Malimu demography
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Transcript of Malimu demography
1
INTRODUCTION TO DEMOGRAPHY
MALIMU, PhDDept of Epidemiology/Biostatistics,School of Public Health and Social Sciences,Muhimbili University of Health and Allied Sciences.
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OBJECTIVES
To understand basic concepts of demography
To understand common vital statistics
To understand sources of vital events
Able to calculate and interpret common
indices in public health
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OUTLINE OF THE PRESENTATION
Introduction and some definitions
Sources of data
Common indices used in public health
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Demography
Science of human population
Formal demography
Size (number)
Distribution (geographical)
Structure (age and sex)
Change (decline or increase)
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Extended meaning of demography
Ethnic characteristics
Race, nationality, mother tongue, etc
Social characteristics
Marital, pace of birth, literacy, education
Economic characteristics
Employment, occupation,, industry, income, etc
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Vital statistics
The most common are:
(1) Births (Natality)
(2) Deaths (Mortality)
(3) Marriages (Nuptiality)
(4) Movements (Migrations)
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Demographic equation
• In order to project population in future
• Pt = Po + (B – D) +( I – O)
• ΔP = NI – NM
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Use of demography Understand magnitude
Understand the pattern (trend)
Understand causes (etiology)/ risk factors
Utulization of health care services
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Consequences for lack of Consequences for lack of statisticsstatistics
Unknown number of population “at risk”
Unknown number of cases
Unknown risk factors
Poor planning
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Sources of vital statisticsSources of vital statistics
CensusCensus
Vital registrationVital registration
Sample surveysSample surveys
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Census
Systematic routine of counting subjects
Produce record of individual at a particular time
Outcome: size and structure
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Census
Covers ALL subjects
A single point figure (cross-sectional)
Legal
Within or between census comparison (numbers, %)
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Enumeration methods
De facto (“in fact” present)
De jure
People who live there or have the right to be
there
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Census
100% coverage
Not helpful for health programs when population
characteristics change rapidly.
Projections used
Detailed questions on fertility and mortality.
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Vital registration
Events during a particular time interval (year)
Dynamic information
Events are affected by numbers at risk, used to calculate
“rates”
Possible to compare levels
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Vital registration
Common in industrialized countries
Less developed countries, incomplete
Simple and few questions
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Vital registration
Examples: death, birth or marriage certificates
Weaknesses:
Incomplete
Selective
Practically “unreliable”
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E.g. Birth registration
0 20 40 60
Total
No educ
Sec educ
Rural
Urban
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Sample surveys
Study a small part of population
Less costly
Quicker
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Sample surveys
Detailed
Chances of errors
Examples: DHS, HIV/AIDS Surveillance
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Sources of morbidity dataSources of morbidity data
Admission registers, these can be:
Institutional-based (Hospitals)
Community-based (surveillance,
environmental safety)
Example: Cancer, accident
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Sources of morbidity dataSources of morbidity data
Clinical records
Indicate history of illness
For example, laboratory records
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Sources of morbidity dataSources of morbidity data
Hospital discharge summaries
Found in Health isntitutions
HMIS
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Sources of morbidity dataSources of morbidity data
Disease surveillance and screening programs
Screening and investigations for epidemics
Records show prevalence (symptoms and non-)
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Sources of morbidity dataSources of morbidity data
Sources outside health facilitiesSources outside health facilities
MCHMCH
EDPEDP
EPIEPI
Mental Health ProgrammeMental Health Programme
Oral, Dental and Eye care CentresOral, Dental and Eye care Centres
Nutritional programmesNutritional programmes
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Sources of mortality dataSources of mortality data
Death certificate
Disease control programs
Census
Special surveys
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FERTILITY
• Number of live births the woman has ever had• Fertile = woman had at least one child
• Opp: infertility (childless)• Physiological ability to bear children (fecundity)
• Opp: sterility• Physiological ability to conceive (in a menstrual
cycle) = fecundability
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Measures of fertility
• Crude Birth Rate (CBR)
Not a ‘rate’ but ‘ratio’ CBR=Live births/year x 1000
Total population
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Measures of fertility
• 2002 Tanzania Population and Household Census:
• Total births = 1,191,084
• Total population = 34,443,603
• CBR = 34.6 births per 1000 population
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Measures of fertility
• CBR is simple to calculate
• Requires few data
• Easy to understand
• Used for crude RNI
• RNI = CBR - CDR
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Measures of fertility
• General Fertility Rate (GFR)General Fertility Rate (GFR)
Acceptable “rate”Acceptable “rate”
GFR=LB (year) x 1000 Mid year WRA
Refined fertility measure
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Measures of fertility
• Ranges between 50 and 300
• 2002 PHC:
• Total births = 1,191,084
• WRA = 8,245,388
• GFT = 144.5 per 1000 WRA.
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Total Fertility Rate (TFR)
Based on specific F-rates
Hypothetical measures
Reproductive experience
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Total Fertility Rate (TFR)
Average number of children per woman
In their reproductive life
Given that she survives to age 50
Given that current age specific fertility rates
would still be applicable during all these 35 years
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Example
Table 12.1 Age-Specific Fertility Rates (ASFR): Tanzania, TDHS, 1996.
Age groups
Number of women
Births ASFR per 1000
15-1920-2425-2930-3435-3940-4445-49
1729169414151135 896 670 581
233440361246150 58 24
135260255217167 87 42
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Calculation of TFR
TFR = cΣ(ASFR): c = age interval
= 1.163 x 5 = 5.815
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Interpretation of TFR
On average, each woman would have 6
children IF she survives through her
reproductive life AND ASFRs do not change
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Gross Reproduction Rate (GRR)
GRR similar to TFR
Considers ONLY female live born babies
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Gross Reproduction Rate (GRR)
Average number of DAUGHTERS a woman would
have if she survives up to her 50th birthday and
experiences the given females ASFRs
GRR=1 (able to reproduce)
GRR=2 (population doubling)
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Example: GRRTable 12.1 Age-Specific Fertility Rates (ASFR): Tanzania, TDHS, 1996.
Age groups Number of women
Female Births Female ASFR per 1000
15-1920-2425-2930-3435-3940-4445-49
1729169414151135 896 670 581
117221181124 76 30 11
68130128109 85 45 19
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Gross Reproduction Rate (GRR)
GRR = cΣ(FASFR): c = age interval
= (68 + 130 + … + 19) x 5/ 1000 = 2.9
Each woman will almost produce three daughters by the
end of her reproductive life given that she survives up to
age 50 and the female ASFR remain constant
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Gross Reproduction Rate (GRR)
If SR = sex ratio at birth
GRR = TFR x proportion of female births
= TFR x SR/(100 + SR).
GRR = TFR x SR = 5.815 x 101.1 = 2.9
SR + 100 101.1 + 100
(Same as 5.5151 x 760/1512 = 2.9)
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Measures of mortality
• Crude Death Rate (CDR)
Not a ‘rate’ but ‘ratio’
Total deaths/year x 1000 Total population
Why “crude”?
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Measures of mortality
• Crude Death Rate (CDR)
Not a ‘rate’ but ‘ratio’
Total deaths/year x 1000 Total population
Why “crude”?
In Tanzania, CDR ≈ 13 per 1000 population
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IMR
• Infant mortality rate (IMR)
• The probability of infant dying before the first birth-
day
• = Deaths under one year x 100 Total live births in a year
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IMR
• May be a ratio
• In Tanzania, it is about 68 per 1000 LB
• (Finland, = 2 per 1000 LB)
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Other Infant mortality break-down are: Neonatal mortality rate = number of deaths in a year under 28 days of age x1000 Number of live births in a year
Early neonatal mortality 'rate' = number of deaths aged under one week in a year x 1000 Number of live births in a year Late neonatal mortality 'rate‘ = number of deaths between 1 - 4 weeks in a year x 1000 Number of live births in a year Post neonatal mortality 'rate‘ = number of deaths between 4 - 52 weeks in a year x 1000 Number of live births in a yearStillbirth rate
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Other mortality measuresStillbirth Rate (SBR) = number of stillbirths in a year x 1000 Total number of live and still-births in a year
Perinatal Mortality Rate (PMR): 36/1000 = SB + deaths <1 week x 1000Total number of live and still-births in a year
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Maternal mortalityRate: (6/1000)
Number of maternal deaths x 100000 WRA
Ratio: (578/100,000)Number of maternal deaths x 100000 LB
Limitations with vital statistics
Difficulty in some definitions
Defining cases (e.g. stillbirth, maternal death)
Defining population at risk (denominator)
Inaccuracy of diagnosis Correctness of case fatality rate)
Limitations with vital statistics
Incompleteness of data
Incomplete or wrong data