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Epidemiology of tuberculosis in Kazakhstan: data from the Unified National Electronic Healthcare System 2014–2019

OBJECTIVES: This study aims to estimate tuberculosis (TB) incidence, mortality rates and survival HRs in Kazakhstan, using large-scale administrative health data records during 2014–2019. DESIGN: A retrospective cohort study. SETTINGS: Data for patients with TB in Kazakhstan during 2014–2019, report...

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Autores principales: Sakko, Yesbolat, Madikenova, Meruyert, Kim, Alexey, Syssoyev, Dmitriy, Mussina, Kamilla, Gusmanov, Arnur, Zhakhina, Gulnur, Yerdessov, Sauran, Semenova, Yuliya, Crape, Byron Lawrence, Sarria-Santamera, Antonio, Gaipov, Abduzhappar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582847/
https://www.ncbi.nlm.nih.gov/pubmed/37821138
http://dx.doi.org/10.1136/bmjopen-2023-074208
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author Sakko, Yesbolat
Madikenova, Meruyert
Kim, Alexey
Syssoyev, Dmitriy
Mussina, Kamilla
Gusmanov, Arnur
Zhakhina, Gulnur
Yerdessov, Sauran
Semenova, Yuliya
Crape, Byron Lawrence
Sarria-Santamera, Antonio
Gaipov, Abduzhappar
author_facet Sakko, Yesbolat
Madikenova, Meruyert
Kim, Alexey
Syssoyev, Dmitriy
Mussina, Kamilla
Gusmanov, Arnur
Zhakhina, Gulnur
Yerdessov, Sauran
Semenova, Yuliya
Crape, Byron Lawrence
Sarria-Santamera, Antonio
Gaipov, Abduzhappar
author_sort Sakko, Yesbolat
collection PubMed
description OBJECTIVES: This study aims to estimate tuberculosis (TB) incidence, mortality rates and survival HRs in Kazakhstan, using large-scale administrative health data records during 2014–2019. DESIGN: A retrospective cohort study. SETTINGS: Data for patients with TB in Kazakhstan during 2014–2019, reported in the Unified National Electronic Healthcare System. PARTICIPANTS: Patients with TB in Kazakhstan (ICD-10 (The International Classification of Diseases, 10th revision) codes: A15–A19). OUTCOME MEASURES: Demographic factors, diagnoses and comorbidities were analysed using descriptive, bivariate and multivariable statistical analyses. TB incidence and mortality rates were calculated, and Cox regression and Kaplan-Meier survival analysis were performed to assess risk factors for survival rates. RESULTS: Of the 149 122 patients with TB, 91 437 (61%) were males, and 139 931 (94%) had respiratory TB. From 2014 to 2019, TB incidence declined from 227 to 15.2 per 100 000 individuals, while all-cause mortality increased from 8.4 to 15.2 per 100 000. Age-specific TB incidence was lowest for 0–10 years of age and highest for 20 years of age. Being older, man, urban residence versus rural, retired versus employed, having HIV and having diabetes versus no comorbidities were associated with lower survival rates. CONCLUSION: To date, this is the largest TB published study for Kazakhstan, characterising TB incidence and mortality trends by demographic factors, and risk factors for survival rates. The findings highlight the need for targeted interventions to address the growing burden of TB, particularly among older adults, men, urban residents and those with HIV and diabetes. The study underscores the importance of using administrative health data to inform policy and health system responses to TB in Kazakhstan.
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spelling pubmed-105828472023-10-19 Epidemiology of tuberculosis in Kazakhstan: data from the Unified National Electronic Healthcare System 2014–2019 Sakko, Yesbolat Madikenova, Meruyert Kim, Alexey Syssoyev, Dmitriy Mussina, Kamilla Gusmanov, Arnur Zhakhina, Gulnur Yerdessov, Sauran Semenova, Yuliya Crape, Byron Lawrence Sarria-Santamera, Antonio Gaipov, Abduzhappar BMJ Open Epidemiology OBJECTIVES: This study aims to estimate tuberculosis (TB) incidence, mortality rates and survival HRs in Kazakhstan, using large-scale administrative health data records during 2014–2019. DESIGN: A retrospective cohort study. SETTINGS: Data for patients with TB in Kazakhstan during 2014–2019, reported in the Unified National Electronic Healthcare System. PARTICIPANTS: Patients with TB in Kazakhstan (ICD-10 (The International Classification of Diseases, 10th revision) codes: A15–A19). OUTCOME MEASURES: Demographic factors, diagnoses and comorbidities were analysed using descriptive, bivariate and multivariable statistical analyses. TB incidence and mortality rates were calculated, and Cox regression and Kaplan-Meier survival analysis were performed to assess risk factors for survival rates. RESULTS: Of the 149 122 patients with TB, 91 437 (61%) were males, and 139 931 (94%) had respiratory TB. From 2014 to 2019, TB incidence declined from 227 to 15.2 per 100 000 individuals, while all-cause mortality increased from 8.4 to 15.2 per 100 000. Age-specific TB incidence was lowest for 0–10 years of age and highest for 20 years of age. Being older, man, urban residence versus rural, retired versus employed, having HIV and having diabetes versus no comorbidities were associated with lower survival rates. CONCLUSION: To date, this is the largest TB published study for Kazakhstan, characterising TB incidence and mortality trends by demographic factors, and risk factors for survival rates. The findings highlight the need for targeted interventions to address the growing burden of TB, particularly among older adults, men, urban residents and those with HIV and diabetes. The study underscores the importance of using administrative health data to inform policy and health system responses to TB in Kazakhstan. BMJ Publishing Group 2023-10-11 /pmc/articles/PMC10582847/ /pubmed/37821138 http://dx.doi.org/10.1136/bmjopen-2023-074208 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Epidemiology
Sakko, Yesbolat
Madikenova, Meruyert
Kim, Alexey
Syssoyev, Dmitriy
Mussina, Kamilla
Gusmanov, Arnur
Zhakhina, Gulnur
Yerdessov, Sauran
Semenova, Yuliya
Crape, Byron Lawrence
Sarria-Santamera, Antonio
Gaipov, Abduzhappar
Epidemiology of tuberculosis in Kazakhstan: data from the Unified National Electronic Healthcare System 2014–2019
title Epidemiology of tuberculosis in Kazakhstan: data from the Unified National Electronic Healthcare System 2014–2019
title_full Epidemiology of tuberculosis in Kazakhstan: data from the Unified National Electronic Healthcare System 2014–2019
title_fullStr Epidemiology of tuberculosis in Kazakhstan: data from the Unified National Electronic Healthcare System 2014–2019
title_full_unstemmed Epidemiology of tuberculosis in Kazakhstan: data from the Unified National Electronic Healthcare System 2014–2019
title_short Epidemiology of tuberculosis in Kazakhstan: data from the Unified National Electronic Healthcare System 2014–2019
title_sort epidemiology of tuberculosis in kazakhstan: data from the unified national electronic healthcare system 2014–2019
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582847/
https://www.ncbi.nlm.nih.gov/pubmed/37821138
http://dx.doi.org/10.1136/bmjopen-2023-074208
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