Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review

Modeling tuberculosis transmission flow in China, 2010-2012

BACKGROUND

China has the third largest number of TB cases in the world, and the average annual floating population in China is more than 200 million, the increasing floating population across regions has a tremendous potential for spreading infectious diseases, however, the role of increasing massive floating population in tuberculosis transmission is yet unclear in China.

METHODS

29,667 tuberculosis flow data were derived from the new smear-positive pulmonary tuberculosis cases in China. Spatial variation of TB transmission was measured by geodetector q-statistic and spatial interaction model was used to model the tuberculosis flow and the regional socioeconomic factors.

RESULTS

Tuberculosis transmission flow presented spatial heterogeneity. The Pearl River Delta in southern China and the Yangtze River Delta along China's east coast presented as the largest destination and concentration areas of tuberculosis inflows. Socioeconomic factors were determinants of tuberculosis flow. Some impact factors showed different spatial associations with tuberculosis transmission flow. A 10% increase in per capita GDP was associated with 10.2% in 2010 or 2.1% in 2012 decrease in tuberculosis outflows from the provinces of origin, and 1.2% in 2010 or 0.5% increase in tuberculosis inflows to the destinations and 18.9% increase in intraprovincial flow in 2012. Per capita net income of rural households and per capita disposable income of urban households were positively associated with tuberculosis flows. A 10% increase in per capita net income corresponded to 14.0% in 2010 or 3.6% in 2012 increase in outflows from the origin, 44.2% in 2010 or 12.8% increase in inflows to the destinations and 47.9% increase in intraprovincial flows in 2012. Tuberculosis incidence had positive impacts on tuberculosis flows. A 10% increase in the number of tuberculosis cases corresponded to 2.2% in 2010 or 1.1% in 2012 increase in tuberculosis inflows to the destinations, 5.2% in 2010 or 2.0% in 2012 increase in outflows from the origins, 11.5% in 2010 or 2.2% in 2012 increase in intraprovincial flows.

CONCLUSIONS

Tuberculosis flows had clear spatial stratified heterogeneity and spatial autocorrelation, regional socio-economic characteristics had diverse and statistically significant effects on tuberculosis flows in the origin and destination, and income factor played an important role among the determinants.

Spatiotemporal analysis and seasonality of tuberculosis in Pudong New Area of Shanghai, China, 2014-2023

BACKGROUND

Spatiotemporal analysis is a vital method that plays an indispensable role in monitoring epidemiological changes in diseases and identifying high-risk clusters. However, there is still a blank space in the spatial and temporal distribution of tuberculosis (TB) incidence rate in Pudong New Area, Shanghai. Consequently, it is crucial to comprehend the spatiotemporal distribution of TB in this district, this will guide the prevention and control of TB in the district.

METHODS

Our research used Geographic Information System (GIS) visualization, spatial autocorrelation analysis, and space-time scan analysis to analyze the TB incidence reported in the Pudong New Area of Shanghai from 2014 to 2023, and described the spatiotemporal clustering and seasonal hot spot distribution of TB incidence.

RESULTS

From 2014 to 2023, the incidence of TB in the Pudong New Area decreased, and the mortality was at a low level. The incidence of TB in different towns/streets has declined. The spatial autocorrelation analysis revealed that the incidence of TB was spatially clustered in 2014, 2016-2018, and 2022, with the highest clusters in 2014 and 2022. The high clustering area was mainly concentrated in the northeast. The space-time scan analysis indicated that the most likely cluster was located in 12 towns/streets, with a period of 2014-2018 and a radiation radius of 15.74 km. The heat map showed that there was a correlation between TB incidence and seasonal variations.

CONCLUSIONS

From 2014 to 2023, the incidence of TB in the Pudong New Area of Shanghai declined, but there were spatiotemporal clusters and seasonal correlations in the incidence area. Local departments should formulate corresponding intervention measures, especially in high-clustering areas, to achieve accurate prevention and control of TB within the most effective time and scope.

Geographical targeting of active case finding for tuberculosis in Pakistan using hotspots identified by artificial intelligence software (SPOT-TB): study protocol for a pragmatic stepped wedge cluster randomised control trial

INTRODUCTION

Pakistan has significantly strengthened its capacity for active case finding (ACF) for tuberculosis (TB) that is being implemented at scale in the country. However, yields of ACF have been lower than expected, raising concerns on its effectiveness in the programmatic setting. Distribution of TB in communities is likely to be spatially heterogeneous and targeting of ACF in areas with higher TB prevalence may help improve yields. The primary aim of SPOT-TB is to investigate whether a policy change to use a geographically targeted approach towards ACF supported by an artificial intelligence (AI) software, MATCH-AI, can improve yields in Pakistan.

METHODS AND ANALYSIS

SPOT-TB will use a pragmatic, stepped wedge cluster randomised design. A total of 30 mobile X-ray units and their field teams will be randomised to receive the intervention. Site selection for ACF in the intervention areas will be guided primarily through the use of MATCH-AI software that models subdistrict TB prevalence and identifies potential disease hotspots. Control areas will use existing approaches towards site selection that are based on staff knowledge, experience and analysis of historical data. The primary outcome measure is the difference in bacteriologically confirmed incident TB detected in the intervention relative to control areas. All remaining ACF-related procedures and algorithms will remain unaffected by this trial.

ETHICS AND DISSEMINATION

Ethical approval has been obtained from the Health Services Academy, Islamabad, Pakistan (7-82/IERC-HSA/2022-52) and from the Common Management Unit for TB, HIV and Malaria, Ministry of Health Services, Regulation and Coordination, Islamabad, Pakistan (26-IRB-CMU-2023). Findings from this study will be disseminated through publications in peer-reviewed journals and stakeholder meetings in Pakistan with the implementing partners and public-sector officials. Findings will also be presented at local and international medical and public health conferences.

TRIAL REGISTRATION NUMBER

NCT06017843.

Neighbourhood factors and tuberculosis incidence in Cape Town: A negative binomial regression and spatial analysis

OBJECTIVES

Although the link between poverty and tuberculosis (TB) is widely recognised, limited studies have investigated the association between neighbourhood factors and TB incidence. Since the factors influencing different episodes of TB might be different, this study focused on the first episode of TB disease (first-episode TB).

METHODS

All first episodes in previously linked and geocoded TB notification data from 2007 to 2015 in Cape Town, South Africa, were aggregated at the neighbourhood level and merged with the 2011 census data. We conducted an ecological study to assess the association between neighbourhood incidence of first-episode TB and neighbourhood factors (total TB burden [all episodes] in the previous year, socioeconomic index, mean household size, mean age, and percentage males) using a negative binomial regression. We also examined the presence of hotspots in neighbourhood TB incidence with the Global Moran's I statistic and assessed spatial dependency in the association between neighbourhood factors and TB incidence using a spatial lag model.

RESULTS

The study included 684 neighbourhoods with a median first-episode TB incidence rate of 114 (IQR: 0-345) per 100,000 people. We found lower neighbourhood socioeconomic index (SEI), higher neighbourhood total TB burden, lower neighbourhood mean household size, and lower neighbourhood mean age were associated with increased neighbourhood first-episode TB incidence. Our findings revealed a hotspot of first-episode TB incidence in Cape Town and evidence of spatial dependency in the association between neighbourhood factors and TB incidence.

CONCLUSION

Neighbourhood TB burden and SEI were associated with first-episode TB incidence, and there was spatial dependency in this association. Our findings can inform targeted interventions to reduce TB in high-risk neighbourhoods, thereby reducing health disparities and promoting health equity.

Mapping TB incidence across districts in Uganda to inform health program activities

BACKGROUND

Identifying spatial variation in TB burden can help national TB programs effectively allocate resources to reach and treat all people with TB. However, data limitations pose challenges for subnational TB burden estimation.

METHODS

We developed a small-area modeling approach using geo-positioned prevalence survey data, case notifications, and geospatial covariates to simultaneously estimate spatial variation in TB incidence and case notification completeness across districts in Uganda from 2016-2019. TB incidence was estimated using 1) cluster-level data from the national 2014-2015 TB prevalence survey transformed to incidence, and 2) case notifications adjusted for geospatial covariates of health system access. The case notification completeness surface was fit jointly using observed case notifications and estimated incidence.

RESULTS

Estimated pulmonary TB incidence among adults varied >10-fold across Ugandan districts in 2019. Case detection increased nationwide from 2016 to 2019, and the number of districts with case detection rates >70% quadrupled. District-level estimates of TB incidence were five times more precise than a model using TB prevalence survey data alone.

CONCLUSION

A joint spatial modeling approach provides useful insights for TB program operation, outlining areas where TB incidence estimates are highest and health programs should concentrate their efforts. This approach can be applied in many countries with high TB burden.

Tuberculosis prevalence and demographic characteristics of population in Azad Jammu and Kashmir (Pakistan): A retrospective study

Tuberculosis (TB) remains a serious problem for public health and a leading cause of death after COVID-19 and superior to even HIV/AIDS. It is a social health issue and can cause stigma and economic loss as the person cannot perform professionally due to lethargy caused by disease. It is a retrospective study done on data from National TB program Muzaffarabad chapter. The details were noted on SPSS and analysis was done to find important demographic characteristics. The total number of patients was 3441; among which 48.76% were males. Most of them (81.11%) belonged to the Muzaffarabad division of Azad Jammu and Kahmir (AJK). The microbiologically or culture positive cases were 440. Rifampicin resistance was present in 147 cases, further categorized as high (n = 143), very high (n = 3), or true positive (n = 1) resistance. Muti drug resistance was found in 19 cases. The microscopy culture is more sensitive (AUC = 0.511) than MTB/RIF or serology (AUC = 0.502) according to ROC. The rate of positive smear results is not very satisfactory in the present study as it cannot detect dormant or latent cases. There is a need to establish more sensitive tests for detection of cases and more research to combat the disease.

Spatial distribution and determinants of tuberculosis incidence in Mozambique: A nationwide Bayesian disease mapping study

INTRODUCTION

Mozambique is a high-burden country for tuberculosis (TB). International studies show that TB is a disease that tends to cluster in specific regions, and different risk factors (HIV prevalence, migration, overcrowding, poverty, house condition, temperature, altitude, undernutrition, urbanization, and inadequate access to TB diagnosis and treatment) are reported in the literature to be associated with TB incidence. Although Mozambique has a higher burden of TB, the spatial distribution, and determinants of TB incidence at the sub-national level have not been studied yet for the whole country. Therefore, we aimed to analyze the spatial distribution and determinants of tuberculosis incidence across all 154 districts of Mozambique and identify the hotspot areas.

METHOD

We conducted an ecological study with the district as our unit of analysis, where we included all cases of tuberculosis diagnosed in Mozambique between 2016 and 2020. We obtained the data from the Mozambique Ministry of Health and other publicly available open sources. The predictor variables were selected based on the literature review and data availability at the district level in Mozambique. The parameters were estimated through Bayesian hierarchical Poisson regression models using Markov Chain Monte Carlo simulation.

RESULTS

A total of 512 877 people were diagnosed with tuberculosis in Mozambique during our five-year study period. We found high variability in the spatial distribution of tuberculosis incidence across the country. Sixty-two districts out of 154 were identified as hotspot areas. The districts with the highest incidence rate were concentrated in the south and the country's central regions. In contrast, those with lower incidence rates were mainly in the north. In the multivariate analysis, we found that TB incidence was positively associated with the prevalence of HIV (RR: 1.23; 95 % CrI 1.13 to 1.34) and negatively associated with the annual average temperature (RR: 0.83; 95 % CrI 0.74 to 0.94).

CONCLUSION

The incidence of tuberculosis is unevenly distributed across the country. Lower average temperature and high HIV prevalence seem to increase TB incidence. Targeting interventions in higher-risk areas and strengthening collaboration between HIV and TB programs is paramount to ending tuberculosis in Mozambique, as established by the WHO's End TB strategy and the Sustainable Development Goals.

Assessing risks for bovine and zoonotic tuberculosis through spatial analysis and a questionnaire survey in Fiji - A pilot study

Mycobacterium bovis causes tuberculosis in cattle and when transmitted to humans typically causes extra-pulmonary tuberculosis (EPTB). Bovine tuberculosis (bTB) has a global distribution and is controlled in most countries to protect animal and public health. Recent studies revealed that bTB is established on dairy farms in Fiji where EPTB cases have been reported in people. The aims of this pilot investigation were to look for putative zoonotic TB (EPTB) cases in people and to evaluate practices that might contribute to the persistence and transmission of M. bovis between cattle and to humans. Existing data sets were shared between the Fiji Ministry of Agriculture and Ministry of Health and a questionnaire-based survey was implemented using One Health principles. Statistically significant co-location and close proximity of EPTB cases and bovine TB affected farms were identified. The bTB infection status of farms was significantly associated with unfenced water sources where cattle grazed. Of 247 households, 65 % shared drinking water sources with cattle and 36 % consumed raw milk without boiling, while 62 % of participants reported backyard slaughter of cattle. Several participants reported current symptoms potentially suggestive of TB (chronic cough) but the impact of smoking and history of previous TB treatment could not be evaluated. Farmers had limited understanding of the practices required to prevent bTB at farm level. Further study is recommended and should include an assessment of lifetime EPTB diagnoses, classification of farms based on more recent bTB test data and molecular typing of mycobacterial isolates from humans, cattle and the environment. A targeted awareness and education approach is required to reduce the future risk of zoonotic TB and to help ensure uptake of recommendations and practices aimed at controlling and preventing bTB.

Geospatial analysis of the hospitalisation rate of patients with rheumatoid arthritis in Hunan: a cross-sectional Chinese study

OBJECTIVE

Little is known about spatial variability of hospitalisation rate (HR) of patients with rheumatoid arthritis (RA) worldwide, especially in China.

METHODS

A cross-sectional study was conducted among patients with RA admitted to hospitals in Hunan Province. Global Moran's I and local indicators of spatial association were used to explore the geospatial pattern of the HR of patients with RA. Generalised estimating equation analysis and geographically weighted regression were used to identify the potential influencing factors of the HR of patients with RA.

RESULTS

There were a total of 11 599 admissions, and the average HR was 1.57 per 10 000 population in Hunan. We detected different cluster patterns of the HR among patients with RA by local indicators of spatial association. Age, ethnicity, average temperature, average temperature range, average rainfall, regions, gross domestic product per capita, and doctors and hospitals per 10 000 people were risk factors for the HR. However, only average temperature, gross domestic product per capita and hospitals per 10 000 people showed different regression coefficients on the HR in different counties. The increase in hospitals increased the probability of HR from east to west in Hunan with a positive coefficient, while temperature decreases increased the risk of HR from south to north negatively. Similarly, the growth of gross domestic product per capita decreased the probability of HR from southwest to northeast.

CONCLUSION

A non-random spatial distribution of the HR of patients with RA was demonstrated in Hunan, and average temperature, gross domestic product per capita and hospitals per 10 000 people showed different regression coefficients on the HR in different counties. Our study indicated that spatial and geostatistics may be useful approaches for further study among patients with RA.

Geographic distribution and predictors of diagnostic delays among possible TB patients in Uganda

BACKGROUND

Understanding the geographic distribution and factors associated with delayed TB diagnosis may help target interventions to reduce delays and improve patient outcomes.

METHODS

We conducted a secondary analysis of adults undergoing TB evaluation within a public health demonstration project in Uganda. Using Global Moran's I (GMI) and Getis-Ord GI* statistics, we evaluated for residential clustering and hotspots associated with patient-related and health system-related delays. We performed multivariate logistic regression to identify individual predictors of both types of delays.

RESULTS

Of 996 adults undergoing TB evaluation (median age: 37 years, IQR 28-49), 333 (33%) experienced patient delays, and 568 (57%) experienced health system delays. Participants were clustered (GMI 0.47-0.64, P ⩽ 0.001) at the sub-county level, but there were no statistically significant hotspots for patient or health system delays. Married individuals were less likely to experience patient delays (OR 0.6, 95% CI 0.48-0.75; P < 0.001). Those aged 38-57 years (OR 1.2, 95% CI 1.07-1.38; P = 0.002) were more likely than those aged ⩾58 years to experience patient delays. Knowledge about TB (OR 0.8, 95% CI 0.63-0.98; P = 0.03) protected against health system delays.

CONCLUSIONS

We did not identify geographic hotspots for TB diagnostic delays. Instead, delays were associated with individual factors such as age, marital status and TB knowledge.

Geographic patterns and hotspots of pediatric tuberculosis: the role of socioeconomic determinants

OBJECTIVE

Children are an important demographic group for understanding overall tuberculosis epidemiology, and monitoring of childhood tuberculosis is essential for appropriate prevention. The present study sought to characterize the spatial distribution of childhood tuberculosis notification rates in continental Portugal; identify high-risk areas; and evaluate the association between childhood tuberculosis notification rates and socioeconomic deprivation.

METHODS

Using hierarchical Bayesian spatial models, we analyzed the geographic distribution of pediatric tuberculosis notification rates across 278 municipalities between 2016 and 2020 and determined high-risk and low-risk areas. We used the Portuguese version of the European Deprivation Index to estimate the association between childhood tuberculosis and area-level socioeconomic deprivation.

RESULTS

Notification rates ranged from 1.8 to 13.15 per 100,000 children under 5 years of age. We identified seven high-risk areas, the relative risk of which was significantly above the study area average. All seven high-risk areas were located in the metropolitan area of Porto or Lisbon. There was a significant relationship between socioeconomic deprivation and pediatric tuberculosis notification rates (relative risk = 1.16; Bayesian credible interval, 1.05-1.29).

CONCLUSIONS

Identified high-risk and socioeconomically deprived areas should constitute target areas for tuberculosis control, and these data should be integrated with other risk factors to define more precise criteria for BCG vaccination.

Assessing the spatial heterogeneity of tuberculosis in a population with internal migration in China: a retrospective population-based study

Background

Internal migrants pose a critical threat to eliminating Tuberculosis (TB) in many high-burden countries. Understanding the influential pattern of the internal migrant population in the incidence of tuberculosis is crucial for controlling and preventing the disease. We used epidemiological and spatial data to analyze the spatial distribution of tuberculosis and identify potential risk factors for spatial heterogeneity.

Methods

We conducted a population-based, retrospective study and identified all incident bacterially-positive TB cases between January 1st, 2009, and December 31st, 2016, in Shanghai, China. We used Getis-Ord Gi* statistics and spatial relative risk methods to explore spatial heterogeneity and identify regions with spatial clusters of TB cases, and then used logistic regression method to estimate individual-level risk factors for notified migrant TB and spatial clusters. A hierarchical Bayesian spatial model was used to identify the attributable location-specific factors.

Results

Overall, 27,383 bacterially-positive tuberculosis patients were notified for analysis, with 42.54% (11,649) of them being migrants. The age-adjusted notification rate of TB among migrants was much higher than among residents. Migrants (aOR, 1.85; 95%CI, 1.65-2.08) and active screening (aOR, 3.13; 95%CI, 2.60-3.77) contributed significantly to the formation of TB high-spatial clusters. With the hierarchical Bayesian modeling, the presence of industrial parks (RR, 1.420; 95%CI, 1.023-1.974) and migrants (RR, 1.121; 95%CI, 1.007-1.247) were the risk factors for increased TB disease at the county level.

Conclusion

We identified a significant spatial heterogeneity of tuberculosis in Shanghai, one of the typical megacities with massive migration. Internal migrants play an essential role in the disease burden and the spatial heterogeneity of TB in urban settings. Optimized disease control and prevention strategies, including targeted interventions based on the current epidemiological heterogeneity, warrant further evaluation to fuel the TB eradication process in urban China.

Identification of growing tuberculosis incidence clusters in a region with a decrease in tuberculosis prevalence in Moscow (2000-2019)

Background

The control of tuberculosis (TB) may benefit from a prospective identification of areas where the incidence may increase in addition to the traditionally identified foci of high incidence. We aimed to identify residential areas with growing tuberculosis incidence rates and assess their significance and stability.

Methods

We analysed the changes in TB incidence rates using case data georeferenced with spatial granularity to apartment buildings in the territory of Moscow from 2000 to 2019. We identified sparsely distributed areas with significant increases in the incidence rate inside residential areas. We tested the stability of found growth areas to case underreporting via stochastic modelling.

Results

For 21 350 cases with smear- or culture-positive pulmonary TB among residents from 2000 to 2019, we identified 52 small-scale clusters of growing incidence rate responsible for 1% of all registered cases. We tested clusters of disease growth for underreporting and found them to be relatively unstable to resampling with case drop-out, but their spatial displacement was small. Territories with a stable increase in TB incidence rate were identified and compared to the rest of the city, which is characterised by a significant decrease in incidence.

Conclusions

Identified areas with a tendency for an increase in the TB incidence rate may be important targets for disease control services.

Use of High-Resolution Geospatial and Genomic Data to Characterize Recent Tuberculosis Transmission, Botswana

Combining genomic and geospatial data can be useful for understanding Mycobacterium tuberculosis transmission in high-burden tuberculosis (TB) settings. We performed whole-genome sequencing on M. tuberculosis DNA extracted from sputum cultures from a population-based TB study conducted in Gaborone, Botswana, during 2012-2016. We determined spatial distribution of cases on the basis of shared genotypes among isolates. We considered clusters of isolates with ≤5 single-nucleotide polymorphisms identified by whole-genome sequencing to indicate recent transmission and clusters of ≥10 persons to be outbreaks. We obtained both molecular and geospatial data for 946/1,449 (65%) participants with culture-confirmed TB; 62 persons belonged to 5 outbreaks of 10-19 persons each. We detected geospatial clustering in just 2 of those 5 outbreaks, suggesting heterogeneous spatial patterns. Our findings indicate that targeted interventions applied in smaller geographic areas of high-burden TB identified using integrated genomic and geospatial data might help interrupt TB transmission during outbreaks.

Geographical evolutionary pathway of global tuberculosis incidence trends

BACKGROUNDS

Tuberculosis (TB) remains a serious public health and human development problem, especially in developing countries. Despite the effectiveness of directly observed therapy, short course programs in reducing transmission and progression of TB, poverty reduction and socioeconomic development remain crucial factors in decreasing TB incidence. However, the geographical pathway on the planet is not yet clear.

OBJECTIVES

This study was to reconstruct the geographical evolutionary process of TB in 173 countries and territories from 2010 to 2019 to analyze the socioeconomic determinants that impact the global TB epidemic. In addition, the TB incidence in 2030 was predicted.

METHODS

This study analyses TB incidence data from 173 countries and territories between 2010 and 2019. The Geotree model would be used to reconstruct the geographical evolutionary process of TB, which provides a simplified schema for geo-visualizing the trajectories of TB incidence and their socioeconomic drivers. Additionally, to estimate the future TB incidence in 2030, a multilevel model was utilized in conjunction with the hierarchical nature of the Geotree based on a stratified heterogeneity analysis.

RESULTS

Global TB incidence was found to be associated with the country type and development stages. Between 2010 and 2019, the average TB incidence rate in 173 countries and territories was -27.48%, with marked spatially stratified heterogeneity by country type and development stage. Low-income and lower-middle-income countries were most vulnerable to TB. Upper-middle-income countries experienced a faster decline in TB incidence than high-income countries, and TB incidence generally decreased as the development stage increased, except for the lower-middle development stage in 2019.The highest average rate of decline in TB incidence was observed in the upper-middle development stage of high-income countries, with a reduction of 45.24%. Meanwhile, 37 high-income countries in the high development stage demonstrated an average rate of change of -13.93%. Socioeconomic determinants, including gross domestic product per capita, urbanization rate, and sociodemographic index, were found to inhibit TB incidence. Based on current trends, the predicted average global TB incidence in 2030 is 91.581 per 100,000 population.

CONCLUSIONS

The trajectories of the global TB incidence have been reconstructed to formulate targeted public health responses. To eliminate TB, countries at similar development stage can draw on the experiences of countries at higher development stages that are tailored to their unique characteristics. By learning from successful TB control strategies, countries can take strategic steps toward eradicating TB and improving public health outcomes.

The application of spatial analysis to understanding the association between area-level socio-economic factors and suicide: a systematic review

BACKGROUND

Little is known about what impact the use of different spatial methodological approaches may have on understanding the relationship between area-level socio-economic factors and suicide.

METHODS

In this systematic review, we searched PubMed, Embase, CINAHL and PsycInfo for original empirical studies examining the relationship between socio-economic factors and suicide with a spatial lens, published up to January 22, 2022. Data on applied spatial methods, indicators of socio-economic factors, and risk of suicide related to socio-economic factors were extracted. The protocol for this systematic review was registered with PROSPERO (CRD42021251387).

RESULTS

A systematic search yielded 6290 potentially relevant results; 58 studies met the inclusion criteria for review. Of the 58 included studies, more than half of the studies (n = 34; 58.6%) used methods that accounted for spatial effects in analyses of the association between socio-economic factors and suicide or examined spatial autocorrelation, while 24 (41.4%) studies applied univariate and multivariate models without considering spatial effects. Bayesian hierarchical models and spatial regression models were commonly used approaches to correct for spatial effects. The risk of suicide relating to socio-economic factors varied greatly by local areas and between studies using various socio-economic indicators. Areas with higher deprivation, higher unemployment, lower income, and lower education level were more likely to have higher suicide risk. There was no significant difference in results between studies using conventional versus spatial statistic methods.

CONCLUSION

An increasing number of studies have applied spatial methods, including Bayesian spatial models and spatial regression models, to explore the relationship between area-level socio-economic factors and suicide. This review of spatial studies provided further evidence that area-level socio-economic factors are generally inversely associated with suicide risk, with or without accounting for spatial autocorrelation.

A spatial analysis of TB cases and abnormal X-rays detected through active case-finding in Karachi, Pakistan

Tuberculosis (TB) is the leading cause of avoidable deaths from an infectious disease globally and a large of number of people who develop TB each year remain undiagnosed. Active case-finding has been recommended by the World Health Organization to bridge the case-detection gap for TB in high burden countries. However, concerns remain regarding their yield and cost-effectiveness. Data from mobile chest X-ray (CXR) supported active case-finding community camps conducted in Karachi, Pakistan from July 2018 to March 2020 was retrospectively analyzed. Frequency analysis was carried out at the camp-level and outcomes of interest for the spatial analyses were mycobacterium TB positivity (MTB+) and X-ray abnormality rates. The Global Moran's I statistic was used to test for spatial autocorrelation for MTB+ and abnormal X-rays within Union Councils (UCs) in Karachi. A total of 1161 (78.1%) camps yielded no MTB+ cases, 246 (16.5%) camps yielded 1 MTB+, 52 (3.5%) camps yielded 2 MTB+ and 27 (1.8%) yielded 3 or more MTB+. A total of 79 (5.3%) camps accounted for 193 (44.0%) of MTB+ cases detected. Statistically significant clustering for MTB positivity (Global Moran's I: 0.09) and abnormal chest X-rays (Global Moran's I: 0.36) rates was identified within UCs in Karachi. Clustering of UCs with high MTB positivity were identified in Karachi West district. Statistically significant spatial variation was identified in yield of bacteriologically positive TB cases and in abnormal CXR through active case-finding in Karachi. Cost-effectiveness of active case-finding programs can be improved by identifying and focusing interventions in hotspots and avoiding locations with no known TB cases reported through routine surveillance.

Spatial distribution and computational modeling for mapping of tuberculosis in Pakistan

Background

Tuberculosis (TB) like many other infectious diseases has a strong relationship with climatic parameters.

Methods

The present study has been carried out on the newly diagnosed sputum smear-positive pulmonary TB cases reported to National TB Control Program across Pakistan from 2007 to 2020. In this study, spatial and temporal distribution of the disease was observed through detailed district wise mapping and clustered regions were also identified. Potential risk factors associated with this disease depending upon population and climatic variables, i.e. temperature and precipitation were also identified.

Results

Nationwide, the incidence rate of TB was observed to be rising from 7.03% to 11.91% in the years 2007–2018, which then started to decline. However, a declining trend was observed after 2018–2020. The most populous provinces, Punjab and Sindh, have reported maximum number of cases and showed a temporal association as the climatic temperature of these two provinces is higher with comparison to other provinces. Machine learning algorithms Maxent, Support Vector Machine (SVM), Environmental Distance (ED) and Climate Space Model (CSM) predict high risk of the disease with14.02%, 24.75%, 34.81% and 43.89% area, respectively.

Conclusion

SVM has a higher significant probability of prediction in the diseased area with a 1.86 partial receiver-operating characteristics (ROC) value as compared with other models.

Connecting the dots: understanding how human mobility shapes TB epidemics

Tuberculosis (TB) remains a leading infectious cause of death worldwide. Reducing TB infections and TB-related deaths rests ultimately on stopping forward transmission from infectious to susceptible individuals. Critical to this effort is understanding how human host mobility shapes the transmission and dispersal of new or existing strains of Mycobacterium tuberculosis (Mtb). Important questions remain unanswered. What kinds of mobility, over what temporal and spatial scales, facilitate TB transmission? How do human mobility patterns influence the dispersal of novel Mtb strains, including emergent drug-resistant strains? This review summarizes the current state of knowledge on mobility and TB epidemic dynamics, using examples from three topic areas, including inference of genetic and spatial clustering of infections, delineating source-sink dynamics, and mapping the dispersal of novel TB strains, to examine scientific questions and methodological issues within this topic. We also review new data sources for measuring human mobility, including mobile phone-associated movement data, and discuss important limitations on their use in TB epidemiology.

Small-scale spatiotemporal epidemiology of notifiable infectious diseases in China: a systematic review

Background

The prevalence of infectious diseases remains one of the major challenges faced by the Chinese health sector. Policymakers have a tremendous interest in investigating the spatiotemporal epidemiology of infectious diseases. We aimed to review the small-scale (city level, county level, or below) spatiotemporal epidemiology of notifiable infectious diseases in China through a systematic review, thus summarizing the evidence to facilitate more effective prevention and control of the diseases.

Methods

We searched four English language databases (PubMed, EMBASE, Cochrane Library, and Web of Science) and three Chinese databases (CNKI, WanFang, and SinoMed), for studies published between January 1, 2004 (the year in which China’s Internet-based disease reporting system was established) and December 31, 2021. Eligible works were small-scale spatial or spatiotemporal studies focusing on at least one notifiable infectious disease, with the entire territory of mainland China as the study area. Two independent reviewers completed the review process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Results

A total of 18,195 articles were identified, with 71 eligible for inclusion, focusing on 22 diseases. Thirty-one studies (43.66%) were analyzed using city-level data, 34 (47.89%) were analyzed using county-level data, and six (8.45%) used community or individual data. Approximately four-fifths (80.28%) of the studies visualized incidence using rate maps. Of these, 76.06% employed various spatial clustering methods to explore the spatial variations in the burden, with Moran’s I statistic being the most common. Of the studies, 40.85% explored risk factors, in which the geographically weighted regression model was the most commonly used method. Climate, socioeconomic factors, and population density were the three most considered factors.

Conclusions

Small-scale spatiotemporal epidemiology has been applied in studies on notifiable infectious diseases in China, involving spatiotemporal distribution and risk factors. Health authorities should improve prevention strategies and clarify the direction of future work in the field of infectious disease research in China.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07669-9.

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.

Spatio-temporal distribution of tuberculosis and the effects of environmental factors in China

Background

Although the World Health Organization reports that the incidence of tuberculosis in China is decreasing every year, the burden of tuberculosis in China is still very heavy. Understanding the spatial and temporal distribution pattern of tuberculosis in China and its influencing environmental factors will provide effective reference for the prevention and treatment of tuberculosis.

Methods

Data of TB incidence from 2010 to 2017 were collected. Time series and global spatial autocorrelation were used to analyze the temporal and spatial distribution pattern of tuberculosis incidence in China, Geodetector and Geographically Weighted Regression model were used to analyze the environmental factors affecting the TB incidence.

Results

In addition to 2007 and 2008, the TB incidence decreased in general. TB has a strong spatial aggregation. Cities in Northwest China have been showing a trend of high-value aggregation. In recent years, the center of gravity of high-value aggregation area in South China has moved further south. Temperature, humidity, precipitation, PM₁₀, PM_2.5, O₃, NO₂ and SO₂ have impacts on TB incidence, and in different regions, the environmental factors show regional differences.

Conclusions

Residents should pay more attention to the risk of developing TB caused by climate change and air pollutant exposure. Increased efforts should be placed on areas with high-value clustering in future public resource configurations.

Neighbourhood prevalence-to-notification ratios for adult bacteriologically-confirmed tuberculosis reveals hotspots of underdiagnosis in Blantyre, Malawi

Local information is needed to guide targeted interventions for respiratory infections such as tuberculosis (TB). Case notification rates (CNRs) are readily available, but systematically underestimate true disease burden in neighbourhoods with high diagnostic access barriers. We explored a novel approach, adjusting CNRs for under-notification (P:N ratio) using neighbourhood-level predictors of TB prevalence-to-notification ratios. We analysed data from 1) a citywide routine TB surveillance system including geolocation, confirmatory mycobacteriology, and clinical and demographic characteristics of all registering TB patients in Blantyre, Malawi during 2015-19, and 2) an adult TB prevalence survey done in 2019. In the prevalence survey, consenting adults from randomly selected households in 72 neighbourhoods had symptom-plus-chest X-ray screening, confirmed with sputum smear microscopy, Xpert MTB/Rif and culture. Bayesian multilevel models were used to estimate adjusted neighbourhood prevalence-to-notification ratios, based on summarised posterior draws from fitted adult bacteriologically-confirmed TB CNRs and prevalence. From 2015-19, adult bacteriologically-confirmed CNRs were 131 (479/371,834), 134 (539/415,226), 114 (519/463,707), 56 (283/517,860) and 46 (258/578,377) per 100,000 adults per annum, and 2019 bacteriologically-confirmed prevalence was 215 (29/13,490) per 100,000 adults. Lower educational achievement by household head and neighbourhood distance to TB clinic was negatively associated with CNRs. The mean neighbourhood P:N ratio was 4.49 (95% credible interval [CrI]: 0.98-11.91), consistent with underdiagnosis of TB, and was most pronounced in informal peri-urban neighbourhoods. Here we have demonstrated a method for the identification of neighbourhoods with high levels of under-diagnosis of TB without the requirement for a prevalence survey; this is important since prevalence surveys are expensive and logistically challenging. If confirmed, this approach may support more efficient and effective targeting of intensified TB and HIV case-finding interventions aiming to accelerate elimination of urban TB.

Distribution of bacteriologically positive and bacteriologically negative pulmonary tuberculosis in Northwest China: spatiotemporal analysis

Pulmonary tuberculosis (PTB) is a major health issue in Northwest China. Most previous studies on the spatiotemporal patterns of PTB considered all PTB cases as a whole; they did not distinguish notified bacteriologically positive PTB (BP-PTB) and notified bacteriologically negative PTB (BN-PTB). Thus, the spatiotemporal characteristics of notified BP-PTB and BN-PTB are still unclear. A retrospective county-level spatial epidemiological study (2011-2018) was conducted in Shaanxi, Northwest China. In total, 44,894 BP-PTB cases were notified, with an average annual incidence rate of 14.80 per 100,000 persons between 2011 and 2018. Global Moran's I values for notified BP-PTB ranged from 0.19 to 0.49 (P < 0.001). Anselin's local Moran's I analysis showed that the high-high (HH) cluster for notified BP-PTB incidence was mainly located in the southernmost region. The primary spatiotemporal cluster for notified BP-PTB (LLR = 612.52, RR = 1.77, P < 0.001) occurred in the central region of the Guanzhong Plain in 2011. In total, 116,447 BN-PTB cases were notified, with an average annual incidence rate of 38.38 per 100,000 persons between 2011 and 2018. Global Moran's I values for notified BN-PTB ranged from 0.39 to 0.69 (P < 0.001). The HH clusters of notified BN-PTB were mainly located in the north between 2011 and 2014 and in the south after 2015. The primary spatiotemporal cluster for notified BN-PTB (LLR = 1084.59, RR = 1.85, P < 0.001) occurred in the mountainous areas of the southernmost region from 2014 to 2017. Spatiotemporal clustering of BP-PTB and BN-PTB was detected in the poverty-stricken mountainous areas of Shaanxi, Northwest China. Our study provides evidence for intensifying PTB control activities in these geographical clusters.

Population estimation beyond counts-Inferring demographic characteristics

Mapping population distribution at a fine spatial scale is essential for urban studies and planning. Numerous studies, mainly supported by geospatial and statistical methods, have focused primarily on predicting population counts. However, estimating their socio-economic characteristics beyond population counts, such as average age, income, and gender ratio, remains unattended. We enhance traditional population estimation by predicting not only the number of residents in an area, but also their demographic characteristics: average age and the proportion of seniors. By implementing and comparing different machine learning techniques (Random Forest, Support Vector Machines, and Linear Regression) in administrative areas in Singapore, we investigate the use of point of interest (POI) and real estate data for this purpose. The developed regression model predicts the average age of residents in a neighbourhood with a mean error of about 1.5 years (the range of average resident age across Singaporean districts spans approx. 14 years). The results reveal that age patterns of residents can be predicted using real estate information rather than with amenities, which is in contrast to estimating population counts. Another contribution of our work in population estimation is the use of previously unexploited POI and real estate datasets for it, such as property transactions, year of construction, and flat types (number of rooms). Advancing the domain of population estimation, this study reveals the prospects of a small set of detailed and strong predictors that might have the potential of estimating other demographic characteristics such as income.

Intra-urban variation in tuberculosis and community socioeconomic deprivation in Lisbon metropolitan area: a Bayesian approach

Background

Multidrug resistant tuberculosis (MDR-TB) is a recognized threat to global efforts to TB control and remains a priority of the National Tuberculosis Programs. Additionally, social determinants and socioeconomic deprivation have since long been associated with worse health and perceived as important risk factors for TB. This study aimed to analyze the spatial distribution of non-MDR-TB and MDR-TB across parishes of the Lisbon metropolitan area of Portugal and to estimate the association between non-MDR-TB and MDR-TB and socioeconomic deprivation.

Methods

In this study, we used hierarchical Bayesian spatial models to analyze the spatial distribution of notification of non-MDR-TB and MDR-TB cases for the period from 2000 to 2016 across 127 parishes of the seven municipalities of the Lisbon metropolitan area (Almada, Amadora, Lisboa, Loures, Odivelas, Oeiras, Sintra), using the Portuguese TB Surveillance System (SVIG-TB). In order to characterise the populations, we used the European Deprivation Index for Portugal (EDI-PT) as an indicator of poverty and estimated the association between non-MDR-TB and MDR-TB and socioeconomic deprivation.

Results

The notification rates per 10,000 population of non-MDR TB ranged from 18.95 to 217.49 notifications and that of MDR TB ranged from 0.83 to 3.70. We identified 54 high-risk areas for non-MDR-TB and 13 high-risk areas for MDR-TB. Parishes in the third [relative risk (RR) = 1.281, 95% credible interval (CrI): 1.021–1.606], fourth (RR = 1.786, 95% CrI: 1.420–2.241) and fifth (RR = 1.935, 95% CrI: 1.536–2.438) quintile of socioeconomic deprivation presented higher non-MDR-TB notifications rates. Parishes in the fourth (RR = 2.246, 95% CrI: 1.374–3.684) and fifth (RR = 1.828, 95% CrI: 1.049–3.155) quintile of socioeconomic deprivation also presented higher MDR-TB notifications rates.

Conclusions

We demonstrated significant heterogeneity in the spatial distribution of both non-MDR-TB and MDR-TB at the parish level and we found that socioeconomically disadvantaged parishes are disproportionally affected by both non-MDR-TB and MDR-TB. Our findings suggest that the emergence of MDR-TB and transmission are specific from each location and often different from the non-MDR-TB settings. We identified priority areas for intervention for a more efficient plan of control and prevention of non-MDR-TB and MDR-TB.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-022-00949-1.

A spatio-temporal study of state-wide case-fatality risks during the first wave of the COVID-19 pandemic in Mexico

spatio-temporal analysis of the first wave of the coronavirus (COVID-19) pandemic in Mexico (April to September 2020) was performed by state. Descriptive analyses through diagrams, mapping, animations and time series representations were carried out. Greater risks were observed at certain times in specific regions. Various trends and clusters were observed and analysed by fitting linear mixed models and time series clustering. The association of co-morbidities and other variables were studied by fitting a spatial panel data linear model (SPLM). On average, the greatest risks were observed in Baja California Norte, Chiapas and Sonora, while some other densely populated states, e.g., Mexico City, had lower values. The trends varied by state and a four-order polynomial, including fixed and random effects, was necessary to model them. The most common risk development was observed in states belonging to two clusters and consisted of an initial increase followed by a decrease. Some states presented cluster configurations with a retarded risk increase before the decrease, while the risk increased throughout the time of study in others. A cyclic behaviour with a second increasing trend was also observed in some states. The SPLM approach revealed a positive significant association with respect to case fatality risk between certain groups, such as males and individuals aged 50 years and more, and the prevalence of chronic kidney disease, cardiovascular disease, asthma and hypertension. The analysis may provide valuable insight into COVID-19 dynamics applicable in future outbreaks, as well as identify determinants signifying certain trends at the state level. The combination of spatial and temporal information may provide a better understanding of the fatalities due to COVID-19.

Tuberculosis burden caused by migrant population in Eastern China: evidence from notification records in Zhejiang Province during 2013-2017

Background

Internal migrants have an enormous impact on tuberculosis (TB) epidemic in China. Zhejiang Province, as one of the developed areas, also had a heavy burden caused by TB.

Methods

In this study, we collected all cases in Zhejiang Province through the TB Management Information System from 2013 to 2017. Description analysis and Spatio-temporal analysis using R software and ArcGIS were performed to identify the epidemiological characteristics and clusterings, respectively.

Results

48,756 individuals in total were notified with TB among the migrant population (TBMP), accounting for one-third of all cases identified. The primary sources of TB from migrants outside the province were from Guizhou, Sichuan, and Anhui. Wenzhou, Taizhou, and Lishui were the three mainly outflowing cities among the intra-provincial TBMP and Hangzhou as the primarily inflowing city. Also, results implied that the inconsistency of the TBMP in spatial analysis and the border area of Quzhou and Lishui city had the highest risk of TB occurrence among the migrants. Additionally, one most likely cluster and four secondary clusters were identified by the spatial–temporal analysis.

Conclusion

The effective control of TB in extra-provincial MP was critical to lowering the TB burden of MP in Zhejiang Province. Also, it is suggested that active TB screening for migrant employees outflowed from high epidemic regions should be strengthened, and further traceability analysis needs to be investigated to clarify the mechanism of TB transmission in clustered areas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07071-5.

TB Hackathon: Development and Comparison of Five Models to Predict Subnational Tuberculosis Prevalence in Pakistan

Pakistan's national tuberculosis control programme (NTP) is among the many programmes worldwide that value the importance of subnational tuberculosis (TB) burden estimates to support disease control efforts, but do not have reliable estimates. A hackathon was thus organised to solicit the development and comparison of several models for small area estimation of TB. The TB hackathon was launched in April 2019. Participating teams were requested to produce district-level estimates of bacteriologically positive TB prevalence among adults (over 15 years of age) for 2018. The NTP provided case-based data from their 2010-2011 TB prevalence survey, along with data relating to TB screening, testing and treatment for the period between 2010-2011 and 2018. Five teams submitted district-level TB prevalence estimates, methodological details and programming code. Although the geographical distribution of TB prevalence varied considerably across models, we identified several districts with consistently low notification-to-prevalence ratios. The hackathon highlighted the challenges of generating granular spatiotemporal TB prevalence forecasts based on a cross-sectional prevalence survey data and other data sources. Nevertheless, it provided a range of approaches to subnational disease modelling. The NTP's use and plans for these outputs shows that, limitations notwithstanding, they can be valuable for programme planning.

A role for community-level socioeconomic indicators in targeting tuberculosis screening interventions

Tuberculosis screening programs commonly target areas with high case notification rates. However, this may exacerbate disparities by excluding areas that already face barriers to accessing diagnostic services. We compared historic case notification rates, demographic, and socioeconomic indicators as predictors of neighborhood-level tuberculosis screening yield during a mobile screening program in 74 neighborhoods in Lima, Peru. We used logistic regression and Classification and Regression Tree (CART) analysis to identify predictors of screening yield. During February 7, 2019-February 6, 2020, the program screened 29,619 people and diagnosed 147 tuberculosis cases. Historic case notification rate was not associated with screening yield in any analysis. In regression analysis, screening yield decreased as the percent of vehicle ownership increased (odds ratio [OR]: 0.76 per 10% increase in vehicle ownership; 95% confidence interval [CI]: 0.58-0.99). CART analysis identified the percent of blender ownership (≤ 83.1% vs > 83.1%; OR: 1.7; 95% CI: 1.2-2.6) and the percent of TB patients with a prior tuberculosis episode (> 10.6% vs ≤ 10.6%; OR: 3.6; 95% CI: 1.0-12.7) as optimal predictors of screening yield. Overall, socioeconomic indicators were better predictors of tuberculosis screening yield than historic case notification rates. Considering community-level socioeconomic characteristics could help identify high-yield locations for screening interventions.

Epidemiological profile of tuberculosis in Iraq during 2011-2018

BACKGROUND

Tuberculosis (TB) is one of the most progressive infectious diseases caused by Mycobacterium tuberculosis. The pathogen is the first cause of mortality linked to a single pathogen worldwide, especially in poor and developing countries.

METHODS

A cross-sectional descriptive study was conducted to estimate incidence rate (IR) of TB in Iraq during a period of eight years (2011-2018). TB data were extracted from the computer system of the National Specialized Center for Chest and Respiratory Diseases in Baghdad.

RESULTS

During 2011-2018, 65,102 confirmed TB cases were reported in Iraq; 39,640 pulmonary TB (PTB) and 25,462 extra-pulmonary TB (EPTB). The average IR (case/100,000 inhabitants) of TB was 23.4 (14.2 for PTB and 9.1 for EPTB). Annual rate of TB cases showed a gradual decline over years (from 29.2 in 2011 to 18.6 in 2018). The decline in IR was more pronounced in PTB than EPTB. However PTB/EPTB ratio showed a gradual decreasing over years (from 2.04 in 2011 to 1.56 in 2018). GIS-mapping revealed that PTB and EPTB IRs show variations between the 18 governorates of Iraq. Most of the recorded PTB cases were new (average: 90.5%), followed by relapse cases (average: 7.9%). Among the reported PTB cases, percentage of males was greater than females (average: 52.1 vs. 47.9%), whereas an opposite trend was observed in EPTB (42.9 vs. 57.1%). The frequency distribution of PTB and EPTB varied between age groups, and lowest average frequency was recorded in age groups 1-4 and 5-14 year.

CONCLUSIONS

TB is still a public health threat, and although a declining trend in incidence was depicted over the years 2011-2018, the disease is still out of control in Iraq, and more investments of resource are necessitated to eliminate the disease. In this context, EPTB and PTB relapse need a recognized attention.

Addressing context dependence in ecology

Context dependence is widely invoked to explain disparate results in ecology. It arises when the magnitude or sign of a relationship varies due to the conditions under which it is observed. Such variation, especially when unexplained, can lead to spurious or seemingly contradictory conclusions, which can limit understanding and our ability to transfer findings across studies, space, and time. Using examples from biological invasions, we identify two types of context dependence resulting from four sources: mechanistic context dependence arises from interaction effects; and apparent context dependence can arise from the presence of confounding factors, problems of statistical inference, and methodological differences among studies. Addressing context dependence is a critical challenge in ecology, essential for increased understanding and prediction.

Spatial pattern of tuberculosis (TB) and related socio-environmental factors in South Korea, 2008-2016

Tuberculosis (TB) incidence and corresponding mortality rates in S. Korea are unusual and unique compared to other economically developed countries. Korea has the highest TB incidence rate in Organization for Economic Co-operation and Development (OECD) countries. TB is known as a disease reflecting socio-economic and environmental conditions of a society. Besides, TB is an infectious disease spread through the air, naturally forming spatial dependence of its incidence. This study investigates TB incidences in Korea in socio-economic and environmental perspectives. Eigenvector spatial filtering applied accounts for spatial autocorrelation in the TB incidence, and Getis-Ord Gi* statistic tracks the changes of TB clusters at given time. The results show that population composition ratio, population growth rate, health insurance payment, and public health variables are significant throughout the study period. Environmental variables make minor effects on TB incidence. This study argues that unique demographic features of Korea are a potential threat to TB control in the future.

Effectiveness of spatially targeted interventions for control of HIV, tuberculosis, leprosy and malaria: a systematic review

BACKGROUND

As infectious diseases approach global elimination targets, spatial targeting is increasingly important to identify community hotspots of transmission and effectively target interventions. We aimed to synthesise relevant evidence to define best practice approaches and identify policy and research gaps.

OBJECTIVE

To systematically appraise evidence for the effectiveness of spatially targeted community public health interventions for HIV, tuberculosis (TB), leprosy and malaria.

DESIGN

Systematic review.

DATA SOURCES

We searched Medline, Embase, Global Health, Web of Science and Cochrane Database of Systematic Reviews between 1 January 1993 and 22 March 2021.

STUDY SELECTION

The studies had to include HIV or TB or leprosy or malaria and spatial hotspot definition, and community interventions.

DATA EXTRACTION AND SYNTHESIS

A data extraction tool was used. For each study, we summarised approaches to identifying hotpots, intervention design and effectiveness of the intervention.

RESULTS

Ten studies, including one cluster randomised trial and nine with alternative designs (before-after, comparator area), satisfied our inclusion criteria. Spatially targeted interventions for HIV (one USA study), TB (three USA) and leprosy (two Brazil, one Federated States of Micronesia) each used household location and disease density to define hotspots followed by community-based screening. Malaria studies (one each from India, Indonesia and Kenya) used household location and disease density for hotspot identification followed by complex interventions typically combining community screening, larviciding of stagnant water bodies, indoor residual spraying and mass drug administration. Evidence of effect was mixed.

CONCLUSIONS

Studies investigating spatially targeted interventions were few in number, and mostly underpowered or otherwise limited methodologically, affecting interpretation of intervention impact. Applying advanced epidemiological methodologies supporting more robust hotspot identification and larger or more intensive interventions would strengthen the evidence-base for this increasingly important approach.

PROSPERO REGISTRATION NUMBER

CRD42019130133.

Identification of TB space-time clusters and hotspots in Ouest département, Haiti, 2011-2016

BACKGROUND

Haiti has the highest incidence rate of TB in the Western Hemisphere, with an estimated 170 cases per 100,000 in 2019. Since 2010, control efforts have focused on targeted case-finding activities in urban areas, implementation of rapid molecular diagnostics at high-volume TB centers, and improved reporting. TB analyses are rarely focused on lower geographic units; thus, the major goal was to determine if there were focal areas of TB transmission from 2011 to 2016 at operational geographic levels useful for the National TB Control Program (PNLT).

METHODS

We created a geocoder to locate TB cases at the smallest geographic level. Kulldorff's space-time permutation scan, Anselin Moran's I, and Getis-Ord Gi* statistics were used to determine the spatial distribution and clusters of TB.

RESULTS

With 91% of cases linked using the geocoder, TB clusters were identified each year. Getis-Ord Gi* analysis revealed 14 distinct spatial clusters of high incidences in the Port-au-Prince metropolitan area. One hundred retrospective space-time clusters were detected.

CONCLUSION

Our study confirms the presence of TB hotspots in the Ouest département, with most clusters in the Port-au-Prince metropolitan area. Results will help the PNLT and its partners better design case-finding strategies for these areas.

Geospatial clustering and modelling provide policy guidance to distribute funding for active TB case finding in Ethiopia

Tuberculosis (TB) exhibits considerable spatial heterogeneity, occurring in clusters that may act as hubs of community transmission. We evaluated the impact of an intervention targeting spatial TB hotspots in a rural region of Ethiopia. To evaluate the impact of targeted active case finding (ACF), we used a spatially structured mathematical model that has previously been described. From model equilibrium, we simulated the impact of a hotspot-targeted strategy (HTS) on TB incidence ten years from intervention commencement and the associated cost-effectiveness. HTS was also compared with an untargeted strategy (UTS). We used logistic cost-coverage analysis to estimate cost-effectiveness of interventions. At a community screening coverage level of 95 % in a hotspot region, which corresponds to screening 20 % of the total population, HTS would reduce overall TB incidence by 52 % compared with baseline. For UTS to achieve an equivalent effect, it would be necessary to screen more than 80 % of the total population. Compared to the existing passive case detection strategy, the HTS at a CDR of 75 percent in hotspot regions is expected to avert 1,023 new TB cases over ten years saving USD 170 per averted case. Similarly, at the same CDR, the UTS will detect 1316 cases over the same period saving USD 3 per averted TB case. The incremental-cost effectiveness-ratio (ICER) of UTS compared with HTS is USD 582 per averted case corresponding to 293 more TB cases averted at an additional cost of USD 170,700. Where regional TB program spending was capped at current levels, maximum gains in incidence reduction were seen when the regional budget was shared between hotspots and non-hotspot regions in the ratio of 40% : 60%. Our analysis suggests that a spatially targeted strategy is efficient and cost-saving, with the potential for significant reduction in overall TB burden.

Spatial drivers of COVID-19 vulnerability in Nigeria

INTRODUCTION

the spread and diffusion of COVID-19 undoubtedly shows strong spatial connotations and alignment with the physical indices of civilization and globalization. Several spatial risk factors have possible influence on its dispersal trajectory. Understanding their influence is critical for mobilization, sensitization and managing non-pharmaceutical interventions at the appropriate spatial-administrative units.

METHODS

on 01 April 2020, we constructed a rapid spatial diagnostics and generated vulnerability map for COVID-19 infection spread at state level using 12 core spatial drivers. The risk factors used include established COVID-19 cases (as at 01 April 2020), population, proximity to the airports, inter-state road traffic, intra-state road traffic, intra city traffic, international road traffic, possible influx of elites from abroad, preponderance of high risk political elite, likelihood of religious gathering, likelihood of other social gatherings, and proximity to existing COVID-19 test centers. These were also tested as predictors of COVID-19 spread using multiple regression analysis.

RESULTS

the results show that 6 States - Lagos, Kano, Katsina, Kaduna, Oyo and Rivers - and the Federal Capital Territory have very high vulnerability, 17 states have high vulnerability and 13 states have medium vulnerability to COVID-19 transmission. Several drivers show a strong association with COVID-19 with the coefficient of correlation ranging from 0.983 - 0.995. The regression analysis indicates that between 96.6 and 99.0 percent of the total variation in the COVID-19 infections across Nigeria can be explained by the predictors.

CONCLUSION

the spatial pattern of infection across the states are substantially consistent with the predicted pattern of vulnerability.

Quantifying geographic heterogeneity in TB incidence and the potential impact of geographically targeted interventions in South and North City Corporations of Dhaka, Bangladesh: a model-based study

In rapidly growing and high-burden urban centres, identifying tuberculosis (TB) transmission hotspots and understanding the potential impact of interventions can inform future control and prevention strategies. Using data on local demography, TB reports and patient reporting patterns in Dhaka South City Corporation (DSCC) and Dhaka North City Corporation (DNCC), Bangladesh, between 2010 and 2017, we developed maps of TB reporting rates across wards in DSCC and DNCC and identified wards with high rates of reported TB (i.e. 'hotspots') in DSCC and DNCC. We developed ward-level transmission models and estimated the potential epidemiological impact of three TB interventions: active case finding (ACF), mass preventive therapy (PT) and a combination of ACF and PT, implemented either citywide or targeted to high-incidence hotspots. There was substantial geographic heterogeneity in the estimated TB incidence in both DSCC and DNCC: incidence in the highest-incidence wards was over ten times higher than in the lowest-incidence wards in each city corporation. ACF, PT and combined ACF plus PT delivered to 10% of the population reduced TB incidence by a projected 7%-9%, 13%-15% and 19%-23% over five years, respectively. Targeting TB hotspots increased the projected reduction in TB incidence achieved by each intervention 1.4- to 1.8-fold. The geographical pattern of TB notifications suggests high levels of ongoing TB transmission in DSCC and DNCC, with substantial heterogeneity at the ward level. Interventions that reduce transmission are likely to be highly effective and incorporating notification data at the local level can further improve intervention efficiency.

Spatial patterns of pulmonary tuberculosis (TB) cases in Zimbabwe from 2015 to 2018

INTRODUCTION

Accurate mapping of spatial heterogeneity in tuberculosis (TB) cases is critical for achieving high impact control as well as guide resource allocation in most developing countries. The main aim of this study was to explore the spatial patterns of TB occurrence at district level in Zimbabwe from 2015 to 2018 using GIS and spatial statistics as a preamble to identifying areas with elevated risk for prioritisation of control and intervention measures.

METHODS

In this study Getis-Ord Gi* statistics together with SaTscan were used to characterise TB hotspots and clusters in Zimbabwe at district level from 2015 to 2018. GIS software was used to map and visualise the results of cluster analysis.

RESULTS

Results show that TB occurrence exhibits spatial heterogeneity across the country. The TB hotspots were detected in the central, western and southern part of the country. These areas are characterised by artisanal mining activities as well as high poverty levels.

CONCLUSIONS AND RECOMMENDATIONS

Results of this study are useful to guide TB control programs and design effective strategies which are important in achieving the United Nations Sustainable Development goals (UNSDGs).

Community transmission of multidrug-resistant tuberculosis is associated with activity space overlap in Lima, Peru

Background

Transmission of multidrug-resistant tuberculosis (MDRTB) requires spatial proximity between infectious cases and susceptible persons. We assess activity space overlap among MDRTB cases and community controls to identify potential areas of transmission.

Methods

We enrolled 35 MDRTB cases and 64 TB-free community controls in Lima, Peru. Cases were whole genome sequenced and strain clustering was used as a proxy for transmission. GPS data were gathered from participants over seven days. Kernel density estimation methods were used to construct activity spaces from GPS locations and the utilization distribution overlap index (UDOI) was used to quantify activity space overlap.

Results

Activity spaces of controls (median = 35.6 km², IQR = 25.1–54) were larger than cases (median = 21.3 km², IQR = 17.9–48.6) (P = 0.02). Activity space overlap was greatest among genetically clustered cases (mean UDOI = 0.63, sd = 0.67) and lowest between cases and controls (mean UDOI = 0.13, sd = 0.28). UDOI was positively associated with genetic similarity of MDRTB strains between case pairs (P < 0.001). The odds of two cases being genetically clustered increased by 22% per 0.10 increase in UDOI (OR = 1.22, CI = 1.09–1.36, P < 0.001).

Conclusions

Activity space overlap is associated with MDRTB clustering. MDRTB transmission may be occurring in small, overlapping activity spaces in community settings. GPS studies may be useful in identifying new areas of MDRTB transmission.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-05953-8.

Using Bayesian spatial models to map and to identify geographical hotspots of multidrug-resistant tuberculosis in Portugal between 2000 and 2016

Multidrug-resistant tuberculosis (MDR-TB) is a major threat to the eradication of tuberculosis. TB control strategies need to be adapted to the necessities of different countries and adjusted in high-risk areas. In this study, we analysed the spatial distribution of the MDR- and non-MDR-TB cases across municipalities in Continental Portugal between 2000 and 2016. We used Bayesian spatial models to estimate age-standardized notification rates and standardized notification ratios in each area, and to delimitate high- and low-risk areas, those whose standardized notification ratio is significantly above or below the country’s average, respectively. The spatial distribution of MDR- and non-MDR-TB was not homogeneous across the country. Age-standardized notification rates of MDR-TB ranged from 0.08 to 1.20 and of non-MDR-TB ranged from 7.73 to 83.03 notifications per 100,000 population across the municipalities. We identified 36 high-risk areas for non-MDR-TB and 8 high-risk areas for MDR-TB, which were simultaneously high-risk areas for non-MDR-TB. We found a moderate correlation (ρ = 0.653; 95% CI 0.457–0.728) between MDR- and non-MDR-TB standardized notification ratios. We found heterogeneity in the spatial distribution of MDR-TB across municipalities and we identified priority areas for intervention against TB. We recommend including geographical criteria in the application of molecular drug resistance to provide early MDR-TB diagnosis, in high-risk areas.

The Importance of Heterogeneity to the Epidemiology of Tuberculosis

Although less well-recognized than for other infectious diseases, heterogeneity is a defining feature of tuberculosis (TB) epidemiology. To advance toward TB elimination, this heterogeneity must be better understood and addressed. Drivers of heterogeneity in TB epidemiology act at the level of the infectious host, organism, susceptible host, environment, and distal determinants. These effects may be amplified by social mixing patterns, while the variable latent period between infection and disease may mask heterogeneity in transmission. Reliance on notified cases may lead to misidentification of the most affected groups, as case detection is often poorest where prevalence is highest. Assuming that average rates apply across diverse groups and ignoring the effects of cohort selection may result in misunderstanding of the epidemic and the anticipated effects of control measures. Given this substantial heterogeneity, interventions targeting high-risk groups based on location, social determinants, or comorbidities could improve efficiency, but raise ethical and equity considerations.

Spatial distribution of people diagnosed with tuberculosis through routine and active case finding: a community-based study in Kampala, Uganda

BACKGROUND

Routine tuberculosis (TB) notifications are geographically heterogeneous, but their utility in predicting the location of undiagnosed TB cases is unclear. We aimed to identify small-scale geographic areas with high TB notification rates based on routinely collected data and to evaluate whether these areas have a correspondingly high rate of undiagnosed prevalent TB.

METHODS

We used routinely collected data to identify geographic areas with high TB notification rates and evaluated the extent to which these areas correlated with the location of undiagnosed cases during a subsequent community-wide active case finding intervention in Kampala, Uganda. We first enrolled all adults who lived within 35 contiguous zones and were diagnosed through routine care at four local TB Diagnosis and Treatment Units. We calculated average monthly TB notification rates in each zone and defined geographic areas of "high risk" as zones that constituted the 20% of the population with highest notification rates. We compared the observed proportion of TB notifications among residents of these high-risk zones to the expected proportion, using simulated estimates based on population size and random variation alone. We then evaluated the extent to which these high-risk zones identified areas with high burdens of undiagnosed TB during a subsequent community-based active case finding campaign using a chi-square test.

RESULTS

We enrolled 45 adults diagnosed with TB through routine practices and who lived within the study area (estimated population of 49 527). Eighteen zones reported no TB cases in the 9-month period; among the remaining zones, monthly TB notification rates ranged from 3.9 to 39.4 per 100 000 population. The five zones with the highest notification rates constituted 62% (95% CI: 47-75%) of TB cases and 22% of the population-significantly higher than would be expected if population size and random chance were the only determinants of zone-to-zone variation (48%, 95% simulation interval: 40-59%). These five high-risk zones accounted for 42% (95% CI: 34-51%) of the 128 cases detected during the subsequent community-based case finding intervention, which was significantly higher than the 22% expected by chance (P < 0.001) but lower than the 62% of cases notified from those zones during the pre-intervention period (P = 0.02).

CONCLUSIONS

There is substantial heterogeneity in routine TB notification rates at the zone level. Using facility-based TB notification rates to prioritize high-yield areas for active case finding could double the yield of such case-finding interventions.

Mapping tuberculosis prevalence in Ethiopia: protocol for a geospatial meta-analysis

INTRODUCTION

Tuberculosis (TB), a major public health concern in Ethiopia, is distributed heterogeneously across the country. Mapping TB prevalence at national and subnational levels can provide information for designing and implementing control strategies. Data for spatial analysis can be obtained through systematic review of the literature, and spatial prediction can be done by meta-analysis of published data (geospatial meta-analysis). Geospatial meta-analysis can increase the power of spatial analytic models by making use of all available data. It can also provide a means for spatial prediction where new survey data in a given area are sparse or not available. In this report, we present a protocol for a geospatial meta-analysis to investigate the spatial patterns of TB prevalence in Ethiopia.

METHODS AND ANALYSIS

To conduct this study, a national TB prevalence survey, supplemented with data from a systematic review of published reports, will be used as the source of TB prevalence data. Systematic searching will be conducted in PubMed, Scopus and Web of Science for studies published up to 15 April 2020 to identify all potential publications reporting TB prevalence in Ethiopia. Data for covariates for multivariable analysis will be obtained from different, readily available sources. Extracted TB survey and covariate data will be georeferenced to specific locations or the centroids of small administrative areas. A binomial logistic regression model will be fitted to TB prevalence data using both fixed covariate effects and random geostatistical effects based on the approach of model-based geostatistics. Markov Chain Monte Carlo simulation will be conducted to obtained posterior parameter estimations, including spatially predicted prevalence.

ETHICS AND DISSEMINATION

Ethical approval will not be required for this study as it will be based on deidentified, aggregate published data. The final report of this review will be disseminated through publication in a peer-reviewed scientific journal and will also be presented at relevant conferences.

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.

Spatio-temporal co-occurrence of hotspots of tuberculosis, poverty and air pollution in Lima, Peru

Growing evidence suggests pollution and other environmental factors have a role in the development of tuberculosis (TB), however, such studies have never been conducted in Peru. Considering the association between air pollution and specific geographic areas, our objective was to determine the spatial distribution and clustering of TB incident cases in Lima and their co-occurrence with clusters of fine particulate matter (PM2.5) and poverty. We found co-occurrences of clusters of elevated concentrations of air pollutants such as PM2.5, high poverty indexes, and high TB incidence in Lima. These findings suggest an interplay of socio-economic and environmental in driving TB incidence.

Spatiotemporal analysis of drug-resistant TB patients registered in selected districts of Karnataka, South India: a cross-sectional study

Background

Tuberculosis (TB) depicts heterogeneous spatial patterns with geographical aggregation of TB cases due to either ongoing person-to-person transmission or reactivation of latent infection in a community sharing risk factor. In this regard, we aimed to assess the spatiotemporal aggregation of drug-resistant TB (DR-TB) patients notified to the national TB program (NTP) from 2015 to 2018 in selected districts of Karnataka, South India.

Methods

This was a cross-sectional study among DR-TB patients notified from Dakshina Kannada, Udupi, and Chikamagalur districts of the state of Karnataka. Clinico-demographic details were extracted from treatment cards. The registered addresses of the patients were geocoded (latitude and longitude) using Google Earth. Using the QGIS software, spot map, heat maps and grid maps 25 km² with more than the expected count of DR-TB patients were constructed.

Results

Of the total 507 patients studied, 376 (74%) were males and the mean (standard deviation) age of the study participants was 41.4 (13.9) years. From 2015 to 2018, the number of patients increased from 85 to 209 per year, the area of aggregation in square kilometers increased from 113.6 to 205.7, and the number of rectangular grids with more than the expected DR-TB patients (> 1) increased from 12 to 47.

Conclusions

The increase in the number of DR-TB patients, area of aggregation, and grids with more than the expected count is a cause for concern. The NTP can use routine programmatic data to develop maps to identify areas of aggregation of disease for targeted TB control activities.

Clustering and spatial heterogeneity of bovine tuberculosis at the livestock/wildlife interface areas in Namwala District of Zambia

BACKGROUND AND AIM

Bovine tuberculosis (bTB) remains a major public health issue in Zambia and has been exacerbated by human immunodeficiency virus prevalence and consumption of unpasteurized milk in the Southern Province of the country. The prevalence of bTB has been established to be linked to Kafue Lechwe, which act as reservoir hosts and share grazing fields with domestic cattle. No studies have so far used geographic information system (GIS) to investigate the relationship between the reservoir hosts (Kafue Lechwe) and domestic animals. This study, therefore, aimed to apply GIS to investigate the spatial distribution of bTB in Namwala District of the Southern Province of the country.

MATERIALS AND METHODS

To investigate the spatial distribution of bTB, geographical positioning system (GPS) coordinates representing 96 cattle herds across 20 independent villages were captured alongside risk factor data. The 96 herds were based on abattoir reports of condemned carcasses and a trace back. Positive herds were confirmed by cross-reference to purified protein derivative tests conducted by the District Veterinary Office. The GPS coordinates were transferred into ArcView 3.2 and laid on the map of Namwala District alongside physical features, including national parks, game management areas, and flood plains. Questionnaires were administered across 96 independent households to assess risk factors of bTB transmission.

RESULTS

The results revealed a "clustered" spatial distribution of the disease in cattle in Namwala District of Zambia, particularly significant in the eastern interface areas of the district (p=0.006 using Moran's I). Abattoir to production area trace back revealed a herd-level prevalence of 36.4% (95% CI=26.7-46.3%) among cattle herds in Namwala District, whereas individual animal prevalence ranged from 0% to 14% (95% CI=2.4-26.2%). Further, GPS data indicated that the majority of the positive herds were located at the livestock/wildlife interface area. Contacts with wildlife, coupled with sharing grazing, and watering points were found to be significant risk factors for bTB transmission.

CONCLUSION

This study demonstrated the presence of bTB in cattle and associated spatial risk factors. In particular, bTB was observed to be a function of animal location within the livestock/wildlife interface area. GIS is thus an applicable and important tool in studying disease distribution.