Identifying Patterns of Potentially Preventable Emergency Mining Theses/Kathleen Alber... ·...

Preventable ED Utilization by Children

Identifying Patterns of Potentially Preventable Emergency

Department Utilization by American Children

Kathleen M. Alber

A Thesis

Submitted in Partial Fulfillment of the

Requirements for the Degree of

Master of Science in Data Mining

Department of Mathematical Sciences

Central Connecticut State University

New Britain, Connecticut

January 2007

Thesis Advisor

Dr. Daniel T. Larose



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Identifying Patterns of Potentially Preventable Emergency

Department Utilization by American Children

Kathleen M. Alber

An Abstract of a Thesis

Submitted in Partial Fulfillment of the

Requirements for the Degree of

Master of Science in Data Mining


Central Connecticut State University

New Britain, Connecticut

January 2007

Thesis Advisor

Dr. Daniel T. Larose


Key Words: Emergency Department Utilization, Children, National Survey of Children’s Health, Medical Home


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ABSTRACT Emergency department care and primary care are ideally distinct parts of the

health care delivery system. In theory, each answers a specific and different health

care need. However, in practice this distinction blurs. Many visits to hospital

emergency departments are potentially preventable by timely and appropriate primary

care. This paper employs a descriptive data mining approach to the analysis of data

collected by the National Survey of Children’s Health 2003 with the purpose of

identifying global and local patterns of potentially preventable emergency department

utilization by American children. Understanding the factors influencing the decision

to seek emergency department care is an essential step in ensuring adequate and

appropriate health care for all children.

Using SPSS Clementine® data mining software, classification models are

employed to discover nationally significant and geographically specific patterns of

potentially preventable emergency department utilization by children. This paper

identifies numerous patterns. Globally, requirement for care, age, and insurance type

were found to be the most significant predictors of the target behavior. Local patterns

characterize several subsets of the population who are significantly associated with

potentially preventable emergency department utilization. These characterizations

often logically suggest theories which explain a group’s association with such

behavior. In many cases, further directed research would be required to confirm and

clarify these assumptions. These are noted throughout this paper.

Access to quality primary care logically influences the choice of emergency

department care to treat a potentially preventable condition. The American Academy


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of Pediatrics has developed the concept of a ‘medical home’ and detailed the specific

requirements for care defining its existence. This study describes a method of

utilizing survey responses to measure each child’s association with a medical home.

Association rule mining is used to characterize groups of children according to

association with a medical home and investigate potentially preventable emergency

department utilization in the context of access to primary care. Differences in

characteristic patterns of health care utilization for these groups are evident,

suggesting that efforts to encourage the appropriate use of primary care in place the

emergency care would most effectively be tailored according to level of access to

primary care.


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TABLE OF CONTENTS

ABSTRACT.................................................................................................................. 3

INTRODUCTION ........................................................................................................ 6

RELATED RESEARCH .............................................................................................. 9

METHODS ................................................................................................................. 13

Data Preprocessing.................................................................................................. 13

Exploratory Data Analysis...................................................................................... 15

Target Variable – PPA_Visits............................................................................. 15

Predictive Attributes ........................................................................................... 16

Non-Predictive Attributes ................................................................................... 45

Data Preparation for Modeling ............................................................................... 45

Clustering................................................................................................................ 46

Classification Modeling.......................................................................................... 47

National Model ................................................................................................... 48

Insurance Type Model......................................................................................... 58

State Models........................................................................................................ 74

Association Rule Mining ........................................................................................ 86

RESULTS ................................................................................................................... 95

DISCUSSION........................................................................................................... 101

REFERENCES ......................................................................................................... 104

Appendix A: Modeling Attributes ............................................................................ 110

Appendix B: Percent of Children Associated with PPA Visits by State ................. 113

Appendix C: Sources and Derivation of State Characteristic Attributes................. 115


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INTRODUCTION

Each year, approximately 30 million visits to American hospital emergency

departments are made by children aged 18 years or younger (Institute of Medicine

[IOM], 2006; McCraig & Burt, 2005). Many of these visits are avoidable, addressing

conditions which would more appropriately be treated in a primary care setting. Such

visits contribute to overcrowding of emergency departments and are indicative of the

use of emergency department services as a proxy for primary care.

It is generally recognized that health care delivery is optimized on both an

individual and system-wide level when children receive appropriate and consistent

primary care directed by a primary care professional and utilization of hospital

emergency departments is limited to unpreventable medical emergencies. The

positive effect of comprehensive pediatric primary care on children’s health has been

documented (Alpert, Robertson, Kosa, Heagarty, & Haggerty, 1976). Optimally, the

primary care professional has a relationship with the patient and therefore is familiar

with the child’s medical history and family issues and provides not only preventative

and illness care but also follow-up attention and focused advice as necessary. The

parent who is comfortable in his/her relationship with the child’s primary care

professional is more likely to seek advice and care for problems before they escalate

to a more serious and urgent status. Emergency department care is focused on

addressing an immediate concern rather than providing ongoing care. It is a more

expensive and often less medically appropriate alternative to primary care for

children. A recent Institute of Medicine report (IOM, 2006) noted that many hospital

emergency departments are not well equipped to handle pediatric patients. Many


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don’t have the essential supplies on hand for these smallest patients and the majority

lack medical personnel with specialized pediatric training.

Several previous studies have sought to discern factors related to the choice of

emergency rather than primary care for non-urgent conditions (Cunningham, Clancy,

Cohen, & Wilets, 1995; Doobinin, Heidt-Davis, Gross, & Isaacman, 2003; Phelps et

al., 2000; Walls, Rhodes, & Kennedy, 2002). Use of emergency department resources

for the treatment of non-urgent cases is one indicator of a breakdown of pediatric

primary care delivery. Another, with more potentially serious medical consequences,

is exemplified by the ill child who is not provided with appropriate, timely primary

care treatment for a non-urgent condition thus resulting in a deterioration of health

status which requires emergency care. To illustrate, Dombkowski, Stanley, and Clark

(2004) found that asthmatic children who were regularly followed and appropriately

medicated were less likely to require emergency intervention. Another study

(Johnson & Rimsza, 2004) identified several emergent conditions, including epileptic

convulsions, severe ear, nose, and throat infections, and bacterial pneumonia, with

which children who received primary care were four times less likely to be

associated. Putting aside the beneficial effect of the primary care professional as a

health/safety educator, accidents, injuries and poisonings are validly considered not

preventable by primary care professional intervention. For the purpose of this study,

all others are considered at least potentially preventable. This includes most non-

emergent conditions and emergent conditions which may not have escalated to an

urgent nature had proper earlier intervention been sought in a primary care setting.


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This study identifies factors associated with children likely to be brought to an

emergency department for potentially preventable conditions. It employs a data

mining approach to the analysis of data collected for the National Survey of

Children’s Health 2003 [NSCH] which includes data pertaining to 102,353 children

less than 18 years of age. Residents of each of the fifty states and Washington, DC are

represented. Survey responses were provided by a parent or caregiver and describe

the referenced child’s demographic and economic conditions as well as profiling

health status and access and utilization of health care resources. It is noted that

factors which affect children’s emergency department utilization patterns may differ

from those which contribute to adult behavior in this regard. Usually, the decision

about whether or where to seek care for a child is made not by the child himself, but

by a parent or other caregiver and the factors influencing that decision are often more

complicated than those required for an adult determining his or her own personal

medical requirements. Therefore, when examining potential contributing factors,

caregiver and family characteristics which might influence the decision-making

process are considered as well.

As the name implies, the NSCH data is national in scope allowing a more

comprehensive study of utilization patterns than most previous research which

analyzed data collected on a local or statewide level. This thesis initially focuses on

national patterns of potentially preventable utilization of emergency department

resources by children and then investigates geographic variation through the

comparison of factors associated with these patterns on the state level.


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RELATED RESEARCH

There have been numerous previous studies which investigated aspects of

emergency department utilization by children. For the most part, the data analyzed by

these samples represent a limited population, often a single hospital and/or

exclusively publicly insured patients.

Several studies have sought to identify factors associated with non-urgent use

of hospital emergency departments by children. Phelps et al. (2000) investigated the

relationship between non-urgent visits and caretaker characteristics including stated

reasons for bringing the child to the emergency department. This study suggested the

importance of types of family unit and insurance as well as the “modeling effect” of

where the caregiver was taken as a child for ill care. Newcomb (2005) focused on

variables measuring Medicaid children’s access to primary care as well as caretaker

characteristics. The author concluded that non-urgent visits to the emergency

department by a localized group of publicly insured children were at least partly due

to lack of ready access to primary care. Doobinin et al. (2003) surveyed the parents

of children brought to a single urban pediatric emergency department with non-urgent

illnesses to determine their reasons for choosing the emergency department for care.

Considered were issues of convenience as well as parental discrimination of urgency

and knowledge of insurance requirements.

Another research approach focuses on the identification of factors

associated with children who visit an emergency department for any reason and/or the

effects of the implementation of certain policies on emergency department utilization

patterns. These studies classify according to whether or not a child uses the


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emergency department, not the nature or urgency of the complaint. Much of the

research found had a narrowly defined purpose. For example, Pollack et al. (2004)

sought to discover sources of variation in emergency department utilization by

Medicaid-insured Michigan children with chronic or complex health conditions while

Fredrickson, Molgaard, Dismuke, Schukman, & Walling (2004) studied Medicaid-

insured asthmatic children in rural Kansas with similar goals. Kotagal et al. (2002)

explored the relationship between primary care and emergency department utilization

in early infancy by studying Medicaid-insured newborns in Ohio. Likewise, the

potential correlation between continuity/consistency of primary care and emergency

department utilization has been the focus of studies (Christakis, Wright, Koepsell,

Emerson, & Connell, 1999; Gill, Mainous, & Nsereko, 2000; Ryan, Riley, Kang, &

Starfield, 2001).

While others took a dichotomous approach to classification of emergency

department visits, Weinick, Billings, and Burstin (2002) introduced a four class

model which differentiated not only between urgent and non-urgent visits, but further

between emergent conditions which could have been treated or prevented in a

primary care setting and those which required emergency department care and were

not preventable.

Much research is limited to or focused on children who are insured under

Medicaid for a variety of reasons including the established tendency of Medicaid

insured children to visit emergency departments with greater frequency than children

of other insurance classifications (Dombkowski et al., 2004; McCraig & Burt, 2005;

Phelps et al., 2000). This greater utilization may be due in part to some Medicaid


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delivery systems which do not financially penalize patients who make use of the more

expensive emergency department alternative for non-urgent illnesses (Phelps et al.,

2000). Tailoring studies to Medicaid enrollees also has the advantage of practical

applicability of results. When research demonstrates problem areas, public policies

can be implemented to encourage and facilitate more appropriate utilization of

medical resources. Managed care Medicaid programs are an example of such policies

and determination of their success in this regard has been the focus of several studies

(Alessandrini, Shaw, Bilker, Perry, Baker, & Schwarz, 2001; Dombkowski et al.,

2004; Piehl, Clemens, & Jones, 2000). Research using Medicaid data is perhaps also

prevalent because state Medicaid data bases provide a rich, convenient, relatively

available source of data to be analyzed.

As was noted above, most of the research obtained for this review had a

narrow geographic focus. One exception was a study by Luo, Liu, Frush, & Hey

(2003) who investigated whether type of insurance coverage affects the likelihood

that a child visits the emergency department. This study utilized the 1997 Medical

Expenditure Panel Survey, a national survey. The authors studied data pertaining to

10,193 children from across the country and included potential predictor variables

describing basic demographic information and type of insurance. They achieved

some different results than many of the more limited studies indicating that a more

diverse dataset may provide different insight. Ryan et al. (2001) also pointed out the

importance of diversifying study populations. Noting that data for most previous

studies originated in urban hospital/clinic settings, they focused their study on rural

adolescents.


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Certain correlations with various forms of emergency department utilization

were noted by multiple studies. The first, mentioned above, is between insurance type

and emergency department utilization by children. The heavier utilization of

emergency department services by Medicaid insured children was noted by most

studies. Another is the child’s age. Infants and adolescents were observed to be more

likely visitors to hospital emergency departments than children of the ages in between

these two groups (Johnson & Rimsza, 2004; Pollack et al., 2004). A care-giver’s

marital status has also been found to be predictive of the tendency to bring a child to

the emergency department for care (Kotogel et al., 2002; Phelps et al., 2000).

Additionally, the mother’s level of education has been associated with the use of

emergency department services (Alessandrini et al., 2001; Ryan et al.,2001). These

correlations were considered in designing this study and interpreting results.


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METHODS

This study utilizes SPSS Clementine® 10.0 data mining software to analyze

the NSCH 2003 data. The primary data mining task is descriptive in nature with the

goal of identifying patterns and trends of preventable emergency department

utilization by children. Bivariate analysis was employed to explore the relationship

between potentially pertinent survey response attributes and the target behavior.

Additionally, Clementine® decision tree and association rule modeling tools were

utilized to allow the identification and clarification of multivariate patterns.

Data Preprocessing

The NCHS data set includes 301 variables describing diverse aspects of

children’s health. Many of these are unrelated to hospital emergency department

utilization for potentially preventable conditions and therefore were disregarded by

this study. Retained for consideration were variables describing (a) demographic

characteristics including age, gender, family structure, primary language, race, and

caregiver education; (b) health status; (c) insurance type; (d) family income; (e)

health care access and utilization; (f) family risk behaviors; and (g) geographical

home.

Assessing the quality and completeness of the data is a crucial step in the

successful modeling process. Missing attribute values in the NSCH data are

prevalent for several reasons. The design of the survey is such that many questions

are asked only when responses to other questions indicate the appropriateness. For

example, if a respondent indicates that the child has not received any medical


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attention in the referenced year, further questions regarding frequency or type of

medical care received are omitted. Consequently, records associated with such

children are missing values for these attributes. Two sections of the survey are age-

specific. Each respondent answers only the questions targeted to the referenced

child’s age group resulting in missing values for attributes associated with questions

of the section applying to the other age group. Additionally some missing values are

the result of the respondent’s inability or refusal to answer a question or errors in

capturing or storing the response. Missing values were handled as follows:

1. Where there was a logical means of discerning the appropriate replacement

for the missing data, that replacement was made. In the example above, a

response indicating that a child received no medical attention in the

referenced year clearly implies zero values for attributes reflecting the

number of visits for preventative primary care, sick care, and emergency

department care.

2. Where the correct replacement was less obvious but a reasonable

approximation could be determined, that replacement was made. For 57%

of the attributes, the majority class was assigned. Others required more

involved deductive processes which are detailed in the exploratory data

analysis section which follows.

3. Where an appropriately logical replacement value could not be discerned or

approximated, the record was removed from consideration. This resulted in

the removal of a very small subset (1.3%) of records which were missing

values for attributes such as gender or education level.


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Each variable in the data set is identified by a question code. For ease of

modeling, attributes were renamed so as to be understandable. Additional attributes

were derived using the information conveyed in NSCH responses. Appendix A lists

the 37 attributes utilized in modeling and references the corresponding data set

question code(s).

The NCHS survey implemented a design of top-coded variables which

effectively suppress outliers.

Exploratory Data Analysis

A significant challenge in the application of data mining to the NSCH data set

is the derivation of an effectual attribute set. NSCH attributes are answers to specific

survey questions. Some can be individually considered as potential predictors, but

most attributes are derived using a selection of responses.

Target Variable – PPA_Visits

The target behavior is the use of a hospital emergency department for

potentially preventable conditions. The NSCH data includes three variables which

were used to derive a single attribute, PPA_visits, to classify each record according to

whether the referenced child made at least one such visit. The first refers to the total

number of emergency department visits. If that number is one, a second attribute

indicates whether the visit was in response to an accident injury or poisoning. If more

than one, a third variable holds the number of visits in response to accident injury or

poisoning.


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PPA_visits is set as false if survey responses indicate that the referenced child

made no visits to an emergency department or that all such visits were due to

accidents, injuries, or poisonings. The target variable is true if the referenced child

made one or more visits to an emergency department which were not due to accident,

injury, or poisoning. Using these criteria, 11.62%, or 11,890 of the NSCH

respondents are identified as having visited the emergency department for potentially

preventable reasons.

Predictive Attributes

Age

Analysis of the NSCH data indicates that patterns of potentially preventable

hospital emergency department utilization vary by age, with younger children

generally making more such visits than those who are older. Survey respondents

specified the age of the referenced child in years. For the purposes of this study, ages

were binned into groups: (a) infants of ages 0 or 1 years, (b) preschoolers between the

ages of 2 and 5 years, (c) young school age children between the ages of 6 and 9

years, (d) middle school aged children between the ages of 10 and 12 years, and (e)

adolescents between the ages of 13 and 17 years. Among the children referenced by

the survey, infants were most likely to be linked with a potentially preventable

emergency department visit. Close to one-quarter of infants were associated with the

target behavior in the studied year. As age increases, the tendency to make such a

visit decreases. Only 7.37% of the adolescents referenced in the survey indicate a trip

to the emergency department for a problem not related to an accident, injury or

poisoning.


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Table 1

Percent of Children Associated with a Potentially Preventable/Avoidable Emergency Department Visit by Age Group

Age Group Percent Associated with PPA Visit 0-1 23.58 2-5 15.85 6-9 9.83

10-12 7.52 13-17 7.37

Caregiver Structure

The decision to seek emergency care for a child is usually made by that

child’s caregiver(s). It has been suggested that the number and relationship of

caregivers residing with the child influences that decision. The NSCH provides two

variables which can be used to ascertain the caregiver structure of the child’s family

unit. The RELATION field refers to the relationship of the person providing the

information for the survey. By survey design, this respondent is the parent or

guardian who lives in the household and knows the most about the health and health

care of the child. The TOTADULT3 field specifies the number of adults living in the

household. Using these two variables, a new attribute is derived which indicates

whether the child resides with (a) a single mother, (b) a single father, (c) a single

other caregiver, (d) two adults, or (e) three or more adults.

Phelps et al. (2000) noted that single caregivers were more likely bring a child

for a non-urgent emergency department visit and speculated that this may be due to

lack of input from another adult in the evaluation of the necessity of such care.

Children of single mothers represented in the NSCH data are indeed more likely to be

associated with a potentially preventable visit to a hospital emergency department

than those living in households with two or more adults. This is also true of children


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with a single caregiver who is neither mother nor father. However this data indicates

that single fathers are the least likely caregivers to seek preventable emergency care

for their children. This suggests a gender difference in a caregiver’s inclination to

seek emergency care for a child.

Caregiver Education

The NSCH data includes indication of the highest education level attained by

any member of household. For the studied children, a higher level of household

member education is associated with a lesser likelihood of a visit to an emergency

department for potentially preventable reasons. The difference is most notable for

those who have continued their education past high school graduation. Links exist

between education level and income and insurance type, suggesting the possibility of

education level functioning as a covariant of these factors. However data

investigation showed that education level is indeed an independent factor in the

decision to seek potentially preventable emergency department care for a child,

particularly in middle income and/or privately insured families.

Primary Language/Interpreter

Of those interviewed in this nationwide survey, 7.56% identified some

language other than English as their primary home language. These households are

more likely to be associated with a child’s visit to an emergency department for

potentially preventable conditions. An additional survey question was posed of

respondents who indicated a non-English primary language to ascertain whether an

interpreter would be required for effective communication between a

patient/caregiver and medical personnel. By segmenting the group of records


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indicating a non-English primary language according to the need for an interpreter, a

clear association between this requirement and potentially preventable emergency

department utilization was established. In fact, children from families with primary

language other than English who do not require an interpreter to effectively

communicate with medical personnel are approximately equally likely to visit the

hospital emergency department with potentially preventable problems as children

who come from homes where English is the primary language. In contrast, those who

require an interpreter are almost twice as likely to make such as visit as their peers

with greater facility for the English language.

Race/Ethnicity

The NSCH identifies four racial designations: white, black, multiple race or

other. A new classification, ‘unknown’, is created to apply to children of respondents

who do not know or refuse to provide a racial designation. A separate survey answer

indicates whether the child is of Hispanic or Latino origin. Data exploration led to

the discovery that most of the children classified as of unknown race are Hispanic.

This suggests that the absence of Hispanic designation as one of the survey choices

for race left respondents uncertain about the appropriate response to describe the race

of the referenced child. Consequently, a new variable was derived combining the two

survey responses in order to allow Hispanic as a possible primary race/ethnicity. This

new variable includes in the multiple race category those children who were

identified as black or white race and also of Hispanic origin. All other records

indicating Hispanic origin are categorized as Hispanic.


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Distribution of data by race with regard to the target variable indicates that

black children are most likely to be brought to a hospital emergency department with

potentially preventable conditions. Hispanic and multiple race children are slightly

less likely than black children to make such visits, but more likely than white

children.

Insurance Type

Multiple survey responses provide information as to the existence and type of

health care insurance coverage for the referenced child. A single attribute was

derived combining this information to identify each referenced child as covered by

Medicaid, private, or no insurance. Examining this variable with respect to

associated potentially preventable emergency department visits indicates a

significantly greater tendency for such visits by Medicaid insured patients.

Table 2 Rate of Potentially Preventable Emergency Department Utilization by Insurance Type

Insurance Type % Associated with a PPA Visit Medicaid 19.65% Private 9.23%

Uninsured 9.48%

This strong association between Medicaid insurance and potentially

preventable emergency department utilization is consistent across all three major

racial/ethnic categories. Concerning other insurance classifications, it is noted that

uninsured Hispanic and white children are slightly less likely to be brought to an

emergency department with preventable conditions than those who are privately

insured. Black children without health insurance on the other hand are even more


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likely to make a potentially preventable visit to an emergency department than their

peers with private insurance.

Table 3 Percent of Children Associated with a Potentially Preventable/Avoidable Emergency Department Visit by Race/Ethnicity and Insurance Type RACE/ETHNICITY NONE MEDICAID PRIVATE

Black 13.66 21.16 11.93 Hispanic 9.71 19.83 10.76

White 8.16 18.47 8.71 Family Income

The NSCH used Department of Health and Human Services guidelines to

derive the household poverty status for each respondent based upon household

income and number of people residing in the household. Each record was assigned to

one of eight poverty status categories. A new variable was derived for this study

collapsing this information into three income categories: (a) lower – less than 150%

of poverty level, (b) middle - 150% to 300% of poverty level, or (c) upper - over

300% of poverty level.

Approximately nine percent of the records have no poverty status designation

because the respondent did not know or refused to provide family income

information. Prior to data collection, survey designers assumed that these responses

would be most associated with actual family incomes in excess of 300% of the

poverty level and subsequent income related questions were asked based upon that

assumption. However, as evidenced by Figure 1, the distribution of these non-

assigned ($null$) records with regard to insurance status suggests that they are more

associated with the middle income group. The percentage associated with Medicaid


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insurance is particularly telling. Medicaid is allocated primarily on the basis of

income. While income requirements vary by state, it is logically expected that there

would be few Medicaid recipients in the upper income category. Additionally, the

lack of medical insurance is atypical of upper income families and yet a significant

minority of the unassigned records indicates no coverage.

Figure 1. Association of insurance type with income group.

While it can be assumed that the respondents who did not know or refused to specify

household income are not exclusively representative of a single income group, it

appears that they are most representative of the middle income group and therefore,

these records were assigned as such for the purposes of this study.

There is a strong association between family income and potentially

preventable emergency department visits. The likelihood of such visits increases as

income level decreases. However, given the aforementioned relationship between

family income and Medicaid coverage, this study considered the possibility of this

association as a covariant effect of insurance type. Records were segmented by

insurance type. Within each insurance type, preventable emergency department

utilization was most strongly associated with the lower income group and that

association weakened as income increased. In fact, this pattern was most pronounced

among the privately insured which comprise close to 70% of the surveyed population.


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Thus it was concluded that both income level and insurance type separately influence

the likelihood of potentially preventable emergency department visits.

General Health

The NSCH data includes an attribute which reflects the survey respondent’s

description of the referenced child’s general health as (a) excellent, (b) very good,

(c) good, (d) fair, or (e) poor. Fortunately, a large majority of children, 87.11%, enjoy

excellent or very good health. However, general health status is clearly linked with

the propensity for potentially preventable emergency department visits. Children

associated with less well conditions are more strongly linked with these visits.

Figure 2. Percent of children associated with a potentially preventable/avoidable emergency department visit by general health specification.

Primary Care Variables As this study seeks to identify factors associated with hospital emergency

department visits which might have been prevented by appropriate and timely


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utilization of primary care resources, variables which measure necessity, access, and

quality of primary care are of great interest.

Medical home.

It is logically assumed that access to quality primary care would be a deterrent

to potentially preventable emergency department utilization. The concept of a

medical home is advanced by the American Academy of Pediatrics [AAP]

(Children’s Health Topics: Medical Home, n.d.). Historically, the existence of a

medical home was defined solely by association with a primary care physician. The

NSCH data suggests that such an association does not have a significant effect on the

likelihood of a potentially preventable visit to a hospital emergency department.

However, the AAP has expanded the definition of the concept of a medical home to

be primary care which is accessible, continuous, comprehensive, family-centered,

coordinated, compassionate, and culturally effective. Under this definition, the

existence of a medical home requires not just the presence of a doctor, but the

availability of high quality primary care. By utilizing information obtained in

response to NSCH survey questions, a consideration of medical home which is more

in keeping with this expanded definition can be attained and considered as potentially

predictive of the target behavior. Bethell, Read, and Brockwood (2004) discussed

using population-based national data sets to ascertain the degree of association with a

medical home for children with special health care needs. They described the

necessity and challenge of the development of a quantitative measure of medical

“homeness” and discussed how survey responses could be used to make at least an

approximate measurement of medical home for children referenced in the surveys.


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Their work led to the inclusion of several questions in the NSCH survey pertaining to

the discernment of a medical home. Building upon this work, this study utilized

responses to these and other survey questions to develop a scoring method to provide

an indication of each referenced child’s association with a medical home.

The AAP definition identifies seven dimensions to be considered when

determining the existence of a medical home for a child. The NSCH survey

instrument was analyzed and questions identified which provide some measure of six

of these seven dimensions. It was determined that no measure of family-centeredness

was available from this data. This process is detailed in Table 4. Obviously, these are

not comprehensive or ideal measures of each dimension. For example, a primary care

practice which employs bilingual doctors and nurses would be arguably more

culturally competent than one which calls in an interpreter as needed. However, this

exercise in determining the existence of a medical home is limited by the information

available in the survey data which provides no indication of the linguistic abilities of

medical practice personnel. The goal is to achieve the best approximation of a child’s

association with a medical home that is possible given the constraints of the questions

asked.


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Table 4 NSCH Questions Addressing the Seven Dimensions of Medical Home

Dimension Question Description S5Q06A How often can help be obtained from personal

MD on phone? accessible

S5Q07A How often is immediate care available from personal MD?

family-centered none continuous S5Q01 Does child have a personal doctor or nurse?

PREV_CARE (derived from

S5Q08A, S5Q08B, S4Q03R)

During the previous year, did the child make the appropriate number of preventative care visits to their personal doctor according the schedule recommended by the American Academy of Pediatrics for children of their age?

S4Q07 During the previous year, did the child receive all the medical care necessary?

S5Q02 How often does personal MD spend enough time with child?

S5Q04 How often does personal MD explain things in an understandable way?

S6Q28 Did personal MD ask about learning, development or behavioral concerns?

comprehensive

S6Q29 IF LDB concerns, did personal MD give information to address those concerns?

S5Q09B If specialist was needed, did personal MD help get specialist care?

coordinated

S5Q10B If special services, equipment or other health care was needed, did personal MD help get it?

S5Q09C If specialist was needed, how often did personal MD talk to patient/caregiver about what would happen at specialist visit?

compassionate

S5Q10C If special services, equipment or health care was needed, how often did personal MD talk to patient/caregiver about it?

culturally competent

S5Q13A If an interpreter was required, how often was one available to assist with personal MD visits?

A medical home score variable was derived to represent each child’s

relationship with a medical home on a scale of 0-100. The existence of one or more

medical professionals identified by the caregiver as the referenced child’s personal


27

doctor or nurse is considered a necessary, though not sufficient, measure of medical

home. Therefore records indicating the lack of association with a personal doctor or

nurse were assigned a medical home score of zero. Not all of the fourteen measures

of medical home described above are collected from each survey respondent.

However, there are five core measures which are collected concerning every child

who has identified a personal doctor or nurse and has sought medical care of any kind

in the survey reference period. The other nine variables contain information collected

only if the child required certain health care in the referenced period. For example,

Question S5Q06A is asked only if the caregiver indicates that phone assistance was

required and sought and Question S5Q13A is asked if the primary household

language is other than English and the caregiver indicated that an interpreter was

required for adequate communication with the medical professional. Therefore, in

order to assure accurate and comparable medical home scores both for healthy

children who used few non-preventative medical resources in the referenced period as

well as those for whom more resources were required, the medical home score was

designed to include two components. The first is a base score which incorporates the

more universal measures of continuous, comprehensive medical care. Each of these

is assigned a value and the values are summed to attain a base measure of medical

home. For children over five years of age who needed and received only preventative

care during the referenced year and required no interpreter, the medical home score is

determined solely on the basis of these five variables. Those who have received their

recommended preventative care from a health care professional who always devotes


28

ample time and provides understandable guidance receive a perfect medical home

score of 100.

Table 5 Measures of Care Contributing to Base Medical Home Score

Measure Description Value Personal_MD Child has a personal doctor or nurse. 40

Prev_Care Child received AAP recommended # of preventative visits,

15

Got_Care Child received all necessary medical care during the past year.

15

MD_Time How often did doctor spend enough time with the child?

always – 15 usually – 10 sometimes – 5 never - 0

MD_Explain How often did doctor explain things in an understandable way?

always – 15 usually – 10 sometimes – 5 never - 0

The second component adjusts the medical home score to reflect inadequate provision

of primary care services addressing individual health care needs. For example,

comprehensive medical care for children under five requires that the personal doctor

provide guidance about learning, developmental, and behavioral concerns. If he/she

neglects to provide this, the medical home score is decremented. Likewise, if the

child (or caregiver in support of the child) required assistance by phone, immediate

care for illness or injury, referral to and help with a specialist or special medical

equipment or care and any of these needs were not met by their personal medical

practice or clinic, the value of the medical home score is decreased. Each of these

measures of failure to provide a medical home is assigned a value and the total

medical home score is decremented by that value if response to the pertinent survey


29

question indicates the appropriateness of this action. If the response indicates that the

need was adequately met or if the question did not apply to the referenced child, the

medical home score is not adjusted. The medical home score then is defined as the

total of the base measure of comprehensive care less adjustments.

Table 6

Adjustments to Base Measure of Medical Home

Measure Description Decrement Value

MD_Phone_Help How often did doctor provide help by phone when needed?

never – 15 sometimes – 10 usually – 5 always- 0

Got_Immed_MD How often did doctor provide immediate care when needed for illness or injury?

never – 15 sometimes – 10 usually – 5 always - 0

LDB_MD_Conc Doctor did not ask about learning, development or behavioral concerns for child under 5.

10

LDB_MD_Info Doctor did not provide information to address stated concerns about learning, development or behavior for child under 5.

10

Spec_PMD_Access Doctor did not help patient get specialist care when needed.

10

Spec_PMD_Explain How often did doctor talk to patient/caregiver about what would take place during specialist visit (if specialist required)?

never -5 sometimes – 5 didn’t go - 5 otherwise - 0

SCare_PMD_Access Doctor did not help patient get special equipment or care when needed.

10

SCare_PMD_Explain How often did doctor explain about needed special equipment or care?

never – 5 sometimes – 5 didn’t get – 5 otherwise - 0

Got_Interp How often was an interpreter provided if one was needed to adequately communicate with doctor?

never -5 sometimes – 5 otherwise - 0


30

The medical home score provides a measure of the strength of the association

of the referenced child with a medical home. Fortunately, for those who have a

personal doctor or nurse and therefore a non-zero medical home score, higher scores

are more prevalent than lower.

Figure 3. Distribution of survey respondents according to medical home score.

It was hypothesized that children with stronger associations with quality medical

homes would be less likely to utilize the emergency department for potentially

preventable conditions. In general, this hypothesis was not supported by the data.

However, for the very small segment of the survey population comprised of children

associated with a personal doctor or nurse and medical home scores less than 50,

there is a link between lower medical home score and greater likelihood that the child

has made at least one potentially preventable visit to the emergency department. This

is illustrated by Figure 4. Only approximately 1% of the children referenced by the

survey belong to this group. These children identify a personal health care provider


31

but apparently receive very few primary care services from him/her. In fact, over 98%

of these children do not receive the recommended preventative care. They also

receive considerably less sick care outside of a hospital.

Figure 4. Percent of children associated with a potentially preventable/avoidable emergency department visit by medical home score.

While the medical home score provides a measure of the child’s access to

quality primary care, for the vast majority of survey respondents it is not a

particularly strong indicator of potentially preventable emergency department

utilization. A more predictive relationship exists between the base medical home

score, the first component of the medical home score calculation, and the target

behavior. Recall that this base score measures the referenced child’s consistent access

to a personal health care professional who offers recommended preventative care and

provides adequate time and explanations as necessary. It is also dependent on whether

the child receives all necessary medical care, but does not reflect the adjustments

indicating the degree to which special health care needs are met. By definition, a

base medical home score is either zero, if the child has no personal doctor or nurse, or


32

40-100 if there is an associated personal medical professional. Figure 5, the

normalized histogram of base medical home scores, shows the proportionate

association with potentially preventable emergency department visits.

Figure 5. Percent of children associated with a potentially preventable/avoidable emergency department visit by base medical home score

As the base medical home is more predictive of the target behavior, it is

adopted as the measure of access to quality primary care for the purpose of this study.

Children associated with base medical home scores of 75 or more are considered as

having a medical home. Note the difference in behavior with regard to potentially

preventable emergency visits for those children who claim to have a personal doctor

but based upon their base medical home score do not have an established medical

home. This 8.65% of the population are far less likely to visit the emergency

department with such problems than those with a medical home. They are also far

less likely to make such a visit than children who have no personal doctor.

A medical home variable was derived which indicates a child’s level of access

to adequate basic primary care. Each record is categorized according to association


33

with (a) a personal medical professional who provides a medical home, (b) a personal

medical professional but no medical home, or (c) no personal medical professional.

Attributes measuring the specialized care features which were reflected as deductions

in the more comprehensive medical home calculation are considered as separate

potential predictors, with the exception of those relating to learning, developmental

and behavioral concerns which were found to be unrelated to the target behavior.

Specialized care features.

Figures 6 and 7 demonstrate how improved access to medical advice by phone

or immediate primary care for illness or injury decreases the likelihood of a

potentially preventable emergency department visit.

Figure 6. Association of access to medical phone advice with potentially preventable/avoidable emergency department use.

Figure 7. Association of access to immediate care for illness or injury with potentially preventable/avoidable emergency department use.


34

On the other hand, patients with access to a primary care provider who consistently

provides explanations as to the necessity for and process of obtaining required

specialized equipment are more likely to make a potentially preventable visit to a

hospital emergency department. It is possible that greater attention is devoted to those

patients with the most significant health care needs and these would be the patients

most likely to require emergency care.

Figure 8. Association of frequency of primary care provider’s explanation of specialist care with potentially preventable/avoidable emergency department use.

Likewise, children with a primary care provider who coordinates access to a

specialist or specialized care or equipment are more, not less, apt to make a

potentially preventable emergency department visit.

Figure 9. Association of primary care provider’s assistance in obtaining specialist with potentially preventable/avoidable emergency department.

While the availability and nature of primary care influences the likelihood of a

potentially preventable emergency department visit for children who require


35

specialized care, figures 6 through 9 clearly indicate that the children least associated

with such visits are those who require or seek no special care.

Sick care/Sick days.

Sick care refers to the number of times the referenced child saw a medical

professional for non-preventative reasons outside of a hospital in the twelve months

prior to the survey. The data indicates a clear relationship between number of sick

visits and a potentially preventable emergency department visit. The more often a

child seeks medical care outside of the hospital, the more likely he/she will seek

potentially preventable care in a hospital emergency department. This relationship

applies throughout the range of number of sick care visits. This suggests that this

attribute primarily indicates level of wellness rather than discriminate between

primary or emergency care when a child’s illness is primary care treatable. Very few

of the surveyed children used the emergency department exclusively in place of

primary sick care. Only 1.8% of the referenced children are associated with a

potentially preventable emergency department visit but not a sick care visit to a

primary health care provider. Sicker children who require more medical care are more

likely to seek or be sent by a medical professional to the emergency department for

some of that care. In fact, nearly half the referenced children who made more than 12

sick care visits also made at least one potentially preventable emergency department

visit.

The number of school days missed due to illness provides another measure of

wellness for all school-age children, including those who are not typically brought to

a medical professional for treatment of illnesses. As the number of sick days


36

increases, so does the likelihood of a potentially preventable emergency department

visit.

Health Conditions

NSCH survey questions investigated the existence of particular health

conditions which require treatment. Ideally this treatment would be provided in a

primary care setting. However, when primary care is not sought or provided in a

timely and effective manner, children dealing with these conditions are more likely to

require emergency department services identified by this study as potentially

preventable. Asthma and allergies (respiratory, food, or skin) are two of these

conditions. Each of these is associated with more than ten percent of the study of

population and is therefore considered individually as a possible risk factor for

potentially preventable emergency department utilization. Several other conditions

occur in smaller segments of the pediatric population. Each one is associated with a

greater likelihood of potentially preventable visits to emergency departments.

However, they are optimally not considered individually because their numbers are

too small to be statistically significant. Instead, they are grouped into a single flag,

Health_Conditions which is true if the child exhibits one or more of these conditions.

Asthma.

Of the children studied by the NSCH, 11.92% were reported to have been

informed by a health care professional at some point that they had asthma. Caregiver

responses indicated that 8.5% of the children still had the condition at the time of the

survey. This latter group can be segmented according to whether the referenced child

experienced an episode of asthma (asthma attack) within the 12 months prior to the


37

survey. Such an episode serves as an indicator of unsuccessful management of the

child’s asthma. For the purposes of this study, children identified by their caregiver as

no longer having asthma were grouped with those who never had asthma in the ‘no

asthma’ category. As is quantified in Table 7, records corresponding to children who

have recently suffered an asthma attack are considerably more likely to be also

associated with a potentially preventable emergency department visit. This is hardly

surprising. Asthma attacks which require emergency department care are classified as

such as they theoretically could be prevented by appropriate, regular treatment and

medication.

Table 7 Percent of Children Associated with a Potentially Preventable/Avoidable Emergency Department Visit by Asthma Classification

Asthma Classification Percent Associated with a PPA Visit Asthma with recent attack 27.81 Asthma with no recent attack 15.40 No asthma 10.51

The link between regular, appropriate medication and decreased necessity for

emergency department intervention for children with asthma was reported by

Dombkowski et al (2004). The NSCH data is limited in the information it provides

concerning adherence to an asthma medication regimen. Survey responses provide an

indication of the elapsed time since the child with asthma last received medication for

this condition. There is no distinction as to whether the medication is designed to

prevent asthma attacks or respond to them or the frequency or regularity with which it

is administered. For the referenced children, more recent medication is associated

with an increased likelihood of potentially preventable emergency department


38

utilization. It is noted that children with more severe asthma are more likely to

receive frequent medication. Additionally, those with poorly managed asthma are

likely to frequently require medication in response to episodes. Consequently,

children with more severe or unmanaged forms of the disease are more represented

among the most recently medicated. Therefore, rather than measuring the effect of

appropriate medication in preventing unnecessary emergency department attention for

asthmatic conditions, it appears that this attribute functions as an indicator of asthma

severity and management of the condition. The data also includes a separate variable

which reflects the respondent’s subjective assessment of the severity of the referenced

child’s asthma. There is the clear and logically expected association between greater

perceived severity and increased likelihood of a potentially preventable emergency

department visit.

In summary, while this data does not allow the opportunity to study the effect

of a regular sustained program of medication as it relates to the necessity of

emergency care, it does provide indicators of asthma severity and management. More

severe and/or less managed asthma are strongly associated with potentially

preventable emergency care.

Allergies.

Children with respiratory, food, or skin allergies are more likely to visit a

hospital emergency department with a potentially preventable problem than their non-

allergic peers. This is particularly true for those with food allergies. In this survey,

23.14% of the 3945 children with food allergies made potentially preventable visits to


39

an emergency department. This is almost twice the percentage of the general survey

population associated with a visit of this nature.

Other health conditions.

Other health conditions which afflict fewer children are also associated with

significantly greater potentially preventable emergency department utilization.

Table 8 indicates the prevalence of these conditions and the percentage of those

afflicted who are associated with a preventable emergency department visit.

Table 8 Percent of Children Associated with a Potentially Preventable/Avoidable Emergency Department Visit by Health Condition

Condition Percent of Survey Population Afflicted

Percent Associated with a PPA Visit

Diabetes 0.34 28.65 Depression 4.03 18.08 Bone, Joint, or Muscle Problem 3.47 19.03 Developmental Delay or Physical Impairment

3.57 22.83

Severe Headaches or Migraines 4.61 18.22 >= 3 Ear Infections, Past Year 3.80 24.37 None 87.2 10.86 Of the surveyed children, 15.37% have one or more of these conditions. They are

identified in the modeling process by the derived flag attribute Health_Conditions.

Condition severity.

Children with mild forms of the aforementioned conditions are less likely to

require potentially preventable emergency care than those for whom the conditions

are more severely manifested. The Condition_Severity variable reflects the severity

of the most severe condition for children afflicted with allergies or any of the

conditions combined in the Health_Conditions flag.


40

Figure 10. Association of condition severity with potentially preventable/avoidable emergency department use.

Exercise

The survey questioned the number of days per week that a child six years of

age or older vigorously exercised. For children who exercise, the number of days

does not have a marked effect on the likelihood of a potentially preventable

emergency department visit. However, children who consistently refrain from

exercise are significantly more likely to make such a visit. Consequently, a flag

attribute was derived to indicate if the referenced child of at least six years of age

engages in regular exercise.

Weight

The NSCH data includes a derived variable which identifies the referenced

child as (a) underweight, (b) normal weight, (c) at risk for becoming overweight, or

(d) overweight. The overweight category includes children at or above the 95th

percentile of BMI-for-age. Approximately 18% of the referenced children are

classified as overweight. Children under six years of age who are identified as

overweight are not significantly more likely to be associated with a potentially

preventable visit to an emergency department. However overweight school-age

children are 45% more likely to make such a visit than their peers of other weight

classifications. Overweight school-age children, particularly those ten years of age


41

and older, are also more likely to refrain from regular exercise. However these

children are no more or less likely to use emergency department services for

potentially preventable reasons than their overweight peers who exercise.

The referenced child’s weight is considered in modeling in the form of a

derived flag variable which indicates whether or not the ‘overweight’ classification

was applied by the NSCH formula.

Geographical home

State.

The NSCH survey was designed to provide approximately equal

representation of children from each of the 50 states plus the District of Columbia.

Additionally, survey design ensured that each of these 51 subsets would be

independent data sets allowing for statistically accurate subsetting by state (NSCH

Survey Design, 2005). The percentage of records associated with a potentially

preventable emergency department visit varies significantly by state. Appendix B

contains a table ranking the states from least association to greatest.

States associated with the most extreme patterns of potentially preventable

emergency department utilization were profiled in order to gain insight into shared

characteristics which may influence these patterns. The first consideration was

disproportionate populations of groups which were previously established to be more

represented among those who engage in the target behavior. Poverty status,

education and race have been shown to be predictive of potentially preventable

emergency department utilization. State deviation in prevalence of the target behavior

related to differences in population concentrations with regard to these factors merely


42

confirms the consistent importance of these factors in influencing this behavior.

Additionally, differing access to primary and emergency care within each state may

contribute to variations in emergency department utilization. This study included a

comparison of the number of children per practicing pediatrician and the number of

hospitals per square mile in each state. These attributes were derived using

information obtained from multiple sources as described in Appendix C.

Table 9

Comparison of Characteristics for States Associated with the Lowest Rates of Potentially Preventable Emergency Department Utilization

State %Pop Below

Poverty Level

%HS/College Grad

% Black Child per Pediatrician

# of Hospitals per 1000

square miles

VT 8.5 88.9/31.3 0.6 1236 1.73 UT 9.1 89.4/28.4 0.9 2551 0.58 CT 8.1 87.5/33.5 10.1 1220 9.49 NE 9.8 90.8/26.8 4.3 2608 1.22 CO 9.7 88.7/36.0 4.1 1966 0.81

Table 10

Comparison of Characteristics for States Associated with the Greatest Rates of Potentially Preventable Emergency Department Utilization

State %Pop Below Poverty Level

%HS/College Grad

% Black Child per Pediatrician

# of Hospitals per 1000

square miles

MS 16 81.2/19.3 36.8 2883 2.26 WV 17.4 78.7/15.3 3.2 2068 2.74 LA 17 79.8/22.3 33.0 1994 4.91 DC 16.8 86/46.4 57.7 484 229.51 KY 14.4 82.8/21.3 7.5 2154 2.89

Tables 9 and 10 confirm the relationship between poverty, education and the

target behavior on a state level. Consistent with national patterns, the states


43

associated with the highest rates of potentially preventable emergency department

utilization have proportionately greater populations identified as living below the

poverty level or lacking in educational attainment. Conversely, emergency

departments in states with considerably fewer poor and less educated residents see

fewer potentially preventable cases.

In general, states with the highest rates of potentially preventable emergency

department utilization are home to significant black populations and blacks are much

less represented in those with lower rates. Connecticut and West Virginia are

exceptions to this rule indicating that race is independently less influential in

determining potentially preventable emergency department utilization in these states.

DC is somewhat atypical of states showing greatest association with

potentially preventable emergency department utilization. While its black population

and percentage living below the poverty level clearly associate it with similarly

grouped states, it boasts more college graduates than any state in the country. The

nature of this district is that two very different groups coexist within its boundaries:

the very poor as well as those associated either directly or indirectly with the national

government who tend to be well educated, compensated, and insured. However

further investigation indicates that within DC each group engages in the target

behavior in a manner consistent with similarly characterized groups outside the

district.

The national average for children per clinically active pediatrician is 1769.

There is no consistent deviation from this average among either of the groups of

states which represent the extremes of the target behavior suggesting that this


44

measure of accessibility to a pediatrician has no apparent individual effect upon the

level of utilization of hospital emergency departments for potentially preventable

reasons. On the other hand, there is evidence that the concentration of hospitals

within a state is significant. In general, the states most associated with the target

behavior have more hospitals per 1000 square mile area than those least associated

with it. Again Connecticut, which is second only to the District of Columbia in

hospitals per thousand square miles and yet boasts among the lowest of rates for

potentially preventable emergency department visits, is an exception. Note however

that Connecticut also has a large number of pediatricians with respect to the number

of children within the state, indicating that health care in general is very available in

this state. It is logical that if a hospital is conveniently located, the choice to seek

care there instead of at an alternative facility is more attractive. However if

pediatricians are also readily available, the convenience factor may be less of an

influence. Given the suggestion that these two factors may have a combined effect on

a state’s rate of potentially preventable emergency department visits, new variables

were derived to include in modeling these measures of availability of practicing

pediatricians and hospital emergency care. Values were calculated for each state and

then the new variables assigned to each record according to the associated state of

residence.

Character of residence.

The NSCH data includes an attribute which indicates whether the referenced

child resides within a metropolitan statistical area (MSA). This designation is applied

only to records of children who reside in states with sufficient populations in both


45

MSA and non-MSA areas. There is a slightly greater tendency for children in non-

metropolitan areas to make a potentially preventable visit to a hospital emergency

department.

Non-Predictive Attributes

Several attributes were considered but discovered to be lacking any significant

association with the target behavior. These include gender, age position within the

household, caregiver country of origin, and geographic region. Additionally, the

presence of smoker in the household was considered. In general, this data suggests

weak positive correlations between children who reside with a smoker and potentially

preventable visits to a hospital emergency department and between such children and

the existence or aggravated severity of respiratory allergies or asthma. Unfortunately,

a survey design error resulted in the omission of this question when surveying

caregivers of children less than six years of age for the first six months

(approximately one third) of the period in which the survey was administered (NSCH

Design, 2006). Consequently the survey data includes a group of approximately 13%

of the response records, primarily associated with infants and toddlers, for which the

existence of a smoker in the household is unknown. Rather than remove these

records when modeling, this decision was made to disregard this attribute.

Data Preparation for Modeling

In preparation for modeling, the NSCH data set was randomly split into

training and test data sets, each of which were determined to be characteristic of the

entire set. The training data was balanced in order to assure sufficient representation


46

of the relatively rare target class which indicates a potentially preventable emergency

department visit. The balanced training data set is composed of more than 16,000

records with relatively equal representation of target attribute values.

Clustering

K-means clustering was employed to form two clusters which are

characterized by Table 11. Comparing the clusters, the first is less associated with the

target behavior. In general, the children represented by this cluster are healthier with

fewer allergies, asthma, and other health conditions. Fewer needed immediate care

by a primary care provider or specialist. Almost all of the children who have no

primary care doctor are assigned this cluster. Hispanic children are more than twice

as likely to belong to this cluster. This group also includes the majority of those who

require an interpreter to communicate with a medical professional. Records assigned

to cluster 2, on the other hand, are significantly more likely to be associated with a

potentially preventable emergency department visit. This cluster is characterized by

sicker children who are more likely to suffer from allergies, asthma, or other health

conditions, particularly those with moderate to severe forms of these conditions.

Children referenced by the records in this cluster were more likely to need immediate

or specialized medical care or medical assistance by phone. Almost all are associated

with a medical home and are insured, either under Medicaid or private plans. Given

the disparate concentrations of records associated with a potentially preventable or

avoidable emergency department visit in the two clusters, cluster assignment can be

used as a predictive indicator of target class.


47

Table 11 Cluster Characterization using Full Data Set

CLUSTER 1 CLUSTER 2 % associated with PPA visit 7.26 25.69

Indicators of Wellness

mean # sick care visits 1.24 3.88 mean number of sick days 2.50 5.89

mean general health 1.41 1.87 % with allergies 13.16 62.88

% with multiple allergies 1.20 15.71 % with asthma 3.78 24.04

% with health conditions 6.80 44.05 % with severe health condition 0.75 6.45 % who get no regular exercise 7.06 8.95

% who are overweight 17.55 20.57 % who required specialist 7.42 67.40

% who required specialized equipment or care

2.88 29.30

% who required immediate care

16.78 60.36

Access to Health Care

% with medical home 71.31 96.03 % Medicaid insured 21.42 23.75

% uninsured 9.88 4.58 % who sought medical advice

by phone 27.75 71.97

% who need interpreter 2.07 0.79

Demographics Median age group 6-9 6-9

Median Income Group middle middle % with post high school

education 72.85 81.05

% Hispanic 6.56 3.1 % Black 9.55 8.6

Classification Modeling

Exploratory data analysis identified factors which are individually related to

potentially preventable emergency department utilization and detailed the nature of


48

those relationships. Data mining classification modeling techniques allow the

investigation of more sophisticated multivariate patterns. Since the goal of this study

is the discovery and description of trends, the choice of modeling technique was made

with consideration of transparency of results. Decision tree algorithms produce

interpretable rules which detail the interaction of factors influencing the target

behavior. In addition to providing a global description of the data set with regard to

the target behavior, decision tree rules can be used to identify and characterize

interesting subpopulations which are disproportionately associated with this behavior.

Algorithms which construct decision trees include Classification and Regression Tree

(CART) (Breiman, Friedman, Olshen, & Stone, 1984) as well as C5.0 and its

predecessors ID3 and C4.5 (Quinlan, 1993). An alternative classification approach

would be the application of a covering algorithm such as PRISM or RIPPER which

produce lists of rules which could be used to identify multivariate patterns of

potentially preventable emergency department utilization.

National Model

A CART model was built using the full, balanced training set to gain insight

into nationally significant patterns of potentially preventable emergency department

utilization. CART employs a binary, recursive partitioning of the NSCH data set.

This application of CART analyzes how best to utilize the 38 (including cluster)

predictor variables to split the data into smaller groups of records according to shared

class. For this data, there are two target classes. A record is either associated with

potentially preventable emergency department utilization or it is not. At each node,


49

CART performs an exhaustive search of the variable set to determine the split which

minimizes the Gini impurity index. This index, which was proposed by Breiman et al.

(1984), measures the extent to which the segments of data assigned to each child node

deviate from the ideal of homogeneous target values and is calculated as follows:

#classes

GiniL = 1 - ∑ (Lj/TL)2 (1) j=1 #classes

GiniR = 1 - ∑ (Rj/TR)2 (2) j=1 Impurity Index = (TL* GiniL + TR* GiniR)/n (3) where: n = the number of records at parent node TL = the number of records in the left child node TR = the number of records in the right child node Lj = the number of records of class j in the left child node Rj = the number of records of class j in the right child node CART builds a tree by adding branches which decrease the impurity of the parent

node until no further reduction in impurity is possible or until stopping criteria have

been satisfied. In designing a classification tree, depth is a crucial consideration. A

tree which is grown beyond the optimal length may overfit the data, achieving

admirable classification accuracy on the training data but generalizing poorly to new

data. Conversely, insufficient depth limits the trees predictive capability and often

disregards rules which demonstrate interesting patterns of behavior. To address this

concern, stopping criteria were set to limit the depth of this tree to five levels under

the root node.


50

Figure 11. National decision tree.

By design, the root node attribute is the variable which most cleanly splits the

data according to class. At each subsequent level of the tree, the attribute chosen is

that which most cleanly splits the remaining data according to class. Consequently,

the top splits of this decision tree illustrate some of the most important patterns of

potentially preventable emergency department utilization by children.


51

Table 12

Top Level Split Attributes – National Model Attribute Split

cluster 1 First Split cluster cluster 2

< 6 years old Second Split age group school-age Medicaid insurance type

none or private 0 sick care visits sick care

> 0 sick care visits <= 5 sick days off school

Third Split

sick days > 5 sick days off school

This model stresses the importance of the child’s level of wellness in

determining potentially preventable emergency department utilization. The most

significant partition separates the generally healthy children assigned to cluster 1

from the less-well children, including most with asthma, allergies or health conditions

which require immediate or special care, of cluster 2. The number of sick care visits

outside of a hospital setting or days home sick from school are also considered in

determining the referenced child’s association with illness and hence potentially

preventable emergency department utilization. That ill children are more likely

visitors to an emergency department for any reason is not a particularly startling or

actionable finding. However, this model also addresses factors which influence the

decision to seek care in an emergency department setting for potentially preventable

problems when the child is ill.

Of considerable importance is the age of the child. CART distinguishes the

behavior of school-age children in this regard from that of those younger. Infants and

preschoolers are strongly associated with potentially preventable emergency

department utilization. In contrast, school-age children are considerably less so.


52

Indicators of wellness are particularly important in isolating the group of school-age

children who do visit the emergency department with potentially preventable

concerns.

The type or existence of medical insurance coverage also plays a role in a

caregiver’s decision to seek potentially preventable care in an emergency department.

CART segments the population of generally healthy, cluster 1 children under the age

of six according to this factor. Those with Medicaid are shown to be significantly

more associated with such care than those who have no insurance or are privately

insured. In fact, while CART utilizes this attribute to partition a specific subset,

exploratory data analysis concluded that this relationship holds for the full data set.

Additionally the level of education attained by a caregiver influences the

decision as to where to seek care for a child. Children in cluster 2 generally require

more medical care than other children. CART found that a caregiver with college

education is more likely to refrain from utilizing emergency department services to

obtain that care for their school-age child, particularly when the child is not

significantly ill as is evidenced by no more than five days absent from school.

Finally, this model detailed an effect of lack of association with a personal

doctor. Generally healthy school-age children of cluster 1 without private health care

insurance who have been ill enough to receive care outside the hospital are likely to

also make a potentially preventable visit to the emergency department if they have no

personal doctor. The support for a decision tree rule is the proportion of records

which satisfy the split conditions defining the path from root to the given terminal

node. The support for this rule is a scant 1.775% of the training data. Therefore,


53

further data investigation was undertaken to assure its generality. Confidence refers to

the proportion of records satisfying the split conditions which are correctly classified

by the rule. The noted confidence of 63.2% applies to the balanced training data. In

order to perform a valid comparison, the confidence of this rule as measured on the

unbalanced training records was contrasted with that measured on the test data set.

Table 13 provides evidence that this rule is in fact generally applicable.

Table 13

Comparison of Rule Confidence on Training and Test Set Rule Unbalanced

Training Set Test Set

if cluster 1 and school-age and any sick care and not privately insured and no personal doctor

.169 .171

Still further investigation examined whether the effect of the lack of association with

a personal doctor was limited to the population for which CART designed this rule.

That population was segmented based upon four attribute values: (a) cluster 1, (b)

school-age, (c) sick care visits > 0, and (d) Medicaid or no insurance. However, it was

discovered that the effect of the lack of a personal doctor on potentially preventable

emergency department utilization patterns applies more broadly to all records which

indicate any sick care visits outside of a hospital, regardless of insurance type, age or

cluster. Sick children who have no personal doctor are more likely to seek potentially

preventable care in an emergency department. Figures 12 and 13 illustrate this

pattern. Children without a personal doctor who do not visit a doctor outside of a

hospital for sick care are less likely than those with a medical home to seek

potentially preventable emergency department care. Children of every basic medical

home status are more likely to make a potentially preventable emergency department


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visit if they have sought sick care outside of a hospital. For those with no doctor or

with a personal doctor but no medical home, this change in potentially preventable

emergency department utilization rate is greater than for those with a medical home.

Figure 12. Association with potentially preventable emergency department use by basic medical home category for children who make no sick care visits.

Figure 13. Association with potentially preventable emergency department use by basic medical home category for children associated with 1 or more sick care visits.

This suggests a more general rule than that described by CART.

Table 14 Comparison of Medical Home Decision Rules

Rule Antecedent Consequent Support Confidence CART if cluster 1 and school-age

and any sick care visits and not privately insured and no personal doctor

PPA 0.018 0.632

Generalized if no medical home (no doctor or a doctor but no medical home) and any sick care visits

PPA 0.097 0.65


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This more general rule has greater support and confidence than that constructed by

CART. Note that CART’s more specific rule applied only to cluster 1. This is largely

because the clustering process grouped more than 95% of records associated with the

lack of personal doctor in cluster 1. While the lack of a personal doctor is associated

with increased likelihood for potentially preventable emergency department

utilization for children of both clusters, those few who are assigned cluster 2

overwhelmingly likely to make such a visit.

Figure 14. Association with potentially preventable emergency department use by basic medical home category for children of cluster 2.

Model Evaluation

Classification accuracy provides a quantitative measure of this model’s ability

to determine association with potentially preventable emergency department

utilization. Of the training data records, 75.06% were correctly classified. When

applied to the test data, this model achieved an overall accuracy rate of 74.85%. The

consistency of these two rates indicates the generality of the model, testifying to its

ability to perform equally well on new data.

Only 11.44% of the test data records indicate association with a potentially

preventable emergency department visit while 88.56% are pre-classified as not

associated with this target behavior. Thus, though this model incorrectly classifies

37.32% of the records which are associated with a potentially preventable visit, these


56

comprise a very small portion of the records classified as non-visitors as is reflected

in the false negative rate of 0.059. On the other hand, the 23.58% of non-visitors who

are misclassified are a significant portion of those visitors resulting in a false positive

rate of 0.744.

Figure 15. Predicted vs. actual association with potentially preventable emergency department visit – national model.

Figure 16 graphically illustrates the performance of this model which predicts

potentially preventable emergency department utilization for 8516 (28.05%) of the

test records. This group includes 62.68% of the records which are pre-classified as

associated with the target behavior along with 23.58% of the records which would

correctly have been assigned the opposite classification. This corresponds to a lift of

2.23.

Figure 16. Gains chart for national model.


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If the model less liberally applied the positive classification, the effect would be to

move the arrow to the left along the curve, decreasing the number of records

incorrectly identified as associated with the target behavior (false positives), but also

the number of those correctly classified as such. In this case, the trade-off for

identifying a reasonable percentage of potentially preventable emergency department

visitors is accepting the misclassification of a significant number of records which are

pre-classified as non-visitors but follow patterns identified by the model to be

associated with such a visit.

Achievable accuracy for this model is limited by the attributes’ ability to

measure necessity for potentially preventable care. While available attributes measure

association with illness of any nature, for the most part they are incapable of

discerning the severity or term of illness. For example, a sick care visit in response to

a cold appears to the model as identical to one for treatment of pneumonia. Logically,

children associated with illness of a more serious or persistent nature would be more

likely to seek care in an emergency department. Furthermore, it is not unusual for

children, including those who typically seek non-emergent or preventable care in an

emergency department, to experience years in which they require no non-preventative

medical attention.

As the goal of this study is descriptive in nature, evaluation of the success of

this endeavor is not exclusively measured by the overall model accuracy. In addition

to understanding the interaction of factors which broadly describes potentially

preventable emergency department utilization, this project seeks to identify specific

actionable patterns which describe trends of population subsets. These can be


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examined separately and their classification effectiveness measured by rule

confidence. Table 15 details the rules generated from the national model with the

most significant confidence.

Table 15 Decision Rules Generated from National Model

Antecedent Consequent Support Confidence if cluster 1 and school-age No_PPA 0.382 0.739 if cluster 1 and school-age and no sick care No_PPA 0.175 0.836 if cluster 1 and < 6years of age and Medicaid PPA 0.095 0.676 if cluster 1 and school-age and any sick care and not privately insured and no personal doctor

PPA 0.018 0.632

if cluster 2 PPA 0.350 0.722 if cluster 2 and < 6 years of age PPA 0.145 0.819 if cluster 2 and school-age and > 5 sick days PPA 0.093 0.760 if cluster 2 and school-age and <= 5 sick days and family education <= high school grad

PPA 0.024 0.703

Insurance Type Model

Health insurance status has been established as a significant contributing

factor in the decision to seek potentially preventable emergency department care. In

an effort to better understand how behavior patterns differ among children with

various types of health insurance, the CART interactive tree builder function was

used to build a classification model in which the training data is first split according

to insurance type so that separate branches individually model the training data

associated with each insurance type.

The cluster attribute was intentionally not included in this model. Clustering

groups similar records together. The k-means algorithm when applied to the NSCH

data grouped those records with attribute values which indicated similar degrees of

wellness and requirements for immediate or specialized care. These factors are highly


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predictive of the target behavior. Consequently, clustering was employed as a useful

tool to increase the predictive accuracy of the national model. However, when cluster

is used, the specific factors which influence the choice of classification are somewhat

less transparent. Therefore, in an attempt to maximize descriptive value of this

second model, cluster was omitted from the input list.

The interactive tree builder function implemented in SPSS Clementine® 10.0

allows the data miner to apply knowledge of the data in determining split attributes

and values (Clementine® 10.0 Users Guide, 2005, Appendix C). The interactive tree

can be grown a level at a time. At each split, the data miner is afforded the

opportunity to accept the split determined by CART as most capable of decreasing

the impurity index or instead choose a competing predictor and/or customize the split

conditions. In this decision, he/she has access to information about available

predictors and the change in impurity available with each choice. For the NSCH data,

the CART algorithm recognized Sick_Care as the best split attribute.

Figure 17. Clementine® defined split.


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Given the modeling decision to investigate patterns of potentially preventable

emergency department utilization by insurance type, this choice was overridden in

favor of the Insurance attribute using the ‘Select Predictor’ dialog box.

Figure 18. Customizing split attributes using Clementine® select predictor dialog box.

Note that this dialog box provides information as to the potential improvement

associated with each competing predictor. Improvement is the degree to which the

impurity could be reduced by a split using that predictor. As the Gini index has been

specified for this model as the measure of impurity, improvement is specifically

defined as the difference between the Gini impurity index of the parent and that of the

resulting child nodes. In this case, the choice of Insurance as a split variable in place

of Sick_Care results in less improvement for this initial split but facilitates the

descriptive goals of this model.

As the tree grows, there are nodes at which multiple predictors promise very

similar improvements. In these cases, the decision to override CART’s first choice

can result in a more descriptive or understandable tree with negligible effect on

classification accuracy. Also, customizing splits can have a positive effect on the

descriptive ability of the model. In the interactive insurance model, an

MD_Phone_Help split was designed by CART to group attribute values ‘usually’ and


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‘not sought’ in one branch and ‘never’, ‘sometimes’, and ‘always’ in the other. This

split does not describe an understandable pattern of behavior. The split was

customized, instead grouping ‘usually’ with other attribute values indicating that

medical advice was sought by phone, resulting in no discernible difference in

accuracy and a far more understandable pattern of behavior.

Figure 19. Customizing split values using Clementine® define split dialog box.

Another advantage of the interactive method of building a decision tree is the

ability to test the effect of specific attributes on different subsets of the population.

An attribute split which contributes to the identification of a pattern can be tested on a

node in the same branch closer to the root to determine if the pattern can be

generalized. Likewise, an attribute can be chosen to split the data of various tree

branches in turn to compare the relative influence and effect on the target behavior

when applied to different groups. While these splits may result in less effective


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classification and therefore not become part of the final tree, they afford the

opportunity to gain knowledge of the data.

Interactive modeling allows the data miner to grow or prune a tree by level or

branch providing more specific control over the design and depth of each branch and

optimizing the descriptive value of the resulting tree. The three branches of the NSCH

insurance type tree designed interactively are described in detail in the following

sections.

Medicaid Insurance Branch

It has been noted that children covered by Medicaid health care insurance are

significantly more likely than their privately insured or uninsured counterparts to visit

hospital emergency departments with potentially preventable problems. Nationally,

11.62% of all NSCH respondents indicated such a visit. By comparison, 19.65% of

those who are insured by Medicaid engaged in this target behavior. This strong

association with potentially preventable emergency department utilization is reflected

in the decision rules built by CART to classify records of Medicaid insured children.

Figure 20. Medicaid decision tree branch.


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This model classifies the majority of training records, 78%, as potentially

preventable emergency department visitors. The strongest indicators of such a visit

are evidence of an illness which was treated outside of a hospital setting and age.

Table 16

Split Attributes – Medicaid Insurance Branch Attribute Split

0 sick care visits First split after insurance split

sick care > 0 sick care visits

< 6 years old age group school-age

<= 3 sick care visits

Second split after insurance split


< 6 years old Third split after insurance split

age group school-age

required immediate medical care

Fourth split after insurance split

got_imm_MD

required no immediate medical care black or other Fifth split after insurance

split race

white, Hispanic, multiple race, or unknown

none sixth split after insurance split

health conditions at least one

All Medicaid insured children less than six years old are identified as likely

associated with this target practice. School-age children who make no primary sick

care visits are among the few who are not classified as likely to make a potentially

preventable emergency department visit. Those who have made more than three such

visits are confidently classified as likely to make a potentially preventable emergency

department visit. Finally, those who have made one to three sick care visits are

classified according to their need for immediate medical care, race, and the existence

of health conditions. The necessity for immediate medical care in addition to sick care

results in a classification as potentially preventable emergency department visitor.


64

Black children have previously been shown to seek potentially preventable

emergency department care at a greater rate than children of other races. This model

classifies all black children who have sought any primary sick care as likely

associated with this practice.

Table 17 summarizes rules generated from this model with significant

confidence. Note that support refers to that of the model as a whole, not the individual

branch.

Table 17

Decision Rules Generated from Medicaid Branch Antecedent Consequent Support Confidence

if any sick care visits PPA 0.211 0.746 if 1-3 sick care visits PPA 0.132 0.689 if > 3 sick care visits PPA 0.076 0.840 if 1-3 sick care visits and < 6 years old PPA 0.069 0.756 if no sick care visits and school-age No_PPA 0.042 0.692 if 1-3 sick care visits and school-age and required any immediate medical care

PPA 0.022 0.684

if 1-3 sick care visits and school-age and required any immediate medical care and race = black or other

PPA 0.010 0.710

No Medical Insurance Branch

The NSCH data indicates that children who lack any form of medical

insurance make potentially preventable emergency department visits at a rate which is

consistent with that of privately insured children, but less than half that of Medicaid

insured children. This is the least represented insurance type in the NSCH data. Only

8.69% of the referenced children are uninsured. Consequently, the depth of this

branch is limited by the relative scarcity of records.


65

Figure 21. Uninsured decision tree branch.

As for Medicaid insured children, careful examination of the model indicates

that uninsured children who use the emergency department for potentially preventable

care are most recognizable by their utilization of other health care resources. Those

who seek no primary care for illness in person or medical advice by phone either lack

the necessity for such care or are hesitant to seek care due to financial concerns

imposed by the lack of insurance coverage. These children typically do not seek

potentially preventable care in an emergency department and are so classified by this

model.

Family income determines the classification of those who do visit a health

care professional to address an illness. Uninsured children from upper income

families are considerably less likely to make a potentially preventable emergency

department visit than those from lower or middle income families. The former are

classified by the model as non-visitors and the latter as visitors. Those of lower or

middle income families who seek phone advice in addition to making one or more

sick care visits are particularly likely to use emergency department services to address


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a potentially preventable concern. The use of multiple health care resources may

indicate more frequent, persistent, or serious illness.

Table 18

Split Attributes – Uninsured Decision Tree Branch Attribute Split

0 sick care visits First split after insurance split


sought medical advice by phone

md_phone_help

did not seek medical advice by phone lower, middle


income upper


Third split after insurance split

md_phone_help

did not seek medical advice by phone

For uninsured children, CART recognizes that indication of necessity for care and

income are more predictive of the target behavior than age. However, further data

exploration confirms that younger children without health insurance do make more

potentially preventable emergency department visits than older children who are

similarly uninsured.

Figure 22. Effect of age on potentially preventable emergency department utilization for uninsured children.


67

Table 19 summarizes rules generated from this model which achieved significant

confidence. These illustrate the importance of evidence of necessity for care in

determining association with a potentially preventable emergency department visit.

Once again, support is measured for the entire model. The limited concentration of

records associated with the lack of insurance results in corresponding small support

for these rules.

Table 19

Decision Rules Generated from Uninsured Branch Antecedent Consequent Support Confidence

if no sick care No_PPA 0.035 0.759 if no sick care and no medical advice by phone No_PPA 0.030 0.803 if at least 1 sick care visit and lower or middle income and medical advice sought by phone

PPA 0.013 0.748

Private Insurance Branch

More than 63% of the balanced training records reference children who have

private health care insurance coverage. These children are the most likely to have

access to adequate health care resources outside of a hospital.

Figure 23. Association with basic medical home by insurance type.

Nevertheless, they visit emergency department at a rate similar to that of uninsured

children.


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Figure 24. Private insurance decision tree branch.

For Medicaid or uninsured children, the best discriminator between those who

seek potentially preventable emergency department care and those who do not is the

need for sick care of any kind. For those who are insured privately, it is the need for

immediate medical care. Those with private medical insurance who require no

immediate care are unlikely to visit the emergency department with a potentially

preventable concern. In fact the CART branch dedicated to the classification of this

group identifies only infants and preschoolers with less than excellent health who

have sought primary sick care as likely to make such a visit.

All the attributes used to partition the privately insured training data measure

necessity for care or age. This branch of the decision tree classifies records as

potentially preventable visitors to an emergency department only with evidence of

extensive, immediate or specialized care or less than excellent general health. As with


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the Medicaid branch, the threshold of required care to gain this classification is lower

for infants and preschoolers.

Table 20

Split Attributes – Private Insurance Decision Tree Branch Attribute Split

required immediate medical care

First split after insurance split

got_imm_md

required no immediate medical care


md_phone_help

did not seek medical advice by phone 0 sick care visits



< = 6 sick care visits sick care > 6 sick care visits

< 6 years old

Third split after insurance split

age group school age

< 6 years old age group school age excellent

Fourth split after insurance split

gen_health < excellent

required a specialist Fifth split after insurance split

spec_pmd_explain

did not require or made no visit to a specialist

Table 21 summarizes the generated decision rules with significant confidence.

Table 21

Decision Rules Generated from Private Insurance Branch Antecedent Consequent Support Confidence

if no immediate care needed and no sick care visits

No_PPA 0.159 0.801

if no immediate care needed and no sick care visits and school-age

No_PPA 0.120 0.844

if immediate care needed and phone advice sought and < 7 sick care visits and age < 6

PPA 0.069 0.718

if immediate care needed and medical phone advice sought and > 6 sick care visits

PPA 0.031 0.822


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Comparison of Insurance Type Branches

All three branches of the insurance type model focus primarily on partitioning

the data according to likely necessity for care. Regardless of insurance type, children

are not brought to an emergency department unless care is necessary. What differs is

the typical level of care required for classification as a potentially preventable visitor.

Potentially preventable immediate concerns are those which could be treated with

prompt attention in primary care or emergent conditions which might have been

prevented by earlier primary care. Privately insured children characteristically seek

emergency department care when the concern is immediate in nature and are more

likely to receive all their non-immediate sick care in a primary setting when

compared with those who are covered by Medicaid or no insurance. While uninsured

children and those insured by Medicaid are also quite likely to seek immediate care in

an emergency department when such care is required, many are associated with a

potentially preventable emergency department visit but no need for immediate care.

These children seek care for clearly non-urgent conditions in an emergency

department. CART specifically identified black Medicaid insured children as

associated with this type of utilization. Further investigation linked black children

with this behavior across all insurance types and age groups. However, the disparity

between black children and those of other racial/ethnic classifications in this regard is

greatest among uninsured children. Black children without medical insurance are

almost as likely to seek potentially preventable care in an emergency room as those

insured by Medicaid. Uninsured white and Hispanic children are considerably less


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likely to seek such care, behaving much more like their privately insured peers in this

regard.

Table 22

Comparison of Percentage of Test Records Associated with a Potentially Preventable Emergency Department Visit by Population and Insurance Type

% Associated with PPA Visit Population Medicaid Insurance

Private Insurance

No Insurance

Black 21.09 12.46 18.58 White 17.58 8.73 8.20 Hispanic 18.4 9.66 9.36

Overall, Medicaid insured children are most likely to seek potentially

preventable emergency department care. In fact, the rate at which infants and

preschoolers are brought for such care is so significant that CART requires no

evidence of illness to classify them as likely visitors.

This model relies upon attributes which indicate utilization of primary care

resources in an attempt to gauge level of wellness and necessity for care which might

be sought in an emergency department. As with the general national model, these

measures are imperfect indicators. Many children are treated repeatedly in a primary

care setting and never seek emergency department care. Others utilize the emergency

department in place of primary care. In an effort to understand whether the type of

medical insurance affects the tendency to choose the emergency department

exclusively for care, the percentage of potentially preventable emergency department

visitors who use the emergency department as a sole source of primary care was

calculated by insurance type. Records referencing these children indicate a potentially


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preventable visit to a hospital emergency room but no sick care visits to any health

care professional outside of a hospital.

Table 23

Potentially Preventable Emergency Department Visitors who Use the Emergency Department as a Sole Source of Care – Test Data

Insurance Type PPA, No Sick Care PPA % ED as Sole Source of Care

Medicaid 204 1274 16.01 No Insurance 70 262 26.72

Private 286 1937 14.77 Uninsured children are most likely to seek care exclusively in an emergency

department. Despite the strong association between Medicaid health care insurance

and potentially preventable emergency department utilization, children covered by

Medicaid are considerably less likely than their uninsured peers to receive all their

primary care in an emergency department.

Model Evaluation The insurance type model correctly classifies 73.17% of the test data. Like the

national model, it misclassifies a significant number of records which are not

associated with a potentially preventable emergency department visit, resulting in a

false positive rate of 0.762. It also fails to correctly classify 38.96% of those who are

appropriately associated with such a visit but, given the predominance of the negative

class, these records comprise a small percentage of those assigned the negative

classification. Hence the false negative rate is only 0.063%.


73

Figure 25. Predicted vs. actual association with potentially preventable emergency department visit – insurance model.

Note that the national model and the insurance type model performed

similarly. Both relied predominantly upon indicators of necessity for care and age in

determining class. Cluster, which was chosen as the most significant split attribute by

the national model, was intentionally disregarded in the insurance modeling process

in the interest of improved transparency. Nevertheless, the accuracy of this model

approached that of the national model. The combined lift chart of Figure 26 compares

the performance of the two models. Arrows indicate the lift attained when each model

was applied to the test data.

Figure 26. Comparative lift of national and insurance models as applied to test data.


74

State Models The national and insurance type models identify the most significant

nationally applicable patterns of potentially preventable hospital emergency

department utilization. Segmentation of the training data by state affords the

opportunity to investigate geographic variations in these patterns. Separate CART

models were built for each of the fifty states and the District of Columbia. Each was

tested for general applicability using an independent, hold-out test data set.

The state models consistently reinforced the importance of indicators of

wellness or necessity for care and age in predicting the target behavior. As these

patterns have already been established and discussed, the focus of this section is

limited to rules noted in the state models which suggest patterns which were not

identified by the models of national scope. These CART rules are used as the starting

point of an investigation which seeks to determine if the suggested patterns apply

nationally or are uniquely characteristic of certain geographic areas. Where they do

suggest differences, additional extensive data exploration further defines and clarifies

those differences. This technique identified two factors associated with geographic

variation in potentially preventable emergency department utilization. Additionally,

understanding of generally applicable patterns of such utilization is broadened with

the identification and analysis of rules included in individual state models but not in

models built using the complete data set.


75

Geographic Variation Distance from hospital.

Thirty-five states have significant enough population in both MSA and non-

MSA areas to have this designation of residency applied to data collected there.

When considered as a group, children who reside in non-MSA areas are more likely

to be from less educated, lower or middle income families and are either uninsured or

insured by Medicaid. They are less likely to have a medical home and more likely to

be white. They are also more likely to visit a hospital emergency department with a

potentially preventable concern. However, when the data is segmented by state, there

are three states which, although they adhere to these demographic patterns do not

follow the typical pattern of potentially preventable emergency department

utilization. Children residing in non-MSA areas of Iowa, Nebraska, and Texas are

less likely to make a potentially preventable visit than their peers in MSA areas. An

investigation into shared characteristics which might explain this departure from

common utilization patterns revealed that pediatricians are in short supply in these

states. In fact, when compared to other states, these three have among the greatest

values for number of children per clinically active pediatrician. Aside from this

commonality, Nebraska and Iowa share many similarities with each other and few

with Texas. Nebraska and Iowa have significantly more children living in non-MSA

areas than most other states. In these states, approximately half the children surveyed

lived in rural areas. Both MSA and non-MSA areas in Nebraska and Iowa boast a

greater than average percentage of families with higher levels of education and

income, privately insured children and those who have a medical home. Children of


76

color make up a significantly smaller percentage of the population, particularly in

rural areas. Texas on the other hand, has a larger than average percentage of lower

income citizens with no insurance or doctor and little education. As is the case for

other states with both MSA and non-MSA designations, these characteristics are

somewhat less prevalent in MSA areas. However in Texas the relative disparity

between MSA and non-MSA areas in terms of the prevalence of these characteristics

is considerably less than the average for all states with such designation. Hispanic and

multiple race children are represented in both MSA and non-MSA areas in Texas at

2-3 times their average representation. Children of black or white race are

proportionately less represented than average. In fact, in MSA areas of Texas, white

children comprise only 41.53% of the surveyed population. Texas is unusual among

states with fewer clinically active pediatricians per child in that the vast majority of

children are clustered in MSA areas. In general, the density of pediatricians is

inversely proportional to the percentage of children living in non-MSA areas. In

Texas, 86.84% of the surveyed children reside in MSA areas, a percentage

significantly greater than the average of 72.7%.

Doobinin et al. (2003) investigated the significance of convenience in

determining whether to utilize a hospital emergency department. They considered the

tendency of urban children to seek emergency department care in place of primary

care due to proximity and hours of availability. It is suggested that in rural areas far

removed from hospitals, the opposite tendency is driven by the inconvenience of

traveling great distances to obtain hospital care. In these areas it may be more

convenient to obtain primary care which, given the scarcity of available pediatricians,


77

may be provided by general practitioners who lack specialized training in pediatrics.

These primary care physicians, realizing the travel burden, may be hesitant to suggest

a trip to a hospital emergency department if at all possible to treat the condition

locally. It is also logical that parents residing in such areas would be cognizant of the

inherent time delay in obtaining emergency department care and would therefore be

more likely to address issues before they became emergencies.

As the NSCH does not provide a means of determining the distance from a

hospital for each child, the previous assumptions can not be conclusively supported or

refuted by this data. However, the database created for the Mapping Health Care for

America’s Children Project (AAP, 2003) does present information regarding

geographic location of clinically active pediatricians within each state. Pediatricians

tend to cluster in MSA areas, particularly those with hospitals. There are significant

geographic areas in Nebraska, Iowa, and Texas which not only lack a clinically active

pediatrician, but also are far removed from any clusters of pediatricians which might

indicate the availability of hospital care. This suggests that distance from emergency

care may be one characteristic shared by non-MSA residents of these three states.

Additional data would be required to thoroughly investigate the correlation between

distance from a hospital and rate of potentially preventable emergency department

utilization.

Need for an interpreter.

Children for whom an interpreter is required for effective communication

between caregiver/patient and medical personnel are more likely to be associated with

a potentially preventable emergency department visit. The strength of this association


78

varies widely by state as does the representation of children concerned. While the

NSCH data indicates a national average of 1.76% of children who require an

interpreter, many states, most notably Maine, West Virginia, Montana, North Dakota,

and Vermont, have few or no such children among those referenced by this survey.

On the other hand, 7.11% of the children in CA require an interpreter for effective

communication with health care personnel. Arizona, Nevada, Oregon, Rhode Island

and Texas also have strong representation of this group. The likelihood that a child

requiring an interpreter will make a potentially preventable visit to an emergency

department in states with the greatest concentration of such children is below the

national average of 20.45%. In states with moderate populations of non-English

speaking families requiring interpreters, including Connecticut, Delaware, and New

Jersey, children from such families are far more likely to be brought to an emergency

department with potentially preventable complaints. In these states, over 30% of the

responding children who require interpreters are associated with a potentially

preventable visit. Furthermore, while this group of children is more associated with

Medicaid than any other insurance, the type of medical insurance has no apparent

effect on the tendency for them to seek care in a hospital setting. Perhaps children

who do not speak English are more likely to find bilingual medical personnel or

interpreters in a hospital emergency department. States with the largest

concentrations of non-English speaking families may offer more primary care settings

staffed with personnel who can effectively communicate in patients’ primary

languages. However children in states with more moderate non-English speaking

populations may find it difficult to obtain such care. Further research utilizing data


79

with measures of availability and location of multilingual medical personnel would be

required to confirm this supposition.

General Trends

Most of the rules generated for the individual state models identified patterns

which do not indicate geographic differences in behavior, but rather highlight patterns

of behavior which generally apply to children across the nation. In many cases, the

CART rule defined a very specific subset of children associated with the target

behavior, which additional data analysis proved could be validly generalized to a

larger population.

Asthma.

Numerous state models include rules predicting potentially preventable

emergency department visits according to asthma related factors. Many models

predict a visit to an emergency department with strong confidence if a child has had a

recent asthma attack. Such an attack is the best available indicator of ineffectively

managed asthma. Models included asthma rules applying to various subsets of the

population. Further data investigation verified that unmanaged asthma is a strong

indicator of potentially preventable emergency department utilization independent of

other factors.

Income and Insurance.

The uninsured branch of the insurance type model noted a difference in target

behavior for children who required some sick care according to family income. Those

from families with income classified as ‘upper’ are less associated with a potentially

preventable emergency department visit. Several state models included rules which


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supported this pattern which applies not only to the uninsured but to those who are

privately insured as well. Family income does not have a significant effect on

potentially preventable emergency department utilization for Medicaid insured

children. While most of these children do come from lower income families, the

minority from middle or upper income families engage in the target behavior at a rate

consistent with those of lower incomes.

Figure 27. Combined effect of income and insurance type on potentially preventable emergency department utilization.

Race and age.

Several state models utilized race to segment data. Rules consistently separate

black children from white children in classifying according to the target behavior.


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However, classification of Hispanic children is dependent on age. Hispanic infants

make potentially preventable emergency department visits at rate very similar to

black infants. As age increase, the likelihood of a visit decreases for all races, but

does so most dramatically for Hispanics. By adolescence, Hispanic children behave

most similarly to white children.

Figure 28. Combined effect of race and age on potentially preventable emergency department utilization.

Medical home and sick care.

The effect of access to a medical home on potentially preventable emergency

department utilization differs according to necessity for care as measured by sick care


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visits outside of a hospital. Healthy children with no medical home who require no

sick care are less likely to engage in the target behavior than similarly healthy

children with a medical home. However, when consideration is limited to those who

require sick care, the absence of a medical home is associated with greater likelihood

of a potentially preventable emergency department visit.

Figure 29. Combined effect of medical home status and amount of sick care on potentially preventable emergency department utilization.

Caregiver structure and age.

Exploratory data analysis found that children of single mothers are more

likely to make a potentially preventable emergency department visit than those who

are cared for in two-parent homes while those raised by a single father are least likely

to make such a visit. CART devised rules which suggest age plays a role in

determining the influence of caregiver structure on the decision to seek potentially

preventable emergency care for a child. Further data investigation showed that indeed


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single fathers of infants behave similarly to single mothers in this regard.

Preschoolers with single fathers seek such care at a rate less than that of children with

single mothers but still greater than those cared for by two parents. The characteristic

hesitance of single fathers to seek potentially preventable emergency department care

for their children is evident only for school-age children.

Figure 30. Combined effect of caregiver status and age on potentially preventable emergency department utilization


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Caregiver education and medical advice by phone.

The effect of consistently available medical advice by phone on potentially

preventable hospital emergency department utilization was noted to be dependent on

caregiver level of education. Children of caregivers with less than a high school

education are no less likely to make a potentially preventable emergency department

visit if consistent access to medical advice by phone is available. In more educated

families, the availability of such advice is associated with decreased likelihood of

such a visit.

Figure 31. Combined effect of consistently available medical phone advice and education on potentially preventable emergency department utilization.

Perhaps there is more effective communication between the doctor and caregiver or

the doctor feels more confident in accepting the more educated caregiver’s appraisal

of the nature of the child’s problem. Ineffective communication may result in the

medical decision to direct that the child be brought to the emergency department or


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the degradation of the child’s condition resulting in the necessity of emergency care.

Regular exercise and age.

The school-age child who does not participate in regular exercise is more

likely to make a potentially preventable emergency department visit than his/her peer

who does exercise. While this pattern holds for all school-age children, for the NSCH

data, it is most pronounced for those between the ages of 10 and 12.

Figure 32. Combined effect of lack of exercise and age on potentially preventable emergency department utilization.


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Association Rule Mining

As this thesis focuses on the utilization of hospital emergency department

services which are preventable by appropriate timely primary care, it is instructive to

thoroughly investigate how access to and utilization of adequate primary care

resources impacts such emergency department utilization. A final modeling exercise

therefore employs a supervised a priori association rule mining technique to develop

an understanding of health care requirements and utilization characteristic of groups

of children who are (a) associated with a medical home, (b) associated with a

personal doctor who provides less than the standard of care required of a medical

home, or (c) lacking association with a personal doctor and hence, with a medical

home. Clarification of the distinct manner in which each of these groups utilize health

care resources provides insight into how and why each typically seeks potentially

preventable emergency department care.

The a priori algorithm was developed by Agrawal and Srikant (1994) to

address the challenge of efficiently determining associations among attributes in a

large database. The SPSS Clementine® version of a priori was applied to the NSCH

data to generate rules which demonstrate the associations of other attribute values

with each medical home class. Each association is quantified by measures of support,

confidence, and lift. Rule support refers to the coverage of the rule measured as the

proportion of all records for which the rule is true. Antecedent support is the prior

probability of the antecedent within the data set. The confidence or accuracy of the

rule measures the ability of the antecedent to predict the consequent. It is calculated

by dividing the rule support by the antecedent support. Clementine® also calculates


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lift which is the ratio of the rule confidence to the prior confidence of the consequent.

A rule with a lift which exceeds one demonstrates a positive association between

antecedent and consequent. Greater lifts imply stronger associations.

The practical value of an association rule is determined by the rule confidence

deviation from the prior confidence of the consequent. In this supervised application

of the a priori algorithm, consequents are restricted to Base_Med_Home attribute

values which describe access to a medical home. Therefore, prior confidences are

determined by the distribution of this attribute.

Figure 33. Base_Med_Home distribution determining prior probabilities for association rules.

A priori limits the number of rules generated by adhering to minimum rule

support and confidence specifications. Because two of the consequent classes are

relatively rare, minimum support and confidence must be set sufficiently low to allow

generation of rules describing associations for these groups of children. This could

result in a prohibitively large number of rules to analyze. However, Clementine®’s a

priori algorithm allows the use of a confidence ratio evaluation measure to restrict the

rules formed to those for which the difference from one of the ratio of rule confidence

to prior confidence of the consequent (or its reciprocal if this ratio is greater than one)

exceeds some specified minimum. Stated more succinctly, rules are included if

| Lift -1| > evaluation measure lower bound (4)


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This method addresses the challenge of recognizing proportionately significant

changes in confidence when outcomes are not evenly distributed as is the case with

medical home classifications. It limits the generation of rules of little value while

allowing the inclusion of rules describing associations with rare categories such as the

No_Med_Home classification.

The a priori algorithm requires categorical inputs. The few attributes utilized

in this study which are not categorical can be logically discretized without

appreciable loss of information. In this process, logical groupings were determined by

analysis of CART rules formed using these variables during the classification mining

phase of this project. For example, the categorical representation of sick care

identifies the number of visits to health care professionals outside of a hospital for

treatment of illness as ‘none’, few (1-3), or many (4 or more) because CART

determined the significance of these groupings in earlier health care utilization

analysis. Similarly, general health was grouped as ‘excellent’, ‘very good’, or ‘less

than very good’. Additionally, it was noted that CART rules used attributes which

detailed relative availability of medical assistance by phone and immediate access to

primary care as dichotomous indicators of utilization of such resources.

Consequently, flags were derived to more effectively serve this purpose in association

mining.

Association Rules by Medical Home Category

Association with a Medical Home

The majority of children referenced by the NSCH, 76.95%, have access to a

basic medical home. Table 24 identifies the attributes most associated with these


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children. In addition to Clementine® measures of data set antecedent support,

confidence, and lift, this table includes a measure of the rule support within the

medical home classification which indicates the percentage of records so classified

for which the antecedent is true. To illustrate using the first rule in Table 24, this

measure indicates that 79.976% of those children associated with a medical home

received all recommended preventative care. This provides an additional means of

gauging associations and characterizing the group.

Table 24 Rules Identified with Association with a Medical Home

Antecedent Antecedent Support

Confidence Support within Med Home = Yes

Lift

Prev_Care = yes 62.406 98.615 79.976 1.282 Immed_Care = yes 26.730 96.533 33.533 1.254 Phone_Help = yes 37.844 96.057 47.241 1.248 Sick_Care_Visits = many 15.225 89.237 17.656 1.160 Asthma = recent attack 4.969 87.378 5.642 1.135 Sick_Care_Visits = few 46.568 86.629 52.425 1.126 Children with a medical home characteristically make significant use of all

medical resources at their disposal. Of the three groups, they are most likely to

receive all recommended preventative care, immediate and non-immediate ill care in

a primary care setting, and medical advice by phone. They are also most likely to use

emergency department resources for potentially preventable concerns. Of the

children referenced in the NSCH study who were associated with a medical home,

12.29% indicated at least one such visit to an emergency department. Contributing to

their greater health care utilization, these children are more likely than their peers to

require significant care in the form of many ill visits, immediate care, or uncontrolled

asthma.


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Association with a Personal Doctor but No Medical Home

Most children who identify a personal doctor receive sufficient care to

indicate association with a medical home. However, 8.65% of the children referenced

by the NSCH claimed association with a personal health care provider but did not

receive the standard or care required of a medical home.

Table 25 Rules Identified with Access to a Personal Doctor but Lack of Association with a Medical Home


Confidence Support within Med_Home =

No

Lift

Prev_Care = no 37.594 20.703 89.978 2.394 Income = middle and Sick_Care_Visits = none

14.474 20.220 33.834 2.338

General_Health = very good and Sick_Care_Visits = none

7.312 20.014 16.918 2.314

Sick_Care_Visits = none and Insurance = Private

24.924 18.579 53.533 2.149

Sick_Care_Visits = none 38.207 18.412 81.326 2.129 Sick_Care_Visits = none and General_Health = less than very good

4.118 18.322 8.723 2.119

Sick_Care_Visits = none and General_Health = Excellent

26.777 17.989 55.687 2.080

Sick_Care_Visits = none and Insurance = Medicaid

8.465 17.181 16.814 1.987

Sick_Care_Visits = none and Income = upper

15.016 16.089 27.930 1.860

Insurance = none 8.668 13.501 13.529 1.561 EDUCATIONR = less than high school

4.520 13.165 6.879 1.522

Race = Hispanic 5.769 11.635 7.760 1.346 Phone_Help = no 62.156 11.512 82.721 1.331 EDUCATIONR = high school grad

20.760 11.203 26.887 1.296

Income = lower 19.778 10.748 24.575 1.243 Immed_Care = no 73.270 10.538 89.262 1.219


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These children characteristically underutilize all medical resources. Only 10% adhere

to the recommended schedule of preventative care. Most make little or no use of

primary care resources in the form of immediate or non-immediate sick care visits or

medical assistance by phone. Additionally they are unlikely to be brought to an

emergency department for potentially preventable care. Only 4.28% of the NSCH

children in this group make such a visit. They are more likely to come from less

educated, lower income, uninsured families than their peers with a medical home but

considerably less so than those without any personal doctor. Those of this group who

are insured or come from families of higher income level are likely associated with no

primary sick care visits. In fact the single attribute which most distinguishes this

group is the lack of primary sick care received. Only 18.67% of these children make

a sick care visit as compared with 70.08% of those with a medical home and 43.41%

of those without a personal doctor. While some of these children enjoy excellent

health and receive no sick care because none is required, not all forgo such care due

to good health. Children of every general health status who make no sick care visits

are significantly represented.

Lack of Association with a Personal Doctor

Children who have no personal doctor comprise 14.4% of those studied by the

NSCH. By survey design, when a respondent indicates the lack of association with a

personal health care provider, questions regarding access to health care generally

provided by a personal doctor or nurse are omitted. These include preventative care,

medical phone advice, and immediate primary care. Consequently, every child of this

group is associated with the lack of access to and utilization of these resources.


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Table 26 Rules Identified with Lack of Association with a Personal Doctor


Confidence Support within Med Home =

No Doc

Lift

Prev_Care = no 37.594 38.306 100 2.660 EDUCATIONR = less than high school

4.520 36.254 11.380 2.517

Insurance = none 8.668 35.785 21.541 2.485 Race = Hispanic 5.769 31.629 12.671 2.196 Insurance = none and General_Health = Excellent

4.545 30.741 9.703 2.135

Income = lower 19.778 24.625 33.822 1.710 Phone_Help = no 62.156 23.169 100 1.609 General_Health = less than very good

12.640 21.722 19.067 1.508

Race = multiple race 10.920 21.534 16.330 1.495 EDUCATIONR = high school grad

20.760 21.329 30.749 1.481

Sick_Care_Visits = none 38.207 21.327 56.586 1.481 Race = black 9.332 20.987 13.601 1.457 num_sick_days = none 23.867 20.726 34.352 1.439 Immed_Care = false 73.270 19.655 100 1.365 Insurance = Medicaid 21.948 18.866 28.755 1.310 Children without a personal doctor are most likely to be raised in less educated, lower

income families and to be uninsured. More Hispanic and multiple race children are

represented in this group than any other. These children are more likely to make a

sick care visit than those with a personal doctor but no medical home but make

considerably fewer than those with a medical home. They are more likely to be of

less than very good health and thus require care that may not be available to them in a

primary care setting due to their lack of association with a personal doctor. However

almost 10% of this group enjoys excellent health but lacks health care insurance

coverage. In addition to financial constraints, the lack of requirement for care may

explain the absence of association with a doctor for this latter group.


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Children without a personal doctor are restricted in their access to medical

care outside of a hospital. They seek potentially preventable care in the emergency

department at a rate of 11.38% which approaches that of those with a medical home.

It is logical to assume that lack of access to primary care results in the use of the

emergency department as a proxy for primary care for many of these children.

Practical Application of Association Rule Mining

The diverse characterizations of these groups suggest that effective efforts to

decrease potentially preventable emergency department utilization would differ

according to medical home status.

Children associated with a medical home have access to quality primary care

as well as emergency care. Initiatives which address the choice of the emergency

department for potentially preventable care when primary care resources are also

available are applicable to this group.

Children who have no personal doctor often lack access to primary care

outside of the emergency department as well as medical insurance or sufficient

income to make such care affordable. These children are more likely to use the

emergency department as a proxy for primary care or to allow health care problems to

deteriorate until emergency care is required. Efforts to encourage less potentially

preventable emergency department utilization for this group must address the

availability and accessibility of primary care services to those of limited resources

who lack insurance.


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The concern regarding potentially preventable emergency department

utilization by children with a personal doctor but no medical home is of a different

nature. For these children, the issue is not the choice of emergency department care in

place of primary care, but rather the hesitancy to seek care at all. While these children

are least likely to make potentially preventable emergency department visits, they are

also least likely to receive attention to illness in a primary care setting. Actions aimed

at ensuring more appropriate health care utilization should address factors which

discourage utilization of preventative and primary care services provided by the

child’s personal doctor.


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RESULTS

In Discovering Knowledge in Data, Larose (2005, p. 197-8) distinguishes

descriptive global models which summarize the relationship of the entire data set with

the target and local patterns which apply to limited subsets of the data. This study

analyzed the NSCH data from both a global and local perspective.

Globally, the most important factor in determining potentially preventable

emergency department utilization is the child’s level of wellness. Before attempting

to ascertain the likelihood that the emergency department will be chosen instead of

primary care when care is required, models first consider whether care will be

required. Evidence of illness in the referenced year in the form of immediate and non-

immediate sick care visits to a primary care provider or requests for medical advice

by phone are indicators of required care. Clusters formed by the k-means algorithm

which are distinguished by their association with level of wellness and health care

utilization also serve this purpose. For those who do require potentially preventable

care, age and insurance coverage influence the choice to seek such care in an

emergency department. Younger children and those with Medicaid insurance are

most likely to do so. Privately insured children are more likely to receive non-

immediate care in a primary care setting and seek care in the emergency department

for immediate concerns.

While the global model provides a broad understanding of potentially

preventable emergency department utilization, local patterns identify how factors

which apply to more limited populations can influence such utilization. These

patterns provide a more complete understanding of the target behavior and often


96

suggest methods of steering distinct groups of children with potentially preventable

conditions towards timely utilization of primary care rather than emergency services.

The following local patterns were identified by this study and quantified using the full

NSCH data set:

1. Children insured by Medicaid health plans are more than twice as

associated with potentially preventable emergency department utilization than

privately or uninsured children. This dramatic association exists regardless of general

health status, age or racial/ethnic classification.

2. Black children are 47% more associated with potentially preventable

emergency department visits than children of other races or ethnic backgrounds. This

pattern of behavior is independent of insurance status or income level which suggests

a cultural component in the decision as to where to seek medical attention for a child.

3. Privately or uninsured children from families with greater income are less

likely to make potentially preventable visits to emergency departments than those

with lesser family incomes. This pattern is particularly evident among children with

no health insurance. Uninsured children from upper income families are 42% less

associated with potentially preventable emergency department utilization than

uninsured lower or middle income children. Presumably these families can afford

primary care which is not covered by insurance.

4. Children who have required and received primary sick care but have no

access to a medical home have a 28% greater likelihood of seeking potentially

preventable emergency department care than their peers with a medical home.


97

5. Children with asthma are more than twice as likely to seek emergency

department care as those who are not associated with this condition. The association

with such care is most dramatic for children with asthma which is not well managed

as is indicated by a recent asthma attack.

6. School-age children who do not regularly exercise are 51% more likely to

make a potentially preventable visit to an emergency department than their peers who

do exercise. The NSCH data indicates that this pattern most pertains to children

between the ages of 10 and 12.

7. Children from more educated families are less associated with potentially

preventable emergency department visits. They are also more likely to have a

personal health care provider who usually or always provides necessary phone advice.

Furthermore, among all families who have access to consistently helpful medical

advice by phone, greater education is associated with a lesser likelihood that the child

will seek potentially preventable care in an emergency department. Children from

such families with post-high school education are 36% less likely to seek such care

than those from families with lesser educational attainment.

8. Children of single mothers are 36% more likely to be taken to an

emergency department with potentially preventable concerns than children of families

with any other caretaker structure. Single fathers are the least likely to seek such care

for their children. Children in the care of a single father are associated with

potentially preventable emergency department visits at a rate which is 45% less than

that of children with a single mother caregiver. This suggests a gender difference in

caregiver determination of appropriate action in response to a child’s medical need.


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The hesitance of single fathers to seek potentially preventable care in an emergency

department however is influenced by the age of the child, applying most strongly to

children of school age.

9. Overweight school-age children are 45% more likely to use an emergency

department for potentially preventable care than their peers of lesser weight. For

these children, a regular exercise regimen does not decrease the likelihood of this

practice.

10. Children from non-English speaking households who require an

interpreter for effective communication between patient/caregiver and medical

personnel are generally more likely than their English-speaking peers to seek

potentially preventable care in a hospital emergency department. The degree to which

this pattern holds varies by state. In states with the most significant concentrations of

non-English speaking families, children from these families are less associated with

this type of emergency department utilization than similar children residing in states

with smaller non-English speaking populations. While many such children are

insured under Medicaid, this pattern is independent of insurance type. Logically,

bilingual primary care would be more available in areas with large concentrations of

non-English speaking patients. This finding suggests that the availability of

interpreters or bilingual medical personnel in primary care settings would decrease

the rate of utilization of hospital emergency departments for children from non-

English speaking families.

11. Children residing in rural areas which are far removed from hospitals

apparently make fewer potentially preventable emergency department visits. This


99

supports the logical assumption that the decision to seek potentially preventable care

in an emergency department is influenced by the geographic distance from residence

to the hospital. More information would be required to support or refute this

suggestion as this data does not include a specific measure of distance between

residence and nearest hospital for each referenced child.

12. Adolescents are the least likely age group to make a potentially

preventable emergency department visit. They make such visits at a rate which is

45% less than that of younger children. While adolescents are frequent visitors to the

emergency department, their visits are predominantly due to accidents, injuries or

poisonings.

13. Uninsured children are most likely to rely exclusively on the emergency

department for care, doing so at a rate which is 53% greater than that of Medicaid

insured children and 68% greater than that of privately insured children.

14. Hispanic children are least likely to be insured or associated with a

medical home. They are three times as likely as children of other racial/ethnic

categories to be uninsured and twice as likely to lack association with a medical

home.

Finally, this study clarified the effect of consistent access to adequate primary

care on potentially preventable emergency department utilization. Health care

utilization patterns were compared by medical home classification providing insight

into how and why each group utilizes the emergency department as part of the

delivery of treatment potentially available in a primary care setting. Children with

access to a quality medical home require and use the most health care resources and


100

are most likely to make a potentially preventable emergency department visit.

Caregivers of these children, sometimes with the direction of a primary care

professional, choose the emergency department for care in place of available primary

care. Children who are not associated with a medical home are more likely to utilize

the emergency department for potentially preventable care due to lack of available or

accessible primary care. Children without a personal doctor are most likely to seek

such care in an emergency department. Many lack access to primary care and use the

emergency department in its place. Those who do have a personal doctor but do not

receive the standard of care required of a medical home are least likely to seek

potentially preventable emergency care or any other care. For these children, the

choice is not where to seek care, but whether to seek care. Differences in health care

utilization for these diverse groups suggest that effort to direct children away from the

emergency department and towards primary care for potentially preventable problems

would optimally consider access to a medical home and be tailored accordingly.


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DISCUSSION

This study showed that patterns of potentially preventable emergency

department utilization are relatively consistent nationally. With a few noted

exceptions, state by state analysis demonstrated that most of the studied factors had a

consistent effect on potentially preventable emergency department utilization.

Differences in rules generated by state patterns were most often attributable to

different local concentrations of the subpopulations of focus not differences in

behavior associated with the attributes which define them.

Some of the patterns noted by this study merely confirm previous research and

observational experience in emergency departments across the country. That sick

children, infants, or those covered by Medicaid are more likely to visit an emergency

department for potentially preventable care has been widely noted by those who

provide and study such care. However, others illustrate less obvious and more

actionable influences on the target behavior. Some discerned patterns provide

insightful clarification of the effect of previously studied factors such as age, access

to care, and caregiver structure. Others provide information as to the effect of factors

not evident in previous research including the influence of regular exercise or weight.

Data mining analysis allowed the discernment of multivariate influences on

potentially preventable emergency department utilization leading to a more

sophisticated understanding of how factors interact to affect potentially preventable

emergency department utilization.

This study devised a method of utilizing NSCH data to classify each child

according to access to quality primary care in the form of a medical home. It was


102

determined that the degree of association with a medical home significantly

influences health care utilization patterns suggesting that efforts to address

motivations for seeking potentially preventable emergency department care should

consider this indicator of access to quality primary care.

Limitations of Study

As with any study, there are certain limitations which should be noted. First,

the National Survey of Children’s Health data was collected by interviewing a parent

or caregiver of each focal child. Accuracy of the information provided is contingent

upon the respondent’s accurate memory and correct reporting. As such some

inaccuracies are to be expected.

Secondly, the design of this study differentiates as potentially preventable all

emergency department visits which were not the result of accident, injury or

poisoning. This criterion for classification is a reasonably but not absolutely accurate

distinguisher of conditions preventable by primary care. There are uncommon

emergent conditions not the result of accidents, injuries, or poisonings which are

arguably not preventable with earlier intervention. There are also individual cases

where a primary care professional is consulted in a timely manner and the child’s

condition nevertheless deteriorates necessitating emergency care. Since the

designation as potentially preventable is based upon category of complaint rather than

a medical diagnosis by individual case, it is likely that some records will be classified

differently than they might be by individual medical review. Nevertheless, given the

volume of data, it’s reasonable to assume that identification of patterns will not be


103

seriously hampered by the possible misclassification of a relatively small number of

records.

Finally, as is common in data mining applications, the data being analyzed

was collected for some other purpose rather than specifically for this project. Hence,

the analyst is restricted to utilization of available and derivable variables.


104

REFERENCES

Alessandrini, E., Shaw, K., Bilker, W., Perry, K., Baker, D., & Schwarz, D. (2001).

Effects of Medicaid managed care on health care use: infant emergency

department and ambulatory services. Pediatrics, 108, 103-110.

Alpert, J., Robertson, L., Kosa, J., Heagarty, M., & Haggerty, R. (1976). Delivery of

health care for children: report of an experiment. Pediatrics, 57, 917-930.

American Academy of Pediatrics. (1999). Recommendations for Preventative

Pediatric Health Care (RE9535). Retrieved September 14, 2006, from

American Academy of Pediatrics website. [link]

American Academy of Pediatrics (2003) Mapping Health Care Delivery for

America’s Children. Retrieved November 15, 2006, from American Academy

of Pediatrics website. [link]

American Academy of Pediatrics. (n.d.). Children’s Health Topics: MEDICAL

HOME. Retrieved October 30, 2006, from American Academy of Pediatrics

website. [link]

Agrawal, R., Srikant, R. (1994). Fast algorithms for mining association rules in large

databases. In J. Bocca, M. Jarke, and C. Zanieolo (Eds.), Proceedings of the

International Conference on Very Large Data Bases, Santiago, Chile. San

Francisco: Morgan Kaufmann, pp. 478-499.

Bethell, C., Read, D., & Brockwood, K. (2004, May). Using existing population-

based data sets to measure the American Academy of Pediatrics definition of

medical home for all children and children with special health care needs.

Pediatrics, 113(5), 1529-1537.

http://aappolicy.aappublications.org/cgi/reprint/pediatrics;105/3/645.pdf

http://www.aap.org/mapping/

http://www.aap.org/healthtopics/medicalhome.cfm


105

Breiman, L., Friedman, J., Olshen, R., Stone, C. (1984). Classification and

Regression Trees. Boca Raton, FL: Chapman & Hall/CRC Press.

Christakis, D., Wright, J., Koepsell, T., Emerson, S., & Connell, F. (1999). Is greater

continuity of care associated with less emergency department utilization?

Pediatrics, 103(4), 738-742.

Cunningham, P., Clancy, C., Cohen, J., & Wilets, M. (1995). The use of hospital

emergency departments for nonurgent health problems: a national perspective.

Medical Care Research and Review, 52(4), 453-474.

Dombkowski, K., Stanley, R., & Clark, S. (2004). Influence of Medicaid managed

care enrollment on emergency department utilization by children. Archives of

Pediatrics & Adolescent Medicine, 158, 17-21. [link]

Doobinin, K., Heidt-Davis, P., Gross, T., & Isaacman, D. (2003). Nonurgent pediatric

emergency department visits: care-seeking behavior and parental knowledge

of insurance. Pediatric Emergency Care, 19(1), 10-14.

Fredrickson, D., Molgaard, C., Dismuke, S., Schukman, J., & Walling, A. (2004).

Understanding frequent emergency room use by Medicaid-insured children

with asthma: a combined quantitative and qualitative study. Journal of the

American Board of Family Practice, 17, 96-100. [link]

Gill, J., Mainous, A., & Nsereko, M. (2000). The effect of continuity of care on

emergency department use. Archives of Family Medicine, 9, 333-339.

http://archpedi.ama-assn.org/cgi/reprint/158/1/17.pdf#search=%22rachel%20stanley%20michigan%20er%22

http://www.jabfm.org/cgi/content/full/17/2/96


106

Health Resources and Services Administration Maternal and Child Health Bureau and

Centers for Disease control and Prevention National Center for Health

Statistics. (n.d.). National Survey of Children’s Health, 2003 State and Local

Area Integrated Telephone Survey. Retrieved August 25, 2006, from Centers

for Disease Control and Prevention website. [link]

Institute of Medicine of the National Academy of Sciences. (2006, June). Emergency

care for children: growing pains. In The future of emergency care in the

United States health system. Washington, DC: National Academies Press.

[link]

Johnson, W., & Rimsza, M. (2004). The effects of access to pediatric care and

insurance coverage on emergency department utilization. Pediatrics, 113(3),

483-487. [link]

Kotagal, U., Schoettker, P., Atherton, H., Hormung, R., Bush, D, Pomerantz, W., &

Schubert, C. (2002). Relationship between early primary care and emergency

department use in early infancy by the Medicaid Population. Archives of

Pediatrics & Adolescent Medicine, 156, 710-716.

Larose, D. (2005). Discovering Knowledge in Data: An Introduction to Data Mining.

Hoboken, New Jersey: John Wiley and Sons.

Luo, X., Liu, G., Frush, K., & Hey, L. (2003). Children’s health insurance status and

emergency department utilization in the United States. Pediatrics, 112(2),

314-319. [link]

http://www.cdc.gov/nchs/data/slaits/NSCH_Questionnaire.pdf

http://www.nap.edu/

http://pediatrics.aappublications.org/cgi/content/full/113/3/483

http://pediatrics.aappublications.org/cgi/content/full/112/2/314


107

McCraig, L., & Burt, C. (2005, May 26). National hospital ambulatory medical care

survey: 2003 emergency department summary (Advance Data from Vital and

Health Statistics Number 358). Hyattsville, MD: U.S. Department of Health

and Human Services Centers for Disease Control and Prevention National

Center for Health Statistics. [link]

National Survey of Children’s Health, 2003 [Data file]. Hyattsville, MD: Health

Resources and Services Administration Maternal and Child Health Bureau

and Centers for Disease control and Prevention National Center for Health

Statistics. [link]

Newcomb, Patricia. (2005). Predicting choice of health services for publicly insured

children with non-urgent illnesses. Southern Online Journal of Nursing

Research, 6(3). Retrieved September 26, 2006, from Southern Nursing

Research Society website. [link]

Phelps, K., Taylor, C., Kimmel, S., Nagel, R., Klein, W., & Puczynski, S. (2000).

Factors associated with emergency department utilization for nonurgent

pediatric problems. Archives of Family Medicine, 9, 1086-1092. [link]

Piehl, M., Clemens, C., Jones, J. (2000). “Narrowing the gap” Decreasing emergency

department use by children enrolled in the Medicaid program by improving

access to primary care. Archives of Pediatrics and Adolescent Medicine,

154(8), 791-795.

http://www.cdc.gov/nchs/data/ad/ad358.pdf

http://www.cdc.gov/nchs/about/major/slaits/nsch.htm

http://www.snrs.org/publications/SOJNR_articles/iss03vol06.pdf#search=%22predicting%20choice%20health%20services%20publicly%20insured%20children%20non-urgent%20illnesses%20newcomb%22

http://archfami.ama-assn.org/cgi/reprint/9/10/1086.pdf


108

Pollack, H., Dombkowski, K., Zimmerman, J., Davis, M., Cowan, A., Wheeler, J.,

Hillemeier, A., & Freed, G. (2004, June). Emergency department use among

Michigan children with special health care needs: an introductory study.

Health Services Research. Retrieved September 20, 2006, from the

LookSmart® FindArticles database. [link]

Quinlan, R. (1993). C4.5: Programs for Machine Learning. San Mateo, CA: Morgan

Kaufmann Publishers.

Ryan, S., Riley, A., Kang, M. & Starfield, B. (2001). The effects of regular source of

care and health need on medical care use among rural adolescents. Archives of

Pediatrics & Adolescent Medicine, 155, 184-190.

SPSS Incorporated. (2005). Clementine® 10.0 Users Guide. Chicago: SPSS Inc.

State Health Statistics (n.d.). Retrieved November 30, 2006, from State Health

Statistics website. [link]

United States Census Bureau. (n.d.). Census Regions and Divisions of the United

States. Retrieved September 2, 2006, from United States Census Bureau

website. [link]

United States Census Bureau (n.d. ). State and County QuickFacts. Retrieved

November 15, 2006, from United States Census Bureau website. [link]

U.S. Department of Health and Human Services Centers for Disease Control and

Prevention National Center for Health Statistics. (2006). Design and

Operation of the National Survey of Children’s Health, 2003 (Vital and

Health Statistics, Series 1, Number 43). Washington, DC: U.S. Government

Printing Office. [link]

http://findarticles.com/p/articles/mi_m4149/is_3_39/ai_n6066528

http://www.shstats.org/series/index/100000

http://www.census.gov/geo/www/us_regdiv.pdf#search=%22US%20REGIONS%20BY%20STATE%22

http://quickfacts.census.gov/qfd/

http://www.cdc.gov/nchs/data/series/sr_01/sr01_043.pdf


109

U.S. Department of Health and Human Services Centers for Disease Control and

Prevention National Center for Health Statistics. (2006, February). Summary

Health Statistics for U.S. Children: National Health Interview Survey, 2004

(Vital and Health Statistics, Series 10, Number 227). Washington, DC: U.S.

Government Printing Office. [link]

Walls, C., Rhodes, K., & Kennedy, J. (2002). The emergency department as usual

source of medical care: estimates from the 1998 National Health Interview

Survey. Academic Emergency Medicine 9(11), 1140-1145.

Weinick, R., Billings, J., & Burstin, H. (2002, January). What is the role of primary

care in emergency department overcrowding? Publications of the Council on

Health Care Economics and Policy. Retrieved September 20, 2006, from The

Council on Healthcare Economics and Policy website. [link]

http://www.cdc.gov/nchs/data/series/sr_10/sr10_227.pdf

http://council.brandeis.edu/pubs/WeinickED.pdf#search=%22children%20emergency%20department%20preventable%22


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Appendix A: Modeling Attributes

ATTRIBUTE NSCH ATTRIBUTES

USED IN DERIVATION

DESCRIPTION

Age_Group AGEYR_CHILD child’s age group Age_Position AGEPOS4 child’s age relative to the age of other

children residing in household Allergy S2Q38

S2Q39 S2Q40

true if child has respiratory, food, or skin allergies

Asthma S2Q49 S2Q52A

asthma classification

Asthma_Severity S2Q50 caregiver’s assessment of asthma severity

Base_Med_Home S4Q03R S4Q07 S5Q01 S5Q02 S5Q04

S5Q08A S5Q08B

basic medical home classification

Caregiver RELATION TOTADULT3

cargiver structure of household in which child resides

Condition_Severity S2Q22 S2Q24 S2Q26 S2Q37 S2Q41 S2Q44 S2Q47

severity of most severe health condition (excluding asthma but

including allergies)

EDUCATIONR EDUCATIONR highest level of education attained by any member of child’s household

Gender S1Q01 gender of child GEN_HEALTH S2Q01 child’s general health status Got_Immed_MD S5Q07A how often immediate care for illness

or injury is available from PMP Health_Conditions S2Q22

S2Q24 S2Q26 S2Q37 S2Q41 S2Q44

true if child has diabetes, depression, bone, joint or muscle problem,

developmental delay or physical impairment, severe

headaches/migraines, or multiple ear infections

Income POVERTY_LEVELR family income category


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Insurance S3Q01 S3Q02

type/existence of medical insurance coverage

MD_Phone_Help S5Q06A how often phone advice is available from personal medical professional

(PMP) Multiple Allergies S2Q38

S2Q39 S2Q40

true if child has multiple allergic conditions

NEED_INTERP NEED_INTERP true if child/caregiver requires an interpreter to communicate with

medical personnel No_Exercise S7Q21 true if school-age child engages in no

regular, vigorous exercise NonUS_Mom RELATION

S11Q03 S11Q04

true if primary caregiver is not a United States native

Obesity BMICLASS true when child is above the 95th percentile of BMI-for-age

PPA_Visits S4Q04A S4Q04R S4Q05R

Target: true if at least one potentially preventable ED visit

PREV_CARE AGEYR_CHILD S4Q03R S5Q01

S5Q08A S5Q08B

true if child receives all preventative care recommended by the American

Academy of Pediatrics

Race RACER Hispanic

race/ethnicity of child

Region STATE US Census Bureau Region Residence MSA_STAT

STATE urban/rural character of residence

Sick_Care S4Q06R number of visits to a health care professional outside of a hospital for

sick care Sick_Days S7Q02R number of days off school due to

illness SCare_PMD_Access S5Q10B true if PMP helps get specialized

equipment or care when needed SCare_PMD_Explain S5Q10C how often does PMP explain about

neede equipment or care Smoker S9Q11B true if any member of child’s

household smokes Spec_PMD_Access S5Q09B true if PMP helps patient get

specialist when needed Spec_PMD_Explain S5Q09C how often did PMP explain purpose

and process of specialist care


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State STATE U.S. state of residence State Division STATE US Census Bureau State Division

State_Hosp_Density See Appendix C number of hospitals per square mile in state of residence

State_PMD_Density See Appendix C number of children per clinically active pediatrician in state of

residence


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Appendix B: Percent of Children Associated with Potentially Preventable/Avoidable Emergency Department Visits by State

# STATE # PPA # RECORDS % PPA 1 VT 137 1894 7.23 2 UT 122 1475 8.27 3 CT 205 2129 9.62 4 NE 180 1863 9.66 5 CO 181 1846 9.80 6 HI 198 2006 9.87 7 NH 189 1914 9.87 8 ND 194 1947 9.96 9 PA 219 2185 10.02 10 KS 188 1837 10.23 11 NV 211 2041 10.34 12 MD 219 2114 10.36 13 MT 201 1932 10.40 14 VA 226 2164 10.44 15 CA 233 2195 10.62 16 WA 204 1920 10.63 17 SD 198 1859 10.65 18 OR 210 1954 10.75 19 MN 200 1852 10.80 20 WI 212 1955 10.84 21 IN 203 1859 10.92 22 NY 220 2003 10.98 23 IA 214 1941 11.03 24 ID 2.05 1848 11.09 25 MO 247 2205 11.20 26 NC 234 2061 11.35 27 NJ 238 2086 11.41 28 IL 245 2139 11.45 29 AK 219 1882 11.64 30 OH 264 2225 11.87 31 WY 226 1875 12.05 32 DE 258 2137 12.07 33 TN 232 1909 12.15 34 FL 258 2097 12.30 35 OK 237 1925 12.31 36 TX 265 2144 12.36 37 SC 268 2143 12.51 38 AL 270 2152 12.55 39 NM 230 18.30 12.57 40 AR 235 1868 12.58 41 MA 266 2101 12.66


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42 GA 245 1842 13.30 43 MI 291 2173 13.39 44 AZ 258 1897 13.60 45 ME 260 1910 13.61 46 RI 279 2002 13.94 47 KY 275 1945 14.14 48 DC 289 2027 14.26 49 LA 324 2235 14.50 50 WV 310 2012 15.41 51 MS 325 2018 16.11


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Appendix C: Sources and Derivation of State Characteristic Attributes

Number of Clinically Active Pediatricians

The American Academy of Pediatrics in collaboration with the Dartmouth

Medical School Center for Evaluative Medical Sciences developed a Web-based

database providing access to information concerning health care delivery to children

on a national, state, and Primary Care Service Area (PCSA) level. This database

provided the number of children per clinically active pediatrician for each state (AAP,

2003).

Hospitals per Square Mile

The number of hospitals in each state that participate in Medicare hospital

insurance program was obtained from the State Health Statistics website. Given the

great diversity in the size of states, a fair comparison of hospital availability requires

consideration of the geographic area served by each hospital. Hence, the number of

square miles in each state was obtained from the United States Census Bureau State

and County QuickFacts website and an approximation of the service area of the

hospital was then calculated by determining the number of hospitals per thousand

square miles in the state.

Poverty Level, Education

The State Health Statistics website provided the percentages of state

populations living below the federally determined poverty level as well as the

percentage who achieved high school and college degrees.


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Race

The percentage of state population (per 2000 census figures) identified as

black persons was obtained from the United States Census Bureau State and County

QuickFacts website.


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BIOGRAPHICAL STATEMENT

Kathleen Alber holds a Bachelor’s of Science degree in Mathematics from Sacred

Heart University as well as a Master’s of Engineering in Biomedical Engineering

from the University of Virginia at Charlottesville. She has been employed as a

software engineer and systems programmer by the International Business Machines

Corporation and served as a database consultant for the Catholic Diocese of

Bridgeport. In recent years, her primary focus has been parenting her four daughters.

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