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1.
PLoS One ; 18(8): e0287628, 2023.
Article in English | MEDLINE | ID: mdl-37552679

ABSTRACT

BACKGROUND: Tuberculosis (TB) is the world's major public health problem. We assessed the proportion, reasons, and associated factors for anti-TB treatment nonadherence in the communities in Indonesia. METHODS: This national coverage cross-sectional survey was conducted from 2013 to 2014 with stratified multi-stage cluster sampling. Based on the region and rural-urban location. The 156 clusters were distributed in 136 districts/cities throughout 33 provinces, divided into three areas. An eligible population of age ≥15 was interviewed to find TB symptoms and screened with a thorax x-ray. Those whose filtered result detected positive followed an assessment of Sputum microscopy, LJ culture, and Xpert MTB/RIF. Census officers asked all participants about their history of TB and their treatment-defined Nonadherence as discontinuation of anti-tuberculosis treatment for <6 months. Data were analyzed using STATA 14.0 (College Station, TX, USA). RESULTS: Nonadherence to anti-TB treatment proportion was 27.24%. Multivariate analysis identified behavioral factors significantly associated with anti-TB treatment nonadherence, such as smoking (OR = 1.78, 95% CI (1.47-2.16)); place of first treatment received: government hospital (OR = 1.45, 95% CI:1.06-1.99); private hospital (OR = 1.93, 95% CI: 1.38-2.72); private practitioner (OR = 2.24, 95% CI: 1.56-3.23); socio-demographic and TB status included region: Sumatera (OR = 1.44, 95% CI: 1.05-1.98); other areas (OR = 1.84, 95% CI: 1.30-2.61); low level of education (OR = 1.60, 95% CI: 1.27-2.03); and current TB positive status (OR = 2.17, 95% CI: 1.26-3.73). CONCLUSIONS: Nonadherence to anti-TB drugs was highly related to the personal perception of the respondents, despite smoking, current TB status, a place for the first treatment, education, and region. The position of the first TB treatment at the private practitioner was significantly associated with the risk of Nonadherence to treatment.


Subject(s)
Tuberculosis, Pulmonary , Humans , Male , Female , Adolescent , Young Adult , Adult , Middle Aged , Logistic Models , Indonesia/epidemiology , Tuberculosis, Pulmonary/drug therapy , Tuberculosis, Pulmonary/epidemiology , Antitubercular Agents/therapeutic use , Cross-Sectional Studies
2.
Travel Med Infect Dis ; 32: 101437, 2019.
Article in English | MEDLINE | ID: mdl-31362115

ABSTRACT

BACKGROUND: Dengue fever control in the tropical island of Bali in Indonesia carries important significance both nationally and globally, as it is one of the most endemic islands in Indonesia and a worldwide popular travel destination. Despite its importance, the spatial and temporal heterogeneity in dengue risk and factors associated with its variation in risk across the island has not been not well explored. This study was aimed to analyze for the first time the geographical and temporal patterns of the incidence of dengue and to quantify the role of environmental and social factors on the spatial heterogeneity of dengue incidence in Bali. METHODS: We analyzed retrospective dengue notification data at the sub-district level (Kecamatan) from January 2012 to December 2017 which obtained from the Indonesian Ministry of Health. Seasonality in notified dengue incidence was assessed by seasonal trend decomposition analysis with Loess (STL) smoothing. Crude standardized morbidity rates (SMRs) of dengue were calculated. Moran's I and local indicators of spatial autocorrelation (LISA) analysis were employed to assess spatial clustering and high-risk areas over the period studied. Bayesian spatial and temporal conditional autoregressive (CAR) modeling was performed to quantify the effects of rainfall, temperature, elevation, and population density on the spatial distribution of risk of dengue in Bali. RESULTS: Strong seasonality of dengue incidence was observed with most cases notified during January to May. Dengue incidence was spatially clustered during the period studied with high-risk kecamatans concentrated in the south of the island, but since 2014, the high-risk areas expanded toward the eastern part of the island. The best-fitted CAR model showed increased dengue risk in kecamatans with high total annual rainfall (relative risk (RR): 1.16 for each 1-mm increase in rainfall; 95% Credible interval (CrI): 1.03-1.31) and high population density (RR: 7.90 per 1000 people/sq.km increase; 95% CrI: 3.01-20.40). The RR of dengue was decreased in kecamatans with higher elevation (RR: 0.73 for each 1-m increase in elevation; 95% CrI: 0.55-0.98). No significant association was observed between dengue RR and year except in 2014, where the dengue RR was significantly lower (RR: 0.53; 95% CrI: 0.30-0.92) relative to 2012. CONCLUSIONS: Dengue incidence was strongly seasonal and spatially clustered in Bali. High-risk areas were spread from kecamatans in Badung and Denpasar toward Karangasem and Klungkung. The spatial heterogeneity of dengue risk across Bali was influenced by rainfall, elevation, and population density. Surveillance and targeted intervention strategies should be prioritized in the high-risk kecamatans identified in this study to better control dengue transmission in this most touristic island in Indonesia. Local health authorities should recommend travelers to use personal protective measures, especially during the peak epidemic period, before visiting Bali.

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