Demographic, socio-economic and behavior as risk factors of tuberculosis in Malaysia: a systematic review of the literature

Mohidem, Nur Adibah; Hashim, Zailina; Osman, Malina; Shaharudin, Rafiza; Muharam, Farrah Melissa; Makeswaran, Punitha

doi:10.1515/reveh-2018-0026

Cited by 16 publications

(19 citation statements)

References 36 publications

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“…Sociodemography characteristics are considered as risk factors for TB and MDR-TB incidence [ 34 ]. Our results showed that TB cases were higher in men, similar to most studies in Malaysia [ 35 ]. Active TB cases are shown to affect individuals of productive age, as shown in a Java island, Indonesia study [ 35 – 38 ].…”

Section: Main Textsupporting

confidence: 91%

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Genetic variation of ABCB1 (rs1128503, rs1045642) and CYP2E1 rs3813867 with the duration of tuberculosis therapy: a pilot study among tuberculosis patients in Indonesia

et al. 2021

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Objective The risk of contracting tuberculosis (TB) and the efficacy of TB therapy are affected by several factors, including genetic variation among populations. In the Indonesian population, data on the genes involved in drug transport and metabolism of TB therapy are limited. The aim of this study was to identify the genetic profile of the ABCB1 gene (rs1128503 and rs1045642) and CYP2E1 gene (rs3813867) in Indonesians with TB. This study was a cross-sectional study of 50 TB outpatients in Jambi city, Indonesia. Sociodemographic characteristics were obtained from medical records. Whole blood was collected, and genomic DNA was isolated. Single nucleotide polymorphisms were determined using polymerase chain reaction-restriction fragment length polymorphism with HaeIII, MboI, and PstI for rs1128503, rs1045642 (ABCB1), and rs3813867 (CYP2E1), respectively. Result The frequency of alleles of each gene was analyzed by Hardy–Weinberg equilibrium. The genetic profiles of ABCB1 rs1128503 and rs1045642 were varied (CC, CT, TT), while CYP2E1 rs3813867 was present in CC (wild type). The genetic variations of ABCB1 and CYP2E1 may have no significant correlation with the duration of TB therapy. Nevertheless, this study may provide as preliminary results for the genetic profiles of ABCB1 (rs1128503, rs1045642) and CYP2E1 (rs3813867) in the Indonesia population.

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Section: Main Textsupporting

confidence: 91%

“…Our results showed that TB cases were higher in men, similar to most studies in Malaysia [ 35 ]. Active TB cases are shown to affect individuals of productive age, as shown in a Java island, Indonesia study [ 35 – 38 ]. However, we observed 17 patients who were less than 40 years of age and 33 who were more than 40 years old.…”

Section: Main Textsupporting

confidence: 91%

Genetic variation of ABCB1 (rs1128503, rs1045642) and CYP2E1 rs3813867 with the duration of tuberculosis therapy: a pilot study among tuberculosis patients in Indonesia

et al. 2021

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“…All these data were retrieved via MyTB and double checked from TBIS to prevent errors. These variables were selected through the consideration of the real situation in Malaysia from the references of previous literatures [ 56 , 57 ]. Other data such as individual’s ID, type of TB (smear positive, smear negative, extrapulmonary, drug-resistant), and date of diagnosis (month, year) were also collected.…”

Section: Methodsmentioning

confidence: 99%

Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia

et al. 2021

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Tuberculosis (TB) cases have increased drastically over the last two decades and it remains as one of the deadliest infectious diseases in Malaysia. This cross-sectional study aimed to establish the spatial distribution of TB cases and its association with the sociodemographic and environmental factors in the Gombak district. The sociodemographic data of 3325 TB cases such as age, gender, race, nationality, country of origin, educational level, employment status, health care worker status, income status, residency, and smoking status from 1st January 2013 to 31st December 2017 in Gombak district were collected from the MyTB web and Tuberculosis Information System (TBIS) database at the Gombak District Health Office and Rawang Health Clinic. Environmental data consisting of air pollution such as air quality index (AQI), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter 10 (PM10,) were obtained from the Department of Environment Malaysia from 1st July 2012 to 31st December 2017; whereas weather data such as rainfall were obtained from the Department of Irrigation and Drainage Malaysia and relative humidity, temperature, wind speed, and atmospheric pressure were obtained from the Malaysia Meteorological Department in the same period. Global Moran’s I, kernel density estimation, Getis-Ord Gi* statistics, and heat maps were applied to identify the spatial pattern of TB cases. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to determine the spatial association of sociodemographic and environmental factors with the TB cases. Spatial autocorrelation analysis indicated that the cases was clustered (p<0.05) over the five-year period and year 2016 and 2017 while random pattern (p>0.05) was observed from year 2013 to 2015. Kernel density estimation identified the high-density regions while Getis-Ord Gi* statistics observed hotspot locations, whereby consistently located in the southwestern part of the study area. This could be attributed to the overcrowding of inmates in the Sungai Buloh prison located there. Sociodemographic factors such as gender, nationality, employment status, health care worker status, income status, residency, and smoking status as well as; environmental factors such as AQI (lag 1), CO (lag 2), NO2 (lag 2), SO2 (lag 1), PM10 (lag 5), rainfall (lag 2), relative humidity (lag 4), temperature (lag 2), wind speed (lag 4), and atmospheric pressure (lag 6) were associated with TB cases (p<0.05). The GWR model based on the environmental factors i.e. GWR2 was the best model to determine the spatial distribution of TB cases based on the highest R2 value i.e. 0.98. The maps of estimated local coefficients in GWR models confirmed that the effects of sociodemographic and environmental factors on TB cases spatially varied. This study highlighted the importance of spatial analysis to identify areas with a high TB burden based on its associated factors, which further helps in improving targeted surveillance.

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“…The TB incidence was positively associated with the temperature, precipitation, and wind speed (all P-values < 0.05) (Moosazadeh, Khanjani and Parsaee, 2015). The risk factors for the development of TB, specifically in a high-risk population, should be targeted through the implementation of specialized interventions (Mohidem et al, 2018).…”

Section: Contact With Patientsmentioning

confidence: 99%

Socio-Economic and Environmental Risk Factors of Tuberculosis in Wonosobo, Central Java, Indonesia

Pratiwi¹,

Pramono

Junaedi³

2020

Kemas

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This study discusses the dominant socio-economic and environmental risk factors for TB disease. The design of this study was a case-control study with 70 case samples and control with a contribution of n = 1. Variables from this study contacted personal, ventilation of the house, humidity, the temperature of the house, density of the house, kitchen, and family earnings. Multivariate data analysis uses multiple logistic regressions. The study notes that from 140 samples, 47% have basic education, and 30% are farmers. People who had a past of contact with TB cases were ten times more likely to contract TB than those who had no contacted (OR = 10.00; p <0.001). Personalities who live in poorly ventilated homes who have a risk of contracting TB are 2.2 times greater than those who live in homes with standard ventilation (OR = 2.20; p <0.018). The moisture increases the risk of TB by four times the low moisture (OR = 4.00; p = 0.001). Living in a house with a higher temperature of TB is 3.8 times higher than a lower temperature (OR = 3.80; p = 0.009). Living in a high population density of the house improves TB five times more than living in a lesser home (OR = 5.00; p <0.001). Kitchen gas enhances the risk of TB 2.5 times greater than gasless (OR = 2.50; p = 0.007). Low family earnings raise the risk of TB three times greater than high family earnings (OR = 3.00; p = 0.002). A past of contact, poorly ventilated homes, high humidity, hothouse temperature, population density, kitchen gas, and low family earnings, are risk factors for TB in Wonosobo, Central Java.

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Demographic, socio-economic and behavior as risk factors of tuberculosis in Malaysia: a systematic review of the literature

Cited by 16 publications

References 36 publications

Genetic variation of ABCB1 (rs1128503, rs1045642) and CYP2E1 rs3813867 with the duration of tuberculosis therapy: a pilot study among tuberculosis patients in Indonesia

Genetic variation of ABCB1 (rs1128503, rs1045642) and CYP2E1 rs3813867 with the duration of tuberculosis therapy: a pilot study among tuberculosis patients in Indonesia

Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia

Socio-Economic and Environmental Risk Factors of Tuberculosis in Wonosobo, Central Java, Indonesia

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