Socio-political and organizational influences on national infectious disease surveillance for refugees: A qualitative case study in Lebanon.

Abstract

Infectious disease surveillance provides actionable information on displaced populations and helps identify outbreaks. Though not a signatory to the 1951 Refugee Convention, Lebanon has experienced large refugee influxes (e.g. Palestinians in 1948, Syrians in 2011), yet information on socio-political and organizational influences shaping surveillance targeting refugees is limited. We thus aimed to examine how Lebanese socio-politics affected infectious disease surveillance for refugees in Lebanon. We conducted a qualitative multimethod single case study of government engagement with refugee infectious disease surveillance (2011-2018) drawing from document analysis, semi-structured observations, and semi-structured key informant interviews at four surveillance sites in Lebanon. We analysed data thematically, using deductive and inductive coding. National politics delayed government and thus its epidemiological surveillance program's (ESU) engagement with refugee disease surveillance, largely due to Lebanon not being a 1951 Refugee Convention signatory and internal policy disagreements. Thus, it was initially difficult for the ESU to lead surveillance activities, though it later became more active. The ESU was limited by unclear reporting mechanisms and resources and its reliance on aggregated surveillance data prevented provision of data-informed responses. Though the ESU led surveillance nationally, and we identified positive provincial level collaborations due to individual efforts, some partners still conducted parallel surveillance. We found no systematic approach to infectious disease surveillance for refugees. The ESU could improve surveillance for refugees by collaborative strategic planning with partners for preparedness, surveillance, reporting, and sustainable resource allocation during refugee crises. Further suggestions include collecting disaggregated data, and piloting potentially more efficient syndromic surveillance, based on symptom clusters, for refugee populations.