TY - JOUR
T1 - Rapid sociometric mapping of community health workers to identify opinion leaders using an SMS platform
T2 - A short report
AU - Odeny, Thomas A.
AU - Petersen, Maya
AU - Muga, Charles T.
AU - Lewis-Kulzer, Jayne
AU - Bukusi, Elizabeth A.
AU - Geng, Elvin H.
N1 - Funding Information:
Research reported in this publication was supported by the National Institute of Mental Health of the National Institutes of Health under Award Number R01MH104123. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Funding Information:
This study was conducted in May 2015 in three subcounties in western Kenya: Kisumu (urban), Migori (semi-rural), and Rongo (semi-rural). Eligible participants were all lay peer health workers, also called clinical and community health assistants (CCHA), associated with the Family AIDS Care and Education Services (FACES) program in Kenya. FACES is a HIV prevention, care, and treatment program funded by the US President’s Emergency Plan for AIDS Relief (PEPFAR). It is a collaboration between the Kenya Medical Research Institute and the University of California, San Francisco [14]. At the time, FACES cared for over 80,000 patients and supported 132 government health facilities spread across western Kenya to offer HIV services. This region has the highest HIV prevalence in Kenya (15%) [15]. The participants were selected from four health facilities involved in a larger study on adaptive interventions including peer navigator approaches for improving engagement in HIV care (ClinicalTrials.gov #NCT02338739). We aimed to identify opinion leaders who would serve as referral contacts for CCHA peer navigator work. All CCHA at these facilities were invited to participate. The CCHA are lay healthcare workers unique to FACES trained as part of task shifting to provide peer counseling, HIV education, patient tracing, and other non-clinical or minor clinical tasks at health facilities that provide HIV care and treatment services [14]. While some of their tasks are similar to those of typical community health workers, they have no government-equivalent cadre due to the wide variety of tasks they are trained by FACES to carry out. They receive a monthly stipend from FACES of between $200 and $400 depending on experience and number on average five per health facility. A community liaison officer also employed by FACES oversees all the CCHA within a sub-county. In general, the CCHA collaborate across sub-counties by virtue of their patient tracing activities, which typically go beyond their assigned sub-county depending on patients’ location.
Publisher Copyright:
© 2017 The Author(s).
PY - 2017/6/26
Y1 - 2017/6/26
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85021301356&partnerID=8YFLogxK
U2 - 10.1186/s13012-017-0611-y
DO - 10.1186/s13012-017-0611-y
M3 - Article
C2 - 28651602
AN - SCOPUS:85021301356
VL - 12
JO - Implementation Science
JF - Implementation Science
SN - 1748-5908
IS - 1
M1 - 80
ER -