Original Article
Community-based field implementation scenarios of a short message service reporting tool for lymphatic filariasis case estimates in Africa and Asia
Abstract
Background: Lymphatic filariasis (LF) is a neglected tropical disease (NTD) targeted for global elimination by 2020. Currently there is considerable international effort to scale-up morbidity management activities in endemic countries, however there remains a need for rapid, cost-effective methods and adaptable tools for obtaining estimates of people presenting with clinical manifestations of LF, namely lymphoedema and hydrocele. The mHealth tool ‘MeasureSMS-Morbidity’ allows health workers in endemic areas to use their own mobile phones to send clinical information in a simple format using short message service (SMS). The experience gained through programmatic use of the tool in five endemic countries across a diversity of settings in Africa and Asia is used here to present implementation scenarios that are suitable for adapting the tool for use in a range of different programmatic, endemic, demographic and health system settings.
Methods: A checklist of five key factors and sub-questions was used to determine and define specific community-based field implementation scenarios for using the MeasureSMS-Morbidity tool in a range of settings. These factors included: (I) tool feasibility (acceptability; community access and ownership); (II) LF endemicity (high; low prevalence); (III) population demography (urban; rural); (IV) health system structure (human resources; community access); and (V) integration with other diseases (co-endemicity).
Results: Based on experiences in Bangladesh, Ethiopia, Malawi, Nepal and Tanzania, four implementation scenarios were identified as suitable for using the MeasureSMS-Morbidity tool for searching and reporting LF clinical case data across a range of programmatic, endemic, demographic and health system settings. These include: (I) urban, high endemic setting with two-tier reporting; (II) rural, high endemic setting with one-tier reporting; (III) rural, high endemic setting with two-tier reporting; and (IV) low-endemic, urban and rural setting with one-tier reporting.
Conclusions: A decision-making framework built from the key factors and questions, and the resulting four implementation scenarios is proposed as a means of using the MeasureSMS-Morbidity tool. This framework will help national LF programmes consider appropriate methods to implement a survey using this tool to improve estimates of the clinical burden of LF. Obtaining LF case estimates is a vital step towards the elimination of LF as a public health problem in endemic countries.
Methods: A checklist of five key factors and sub-questions was used to determine and define specific community-based field implementation scenarios for using the MeasureSMS-Morbidity tool in a range of settings. These factors included: (I) tool feasibility (acceptability; community access and ownership); (II) LF endemicity (high; low prevalence); (III) population demography (urban; rural); (IV) health system structure (human resources; community access); and (V) integration with other diseases (co-endemicity).
Results: Based on experiences in Bangladesh, Ethiopia, Malawi, Nepal and Tanzania, four implementation scenarios were identified as suitable for using the MeasureSMS-Morbidity tool for searching and reporting LF clinical case data across a range of programmatic, endemic, demographic and health system settings. These include: (I) urban, high endemic setting with two-tier reporting; (II) rural, high endemic setting with one-tier reporting; (III) rural, high endemic setting with two-tier reporting; and (IV) low-endemic, urban and rural setting with one-tier reporting.
Conclusions: A decision-making framework built from the key factors and questions, and the resulting four implementation scenarios is proposed as a means of using the MeasureSMS-Morbidity tool. This framework will help national LF programmes consider appropriate methods to implement a survey using this tool to improve estimates of the clinical burden of LF. Obtaining LF case estimates is a vital step towards the elimination of LF as a public health problem in endemic countries.
