Back

Effect of Ambient Temperature on Diabetes Patient Visits at Primary and Referral Healthcare Services in Yogyakarta Province, Indonesia: An Analysis of Indonesian National Health Insurance Data 2021

Ramadona, A. L.; Qaimamunazzala, H.; Kusnanto, H.; Lazuardi, L.; Danawati, C. W.; Fuad, A.; Dewi, F. S. T.

2023-08-16 public and global health
10.1101/2023.08.16.23291838 medRxiv
Show abstract

The global incidence of diabetes mellitus is on the rise, posing a significant health challenge worldwide. Recent evidence indicates a possible link between diabetes mellitus and temperature. This study aimed to examine the effect of temperature on diabetes patient visits in Yogyakarta Province, Indonesia, which is currently experiencing a high burden of diabetes and an increasingly aging population. Daily temperature data for 2021 were obtained from the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG). The number of diabetes patient visits was gathered from BPJS Health data samples for primary and referral healthcare facilities in the province. The relationship between temperature and diabetes patient visits was explored through data visualization, Pearson correlation, and Poisson regression models. We found a short-term correlation between higher temperatures and an increase in patient visits daily. However, observations on Sundays and holidays may not reflect the delayed effect of temperature on patient visits since not all primary and referral care providers offer services on these days. Nonetheless, including the observations on these days is necessary to assess the overall impact of temperature on patient visits weekly and monthly. The regression analysis revealed that for each additional 1{degrees}C increase in the average monthly temperature, the estimated number of patients for primary care increased by 15% and 14% for referral care services.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.