The impact of reducing and eliminating user fees on facility-based delivery: a controlled interrupted time series in Burkina Faso

Abstract

User fee reduction and removal policies have been the object of extensive research, but little rigorous evidence exists on their sustained effects in relation to use of delivery care services, and no evidence exists on the effects of partial reduction compared with full removal of user fees. We aimed to fill these knowledge gaps by assessing sustained effects of both partial reduction and complete removal of user fees on utilization of facility-based delivery. Our study took place in four districts in the Sahel region of Burkina Faso, where the national user fee reduction policy (SONU) launched in 2007 (lowering fees at point of use by 80%) co-existed with a user fee removal pilot launched in 2008. We used Health Management Information System data to construct a controlled interrupted time-series analysis and examine both immediate and sustained effects of SONU and the pilot from January 2004 to December 2014. We found that both SONU and the pilot led to a sustained increase in the use of facility-based delivery. SONU produced an accumulative increase of 31.4% (P < 0.01) over 8 years in the four study districts. The pilot further enhanced utilization and produced an additional increase of 23.2% (P < 0.001) over 6 years. These increasing trends did not continue to reach full coverage, i.e. ensuring that all women had a facility-based delivery. Instead, they stabilized 3 years and 4 years after the onset of SONU and the pilot, respectively. Our study provides further evidence that user fee reduction and removal policies are effective in increasing service use in the long term. However, they alone are not sufficient to achieve full coverage. This calls for the need to implement additional measures, targeting for instance geographical barriers and knowledge gaps, to achieve the target of all women delivering in the presence of a skilled attendant.

Removal of user fees and system strengthening improves access to maternity care, reducing neonatal mortality in a district hospital in Lesotho

“…This lack of evidence results from both the number and quality of existing studies [23]. Despite this, there is consistency in the direction of effect, particularly when assessing the impact of fees on facility (primary health care-and hospital-level) deliveries [3][4][5][6][7][8][9][10][11][12]. Our findings are consistent with this trend and importantly are able to assess not only access to care but also delivery outcomes and the presentation of complications during delivery.…”

“…Maternal deaths can be greatly reduced by increasing the number of births attended by a skilled birth attendant, alongside timely access to secondary and tertiary care. There is a growing body of evidence to suggest removal or minimisation of fees for antenatal and delivery services improves maternal and neonatal outcomes and increases facilitybased deliveries [3][4][5][6][7][8][9][10][11][12]. Lesotho could improve its maternal mortalityamong the highest globallyby removing user fees for hospital delivery care.…”

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] .…”

“…These strategies consisted of exclusion, imputation, interpolation, veri cation, and accounting for missing data in modeling. Exclusion of missing data was the most common practice, and among studies that used this technique, they excluded facilities from the analytic samples 38,41,45,52,65,91,[93][94][95][96][97][98] , restricted the study period based on explicit criteria 54,99 , or applied sensitivity analysis to compare various exclusion criteria 41,100,101 . Imputation methods varied from assigning speci c values to the missing observation 42,86,91,[102][103][104] , to various modeling strategies such as conditional autoregressive model 87 , generalized linear regression 103 , and iterative singular value decomposition 103 .…”

“…Thresholds were set based on the distribution of the data, such as proportions or standard deviations from univariate regression. Several studies used visual inspection of outliers 38,81 , while the use of jackkni ng analysis and the identi cation of in uential points through Cook's distance statistics were also applied 107,108 . Upon identi cation of extreme values, several strategies were utilized: exclusion, replacement with the average value, replacement with the missing value, veri cation with a data source, or discounting the observation in statistical estimation.…”