RESUMO
Introduction: People from backgrounds that are economically/socially disadvantaged experienced disproportionately high COVID-19 death rates and had lower vaccination rates. Effective outreach strategies for increasing vaccine uptake during the pandemic are not fully known. Among patients receiving care at a Federally Qualified Health Center, we tested whether community engaged digitally-enabled outreach increased COVID vaccine uptake. Study Design Setting and Participants: A 3-parallel-arm randomized controlled trial with a hybrid effectiveness-implementation design was conducted among patients ≥18 years old on study enrollment during 2021 with 1,650 assigned in 3:10:20 ratio; 2,328 were later selected for two subsequent implementation rounds. Interventions: From April 13 to June 10, 2021, patients were proactively sent a text-messaging invitation to make an appointment for vaccination as part of the routine practice (Arm 1, n=150) with added personalized clinician recommendation alone (Arm 2, n=500) or with an explicit nudge for answers to frequently asked questions (Arm 3, n=1,000). Further implementation used messaging addressing vaccine hesitancy (n=1,323) or adverse reactions to vaccines (n=1,005). Main Outcomes and Measures: The primary outcome was the completion of the first SARS-Cov-2 vaccine dose determined at 14, 30 and 90 days after outreach. Results: Of 1,650 patients in effectiveness Arms, 61% was female. Vaccination rates for Arms 1, 2, and 3, were 6% (n=9), 5.4% (n=27) and 3.3% (n=33) at 14 days, and 11.5% (n=17), 11.6% (n=58), and 8.5% (n=85) at 90 days, respectively, which were similar in pairwise comparisons. At 90 days, vaccination rates were similar across the two implementation rounds (3.9% vs. 3.6%) and were similar to the rate (3.3%) among patients who were not selected for intervention arms or implementation rounds (n=8,671). Conclusions: Digitally-enabled outreach that included SMS messaging outreach augmented with clinician recommendations did not improve COVID-19 vaccination rates. Trial Registration: This study is registered at ClinicalTrails.gov Identifier: NC-T04952376.
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Creating typologies is a way to summarize the large heterogeneity of smallholder farming systems into a few farm types. Various methods exist, commonly using statistical analysis, to create these typologies. We demonstrate that the methodological decisions on data collection, variable selection, data-reduction and clustering techniques can bear a large impact on the typology results. We illustrate the effects of analysing the diversity from different angles, using different typology objectives and different hypotheses, on typology creation by using an example from Zambia's Eastern Province. Five separate typologies were created with principal component analysis (PCA) and hierarchical clustering analysis (HCA), based on three different expert-informed hypotheses. The greatest overlap between typologies was observed for the larger, wealthier farm types but for the remainder of the farms there were no clear overlaps between typologies. Based on these results, we argue that the typology development should be guided by a hypothesis on the local agriculture features and the drivers and mechanisms of differentiation among farming systems, such as biophysical and socio-economic conditions. That hypothesis is based both on the typology objective and on prior expert knowledge and theories of the farm diversity in the study area. We present a methodological framework that aims to integrate participatory and statistical methods for hypothesis-based typology construction. This is an iterative process whereby the results of the statistical analysis are compared with the reality of the target population as hypothesized by the local experts. Using a well-defined hypothesis and the presented methodological framework, which consolidates the hypothesis through local expert knowledge for the creation of typologies, warrants development of less subjective and more contextualized quantitative farm typologies.