Evidence map›Paper›PMID 35436946›Full record

ArticleHuman resources for health2022

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

Leslie Berman, Margaret L Prust, Agnes Maungena Mononga, Patrick Boko, Macfarlane Magombo, Mihereteab Teshome, Levison Nkhoma, Grace Namaganda, Duff Msukwa, Andrews Gunda

Abstract read
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Article in Human resources for health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Leslie BermanClinton Health Access Initiative, Inc. (CHAI) Malawi, Lilongwe, Malawi. leslie.r.berman@gmail.com.ORCID 0000-0003-3837-7041
Margaret L PrustAnalytics and Implementation Research Team, Clinton Health Access Initiative, Inc. (CHAI), Boston, MA, USA.
Agnes Maungena MonongaDepartment of Human Resources Management and Development, Ministry of Health, Lilongwe, Malawi.
Patrick BokoDepartment of Human Resources Management and Development, Ministry of Health, Lilongwe, Malawi.
Macfarlane MagomboDepartment of Human Resources Management and Development, Ministry of Health, Lilongwe, Malawi.
Mihereteab TeshomeClinton Health Access Initiative, Inc. (CHAI) Malawi, Lilongwe, Malawi.
Levison NkhomaClinton Health Access Initiative, Inc. (CHAI) Malawi, Lilongwe, Malawi.
Grace NamagandaHuman Resources for Health 2030 (HRH2030), Chemonics International, Lilongwe, Malawi.
Duff MsukwaDepartment of Human Resources Management and Development, Ministry of Health, Lilongwe, Malawi.
Andrews GundaClinton Health Access Initiative, Inc. (CHAI) Malawi, Lilongwe, Malawi.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA 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.

methodsThree 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.

resultsAs 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.

conclusionsGiven 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.

Indexed as

Health WorkforceStrategic PlanningHealth PlanningHumansMalawiWorkforceHuman resources for healthMalawiRecruitmentStrategic planningWorkforce development

Identifiers

PMID35436946
PMCPMC9014573

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.