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Background: The last decade has seen widespread retreat from user fees with the intention to reduce financial constraints to users in accessing health care and in particular improving access to reproductive, maternal and newborn health services. This has had important benefits in reducing financial barriers to access in a number of settings. If the policies work as intended, service utilization rates increase. However this increases workloads for health staff and at the same time, the loss of user fee revenues can imply that health workers lose bonuses or allowances, or that it becomes more difficult to ensure uninterrupted supplies of health care inputs.This research aimed to assess how policies reducing demand-side barriers to access to health care have affected service delivery with a particular focus on human resources for health.Methods: We undertook case studies in five countries (Ghana, Nepal, Sierra Leone, Zambia and Zimbabwe). In each we reviewed financing and HRH policies, considered the impact financing policy change had made on health service utilization rates, analysed the distribution of health staff and their actual and potential workloads, and compared remuneration terms in the public sectors.Results: We question a number of common assumptions about the financing and human resource inter-relationships. The impact of fee removal on utilization levels is mostly not sustained or supported by all the evidence. Shortages of human resources for health at the national level are not universal; maldistribution within countries is the greater problem. Low salaries are not universal; most of the countries pay health workers well by national benchmarks.Conclusions: The interconnectedness between user fee policy and HRH situations proves difficult to assess. Many policies have been changing over the relevant period, some clearly and others possibly in response to problems identified associated with financing policy change. Other relevant variables have also changed.However, as is now well-recognised in the user fee literature, co-ordination of health financing and human resource policies is essential. This appears less well recognised in the human resources literature. This coordination involves considering user charges, resource availability at health facility level, health worker pay, terms and conditions, and recruitment in tandem. All these policies need to be effectively monitored in their processes as well as outcomes, but sufficient data are not collected for this purpose. © 2013 McPake et al.; licensee BioMed Central Ltd.
This study consisted of the following components: literature review, desk-based analysis and document review, field studies and analysis. No experimental research or research on humans was involved in this work. We undertook a review of the current literature on the removal of, exemption from or waivers of user fees in low- and middle-income countries in relation to RMNH and the consequences for human resources for health working in RMNH. First, to be included, studies had to address either the removal of user charges or the application of exemptions and/or waivers in order to facilitate access to RMNH services in low- and middle-income countries. The user fee, exemption and waiver mechanisms at national, provincial and district level were explored. The second criterion for inclusion was consideration of the effect of these financing instruments on RMNH health personnel, particularly cadres of skilled birth attendants (SBAs), including nurses, midwives, doctors and clinical officers and the paramedical, support and ancillary staff. The final criterion was publication date, which was restricted to 2001 to 2011, with some exceptions, where studies on the introduction of user fees from the 1980s to 1990s were included for historical context. Only studies and reports written in the English language were collected, collated and consolidated in the bibliography. The following databases and sources were searched: PubMed, Popline, SCOPUS, Science Direct, Web of Knowledge, Human Resources for Health Journal, Equinet, MNCH knowledge portal, ELDIS, HRH Global Resource Centre, World Health Organization, Alliance for Health Policy and Health Systems Research, and Google Scholar, using a list of 66 keywords. In the initial search, 500 articles were identified, out of which 267 were shortlisted based on the keywords above; the abstracts were then reviewed independently by two researchers and 115 were shortlisted. Following a further refinement of the search parameters, in which the keywords were narrowed to exclude any articles not including reference to human resources engaged with RMNH activity, a final list of 67 was included and the full articles were included and reviewed. Similarly, the grey literature search furnished 200 documents and 35 were included following the aforesaid procedure. We sought data on: • Human resource numbers and distribution (by cadre and district) in public and private sectors and before and after the financing policy change of interest, where relevant; • Public and private sector remuneration and allowances, and trends; • RNMH need as measured by the population and birth rate by district; • Health-management information-system data on levels of use of antenatal care, postnatal care, deliveries, newborn care, abortions, and family planning, gynaecological, sexually transmitted diseases (STD) and HIV clinic services. Access to data sets held by Ministries of Health, Central Statistical Offices and similar offices was secured along with policy and planning documents, through the recruitment of local collaborators in a position to access these. Grey literature was located by web search and by contacting relevant local agencies. The search for data and documents was undertaken during 2011. Much of the data sought proved unavailable. Trend data were generally unavailable either due to an absence of maintenance of a historic database, or because previous estimates of variables were made in a way not comparable with those of present estimates. Private sector data were difficult to access and sparse where available at all. Field studies were undertaken in two countries (Sierra Leone and Zimbabwe) to gain more in-depth understanding in both HRH and financing domains. These countries were selected because there was a smaller literature base on user fees and their removal, in these countries than in others. In Sierra Leone, the time was spent accessing documents and secondary data and seeking clarifications in relation to data that appeared inconsistent. Data quality was poor, and there remain considerable gaps in what we were able to collect. In each country we analysed available data and research reports to review: (1) how financing policy change had affected utilization levels; (2) the geographical distribution of the health workforce; (3) delivery workloads and how actual workloads and potential workloads (based on the total number of births that are estimated for the country) compared to what is considered by the WHO to be a feasible workload; and (4) remuneration and terms and conditions. In the discussion section, we address to what extent a review of these data help to answer our research questions concerning the inter-relationships between workforce and financing situations and policies. Qualitative data were transcribed and analysed thematically, starting from the topics outlined in the interview guides, but allowing for identification of new themes arising from the discussions. Analysis of the distribution of the health workforce in each country computed concentration indices (CIs). These are constructed by ordering districts by increasing population density (from most sparsely to most densely populated districts) and measuring the distance between actual and equal shares of health workers per head of population in each district. A hypothetical situation where health workers are distributed equally in proportion to population across the country produces a CI of zero (no distance from actual to equal share). In a situation where the distribution favours densely populated areas, the index will be greater than zero. Maximum, pro-urban, concentration is where the whole of the staff is based in the most densely populated district and the corresponding CI is one.
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