Ontology highlight
ABSTRACT: Objectives
To examine the effect of individual and area-level characteristics on the probability of public-private mix (PPM) support (PPM coverage) for tuberculosis (TB).Methods
This study is a retrospective cohort design using TB reporting and treatment management data in Korea. We analyzed PPM coverage through multilevel logistic regression and empirical Bayesian estimation according to individual and area-level characteristics and their interaction.Results
Patients aged 0-29 years, women, of Korean nationality, treated at a general hospital, a one-time reporting, urban areas, and the lowest deprivation index (DI) showed higher PPM coverage. Due to the cross-level interaction, PPM coverage in the urban areas (slope=-0.048, p<0.001) had a higher level but a steeper negative deprivation gradient than in rural areas (slope= -0.015, p<0.001). For a general hospital, the PPM coverage in urban is high but more significantly decreased than in rural areas with the higher DI (urban: slope=-0.047, p<0.001; rural: slope=-0.031, p<0.001). For clinics and hospitals, the effect of DI did not appear in urban areas, but in rural areas, the higher the DI, the higher the PPM coverage with a slope of 0.046 (p<0.001) and 0.063 (p<0.001), respectively.Conclusions
The PPM program created a significant disparity in PPM coverage between urban-rural areas and type of healthcare provider according to DI. Considering the high risk of TB incidence in areas with higher DI, institutional improvement and program redesign are needed to improve accessibility and equity.
SUBMITTER: Son H
PROVIDER: S-EPMC10266928 | biostudies-literature | 2023
REPOSITORIES: biostudies-literature
Epidemiology and health 20221207
<h4>Objectives</h4>To examine the effect of individual and area-level characteristics on the probability of public-private mix (PPM) support (PPM coverage) for tuberculosis (TB).<h4>Methods</h4>This study is a retrospective cohort design using TB reporting and treatment management data in Korea. We analyzed PPM coverage through multilevel logistic regression and empirical Bayesian estimation according to individual and area-level characteristics and their interaction.<h4>Results</h4>Patients age ...[more]