Governmental and non-governmental organizations continue to prioritize efforts to reduce the global burden of HIV, TB and malaria. To assist stakeholders in LMICs with HRH workforce planning to combat these diseases, we developed an open access, user-friendly calculator tool featuring two MS Excel components – “Lives saved” and “Coverage target”. The tool demonstrates the feasibility of estimating improvements in service treatment and lives saved for HIV, TB and malaria following investments in the HRH workforce. Our review of the literature, as well as the empirical results of our novel analytic methodology, indicate positive associations among HRH investments, four key treatment service indicators, and health impact on the burden of HIV, TB, and malaria. In addition, pilot tests from LMICs confirm the feasibility of using the tool to assist with HRH planning.
In most countries with a considerable burden of HIV, TB, and malaria, the health workforce investments supported in the context of programmes funded by global health initiatives such as the Global Fund include a mix of pre-service training, full remuneration of new hires, various forms of incentives and in-service training. These investments span across DNMs and CHWs. The investments, based on our pilot studies, typically occurred among CHWs. The modelled estimates developed for illustrative purposes and through country case studies suggest that HRH investments result in lives saved across the four treatment coverage areas. Further, they show that attainment of high targets of specific treatment coverage indicators would require a substantially greater health workforce than what is currently available in most LMICs. Whereas investment in existing workers to improve their competencies and productivity is one useful approach, increasing the total number of HRH workers in these contexts represents a crucial long-term effort to achieve reductions in the burden of HIV, TB, and malaria, echoing predictions elsewhere [24]. Government strategies to increase the education and employment of the health workforce should provide the policy and investment framework to operationalize the support provided by development partners.
The applicability of the workforce tool depends on availability of HRH output data as well as epidemiologic data. This information would include the country’s number of new health workers entering the workforce and the existing number of health workers trained, remunerated, incentivized or otherwise supported. Programmes and development partners, such as the Global Fund, that make a substantial investment in HRH should consider requiring the reporting of LMIC-specific information on these investments. Such required reporting would permit better tracking of “value added” from HRH investments and enable more precise estimates of the relation between HRH inputs and specific treatment service outputs.
Strengths of the tool include its user-friendly nature, in that our country counterparts for the piloting exercise could navigate and understand the various HRH and global burden data inputs. In addition, the most up-to-date literature and our empirical findings support positive associations between HRH inputs and health impact for HIV, TB, and malaria. Whereas components of the conceptual model have been applied in previous exercises to develop general benchmarks of DNM densities [25–27], we know of no prior effort which permits tailoring HRH planning to the particular country context. The current tool, moreover, gives the end-user substantial flexibility in entering various HRH investment scenarios such that they could compare the influence of these scenarios on HIV, TB, and malaria treatment coverage.
A key limitation involves the descriptive nature of the associations discovered in our regression results. We used cross-sectional information to estimate the associations between country-level DNM density and treatment service coverage for HIV, TB, and malaria. The estimation strategy does not account for potential “third” variables which could cause both gains in DNM density and health. We therefore caution against interpreting as causal, the associations which underpin the HRH tool. Methodological approaches other than ours (e.g., simultaneous equation modelling—see, for example, Scheffler and colleagues [25]) may be better equipped to estimate causal effects. Such approaches would likely require HRH and health data with finer geographic and temporal resolution—but with much less country coverage—than the publicly available datasets we utilize.
We also acknowledge a detailed set of simplifying assumptions (supplementary file 1). For instance, we assumed that new HRH investments do not affect existing HRH dynamics (e.g., inflows from immigration, outflows from departures or retirements). In addition, we applied the co-treatment rate for HIV/TB co-infected individuals, averaged across all countries, when estimating treatment service coverage following increased HRH investments. This averaging may mask important country-level differences in the extent to which LMICs aggressively screen for TB among HIV-positive persons. Whereas we make explicit these assumptions, the end-user should avoid interpreting any cell in the MS Excel calculator sheets as “deterministic,” precise estimates.
Regarding uncertainty analyses, our methodology assumes that the relations between the many health worker inputs and treatment service outputs are measured with no uncertainty. This simplifying assumption permits calculation of point estimates in terms of additional lives saved, for example, but does not provide confidence bounds for each point estimate.
Finally, while this tool does factor in different types of HRH investments and different occupational groups, it is not an allocative efficiency tool. For this reason, the tool cannot be used as a guide on which type of HRH investment or which occupational group (based on a marginal investment logic) should be prioritized.
Investments in human resources for health have the potential for positive results for a range of health services and health outcomes, beyond the three diseases we examined. Future research may explore the application of our methodology to other health service areas. It is also acknowledged that HRH investments have broader positive development outcomes, including through the creation of qualified employment opportunities, particularly for women, spurring sustainable economic growth and contributing to gender empowerment. These other dimensions are of critical importance and deserve further scholarly attention.