Self-reported prevalence of tuberculosis: unveiling spatial representation in the districts of Tamil Nadu.

IF 2.3 4区医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH International Health Pub Date : 2024-10-18 DOI:10.1093/inthealth/ihae072

Malaisamy Muniyandi, Kavi Mathiyazhagan, Nagarajan Karikalan

{"title":"Self-reported prevalence of tuberculosis: unveiling spatial representation in the districts of Tamil Nadu.","authors":"Malaisamy Muniyandi, Kavi Mathiyazhagan, Nagarajan Karikalan","doi":"10.1093/inthealth/ihae072","DOIUrl":null,"url":null,"abstract":"Background: The objective of the current study was to estimate the self-reported individual-level crude prevalence and cluster-level adjusted prevalence of TB for the districts of Tamil Nadu and to understand the spatial distribution of TB cases through spatial autocorrelation and hotspot analysis.Methods: National Family Health Survey (NFHS) data, gathered during 2014-2015 (NFHS-4) and 2019-2021 (NFHS-5), were used in the current study to estimate district-wise, individual-level crude and cluster-level adjusted TB prevalence per 100 000 population in Tamil Nadu. This was illustrated with the help of spatial geographic representation for various districts of Tamil Nadu using SPSS and QGIS software. The spatial autocorrelation and hotspot analysis were performed using Geoda software.Results: The overall self-reported individual-level crude prevalence of TB was 337 (95% CI 302 to 375) and 169 (95% CI 144 to 197) per 100 000 population, whereas the cluster-level adjusted prevalence of TB was 356 (95% CI 311 to 405) and 184 (95% CI 154 to 219) per 100 000 population in NFHS-4 and NFHS-5, respectively.Conclusions: This study highlights those geographical areas with high rates of TB prevalence. This information would be useful for the state and district programme managers to identify areas of high TB prevalence where interventions can be focused.","PeriodicalId":49060,"journal":{"name":"International Health","volume":" ","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Health","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/inthealth/ihae072","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}

引用次数: 0

Abstract

Background: The objective of the current study was to estimate the self-reported individual-level crude prevalence and cluster-level adjusted prevalence of TB for the districts of Tamil Nadu and to understand the spatial distribution of TB cases through spatial autocorrelation and hotspot analysis.

Methods: National Family Health Survey (NFHS) data, gathered during 2014-2015 (NFHS-4) and 2019-2021 (NFHS-5), were used in the current study to estimate district-wise, individual-level crude and cluster-level adjusted TB prevalence per 100 000 population in Tamil Nadu. This was illustrated with the help of spatial geographic representation for various districts of Tamil Nadu using SPSS and QGIS software. The spatial autocorrelation and hotspot analysis were performed using Geoda software.

Results: The overall self-reported individual-level crude prevalence of TB was 337 (95% CI 302 to 375) and 169 (95% CI 144 to 197) per 100 000 population, whereas the cluster-level adjusted prevalence of TB was 356 (95% CI 311 to 405) and 184 (95% CI 154 to 219) per 100 000 population in NFHS-4 and NFHS-5, respectively.

Conclusions: This study highlights those geographical areas with high rates of TB prevalence. This information would be useful for the state and district programme managers to identify areas of high TB prevalence where interventions can be focused.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

自我报告的肺结核发病率：揭示泰米尔纳德邦各地区的空间代表性。

研究背景本研究旨在估算泰米尔纳德邦各县自我报告的肺结核个人水平粗流行率和集群水平调整流行率，并通过空间自相关性和热点分析了解肺结核病例的空间分布：本研究使用了 2014-2015 年（NFHS-4）和 2019-2021 年（NFHS-5）期间收集的全国家庭健康调查（NFHS）数据，以估算泰米尔纳德邦各地区、个人层面的粗略结核病患病率和每 10 万人口的集群层面调整结核病患病率。在 SPSS 和 QGIS 软件的帮助下，泰米尔纳德邦各地区的空间地理表示法对此进行了说明。使用 Geoda 软件进行了空间自相关性和热点分析：结果：在 NFHS-4 和 NFHS-5 中，个人自我报告的结核病总粗流行率分别为每 10 万人 337 例（95% CI 302 至 375 例）和 169 例（95% CI 144 至 197 例），而集群调整后的结核病流行率分别为每 10 万人 356 例（95% CI 311 至 405 例）和 184 例（95% CI 154 至 219 例）：本研究强调了结核病高发的地理区域。这些信息将有助于州和地区项目管理人员确定结核病高发地区，并在这些地区重点采取干预措施。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

International Health PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-

CiteScore

4.50

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： International Health is an official journal of the Royal Society of Tropical Medicine and Hygiene. It publishes original, peer-reviewed articles and reviews on all aspects of global health including the social and economic aspects of communicable and non-communicable diseases, health systems research, policy and implementation, and the evaluation of disease control programmes and healthcare delivery solutions. It aims to stimulate scientific and policy debate and provide a forum for analysis and opinion sharing for individuals and organisations engaged in all areas of global health.