Nernst-Atwood Raphael , Pierre Anthony Garraud , Maroussia Roelens , Jean Patrick Alfred , Milo Richard , Janne Estill , Olivia Keiser , Aziza Merzouki
{"title":"2018 至 2019 年海地结核病治疗结果评估:竞争风险分析","authors":"Nernst-Atwood Raphael , Pierre Anthony Garraud , Maroussia Roelens , Jean Patrick Alfred , Milo Richard , Janne Estill , Olivia Keiser , Aziza Merzouki","doi":"10.1016/j.ijregi.2024.03.005","DOIUrl":null,"url":null,"abstract":"<div><h3>Objectives</h3><p>This study assesses tuberculosis (TB) treatment outcomes in Haiti.</p></div><div><h3>Methods</h3><p>Data from drug-susceptible patients with TB (2018-2019) were analyzed using the Fine & Gray model with multiple imputation.</p></div><div><h3>Results</h3><p>Of the 16,545 patients, 14.7% had concurrent HIV coinfection, with a 66.2% success rate. The median treatment duration was 5 months, with patients averaging 30 years (with an interquartile range of 22-42 years). The estimated hazard of achieving a successful treatment outcome decreased by 2.5% and 8.1% for patients aged 45 and 60 years, respectively, compared with patients aged 30 years. Male patients had a 6.5% lower estimated hazard of success than their female counterparts. In addition, patients coinfected with HIV experienced a 35.3% reduction in the estimated hazard of achieving a successful treatment outcome compared with those with a negative HIV serologic status.</p></div><div><h3>Conclusions</h3><p>Integrated health care approaches should be implemented, incorporating innovative solutions, such as machine learning algorithms combined with geographic information systems and non-conventional data sources (including social media), to identify TB hotspots and high-burden households.</p></div>","PeriodicalId":73335,"journal":{"name":"IJID regions","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772707624000201/pdfft?md5=9aca51ae6f0f4bed7eda115b5dc942f0&pid=1-s2.0-S2772707624000201-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Evaluating tuberculosis treatment outcomes in Haiti from 2018 to 2019: A competing risk analysis\",\"authors\":\"Nernst-Atwood Raphael , Pierre Anthony Garraud , Maroussia Roelens , Jean Patrick Alfred , Milo Richard , Janne Estill , Olivia Keiser , Aziza Merzouki\",\"doi\":\"10.1016/j.ijregi.2024.03.005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objectives</h3><p>This study assesses tuberculosis (TB) treatment outcomes in Haiti.</p></div><div><h3>Methods</h3><p>Data from drug-susceptible patients with TB (2018-2019) were analyzed using the Fine & Gray model with multiple imputation.</p></div><div><h3>Results</h3><p>Of the 16,545 patients, 14.7% had concurrent HIV coinfection, with a 66.2% success rate. The median treatment duration was 5 months, with patients averaging 30 years (with an interquartile range of 22-42 years). The estimated hazard of achieving a successful treatment outcome decreased by 2.5% and 8.1% for patients aged 45 and 60 years, respectively, compared with patients aged 30 years. Male patients had a 6.5% lower estimated hazard of success than their female counterparts. In addition, patients coinfected with HIV experienced a 35.3% reduction in the estimated hazard of achieving a successful treatment outcome compared with those with a negative HIV serologic status.</p></div><div><h3>Conclusions</h3><p>Integrated health care approaches should be implemented, incorporating innovative solutions, such as machine learning algorithms combined with geographic information systems and non-conventional data sources (including social media), to identify TB hotspots and high-burden households.</p></div>\",\"PeriodicalId\":73335,\"journal\":{\"name\":\"IJID regions\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-03-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2772707624000201/pdfft?md5=9aca51ae6f0f4bed7eda115b5dc942f0&pid=1-s2.0-S2772707624000201-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IJID regions\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2772707624000201\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"INFECTIOUS DISEASES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IJID regions","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772707624000201","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"INFECTIOUS DISEASES","Score":null,"Total":0}
Evaluating tuberculosis treatment outcomes in Haiti from 2018 to 2019: A competing risk analysis
Objectives
This study assesses tuberculosis (TB) treatment outcomes in Haiti.
Methods
Data from drug-susceptible patients with TB (2018-2019) were analyzed using the Fine & Gray model with multiple imputation.
Results
Of the 16,545 patients, 14.7% had concurrent HIV coinfection, with a 66.2% success rate. The median treatment duration was 5 months, with patients averaging 30 years (with an interquartile range of 22-42 years). The estimated hazard of achieving a successful treatment outcome decreased by 2.5% and 8.1% for patients aged 45 and 60 years, respectively, compared with patients aged 30 years. Male patients had a 6.5% lower estimated hazard of success than their female counterparts. In addition, patients coinfected with HIV experienced a 35.3% reduction in the estimated hazard of achieving a successful treatment outcome compared with those with a negative HIV serologic status.
Conclusions
Integrated health care approaches should be implemented, incorporating innovative solutions, such as machine learning algorithms combined with geographic information systems and non-conventional data sources (including social media), to identify TB hotspots and high-burden households.