Epidemiological and spatial factors for tuberculosis: a matched case-control study in Nagata, Japan

Murakami, Ryo; Matsuo, Nobutake; Ueda, Kimiyo; Nakazawa, Minato

doi:10.5588/ijtld.18.0369

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…In this study, the population (kernel) density per area (m 2 ) was positively associated with the risk of TB, thus indicating that a one-unit increase per area would greatly increase the risk of TB, as shown by the spatial analysis of TB cases conducted by Murakami et al [85] and Alves et al [86] in Japan and Brazil, respectively. Six high-risk clusters of high-density regions were found through the kernel density estimation, whereas the rest of the regions presented low-risk areas.…”

Section: Discussionsupporting

confidence: 61%

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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Section: Discussionsupporting

confidence: 61%

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

et al. 2021

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“…Since NTC receives the TB referral cases from all over the country, the cases represent the general population. The controls selected were relatives of non-TB patients matched with on average of 15-year band, and residing to the same local unit within the district with the cases [ 7 , 12 ]. The respondents were not matched with sex as it may be the potential risk factors and may confound other variables.…”

Section: Methodsmentioning

confidence: 99%

“…As per the Global TB report 2019, as many as 7000 people die annually from TB in Nepal. Numerous studies identified sociodemographic, environmental, and behavioral risk factors such as age, sex, occupations, indoor air pollutions, smoking, and alcohol consumption [ [6] , [7] , [8] , [9] , [10] , [11] , [12] ] as being associated with the development of TB in humans.…”

Section: Introductionmentioning

confidence: 99%

Risk factors of tuberculosis in human and its association with cattle TB in Nepal: A one health approach

Gompo¹,

Shrestha

Ranjit

et al. 2020

One Health

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Tuberculosis (TB) in humans is primarily caused by Mycobacterium tuberculosis (M. tuberculosis), with millions of infections and hundreds of thousands of deaths worldwide. It creates a substantial economic burden on the community. Unlike M. tuberculosis, Mycobacterium bovis infects cattle and causes bovine TB, also known as zoonotic TB. People can contract zoonotic TB after consumption of unpasteurized dairy products, handling the sick animals, and via occupational exposures. The association between the zoonotic TB in humans and cattle is not well known in Nepal. The study examined the associated risk factors, including exposure to infected cattle, that contribute to TB's development in human beings in Nepal. The study consists of human and animal subjects. Firstly, a retrospective matched case-control study was conducted at the National Tuberculosis Center (NTC), Bhaktapur, Nepal. A total of 290 people (equal numbers of TB cases and control subjects) were interviewed to obtain information on socio-demographic, behavioral, and occupational risks, including the history of cattle related exposures. Secondly, a cross-sectional study was performed among the cattle owned by the TB-confirmed patients. Comparative tuberculin skin test, rapid antibody test, and ELISA were used in parallel to detect M. bovis infection in cattle. The risk factors for the development of TB in humans were smokers (OR = 4.6, 95% CI: 2.1–10.0, p < 0.001), previous history of TB (OR = 7.9, 95% CI: 3.0–20.6, p < 0.001) and history of cattle exposures (OR = 3.9, 95% CI: 2.1–7.4, p = 0.001). Out of 123 cattle sampled, 12 cattle (9.76%, 95% CI: 5.37–16.76, p < 0.0001) were positive by the tuberculin test, 46 (37.4%, 95% CI: 28.97–46.62, p = 0.007) were tested positive by the rapid test, and 7 (5.7%, 95% CI: 2.52–11.80, p < 0.0001) by ELISA test. The inter-test agreement between the tuberculin and ELISA was very strong (κ = 0.72, 95% CI: 0.48–0.95, p < 0.01). This study indicates that exposure to infected cattle and socio-demographic risk factors can contribute to the development of TB in human beings.

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“…The condition of settlements in each village/kelurahan is different, there are several villages that have a large distance from one house to another, so that the air flow to each house is better than villages/ kelurahan which have narrow and densely populated houses, where sometimes the lighting for passing through the window is often closed by other houses which causes suboptimal humidity and temperature in the house (Murakami et al, 2019).…”

Section: Contextual Effect Of Village On the Risk Of Tb In Childrenmentioning

confidence: 99%

Contextual Effect of Village on the Incidence of Tuberculosis in Children in Surakarta, Central Java: A Multilevel Analysis Evidence

Saputri

Dewi

Murti

2019

J EPIDEMIOL PUBLIC HEALT

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Background: Indonesia is in the second place with the highest burden of tuberculosis (TB) in the world. Childhood is a period of rapid growth that requires efforts to maintain children's health. At the time the child still has a low immune system making it vulnerable to a disease including TB. The problem of TB in children requires better attention in child TB control programs. This study aimed to analyze the contextual influence of villages on the incidence of TB in children. Subjects and Method: This was a case-control study conducted in 25 villages in Surakarta, Central Java, from August to September 2019. A sample of 200 children aged 0 to 18 years was selected by fixed disease sampling. The dependent variable was the incidence of TB in children. The independent variables were birth weight, BCG immunization, exclusive breastfeeding, nutritional status, parental income, home sanitation, exposure to cigarette smoke, family history of TB, and contextual effect of village. Data on TB were obtained from medical record in Surakarta community health center. The other data was collected by questionnaire. The data was analyzed by multilevel multiple logistic regression with Stata 13. Results: The risk of TB in children increased poor house sanitation

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Epidemiological and spatial factors for tuberculosis: a matched case-control study in Nagata, Japan

Cited by 6 publications

References 12 publications

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

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

Risk factors of tuberculosis in human and its association with cattle TB in Nepal: A one health approach

Contextual Effect of Village on the Incidence of Tuberculosis in Children in Surakarta, Central Java: A Multilevel Analysis Evidence

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