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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 t...
Data analysis
Availability and accessibility
Applicability
Reliability
Indicators
Data collection
Communication
Cooperatio...
Availability
Accessibility
Both raw data and
report are available
Sub group analysis
can be done
Applicability
Covers 12 U...
Availability
Accessibility
Report is available;
raw data not
available
Sub group analysis
can NOT be done
Applicability
Co...
Availability
Accessibility
Both raw data and
report are available
Sub group analysis
can be done
Applicability
Covers 2 Ur...
Availability
Accessibility
Preliminary report
is available
Raw data and final
report NOT
available online
Sub-group analys...
Availability
Accessibility
Preliminary report
is available
Raw data and final
report NOT
available online
Sub group analys...
Availability
Accessibility
Report available
online.
Request raw data
from MoHFW
Sub group analysis
can be done
Applicabili...
Urban HEART Indicators NOT covered
Indicator
Road traffic injuries (core) Recommend to include in DHS
Prevalence of tobacc...
Geographical coverage in analysis
Division
City
Corporation Muni.
2013
MICS V
(7 divisions)
V
2011
BDHS V
(7 divisions)
V
...
Definitions of Inequity Used in Each Report
BDHS NTP UHS PEHUP MICS STEPS
Urban/rural
specific
wealth
quintile (20%)
 Poo...
Are the urban poor being identified?
DHS wealth
quintile
category
Urban n
(unweighted)
Urban % Rural n
(unweighted)
Rural ...
Response rate of urban poor(est) in DHS
Indicator
Total
respondent
Total urban
respondent
%
Urban
poorest and
poorer
respo...
1. DHS, MICS, UHS, STEPs: 1st-stage sampling from census data,
and 2nd-stage listing of households misses many urban-
poor...
Recommendation
1. Specific or booster surveys of the urban poor
Household data can capture sufficient number of
urban poo...
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Household surveys in Bangladesh - How well are the urban poor represented?

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Presentation by Advancement through Research and Knowledge (ARK) Foundation on the persisting health inequalities within urban settings in Bangladesh and the representation of the urban poor in household surveys. First presented at the 12th International Conference on Urban Health 2015, Dhaka, Bangladesh.

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Household surveys in Bangladesh - How well are the urban poor represented?

  1. 1. HOUSEHOLD SURVEYS IN BANGLADESH How well are the urban poor represented? Ru-Yi Lin
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 17. Thank you for your attention

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