A viable, cost-effective solution at scale has been developed and implemented for collecting electronic data during household visits in a resource-constrained setting.

Background Adolescents tend to experience heightened vulnerability to risky and reckless behavior. Adolescents living in rural settings may often experience poverty and a host of risk factors which can increase their vulnerability to various forms of health risk behavior (HRB). Understanding HRB clustering and its underlying factors among adolescents is important for intervention planning and health promotion. This study examines the co-occurrence of injury and violence, substance use, hygiene, physical activity, and diet-related risk behaviors among adolescents in a rural setting on the Kenyan coast. Specifically, the study objectives were to identify clusters of HRB; based on five categories of health risk behavior, and to identify the factors associated with HRB clustering. Methods A cross-sectional survey was conducted of a random sample of 1060 adolescents aged 13–19 years living within the area covered by the Kilifi Health and Demographic Surveillance System. Participants completed a questionnaire on health behaviors which was administered via an Audio Computer-Assisted Self–Interview. Latent class analysis on 13 behavioral factors (injury and violence, hygiene, alcohol tobacco and drug use, physical activity, and dietary related behavior) was used to identify clustering and stepwise ordinal logistic regression with nonparametric bootstrapping identified the factors associated with clustering. The variables of age, sex, education level, school attendance, mental health, form of residence and level of parental monitoring were included in the initial stepwise regression model. Results We identified 3 behavioral clusters (Cluster 1: Low-risk takers (22.9%); Cluster 2: Moderate risk-takers (67.8%); Cluster 3: High risk-takers (9.3%)). Relative to the cluster 1, membership of higher risk clusters (i.e. moderate or high risk-takers) was strongly associated with older age (p<0.001), being male (p<0.001), depressive symptoms (p = 0.005), school non-attendance (p = 0.001) and a low level of parental monitoring (p<0.001). Conclusion There is clustering of health risk behaviors that underlies communicable and non-communicable diseases among adolescents in rural coastal Kenya. This suggests the urgent need for targeted multi-component health behavior interventions that simultaneously address all aspects of adolescent health and well-being, including the mental health needs of adolescents.

Background. International recommendations for the control of the coronavirus disease 2019 (COVID-19) pandemic emphasize the central role of laboratory testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent, at scale. The availability of testing reagents, laboratory equipment and qualified staff are important bottlenecks to achieving this. Elsewhere, pooled testing (i.e. combining multiple samples in the same reaction) has been suggested to increase testing capacities in the pandemic period. Methods. We discuss our experience with SARS-CoV-2 pooled testing using real-time reverse transcription polymerase chain reaction (RT-PCR) on the Kenyan Coast. Results. In mid-May, 2020, our RT-PCR testing capacity for SARS-CoV-2 was improved by ~100% as a result of adoption of a six-sample pooled testing strategy. This was accompanied with a concomitant saving of ~50% of SARS-CoV-2 laboratory test kits at both the RNA extraction and RT-PCR stages. However, pooled testing came with a slight decline of test sensitivity. The RT-PCR cycle threshold value (ΔCt) was ~1.59 higher for samples tested in pools compared to samples tested singly. Conclusions. Pooled testing is a useful strategy to increase SARS-CoV-2 laboratory testing capacity especially in low-income settings.

Background: There is uncertainty about the mortality impact of the COVID-19 pandemic in Africa because of poor ascertainment of cases and limited national civil vital registration. We analysed excess mortality from 1st January 2020-5th May 2022 in a Health and Demographic Surveillance Study in Coastal Kenya where the SARS-CoV-2 seroprevalence reached 75% among adults in March 2022 despite vaccine uptake of only 17%. Methods: We modelled expected mortality in 2020-2022 among a population of 306,000 from baseline surveillance data between 2010-2019. We calculated excess mortality as the ratio of observed/expected deaths in 5 age strata for each month and for each national wave of the pandemic. We estimated cumulative mortality risks as the total number of excess deaths in the pandemic per 100,000 population. We investigated observed deaths using verbal autopsy. Findings: We observed 16,236 deaths among 3,410,800 person years between 1st January 2010 and 5th May 2022. Across 5 waves of COVID-19 cases during 1st April 2020-16th April 2022, population excess mortality was 4.1% (95% PI -0.2%, 7.9%). Mortality was elevated among those aged ≥65 years at 14.3% (95% PI 7.4%, 21.6%); excess deaths coincided with wave 2 (wild-type), wave 4 (Delta) and wave 5 (Omicron BA1). Among children aged 1-14 years there was negative excess mortality of -20.3% (95% PI -29.8%, -8.1%). Verbal autopsy data showed a transient reduction in deaths from acute respiratory infections in 2020 at all ages. For comparison with other studies, cumulative excess mortality risk for January 2020-December 2021, age-standardized to the Kenyan population, was 47.5/100,000. Interpretation: Net excess mortality during the pandemic was substantially lower in Coastal Kenya than in many high income countries. However, adults, aged ≥65 years, experienced substantial excess mortality suggesting that targeted COVID-19 vaccination of older persons may limit further COVID-19 deaths by protecting the residual pool of naive individuals.