tetano
Editor, Senior Moderator
PLoS One
. 2024 Oct 28;19(10):e0312717.
doi: 10.1371/journal.pone.0312717. eCollection 2024. Exploring demographic, healthcare, and socio-economic factors as predictors of COVID-19 incidence rate: A spatial regression analysis
Kittipong Sornlorm[SUP] 1 [/SUP], Ei Sandar U[SUP] 1 [/SUP], Wongsa Laohasiriwong[SUP] 1 [/SUP], Wor Mi Thi[SUP] 1 [/SUP]
Affiliations
This study investigated the relationship between demographic, healthcare, and socio-economic factors, and COVID-19 incidence rate per 100,000 population in Thailand at the province level between January 2020 and March 2022, using a five-phase approach by spatial analysis. OLS models were initially used with significant variables: household, hospital, and industry density, nighttime light index (NTLI). Spatial dependency led to spatial error (SEM) and spatial lag models (SLM), performing better with similar significant variables being applied. SEM explains 58, 65 and, 70 percent in Wave 1, 4 and 5 of COVID-19 variation. SLM explains 25 and 76 percent in Wave 2 and 3 of incidence rate. Positive associations were found between incidence and household density, hospital/medical establishments with beds, Nighttime Light Index (NTLI), and negative with population, hospital, and industry density. Wave 5 showed significant changes with negative for household, hospital, and industry density, urban population; positive for hospital/medical establishments with beds, internet access, NTLI. The study showed that significant predictors of COVID-19 incidence rate vary across waves. Population, household and hospital density, urbanization, access to medical facilities, industrialization, internet access, and NTLI all play a role. The study suggests SEM and SLM models are more appropriate, providing useful information for policymakers and health officials in managing pandemic in Thailand.
. 2024 Oct 28;19(10):e0312717.
doi: 10.1371/journal.pone.0312717. eCollection 2024. Exploring demographic, healthcare, and socio-economic factors as predictors of COVID-19 incidence rate: A spatial regression analysis
Kittipong Sornlorm[SUP] 1 [/SUP], Ei Sandar U[SUP] 1 [/SUP], Wongsa Laohasiriwong[SUP] 1 [/SUP], Wor Mi Thi[SUP] 1 [/SUP]
Affiliations
- PMID: 39466800
- DOI: 10.1371/journal.pone.0312717
This study investigated the relationship between demographic, healthcare, and socio-economic factors, and COVID-19 incidence rate per 100,000 population in Thailand at the province level between January 2020 and March 2022, using a five-phase approach by spatial analysis. OLS models were initially used with significant variables: household, hospital, and industry density, nighttime light index (NTLI). Spatial dependency led to spatial error (SEM) and spatial lag models (SLM), performing better with similar significant variables being applied. SEM explains 58, 65 and, 70 percent in Wave 1, 4 and 5 of COVID-19 variation. SLM explains 25 and 76 percent in Wave 2 and 3 of incidence rate. Positive associations were found between incidence and household density, hospital/medical establishments with beds, Nighttime Light Index (NTLI), and negative with population, hospital, and industry density. Wave 5 showed significant changes with negative for household, hospital, and industry density, urban population; positive for hospital/medical establishments with beds, internet access, NTLI. The study showed that significant predictors of COVID-19 incidence rate vary across waves. Population, household and hospital density, urbanization, access to medical facilities, industrialization, internet access, and NTLI all play a role. The study suggests SEM and SLM models are more appropriate, providing useful information for policymakers and health officials in managing pandemic in Thailand.