This document assesses the appropriateness of existing data sources in Bangladesh to identify health inequities in urban areas and the health needs of the urban poor. It analyzes several major surveys including the Bangladesh Demographic and Health Survey, Urban Health Survey, Multiple Indicator Cluster Survey, and National Tuberculosis Control Programme annual report. It finds that these surveys have small urban samples not fully representative of the urban poor, use wealth indices that do not capture income/expenditures, and have challenges in accessing raw data. It recommends specific surveys of the urban poor with improved sampling methods and easier access to raw data to better identify health inequities.

1. HOUSEHOLD SURVEYS IN
BANGLADESH
How well are the urban poor
represented?
Ru-Yi Lin

2. STUDY DESIGN
AIM To identify the health inequity within urban areas in
Bangladesh
OBJECTIVE To identify data sources for the Urban HEART indicators
To 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

3. Data analysis
Availability and accessibility
Applicability
Reliability
Indicators
Data collection
Communication
Cooperation
7 organizations and government
institutions
Selected data type
Nationwide survey
Urban health related study
done by organization
Specific target groups or for rural areas
No information related to selected
indicators

4. Availability
Accessibility
Both raw data and
report are available
Sub group analysis
can be done
Applicability
Covers 12 Urban
HEART indicators
No further
information about
slum/non-slum
groups
Reliability
Sample size:
18,000 household
Urban: 6210 HHs;
Rural: 11790 HHs
Two stages cluster
randomize
sampling*
Covers 7 divisions
Used standard
sample size formula
for key indicators at
subnational-level
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

5. Availability
Accessibility
Report is available;
raw data not
available
Sub group analysis
can NOT be done
Applicability
Covers 1 Urban
HEART indicator
Nationwide data
Result can be
divided into
urban/rural areas
No further
information about
patient’s
socioeconomic
status
Reliability
Data collected from
patient register
system
Cover 6 divisions
Challenges of
routine data
collection including
duplication and
human error
The most
vulnerable may not
have access to
health system
2013 National Tuberculosis Control Programme
(NTP) annual report

6. Availability
Accessibility
Both raw data and
report are available
Sub group analysis
can be done
Applicability
Covers 2 Urban
HEART indicator
Results for slum
areas in cities
Reliability
Sample size: 950
Households
Cover 3 City
Corporation
Sample size
calculation in
report
2014 Promoting Environmental Health for the Urban
Poor: Mid-term assessment of Water Aid project

7. Availability
Accessibility
Preliminary report
is available
Raw data and final
report NOT
available online
Sub-group analysis
can be done
Applicability
Covers 8 Urban HEART
indicator
Data from urban areas
Slum/non slum
disaggregation by
socio-
economic/wealth
quintiles
Reliability
Sample size: 53790
Households in
urban areas*
Covers 9 City
Corporations
Done, but unknown
because report is
not online yet
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

8. Availability
Accessibility
Preliminary report
is available
Raw data and final
report NOT
available online
Sub group analysis
can be done
Applicability
Covers 5 Urban HEART
indicator
Only women and
children
Reliability
Sample size: 55120
HHs
Covers 7 divisions
and municipalities
Used MICS-5
sample size
formula for key
MCH indicators at
subnational-level
2012-2013 Multiple Indicator Cluster Survey: Key
District Level Findings

9. Availability
Accessibility
Report available
online.
Request raw data
from MoHFW
Sub group analysis
can be done
Applicability
Covers 5 Urban HEART
indicator
Adult women and
men
Reliability
Sample size: 9275
HHs
Covers urban and
rural area
Sample size
calculation in
report
2010 STEPs: Non-Communicable Disease Risk
Factor Survey Bangladesh 2010

10. Urban HEART Indicators NOT covered
Indicator
Road 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 cancers
CVDs Diabetes and hypertension covered in DHS as pre indicator to
develop CVDs
Respiratory 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)

11. Geographical coverage in analysis
Division
City
Corporation Muni.
2013
MICS V
(7 divisions)
V
2011
BDHS V
(7 divisions)
V
2013
NTP
V
(6 divisions)
2013
UH
survey
V
(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

12. 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

13. Are the urban poor being identified?
DHS wealth
quintile
category
Urban n
(unweighted)
Urban % Rural n
(unweighted)
Rural %
poorest 515 3.00 3021 17.62
poorer 433 2.53 2857 16.67
middle 621 3.62 2565 14.96
richer 1465 8.55 1931 11.27
richest 2834 16.53 899 5.24
Total 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

14. Response rate of urban poor(est) in DHS
Indicator
Total
respondent
Total urban
respondent
%
Urban
poorest and
poorer
respondent
%
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%

15. 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

16. Recommendation
1. Specific or booster surveys of the urban poor
Household data can capture sufficient number of
urban poorest people.
2. Improved sampling methods
Households data can be representative of urban
poorest people.
3. Mechanisms for sharing information
 Easier mechanisms to access raw data.

17. Thank you for your attention