Widening the lens of population-based health research to climate change impacts and adaptation: The Climate and Health Surveillance and Response System (CHES-RS) (Preprint)
Authors: Barteit Sandra , Sié Ali , Zabré Pascal , Traoré Issouf , Ouedraogo Aristide , Boudo Valentin , Munga Stephen , Khagayi Sammy , Obor David , Muok Erick , Franke Jonas , Blass Klaus , Su TinTin , Bärnighausen Till , Sankoh Osman , Sauerborn Rainer 
Published: November 2022

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.