Role of Nighttime Lights on Cardiovascular Risk in Thailand: A Preliminary Ecological Analysis, 2024
DOI:
https://doi.org/10.59096/osir.v19i1.278867Keywords:
ecological study, nighttime light, cardiovascular disease, incidence, ThailandAbstract
Objectives: Nighttime light (NTL) may serve as a proxy for urbanicity and circadian disruption, both of which are relevant to cardiovascular disease (CVD). This study aimed to examine the association between province-level NTL intensity and province-level CVD admission rates.
Methods: We conducted a nationwide ecological study of Thai provinces, linking satellite-derived NTL with 2024 inpatient CVD admissions for adults aged ≥40 years using a nationwide database from the Ministry of Public Health. Admissions for heart failure, acute myocardial infarction (MI), stroke, and atrial fibrillation (AF) were aggregated annually by province. The exposure was the intensity-based location quotient of smoothed nighttime light (LQSNL), using data from the Defense Meteorological Satellite Program—Operational Linescan System for 2010 and, in sensitivity analyses, data from the Suomi National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite for 2023. Associations were assessed with Pearson correlation and linear regression adjusted for the hospital bed capacity.
Results: Provinces with the highest LQSNL quartile had the highest CVD admission rates. LQSNL in 2010 correlated with MI (r 0.31, p<0.001), stroke (r 0.24, p 0.035), and AF (r 0.47, p<0.001) admissions. Using the 2023 LQSNL data yielded similar patterns. In multivariable models, higher LQSNL remained significantly associated with higher admission rates for all CVD outcomes across both exposure years.
Public Health Recommendations: These findings support the use of satellite-derived NTL as a practical proxy for spatial variation in estimating the CVD admission burden, with potential applications in surveillance and resource planning. Given the ecological design and potential for residual confounding, future studies should incorporate individual-level exposure and additional covariates, including demographic, environmental, and health system variables.
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