User fee exemption and maternal health care utilisation at mission health facilities in Malawi: An application of disequilibrium theory of demand and supply

Abstract

The literature on health care utilisation has focussed on the interaction of supply and demand factors in determining utilisation. At the aggregate level, studies have modelled the simultaneity of demand and supply, and different methods have been used. This study proposes an alternative framework for modelling utilisation, which yet separates demand and supply factors, the disequilibrium theory of demand and supply. This theory is useful in modelling data that reflect that not all health care demand is met by health care providers and not all health care supply is taken by consumers. Such disequilibrium arises due to rigid prices and quantity rationing. We use the theory to model maternal health care utilisation and user fee exemption at mission health care facilities in Malawi. The study uses switching regression methods and data from the Malawi Health Management Information System. Results show that user fee exemption is associated with increased utilisation of maternal health care. Demand and supply regime classification shows that many of the health facilities met much of the demand, whereas the rest only provided as much maternal care as their maximum capacity. In the latter case, intended maternal health care utilisation targets may not have been met.

Using routine health information data for research in low- and middle-income countries: a systematic review

“…RHIS data have been used to evaluate a wide range of interventions, ranging from programs that targeted specific diseases to interventions or policies that affected multiple types of diseases or health services. These included: the effect of malaria control strategies [30][31][32][33][34][35][36], user fee exemption policies [37][38][39][40], health financing schemes [41][42][43][44], interventions on health governance [45][46][47][48][49][50][51][52][53], the administration of new vaccines and vaccination campaigns [54][55][56], as well as community-level interventions such as approaches to enhance community participation and improve referrals from traditional birth attendants in increasing the demand for maternal and child care [57][58][59].…”

“…Many of the studies utilized the highly disaggregated nature of the data by using either facility or district level data, with the exception of two studies which modelled national trends [33,116]. Studies commonly applied strategies to account for temporal autocorrelation and the correlation between geographical units, including generalized linear models [58], multi-level analysis [77,78], and ordinary least-squares regression with adjustment for seasonality and lag [34,37,117]. Among studies that modelled multiple facilities or administrative regions, random effects were commonly applied to account for heterogeneity.…”

Using Routine Health Information Data for Research in Low- and Middle-Income Countries: A Systematic Review

“…RHIS data have been used to evaluate a wide range of interventions, ranging from programs that targeted speci c diseases to interventions or policies that affected multiple types of diseases or health services. These included: the effect of malaria control strategies [30][31][32][33][34][35][36] , user fee exemption policies [37][38][39][40] , health nancing schemes [41][42][43][44] , interventions on health governance [45][46][47][48][49][50][51][52][53] , the administration of new vaccines and vaccination campaigns [54][55][56] , as well as community-level interventions such as approaches to enhance community participation and improve referrals from traditional birth attendants in increasing the demand for maternal and child care [57][58][59] .…”

“…Many of the studies utilized the highly disaggregated nature of the data by using either facility or district level data, with the exception of two studies which modelled national trends 33,74 . Studies commonly applied strategies to account for temporal autocorrelation and the correlation between geographical units, including generalized linear models 58 , multi-level analysis 75,76 , and ordinary least-squares regression with adjustment for seasonality and lag 34,37,77 . Among studies that modelled multiple facilities or administrative regions, random effects were commonly applied to account for heterogeneity.…”

“…Other studies incorporated information from other national community surveys, health facility surveys, and program data as covariates 34,75 . While most studies controlled for potential confounders by including covariates in analytic models, one study on maternal health service applied propensity score matching to further remove biases from differences in covariate distribution 37 .…”