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Targeting subsidised inpatient services to the poor in a setting with limited state capacity: proxy means testing in Myanmar's hospital equity fund scheme
Author(s) -
Htet Soe,
Ludwick Teralynn,
Mahal Ajay
Publication year - 2019
Publication title -
tropical medicine and international health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.056
H-Index - 114
eISSN - 1365-3156
pISSN - 1360-2276
DOI - 10.1111/tmi.13286
Subject(s) - proxy (statistics) , equity (law) , quartile , business , household income , consumption (sociology) , medicine , environmental health , geography , statistics , mathematics , political science , law , confidence interval , social science , archaeology , sociology
Objectives Many low‐ and middle‐income countries ( LMIC s) provide subsidised access to health services for the poor. Proxy means tests ( PMT s) for income are typically employed to identify eligible beneficiaries for subsidised services but often result in significant mistargeting of benefits. We assessed the PMT approach used in Myanmar's hospital equity fund ( HEF ). Methods We analysed inclusion/exclusion errors by comparing household eligibility under the PMT used for HEF with household consumption (the gold standard proxy for income in LMIC s). We assessed receipt of benefits post‐hospitalisation against HEF eligibility rules and household income. Focus groups/interviews were conducted to understand administrative factors that influence targeting. We modelled (linear regression) predictors of household consumption to improve PMT accuracy. Results We found large targeting errors (86% of households in the bottom consumption quartile would be excluded and 15% of households in the top consumption quartile deemed eligible). HEF scores for PMT held little explanatory power for household income: 93% of individuals meeting the HEF eligibility criteria did not receive benefits post‐hospitalisation, while 23% of ineligible individuals received programme support. Re‐weighting PMT indicators on electricity access, land ownership and livestock ownership, and assigning weights to home‐ownership, households with elderly/disabled members and household head education levels could significantly improve targeting accuracy. Poor programme awareness and uneven adherence to official eligibility determination procedures among staff likely affected targeting. Conclusions Re‐weighting PMT indicators and increasing training and communication about qualification procedures could improve allocation of limited funds, though accurate targeting may continue to be challenging in contexts of low state capacity.