USING GIS TO ESTIMATE HOW MOTHERS’ BREASTFEEDING DECISIONS RESPOND TO EXPOSURE TO ARSENIC-CONTAMINATED DRINKING WATER

KARTINI SHASTRY (WITH PINAR KESKIN AND HELEN WILLIS) GIS DAY 2015, WELLESLEY COLLEGE

MOTIVATION AND RESEARCH QUESTION

Breastfeeding has health benefits for infants when water-related diseases/contaminants are prevalent

Do mothers increase duration of breastfeeding in response to concerns about water quality? Context: millions of people in Bangladesh exposed

to arsenic in their drinking water

1970’s: millions of tube wells installed

1990’s: arsenic discovered in many tube wells

1999-2006: information campaign, tested wells, painted contaminated wells red

HISTORY OF SAFE DRINKING WATER IN BANGLADESH

OVERVIEW: STRATEGY AND RESULTS

Sources of variation Compare children born before and after campaign Compare children living in more and less contaminated

villages Geography of contamination means that people in different

villages have varying access to uncontaminated water, even conditional on exposure

Mothers breastfeed children longer More likely to breastfeed infants exclusively Improved health for infants (reduced mortality rate, reduced

diarrheal incidence)

THE DATA CHALLENGE

Data on children born 1995-2007 (Bangladesh DHS) GPS coordinates of village

Need measures of arsenic exposure and access to clean water

Two imperfect sources of data on arsenic levels of wells 1. Original data: British Geological Society (BGS)

3500 wells with GPS coordinates, covers entire country

2. New data: Bangladesh Arsenic Mitigation Water Supply Program (BAMWSP) 4.5 million wells but no GPS coordinates, doesn’t cover entire country

Does have mouza identifer (village cluster)

BGS VS BAMWSP DATA

1998-1999 Tested 3,500 wells

ORIGINAL DATA 1 POINT=1 WELL

NEW DATA 1 POINT=20-30WELLS

1999-2004 Tested 4.5 million wells

Many mouzas that do not appear in the BAMWSP well data are ‘arsenic safe’ on this online map

SOME MISSING MOUZAS CONSIDERED SAFE

MISSING MOUZAS IN CONTAMINATED AREAS

Original Data DHS village locations BGS wells

Mouzas shaded by number of wells tested

New Data

Mouzas

shaded by mean As level of tested wells

RESULTS WITH MEASURE OF EXPOSURE

Exposure ~ fraction of wells contaminated Exposure measures highly correlated

ρ ranges from 0.69 - 0.99 Predict household-level arsenic measures

Main results with new measures are consistent In fact, magnitudes are remarkably similar

MEASURING ACCESS TO CLEAN WATER

Estimate access to clean water, conditional on exposure to arsenic-contaminated water Requires more precise information on well’s location

Better to have GPS location or larger sample of wells ???

Option 1: “Inverse Distance Weighting” (IDW, Krigging) Interpolate As level ‘everywhere’ using mean As of wells tested in

a mouza, assigning all of them to the ‘center’ of the mouza

OUTPUT OF INVERSE DISTANCE WEIGHTING

Some villages have better access to clean wells than others

MEASURING ACCESS TO CLEAN WATER

Estimate access to clean water, conditional on exposure to arsenic-contaminated water Requires more precise information on well’s location

Better to have GPS location or larger sample of wells ???

Option 1: “Inverse Distance Weighting” (IDW) or Krigging Interpolate As level ‘everywhere’ using mean As of wells tested in

a mouza, assigning all of them to the ‘center’ of the mouza

Option 2: Simulate measures Distribute tested wells to randomly chosen points in a mouza

GPS DATA BETTER THAN LARGE SAMPLE

All measures seem to relate nicely with each other and with household-level arsenic measures

BGS measures do a slightly better job at predicting who continues to use contaminated water (slightly higher R-squared)

New measures too highly correlated with contamination to estimate triple difference

The End!