Purpose: The drug resistance pattern in tuberculosis (TB) is still under investigated. We analyzed the clinical data from the patients with smear positive TB and applied the model to predict the patients with smear-po...Purpose: The drug resistance pattern in tuberculosis (TB) is still under investigated. We analyzed the clinical data from the patients with smear positive TB and applied the model to predict the patients with smear-positive TB. Materials and Methods: Medical records information of 6977 cases was included from 11,950 inpatients from January 2009 to November 2013. The cases data were divided into a training set, test set and prediction set. Logistic regression analysis was applied to the training set data to establish a prediction classification model, the effect of which was then evaluated using the test set by receiver operating characteristic (ROC) analysis. The model was then applied to the prediction set to identify incidence of snMDR-TB. Results: Sixteen factors which correlate with MDR-TB-including frequency of hospitalization, province of origin, anti-TB drugs, and complications, were identified from the comparison between SP-TB and spMDR-TB. The area under the ROC curve (AUC) of the prediction model was 0.752 (sensitivity = 61.3%, specificity = 83.3%). The percentage of all inpatients with snMDR-TB (snMDR-TB/Total) was 28.7% ± 0.02%, while that of all SN-PTB with snMDR-TB (snMDR-TB/SN-PTB) was 26.5% ± 0.03%. The ratio of snMDR-TB to MDR-TB (snMDR-TB/MDR-TB) was 2.09 ± 0.33. Conclusion: snMDR-TB as an important source of MDR-TB is a significant hidden problem for MDR-TB control and can be identified by the prediction model. A kind of vicious circle with a certain delay effect exists between snMDR-TB and MDR-TB. To better control MDR-TB, it is necessary to pay greater attention to snMDR-TB, conduct further research and develop targeted therapeutic strategies.展开更多
In this study, we investigate the effects of missing data when estimating HIV/TB co-infection. We revisit the concept of missing data and examine three available approaches for dealing with missingness. The main objec...In this study, we investigate the effects of missing data when estimating HIV/TB co-infection. We revisit the concept of missing data and examine three available approaches for dealing with missingness. The main objective is to identify the best method for correcting missing data in TB/HIV Co-infection setting. We employ both empirical data analysis and extensive simulation study to examine the effects of missing data, the accuracy, sensitivity, specificity and train and test error for different approaches. The novelty of this work hinges on the use of modern statistical learning algorithm when treating missingness. In the empirical analysis, both HIV data and TB-HIV co-infection data imputations were performed, and the missing values were imputed using different approaches. In the simulation study, sets of 0% (Complete case), 10%, 30%, 50% and 80% of the data were drawn randomly and replaced with missing values. Results show complete cases only had a co-infection rate (95% Confidence Interval band) of 29% (25%, 33%), weighted method 27% (23%, 31%), likelihood-based approach 26% (24%, 28%) and multiple imputation approach 21% (20%, 22%). In conclusion, MI remains the best approach for dealing with missing data and failure to apply it, results to overestimation of HIV/TB co-infection rate by 8%.展开更多
<span style="font-family:Verdana;"><b></span><b><span style="font-family:Verdana;">Introduction:</span></b><span style="font-family:Verdana;"&...<span style="font-family:Verdana;"><b></span><b><span style="font-family:Verdana;">Introduction:</span></b><span style="font-family:Verdana;"></b></span><b> </b><span style="font-family:Verdana;">TB Surveillance is a critical component of the global TB response. Comprehensive, accurate and timely information on TB is crucial for an effective TB control program hence the need for a robust tuberculosis surveillance system in all countries that contribute to the global burden of TB including Nigeria. Against this backdrop, an intervention was set in motion to triangulate the</span><span style="font-family:Verdana;"> information from the health data reporting systems towards improving the overall surveillance system for TB in the country. </span><span style="font-family:Verdana;"><b></span><b><span style="font-family:Verdana;">Objectives:</span></b><span style="font-family:Verdana;"> </b></span><span style="font-family:Verdana;"> This article highlights the best practices, lessons learnt and challenges associated with the implementation of TB surveillance in Nigeria. In resource-limited</span><span style="font-family:Verdana;"> settings such as Nigeria where health systems including health information management are sub-optimal, there is a heavy reliance on national and sub-national TB surveillance systems. TB data is mainly reported through the National Tuberculosis Control, however the integrated disease surveillance and response (IDSR) system also provides a platform for TB data collation through the LGA and State disease surveillance and notification officers. </b></span><b><span style="font-family:Verdana;">Conclusion:</span></b><span style="font-family:Verdana;"></b> Implementing TB surveillance in Nigeria brought to fore the need for a wider engagement of all health facilities in TB control. As a dividend of the TB surveillance intervention, quality of care was improved in the private health sector through effective linkages to the commodity management system of the NTP and the national treatment guidelines. Strengthening community health surveillance system was identified as a critical element of Tuberculosis control. Also, the efficiency birthed by the integration of TB surveillance into the IDSR structure opened up other potential opportunities such as a unified capacity building of community informants on all notifiable diseases and the integration of reporting and risk communication for all health issues at the community level.展开更多
Objective: To identify the patterns of tuberculosis (TB) notification rates in Phnom Penh and examine their relationships with the population density, socioeconomic, residential and occupational characteristics. Metho...Objective: To identify the patterns of tuberculosis (TB) notification rates in Phnom Penh and examine their relationships with the population density, socioeconomic, residential and occupational characteristics. Methods: The numbers of total TB and smear-positive pulmonary TB cases reported between January 1, 2010 and December 31, 2012 in Phnom Penh were counted for 76 communes in Cambodia according to TB registration records filed under the national TB programme. Population, socioeconomic, residential and occupational characteristics for the communes were obtained from the 2008 General Population Census of Cambodia. The following indicators were developed for individual communes: smear-positive pulmonary TB notification rate (SPTB-NR) (per 100,000 population, in 36 months), population density (per km2), socioeconomic indicators, residential characteristics and occupational characteristics. Geographic patterns of these indicators and characteristics were analysed using ArcGIS. Associations between SPTB-NR and characteristics were analysed. Results: A total of 4102 TB cases were reported in 36 months, including 2046 SPTB cases. SPTB-NR for Phnom Penh was 135 cases per 100,000;median SPTB-NR by commune was 100. SPTB-NR was higher in outlying areas than in city centre communes;population density was high in the centre and low in the outlying areas. SPTB-NR was associated with larger percentage of household members per room (PR: 2.81, 95%CI: 2.68 - 2.93), percentage of population resident in the same commune Conclusions: The SPTB-NR in Phnom Penh did not follow the pattern of population density. Socioeconomic, residential and occupational characteristics by commune were associated with SPTB-NR. Development of prevention and control programmes by considering commune level characteristics is encouraged.展开更多
文摘Purpose: The drug resistance pattern in tuberculosis (TB) is still under investigated. We analyzed the clinical data from the patients with smear positive TB and applied the model to predict the patients with smear-positive TB. Materials and Methods: Medical records information of 6977 cases was included from 11,950 inpatients from January 2009 to November 2013. The cases data were divided into a training set, test set and prediction set. Logistic regression analysis was applied to the training set data to establish a prediction classification model, the effect of which was then evaluated using the test set by receiver operating characteristic (ROC) analysis. The model was then applied to the prediction set to identify incidence of snMDR-TB. Results: Sixteen factors which correlate with MDR-TB-including frequency of hospitalization, province of origin, anti-TB drugs, and complications, were identified from the comparison between SP-TB and spMDR-TB. The area under the ROC curve (AUC) of the prediction model was 0.752 (sensitivity = 61.3%, specificity = 83.3%). The percentage of all inpatients with snMDR-TB (snMDR-TB/Total) was 28.7% ± 0.02%, while that of all SN-PTB with snMDR-TB (snMDR-TB/SN-PTB) was 26.5% ± 0.03%. The ratio of snMDR-TB to MDR-TB (snMDR-TB/MDR-TB) was 2.09 ± 0.33. Conclusion: snMDR-TB as an important source of MDR-TB is a significant hidden problem for MDR-TB control and can be identified by the prediction model. A kind of vicious circle with a certain delay effect exists between snMDR-TB and MDR-TB. To better control MDR-TB, it is necessary to pay greater attention to snMDR-TB, conduct further research and develop targeted therapeutic strategies.
文摘In this study, we investigate the effects of missing data when estimating HIV/TB co-infection. We revisit the concept of missing data and examine three available approaches for dealing with missingness. The main objective is to identify the best method for correcting missing data in TB/HIV Co-infection setting. We employ both empirical data analysis and extensive simulation study to examine the effects of missing data, the accuracy, sensitivity, specificity and train and test error for different approaches. The novelty of this work hinges on the use of modern statistical learning algorithm when treating missingness. In the empirical analysis, both HIV data and TB-HIV co-infection data imputations were performed, and the missing values were imputed using different approaches. In the simulation study, sets of 0% (Complete case), 10%, 30%, 50% and 80% of the data were drawn randomly and replaced with missing values. Results show complete cases only had a co-infection rate (95% Confidence Interval band) of 29% (25%, 33%), weighted method 27% (23%, 31%), likelihood-based approach 26% (24%, 28%) and multiple imputation approach 21% (20%, 22%). In conclusion, MI remains the best approach for dealing with missing data and failure to apply it, results to overestimation of HIV/TB co-infection rate by 8%.
文摘<span style="font-family:Verdana;"><b></span><b><span style="font-family:Verdana;">Introduction:</span></b><span style="font-family:Verdana;"></b></span><b> </b><span style="font-family:Verdana;">TB Surveillance is a critical component of the global TB response. Comprehensive, accurate and timely information on TB is crucial for an effective TB control program hence the need for a robust tuberculosis surveillance system in all countries that contribute to the global burden of TB including Nigeria. Against this backdrop, an intervention was set in motion to triangulate the</span><span style="font-family:Verdana;"> information from the health data reporting systems towards improving the overall surveillance system for TB in the country. </span><span style="font-family:Verdana;"><b></span><b><span style="font-family:Verdana;">Objectives:</span></b><span style="font-family:Verdana;"> </b></span><span style="font-family:Verdana;"> This article highlights the best practices, lessons learnt and challenges associated with the implementation of TB surveillance in Nigeria. In resource-limited</span><span style="font-family:Verdana;"> settings such as Nigeria where health systems including health information management are sub-optimal, there is a heavy reliance on national and sub-national TB surveillance systems. TB data is mainly reported through the National Tuberculosis Control, however the integrated disease surveillance and response (IDSR) system also provides a platform for TB data collation through the LGA and State disease surveillance and notification officers. </b></span><b><span style="font-family:Verdana;">Conclusion:</span></b><span style="font-family:Verdana;"></b> Implementing TB surveillance in Nigeria brought to fore the need for a wider engagement of all health facilities in TB control. As a dividend of the TB surveillance intervention, quality of care was improved in the private health sector through effective linkages to the commodity management system of the NTP and the national treatment guidelines. Strengthening community health surveillance system was identified as a critical element of Tuberculosis control. Also, the efficiency birthed by the integration of TB surveillance into the IDSR structure opened up other potential opportunities such as a unified capacity building of community informants on all notifiable diseases and the integration of reporting and risk communication for all health issues at the community level.
文摘Objective: To identify the patterns of tuberculosis (TB) notification rates in Phnom Penh and examine their relationships with the population density, socioeconomic, residential and occupational characteristics. Methods: The numbers of total TB and smear-positive pulmonary TB cases reported between January 1, 2010 and December 31, 2012 in Phnom Penh were counted for 76 communes in Cambodia according to TB registration records filed under the national TB programme. Population, socioeconomic, residential and occupational characteristics for the communes were obtained from the 2008 General Population Census of Cambodia. The following indicators were developed for individual communes: smear-positive pulmonary TB notification rate (SPTB-NR) (per 100,000 population, in 36 months), population density (per km2), socioeconomic indicators, residential characteristics and occupational characteristics. Geographic patterns of these indicators and characteristics were analysed using ArcGIS. Associations between SPTB-NR and characteristics were analysed. Results: A total of 4102 TB cases were reported in 36 months, including 2046 SPTB cases. SPTB-NR for Phnom Penh was 135 cases per 100,000;median SPTB-NR by commune was 100. SPTB-NR was higher in outlying areas than in city centre communes;population density was high in the centre and low in the outlying areas. SPTB-NR was associated with larger percentage of household members per room (PR: 2.81, 95%CI: 2.68 - 2.93), percentage of population resident in the same commune Conclusions: The SPTB-NR in Phnom Penh did not follow the pattern of population density. Socioeconomic, residential and occupational characteristics by commune were associated with SPTB-NR. Development of prevention and control programmes by considering commune level characteristics is encouraged.
