Mortality and Morbidity: Determinants and Measurement
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Transcript of Mortality and Morbidity: Determinants and Measurement
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this site.
Copyright 2006, The Johns Hopkins University and Henry Mosley. All rights reserved. Use of these materials permitted only in accordance with license rights granted. Materials provided “AS IS”; no representations or warranties provided. User assumes all responsibility for use, and all liability related thereto, and must independently review all materials for accuracy and efficacy. May contain materials owned by others. User is responsible for obtaining permissions for use from third parties as needed.
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Mortality and Morbidity
Data Sources for Measuring MortalityModule 6a
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Learning Objectives
Upon completion of this module, the student will be able to :
Identify different sources of data for measuring mortality and morbidity
Explain some of the problems relating to the completeness and quality of the data
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Mortality and Morbidity as Indicators of Health Status of a Population
Death is a unique and universal event,and as a final event, clearly definedAge at death and cause provide an instant depiction of health statusIn high mortality settings, information on trends of death (by causes) substantiate the progress of health programs
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Mortality and Morbidity as Indicators of Health Status of a Population
As survival improves with modernization and populations age, mortality measures do not give an adequate picture of a population’s health statusIndicators of morbidity such as the prevalence of chronic diseases and disabilities become more important
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Major Sources of Mortality Information
National vital registration systems - a major source in developed countriesSample registration systems (e.g., in China and India)Household surveys - to estimate infant and child mortalitySpecial longitudinal investigations (e.g., maternal mortality studies)
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Vital Registration orVital Statistics Systems
Features
Universal coverage of the population
Continuous operation
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Death Registration:Counting the Events
Definition: official notification that a death has occurredUsually a legal requirement before burial/cremationCounts (rates) by age, sex, location and time provide invaluable health dataConcurrent registration essential for good cause of death determination
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Data Collection for Vital Registration
Events are collected by a local registration office, usually a government agencyWho reports to registration office?– Individual citizens, local officials,
physicians, hospital employees, etc.Main advantage is universal coverageDisadvantages are late or never reporting
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Special Problems of Vital Registration in Developing Countries
Laws vary dramatically across the countriesPublic compliance poor Definitions of vital events variesInadequate resourcesLack of trained personnel to collect dataData infrequently analyzedUnderutilization of data
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National Sample Registration Systems - India
Sample Registration System (SRS)– Began in 1964-65– Over 6000 sampling units (about 10,000,000
population)– Dual registration systems for births and
deaths– Provides fertility and mortality estimates for
every state and territory – Cause of death based on lay reporting
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Data Collection in Developing Countries by Sample Surveys
Systematic national household sample surveys to collect data on population and health began during early 1960’s to measure the demographic impact of family planning programs Family planning and population surveys are still the largest sources of data for health in developing countries
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Data Collection in Developing Countries by Sample Surveys
Major International Household Surveys– 1970s to 1985 -World Fertility Surveys
(WFS)– 1985 to Present - Demographic and
Health Surveys (DHS)Mortality (and morbidity) data limited to infants, children and mothers
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Special Longitudinal Population Studies
Specialized longitudinal studies of selected events– Maternal mortality, in Egypt, Nigeria,
Philippines, Bangladesh, etc.Continuing longitudinal event registration in selected study populations– in Matlab in Bangladesh, Rakai in
Uganda, Navrongo in Ghana, etc.14
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Summary slide
This concludes this lecture. The key concepts introduced in this lecture include– Importance of mortality and morbidity as
indicators of health status of a population
– Major sources of mortality information
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Mortality and Morbidity
Indicators for Measuring MortalityModule 6b
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Learning Objectives
Upon completion of this module, the student will be able to :
Describe, calculate and interpret different mortality and morbidity indicators
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Measures of Mortality
Crude Death RatesAge-Specific Death RatesLife Table Estimates– Life expectancy– Survivorship (by age)
Cause-Specific Death RatesSpecial Indicators– Infant and maternal mortality rates
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Crude Mortality Indicators
Crude Death Rate (CDR)
Number of deaths in a given year per 1000 mid-year population
1000∗− population yearMid
rdeaths/yea of Number
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Crude Death Rate : ExampleUganda’s crude death rate in 1999 is
which indicates that there were about 18 deaths per 1000 inhabitants in the year 1999.
4.181000973,804,22
296,420=×=× k
population year-mid Total deaths of #
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Crude Death Rates in Africa, 1999
Deaths per 1000
19 to 24 (11)*18 (12)14 to 17 (7)11 to 13 (13)
3 to 10 (11)
Data Source: World Population Data sheet,1999, PRB
* Figures in brackets indicate # of countries
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Crude Death Rates Around the World
16
12
68
11
8
02468
1012141618
SSA SouthernAfrica
SouthAmerica
Asia Europe NorthAmerica
Dea
ths p
er 1
000
Data Source: World Population data sheet, 1999, PRB22
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Crude Death Rates Points to Note
Risks of death change by age, so CDR is affected by population age structure
Aging populations can have rising CDRs, even as the health conditions are improving
LDCs with very young populations will often have lower CDRs than MDCs even though their overall health conditions are poorer
Therefore mortality comparisons across countries should always use mortality indicators that are adjusted for differences in age composition
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Matlab, BangladeshPercent distribution of population and deaths, 1987
Source: ICDDR,B
85+80757065605550454035302520151050
0246810121416 0 10 20 30 40 50
Population Deaths
Median age at death
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SwedenPercent distribution of population and deaths, 1985
Source: Keyfitz and Flieger, 1990
85+807570656055504540353025201510
50
0246810 0 5 10 15 20 25
Population Deaths
Medianage atdeath
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Age Specific Death Rates (ASDR)
Number of deaths per year in a specific age (group) per 1000 persons in the age group
Where Da = Number of deaths in age group aPa = Midyear population in age group a
1000PD
a
a ∗=
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Death Rates by Age, Sweden, 1945 and 1996
0
20
40
60
80
100
120
140
160
<1 1-4 5-9 10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
Deat
h ra
te p
er 1
000
popu
latio
n
ASDR, 1945ASDR,1996
27Data Source: UN Demographic Yearbooks, 1948, and 1997
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Why Age Specific Death Rates?
Can compare mortality at different ages
Can compare mortality in the same age groups over time and/or between countries and areas
Can be used to calculate life tables to create an age-independent measure of mortality (life-expectancy)
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The Life Table
A powerful demographic tool used to simulate the lifetime mortality experience of a population, by taking that population’s age-specific death rates and applying them to a hypothetical population of 100,000 people born at the same time
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Measurement of Life ExpectancySu
rviv
ors
(thou
sand
s)
Survivors at each age
Total years of life lived by 100,000 persons
Age
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Life Expectancy at BirthAverage number of years lived among a cohort of births experiencing deaths at each year of age throughout their remaining life-time according to a specific schedule of age specific mortality rates Note: This measure of mortality is independent of the age structure of the population
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Life Expectancy
Estimate of the average number of additional years a person could expect to live if the age-specific death rates for a given year prevailed for rest of his or her life
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Life Expectancy at Birth: Example
If ASDRs for 1999 remain unchanged, males born in Uganda can expect to live 41 years on average; females can expect to live 42 years
The comparative figures for USA are 74 years and 79 years for males and females respectively
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Life Expectancy at Birth for Major World Regions
4952
4956
6968
657273
77
0 20 40 60 80 100
SSA
West Africa
Middle Africa
South Africa
South America
West Asia
SE Asia
East Asia
Europe
North America
Data Source: World Population Data Sheet,1999, PRB 34
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Mortality Indicator Comparisons in Countries With Death Registration
Country(1985)USASwedenJapanKorea
CDR8.7411.266.986.17
Life Expectancy Male Female
71.3 78.473.8 79.875.4 81.166.2 72.5
Source: Keyfitz and Flieger, 199035
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Life Expectancy at Birth: Notes
Most commonly cited life-expectancy measure
Age independent, can be used to compare health conditions in different populations
Good indicator of current health conditions
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Cause Specific Death Rates
Number of deaths attributable to a particular cause c divided by population at risk , usually expressed in deaths per 100,000
100000P
D c ×=
37
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Cause Specific Death Rate: Examples
The cause specific death rate per 100,000 for tuberculosis in South Africa in 1993 was:
Cause specific death rates for TB in Philippines, Mexico and Sweden were 36.7, 5.1, and 0.4 respectively
9.18000,100974,544,39
7474=×=× k
Population TotalTB from Deaths
(UN Demographic year book, 1997)
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Death Rates Due to Specific Causes, South Africa, 1948 and 1993
29.9
2.2 10.4
14.2
0.4
10.3
18.9
0
5
10
15
20
25
30
35
Tuberculosis Malaria Diabetes Measles
Dea
ths
per 1
00,0
00 p
opul
atio
n
19481993
Data Source: UN Demographic Year Books, 1952, and 199739
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Summary Slide
This concludes this module, the key concepts introduced in the module include– Crude death rate– Age specific death rate– Life table and life expectancy– Cause specific death rate
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Mortality and Morbidity
Special Mortality IndicatorsModule 6c
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Learning Objectives
Upon completion of this module, the student will be able to : Describe, calculate and interpret infant mortality rate and different indicators for measuring maternal mortality rateDescribe the differentials in infant mortality rate and maternal mortality rate across different regions of the world
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Special Mortality IndicatorsInfant Mortality Rate (IMR):
Number of deaths of infants under age 1 per year per 1000 live births in the same year
1000
yearthat in births live Total
yeargiven a in infants of deaths of #
IMR ×=
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Special Mortality Indicators
Infant Mortality Rate (IMR): Examples
In 1999, the infant mortality rate of Uganda was 81/1000 while Sweden reported one of the lowest infant mortality rates of 3.6/1000Malawi reported a IMR of 137/1000, which is very high
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Infant Mortality Rates Around the World
9486
1045554
4629
97
35
0 20 40 60 80 100 120
SSA
West Africa
Middle Africa
South Africa
West Asia
SE Asia
East Asia
Europe
North America
South America
Infant Mortality Rate/1000
Data Source: World Population Data Sheet,1999, PRB 45
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Why Infant Mortality Rates ?
The IMR is a good indicator of the overall health status of a population
It is a major determinant of life expectancy at birth
The IMR is sensitive to levels and changes in socio-economic conditions of a population
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Maternal MortalityDefinition:
‘Maternal death’ is death of a woman
while pregnant ,orwithin 42 days of termination of
pregnancyIrrespective of the duration or site of the pregnancyFrom any cause related to, or aggravated by the pregnancy or its management Not from accidental causes
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Maternal Mortality Indicators
Maternal mortality ratio (per 100,000 live births - or per 1000 live births)
Maternal mortality rate (per 100,000 women of childbearing age)
Life-time risk of maternal mortality
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Maternal Mortality Ratio
Number of women who die as a result of complications of pregnancy or childbearing in a given year per 100,000 live births in that year
Represents the risk associated with each pregnancy, i.e., the obstetric risk
000,100×=births live of #
deaths maternal of #
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Maternal Mortality RateNumber of women who die as a result of complications of pregnancy or childbearing in a given year per 100,000 women of childbearing age in the population
Represents both the obstetric risk and the frequency with which women are exposed to this risk
000,100×= 49-15 ages womenof #
deaths maternal of #
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Lifetime Risk of Maternal Death
The risk of an individual woman dying from pregnancy or childbirth during her reproductive lifetime.
Takes into account both the probability of becoming pregnant and the probability of dying as a result of pregnancy cumulated across a woman’s reproductive years
Approximated by product of TFR and maternal mortality ratio
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Women’s Lifetime Risk of Death from Pregnancy, 1990
Region Risk of Death
Africa 1 in 16
Asia 1 in 65
Latin America and Caribbean 1 in 130
Europe 1 in 1400
North America 1 in 3700
All developing countries 1 in 48
All developed countries 1 in 1800
Source: Adapted from Family Care International,199852
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Summary Slide
This concludes this session, the key concepts introduced in this module include
– Indicators for maternal mortality
– Infant mortality rate
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Mortality and Morbidity
Data Sources and Indicators for Measuring Morbidity
Module 6d
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Learning Objectives
Upon completion of this module, the student will be able to : Identify different sources of data for measuring morbidityExplain some of the problems relating to the completeness and quality of the dataDescribe, calculate and interpret different morbidity indicators
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Morbidity
Morbidity refers to the diseases and illness, injuries, and disabilities in a population
Data on frequency and distribution of a illness can aid in controlling its spread and, in some cases, may lead to the identification of its causes
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Morbidity
The major methods for gathering morbidity data are through surveillance systems and sample surveys.
These are both costly procedures and therefore are used only selectively in developing country setting to gather data on health problems of major importance
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Disease Surveillance: Key Elements
The systematic collection of pertinent information about events of interestThe orderly consolidation, analysis, and interpretation of these dataThe prompt dissemination of the results in a useful formTimely and appropriate public health action taken based on the findings
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Disease Surveillance
Initially concerned with infectious diseases Currently includes a wider range of health data including– chronic diseases– environmental risk factors– health care practices– health behaviors
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Sources of Data for SurveillanceNotifiable diseases– Clinic/hospital admissions– Laboratory specimens
Sentinel surveillance Administrative data systems – e.g., insurance recordsOther data sources – e.g., accident and injury reports
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Sample Surveys for Morbidity: Rationale
Economy: of cost, of time -- only limited units are examined and analyzed
Accuracy: quality of enumeration and supervision can be high
Adaptability: many topics can be coveredElaborateness: in-depth information can be
collected
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Sample Surveys: Principle Elements
Subjects of study: individual persons, records, etc.Sample size: determined by the investigators considering precision required for estimates and resources available for the studyUniverse to be sample: dependent on study objectives
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Sample Surveys: Principle Elements
Data collection procedures: unlimited, e.g., in depth interviews, physical, biological or cognitive measurements, direct observations, etc. Frequency of enumeration: variable, i.e., single visit, or multiple rounds to the same individual or to different individuals
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Morbidity - Indicators
Incidence Rate
Number of persons contracting a disease during a given time period per 1000 population at risk
Refers only to new cases during a defined period
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Incidence Rate - Example
Incidence for malaria will be given by:
krisk at Population
period time given a during malaria
developing persons of #
×
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Morbidity - IndicatorsPrevalence Rate
Number of persons who have a particular disease/condition at a given point in time per 1,000 populationA snapshot of an existing health situationIncludes all known cases of a disease that have not resulted in death,cure or remission
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-Prevalence Rate - Example
Prevalence of HIV/AIDS among adults at a given point in time will be
k
49-15 agespopulation TotalHIV/AIDS with
49-15 ages persons of #×
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Adult HIV/AIDS Prevalence by Region, 1998
8
0.7
0.6
0.6
0.3
0.1
0 2 4 6 8 10
SSA
S/SE Asia
Latin Am.
North Am.
W. Europe
E. Europe
Percent of adults ages 15-49 with HIV/AIDS
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Estimated Worldwide Incidence, Prevalence and Deaths For Selected Infectious Diseases, 1990
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DiseaseNew
cases(1000s)
Rate per 100,000
Cases(1000s)
Rate per 100,000
Deaths(1000s)
Rate per 100,000
Malaria 213,743 4,058 2,777 53 856 16
Measles 44,334 842 1,739 33 1,058 20
Tuberculosis 6,346 121 12,739 242 2,040 39
HIV and AIDS 2,153 41 8,823 167 312 6
Poliomyelitis 215 4 10,648 203 27 1
MortalityIncidence Prevalence
Source: C.Murray and A. Lopez, Global Health Statistics: Epidemiologic Tables (1996)
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Summary Slide
This concludes this session. The key concepts introduced in this module include:– Data sources for studying morbidity– Key indicators of morbidity
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