Packaging health services when resources are limited: the example of a cervical cancer screening visit

Abstract

Background: Increasing evidence supporting the value of screening women for cervical cancer once in their lifetime, coupled with mounting interest in scaling up successful screening demonstration projects, present challenges to public health decision makers seeking to take full advantage of the single-visit opportunity to provide additional services. We present an analytic framework for packaging multiple interventions during a single point of contact, explicitly taking into account a budget and scarce human resources, constraints acknowledged as significant obstacles for provision of health services in poor countries.

Methods and findings: We developed a binary integer programming (IP) model capable of identifying an optimal package of health services to be provided during a single visit for a particular target population. Inputs to the IP model are derived using state-transition models, which compute lifetime costs and health benefits associated with each intervention. In a simplified example of a single lifetime cervical cancer screening visit, we identified packages of interventions among six diseases that maximized disability-adjusted life years (DALYs) averted subject to budget and human resource constraints in four resource-poor regions. Data were obtained from regional reports and surveys from the World Health Organization, international databases, the published literature, and expert opinion. With only a budget constraint, interventions for depression and iron deficiency anemia were packaged with cervical cancer screening, while the more costly breast cancer and cardiovascular disease interventions were not. Including personnel constraints resulted in shifting of interventions included in the package, not only across diseases but also between low- and high-intensity intervention options within diseases.

Conclusions: The results of our example suggest several key themes: Packaging other interventions during a one-time visit has the potential to increase health gains; the shortage of personnel represents a real-world constraint that can impact the optimal package of services; and the shortage of different types of personnel may influence the contents of the package of services. Our methods provide a general framework to enhance a decision maker's ability to simultaneously consider costs, benefits, and important nonmonetary constraints. We encourage analysts working on real-world problems to shift from considering costs and benefits of interventions for a single disease to exploring what synergies might be achievable by thinking across disease burdens.

Figure 1. Equations of the Integer Programming Model

The IP model consists of the objective function, which maximizes DALYs averted through the health interventions (equation 1); the constraint of choosing no more than one intervention option per disease (equation 2); a budget constraint, expressed as a per-woman lifetime health expenditure (equation 3); and human resource constraints, limiting the available general (equation 4a) and specialized (equation 4b) personnel time separately.

Figure 2. Packages of Interventions under a Lifetime Budget of $75 per Woman in AFR-D,E

Each curve shows the order in which interventions are added (or substituted) when the budget is set at $75 per woman under the different scenarios of personnel time constraints: no personnel time constraints (blue), 50% constraint on specialized personnel time only (pink), and 50% constraint on both general and specialized personnel time (green). Curves in gray represent data from alternative lifetime budget set at $100 per woman (Figure 3). The dotted line represents the budget constraint.

Figure 3. Packages of Interventions under a Lifetime Budget of $100 per Woman in AFR-D,E

Each curve shows the order in which interventions are added (or substituted) when the budget is set at $100 per woman under the different scenarios of personnel time constraints: no personnel time constraints (blue), 50% constraint on specialized personnel time only (pink), and 50% constraint on both general and specialized personnel time (green). Curves in gray represent data from alternative lifetime budget set at $75 per woman (Figure 2). The dotted line represents the budget constraint.