Rapid sociometric mapping of community health workers to identify opinion leaders using an SMS platform: a short report

Abstract

Background: Using opinion leaders to accelerate the dissemination of evidence-based public health practices is a promising strategy for closing the gap between evidence and practice. Network interventions (using social network data to accelerate behavior change or improve organizational performance) are a promising but under-explored strategy. We aimed to use mobile phone technology to rapidly and inexpensively map a social network and identify opinion leaders among community health workers in a large HIV program in western Kenya.

Methods: We administered a five-item socio-metric survey to community health workers using a mobile phone short message service (SMS)-based questionnaire. We used the survey results to construct and characterize a social network of opinion leaders among respondents. We calculated the extent to which a particular respondent was a popular point of reference ("degree centrality") and the influence of a respondent within the network ("eigenvector centrality").

Results: Surveys were returned by 38/39 (97%) of peer health workers contacted; 52% were female. The median survey response time was 13.75 min (inter-quartile range, 8.8-38.7). The total cost of relaying survey questions through a secure cloud-based SMS aggregator was $8.46. The most connected individuals (high degree centrality) were also the most influential (high eigenvector centrality). The distribution of influence (eigenvector centrality) was highly skewed in favor of a single influential individual at each site.

Conclusions: Leveraging increasing access to SMS technology, we mapped the network of influence among community health workers associated with a HIV treatment program in Kenya. Survey uptake was high, response rates were rapid, and the survey identified clear opinion leaders. In sum, we offer proof of concept that a "mobile health" (mHealth) approach can be used in resource-limited settings to efficiently map opinion leadership among health care workers and thus open the door to reproducible, feasible, and efficient empirically based network interventions that seek to spread novel practices and behaviors among health care workers.

Fig. 1

SMS survey flow illustration

Fig. 2

Distribution of SMS survey completion times

Fig. 3

Distribution of time spent (in hours) on survey development tasks per sub-county. The total time spent was 19.75 h distributed as follows: (1) devise survey questions 1.5 h (8%); (2) program questions in SMS system 3 h (15%); (3) announce process to staff 0.75 h (4%); (4) pilot survey 0.5 h (3%); (5) administer survey 8 h (40%); and (6) analysis of survey questions 6 h (30%)

Fig. 4

Network structure visualization. Each node represents a survey respondent, and each arrow points to the person whose opinion would be sought in response to a survey question. Each arrow represents one survey question. An arrow pointing back to the node of origin represents a survey respondent who, in response to a question asking whose opinion they would seek, indicated that they would rely on their own opinion. Isolated nodes (no incoming or outgoing arrows) represent respondents who neither selected others as a reference for opinions nor selected themselves

Fig. 5

Distribution of eigenvector centrality values. KSM Kisumu sub-county, RDH Rongo sub-county, MDH Migori sub-county