Assessment of Heat Exposure and Health Outcomes in Rural Populations of Western Kenya by Using Wearable Devices: Observational Case Study.

BACKGROUND: Climate change increasingly impacts health, particularly of rural populations in sub-Saharan Africa due to their limited resources for adaptation. Understanding these impacts remains a challenge, as continuous monitoring of vital signs in such populations is limited. Wearable devices (wearables) present a viable approach to studying these impacts on human health in real time. OBJECTIVE: The aim of this study was to assess the feasibility and effectiveness of consumer-grade wearables in measuring the health impacts of weather exposure on physiological responses (including activity, heart rate, body shell temperature, and sleep) of rural populations in western Kenya and to identify the health impacts associated with the weather exposures. METHODS: We conducted an observational case study in western Kenya by utilizing wearables over a 3-week period to continuously monitor various health metrics such as step count, sleep patterns, heart rate, and body shell temperature. Additionally, a local weather station provided detailed data on environmental conditions such as rainfall and heat, with measurements taken every 15 minutes. RESULTS: Our cohort comprised 83 participants (42 women and 41 men), with an average age of 33 years. We observed a positive correlation between step count and maximum wet bulb globe temperature (estimate 0.06, SE 0.02; P=.008). Although there was a negative correlation between minimum nighttime temperatures and heat index with sleep duration, these were not statistically significant. No significant correlations were found in other applied models. A cautionary heat index level was recorded on 194 (95.1%) of 204 days. Heavy rainfall (>20 mm/day) occurred on 16 (7.8%) out of 204 days. Despite 10 (21%) out of 47 devices failing, data completeness was high for sleep and step count (mean 82.6%, SD 21.3% and mean 86.1%, SD 18.9%, respectively), but low for heart rate (mean 7%, SD 14%), with adult women showing significantly higher data completeness for heart rate than men (2-sided t test: P=.003; Mann-Whitney U test: P=.001). Body shell temperature data achieved 36.2% (SD 24.5%) completeness. CONCLUSIONS: Our study provides a nuanced understanding of the health impacts of weather exposures in rural Kenya. Our study's application of wearables reveals a significant correlation between physical activity levels and high temperature stress, contrasting with other studies suggesting decreased activity in hotter conditions. This discrepancy invites further investigation into the unique socioenvironmental dynamics at play, particularly in sub-Saharan African contexts. Moreover, the nonsignificant trends observed in sleep disruption due to heat expose the need for localized climate change mitigation strategies, considering the vital role of sleep in health. These findings emphasize the need for context-specific research to inform policy and practice in regions susceptible to the adverse health effects of climate change.

Exploring Natural Immune Responses to Shigella Exposure Using Multiplex Bead Assays on Dried Blood Spots in High-Burden Countries: Protocol From a Multisite Diarrhea Surveillance Study.

Background: Molecular diagnostics on human fecal samples have identified a larger burden of shigellosis than previously appreciated by culture. Evidence of fold changes in immunoglobulin G (IgG) to conserved and type-specific Shigella antigens could be used to validate the molecular assignment of type-specific Shigella as the etiology of acute diarrhea and support polymerase chain reaction (PCR)-based microbiologic end points for vaccine trials. Methods: We will test dried blood spots collected at enrollment and 4 weeks later using bead-based immunoassays for IgG to invasion plasmid antigen B and type-specific lipopolysaccharide O-antigen for Shigella flexneri 1b, 2a, 3a, and 6 and Shigella sonnei in Shigella-positive cases and age-, site-, and season-matched test-negative controls from all sites in the Enterics for Global Health (EFGH) Shigella surveillance study. Fold antibody responses will be compared between culture-positive, culture-negative but PCR-attributable, and PCR-positive but not attributable cases and test-negative controls. Age- and site-specific seroprevalence distributions will be identified, and the association between baseline antibodies and Shigella attribution will be estimated. Conclusions: The integration of these assays into the EFGH study will help support PCR-based attribution of acute diarrhea to type-specific Shigella, describe the baseline seroprevalence of conserved and type-specific Shigella antibodies, and support correlates of protection for immunity to Shigella diarrhea. These insights can help support the development and evaluation of Shigella vaccine candidates.

Shigella Detection and Molecular Serotyping With a Customized TaqMan Array Card in the Enterics for Global Health (EFGH): Shigella Surveillance Study.

Background: Quantitative polymerase chain reaction (qPCR) targeting ipaH has been proven to be highly efficient in detecting Shigella in clinical samples compared to culture-based methods, which underestimate Shigella burden by 2- to 3-fold. qPCR assays have also been developed for Shigella speciation and serotyping, which is critical for both vaccine development and evaluation. Methods: The Enterics for Global Health (EFGH) Shigella surveillance study will utilize a customized real-time PCR-based TaqMan Array Card (TAC) interrogating 82 targets, for the detection and differentiation of Shigella spp, Shigella sonnei, Shigella flexneri serotypes, other diarrhea-associated enteropathogens, and antimicrobial resistance (AMR) genes. Total nucleic acid will be extracted from rectal swabs or stool samples, and assayed on TAC. Quantitative analysis will be performed to determine the likely attribution of Shigella and other particular etiologies of diarrhea using the quantification cycle cutoffs derived from previous studies. The qPCR results will be compared to conventional culture, serotyping, and phenotypic susceptibility approaches in EFGH. Conclusions: TAC enables simultaneous detection of diarrheal etiologies, the principal pathogen subtypes, and AMR genes. The high sensitivity of the assay enables more accurate estimation of Shigella-attributed disease burden, which is critical to informing policy and in the design of future clinical trials.

The Enterics for Global Health (EFGH) Shigella Surveillance Study in Kenya.

Background: Although Shigella is an important cause of diarrhea in Kenyan children, robust research platforms capable of conducting incidence-based Shigella estimates and eventual Shigella-targeted clinical trials are needed to improve Shigella-related outcomes in children. Here, we describe characteristics of a disease surveillance platform whose goal is to support incidence and consequences of Shigella diarrhea as part of multicounty surveillance aimed at preparing sites and assembling expertise for future Shigella vaccine trials. Methods: We mobilized our preexisting expertise in shigellosis, vaccinology, and diarrheal disease epidemiology, which we combined with our experience conducting population-based sampling, clinical trials with high (97%-98%) retention rates, and healthcare utilization surveys. We leveraged our established demographic surveillance system (DSS), our network of healthcare centers serving the DSS, and our laboratory facilities with staff experienced in performing microbiologic and molecular diagnostics to identify enteric infections. We joined these resources with an international network of sites with similar capabilities and infrastructure to form a cohesive scientific network, designated Enterics for Global Health (EFGH), with the aim of expanding and updating our knowledge of the epidemiology and adverse consequences of shigellosis and enriching local research and career development priorities. Conclusions: Shigella surveillance data from this platform could help inform Shigella vaccine trials.

Evaluation of Fecal Inflammatory Biomarkers to Identify Bacterial Diarrhea Episodes: Systematic Review and Protocol for the Enterics for Global Health Shigella Surveillance Study.

Background: The measurement of fecal inflammatory biomarkers among individuals presenting to care with diarrhea could improve the identification of bacterial diarrheal episodes that would benefit from antibiotic therapy. We reviewed prior literature in this area and describe our proposed methods to evaluate 4 biomarkers in the Enterics for Global Health (EFGH) Shigella surveillance study. Methods: We systematically reviewed studies since 1970 from PubMed and Embase that assessed the diagnostic characteristics of inflammatory biomarkers to identify bacterial diarrhea episodes. We extracted sensitivity and specificity and summarized the evidence by biomarker and diarrhea etiology. In EFGH, we propose using commercial enzyme-linked immunosorbent assays to test for myeloperoxidase, calprotectin, lipocalin-2, and hemoglobin in stored whole stool samples collected within 24 hours of enrollment from participants in the Bangladesh, Kenya, Malawi, Pakistan, Peru, and The Gambia sites. We will develop clinical prediction scores that incorporate the inflammatory biomarkers and evaluate their ability to identify Shigella and other bacterial etiologies of diarrhea as determined by quantitative polymerase chain reaction (qPCR). Results: Forty-nine studies that assessed fecal leukocytes (n = 39), red blood cells (n = 26), lactoferrin (n = 13), calprotectin (n = 8), and myeloperoxidase (n = 1) were included in the systematic review. Sensitivities were high for identifying Shigella, moderate for identifying any bacteria, and comparable across biomarkers. Specificities varied depending on the outcomes assessed. Prior studies were generally small, identified red and white blood cells by microscopy, and used insensitive gold standard diagnostics, such as conventional bacteriological culture for pathogen detection. Conclusions: Our evaluation of inflammatory biomarkers to distinguish diarrhea etiologies as determined by qPCR will provide an important addition to the prior literature, which was likely biased by the limited sensitivity of the gold standard diagnostics used. We will determine whether point-of-care biomarker tests could be a viable strategy to inform treatment decision making and increase appropriate targeting of antibiotic treatment to bacterial diarrhea episodes.

Comparative analysis of SARS-CoV-2 detection methods using stool, blood, and nasopharyngeal swab samples.

Introduction: as a public health policy, the ongoing global coronavirus disease 2019 vaccination drives require continuous tracking, tracing, and testing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Diagnostic testing is important in virus detection and understanding its spread for timely intervention. This is especially important for low-income settings where the majority of the population remains untested. This is well supported by the fact that of about 9% of the Kenyan population had been tested for the virus. Methods: this was a cross-sectional study conducted at the Kisumu and Siaya Referral Hospitals in Kenya. Here we report on the sensitivity and specificity of the rapid antigen detection test (Ag-RDT) of SARS-CoV-2 compared with the quantitative reverse transcriptase polymerase chain reaction (RT-qPCR) using stool and nasopharyngeal swab samples. Further, the mean Immunoglobulin M (IgM) and Immunoglobulin G (IgG) antibody levels among symptomatic and asymptomatic individuals in western Kenya were evaluated. Results: the sensitivity and specificity of Ag-RDT were 76.3% (95% CI, 59.8-88.6%) and 96.3% (95% CI, 87.3-99.5%) with a negative and positive predictive value of 85% (95% CI, 73.8%-93.0%) and 93% (95% CI, 78.6%-99.2%) respectively. There was substantial agreement of 88% (Kappa value of 0.75, 95% CI, 0.74-0.77) between Ag-RDT and nasopharyngeal swab RT-qPCR, and between stool and nasopharyngeal swab RT-qPCR results (83.7% agreement, Kapa value 0.62, 95% CI 0.45-0.80). The mean IgM and IgG antibody response to SARS-CoV-2 were not different in asymptomatic individuals, 1.11 (95% CI, 0.78-1.44) and 0.88 (95% CI, 0.65-1.11) compared to symptomatic individuals 4.30 (95% CI 3.30-5.31) and 4.16 (95% CI 3.32 -5.00). Conclusion: the choice of an appropriate SARS-CoV-2 diagnostic, screening, and surveillance test should be guided by the specific study needs and a rational approach for optimal results.

Evaluation of community-based vector surveillance system for routine entomological monitoring under low malaria vector densities and high bed net coverage in western Kenya.

BACKGROUND: Entomological surveillance is traditionally conducted by supervised teams of trained technicians. However, it is expensive and limiting in the number of sites visited. Surveillance through community-based collectors (CBC) may be more cost-effective and sustainable for longitudinal entomological monitoring. This study evaluated the efficiency of CBCs in monitoring mosquito densities compared to quality-assured sampling conducted by experienced entomology technicians. METHODS: Entomological surveillance employing CBCs was conducted in eighteen clusters of villages in western Kenya using indoor and outdoor CDC light traps and indoor Prokopack aspiration. Sixty houses in each cluster were enrolled and sampled once every month. Collected mosquitoes were initially identified to the genus level by CBCs, preserved in 70% ethanol and transferred to the laboratory every 2 weeks. Parallel, collections by experienced entomology field technicians were conducted monthly by indoor and outdoor CDC light traps and indoor Prokopack aspiration and served as a quality assurance of the CBCs. RESULTS: Per collection, the CBCs collected 80% fewer Anopheles gambiae sensu lato (s.l.) [RR = 0.2; (95% CI 0.14-0.27)] and Anopheles coustani [RR = 0.2; (95% CI 0.06-0.53)] and 90% fewer Anopheles funestus [RR = 0.1; (95% CI 0.08-0.19)] by CDC light traps compared to the quality assured (QA) entomology teams. Significant positive correlations were however observed between the monthly collections by CBCs and QA teams for both An. gambiae and An. funestus. In paired identifications of pooled mosquitoes, the CBCs identified 4.3 times more Anopheles compared to experienced technicians. The cost per person-night was lower in the community-based sampling at $9.1 compared to $89.3 by QA per collection effort. CONCLUSION: Unsupervised community-based mosquito surveillance collected substantially fewer mosquitoes per trap-night compared to quality-assured collection by experienced field teams, while consistently overestimating the number of Anopheles mosquitoes during identification. However, the numbers collected were significantly correlated between the CBCs and the QA teams suggesting that trends observed by CBCs and QA teams were similar. Further studies are needed to evaluate whether adopting low-cost, devolved supervision with spot checks, coupled with remedial training of the CBCs, can improve community-based collections to be considered a cost-effective alternative to surveillance conducted by experienced entomological technicians.

Widening the lens of population-based health research to climate change impacts and adaptation: the climate change and health evaluation and response system (CHEERS).

Background: Climate change significantly impacts health in low-and middle-income countries (LMICs), exacerbating vulnerabilities. Comprehensive data for evidence-based research and decision-making is crucial but scarce. Health and Demographic Surveillance Sites (HDSSs) in Africa and Asia provide a robust infrastructure with longitudinal population cohort data, yet they lack climate-health specific data. Acquiring this information is essential for understanding the burden of climate-sensitive diseases on populations and guiding targeted policies and interventions in LMICs to enhance mitigation and adaptation capacities. Objective: The objective of this research is to develop and implement the Change and Health Evaluation and Response System (CHEERS) as a methodological framework, designed to facilitate the generation and ongoing monitoring of climate change and health-related data within existing Health and Demographic Surveillance Sites (HDSSs) and comparable research infrastructures. Methods: CHEERS uses a multi-tiered approach to assess health and environmental exposures at the individual, household, and community levels, utilizing digital tools such as wearable devices, indoor temperature and humidity measurements, remotely sensed satellite data, and 3D-printed weather stations. The CHEERS framework utilizes a graph database to efficiently manage and analyze diverse data types, leveraging graph algorithms to understand the complex interplay between health and environmental exposures. Results: The Nouna CHEERS site, established in 2022, has yielded significant preliminary findings. By using remotely-sensed data, the site has been able to predict crop yield at a household level in Nouna and explore the relationships between yield, socioeconomic factors, and health outcomes. The feasibility and acceptability of wearable technology have been confirmed in rural Burkina Faso for obtaining individual-level data, despite the presence of technical challenges. The use of wearables to study the impact of extreme weather on health has shown significant effects of heat exposure on sleep and daily activity, highlighting the urgent need for interventions to mitigate adverse health consequences. Conclusion: Implementing the CHEERS in research infrastructures can advance climate change and health research, as large and longitudinal datasets have been scarce for LMICs. This data can inform health priorities, guide resource allocation to address climate change and health exposures, and protect vulnerable communities in LMICs from these exposures.

Malaria, climate variability, and interventions: modelling transmission dynamics.

Assessment of the relative impact of climate change on malaria dynamics is a complex problem. Climate is a well-known factor that plays a crucial role in driving malaria outbreaks in epidemic transmission areas. However, its influence in endemic environments with intensive malaria control interventions is not fully understood, mainly due to the scarcity of high-quality, long-term malaria data. The demographic surveillance systems in Africa offer unique platforms for quantifying the relative effects of weather variability on the burden of malaria. Here, using a process-based stochastic transmission model, we show that in the lowlands of malaria endemic western Kenya, variations in climatic factors played a key role in driving malaria incidence during 2008-2019, despite high bed net coverage and use among the population. The model captures some of the main mechanisms of human, parasite, and vector dynamics, and opens the possibility to forecast malaria in endemic regions, taking into account the interaction between future climatic conditions and intervention scenarios.

Malaria control and elimination in Kenya: economy-wide benefits and regional disparities.

BACKGROUND: Malaria remains a public health problem in Kenya despite several concerted control efforts. Empirical evidence regarding malaria effects in Kenya suggests that the disease imposes substantial economic costs, jeopardizing the achievement of sustainable development goals. The Kenya Malaria Strategy (2019-2023), which is currently being implemented, is one of several sequential malaria control and elimination strategies. The strategy targets reducing malaria incidences and deaths by 75% of the 2016 levels by 2023 through spending around Kenyan Shillings 61.9 billion over 5 years. This paper assesses the economy-wide implications of implementing this strategy. METHODS: An economy-wide simulation model is calibrated to a comprehensive 2019 database for Kenya, considering different epidemiological zones. Two scenarios are simulated with the model. The first scenario (GOVT) simulates the annual costs of implementing the Kenya Malaria Strategy by increasing government expenditure on malaria control and elimination programmes. The second scenario (LABOR) reduces malaria incidences by 75% in all epidemiological malaria zones without accounting for the changes in government expenditure, which translates into rising the household labour endowment (benefits of the strategy). RESULTS: Implementing the Kenya Malaria Strategy (2019-2023) enhances gross domestic product at the end of the strategy implementation period due to more available labour. In the short term, government health expenditure (direct malaria costs) increases significantly, which is critical in controlling and eliminating malaria. Expanding the health sector raises the demand for production factors, such as labour and capital. The prices for these factors rise, boosting producer and consumer prices of non-health-related products. Consequently, household welfare decreases during the strategy implementation period. In the long run, household labour endowment increases due to reduced malaria incidences and deaths (indirect malaria costs). However, the size of the effects varies across malaria epidemiological and agroecological zones depending on malaria prevalence and factor ownership. CONCLUSIONS: This paper provides policymakers with an ex-ante assessment of the implications of malaria control and elimination on household welfare across various malaria epidemiological zones. These insights assist in developing and implementing related policy measures that reduce the undesirable effects in the short run. Besides, the paper supports an economically beneficial long-term malaria control and elimination effect.

The relative effect of climate variability on malaria incidence after scale-up of interventions in western Kenya: A time-series analysis of monthly incidence data from 2008 to 2019.

Background: Despite considerable progress made over the past 20 years in reducing the global burden of malaria, the disease remains a major public health problem and there is concern that climate change might expand suitable areas for transmission. This study investigated the relative effect of climate variability on malaria incidence after scale-up of interventions in western Kenya. Methods: Bayesian negative binomial models were fitted to monthly malaria incidence data, extracted from records of patients with febrile illnesses visiting the Lwak Mission Hospital between 2008 and 2019. Data pertaining to bed net use and socio-economic status (SES) were obtained from household surveys. Climatic proxy variables obtained from remote sensing were included as covariates in the models. Bayesian variable selection was used to determine the elapsing time between climate suitability and malaria incidence. Results: Malaria incidence increased by 50% from 2008 to 2010, then declined by 73% until 2015. There was a resurgence of cases after 2016, despite high bed net use. Increase in daytime land surface temperature was associated with a decline in malaria incidence (incidence rate ratio [IRR] = 0.70, 95% Bayesian credible interval [BCI]: 0.59-0.82), while rainfall was associated with increased incidence (IRR = 1.27, 95% BCI: 1.10-1.44). Bed net use was associated with a decline in malaria incidence in children aged 6-59 months (IRR = 0.78, 95% BCI: 0.70-0.87) but not in older age groups, whereas SES was not associated with malaria incidence in this population. Conclusions: Variability in climatic factors showed a stronger effect on malaria incidence than bed net use. Bed net use was, however, associated with a reduction in malaria incidence, especially among children aged 6-59 months after adjusting for climate effects. To sustain the downward trend in malaria incidence, this study recommends continued distribution and use of bed nets and consideration of climate-based malaria early warning systems when planning for future control interventions.

Self-Reported Symptoms at Last Febrile Illness as a Predictor of Treatment-Seeking in Western Kenya: A Cross-Sectional Study.

Timely treatment-seeking behavior can reduce morbidity and mortality due to infectious diseases. Patterns of treatment-seeking behavior can differ by access to health care, and perceptions of disease severity and symptoms. We evaluated the association between self-reported symptoms at last illness and the level of treatment-seeking behaviors. We analyzed cross-sectional data from 1,037 participants from the lowlands and highlands of Western Kenya from 2015 using logistic regression models. There was considerable heterogeneity in the symptoms and treatment-seeking behaviors reported among individuals who were febrile at their last illness. A greater number of self-reported categories of symptoms tended to be associated with a higher likelihood of treatment-seeking in both sites. Participants were significantly more likely to seek treatment if they reported fever, aches, and digestive symptoms at last illness than just fever and aches or fever alone, but the frequency of treatment-seeking for fever in combination with aches and respiratory symptoms did not follow a consistent pattern. Among those who sought treatment, most used a formal source, but the patterns were inconsistent across sites and by the number of symptoms categories. Understanding the drivers of treatment-seeking behavior after febrile illness is important to control and treat infectious diseases in Kenya.

High seroprevalence of Immunoglobulin G (IgG) and IgM antibodies to SARS-CoV-2 in asymptomatic and symptomatic individuals amidst vaccination roll-out in western Kenya.

The population's antibody response is a key factor in comprehending SARS-CoV-2 epidemiology. This is especially important in African settings where COVID-19 impact, and vaccination rates are relatively low. This study aimed at characterizing the Immunoglobulin G (IgG) and Immunoglobulin M (IgM) in both SARS-CoV-2 asymptomatic and symptomatic individuals in Kisumu and Siaya counties in western Kenya using enzyme linked immunosorbent assays. The IgG and IgM overall seroprevalence in 98 symptomatic and asymptomatic individuals in western Kenya between December 2021-March 2022 was 76.5% (95% CI = 66.9-84.5) and 29.6% (95% CI = 20.8-39.7) respectively. In terms of gender, males had slightly higher IgG positivity 87.5% (35/40) than females 68.9% (40/58). Amidst the ongoing vaccination roll-out during the study period, over half of the study participants (55.1%, 95% CI = 44.7-65.2) had not received any vaccine. About one third, (31.6%, 95% CI = 22.6-41.8) of the study participants had been fully vaccinated, with close to a quarter (13.3% 95% CI = 7.26-21.6) partially vaccinated. When considering the vaccination status and seroprevalence, out of the 31 fully vaccinated individuals, IgG seropositivity was 81.1% (95% CI = 70.2-96.3) and IgM seropositivity was 35.5% (95% CI = 19.22-54.6). Out of the participants that had not been vaccinated at all, IgG seroprevalence was 70.4% (95% CI 56.4-82.0) with 20.4% (95% CI 10.6-33.5) seropositivity for IgM antibodies. On PCR testing, 33.7% were positive, with 66.3% negative. The 32 positive individuals included 12(37.5%) fully vaccinated, 8(25%) partially vaccinated and 12(37.5%) unvaccinated. SARs-CoV-2 PCR positivity did not significantly predict IgG (p = 0.469 [95% CI 0.514-4.230]) and IgM (p = 0.964 [95% CI 0.380-2.516]) positivity. These data indicate a high seroprevalence of antibodies to SARS-CoV-2 in western Kenya. This suggests that a larger fraction of the population was infected with SARS-CoV-2 within the defined period than what PCR testing could cover.

Wearables for Measuring Health Effects of Climate Change-Induced Weather Extremes: Scoping Review.

BACKGROUND: Although climate change is one of the biggest global health threats, individual-level and short-term data on direct exposure and health impacts are still scarce. Wearable electronic devices (wearables) present a potential solution to this research gap. Wearables have become widely accepted in various areas of health research for ecological momentary assessment, and some studies have used wearables in the field of climate change and health. However, these studies vary in study design, demographics, and outcome variables, and existing research has not been mapped. OBJECTIVE: In this review, we aimed to map existing research on wearables used to detect direct health impacts and individual exposure during climate change-induced weather extremes, such as heat waves or wildfires. METHODS: We conducted a scoping review according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework and systematically searched 6 databases (PubMed [MEDLINE], IEEE Xplore, CINAHL [EBSCOhost], WoS, Scopus, Ovid [MEDLINE], and Google Scholar). The search yielded 1871 results. Abstracts and full texts were screened by 2 reviewers (MK and IM) independently using the inclusion and exclusion criteria. The inclusion criteria comprised studies published since 2010 that used off-the-shelf wearables that were neither invasive nor obtrusive to the user in the setting of climate change-related weather extremes. Data were charted using a structured form, and the study outcomes were narratively synthesized. RESULTS: The review included 55,284 study participants using wearables in 53 studies. Most studies were conducted in upper-middle-income and high-income countries (50/53, 94%) in urban environments (25/53, 47%) or in a climatic chamber (19/53, 36%) and assessed the health effects of heat exposure (52/53, 98%). The majority reported adverse health effects of heat exposure on sleep, physical activity, and heart rate. The remaining studies assessed occupational heat stress or compared individual- and area-level heat exposure. In total, 26% (14/53) of studies determined that all examined wearables were valid and reliable for measuring health parameters during heat exposure when compared with standard methods. CONCLUSIONS: Wearables have been used successfully in large-scale research to measure the health implications of climate change-related weather extremes. More research is needed in low-income countries and vulnerable populations with pre-existing conditions. In addition, further research could focus on the health impacts of other climate change-related conditions and the effectiveness of adaptation measures at the individual level to such weather extremes.

Evaluation of the protective efficacy of a spatial repellent to reduce malaria incidence in children in western Kenya compared to placebo: study protocol for a cluster-randomized double-blinded control trial (the AEGIS program).

BACKGROUND: Spatial repellents are widely used for prevention of mosquito bites and evidence is building on their public health value, but their efficacy against malaria incidence has never been evaluated in Africa. To address this knowledge gap, a trial to evaluate the efficacy of Mosquito Shield™, a spatial repellent incorporating transfluthrin, was developed for implementation in Busia County, western Kenya where long-lasting insecticidal net coverage is high and baseline malaria transmission is moderate to high year-round. METHODS: This trial is designed as a cluster-randomized, placebo-controlled, double-blinded clinical trial. Sixty clusters will be randomly assigned in a 1:1 ratio to receive spatial repellent or placebo. A total of 6120 children aged ≥6 months to 10 years of age will be randomly selected from the study clusters, enrolled into an active cohort (baseline, cohort 1, and cohort 2), and sampled monthly to determine time to first infection by smear microscopy. Each cohort following the implementation of the intervention will be split into two groups, one to estimate direct effect of the spatial repellent and the other to estimate degree of diversion of mosquitoes and malaria transmission to unprotected persons. Malaria incidence in each cohort will be estimated and compared (primary indicator) to determine benefit of using a spatial repellent in a high, year-round malaria transmission setting. Mosquitoes will be collected monthly using CDC light traps to determine if there are entomological correlates of spatial repellent efficacy that may be useful for the evaluation of new spatial repellents. Quarterly human landing catches will assess behavioral effects of the intervention. DISCUSSION: Findings will serve as the first cluster-randomized controlled trial powered to detect spatial repellent efficacy to reduce malaria in sub-Saharan Africa where transmission rates are high, insecticide-treated nets are widely deployed, and mosquitoes are resistant to insecticides. Results will be submitted to the World Health Organization Vector Control Advisory Group for assessment of public health value towards an endorsement to recommend inclusion of spatial repellents in malaria control programs. TRIAL REGISTRATION: ClinicalTrials.gov NCT04766879 . Registered February 23, 2021.

Factors related to long-lasting insecticidal net (LLIN) use during travel in western Kenya: A descriptive analysis.

BACKGROUND: Overnight travel predicts increased likelihood of Plasmodium infection and may introduce parasite strains to new areas, but deviations from routine at-home use of long-lasting insecticidal nets (LLINs) during travel are not well studied. METHODS: Cross-sectional data were taken in 2015 from the western Kenyan highlands and lowlands. Household surveys assessed individual travel activity during the previous month, LLIN use (at home and away), and current Plasmodium infection status. Crude and adjusted logistic regression was used to estimate the odds ratios (OR) of current malaria infection relative to travel within the last month. RESULTS: Highland residents who had traveled were more likely to have Plasmodium infection at the time of interview than highland residents who had not traveled (adjusted OR = 4.09 [1.60, 10.52]). Alternately, in the lowlands those who traveled overnight were significantly less likely to be infected vs non-travelers (adjusted OR = 0.56 [0.39,0.96]). Rates of LLIN use during travel were lower than reported rates while at home. Despite this, among travelers, LLIN use during travel was not associated with likelihood of Plasmodium infection for either region. CONCLUSIONS: Travel had heterogeneous associations with infection status for the lowlands and highlands of western Kenya. Given the higher prevalence of malaria in the lowlands, travel is unlikely to increase likelihood of exposure. Conversely, travel from the lower-prevalence highlands may have taken respondents to higher prevalence areas. LLIN use while traveling differed from at-home habits and may depend on availability of LLINs where the traveler sleeps.

The Impact of Wearable Technologies in Health Research: Scoping Review.

BACKGROUND: Wearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen substantially in recent years. However, there is a shortage of aggregated insights into how wearables have been used in health research. OBJECTIVE: In this review, we aim to broadly overview and categorize the current research conducted with affordable wearable devices for health research. METHODS: We performed a scoping review to understand the use of affordable, consumer-grade wearables for health research from a population health perspective using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A total of 7499 articles were found in 4 medical databases (PubMed, Ovid, Web of Science, and CINAHL). Studies were eligible if they used noninvasive wearables: worn on the wrist, arm, hip, and chest; measured vital signs; and analyzed the collected data quantitatively. We excluded studies that did not use wearables for outcome assessment and prototype studies, devices that cost >€500 (US $570), or obtrusive smart clothing. RESULTS: We included 179 studies using 189 wearable devices covering 10,835,733 participants. Most studies were observational (128/179, 71.5%), conducted in 2020 (56/179, 31.3%) and in North America (94/179, 52.5%), and 93% (10,104,217/10,835,733) of the participants were part of global health studies. The most popular wearables were fitness trackers (86/189, 45.5%) and accelerometer wearables, which primarily measure movement (49/189, 25.9%). Typical measurements included steps (95/179, 53.1%), heart rate (HR; 55/179, 30.7%), and sleep duration (51/179, 28.5%). Other devices measured blood pressure (3/179, 1.7%), skin temperature (3/179, 1.7%), oximetry (3/179, 1.7%), or respiratory rate (2/179, 1.1%). The wearables were mostly worn on the wrist (138/189, 73%) and cost <€200 (US $228; 120/189, 63.5%). The aims and approaches of all 179 studies revealed six prominent uses for wearables, comprising correlations-wearable and other physiological data (40/179, 22.3%), method evaluations (with subgroups; 40/179, 22.3%), population-based research (31/179, 17.3%), experimental outcome assessment (30/179, 16.8%), prognostic forecasting (28/179, 15.6%), and explorative analysis of big data sets (10/179, 5.6%). The most frequent strengths of affordable wearables were validation, accuracy, and clinical certification (104/179, 58.1%). CONCLUSIONS: Wearables showed an increasingly diverse field of application such as COVID-19 prediction, fertility tracking, heat-related illness, drug effects, and psychological interventions; they also included underrepresented populations, such as individuals with rare diseases. There is a lack of research on wearable devices in low-resource contexts. Fueled by the COVID-19 pandemic, we see a shift toward more large-sized, web-based studies where wearables increased insights into the developing pandemic, including forecasting models and the effects of the pandemic. Some studies have indicated that big data extracted from wearables may potentially transform the understanding of population health dynamics and the ability to forecast health trends.

Real world external validation of metabolic gestational age assessment in Kenya.

Using data from Ontario Canada, we previously developed machine learning-based algorithms incorporating newborn screening metabolites to estimate gestational age (GA). The objective of this study was to evaluate the use of these algorithms in a population of infants born in Siaya county, Kenya. Cord and heel prick samples were collected from newborns in Kenya and metabolic analysis was carried out by Newborn Screening Ontario in Ottawa, Canada. Postnatal GA estimation models were developed with data from Ontario with multivariable linear regression using ELASTIC NET regularization. Model performance was evaluated by applying the models to the data collected from Kenya and comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound. Heel prick samples were collected from 1,039 newborns from Kenya. Of these, 8.9% were born preterm and 8.5% were small for GA. Cord blood samples were also collected from 1,012 newborns. In data from heel prick samples, our best-performing model estimated GA within 9.5 days overall of reference GA [mean absolute error (MAE) 1.35 (95% CI 1.27, 1.43)]. In preterm infants and those small for GA, MAE was 2.62 (2.28, 2.99) and 1.81 (1.57, 2.07) weeks, respectively. In data from cord blood, model accuracy slightly decreased overall (MAE 1.44 (95% CI 1.36, 1.53)). Accuracy was not impacted by maternal HIV status and improved when the dating ultrasound occurred between 9 and 13 weeks of gestation, in both heel prick and cord blood data (overall MAE 1.04 (95% CI 0.87, 1.22) and 1.08 (95% CI 0.90, 1.27), respectively). The accuracy of metabolic model based GA estimates in the Kenya cohort was lower compared to our previously published validation studies, however inconsistency in the timing of reference dating ultrasounds appears to have been a contributing factor to diminished model performance.

Feasibility, acceptability and validation of wearable devices for climate change and health research in the low-resource contexts of Burkina Faso and Kenya: Study protocol.

As the epidemiological transition progresses throughout sub-Saharan Africa, life lived with diseases is an increasingly important part of a population's burden of disease. The burden of disease of climate-sensitive health outcomes is projected to increase considerably within the next decades. Objectively measured, reliable population health data is still limited and is primarily based on perceived illness from recall. Technological advances like non-invasive, consumer-grade wearable devices may play a vital role in alleviating this data gap and in obtaining insights on the disease burden in vulnerable populations, such as heat stress on human cardiovascular response. The overall goal of this study is to investigate whether consumer-grade wearable devices are an acceptable, feasible and valid means to generate data on the individual level in low-resource contexts. Three hundred individuals are recruited from the two study locations in the Nouna health and demographic surveillance system (HDSS), Burkina Faso, and the Siaya HDSS, Kenya. Participants complete a structured questionnaire that comprises question items on acceptability and feasibility under the supervision of trained data collectors. Validity will be evaluated by comparing consumer-grade wearable devices to research-grade devices. Furthermore, we will collect demographic data as well as the data generated by wearable devices. This study will provide insights into the usage of consumer-grade wearable devices to measure individual vital signs in low-resource contexts, such as Burkina Faso and Kenya. Vital signs comprising activity (steps), sleep (duration, quality) and heart rate (hr) are important measures to gain insights on individual behavior and activity patterns in low-resource contexts. These vital signs may be associated with weather variables-as we gather them from weather stations that we have setup as part of this study to cover the whole Nouna and Siaya HDSSs-in order to explore changes in behavior and other variables, such as activity, sleep, hr, during extreme weather events like heat stress exposure. Furthermore, wearable data could be linked to health outcomes and weather events. As a result, consumer-grade wearables may serve as a supporting technology for generating reliable measurements in low-resource contexts and investigating key links between weather occurrences and health outcomes. Thus, wearable devices may provide insights to better inform mitigation and adaptation interventions in these low-resource settings that are direly faced by climate change-induced changes, such as extreme weather events.