Spillover effects on health outcomes in low- and middle-income countries: a systematic review

Abstract

Background: Many interventions delivered to improve health may benefit not only direct recipients but also people in close physical or social proximity. Our objective was to review all published literature about the spillover effects of interventions on health outcomes in low-middle income countries and to identify methods used in estimating these effects.

Methods: We searched 19 electronic databases for articles published before 2014 and hand-searched titles from 2010 to 2013 in five relevant journals. We adapted the Cochrane Collaboration's quality grading tool for spillover estimation and rated the quality of evidence.

Results: A total of 54 studies met inclusion criteria. We found a wide range of terminology used to describe spillovers, a lack of standardization among spillover methods and poor reporting of spillovers in many studies. We identified three primary mechanisms of spillovers: reduced disease transmission, social proximity and substitution of resources within households. We found the strongest evidence for spillovers through reduced disease transmission, particularly vaccines and mass drug administration. In general, the proportion of a population receiving an intervention was associated with improved health. Most studies were of moderate or low quality. We found evidence of publication bias for certain spillover estimates but not for total or direct effects. To facilitate improved reporting and standardization in future studies, we developed a reporting checklist adapted from the CONSORT framework specific to reporting spillover effects.

Conclusions: We found the strongest evidence for spillovers from vaccines and mass drug administration to control infectious disease. There was little high quality evidence of spillovers for other interventions.

Keywords: Spillover effects; indirect effects; herd effects; herd immunity; diffusion; externalities; interference.

Figure 1

Cluster-level spillover effects. On the x-axis, the cluster-level spillover effect is shown as the % change in outcome among the untreated in the treated cluster from the mean in the control group [i.e., (1-RR) x 100%, where RR is the relative risk]. Outcomes were recoded so that a greater value of the spillover effect indicates an improvement in health (e.g., higher vaccination coverage, lower mortality) and a smaller value indicates poorer health (e.g., lower vaccination coverage, higher mortality). This figure excludes studies of low or very low quality and studies that did not report information that allowed for standardization. Statistical significance was determined based on the measures presented in the paper for the parameter on its original scale. (a) Information required to convert standard errors for risk differences to standard errors for (1-RR) x 100% was not reported, thus 95% confidence intervals are not presented. (b) These studies were conducted in the same country (India) and are subject to dependence.

Figure 2

Cluster-level spillover effects by treatment coverage level. This figure plots cluster-level spillover estimates by the level of treatment coverage within treated clusters. We estimated treatment coverage using information available in each paper. On the y-axis, the cluster-level spillover effect is shown as the % change in outcome among the untreated in the treated cluster from the mean in the control group [i.e., (1-RR) x 100%, where RR is the relative risk]. Outcomes were recoded so that a greater value of the spillover effect indicates an improvement in health (e.g., higher vaccination coverage, lower mortality) and a smaller value indicates worse health (e.g., lower vaccination coverage, higher mortality). This figure excludes studies of low or very low quality and studies that did not report information that allowed for standardization. (a) These studies were conducted in the same country (India) and are subject to dependence. (b) Information required to convert standard errors for risk differences to standard errors for (1-RR) x 100% was not reported, thus 95% confidence intervals are not presented.

Figure 3

Funnel plots for spillover effects. Panel A: This plot includes spillover estimates from 19 studies that reported risk differences for binary outcomes, of which all but one were from studies in the economics literature. These studies evaluated a wide range of interventions including women’s empowerment programs, mass drug administration for infectious disease control, peer group interventions, and nutrition programs. Panel B: This plot includes spillover estimates from 14 studies that reported risk ratios or protective efficacy ((1-RR) x 100%) for binary outcomes, all of which were from studies in the public health literature. These studies evaluated vaccines and mass drug administration for infectious disease control.