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Using multi‐country household surveys to understand who provides reproductive and maternal health services in low‐ and middle‐income countries: a critical appraisal of the Demographic and Health Surveys
Author(s) -
Footman K.,
Benova L.,
Goodman C.,
Macleod D.,
Lynch C. A.,
PennKekana L.,
Campbell O. M. R.
Publication year - 2015
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.12471
Subject(s) - health care , public health , medicine , clarity , developing country , critical appraisal , reproductive health , public sector , population , business , nursing , environmental health , economic growth , political science , alternative medicine , biochemistry , chemistry , pathology , law , economics
Objective The Demographic and Health Surveys ( DHS ) are a vital data resource for cross‐country comparative analyses. This study is part of a set of analyses assessing the types of providers being used for reproductive and maternal health care across 57 countries. Here, we examine some of the challenges encountered using DHS data for this purpose, present the provider classification we used, and provide recommendations to enable more detailed and accurate cross‐country comparisons of healthcare provision. Methods We used the most recent DHS surveys between 2000 and 2012; 57 countries had data on family planning and delivery care providers and 47 countries had data on antenatal care. Every possible response option across the 57 countries was listed and categorised. We then developed a classification to group provider response options according to two key dimensions: clinical nature and profit motive. Results We classified the different types of maternal and reproductive healthcare providers, and the individuals providing care. Documented challenges encountered during this process were limitations inherent in household survey data based on respondents’ self‐report; conflation of response options in the questionnaire or at the data processing stage; category errors of the place vs . professional for delivery; inability to determine whether care received at home is from the public or private sector; a large number of negligible response options; inconsistencies in coding and analysis of data sets; and the use of inconsistent headings. Conclusions To improve clarity, we recommend addressing issues such as conflation of response options, data on public vs . private provider, inconsistent coding and obtaining metadata. More systematic and standardised collection of data would aid international comparisons of progress towards improved financial protection, and allow us to better characterise the incentives and commercial nature of different providers.