Incidence Of Pulmonary Tuberculosis Research Articles

Pengaruh sanitasi lingkungan fisik rumah, sosial budaya dan kontak serumah terhadap kejadian Tuberkulosis Paru di Pulau Bengkalis Kabupaten Bengkalis

The degree of public health is influenced by four factors, namely environment, behavior, health services, and heredity. Data from 2019 WHO Global Tuberculosis Report, Indonesia ranks third with the highest tuberculosis cases in the world. The purpose of this study was to analyze the sanitary conditions of the house environment (temperature, humidity, type of floor, ventilation, lighting), socio-culture (trust), and household contact against the incidence of pulmonary tuberculosis on Bengkalis Island. This research is an analytical study with a case-control design. This research was conducted on Bengkalis Island, namely in two sub-districts (Bengkalis District and Bantan District) and in the working area of Puskesmas Bengkalis, Puskesmas Pematang Duku, Puskesmas Selatbaru and Puskesmas Teluk Pambang. The data used are quantitative data sourced from observations, interviews, and questionnaires. The research sample was 80 people consisting of 40 cases and 40 controls. Data analysis was performed by multiple logistic regression tests. The results of the study concluded that the most dominant variable affecting the incidence of pulmonary tuberculosis was temperature with a p-value of 0.008. Confounding variables for tuberculosis incidence were ventilation, floor type, lighting, and confidence. The multivariate model formed is fit/fit for use. Based on the results of the calculation of the equation, it can be concluded that if a person has temperature risk factors, humidity risk factors, ventilation risk factors, floor type risk factors, lighting risk factors, trust risk factors, and household contact risk factors, then he will have a chance of suffering from pulmonary tuberculosis by 94 percent (with a probability value of 0.94).

Long-term exposure to ambient air pollution and greenness in relation to pulmonary tuberculosis in China: A nationwide modelling study

Previous studies have attempted to clarify the relationship between the occurrence of pulmonary tuberculosis (PTB) and exposure to air pollutants. However, evidence from multi-centres, particularly at the national level, is scarce, and no study has examined the modifying effect of greenness on air pollution–TB associations. In this study, we examined the association between long-term exposure to ambient air pollutants (PM10 p.m.2.5, and O3) and monthly PTB or smear-positive pulmonary tuberculosis (SPPTB) incidence to further evaluate whether these associations were affected by greenness in mainland China using a two-stage analytic procedure. PM2.5 was positively associated with both PTB and SPPTB incidence, with relative risk (RR) of 1.12 (95% confidence interval [CI]: 1.03, 1.22) and 1.08 (95% CI: 1.02, 1.10) per 10 μg/m3 increase, respectively. Furthermore, PM10 was positively associated with PTB incidence, with RR of 1.07 (95% CI: 1.01, 1.13). However, O3 was not associated with the monthly incidence of PTB or SPPTB. The normalized difference vegetation index (NDVI) exhibited a modifying effect on the association between PM2.5 exposure and SPPTB incidence in northern areas, with RR of 1.16 (95% CI: 1.03, 1.31) in lower mean annual NDVI areas than in the higher areas (RR = 0.98, 95% CI: 0.87, 1.09). This nationwide analysis indicated that NDVI could reduce the effect of air pollutants on TB incidence particularly in the northern areas. Long-term exposure to particulate matter (PM) may increase the occurrence of PTB or SPPTB in China, and further studies involving larger numbers of SPPTB cases are required to confirm the effects of PM exposure on SPPTB incidence in the future.

PENGARUH KAMARISASI, PENCAHAYAAN, SUHU DAN KELEMBABAN TERHADAP KEJADIAN TUBERKULOSIS PARU DI KABUPATEN MAROS, SULAWESI SELATAN

Tuberculosis (TB) is an infectious infectious disease caused by Mycobacterium tuberculosis and most often manifests in the lungs called pulmonary tuberculosis. Mycobacterium is transmitted via airborne droplets, so that a person with pulmonary tuberculosis is a source of transmission of pulmonary TB to the surrounding population. The purpose of this study was to determine the effect of the density of room conditions, lighting, temperature and humidity on the incidence of pulmonary tuberculosis in the working area of the Turikale Health Center, Turikale District, Maros Regency. This study uses a descriptive design that describes the factors that influence the incidence of pulmonary tuberculosis. The number of samples of 33 houses with positive pulmonary tuberculosis was determined by purposive sampling. The results showed that there was an influence between the density of room conditions and lighting on the incidence of pulmonary tuberculosis. There is no effect between temperature and humidity on the incidence of pulmonary tuberculosis. Suggestions for families and the home community should have enough space or rooms, especially for pulmonary tuberculosis patients to make a special room to reduce the risk of transmission to other residents of the house.

Faktor-Faktor Yang Mempengaruhi Kejadian TB Paru Di Wilayah Kerja Puskesmas Pamenang Tahun 2021

Tuberculosis is still a public health problem that is a challenge. Global Tuberculosis is an infectious disease caused by Mycobacterium Tuberculosis, Tuberculosis remains the top 10 cause of death in the world and Tuberculosis deaths globally are estimated at 1.3 million patients. Tuberculosis (TB) is still a health problem in the world, although control efforts with the Directly Observed Treatment Shortcourse (DOTS) strategy have been implemented in many countries since 1995.This type of research is a quantitative study with a cross sectional approach, this research was conducted in the work area of ​​the Pamenang Health Center in 2021. The population in this study was 35 people who were registered in the pulmonary TB registration book, and as a control of all neighbors with pulmonary TB as many as 35 people. The results of this study indicate that it is known from 70 houses that were observed to have good knowledge 47 (67.2%) sufficient knowledge 15 (21.4%) less 8 (11.4%), which have low economic status 64 (91.4%), high economic status 6 (8.6%) of the 70 houses were observed, 26 (37.1%) houses had inappropriate humidity and 44 (62.9%) houses had suitable humidity. There is no relationship between knowledge, economic status with the incidence of pulmonary TB and there is a relationship between humidity and the incidence of pulmonary TB in the work area of ​​the Pamenang Health Center in 2021. It is recommended that the Pamenang Health Center provide counseling to the community regarding the relationship between humidity and the incidence of pulmonary TB.

Bridging the Gap in End Tuberculosis Targets in the Elderly Population in Eastern China: Observational Study From 2015 to 2020.

BackgroundWith a progressive increase in the aging process, the challenges posed by pulmonary tuberculosis (PTB) are also increasing for the elderly population.ObjectiveThis study aimed to identify the epidemiological distribution of PTB among the elderly, forecast the achievement of the World Health Organization’s 2025 goal in this specific group, and predict further advancement of PTB in the eastern area of China.MethodsAll notified active PTB cases aged ≥65 years from Zhejiang Province were screened and analyzed. The general epidemiological characteristics were depicted and presented using the ArcGIS software. Further prediction of PTB was performed using R and SPSS software programs.ResultsAltogether 41,431 cases aged ≥65 years were identified by the surveillance system from 2015 to 2020. After excluding extrapulmonary TB cases, we identified 39,832 PTB cases, including laboratory-confirmed (23,664, 59.41%) and clinically diagnosed (16,168, 40.59%) PTB. The notified PTB incidence indicated an evident downward trend with a reduction of 30%; however, the incidence of bacteriologically positive cases was steady at approximately 60/100,000. Based on the geographical distribution, Quzhou and Jinhua Cities had a higher PTB incidence among the elderly. The delay in PTB diagnosis was identified, and a significantly prolonged treatment course was observed in the elderly. Moreover, a 50% reduction of PTB incidence by the middle of 2024 was predicted using a linear regression model. It was found that using the exponential smoothing model would be better to predict the PTB trend in the elderly than a seasonal autoregressive integrated moving average model.ConclusionsMore comprehensive and effective interventions such as active PTB screening combined with physical checkup and succinct health education should be implemented and strengthened in the elderly. A more systematic assessment of the PTB epidemic trend in the elderly population should be considered to incorporate more predictive factors.

Age–period–cohort analysis of pulmonary tuberculosis reported incidence, China, 2006–2020

BackgroundTuberculosis (TB) poses a severe public health challenge in China and worldwide. This study evaluated the effects of age, period, and birth cohort on reported incidence trends of TB based on population and refined the characteristics of high-risk groups.MethodsAggregate data that reported pulmonary tuberculosis (PTB) cases from China Tuberculosis Management Information System (TBIMS) from 2006 to 2020 were used to analyze effect coefficients through the age–period–cohort (APC) model based on intrinsic estimator (IE) method, and converted them into relative risk (RR) to estimate trends.ResultsA total of 14.82 million cases of PTB were reported in China from 2006 to 2020, showing a continuous downward trend. The reporting rate increased with age by age group, with 70–74 years old being 2–3 times higher than that in 20–24 years old. APC analysis model showed that age effects were bimodal in 20–24 years old [RR = 2.29, 95% confidence interval (CI): 1.73–3.03] and 70–74 years old (RR = 1.95, 95% CI: 1.67–2.27), and lower than the overall average in the groups under 15 years old. Stratified results showed that the risk was higher for women under age 40 than men and higher for men over 40. The risk was higher in urban than in rural areas under 30 years old and slightly higher in rural than in urban between 30 and 64 years old. The risk for 15–34 years old was significantly higher in the east than in other regions. The period effects showed a decreasing trend, and the risk was higher in rural in recent years. Except for cohorts born in 1961–1965 and 2001–2005, where the RR increased, the later the cohort was born, the lower the risk. The cohort 1926–1930 in eastern had the highest risk (RR = 3.49, 95% CI: 2.44–4.98).ConclusionsThe reported incidence of PTB continued to decline in China from 2006 to 2020. The young (20–24 years old) and the elderly (70–74 years old) were equally at high risk. There were differences in the age, period and cohort effects on PTB incidence among gender, urban–rural and regions. Our findings better reflected the characteristics of high-risk populations, thus contributing to the development of timely and effective intervention strategies, and providing clues for etiological research.Graphical abstract

The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis.

With the increasing incidence and mortality of pulmonary tuberculosis, in addition to tough and controversial disease management, time-wasting and resource-limited conventional approaches to the diagnosis and differential diagnosis of tuberculosis are still awkward issues, especially in countries with high tuberculosis burden and backwardness. In the meantime, the climbing proportion of drug-resistant tuberculosis poses a significant hazard to public health. Thus, auxiliary diagnostic tools with higher efficiency and accuracy are urgently required. Artificial intelligence (AI), which is not new but has recently grown in popularity, provides researchers with opportunities and technical underpinnings to develop novel, precise, rapid, and automated implements for pulmonary tuberculosis care, including but not limited to tuberculosis detection. In this review, we aimed to introduce representative AI methods, focusing on deep learning and radiomics, followed by definite descriptions of the state-of-the-art AI models developed using medical images and genetic data to detect pulmonary tuberculosis, distinguish the infection from other pulmonary diseases, and identify drug resistance of tuberculosis, with the purpose of assisting physicians in deciding the appropriate therapeutic schedule in the early stage of the disease. We also enumerated the challenges in maximizing the impact of AI in this field such as generalization and clinical utility of the deep learning models.

Incidence of pulmonary tuberculosis under the regular COVID-19 epidemic prevention and control in China

BackgroundThe COVID-19 pandemic has driven public health intervention strategies, including keeping social distance, wearing masks in crowded places, and having good health habits, to prevent the transmission of the novel coronavirus (SARS-CoV-2). However, it is unknown whether the use of these intervention strategies influences morbidity in other human infectious diseases, such as tuberculosis.MethodsIn this study, three prediction models were constructed to compare variations in PTB incidences after January 2020 without or with intervention includes strict and regular interventions, when the COVID-19 outbreak began in China. The non-interventional model was developed with an autoregressive integrated moving average (ARIMA) model that was trained with the monthly incidence of PTB in China from January 2005 to December 2019. The interventional model was established using an ARIMA model with a continuing intervention function that was trained with the monthly PTB incidence in China from January 2020 to December 2020.ResultsStarting with the assumption that no COVID-19 outbreak had occurred in China, PTB incidence was predicted, and then the actual incidence was compared with the predicted incidence. A remarkable overall decline in PTB incidence from January 2020 to December 2020 was observed, which was likely due to the potential influence of intervention policies for COVID-19. If the same intervention strategy is applied for the next 2 years, the monthly PTB incidence would reduce on average by about 1.03 per 100,000 people each month compared with the incidence predicted by the non-interventional model. The annual incidence estimated 59.15 under regular intervention per 100,000 in 2021, and the value would decline to 50.65 with strict interventions.ConclusionsOur models quantified the potential knock-on effect on PTB incidence of the intervention strategy used to control the transmission of COVID-19 in China. Combined with the feasibility of the strategies, these results suggested that continuous regular interventions would play important roles in the future prevention and control of PTB.

Effects and Interaction of Meteorological Factors on Pulmonary Tuberculosis in Urumqi, China, 2013-2019.

BackgroundMost existing studies have only investigated the delayed effect of meteorological factors on pulmonary tuberculosis (PTB). However, the effect of extreme climate and the interaction between meteorological factors on PTB has been rarely investigated.MethodsNewly diagonsed PTB cases and meteorological factors in Urumqi in each week between 2013 and 2019 were collected. The lag-exposure-response relationship between meteorological factors and PTB was analyzed using the distributed lag non-linear model (DLNM). The generalized additive model (GAM) was used to visualize the interaction between meteorological factors. Stratified analysis was used to explore the impact of meteorological factors on PTB in different stratification and RERI, AP and SI were used to quantitatively evaluate the interaction between meteorological factors.ResultsA total of 16,793 newly diagnosed PTB cases were documented in Urumqi, China from 2013 to 2019. The median (interquartile range) temperature, relative humidity, wind speed, and PTB cases were measured as 11.3°C (−5.0–20.5), 57.7% (50.7–64.2), 4.1m/s (3.4–4.7), and 47 (37–56), respectively. The effects of temperature, relative humidity and wind speed on PTB were non-linear, which were found with the “N”-shaped, “L”-shaped, “N”-shaped distribution, respectively. With the median meteorological factor as a reference, extreme low temperature was found to have a protective effect on PTB. However, extreme high temperature, extreme high relative humidity, and extreme high wind speed were found to increase the risk of PTB and peaked at 31.8°C, 83.2%, and 7.6 m/s respectively. According to the existing monitoring data, no obvious interaction between meteorological factors was found, but low temperature and low humidity (RR = 1.149, 95%CI: 1.003–1.315), low temperature and low wind speed (RR = 1.273, 95%CI: 1.146–1.415) were more likely to cause the high incidence of PTB.ConclusionTemperature, relative humidity and wind speed were found to play vital roles in PTB incidence with delayed and non-linear effects. Extreme high temperature, extreme high relative humidity, and extreme high wind speed could increase the risk of PTB. Moreover, low temperature and low humidity, low temperature and low wind speed may increase the incidence of PTB.

A systematic review and meta-analysis on the association between ambient air pollution and pulmonary tuberculosis

There is inconclusive evidence on the association between ambient air pollution and pulmonary tuberculosis (PTB) incidence, tuberculosis-related hospital admission and mortality. This review aimed to assess the extent to which selected air pollutants are associated to PTB incidence, hospital admissions and mortality. This was a systematic review of studies published in English from January 1st, 1946, through May 31st, 2022, that quantitatively assessed the association between PM2.5, PM10, NO2, SO2, CO, O3 and the incidence of, hospital admission or death from PTB. Medline, Embase, Scopus and The Cochrane Library were searched. Extracted data from eligible studies were analysed using STATA software. Random-effect meta-analysis was used to derive pooled adjusted risk and odds ratios. A total of 24 studies (10 time-series, 5 ecologic, 5 cohort, 2 case–control, 1 case cross-over, 1 cross-sectional) mainly from Asian countries were eligible and involved a total of 437,255 tuberculosis cases. For every 10 μg/m3 increment in air pollutant concentration, there was a significant association between exposure to PM2.5 (pooled aRR = 1.12, 95% CI: 1.06–1.19, p < 0.001, N = 6); PM10 (pooled aRR = 1.06, 95% CI: 1.01–1.12, p = 0.022, N = 8); SO2 (pooled aRR = 1.08, 95% CI: 1.04–1.12, p < 0.001, N = 9); and the incidence of PTB. There was no association between exposure to CO (pooled aRR = 1.04, 95% CI: 0.98–1.11, p = 0.211, N = 4); NO2 (pooled aRR = 1.08, 95% CI: 0.99–1.17, p = 0.057, N = 7); O3 (pooled aRR = 1.00, 95% CI: 0.99–1.02, p = 0.910, N = 6) and the incidence of PTB. There was no association between the investigated air pollutants and mortality or hospital admissions due to PTB. Overall quality of evidence was graded as low (GRADE approach). Exposure to PM2.5, PM10 and SO2 air pollutants was found to be associated with an increased incidence of PTB, while exposure to CO, NO2 and O3 was not. There was no observed association between exposure to these air pollutants and hospital admission or mortality due to PTB. The quality of the evidence generated, however, remains low. Addressing the tuberculosis epidemic by 2030 as per the 4th Sustainable Development Goal may require a more rigorous exploration of this association.

Environmental Quality Factors with The Incidence of Pulmonary Tuberculosis: A Literature Review

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis that can attack the lungs and other organs. Tuberculosis (TB) is still a global priority health problem because Indonesia is one of the countries with the highest number of TB cases in the world. It is estimated that around 845.000 Indonesians in 2019 suffer from tuberculosis. Around 92,700 people die from TB in Indonesia per year. The purpose of this study was to determine the environmental risk factors with the incidence of pulmonary tuberculosis (TB). The method in this study uses the literature review method by selecting data sources according to the desired topic and criteria. The data base used in the search for sources is Google Scholar, ejournal.undip.ac.id, and Portal Garuda, with the keywords pulmonary tuberculosis, TB incidence factors, tuberculosis environmental factors, and the physical condition of the TB patient's house. The results of studies from 10 scientific journals on environmental risk factors for TB disease show that ventilation area, occupancy density, lighting intensity, humidity and room temperature are risk factors for TB incidence.

Application of the ARIMA Model in Forecasting the Incidence of Tuberculosis in Anhui During COVID-19 Pandemic from 2021 to 2022.

ObjectiveForecasting the seasonality and trend of pulmonary tuberculosis is important for the rational allocation of health resources. In this study, we predict the incidence of pulmonary tuberculosis by establishing the autoregressive integrated moving average (ARIMA) model and providing support for pulmonary tuberculosis prevention and control during COVID-19 pandemic.MethodsRegistered tuberculosis(TB) cases from January 2013 to December 2020 in Anhui province were analysed using traditional descriptive epidemiological methods. Then we used the monthly incidence rate of TB from January 2013 through June 2020 to construct ARIMA model, and used the incidence rate from July 2020 to December 2020 to evaluate the forecasting accuracy. Ljung Box test, Akaike's information criterion(AICc), Bayesian information criterion(BIC) and Realtive error were used to evaluate the model fitting and forecasting effect, Finally, the optimal model was used to forecast the expected monthly incidence of tuberculosis for 2021 and 2022 to learn about the incidence trend.ResultsA total of 255,656 TB cases were registered. The reported rate of tuberculosis was highest in 2013 and lowest in 2020. The peak incidence was in March, Tongling (71.97/100,000), Chizhou (59.93/100,000), and Huainan (58.36/100,000) had the highest number of cases. The ratio of male to female incidence was 2.59:1, with the largest proportion of people being between 66 and 75 years old. The main occupation of patients was farmer. ARIMA (0, 1, 1) (0, 1, 1)12 model was the optimal model to forecast the incidence trend of TB.ConclusionTongling, Chizhou, and Huainan should strengthen measures for TB. In particular, the government should pay more attention on elderly people to prevent tuberculosis infections. The rate of TB patient registration and reporting has decreased under the pandemic of COVID-19. The ARIMA model can be a useful tool for predicting future TB cases.

Prevalence of pulmonary tuberculosis in Al-Najaf governorate, Iraq

Background: pulmonary tuberculosis (PTB) is one of the most global dangerous infectious diseases worldwide, particularly in developing countries. Mycobacterium tuberculosis (Mtb) is the most infectious agent that caused PTB transferred by air droplets from one person to other. In the present epidemiological study, we aimed to record the total numbers and percentages of patients infected with PTB caused by Mtb during 12 months in AL-Najaf Governorate, Iraq. Methods: This study has been done in 2019 from January to the end of December in the north and south region of AL-Najaf Governorate, Iraq. Sputum of PTB-patients has been stained by Ziehl–Neelsen to Mtb diagnosis according to World Health Organization (WHO) recommendation. Results: We recorded 174 PTB-patients in AL-Najaf Governorate 2019; 89 male and 85 female; 101 and 73 in the north and south sections respectively. The age group 16-24 was the highest infected. Quarter two recorded the highest numbers of PTB-patients; 33 (60%) in the north section and 22 (40%) in the south section. Conclusion: There was low PTB incidence in AL-Najaf Governorate 2019 but the Youngers were the most susceptible to infection. Infection in the north section was higher than in the south section and there were no high differences between genders. Seasons have no high effect on the number of infections.

High incidence of pulmonary tuberculosis in an indigenous Saharia tribe in Madhya Pradesh, central India-A prospective cohort study.

Lack of TB incidence data is an important evidence gap among Saharia-a high TB burden indigenous community in Madhya Pradesh, central India. The present study was undertaken to calculate the incidence of pulmonary tuberculosis in this tribe. To calculate the incidence of pulmonary tuberculosis (PTB) among the Saharia tribal population in Madhya Pradesh, central India. A prospective cohort study was conducted among the Saharia tribal population (aged 15 years and above) of Shivpuri district in Madhya Pradesh state in central India. A total of 9,756 individuals were screened for TB in the baseline TB prevalence survey during 2012-13. All available household members were screened for symptoms suggestive of pulmonary TB (presumptive TB). Two sputum specimens were collected from persons having symptoms suggestive of TB and examined by smear and culture tests. A cohort of all non-TB individuals in the baseline survey was followed-up for one year and re-screened for the sysmptoms of PTB in the year 2014-15. Based on the data collected, the incidence of TB over one year period was estimated per 100,000 population. A total of 9756 and 9044 individuals (≥ 15 years) were screened for symptoms suggestive of PTB during baseline and endline surveys respectively. The sputum specimens of presumptive TB cases were collected and examined by smear and culture tests. The overall incidence of bacteriologically positive (smear and/or culture) PTB over one year period was 1504 (95% Confidence Interval (CI): 1273-1776) per 100,000 in the study population, the incidence of smear-positive PTB was 1106 (95% CI: 910-1343), and the incidence of culture-positive PTB was 1084 (95% CI: 890-1319) per 100,000 population. The incidence for both smear and culture-positive PTB was 686 (95% CI: 535-878) per 100,000 population in the year 2014-15. The incidence of PTB was 2.8 times higher in males compared to females (2259 vs 807 per 100,000) and was positively correlated with age. The findings of the study, the first of its kind in the Saharia tribal population, indicate a high incidence of TB in this tribal community thereby highlighting the urgent need for focused and intensified efforts to achieve the goal of TB elimination in the country.

Analysis of Pulmonary Tuberculosis based on Home Sanitation in Sembung Hamlet, Margopatut East Java

Pulmonary Tuberculosis (TB) disease is a contagious disease. TB can spread quickly in environment with sanitation. The study aimed to analyze the effect of house sanitation toward pulmonary TB incidence in Sembung Hamlet, Margopatut Village, Sawahan District, Nganjuk Regency. The study is quantitative analytic research with a cross sectional approach. The study was conducted toward 212 respondents taken using simple random sampling. The results showed that there was an influence of house sanitation including house ventilation, type of floor, sky, the ceiling of the house, lighting, and residential density of houses toward pulmonary TB incidence in Sembung Hamlet, Margopatut Village, Sawahan District, Nganjuk Regency. People who have house with high density occupancy should rearrange the number occupants and can consider seeking other room with more spacious space. People need arrange the house or room so the room can get better air circulation and natural lighting to the house or room.

Association between climate variables and pulmonary tuberculosis incidence in Brunei Darussalam

We investigated the association between climate variables and pulmonary tuberculosis (PTB) incidence in Brunei-Muara district, Brunei Darussalam. Weekly PTB case counts and climate variables from January 2001 to December 2018 were analysed using distributed lag non-linear model framework. After adjusting for long-term trend and seasonality, we observed positive but delayed relationship between PTB incidence and minimum temperature, with significant adjusted relative risk (adj.RR) at 25.1 °C (95th percentile) when compared to the median, from lag 30 onwards (adj.RR = 1.17 [95% Confidence Interval (95% CI): 1.01, 1.36]), suggesting effect of minimum temperature on PTB incidence after 30 weeks. Similar results were observed from a sub-analysis on smear-positive PTB case counts from lag 29 onwards (adj.RR = 1.21 [95% CI: 1.01, 1.45]), along with positive and delayed association with total rainfall at 160.7 mm (95th percentile) when compared to the median, from lag 42 onwards (adj.RR = 1.23 [95% CI: 1.01, 1.49]). Our findings reveal evidence of delayed effects of climate on PTB incidence in Brunei, but with varying degrees of magnitude, direction and timing. Though explainable by environmental and social factors, further studies on the relative contribution of recent (through primary human-to-human transmission) and remote (through reactivation of latent TB) TB infection in equatorial settings is warranted.

Spatial analysis and influencing factors of pulmonary tuberculosis among students in Nanning, during 2012-2018.

BackgroundEconomically underdeveloped areas in western China are hotspots of tuberculosis, especially among students. However, the related spatial and temporal patterns and influencing factors are still unclear and there are few studies to analyze the causes of pulmonary tuberculosis in students from the perspective of space.MethodsWe collected data regarding the reported incidence of pulmonary tuberculosis (PTB) among students at township level in Nanning, from 2012 to 2018. The reported incidence of pulmonary tuberculosis among students in Nanning was analyzed using spatial autocorrelation and spatial scan statistical analysis to depict hotspots of PTB incidence and spatial and temporal clustering. Spatial panel data of the reported incidence rates and influencing factors at district and county levels in Nanning were collected from 2015 to 2018. Then, we analyzed the spatial effects of incidence and influencing factors using the spatial Durbin model to explore the mechanism of each influencing factor in areas with high disease prevalence under spatial effects.ResultsFrom 2012 to 2018, 1609 cases of PTB were reported among students in Nanning, with an average annual reported incidence rate of 14.84/100,000. Through the Joinpoint regression model, We observed a steady trend in the percentage of cases reported each year (P>0.05). There was spatial autocorrelation between the annual reported incidence and the seven-years average reported incidence from 2012 to 2018. The high-incidence area was distributed in the junction of six urban areas and spread to the periphery, with the junction at the center. The population of college students, per capita financial expenditure on health, per capita gross domestic product, and the number of health technicians per 1,000 population were all influencing factors in the reported incidence of PTB among students.ConclusionWe identified spatial clustering of the reported incidence of PTB among students in Nanning, mainly located in the urban center and its surrounding areas. The clustering gradually decreased from the urban center to the surrounding areas. Spatial effects influenced the reported incidence of PTB. The population density of college students, per capita health financial expenditure, gross domestic product (GDP) per capita, and the number of health technicians per 1,000 were all influencing factors in the reported incidence of PTB among students.

Determinants of Risk Factors on the Event of Lung Tb

Pulmonary tuberculosis (TB) is one of the most prevalent infectious diseases in the world. Based on a report by the World Health Organization (WHO, 2012) one third of the world's population, which is around two billion people infected with Mycobacterium Tuberculosis. More than 8 million populations get active TB every year and around 2 million die. More than 90% of TB cases and deaths come from developing countries, one of which is in Indonesia. This research is an observational analytic study with a case control study design that aims to determine the effect of risk factors for pulmonary TB events using a retrospective approach. The results of this study indicate that the most dominant variable TB incidence in high school education cases is down to 45 people (97.8%) and the smallest House Density is 25 people (54.3%) and the most dominant control variable is no comorbidities namely as many as 47 people (97.9%) and the smallest Respondents' House Density fulfilled the requirements of 26 people (56.5%). Factors that significantly influence the incidence of pulmonary tuberculosis are nutritional status (p = 0,000, OR = 31,263), respondent employment (p = 0,000, OR = 21.77), comorbidities (p = 0,000, OR = 0.022), Ventilation (p = 0.001, OR = 4,680) and House Humidity (p = 0,000, OR = 9,625). The results of the study concluded that the variable that most influenced the incidence of pulmonary TB was Nutrition Status (p = 0.002, Exp (B) = 2.334).

Determinants of Risk Factors on the Event of Lung Tb

Pulmonary tuberculosis (TB) is one of the most prevalent infectious diseases in the world. Based on a report by the World Health Organization (WHO, 2012) one third of the world's population, which is around two billion people infected with Mycobacterium Tuberculosis. More than 8 million populations get active TB every year and around 2 million die. More than 90% of TB cases and deaths come from developing countries, one of which is in Indonesia. This research is an observational analytic study with a case control study design that aims to determine the effect of risk factors for pulmonary TB events using a retrospective approach. The results of this study indicate that the most dominant variable TB incidence in high school education cases is down to 45 people (97.8%) and the smallest House Density is 25 people (54.3%) and the most dominant control variable is no comorbidities namely as many as 47 people (97.9%) and the smallest Respondents' House Density fulfilled the requirements of 26 people (56.5%). Factors that significantly influence the incidence of pulmonary tuberculosis are nutritional status (p = 0,000, OR = 31,263), respondent employment (p = 0,000, OR = 21.77), comorbidities (p = 0,000, OR = 0.022), Ventilation (p = 0.001, OR = 4,680) and House Humidity (p = 0,000, OR = 9,625). The results of the study concluded that the variable that most influenced the incidence of pulmonary TB was Nutrition Status (p = 0.002, Exp (B) = 2.334).

How many of persistent coughers have pulmonary tuberculosis? Population-based cohort study in Ethiopia

ObjectiveMany individuals with persistent cough and smear microscopy-negative sputum test for tuberculosis (TB) remain at risk of developing the disease. This study estimates the incidence of pulmonary TB (PTB) among...