Household costs and time to seek care for pregnancy related complications: The role of results-based financing

Results-based financing (RBF) schemes–including performance based financing (PBF) and conditional cash transfers (CCT)-are increasingly being used to encourage use and improve quality of institutional health care for pregnant women in order to reduce maternal and neonatal mortality in low-income countries. While there is emerging evidence that RBF can increase service use and quality, little is known on the impact of RBF on costs and time to seek care for obstetric complications, although the two represent important dimensions of access. We conducted this study to fill the existing gap in knowledge by investigating the impact of RBF (PBF+CCT) on household costs and time to seek care for obstetric complications in four districts in Malawi. The analysis included data on 2,219 women with obstetric complications from three waves of a population-based survey conducted at baseline in 2013 and repeated in 2014(midline) and 2015(endline). Using a before and after approach with controls, we applied generalized linear models to study the association between RBF and household costs and time to seek care. Results indicated that receipt of RBF was associated with a significant reduction in the expected mean time to seek care for women experiencing an obstetric complication. Relative to non-RBF, time to seek care in RBF areas decreased by 27.3% (95%CI: 28.4–25.9) at midline and 34.2% (95%CI: 37.8–30.4) at endline. No substantial change in household costs was observed. We conclude that the reduced time to seek care is a manifestation of RBF induced quality improvements, prompting faster decisions on care seeking at household level. Our results suggest RBF may contribute to timely emergency care seeking and thus ultimately reduce maternal and neonatal mortality in beneficiary populations.

Malawi is a low income country with a population of 17 million. Its gross domestic product (GDP) is 4.3 billion US$, of which 4.2% is spent on healthcare[22]. Total expenditure on reproductive health (RH) rose from US$50.1 million in 2009/10 to US$74.3 million in 2010/2011, and then declined to US$63.6 million in 2011/12 [23]. On average, US$9.9 per annum was spent on RH over the same period on each woman of reproductive age (15–49 years). Routine and emergency obstetric care are provided free at all public health facilities and at selected private not for profit facilities contracted by the government through Service Levels Agreements. Although Malawi imposes no formal fees for emergency obstetric care (EmOC), evidence shows that some medical costs are still shifted towards patients due to stock outs of drugs and other items needed for surgery[24]. In each district, health centers provide basic emergency obstetric care (BEmOC) and refer complicated cases to respective district hospitals, which provide comprehensive emergency obstetric care (CEmOC). Despite the government’s financial commitment towards reproductive health, maternal mortality ratio in the country is still high at 574/100,000 live births[25]. Details of the RBF4MNH initiative are provided elsewhere [21]. Briefly, the aim of RBF4MNH initiative is to reduce maternal and neonatal mortality through increased access and improved quality of service delivery. The RBF4MNH initiative includes both supply-side and demand-side conditional financial incentives. Supply-side incentives are paid on a quarterly basis to health facilities upon attainment of pre-agreed performance targets: 70% to be divided as top-up among health workers providing maternal and child health (MCH) services and 30% to be invested in improving facility infrastructure and supplies. Hospitals receive 60% for top-up and 40% for investments. The demand-side incentives are paid to pregnant women,irrespective of income or socio-economic status, residing in the designated health facility catchment areas upon delivering in the designated health facility or if referred at the district hospital. The cash transfers, averaging US$10.50 per woman[26], consist of a flat lump sum (US$ 4.0) and of a variable portion, depending on whether a woman remains at a facility 48 hours postpartum and on the distance travelled to access facility care. Although the transfer is only disbursed at delivery, women must register already while pregnant during antenatal visit. Health surveillance agents are responsible to verify women’s village of residence to confirm their eligibility and determine the amount to be received. To ensure sufficient capacity for obstetric care service provision, the RBF4MNH initiative was preceded by a one-time infrastructural and supply upgrade. Additional facility investments were considered to occur based on the earmarked performance rewards. The Ministry of Health (MoH), through district health management teams (DHMTs), is the lead agent in the RBF4MNH implementation. Technical assistance is provided by Options Consulting Services Limited. In 2013, the Malawi MoH selected four districts, Balaka, Dedza,Ntcheu and Mchinji with a combined population of 1.9 million to pilot the RBF4MNH initiative[21]. The districts were purposefully selected so that they were relatively representative of the rest of the 28 districts in the country in terms of maternal/childhood illness patterns and administrative arrangements. Across the four districts, the MoH identified a total of 33 public health facilities (28 BEmOC and 5 CEmOC) eligible to provide EmOC services and selected 17 of those (4 district hospitals/CEmOCs and 13 BEmOCs) to be recipients of the RBF4MNH initiative. One year later, the intervention was expanded with one private not for profit mission hospital/CEmOC and 10 BEmOCs (including 5 private not for profit facilities). The supply-side component was rolled out at the selected CEmOC and BEmOC facilities soon after the official launch of the program in April 2013. Due to implementation challenges, the demand-side component became fully effective across facilities only one year later. Fig 1 illustrates how in terms of intervention exposure, this arrangement entailed that women needing complication care residing in the catchment areas of RBF4MNH facilities (hereafter defined as RBF group) were likely affected by the supply-side incentives provided at both BEmOC and CEmOC level as well as by the conditional cash transfers, while women residing in the catchment areas of non-RBF4MNH facilities (hereafter defined as non-RBF group) were likely affected by the supply-side incentives only if referred to seek complication care in a CEmOC facility. The vertical arrow indicates when supply-side incentives to health workers were applied to Intervention facilities. The intervention facilities in addition received demand-side incentives for women which were fully functional from 2014. Blue horizontal arrow represents interventin facilities. White horozontal arrow represents control facilities. Horizontal axis shows the before and after periods and timing of data collection. Back pointing arrows indicate the 12 months recall period data was collected during each survey round. Data for this study were obtained through three repeated cross-sectional household surveys, conducted at baseline, midterm, and endpoint: April to May 2013, June to July 2014 and June to July 2015 respectively (S1 Data). The data were collected as part of a larger study evaluating the impact of the RBF4MNH strategy, the detailed data collection design and procedures have already been published [27]. Before the first survey, enumeration areas were randomly selected for each health facility. Within each enumeration area, eligible women were interviewed. The surveys targeted all women having completed a pregnancy in the 12 months prior to the survey date. The analysis presented here is limited to the truncated sample of women who reported having experienced a pregnancy-related complication at any point in the course of their pregnancy. Fig 1 provides details on timing of data collection and appropriate recall periods. Trained interviewers collected data from the women using a structured questionnaire, programmed digitally and administered using tablet computers. The questionnaire was administered in Chichewa, the common local language. Information was collected on the women’s social demographic features, self-reported complications and hospital admissions due to complications related to the pregnancy completed within the prior 12 months. The information on self-reported complications was collected in the form of lay person descriptions of a combination of symptoms and signs suggestive of common obstetric complications[20]. This information was validated by interviewers using formal diagnosis recorded in the women’s health passports, where possible. For each self-reported complication, information about relevant out-of-pocket expenditures on medical costs (consultations, drugs, surgical procedures, radiological and laboratory fees) transport costs, food and accommodation were recorded. Time use for seeking and obtaining care for both patients and their informal caregivers was also recorded. All women reporting a complication were asked if they sought care. If the response was yes, the women were then asked to report how quickly after symptoms onset they had decided to seek care, and how many days elapsed before they presented to a facility once decision to seek care was made. Ethical approval for the study was obtained from University of Malawi, College of Medicine Research and Ethics Committee (COMREC) protocol P.02/13/1353 and Ethics Committee of Faculty of Medicine of the University of Heidelberg, Germany, protocol number S-256/2012. Permission to conduct the study was sought from district and village authorities. Written informed consent was obtained from all women prior to the interview. In line with the two objectives, we defined two dependent variables a) Total costs and b) Time to seek care. Total costs were defined as the sum of both direct costs (e.g. medical and transport fees) and indirect costs incurred for each reported complication. We estimated costs of time taken to seek care and actually spent at health facilities using a simplified human capital approach[28]. For each reported complication, we quantified and added up lost patient and informal caregiver’s time in days. Given the high level of self or informal employment in our sample (>80%) and the lack of job specific mean wage information for those in formal employment, we used minimum wages to value lost productivity for both the formally and informally employed. While using minimum wage for those formally employed would bias their wages downwards, this would be offset by using minimum wage for the majority of self or informally employed who probably earn less than the minimum wage. Productivity loss (opportunity cost) was estimated as the product of the time lost and daily minimum wage pertaining to the survey year. Reported minimum wages per day in US$ were 0.87, 1.30, 1.25 for years 2013,2014 and 2015 respectively [29].To compare the costs reported over the years, we used annual Consumer Price Index (CPI) increases from 2013 to 2014, 2014 to 2015 and 2014 to 2015 respectively to adjust the 2013(baseline) and 2014 (midline) costs to 2015(endline) values (1US$ = 550MK). Hereafter, we refer to total costs simply as costs, unless stated otherwise. Time to seek care was defined as duration in days a woman with a reported complication took to present for care at a health facility after symptoms onset. Hereafter, we refer to time to seek care simply as time. The main exposure was receipt of RBF (PBF + CCT) for women in designated health facility areas. To control for confounding in the estimation of the effect, we included independent variables identified as important determinants of care seeking[30] and that have local context and cultural relevance within the framework of understanding obstetric complications care seeking[31, 32]. The variables include age, parity, education, socio-economic status(SES), area of residence, facility type and distance to facility. We additionally included variables indicating if women were registered to receive financial incentives and for those who sought care, whether they were treated as in-patients (a proxy for disease severity) and days spent in facility. We assumed these variables would have bearing on costs. Following standard approaches[33], we generated a wealth index based on household assets ownership using principal component analysis. We used the wealth index to rank the women into three SES terciles. Table 1 provides details of the independent variables. In settings where direct and indirect costs for obstetric complications care are substantial, the apriori effects of cash transfers contingent on facility delivery on household costs is not clear. It would depend on the size of the transfer, the share of women with complications (during labor/delivery) who receive cash and whether receipt of cash actually substitutes for other material support for upkeep or reduce the need for informal caregiver time. For example, if the size of the transfer is large, one would expect an increase in direct costs. If the transfers are used for upkeep while a woman is admitted for care and lessen the need for informal caregivers time/support, one might expect a decrease in indirect costs. Given this lack of clear a prediction, we attempted to answer this question empirically. Although the 3-delay model outlined by Thaddeus and Maine originally applies to delivery care, we expanded its use by applying the same set of concepts to all care pertinent to maternal care, since we assumed that the same set of barriers to access persist along the maternal care continuum. We further extended the model by linking it with performance incentives offered to health providers/facilities, cash transfers offered to women and time to seek care in Fig 2.The framework provides a foundation for studying the relationship between supply-side and demand-side financial incentives and time to seek care, while accounting for numerous factors that interact and may contribute to attainment of prompt emergency care. We hypothesise that, faced with an obstetric complication, perceptions of improved quality of care at health facility resulting from supply-side incentives and guarantees of cash reimbursements would positively influence decision making at household level, leading to reduced likelihood of encountering first delay; and that promises of cash reimbursements would enable households make better transport choices(e.g. more use of motorized transport) leading to reductions in the second delay. Combined, these actions may lead to discernible reductions in average times women with obstetric complications take before presenting themselves for emergency care at health facilities. We provide descriptive summary statistics (means, proportions and corresponding 95% confidence intervals) for social-demographic characteristics of the women with a reported complication in the control and intervention groups. We use t-tests and chi-square tests to assess differences in means and proportions, respectively, between the two groups. Health care data are typically positively skewed, heteroskedastic and may have nontrivial fraction of zeros making it problematic to use parametric analytic approaches[34]. To estimate populations means, E(y|x), while taking into account the non-normal distribution of health care data, generalized linear models (GLMs) are recommended as they allow for making direct inference about expected population means without recourse to complex transformations or re-transformations [35]. Given skewness of the study dependent variables (costs and time), we opted to use GLM to model the dependent variables. As total costs had trivial amounts of zeros (< 3%), a two part model, an approach often used in modeling cost data, is likely to have little effect on the overall predicted mean costs[36]. We thus limited the cost analysis into a single part prediction model. GLMs require explicit specification of the distribution (F) of the dependent variable and the link function (g) describing how independent variables are functionally related to the dependent variable[36]. We used modified Parks test to select appropriate distribution and link functions for the study outcomes[37]. Through this test, we found that a log link with Gamma and Poisson families respectively provide best fits for the costs and time data. The empirical GLMs took the form: Where μi denotes the dependent variable of interest (costs/time) for every unit (pregnant woman seeking care for a reported complication),Yeari is a categorical variable indicating the time point taking value 0 at baseline, 1 at midline and 2 at endline, RBFi, is an indicator variable coded 1 if the unit is in the intervention group, 0 if in the control group, Xi is a vector of independent factors known to influence the dependent variables as outlined above. The estimable quantities of interest are thus: β0, a common constant for all observations,β1, effect of time on each unit, β3 the effect of treatment (and the main target of inference) and β4 representing a vector of coefficients for X(Table 1). Given that the decision to admit women with reported complications for in-patient care was based on clinical assessments, we considered women admitted for care a distinct subgroup. We thus ran two separate models for each of the primary study outcomes: the first model included all women with a reported complication who sought care (full model), while the second was restricted to the women who were admitted (restricted model). As the models have a log link, the exponential of coefficients should be interpreted as the ratio of arithmetic means [35]. We generated robust standard errors and corrected for clustering using the cluster command to allow for clustering of women at health facility levels. StataIC/14 (Stata-corp LP, Texas, USA) was used for the analysis.

• chinkhumba, jobiba
• de allegri, manuela
• mazalale, jacob
• brenner, stephan
• mathanga, don p.
• muula, adamson s.
• robberstad, bjarne