Abstract:
Background: The growing AIDS epidemic in southern Africa is placing an increased strain on health systems, which are experiencing steadily rising patient loads. Health care systems are tackling the barriers to serving large populations in scaled-up operations. One of the most significant challenges in this effort is securing the health care workforce to deliver care in settings where the manpower is already in short supply.
Methods: We have produced a demand-driven staffing model using simple spreadsheet technology, based on
treatment protocols for HIV-positive patients that adhere to Mozambican guidelines. The model can be adjusted
for the volumes of patients at differing stages of their disease, varying provider productivity, proportion who are
pregnant, attrition rates, and other variables.
Results: Our model projects the need for health workers using three different kinds of goals: 1) the number of patients to be placed on anti-retroviral therapy (ART),
2) the number of HIV-positive patients to be enrolled for treatment, and 3) the number of patients to be enrolled in a treatment facility per month.
Conclusion: We propose three scenarios, depending on numbers of patients enrolled. In the first scenario, we start with 8000 patients on ART and increase that number to 58 000 at the end of three years (those were the goals for the country of Mozambique). This would require thirteen clinicians and just over ten nurses by the end of the first year, and 67 clinicians and 47 nurses at the end of the third year. In a second scenario, we start with 34 000 patients enrolled for care (not all of them on ART), and increase to 94 000 by the end of the third year,
requiring a growth in clinician staff from 18 to 28. In a third scenario, we start a new clinic and enrol 200 new patients per month for three years, requiring 1.2 clinicians in year 1 and 2.2 by the end of year 3. Other clinician types in the model include nurses, social workers, pharmacists, phlebotomists, and peer counsellors. This planning tool could lead to more realistic and appropriate estimates of workforce levels required to provide high-quality HIV care in a low-resource settings.
