Evaluating Portuguese primary healthcare through Prevention Quality Indicators (PQIs )

Introdução à Medicina I/IIClass 9Adviser: Alberto Freitas

What are PQIs?

IntroMedI - class 9 - PQIs

QIs

• Not definitive measures

• Use hospital discharge

inpatient data

•Based on readily available dataIntroMedI - class 9 - PQIs

PQIs

• Primary Healthcare

•Ambulatory Care Sensitive Conditions

• “avoidable hospitalization rates are a sensitive indicator for assessing quality of primary ambulatory care” (Niti et al, 2003)IntroMedI - class 9 - PQIs

Prevention starts here.


Source: Ansari Z. Laditka JN. Laditka SB. Access to Health Care and Hospitalization for Ambulatory Care Sensitive Conditions. Med Care Res Rev. 2006; 63:719-742

When were they first introduced?


Studies of the kind in other countries

• United States (Starfied et al.,1991, Sanderson et al., 2000, Kozak et al., 2001)

• Australia (Ansari et al., 2003, 2006)

• New Zealand (Sheerin et al., 2006)

• Canada (Roos et al., 2005, Porter et al., 2007)

• Singapore (Niti & Ng, 2003)

• Spain (Sánchez et al., 2004)

• Italy (Rizza et al., 2007)


Source: Agency for Healthcare Research and Quality. Guide to Prevention Quality Indicators: Hospital Admission for Ambulatory Care Sensitive Conditions. Version 3.1. Rockville, MD: Agency for Healthcare Research and Quality; March 2007.


This kind of studies

• evaluate the quality of the healthcare

conditions

• establish patterns

• allow comparison with past and future

works inside and outside the sampling

area.


Key-words

• Ambulatory Care

•Primary Care

•Quality of Healthcare

•Portugal

•Delivery of Healthcare

•Prevention Quality IndicatorsIntroMedI - class 9 - PQIs

IntroMedII - class 9 - PQIs

Aim

RESEARCH QUESTION

What is the status of the primary health care system in Portugal and how does it compare to past years and among its regions?


AIMS

• Assessment of the primary healthcare

system quality, in an outpatient setting.

• Compare different level 2 NUTS*, trends

2000-2005

• Lay hypotheses for the observed

differences. *except for Azores and Madeira.



Participants and Methods

PARTICIPANTS

• 6199102 patients’ discharge data from

national database (ACSS)

• 94 Acute Care Public Hospitals (continent)


DATA COLLECTION

• Data collected from acute care hospital

database

• Variables of interest present in the

database or calculated from others

• INE (Instituto Nacional de Estatística)

provides populational and other statistical

data


• Division in NUTS II


INCLUSION

• Diagnosis according to PQI

EXCLUSION• Age <18

• MDC = 14 or 15

• Transferred from

• Related non-evaluative conditions



Source: Agency for Healthcare Research and Quality. Guide to Prevention Quality Indicators: Hospital Admission for Ambulatory Care Sensitive Conditions. Version 3.1. Rockville, MD: Agency for Healthcare Research and Quality; March 2007.


INVALID

• Address codes missing. non-existent or

belonging to the Azores or Madeira.

• Absurd age (below 0; over 150)

• Undetermined Sex (3)


STRATIFICATION

• NUT II (Norte, Centro, Lisboa, Alentejo, Algarve)

• Year (2000-2005)

• Gender

• Age (0-17; 18-24; 25-34; 35-44; 45-54; 55-64; 65-

74; 75+)


RESULTS OVERVIEW

IntroMedI - class 9 - PQIs* Overall PQI = Sum of all PQIs except for 2 and 9

*


Significant Associations• Life Quality

• GIP per Capita• Life Expectancy at Birth

• Healthcare Facilities• Number of Health Centers per 100.000 pop.• Medical visits per inhabitant• Number of doctors per 1000 pop.

• Education• Literacy Index• Proportion of active population with minimum education (“3º Ciclo”)• Proportion of active population with secondary education or higher

• Neonatal• Age of first pregnancy


Significant Associations

•Overall PQI

•Acute PQI

•Diabetes PQI

No significant associations found


Doctors (N) r= -0,70

Nurses (N) r= -0,81

Lit. Index r= -0,67

Life exp. r= -0,61

Med. Visits r= -0,59

Health Centres r= 0,74

Hospital Distance r= 0,76


Pop. Density r= -0,96



Life Exp. r= -0,43

Lit. Index r= - 0,40



NONE

Hospital Distance r= -0,53

Min. Education r= -0,39

Med. Visits r= 0,71

Life Exp. r= 0,60

Sec. Education r= -0,36

Min. Education r= -0,35





Lit. Index r= -0,68

Lit. Index r= -0,64

Life Exp. r= -0,40




Sec. Education r= 0,49

Min. Education r= 0,52

GDP r= 0,37


Life Exp. r= -0,51

GDP r= 0,54

Minim. Education r= 0,44

Sec. Education r =0,42

Life Exp. r= -0,50



Minim. Education r= 0,33

Lit. Indexr= -0,62


Med. Visits r= 0,50

GDP r= 0,50

Min. Education r= 0,47

Sec. Education r =0,46

Lit. Index r= 0,38



Age 1st Pregnancy

r= 0,64

Age 1st Pregnancy

r= 0,64

GDPr= 0,70

GDPr= 0,70

MeanFig.6 Mean age of first child vs LBW Linear Regression


Cost Analysis


Discussion


Prevention

• There are different levels of prevention

1. Preventing the onset of the condition

2. Preventing the rampage development of symptoms; i.e

controling the condition


Variable North Centre Lisbon Alentejo Algarve

Population Ageing IndexPopulation Ageing Index 90,9 140,1 105,9 170,8 126,2

CitiesCities 51 41 17 19 11

Pop. DensityPop. Density(N/Km(N/Km22)) 175,6 84,5 946,9 24,3 83,4

SEDISEDI 0,775 0,767 0,854 0,747 0,791COCO22 emissions/Km2 emissions/Km2 620,131 361,722 9029,576 232,786 360,295

Sources: INE, 2005; Cónim C. População e Desenvolvimento Humano- Uma Perspectiva de Quantificação -1970-1999; Marques JL, Martins JM, Castro EA. Análise input-output rectangular inter-regional - emissões de CO2 em Portugal e o protocolo de Quioto

Fig.9 Overall PQI per NUT II


Self-Perception of Health

Source: National Health Survey, 1999

Overall PQI

NUT II Good/Very Good Average Bad/Very Bad Total Male Female Total Male Female Total Male Female

Continent 31,3 38,5 27,1 42,9 41,6 43,7 25,8 19,9 29,3 North 31,4 38,1 27,6 42,2 40,8 43,1 26,4 21,1 29,3

Centre 26 32,4 22,1 44,1 44 44,2 27,9 23,6 30,5 Lisbon 34,3 42,8 49,1 43,3 40,7 44,9 22,4 16,5 26

Alentejo 33,2 40 28,3 42,5 43 42,2 24,3 17 28,5 Algarve 31,5 38,8 27,2 41,7 39,3 43,1 26,9 22 29,7


Patient Profile

• Male

• Elderly (75 years old +)

• Living in areas not densely populated


Studies supporting this association:Lockwood et al .Stress-associated preterm delivery: the role of corticotropin-releasing hormone.


Variable USA Portugal Year Source

Human Human Development Development Index (HDI)Index (HDI)

0,951 (ranked 12th)

0,897 (ranked 29th)

2005 UNDP report 2007/2008

ObesityObesity 32,0% 14.2% 2004 Health 2007 (USA)Overweight and obesity in Portugal: nationalprevalence in 2003–2005

Smoking Smoking prevalenceprevalence

23.9% 35.8% 2005 WHO

COCO2 2 emissionsemissions

(ton per (ton per capita)capita)

20,6 5,6 2004 UNDP report 2007/2008

HyHypertensionpertension 30,2% 20,0% 2004/5 National Health Survey (PT)Health 2007 (USA)

DiabetesDiabetes 7,0% 6,5% 2005 National Institute of DiabetesNational Health Survey (PT)

COPD

Hypertensio

nCHFAsthma

COPD

Asthma

CHFHypertensio

nCHFDiabetes

Hypertension


Variable USA Portugal Year Source

Access to Access to drinking drinking waterwater

99% 99% 2006 WHO

Life Life expectancy at expectancy at birth (years)birth (years)

77,9 77,7 2005 UNDP report 2007/2008

ClimateClimate Dfa/b, Cfa, BSh/k, Csb,

BWk.

Csb (north),

Csa (south)

2006 Kottek et al, World map of Koppen-Geiger climate classification update.

EthniesEthnies Heterogeneous N/A 2006 US Census Bureau.

Male/FemaleMale/Female 49%/51% 48,4% 51,6%

2005 US Census BureauINE

>65 y>65 y 12,4% 17,0% 2005 US Census BureauINE

Wet all seasons: Dfa – snow, fully humid, hot summer ;Dfb – snow, fully humid, warm summer

Humid subtropical:Cfa – warm temperature, flly humid, hot summer

Dry semiaridBSk – arid, steppe, cold aridBSh – arid, steppe, hot arid

Dry aridBWk – arid, desert, cold arid

MediteraneanCsb – warm temperature, summer dry, warm summerCsa – warm temperature, summer dry, hot summer.

Dehydration

Dehydration


0

50

100

150

200

250

Angina

Asthm

a

DPOCCHF

Dehydra

tatio

n

Hyper

tens

ion UTI

Portugal

EspanhaSpain


•Socioeconomic factors:

• gender

• age

• Income

•Propensity to seek care according to perceived health needs

•Hospital bed availability

•Regular source of care or continuity of care

•Differences in healthcare service (Public/Private healthcare; Insurances)

•Coding Issues

•Lack in Epidemiological Studies (or inconsistent)

•Intra-regional variations

Study Limitations


Conclusions

•From 55 years old on:•There is a clear increase in avoidable hospitalizations•Males present higher avoidable hospitalizations rates

•Differences among years are not significant•Norte presents the best quality/cost ratio

•Portugal shows a more efficient primary healthcare system than USA or Spain.•Some heterogeneity among regions points to uneven primary healthcare supply•Further study is required to evaluate yearly trends and precise factors which influence PQ.


Future Research

• Multivariate analysis of correlations

• Litoral/Interior Analysis

• Wider timespan (Pre and Post SNS restructuration)

• Careful analysis of economical indexes and prevalence of

analysed conditions

• Further International Comparison


REFERENCES

PQI Info•Agency for Healthcare Research and Quality. Guide to Prevention Quality Indicators:

Hospital Admission for Ambulatory Care Sensitive Conditions. Version 3.1. Rockville (MD):

Agency for Healthcare Research and Quality; March 2007.

•Farquhar, M. AHRQ Quality Indicators [slides]. Rockville (MD): Agency for Healthcare

Research and Quality; 2005. 20 slides colour.

•General Questions about the AHRQ QIs [Internet]. Rockville (MD): Agency for Healthcare

Research and Quality; July 2004 [cited 2007 Oct 31]. Available from:

http://www.qualityindicators.ahrq.gov/general_faq.htm

•AHRQ Prevention Quality Indicators Overview [Internet]. Rockville (MD): Agency for

Healthcare Research and Quality; July 2004 [cited 2007 Oct 31]. Available from:

http://www.qualityindicators.ahrq.gov/pqi_overview.htm

•Agency for Healthcare Research and Quality. Prevention Quality Indicators: Technical

Specifications. Version 3.2. Rockville (MD): AHRQ; March 2008.


Related Articles• Ansari Z, Laditka JN, Laditka SB. Access to Healthcare and Hospitalization for

Ambulatory Care Sensitive Conditions. Med Care Res Rev. 2006; 63:719-42

• Billings J, Zeitel L, Lukomnick J, Carey TS, Blank AE, Newman L. Impact of socioeconomic status on hospital use in New York City. Health Aff (Millwood). 1993; 2:162-9.

• Starfield B. Primary care and health: a cross-national comparison. JAMA. 1991; 266:2268-71.

• Sanderson C, Dixon J. Conditions for which onset or hospital admission is potentially preventable by timely and effective ambulatory care. J Health Serv Res Policy. 2000, 5:222-30.

• Kozak LJ, Hall MJ, Owings MF. Trends in Avoidable Hospitalizations, 1980-1998. Health Aff. 2001; 2 (20): 225-32.

• Casanova C, Starfield B. Hospitalizations of children and access to primary care: a cross-national comparison. Int J Health Serv. 1995; 25:283-94.

• Ansari Z, Barbetti T, Carson NJ, Auckland MJ, Cicuttini F: The Victorian ambulatory care sensitive conditions study: rural and urban perspectives. Soz Praventivmed. 2003; 48:33-43.


• Sheerin I, Allen G, Henare M, Craig K. Avoidable hospitalizations: potential for primary and public health initiatives in Canterbury, New Zealand. N Z Med J. 2003; 119(1236).

• Roos LL, Walld R, Uhanova J, Bond R: Physician visits, hospitalizations, and socioeconomic status: ambulatory care sensitive conditions in a Canadian setting. Health Serv Res. 2005, 40:1167-85.

• Porter J, Herring J, Lacroix J, Levinton C. Avoidable Admissions and Repeat Admissions:

What Do They Tell Us? Healthc Q. 2007, 10:26-28.

• Niti M, Ng TP. Avoidable hospitalization rates in Singapore, 1991-1998: assessing trends

and inequities of quality primary care, J Epidemiol Community Health. 2003; 57: 17-22.

• Rizza P, Bianco A, Pavia M, Angelillo IF. Preventable hospitalization and access to

primary healthcare in an area of Southern Italy. BMC Health Serv Res. 2007; 7:134.

• Sanchez JLA, Vilalta JS, Perepérez SB, Martínez IM. Characteristics of avoidable

hospitalization in Spain. Med Clin (Barc). 2004; 122(17):653-8.

• Weissman JS, Gatsonis C, Epstein AM. Rates of avoidable hospitalization by insurance

status in Massachusetts and Maryland. JAMA. 1992; 268:2388-94.


• Pappas G, Hadden WC, Kozak LJ, Fisher GF. Potentially Avoidable Hospitalization

inequalities in rates between US socioeconomic groups. Am J Public Health. 1997;

87:811-6.

• Booth GL, Hux JE. Relationship Between Avoidable Hospitalizations for Diabetes Mellitus

and Income Level. Arch Intern Med. 2003; 163:101-6.

Data Sources

• Administração Central do Sistema de Saúde, IP (ACSS). www.acss.min-saude.pt

• Statistics Portugal [Internet]. Lisbon: INE. 1864- [cited 2008 Mar 30]. http://www.ine.pt

• Regulation (EC) No 1059/2003 of the European Parliament and of the Council of 26 May

2003 on the establishment of a common classification of territorial units for statistics

(NUTS) (Official Journal L 154, 21/06/2003)

• Departamento de Gestão Financeira. Serviço Nacional de Saúde – Contas Globais 2000

[Internet]. IGIF; 2002 Sep [cited 2008 Apr 19]. 192p. Available from: http://www.acss.min-

saude.pt/Downloads_ACSS/relatorios_contas/Relatorio_Contas_2000.pdf


• Departamento de Gestão Financeira. Serviço Nacional de Saúde – Contas Globais 2001

[Internet]. IGIF; 2003 Nov [cited 2008 Apr 19]. 171p. Available from: http://www.acss.min-


• Departamento de Consolidação e Controlo de Gestão do SNS. Serviço Nacional de Saúde

– Contas Globais 2002 [Internet]. IGIF; 2004 Apr [cited 2008 Apr 19]. 237p. Available from:

http://www.acss.min-saude.pt/Downloads_ACSS/relatorios_contas/Relatorio_Contas_2002

.pdf


– Contas Globais 2003 [Internet]. Lisbon: Instituto de Gestão Informática e Financeira da

Saúde; 2005 Apr [cited 2008 Apr 19]. 223p. Available from:

http://www.acss.min-saude.pt/Downloads_ACSS/relatorios_contas/Relatorio_Contas_2003

.pdf


– Contas Globais 2004 [Internet]. Lisbon: Instituto de Gestão Informática e Financeira da

Saúde; 2006 Mar [cited 2008 Apr 19]. 187p. Available from: http://www.acss.min-



Software

• SPSS for Windows, Rel. 15.0.0 2006. Chicago (IL): SPSS Inc.

• Microsoft Frontpage 2003, Rel. 11.5516.8202. USA: Microsoft Corporation.

International Data • Health, United States, 2007 ;U.S. Department of Health and Human

Services; Centers for Disease Control and Prevention; National Center for Health Statistics

• World Health Organization. Available from:www.who.int/en

• M. C. Peel, B. L. Finlayson, and T. A. McMahon. Updated world map of the Koppen-Geiger climate classification

• National Diabetes Statistics. Available from: diabetes.niddk.nih.gov/dm/pubs/statistics/

• U.S. Census Bureau. Available from:www.census.gov/


Protocol developed by:

Ana Catarina Moura, [email protected] Margarida Oliveira, [email protected]árbara Mendonça, [email protected]áudia Pereira, [email protected]élio Alves, [email protected]ão Miguel Rego, [email protected]é Pedro Pinto, [email protected] Francisca Costa, [email protected] Guiomar Pinheiro, [email protected] Couto, [email protected] Reis, [email protected] José Alberto Silva Freitas, [email protected]


Thank you for your time

Evaluating Portuguese primary healthcare through Prevention Quality Indicators (PQIs )

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