Socio-Economic Predictors and Distribution of Tuberculosis Incidence in Beijing, China: A Study Using a Combination of Spatial Statistics and GIS Technology

Development and validation of a structured questionnaire for assessing risk factors of medication non-adherence among pulmonary tuberculosis patients in Indonesia

Background: Medication non-adherence is a significant concern in tuberculosis (TB) treatment, requiring a precise understanding of the associated risk factors. However, there is a lack of appropriate means to assess the risk factors among TB patients in Indonesia, leading to the development and validation of a structured questionnaire for this purpose. Method: This study unfolded in two distinct phases, namely, the first included questionnaire construction through framework development, item generation, item screening, and pretesting (in 50 patients). The second comprised questionnaire validation with 346 participants using confirmatory factor analysis (CFA) and structural equation modeling-partial least squares (SEM-PLS). Additionally, reliability testing was conducted using Cronbach's alpha and composite reliability statistical techniques. Results: In the development phase, 168 items were defined, consisting of sociodemographic characteristics (8 items) and risk factors for medication non-adherence (160 items). Expert evaluation reduced the number of items to 60, which decreased to 22 after performing a pilot study. Subsequent SEM-PLS modeling resulted in the identification of 14 valid items, representing five major risk factors, namely, socioeconomics (4 items), healthcare team (4 items), condition (3 items), therapy (2 items), and patient (1 item). Only condition-related factors were found to influence non-adherence, and all constructs showed good reliability based on Cronbach's alpha (>0.6) and composite reliability (0.7) values. Conclusion: The final 22 items that emerged from this rigorous process indicated a valid and robust questionnaire for assessing risk factors of medication non-adherence among pulmonary tuberculosis patients in Indonesia. The developed questionnaire was positioned to be a valuable tool for healthcare professionals, policymakers, and scientists in creating patient-centered strategies and interventions to address non-adherence.

Latent Tuberculosis Infection Diagnosis Using QuantiFERON-TB Gold Plus Kit Among Correctional Workers: A Cross-Sectional Study in Francisco Beltrão-PR, Brazil

Correctional workers form a high-priority group for tuberculosis control measures because of their high exposure and risk. This cross-sectional study conducted in April and May 2022 included 71 criminal police officers from the State Penitentiary of Francisco Beltrão-PR, Brazil. Their sociodemographic and laboratory data were collected. Latent tuberculosis infection (LTBI) was assessed using a QuantiFERON-TB Gold Plus in-tube test kit. Binary logistic regression was applied to calculate the odds ratios (ORs) and 95% confidence intervals (CI) of the LTBI predictors. The prevalence of LTBI was 22.6% (95% CI, 12.8-32.2%). Factors associated with LTBI were age > 43 years (OR, 0.18; 95% CI, 0.04-0.70; p < 0.014) and the use of medications (OR, 5.13; 95% CI, 1.40-18.87; p < 0.014). The prevalence was close to that estimated worldwide for LTBI in correctional workers, reinforcing the need for occupational health control measures consisting of regular screening and treatment of positive cases of latent infection among correctional workers to reduce the risk of illness and spread of infection in the penitentiary system and community.

A Spatio-temporal Bayesian model to estimate risk and influencing factors related to tuberculosis in Chongqing, China, 2014-2020

BACKGROUND

Tuberculosis (TB) is a serious infectious disease that is one of the leading causes of death worldwide. This study aimed to investigate the spatial and temporal distribution patterns and potential influencing factors of TB incidence risk, and to provide a scientific basis for the prevention and control of TB.

METHODS

We collected reported cases of TB in 38 districts and counties in Chongqing from 2014 to 2020 and data on environment, population characteristics and economic factors during the same period. By constructing a Bayesian spatio-temporal model, we explored the spatio-temporal distribution pattern of TB incidence risk and potential influencing factors, identified key areas and key populations affected by TB, compared the spatio-temporal distribution characteristics of TB in populations with different characteristics, and explored the differences in the influence of various social and environmental factors.

RESULTS

The high-risk areas for TB incidence in Chongqing from 2014 to 2020 were mainly concentrated in southeastern and northeastern regions of Chongqing, and the overall relative risk (RR) of TB showed a decreasing trend during the study period, while RR of TB in main urban area and southeast of Chongqing showed an increasing trend. The RR of TB was relatively high in the main urban area for the female population and the population aged 0-29 years, and the RR of TB for the population aged 30-44 years in the main urban area and the population aged 60 years or older in southeast of Chongqing had an increasing trend, respectively. For each 1 μg/m³ increase in SO₂ and 1% increase in the number of low-income per 1000 non-agricultural households (LINA per 1000 persons), the RR of TB increased by 0.35% (95% CI: 0.08-0.61%) and 0.07% (95% CI: 0.05-0.10%), respectively. And LINA per 1000 persons had the greatest impact on the female population and the over 60 years old age group. Although each 1% increase in urbanization rate (UR) was associated with 0.15% (95% CI: 0.11-0.17%) reduction in the RR of TB in the whole population, the RR increased by 0.18% (95% CI: 0.16-0.21%) in the female population and 0.37% (95% CI: 0.34-0.45%) in the 0-29 age group.

CONCLUSION

This study showed that high-risk areas for TB were concentrated in the southeastern and northeastern regions of Chongqing, and that the elderly population was a key population for TB incidence. There were spatial and temporal differences in the incidence of TB in populations with different characteristics, and various socio-environmental factors had different effects on different populations. Local governments should focus on areas and populations at high risk of TB and develop targeted prevention interventions based on the characteristics of different populations.

Investigating Spatial Patterns of Pulmonary Tuberculosis and Main Related Factors in Bandar Lampung, Indonesia Using Geographically Weighted Poisson Regression

Tuberculosis (TB) is a highly infectious disease, representing one of the major causes of death worldwide. Sustainable Development Goal 3.3 implies a serious decrease in the incidence of TB cases. Hence, this study applied a spatial analysis approach to investigate patterns of pulmonary TB cases and its drivers in Bandar Lampung (Indonesia). Our study examined seven variables: the growth rate of pulmonary TB, population, distance to the city center, industrial area, green open space, built area, and slum area using geographically weighted Poisson regression (GWPR). The GWPR model demonstrated excellent results with an R2 and adjusted R2 of 0.96 and 0.94, respectively. In this case, the growth rate of pulmonary TB and population were statistically significant variables. Spatial pattern analysis of sub-districts revealed that those of Panjang and Kedaton were driven by high pulmonary TB growth rate and population, whereas that of Sukabumi was driven by the accumulation of high levels of industrial area, built area, and slums. For these reasons, we suggest that local policymakers implement a variety of infectious disease prevention and control strategies based on the spatial variation of pulmonary TB rate and its influencing factors in each sub-district.

Reclaiming independence in spatial-clustering datasets: A series of data-driven spatial weights matrices

Most spatial models include a spatial weights matrix (W) derived from the first law of geography to adjust the spatial dependence to fulfill the independence assumption. In various fields such as epidemiological and environmental studies, the spatial dependence often shows clustering (or geographic discontinuity) due to natural or social factors. In such cases, adjustment using the first‐law‐of‐geography‐based W might be inappropriate and leads to inaccuracy estimations and loss of statistical power. In this work, we propose a series of data‐driven Ws (DDWs) built following the spatial pattern identified by the scan statistic, which can be easily carried out using existing tools such as SaTScan software. The DDWs take both the clustering (or discontinuous) and the intuitive first‐law‐of‐geographic‐based spatial dependence into consideration. Aiming at two common purposes in epidemiology studies (ie, estimating the effect value of explanatory variable X and estimating the risk of each spatial unit in disease mapping), the common spatial autoregressive models and the Leroux‐prior‐based conditional autoregressive (CAR) models were selected to evaluate performance of DDWs, respectively. Both simulation and case studies show that our DDWs achieve considerably better performance than the classic W in datasets with clustering (or discontinuous) spatial dependence. Furthermore, the latest published density‐based spatial clustering models, aiming at dealing with such clustering (or discontinuity) spatial dependence in disease mapping, were also compared as references. The DDWs, incorporated into the CAR models, still show considerable advantage, especially in the datasets for common diseases.

Influence of Public Health Services on the Goal of Ending Tuberculosis: Evidence From Panel Data in China

Background

The World Health Organization has proposed an initiative to “end tuberculosis (TB).” Unfortunately, TB continues to endanger the health of people worldwide. We investigated the impact of public health services (PHS) in China on TB incidence. In this way, we provided policy ideas for preventing the TB epidemic.

Methods

We used the “New Public Management Theory” to develop two indicators to quantify policy documents: multisector participation (MP) and the Assessable Public Health Service Coverage Rate (ASCR). The panel data from 31 provinces in Chinese mainland were collected from 2005 to 2019 based on 1,129 policy documents and the China Statistical Yearbook. A fixed-effect model was used to determine the impact of MP and the ASCR on TB incidence.

Results

From 2005 to 2019, the average MP increased from 89.25 to 97.70%, and the average ASCR increased from 53.97 to 78.40% in Chinese mainland. However, the development of ASCR between regions was not balanced, and the average level in the western region was lower than that in the eastern coastal provinces. With an increase in MP and the ASCR, the TB incidence had been decreasing gradually in recent years. The panel analysis results showed that MP (β = −0.76, p < 0.05). and ASCR (β = −0.40, p < 0.01) had a negative effect on TB incidence, respectively. Even if the control variables were added, the negative effects of MP (β = −0.86, p < 0.05) and ASCR (β = −0.35, p < 0.01) were still statistically significant.

Conclusions

Promoting the participation of multiple departments, as well as emphasizing the quality of PHS delivery, are important ways to alleviate the TB epidemic. The settings of evaluation indices for PHS provision should be strengthened in the future.

Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018

Background

Effective reduction of tuberculosis (TB) requires information on the distribution of TB incidence rate across time and location. This study aims to identify the spatio-temporal pattern of TB incidence rate in Iran between 2008 and 2018.

Methods

This cross-sectional study was conducted on aggregated TB data (50,500 patients) at the provincial level provided by the Ministry of Health in Iran between 2008 and 2018. The Anselin Local Moran’s I and Getis-Ord Gi* were performed to identify the spatial variations of the disease. Furthermore, spatial scan statistic was employed for purely temporal and spatio-temporal analyses. In all instances, the null hypothesis of no clusters was rejected at p ≤ 0.05.

Results

The overall incidence rate of TB decreased from 13.46 per 100,000 (95% CI: 13.19–13.73) in 2008 to 10.88 per 100,000 (95% CI: 10.65–11.11) in 2018. The highest incidence rate of TB was observed in southeast and northeast of Iran for the whole study period. Additionally, spatial cluster analysis discovered Khuzestan Province, in the West of the country, having significantly higher rates than neighbouring provinces in terms of both total TB and smear-positive pulmonary TB (SPPTB). Purely temporal analysis showed that high-rate and low-rate clusters were predominantly distributed in the time periods 2010–2014 and 2017–2018. Spatio-temporal results showed that the statistically significant clusters were mainly distributed from centre to the east during the study period. Some high-trend TB and SPPTB statistically significant clusters were found.

Conclusion

The results provided an overview of the latest TB spatio-temporal status In Iran and identified decreasing trends of TB in the 2008–2018 period. Despite the decreasing incidence rate, there is still need for screening, and targeting of preventive interventions, especially in high-risk areas. Knowledge of the spatio-temporal pattern of TB can be useful for policy development as the information regarding the high-risk areas would contribute to the selection of areas needed to be targeted for the expansion of health facilities.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11157-1.

Tuberculosis associated with the living conditions in an endemic municipality in the North of Brazil

Objective:

to analyze the association between the occurrence of new tuberculosis cases and the Adapted Living Condition Index, and to describe the spatial distribution in an endemic municipality.

Method:

this is an analytical and ecological study that was developed from new cases in residents of an endemic municipality in the North Region of Brazil. The data were obtained from the Notifiable Diseases Information System and from the 2010 Demographic Census. The Adapted Living Conditions Index was obtained by factor analysis and its association with the occurrence of the disease was analyzed by means of the chi-square test. The type I error was set at 0.05. Kernel estimation was used to describe the density of tuberculosis in each census sector.

Results:

the incidence coefficient was 97.5/100,000 inhabitants. The data showed a statistically significant association between the number of cases and socioeconomic class, with the fact that belonging to the highest economic class reduces the chance of the disease occurring. The thematic maps showed that tuberculosis was distributed in a heterogeneous way with a concentration in the Southern region of the municipality.

Conclusion:

tuberculosis, associated with precarious living conditions, reinforces the importance of discussion on social determinants in the health-disease process to subsidize equitable health actions in risk areas, upon a context of vulnerability.

Spatial distribution of Mycobacterium Tuberculosis in metropolitan Harare, Zimbabwe

INTRODUCTION

The contribution of high tuberculosis (TB) transmission pockets in propagating area-wide transmission has not been adequately described in Zimbabwe. This study aimed to describe the presence of hotspot transmission of TB cases in Harare city from 2011 to 2012 using geospatial techniques.

METHODS

Anonymised TB patient data stored in an electronic database at Harare City Health department was analysed using geospatial methods. Confirmed TB cases were mapped using geographic information system (GIS). Global Moran's I and Anselin Local Moran's I (LISA) were used to assess clustering and the local Getis-Ord Gi* was used to estimate hotspot phenomenon of TB cases in Harare City for the period between 2011 and 2012.

RESULTS

A total of 12,702 TB cases were accessed and mapped on the Harare City map. In both 2011 and 2012, ninety (90%) of cases were new and had a high human immunodeficiency virus (HIV)/TB co-infection rate of 72% across all suburbs. Tuberculosis prevalence was highest in the Southern district in both 2011 and 2012. There were pockets of spatial distribution of TB prevalence across West South West, Southern, Western, South Western and Eastern health districts. TB hot spot occurrence was restricted to the West South West, parts of South Western, Western health districts. West South West district had an increased peri-urban population with inadequate social services including health facilities. These conditions were conducive for increased intensity of TB occurrence, a probable indication of high transmission especially in the presence of high HIV co-infection.

CONCLUSIONS AND RECOMMENDATIONS

Increased TB transmission was limited to a health district with high informal internal migrants with limited health services in Harare City. To minimise spread of TB into greater Harare, there is need to improve access to TB services in the peri-urban areas.

The association between internal migration and pulmonary tuberculosis in China, 2005-2015: a spatial analysis

BACKGROUND

Internal migration places individuals at high risk of contracting tuberculosis (TB). However, there is a scarcity of national-level spatial analyses regarding the association between TB and internal migration in China. In our research, we aimed to explore the spatial variation in cases of sputum smear-positive pulmonary TB (SS + PTB) in China; and the associations between SS + PTB, internal migration, socioeconomic factors, and demographic factors in the country between 2005 and 2015.

METHODS

Reported cases of SS + PTB were obtained from the national PTB surveillance system database; cases were obtained at the provincial level. Internal migration data were extracted from the national population sampling survey and the census. Spatial autocorrelations were explored using the global Moran's statistic and local indicators of spatial association. The spatial temporal analysis was performed using Kulldorff's scan statistic. Fixed effects regression was used to explore the association between SS + PTB and internal migration.

RESULTS

A total of 4 708 563 SS + PTB cases were reported in China between 2005 and 2015, of which 3 376 011 (71.7%) were male and 1 332 552 (28.3%) were female. There was a trend towards decreasing rates of SS + PTB notifications between 2005 and 2015. The result of global spatial autocorrelation indicated that there were significant spatial correlations between SS + PTB rate and internal migration each year (2005-2015). Spatial clustering of SS + PTB cases was mainly located in central and southern China and overlapped with the clusters of emigration. The proportions of emigrants and immigrants were significantly associated with SS + PTB. Per capita GDP and education level were negatively associated with SS + PTB. The internal migration flow maps indicated that migrants preferred neighboring provinces, with most migrating for work or business.

CONCLUSIONS

This study found a significant spatial autocorrelation between SS + PTB and internal migration. Both emigration and immigration were statistically associated with SS + PTB, and the association with emigration was stronger than that for immigration. Further, we found that SS + PTB clusters overlapped with emigration clusters, and the internal migration flow maps suggested that migrants from SS + PTB clusters may influence the TB epidemic characteristics of neighboring provinces. These findings can help stakeholders to implement effective PTB control strategies for areas at high risk of PTB and those with high rates of internal migrants.

A GIS-Based Artificial Neural Network Model for Spatial Distribution of Tuberculosis across the Continental United States

Despite the usefulness of artificial neural networks (ANNs) in the study of various complex problems, ANNs have not been applied for modeling the geographic distribution of tuberculosis (TB) in the US. Likewise, ecological level researches on TB incidence rate at the national level are inadequate for epidemiologic inferences. We collected 278 exploratory variables including environmental and a broad range of socio-economic features for modeling the disease across the continental US. The spatial pattern of the disease distribution was statistically evaluated using the global Moran’s I, Getis–Ord General G, and local Gi* statistics. Next, we investigated the applicability of multilayer perceptron (MLP) ANN for predicting the disease incidence. To avoid overfitting, L1 regularization was used before developing the models. Predictive performance of the MLP was compared with linear regression for test dataset using root mean square error, mean absolute error, and correlations between model output and ground truth. Results of clustering analysis showed that there is a significant spatial clustering of smoothed TB incidence rate (p < 0.05) and the hotspots were mainly located in the southern and southeastern parts of the country. Among the developed models, single hidden layer MLP had the best test accuracy. Sensitivity analysis of the MLP model showed that immigrant population (proportion), underserved segments of the population, and minimum temperature were among the factors with the strongest contributions. The findings of this study can provide useful insight to health authorities on prioritizing resource allocation to risk-prone areas.