Abstract
BackgroundGlobal health implementing organizations benefit most from health impact estimation models that isolate the individual effects of distributed products and services - a feature not typically found in intervention impact models, but which allow comparisons across interventions and intervention settings. Population Services International (PSI), a social marketing organization, has developed a set of impact models covering seven health program areas, which translate product/service distribution data into impact estimates. Each model's primary output is the number of disability-adjusted life-years (DALYs) averted by an intervention within a specific country and population context. This paper aims to describe the structure and inputs for two types of DALYs averted models, considering the benefits and limitations of this methodology.MethodsPSI employs two modeling approaches for estimating health impact: a macro approach for most interventions and a micro approach for HIV, tuberculosis (TB), and behavior change communication (BCC) interventions. Within each intervention country context, the macro approach determines the coverage that one product/service unit provides a population in person-years, whereas the micro approach estimates an individual's risk of infection with and without the product/service unit. The models use these estimations to generate per unit DALYs averted coefficients for each intervention. When multiplied by program output data, these coefficients predict the total number of DALYs averted by an intervention in a country.ResultsModel outputs are presented by country for two examples: Water Chlorination DALYs Averted Model, a macro model, and the HIV Condom DALYs Averted Model for heterosexual transmission, a micro model. Health impact estimates measured in DALYs averted for PSI interventions on a global level are also presented.ConclusionsThe DALYs averted models offer implementing organizations practical measurement solutions for understanding an intervention's contribution to improving health. These models calculate health impact estimates that reflect the scale and diversity of program operations and intervention settings, and that enable comparisons across health areas and countries. Challenges remain in accounting for intervention synergies, attributing impact to a single organization, and sourcing and updating model inputs. Nevertheless, these models demonstrate how DALYs averted can be viably used by the global health community as a metric for predicting intervention impact using standard program output data.
Highlights
Global health implementing organizations benefit most from health impact estimation models that isolate the individual effects of distributed products and services - a feature not typically found in intervention impact models, but which allow comparisons across interventions and intervention settings
We describe the methodology of Population Services International (PSI)’s disability-adjusted life-years (DALYs) averted models, explaining how product and service distribution data are modeled into health impact estimates, with the final model output being the number of DALYs averted by a PSI intervention
To demonstrate how the PSI DALYs averted models operate in practice, we modeled health impact estimates for each of the examples provided, based on data from countries where PSI operates
Summary
Global health implementing organizations benefit most from health impact estimation models that isolate the individual effects of distributed products and services - a feature not typically found in intervention impact models, but which allow comparisons across interventions and intervention settings. Population Services International (PSI), a social marketing organization, has developed a set of impact models covering seven health program areas, which translate product/service distribution data into impact estimates. To demonstrate performance and maintain stakeholder interest in its program initiatives, global health implementing organizations need to be able to articulate the impact of health promotion programs and use evidence to improve decision making. As modelers can utilize different approaches to generate impact estimates and this type of modeling can serve diverse purposes, individual organizations and researchers have developed many different models for policy decisions and program planning. Models have been used to inform the scale up of male circumcision initiatives, to guide decision making and the allocation of resources for family planning services, and to plan malaria control initiatives [6,7,8]
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