Kenya, Nepal, & Malawi: Adjustment to Population Health Needs

Effective surveillance hinges on consistent access to reliable, real-time data that captures a comprehensive range of information on population health needs and events of public health significance 1 2 3 Collecting these data is particularly challenging in countries facing significant resource-constraints and a significant rural or isolated population.  To address this challenge, a number of countries have used community-based or crowd-sourced data, such as InfluenzaNet and social media postings, to complement traditional facility-based data used in surveillance systems.4

Considering the rapid global expansion of community health worker (CHW) programs, CHWs offer a promising platform for strengthening surveillance systems in rural, resource-constrained settings. Several countries are already experimenting with CHW-based participatory-surveillance systems, often through the use of mobile phones.4 In this model, CHWs collect data as a part of their routine course of care. Studies conducted on CHW-based surveillance in Nepal, Malawi, and Kenya have shown promise that CHW-collected data can supplement data collected at local facilities and support surveillance activities.4 5 6 The efficacy of this model relies on factors that affect data validity, reliability, and quality such as local participation, presence of well-trained and supervised CHWs capable of conducting surveillance activities, 7 and ongoing support and quality improvement training. These CHW-based data collection strategies have the potential to improve surveillance capabilities in resource-constrained settings and aid real-time decision-making response to population health needs and resource allocation at the local and policy-making level. While these interventions have shown some success in tracking disease trends, it is important to note that individual, self-reported data can have limitations, including a lack of routine collection and should be supplemented with other sources.


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