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.
BACKGROUND Even though climate change is one of the most significant global health challenges, empirical population-based data on its impacts and adaptation measures to protect population health are still limited. The 56 long-term health cohorts in Africa and Asia, called Health and Demographic Surveillance Systems (HDSSs) are excellent for monitoring climate impact on health and adaptation measures as they: (i) follow quality controlled protocols of data collection across all sites, (ii) provide long-term continuous data, (iii) cover diverse climate hotspots, including coastal areas, rainforests, savannah and highlands, and, (iv) capture about 100-million-person years of data. However, HDSSs have not leveraged their potential for climate and health research and policy, as (i) local meteorological data or remotely sensed data is not incorporated; (ii) there are limited links to downscaled climate impact models for HDSSs; (iii) and at its core, demographic dynamics are captured, with cause of death being the major health indicator tracked over time. OBJECTIVE We introduced major improvements in data collection, database architecture, data transmission, as well as links to locally downsized climate models to capitalize on the strong potential of HDSSs sites for measuring health impacts of climate change, identifying particularly vulnerable groups, and testing the costs and effectiveness of adaptation interventions and policies to protect populations from these climate impacts. This bundle of methods called Climate and Health Surveillance and Response System (CHES-RS) aims to provide a consistent set of climate and health data which are routinely collected. METHODS The CHES-RS has already been piloted in the HDSS at the Nouna Health Research Center in Burkina Faso, and it is currently being rolled out to two other HDSS sites, one in sub-Saharan Africa, respectively in the Siaya HDSS, Kenya, and the other in the South East Asia Community Observatory (SEACO) HDSS in Malaysia. CHES-RSs are ready to conduct research in the following major health sectors: climate/weather, land use and coverage/biodiversity, agriculture/household harvest, food security, household economics, as well as research in the field of one health, including zoonotic disease surveillance. CHES-RS uses digital sensors to measure three levels of exposure: (i) Individual-level data: consumer-grade wearable devices yield objective measures in vulnerable and rural populations, and an expanded HDSS questionnaire includes a full morbidity evaluation (ii) Household-level data comprises both indoor temperature measurements and remote sensing data captured through satellites (iii) Community-level data: comes from fully automated weather stations that record temperature, precipitation, solar radiation, wind speed, and direction. To handle heterogeneous data, we leverage graph databases for data management. RESULTS NA CONCLUSIONS To address current and emerging global health challenges over the next few decades, CHES-RSs will serve as a novel architecture for existing HDSSs and similar infrastructures of population-based surveillance cohorts. They may encourage ecosystems for climate change and health research, as well as big data analysis using artificial intelligence (AI), potentially providing the foundation for calculating climate change-induced disability-adjusted life years (cDALYs) and loss and damages. Using a routine morbidity panel survey and objectively measured health data, CHES-RS generate data-rich cohorts in countries where good quality health data is scarce, allowing for early interventions and earlier detection of risk factors for illnesses.
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