Malimu demography

53
1 INTRODUCTION TO DEMOGRAPHY MALIMU, PhD Dept of Epidemiology/Biostatistics, School of Public Health and Social Sciences, Muhimbili University of Health and Allied Sciences.

Transcript of Malimu demography

Page 1: 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.

Page 2: Malimu demography

2

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

Page 3: Malimu demography

3

OUTLINE OF THE PRESENTATION

Introduction and some definitions

Sources of data

Common indices used in public health

Page 4: Malimu demography

4

Demography

Science of human population

Formal demography

Size (number)

Distribution (geographical)

Structure (age and sex)

Change (decline or increase)

Page 5: Malimu demography

5

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

Page 6: Malimu demography
Page 7: Malimu demography
Page 8: Malimu demography

8

Vital statistics

The most common are:

(1) Births (Natality)

(2) Deaths (Mortality)

(3) Marriages (Nuptiality)

(4) Movements (Migrations)

Page 9: Malimu demography

9

Demographic equation

• In order to project population in future

• Pt = Po + (B – D) +( I – O)

• ΔP = NI – NM

Page 10: Malimu demography

10

Use of demography Understand magnitude

Understand the pattern (trend)

Understand causes (etiology)/ risk factors

Utulization of health care services

Page 11: Malimu demography

11

Consequences for lack of Consequences for lack of statisticsstatistics

Unknown number of population “at risk”

Unknown number of cases

Unknown risk factors

Poor planning

Page 12: Malimu demography

12

Sources of vital statisticsSources of vital statistics

CensusCensus

Vital registrationVital registration

Sample surveysSample surveys

Page 13: Malimu demography

13

Census

Systematic routine of counting subjects

Produce record of individual at a particular time

Outcome: size and structure

Page 14: Malimu demography

14

Census

Covers ALL subjects

A single point figure (cross-sectional)

Legal

Within or between census comparison (numbers, %)

Page 15: Malimu demography

15

Enumeration methods

De facto (“in fact” present)

De jure

People who live there or have the right to be

there

Page 16: Malimu demography

16

Census

100% coverage

Not helpful for health programs when population

characteristics change rapidly.

Projections used

Detailed questions on fertility and mortality.

Page 17: Malimu demography

17

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

Page 18: Malimu demography

18

Vital registration

Common in industrialized countries

Less developed countries, incomplete

Simple and few questions

Page 19: Malimu demography

19

Vital registration

Examples: death, birth or marriage certificates

Weaknesses:

Incomplete

Selective

Practically “unreliable”

Page 20: Malimu demography

20

E.g. Birth registration

0 20 40 60

Total

No educ

Sec educ

Rural

Urban

Page 21: Malimu demography

21

Sample surveys

Study a small part of population

Less costly

Quicker

Page 22: Malimu demography

22

Sample surveys

Detailed

Chances of errors

Examples: DHS, HIV/AIDS Surveillance

Page 23: Malimu demography

23

Sources of morbidity dataSources of morbidity data

Admission registers, these can be:

Institutional-based (Hospitals)

Community-based (surveillance,

environmental safety)

Example: Cancer, accident

Page 24: Malimu demography

24

Sources of morbidity dataSources of morbidity data

Clinical records

Indicate history of illness

For example, laboratory records

Page 25: Malimu demography

25

Sources of morbidity dataSources of morbidity data

Hospital discharge summaries

Found in Health isntitutions

HMIS

Page 26: Malimu demography

26

Sources of morbidity dataSources of morbidity data

Disease surveillance and screening programs

Screening and investigations for epidemics

Records show prevalence (symptoms and non-)

Page 27: Malimu demography

27

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

Page 28: Malimu demography

28

Sources of mortality dataSources of mortality data

Death certificate

Disease control programs

Census

Special surveys

Page 29: Malimu demography

29

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

Page 30: Malimu demography

30

Measures of fertility

• Crude Birth Rate (CBR)

Not a ‘rate’ but ‘ratio’ CBR=Live births/year x 1000

Total population

Page 31: Malimu demography

31

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

Page 32: Malimu demography

32

Measures of fertility

• CBR is simple to calculate

• Requires few data

• Easy to understand

• Used for crude RNI

• RNI = CBR - CDR

Page 33: Malimu demography

33

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

Page 34: Malimu demography

34

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.

Page 35: Malimu demography

35

Total Fertility Rate (TFR)

Based on specific F-rates

Hypothetical measures

Reproductive experience

Page 36: Malimu demography

36

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

Page 37: Malimu demography

37

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

Page 38: Malimu demography

38

Calculation of TFR

TFR = cΣ(ASFR): c = age interval

= 1.163 x 5 = 5.815

Page 39: Malimu demography

39

Interpretation of TFR

On average, each woman would have 6

children IF she survives through her

reproductive life AND ASFRs do not change

Page 40: Malimu demography

40

Gross Reproduction Rate (GRR)

GRR similar to TFR

Considers ONLY female live born babies

Page 41: Malimu demography

41

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)

Page 42: Malimu demography

42

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

Page 43: Malimu demography

43

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

Page 44: Malimu demography

44

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)

Page 45: Malimu demography

45

Measures of mortality

• Crude Death Rate (CDR)

Not a ‘rate’ but ‘ratio’

Total deaths/year x 1000 Total population

Why “crude”?

Page 46: Malimu demography

46

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

Page 47: Malimu demography

47

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

Page 48: Malimu demography

48

IMR

• May be a ratio

• In Tanzania, it is about 68 per 1000 LB

• (Finland, = 2 per 1000 LB)

Page 49: Malimu demography

49

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

Page 50: Malimu demography

50

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

Page 51: Malimu demography

51

Maternal mortalityRate: (6/1000)

Number of maternal deaths x 100000 WRA

Ratio: (578/100,000)Number of maternal deaths x 100000 LB

Page 52: Malimu demography

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)

Page 53: Malimu demography

Limitations with vital statistics

Incompleteness of data

Incomplete or wrong data