Tuberculosis prevalence and demographic characteristics of population in Azad Jammu and Kashmir (Pakistan): A retrospective study.
Soffia KhursheedSamia WazirMuhammad Khurram SaleemAyesha Isani MajeedMumtaz AhmadQudsia Umaira KhanArzu JadoonAmna AkbarSarosh Khan JadoonSabahat TasneemHumayun SaleemMohammad Saleem KhanSarosh AlviPublished in: Medicine (2024)
Tuberculosis (TB) remains a serious problem for public health and a leading cause of death after COVID-19 and superior to even HIV/AIDS. It is a social health issue and can cause stigma and economic loss as the person cannot perform professionally due to lethargy caused by disease. It is a retrospective study done on data from National TB program Muzaffarabad chapter. The details were noted on SPSS and analysis was done to find important demographic characteristics. The total number of patients was 3441; among which 48.76% were males. Most of them (81.11%) belonged to the Muzaffarabad division of Azad Jammu and Kahmir (AJK). The microbiologically or culture positive cases were 440. Rifampicin resistance was present in 147 cases, further categorized as high (n = 143), very high (n = 3), or true positive (n = 1) resistance. Muti drug resistance was found in 19 cases. The microscopy culture is more sensitive (AUC = 0.511) than MTB/RIF or serology (AUC = 0.502) according to ROC. The rate of positive smear results is not very satisfactory in the present study as it cannot detect dormant or latent cases. There is a need to establish more sensitive tests for detection of cases and more research to combat the disease.
Keyphrases
- hiv aids
- mycobacterium tuberculosis
- pulmonary tuberculosis
- public health
- mental health
- healthcare
- antiretroviral therapy
- quality improvement
- coronavirus disease
- ejection fraction
- emergency department
- climate change
- high throughput
- machine learning
- depressive symptoms
- single molecule
- health information
- social support
- high speed
- label free
- adverse drug
- data analysis
- tertiary care
- hepatitis c virus
- mental illness
- risk assessment
- artificial intelligence
- single cell
- sensitive detection
- patient reported outcomes