Using modeling and scenario analysis to support evidence-based health workforce strategic planning in Malawi.

BACKGROUND: A well-trained and equitably distributed workforce is critical to a functioning health system. As workforce interventions are costly and time-intensive, investing appropriately in strengthening the health workforce requires an evidence-based approach to target efforts to increase the number of health workers, deploy health workers where they are most needed, and optimize the use of existing health workers. This paper describes the Malawi Ministry of Health (MoH) and collaborators' data-driven approach to designing strategies in the Human Resources for Health Strategic Plan (HRH SP) 2018-2022. METHODS: Three modelling exercises were completed using available data in Malawi. Staff data from districts, central hospitals, and headquarters, and enrollment data from all health training institutions were collected between October 2017 and February 2018. A vacancy analysis was conducted to compare current staffing levels against established posts (the targeted number of positions to be filled, by cadre and work location). A training pipeline model was developed to project the future available workforce, and a demand-based Workforce Optimization Model was used to estimate optimal staffing to meet current levels of service utilization. RESULTS: As of 2017, 55% of established posts were filled, with an average of 1.49 health professional staff per 1000 population, and with substantial variation in the number of staff per population by district. With current levels of health worker training, Malawi is projected to meet its establishment targets in 2030 but will not meet the WHO standard of 4.45 health workers per 1000 population by 2040. A combined intervention reducing attrition, increasing absorption, and doubling training enrollments would allow the establishment to be met by 2023 and the WHO target to be met by 2036. The Workforce Optimization Model shows a gap of 7374 health workers to optimally deliver services at current utilization rates, with the largest gaps among nursing and midwifery officers and pharmacists. CONCLUSIONS: Given the time and significant financial investment required to train and deploy health workers, evidence needs to be carefully considered in designing a national HRH SP. The results of these analyses directly informed Malawi's HRH SP 2018-2022 and have subsequently been used in numerous planning processes and investment cases in Malawi. This paper provides a practical methodology for evidence-based HRH strategic planning and highlights the importance of strengthening HRH data systems for improved workforce decision-making.

Assessment of interventions to attract and retain health workers in rural Zambia: a discrete choice experiment.

BACKGROUND: Workforce shortages, particularly in rural areas, limit the delivery of health services in Zambia. Policymakers and researchers co-created this study to identify potential non-monetary employment incentives and assess their cost-effectiveness to attract and retain public sector health workers to the rural areas of Zambia. METHODS: The study consisted of two key phases: a discrete choice experiment (DCE), preceded by a qualitative component to inform DCE questionnaire development. Firstly, in qualitative interviews with 25 health workers and focus group discussions (FGDs) with 253 health students, participants were asked to discuss job attributes and potential incentives that would influence their job choices. Based on this exercise and in consultation with policymakers, job attributes were selected for inclusion in a discrete choice experiment (DCE) questionnaire. Secondly, this questionnaire, consisting of hypothetical job "choice sets," was presented to 474 practicing health workers and students. A conditional logit regression model was applied to the data from this DCE questionnaire to estimate preferences for various job attributes. Using administrative data, we estimated the cost of implementing potential attraction and retention strategies per health worker year worked. RESULTS: Although health workers preferred urban jobs to rural jobs (OR 1.39, 95% CI 1.11-1.75), employment incentives influenced health workers' decision to choose rural jobs. If superior housing was offered in a rural area compared to a basic housing allowance in an urban job, participants would be five times as likely to choose the rural job (OR 5.04, 95% CI 4.12-6.18). Education incentives and facility-based improvements also increased the likelihood of rural job uptake. Housing benefits were estimated to have the lowest total costs per health worker year worked, and offer high value in terms of cost per percentage point increase in rural job uptake. CONCLUSIONS: Non-monetary incentives such as housing, education, and facility improvements can be important motivators of health worker choice of location and could mitigate rural health workforce shortages. These results can provide valuable insight into the types of job attributes and incentives that are most likely to be effective in attracting and retaining health workers in rural areas.

Quality improvement intervention to increase adherence to ART prescription policy at HIV treatment clinics in Lusaka, Zambia: A cluster randomized trial.

INTRODUCTION: In urban areas, crowded HIV treatment facilities with long patient wait times can deter patients from attending their clinical appointments and picking up their medications, ultimately disrupting patient care and compromising patient retention and adherence. METHODS: Formative research at eight facilities in Lusaka revealed that only 46% of stable HIV treatment patients were receiving a three-month refill supply of antiretroviral drugs, despite it being national policy for stable adult patients. We designed a quality improvement intervention to improve the operationalization of this policy. We conducted a cluster-randomized controlled trial in sixteen facilities in Lusaka with the primary objective of examining the intervention's impact on the proportion of stable patients receiving three-month refills. The secondary objective was examining whether the quality improvement intervention reduced facility congestion measured through two proxy indicators: daily volume of clinic visits and average clinic wait times for services. RESULTS: The mean change in the proportion of three-month refills among control facilities from baseline to endline was 10% (from 38% to 48%), compared to a 25% mean change (an increase from 44% to 69%) among intervention facilities. This represents a significant 15% mean difference (95% CI: 2%-29%; P = 0.03) in the change in proportion of patients receiving three-month refills. On average, control facilities had 15 more visits per day in the endline than in the baseline, while intervention facilities had 20 fewer visits per day in endline than in baseline, a mean difference of 35 fewer visits per day (P = 0.1). The change in the mean facility total wait time for intervention facilities dropped 19 minutes between baseline and endline when compared to control facilities (95% CI: -10.2-48.5; P = 0.2). CONCLUSION: A more patient-centred service delivery schedule of three-month prescription refills for stable patients is viable. We encourage the expansion of this sustainable intervention in Zambia's urban clinics.

How Connecticut health directors deal with public health budget cuts at the local level.

OBJECTIVES: We investigated the perspectives of local health jurisdiction (LHJ) directors on coping mechanisms used to respond to budget reductions and constraints on their decision-making. METHODS: We conducted in-depth interviews with 17 LHJ directors. Interviews were audio recorded, transcribed, and analyzed using the constant comparative method. RESULTS: LHJ directors use a range of coping mechanisms, including identifying alternative revenue sources, adjusting services, amending staffing arrangements, appealing to local political leaders, and forming strategic partnerships. LHJs also face constraints on their decision-making because of state and local statutory requirements, political priorities, pressures from other LHJs, and LHJ structure. CONCLUSIONS: LHJs respond creatively to budget cuts to maintain important public health services. Some LHJ adjustments to administrative resources may obscure the long-term costs of public health budget cuts in such areas as staff morale and turnover. Not all coping strategies are available to each LHJ because of the contextual constraints of its locality, pointing to important policy questions on identifying optimum jurisdiction size and improving efficiency.