HOUSEHOLD SURVEYS IN BANGLADESH

How well are the urban poor represented?

Ru-Yi Lin

STUDY DESIGN

AIM To identify the health inequity within urban areas in Bangladesh

OBJECTIVE To identify data sources for the Urban HEART indicatorsTo assess the appropriateness of existing data to identify health inequities in urban areas

To identify the health needs of the urban poor in Bangladesh, and where possible how this differs from the non-poor

AvailabilityAccessibility

Both raw data and report are available

Sub group analysis can be done

2011 Bangladesh Demography and Health Survey

*Sample frame: select the Enumeration areas (EAs) covered whole country from 2011 census (113 household/EA). 600 EAs been selected(207 in urban/ 393 in rural)->30 Household been selected in each cluster

AvailabilityAccessibility

Report is available; raw data not available

Sub group analysis can NOT be done

2013 National Tuberculosis Control Programme (NTP) annual report

AvailabilityAccessibility

Sub group analysis can be done

2014 Promoting Environmental Health for the Urban Poor: Mid-term assessment of Water Aid project

AvailabilityAccessibility

Preliminary report is available

Raw data and final report NOT available online

Sub-group analysis can be done

2013 Urban Health Survey: Primary Results

*Sample frame: Three-stage sampling design of Mohallas from 9 city corporations, District Municipalities and large towns with population over 45,000 from the 2011 census

AvailabilityAccessibility

Preliminary report is available

Raw data and final report NOT available online

Sub group analysis can be done

2012-2013 Multiple Indicator Cluster Survey: Key District Level Findings

AvailabilityAccessibility

Report available online.

Request raw data from MoHFW

Sub group analysis can be done

2010 STEPs: Non-Communicable Disease Risk Factor Survey Bangladesh 2010

Urban HEART Indicators NOT covered

IndicatorRoad traffic injuries (core) Recommend to include in DHS

Prevalence of tobacco smoking (core) Missing data in BDHS, GATS Bangladesh 2009 disaggregated by urban/rural

Government spending on health (core) National Health Accounts (Heath Economics Unit)

Maternal mortality

Life expectancy at birth

Morbidity and mortality from cancersCVDs Diabetes and hypertension covered in DHS as pre indicator to

develop CVDsRespiratory disease

HIV/AIDS Respondents may hesitate to answer this question

Homicide From Police data

Mental illness Although stigma – use assessment such as PHQ9

Work related injuries Recommend to include in DHS

Security of tenure Recommend to include in DHS

Voter participation From election data

Insurance coverage From National Health Accounts (Heath Economics Unit)

Geographical coverage in analysis

DivisionCity

Corporation Muni.

2013 MICS

V (7

divisions)V

2011BDHS

V (7

divisions)V

2013NTP

V (6

divisions)

2013 UH

surveyV

(9 City Corporations)

V

2014 PEHUP

V (3 City

Corporations)

2010 STEPs

V (6 divisions)

Comila

Rarayanganj

2013 Urban Health Survey

2011 BDHS

2014 PEHUP

2012 MICS 2013 NTP

2010 STEPs

Definitions of Inequity Used in Each Report

BDHS NTP UHS PEHUP MICS STEPS

Urban/rural specific wealth quintile (20%) Poorest Poorer Middle Richer Richest

Not disaggregated by wealth

Slum/non slum High

density & crowed

Poor housing conditions

Poor water & sewerage condition

Poor & very poor SES

Slum household income levels <=Tk.5000 Tk.5001-

7500 Tk.7501-

10000 Tk.10001-

12500 Tk.12501-

15000 Tk.15001-

17500 Tk.17501-

20000 Tk.20001+

Not disaggregated by wealth

Wealth quartile (25%) 1st 2nd 3rd 4th

Wealth Index Slum/non-slum, wealth index

Income Wealth index Wealth index

Are the urban poor being identified?

DHS wealth quintile category

Urban n (unweighted)

Urban % Rural n (unweighted)

Rural %

poorest 515 3.00 3021 17.62poorer 433 2.53 2857 16.67middle 621 3.62 2565 14.96richer 1465 8.55 1931 11.27richest 2834 16.53 899 5.24Total 5868 34.23 11273 65.77

Absolute numbers and % sample size per wealth quintile across the national DHS 2011 sample (both urban and rural areas) n = sample size

Response rate of urban poor(est) in DHS

Indicator Total respondent

Total urbanrespondent %

Urban poorest and poorerrespondent

% Total rural respondent

%

Infant mortality 9992 3082 30.84% 525 17.03% 6910 69.16%

Diabetes 7565 2489 32.9% 353 14.18% 5076 67.1%

Access to safe water (HHs) 17141 5868 34.2% 948 16.16% 11273 65.8%

Access to improved sanitation 83731 28109 33.57% 4180 14.87% 55622 66.43%

Skilled birth attendance 6410 1994 31.11% 340 17.05% 4416 68.89%

Fully immunized children 1542 506 32.81% 69 13.64% 1036 67.19%

Unemployment 21839 7633 34.95% 1102 14.44% 14206 65.05%

Under-5 mortality 9992 3082 30.84% 525 17.03% 6910 69.16%

Literacy 21839 7633 34.95% 1102 14.44% 14206 65.05

Underweight children 7647 2342 30.63% 396 16.91% 5305 69.37%

Breastfeeding 523 168 32.12% 47 27.98% 355 67.88%

Teenage pregnancy 1911 594 31.08% 128 21.55% 1317 68.92%

1. DHS, MICS, UHS, STEPs: 1st-stage sampling from census data, and 2nd-stage listing of households misses many urban-poorest so urban sample is not representative.

2. BDHS, MICS, STEPs: The sample size is too small to perform sub-urban analysis.

3. DHS, MICS, UHS, STEPs: People who have no house might be excluded in household survey, whom are the extreme poor people (homeless, illegal settlements).

4. DHS, MICS, UHS, STEPs: The wealth index allows us to look at physical assets only; not income, expenditures, savings, or access to credit.

5. All: Requesting access to raw data is often complicated and unclear which prolong the progresses of the study.

Challenge

Recommendation

1. Specific or booster surveys of the urban poorHousehold data can capture sufficient number of

urban poorest people.

2. Improved sampling methodsHouseholds data can be representative of urban

poorest people.

3. Mechanisms for sharing information Easier mechanisms to access raw data.

Thank you for your attention