Spatial clustering of notified tuberculosis in Ethiopia: A nationwide study

Inequalities in tuberculosis control in Ethiopia: A district-level distributional modelling analysis

BACKGROUND

Implementation studies indicate that the addition of tuberculosis diagnosis and treatment services into the community health extension workers' tasks-that is 'task-shifting'-improved case detection and treatment outcomes in Ethiopia. Given resource and operational constraints, only a limited number of areas can be targeted by an expanded task-shifting program. Therefore, we mapped the distributional disparities in tuberculosis services across regions and districts and modelled the equity pathways towards optimising national scale-up of this task-shifting intervention in Ethiopia.

METHODS

We used data from various sources including District Health Information Software 2; demographic, geospatial and topographic data; and previously published implementation study findings. We developed methods to integrate these datasets and to calculate the proportion of health facilities with tuberculosis services, the district population to health centre ratio, and the proportion of district population living within 2h walking distance from a health centre. Equity and disparities were then measured in terms of: tuberculosis services coverage; health centre adequacy, that is the district population served by health centres; and spatial access adequacy, that is the district population with health centre access within a two-hour walking distance. Subsequently, districts were ranked according to these measures to allow prioritisation of the health extension worker task-shifting intervention.

RESULTS

Tuberculosis services coverage varied from 54% in Afar region to 100% in Harari region, and health centre inadequacy ranged from 10% of districts in Benishangul-Gumuz to 87% in Sidama. After spatial access adjustment, health centre inadequacy ranged from 7% of districts in Sidama to 91% in Somali; and tuberculosis services inadequacy from 7% of districts in Sidama to 97% in Afar. Task-shifting implemented in inadequate districts (55% of all districts) could raise national case detection rate from 66% (currently) to 88% and treatment success rate from 93% to 99%; Benishangul-Gumuz achieving the largest increase of all regions.

CONCLUSIONS

Access to effective tuberculosis services presents substantial disparities across districts in Ethiopia, due to both health system and tuberculosis-specific factors. Jointly considering both types of factors would enable prioritisation of districts where health extension workers would be most impactful.

Spatial co-distribution of tuberculosis prevalence and low BCG vaccination coverage in Ethiopia

While bacille-calmette-guerin (BCG) vaccination is one of the recommended strategies for preventing tuberculosis (TB), its coverage is low in several countries, including Ethiopia. This study investigated the spatial co-distribution and drivers of TB prevalence and low BCG coverage in Ethiopia. This ecological study was conducted using data from a national TB prevalence survey and the Ethiopian demographic and health survey (EDHS) to map the spatial co-distribution of BCG vaccination coverage and TB prevalence. A Bayesian geostatistical model was built to identify the drivers for the spatial distribution of TB prevalence and low BCG vaccination coverage. BCG vaccination coverage was defined as the number of children who received the vaccine divided by the total number of children born within five years preceding the EDHS surveys. Parameter estimation was done using binary logistic regression. Prediction maps for the co-distribution of high TB prevalence and low BCG vaccination coverage were created by overlying spatial prediction surfaces of the two outcomes. Posterior means and a 95% Bayesian credible interval (CrI) were used to summarize the parameters of the model. The national prevalence was 0.40% (95% confidence interval (CI) 0.34%, 0.47%) for TB and 47% (95% CI 46%, 48%) for vaccination coverage. Substantial spatial variation in TB prevalence and low BCG coverage was observed at a regional and local level, particularly in border areas of the country, including the Somali, Afar, and Oromia regions. Approximately 58% of the pixels (i.e., geographical area or spatial units) with high TB prevalence exhibited low BCG coverage in the same location. While travel time to cities (Mean = 0.28, 95% BCI: 0.15, 0.41) and distance to health facilities (Mean = 0.43, 95% CI 0.22, 0.63), were positively associated, population density (Mean = -0.04, 95% BCI -0.05, -0.02) was negatively associated, with the proportion of unvaccinated children for BCG indicating areas near health facilities and cities have better BCG coverage. However, there were no significant predictors for TB prevalence. Substantial spatial co-distribution between high TB prevalence and low BCG coverage was observed in some parts of the country, indicating that there are areas where the TB burden is not being adequately managed through the provision of vaccines in Ethiopia. Scaling up BCG vaccination coverage and TB diagnosis and treatment through improving access to health services in border regions such as Somalia and Afar would be important to reduce the prevalence of TB in Ethiopia.

Spatial distribution and predictors of drug-resistant tuberculosis incidence in Mozambique: A nationwide Bayesian disease mapping study

INTRODUCTION

Mozambique is among the highest-burden countries for drug-resistant tuberculosis in the world. However, the spatial distribution of drug-resistant tuberculosis, in the country is unknown. Therefore, we aimed to analyse the spatial distribution, predictors, and hotspot districts for drug-resistant tuberculosis incidence in Mozambique.

METHOD

We carried out an ecological study using the district as the unit of analysis where we included all cases of drug-resistant tuberculosis diagnosed in Mozambique from 2016 to 2020. We obtained the data from the Minister of Health and other open sources. Parameters of interest were estimated through a spatial Bayesian Poisson regression model using Markov Chain Monte Carlo simulation.

RESULTS

A total of 5092 people with drug-resistant tuberculosis in Mozambique were diagnosed during our study period. We found heterogeneity in the spatial distribution of drug-resistant tuberculosis incidence across the country. Higher incidence rates were mainly observed in the south and central regions, and 26 (16.9%) districts out of 154 were identified as hotspot areas. The incidence of drug-resistant tuberculosis increased with an increase in the HIV prevalence (Relative risk [RR]: 1.53; 95% Credibility Interval [CrI]: 1.32 to 1.76), electricity coverage rate (RR: 1.59; 95% CrI: 1.19 to 2.09), and population density (RR: 1.36; 95% CrI: 1.08 to 1.69) and decreased with an increase in the proportion of people with a bank account per district (RR: 0.71; 95% Crl: 0.51 to 0.96).

CONCLUSION

The incidence of drug-resistant tuberculosis was not homogeneous, and it was associated with social determinants of health. Targeting interventions in hotspot districts and addressing social determinants is crucial for tuberculosis elimination in Mozambique.

Ecological-level factors associated with tuberculosis incidence and mortality: A systematic review and meta-analysis

Globally, tuberculosis (TB) is the leading infectious cause of morbidity and mortality, with the risk of infection affected by both individual and ecological-level factors. While systematic reviews on individual-level factors exist, there are currently limited studies examining ecological-level factors associated with TB incidence and mortality. This study was conducted to identify ecological factors associated with TB incidence and mortality. A systematic search for analytical studies reporting ecological factors associated with TB incidence or mortality was conducted across electronic databases such as PubMed, Embase, Scopus, and Web of Science, from each database's inception to October 30, 2023. A narrative synthesis of evidence on factors associated with TB incidence and mortality from all included studies, alongside random-effects meta-analysis where applicable, estimated the effects of each factor on TB incidence. A total of 52 articles were included in the analysis, and one study analysed two outcomes, giving 53 studies. Narrative synthesis revealed predominantly positive associations between TB incidence and factors such as temperature (10/18 studies), precipitation (4/6), nitrogen dioxide (6/9), poverty (4/4), immigrant population (3/4), urban population (3/8), and male population (2/4). Conversely, air pressure (3/5), sunshine duration (3/8), altitude (2/4), gross domestic product (4/9), wealth index (2/8), and TB treatment success rate (2/2) mostly showed negative associations. Particulate matter (1/1), social deprivation (1/1), and population density (1/1) were positively associated with TB mortality, while household income (2/2) exhibited a negative association. In the meta-analysis, higher relative humidity (%) (relative risk (RR) = 1.45, 95%CI:1.12, 1.77), greater rainfall (mm) (RR = 1.56, 95%CI: 1.11, 2.02), elevated sulphur dioxide (μg m-3) (RR = 1.04, 95% CI:1.01, 1.08), increased fine particulate matter concentration (PM2.5) (μg/ m3) (RR = 1.33, 95% CI: 1.18, 1.49), and higher population density (people/km2) (RR = 1.01,95%CI:1.01-1.02) were associated with increased TB incidence. Conversely, higher average wind speed (m/s) (RR = 0.89, 95%CI: 0.82,0.96) was associated with decreased TB incidence. TB incidence and mortality rates were significantly associated with various climatic, socioeconomic, and air quality-related factors. Intersectoral collaboration across health, environment, housing, social welfare and economic sectors is imperative for developing integrated approaches that address the risk factors associated with TB incidence and mortality.

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.

Spatio-Temporal Pattern of Tuberculosis Distribution in Romania and Particulate Matter Pollution Associated With Risk of Infection

The study proposes a dynamic spatio-temporal profile of the distribution of tuberculosis incidence and air pollution in Romania, where this infectious disease induces more than 8,000 new cases annually. The descriptive analysis for the years 2012-2021 assumes an identification of the structuring patterns of mycobacterium tuberculosis risk in the Romanian population, according to gender and age, exploiting spatial modeling techniques of time series data. Through spatial autocorrelation, the degree of similarity between the analyzed territorial systems was highlighted and the relationships that are built between the analysis units in spatial proximity were investigated. By modeling the geographical distribution of tuberculosis, the spatial correlation with particulate matter (PM2.5) pollution was revealed. The identification of clusters of infected persons is an indispensable step in the construction of efficient tuberculosis management systems. The results highlight the link between the distribution of tuberculosis, air pollution and socio-economic development, which requires a detailed analysis of the epidemiological data obtained in the national tuberculosis surveillance and control program from the perspective of geographical distribution.

One size does not fit all: an application of stochastic modeling to estimating primary healthcare needs in Ethiopia at the sub-national level

BACKGROUND

Primary healthcare systems require adequate staffing to meet the needs of their local population. Guidelines typically use population ratio targets for healthcare workers, such as Ethiopia's goal of two health extension workers for every five thousand people. However, fixed ratios do not reflect local demographics, fertility rates, disease burden (e.g., malaria endemicity), or trends in these values. Recognizing this, we set out to estimate the clinical workload to meet the primary healthcare needs in Ethiopia by region.

METHODS

We utilize the open-source R package PACE-HRH for our analysis, which is a stochastic Monte Carlo simulation model that estimates workload for a specified service package and population. Assumptions and data inputs for region-specific fertility, mortality, disease burden were drawn from literature, DHS, and WorldPop. We project workload until 2035 for seven regions and two charted cities of Ethiopia.

RESULTS

All regions and charted cities are expected to experience increased workload between 2021 and 2035 for a starting catchment of five thousand people. The expected (mean) annual clinical workload varied from 2,930 h (Addis) to 3,752 h (Gambela) and increased by 19-28% over fifteen years. This results from a decline in per capita workload (due to declines in fertility and infectious diseases), overpowered by total population growth. Pregnancy, non-communicable diseases, sick child care, and nutrition remain the largest service categories, but their priority shifts substantially in some regions by 2035. Sensitivity analysis shows that fertility assumptions have major implications for workload. We incorporate seasonality and estimate monthly variation of up to 8.9% (Somali), though most services with high variability are declining.

CONCLUSIONS

Regional variation in demographics, fertility, seasonality, and disease trends all affect the workload estimates. This results in differences in expected clinical workload, the level of uncertainty in those estimates, and relative priorities between service categories. By showing these differences, we demonstrate the inadequacy of a fixed population ratio for staffing allocation. Policy-makers and regulators need to consider these factors in designing their healthcare systems, or they risk sub-optimally allocating workforce and creating inequitable access to care.

Spatiotemporal Distribution of Tuberculosis in the Oromia Region of Ethiopia: A Hotspot Analysis

Tuberculosis (TB) is a major public health concern in low- and middle-income countries including Ethiopia. This study aimed to assess the spatiotemporal distribution of TB and identify TB risk factors in Ethiopia's Oromia region. Descriptive and spatiotemporal analyses were conducted. Bayesian spatiotemporal modeling was used to identify covariates that accounted for variability in TB and its spatiotemporal distribution. A total of 206,278 new pulmonary TB cases were reported in the Oromia region between 2018 and 2022, with the lowest annual TB case notification (96.93 per 100,000 population) reported in 2020 (i.e., during the COVID-19 pandemic) and the highest TB case notification (106.19 per 100,000 population) reported in 2019. Substantial spatiotemporal variations in the distribution of notified TB case notifications were observed at zonal and district levels with most of the hotspot areas detected in the northern and southern parts of the region. The spatiotemporal distribution of notified TB incidence was positively associated with different ecological variables including temperature (β = 0.142; 95% credible interval (CrI): 0.070, 0.215), wind speed (β = -0.140; 95% CrI: -0.212, -0.068), health service coverage (β = 0.426; 95% CrI: 0.347, 0.505), and population density (β = 0.491; 95% CrI: 0.390, 0.594). The findings of this study indicated that preventive measures considering socio-demographic and health system factors can be targeted to high-risk areas for effective control of TB in the Oromia region. Further studies are needed to develop effective strategies for reducing the burden of TB in hotspot areas.

Comparative serum lipid and immunohematological values among adult pulmonary tuberculosis and tuberculosis lymphadenitis cases and their association with sputum bacilli load and time to culture positivity in Northwestern Ethiopia

BACKGROUND

The serum lipid and immunohematological values of tuberculosis lymphadenitis (TBLN) patients is poorly documented relative to pulmonary tuberculosis (PTB) cases. Therefore, the aim of this study was to investigate the serum lipid and immunohematological values of patients with TBLN in comparison with PTB (PTB) patients.

METHODS

An institution-based comparative cross-sectional study was conducted in Northwest Ethiopia from March to December 2021. The study participants were bacteriologically confirmed PTB (n = 82) and TBLN (n = 94) cases with no known comorbidity and whose ages was greater than 18 years and with no current pregnancy. Independent sample t-test, one-way ANOVA, box plot, and correlation matrix were used to analyze the data.

RESULTS

The body mass index (BMI), CD4 + T cell count, and high-density lipoprotein-Cholesterol (HDL-C) values were significantly higher among TBLN cases compared with PTB cases. Additionally, the total white blood cell (WBC) count, hemoglobin (Hb), total Cholesterol (CHO) and creatinine (Cr) values were relatively higher among TBLN than PTB (P > 0.05). On the reverse, the platelet count and triacylglycerol (TAG) values were relatively higher among PTB than in TBLN cases. While the mean days of culture positivity were 11.6 days for TBLN, the mean days of culture positivity were 14.0 days for PTB. Anemia and serum lipid values showed no correlation with sputum bacilli load and time to culture positivity.

CONCLUSION

Tuberculous lymphadenitis patients were well-endowed with serum lipid, immunological and nutritional status compared with PTB cases. Hence, the high incidence rate of TBLN in Ethiopia could not be explained by low peripheral immunohematological values, malnutrition, Anemia, and dyslipidemia. Further study for identifying the predictors for TBLN in Ethiopia is highly desirable.

Prevalence of pulmonary tuberculosis in homeless individuals in the Addis Ababa City, Ethiopia

Background

Homeless individuals are at a high risk of infection with Mycobacterium tuberculosis (M. tuberculosis) as compared to the general population. The number of homeless individuals has been increasing in Addis Ababa City during the last three decades due to the migration of rural inhabitants to the City for better living conditions. The objective of this study was to estimate the prevalence of pulmonary tuberculosis (PTB) and evaluate associated risk factors in homeless individuals in Addis Ababa City.

Methods

A total of 5,600 homeless individuals were screened for PTB symptoms using WHO guideline between February 2019 and December 2020. Sputum samples were cultured from individuals with symptoms of PTB for mycobacterial isolation. Logistic regression analysis was used to identify factors associated with PTB.

Results

The prevalence of bacteriologically confirmed cases was 1.1% (59/5,600) or 10.54 per 1000 population. Multinomial logistic regression analysis showed that being homeless for more than 5 years, body mass index (BMI) < 18.5, smoking cigarette, living in a group of more than five individuals, close contact with chronic coughers, imprisonment and HIV infection were significantly associated with the prevalence of PTB in homeless individuals (P < 0.05).

Conclusion

In conclusion, the result of this study indicated that the prevalence of PTB in homeless individuals was higher than the prevalence of PTB in the general population of Addis Ababa City requiring for the inclusion of the homeless individuals in the TB control program.

Implementation of evidence-based multiple focus integrated intensified TB screening to end TB (EXIT-TB) package in East Africa: a qualitative study

INTRODUCTION

Tuberculosis (TB) remains a major cause of morbidity and mortality, especially in sub-Saharan Africa. We qualitatively evaluated the implementation of an Evidence-Based Multiple Focus Integrated Intensified TB Screening package (EXIT-TB) in the East African region, aimed at increasing TB case detection and number of patients receiving care.

OBJECTIVE

We present the accounts of participants from Tanzania, Kenya, Uganda, and Ethiopia regarding the implementation of EXIT-TB, and suggestions for scaling up.

METHODS

A qualitative descriptive design was used to gather insights from purposefully selected healthcare workers, community health workers, and other stakeholders. A total of 27, 13, 14, and 19 in-depth interviews were conducted in Tanzania, Kenya, Uganda, and Ethiopia respectively. Data were transcribed and translated simultaneously and then thematically analysed.

RESULTS

The EXIT-TB project was described to contribute to increased TB case detection, improved detection of Multidrug-resistant TB patients, reduced delays and waiting time for diagnosis, raised the index of TB suspicion, and improved decision-making among HCWs. The attributes of TB case detection were: (i) free X-ray screening services; (ii) integrating TB case-finding activities in other clinics such as Reproductive and Child Health clinics (RCH), and diabetic clinics; (iii), engagement of CHWs, policymakers, and ministry level program managers; (iv) enhanced community awareness and linkage of clients; (v) cooperation between HCWs and CHWs, (vi) improved screening infrastructure, (vii) the adoption of the new simplified screening criteria and (viii) training of implementers. The supply-side challenges encountered ranged from disorganized care, limited space, the COVID-19 pandemic, inadequate human resources, inadequate knowledge and expertise, stock out of supplies, delayed maintenance of equipment, to absence of X-ray and GeneXpert machines in some facilities. The demand side challenges ranged from delayed care seeking, inadequate awareness, negative beliefs, fears towards screening, to financial challenges. Suggestions for scaling up ranged from improving service delivery, access to diagnostic equipment and supplies, and infrastructure, to addressing client fears and stigma.

CONCLUSION

The EXIT-TB package appears to have contributed towards increasing TB case detection and reducing delays in TB treatment in the study settings. Addressing the challenges identified is needed to maximize the impact of the EXIT-TB intervention.

Spatial Clustering of Tuberculosis-HIV Coinfection in Ethiopia at Districts Level

Background

Tuberculosis (TB) is a preventable and treatable disease but it is the leading cause of death among people living with HIV (PLHIV). In addition, the emergence of the HIV pandemic has also had a major impact on TB incidence rates. There are studies in spatial patterns of TB and HIV separately in Ethiopia; there is, however, no information on spatial patterns of TB-HIV coinfection in the country at the districts level at least using yearly data. This paper, therefore, aimed at determining the spatial clustering of TB-HIV coinfection prevalence rates in the country at the districts level on an annual basis over a four-year period, 2015-2018.

Methods

District-level aggregated data on the number of TB-HIV infections were obtained from the Ethiopian Federal Ministry of Health for 2015 to 2018. The univariate and bivariate global Moran's index, Getis-Ord G _i ^∗ local statistic, a chi-square test, and a modified t-test statistic for Spearman's correlation coefficient were used to evaluate the spatial clustering and spatial heterogeneity of TB among PLHIV and HIV among TB patients prevalence rates.

Results

The district-level prevalence rate of HIV among TB patients was positively and significantly spatially autocorrelated with global Moran's I values range between 0.021 and 0.134 (p value <0.001); however, the prevalence of TB among PLHIV was significant only for 2015 and 2017 (p value <0.001). Spearman's correlation also shows there was a strong positive association between the two prevalence rates over the study period. The local indicators of spatial analysis using the Getis-Ord statistic revealed that hot-spots for TB among PLHIV and HIV among TB patients have appeared in districts of various regions and the two city administrations in the country over the study period; however, the geographical distribution of hotspots varies over the study period. Similar trends were also observed for the cold-spots except for 2017 and 2018 where there were no cold-spots for TB among PLHIV.

Conclusions

The study presents detailed knowledge about the spatial clustering of TB-HIV coinfection in Ethiopia at the districts level, and the results could provide information for planning coordinated district-specific interventions to jointly control both diseases in Ethiopia.

Bayesian spatial modelling of tuberculosis-HIV co-infection in Ethiopia

An in-depth analysis of the epidemiological patterns of TB/HIV co-infection is essential since it helps to target high-risk areas with effective control measures. The main objective of this study was to assess the spatial clustering of TB/HIV co-infection prevalence in Ethiopia for the year 2018 using district-level aggregated TB and HIV data obtained from the Ethiopian Federal Ministry of Health. The global Moran's index, Getis-Ord [Formula: see text] local statistic, and Bayesian spatial modeling techniques were applied to analyse the data. The result of the study shows that TB among people living with HIV (PLHIV) and HIV among TB patients prevalence were geographically heterogeneous. The highest prevalence of TB among PLHIV in 2018 was reported in the Gambella region (1.44%). The overall prevalence of TB among PLHIV in Ethiopia in the same year was 0.38% while the prevalence of HIV among TB patients was 6.88%. Both district-level prevalences of HIV among TB patients and TB among PLHIV were positively spatially autocorrelated, but the latter was not statistically significant. The local indicators of spatial analysis using the Getis-Ord statistic also identified hot-spots districts for both types of TB/HIV co-infection data. The results of Bayesian spatial logistic regression with spatially structured and unstructured random effects using the Besag, York, and Mollié prior showed that not all the heterogeneities in the prevalence of HIV among TB patients and TB among PLHIV were explained by the spatially structured random effects. This study expanded knowledge about the spatial clustering of TB among PLHIV and HIV among TB patients in Ethiopia at the district level in 2018. The findings provide information to health policymakers in the country to plan geographically targeted and integrated interventions to jointly control TB and HIV.

Spatial Co-Clustering of Tuberculosis and HIV in Ethiopia

Background: Tuberculosis (TB) and HIV are epidemiologically associated, and their co-dynamics suggest that the two diseases are directly related at the population level and within the host. However, there is no or little information on the joint spatial patterns of the two diseases in Ethiopia. The main objective of the current study was to assess the spatial co-clustering of TB and HIV cases simultaneously in Ethiopia at the district level. Methods: District-level aggregated data collected from the national Health Management Information System (HMIS) for the years 2015 to 2018 on the number of TB cases enrolled in directly observed therapy, short course (DOTS) who were tested for HIV and the number of HIV patients enrolled in HIV care who were screened for TB during their last visit to health care facilities were used in this study. The univariate and bivariate global and local Moran’s I indices were applied to assess the spatial clustering of TB and HIV separately and jointly. Results: The results of this study show that the two diseases were significantly (p-value <0.001) spatially autocorrelated at the district level with minimum and maximum global Moran’s I values of 0.407 and 0.432 for TB, 0.102 and 0.247 for HIV, and 0.152 and 0.251 for joint TB/HIV. The district-level TB/HIV spatial co-clustering patterns in Ethiopia in most cases overlapped with the hot spots of TB and HIV. The TB/HIV hot-spot clusters may appear due to the observed high TB and HIV prevalence rates in the hot-spot districts. Our results also show that there were low-low TB/HIV co-clusters or cold spots in most of the Afar and Somali regions, which consistently appeared for the period 2015−2018. This may be due to very low notifications of both diseases in the regions. Conclusions: This study expanded knowledge about TB and HIV co-clustering in Ethiopia at the district level. The findings provide information to health policymakers in the country to plan geographically targeted and integrated interventions to jointly control TB and HIV.

Mapping BCG vaccination coverage in Ethiopia between 2000 and 2019

Introduction

The Bacille-Calmette–Guerin (BCG) vaccination remains the primary strategy to prevent severe disseminated TB in young children, particularly in high TB-burden countries such as Ethiopia. Accurate knowledge of vaccination coverage in small geographical areas is critically important to developing targeted immunization campaigns. Thus, this study aimed to investigate the spatiotemporal distributions and ecological level determinants of BCG vaccination coverage in Ethiopia.

Method

Bacille-Calmette–Guerin immunization coverage and geographical information data were obtained from five different Demographic and Health Surveys, conducted in Ethiopia between 2000 and 2019. Data for independent variables were obtained from publicly available sources. Bayesian geostatistical models were used to predict the spatial distribution of BCG vaccination coverage in Ethiopia.

Result

The overall national BCG vaccination coverage between 2000 and 2019 was 65.5%. The BCG vaccine coverage was 53.5% in 2000, 56.9% in 2005, 64.4% in 2011, 79.6% in 2016, and 79.0% in 2019. BCG vaccination coverage increased by 47.6% in Ethiopia from 2000 to 2019, but substantial geographical inequalities in BCG coverage remained at sub-national and local levels. High vaccination coverage was observed in northern, western, and central parts of Ethiopia. Climatic and demographic factors such as temperature, altitude, and population density were positively associated with BCG vaccination coverage. Whereas, healthcare access factors such as distance to health facilities and travel time to the nearest cities were negatively associated with BCG vaccine coverage in Ethiopia.

Conclusion

Despite substantial progress in national BCG vaccination coverage, marked spatial variation in BCG coverage persists throughout the country at sub-national and local levels. Healthcare access and climatic and demographic factors determined the spatial distribution of BCG vaccination coverage. Maintaining a high level of vaccination coverage across geographical areas is important to prevent TB in Ethiopia.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07547-4.

Space-Time Distribution Characteristics of Tuberculosis and Its Socioeconomic Factors in Southern China from 2015 to 2019

Purpose

Guangxi is a high prevalence area of tuberculosis (TB) in China, urgent needing of further TB reduction. Our purpose is to analyze the epidemiological characteristics of TB in Guangxi and analyze the relationship between socioeconomic factors and TB from the dimensions of time and space to provide evidence to effectively prevent and control TB.

Patients and Methods

We performed a retrospective analysis of the epidemiology of TB. Moran’s index (I) was used for spatial autocorrelation analysis, and space-time scanning was used to detect temporal, space, and space-time clusters of TB. A Bayesian space-time model was used to analyze related factors of the TB epidemic at the county level in Guangxi.

Results

From 2015 to 2019, a total of 233,623 TB cases were reported in Guangxi. The majority of TB cases were in males; the reported incidence of TB was the highest in people aged ≥65 years. By occupation, farmers were the most frequently affected. The overall reported incidence of TB decreased by 4.95% during this period. Tuberculosis occurs all year round, but the annual reporting peak is usually from March to July. Spatial autocorrelation analysis showed that the reported incidence of TB in 2015–2019 was spatially clustered (Moran’s I > 0, P < 0.05); Kulldorff’s scan revealed that the space-time cluster (log-likelihood ratio = 2683.76, relative risk = 1.60, P < 0.001) was mainly concentrated in northern Guangxi. Using Bayesian space-time modeling, socioeconomic and healthcare factors are related to the high prevalence of TB.

Conclusion

The prevalence of TB is influenced by a space-time interaction effect and is associated with socioeconomic and healthcare status. It is necessary to improve the economic development and health service in areas with a high TB prevalence.

Community detection in large scale congested urban road networks

Traffic congestion in large urban networks may take different shapes and propagates non-uniformly variations from day to day. Given the fact that congestion on a road segment is spatially correlated to adjacent roads and propagates spatiotemporally with finite speed, it is essential to describe the main pockets of congestion in a city with a small number of clusters. For example, the perimeter control with macroscopic fundamental diagrams is one of the effective traffic management tools. Perimeter control adjusts the inflow to pre-specified regions of a city through signal timing on the border of a region in order to optimize the traffic condition within the region. The precision of macroscopic fundamental diagrams depends on the homogeneity of traffic condition on road segments of the region. Hence, previous studies have defined the boundaries of the region under perimeter control subjected to the regional homogeneity. In this study, a cost-effective method is proposed for the mentioned problem that simultaneously considers homogeneity, contiguity and compactness of clusters and has a shorter computational time. Since it is necessary to control the cost and complexity of perimeter control in terms of the number of traffic signals, sparse parts of the network could be potential candidates for boundaries. Therefore, a community detection method (Infomap) is initially adopted and then those clusters are improved by refining the communities in relation to roads with the highest heterogeneity. The proposed method is applied to Shenzhen, China and San Francisco, USA and the outcomes are compared to previous studies. The results of comparison reveal that the proposed method is as effective as the best previous methods in detecting homogenous communities, but it outperforms them in contiguity. It is worth noting that this is the first method that guarantees the connectedness of clusters, which is a prerequisite of perimeter control.

Spatial clustering and genetic diversity of Mycobacterium tuberculosis isolate among pulmonary tuberculosis suspected patients, Arsi Zone, Ethiopia

BACKGROUND

Tuberculosis remains a serious public health concern globally. The enormous social, economic, and health impacts of the diseases are attributed to the lack of updated data on the prevalence, geospatial distribution, population structures, and genotypic variants of the circulating M. tuberculosis.

METHODS

Structured questionnaire, mycobacterial culture, and standard 24-Mycobacterial Interspersed Repeated Units-Variable Number Tandem Repeats (MIRU-VNTR) were employed to collect sociodemographic characters, residence linked information, and genotype the isolates. The retrospective discrete Bernoulli model was used to identify the hot spot districts of sputum smear positivity, and Web-based Miru-VNTRPlus were used for the identification of lineages and sublineages.

RESULTS

Out of 832 presumptive pulmonary tuberculosis (PTB) suspects, 119 (14.3%) were smear-positive. In the multivariate binary logistic model, PTB suspected patients in the age groups of 7-25 and 25-34 and those from rural residents were 4.53 (AOR = 4.53; 95% CI 2.25-9.13), 3.00 (AOR = 3.00; 95% CI 1.41-6.35) and 1.65 (AOR = 1.65; 95% CI 1.01-2.70) times at higher risk of turning smear-positive. Eleven (47.8%) districts of Arsi Zone were shown to have a high rate of clustering (RR = 2.27; 95% CI 1.62-3.2) of smear-positive PTB. Of 72 isolates queried for the lineage assignment, 59 (81.9%) were classified into the previously known lineages and 13 (18.1%) were not assigned to any known lineages. Overall, 42 (58.3%) belong to M. tuberculosis lineage 4 (Euro-American), 16 (22.2%) M. tuberculosis lineage 3 (Delhi/CAS), and 1 (1.4%) M. tuberculosis Lineage 1 (Indo-Oceanic/ East Africa Indian). Further classification to the sublineage indicates that the predominant lineage was Delhi/CAS comprising 16 (22.2%) isolates followed by 15 (20.8%) isolates belonging to Haarlem. The remaining isolates were distributed as 13 (18.1%) TUR, 6 (8.3%) LAM, 4 (5.5%) URAL, 4 (4.5%) NEW-1 and 1 (1.4%) EAI.

CONCLUSION

Our study showed higher smear-positive results among PTB suspected patients and remarkable spatial variation across districts of Arsi Zone in smear-positive PTB. This information together with the genotypic features could be used as input for the efforts of designing control strategies.

Spatiotemporal Patterns of Tuberculosis in Hunan Province, China

Tuberculosis (TB) is the leading cause of death from a bacterial pathogen worldwide. China has the third highest TB burden in the world, with a high reported burden in Hunan Province (amongst others). This study aimed to investigate the spatial distribution of TB and identify socioeconomic, demographic, and environmental drivers in Hunan Province, China. Numbers of reported cases of TB were obtained from the Tuberculosis Control Institute of Hunan Province, China. A wide range of covariates were collected from different sources, including from the Worldclim database, and the Hunan Bureau of Statistics. These variables were summarized at the county level and linked with TB notification data. Spatial clustering of TB was explored using Moran’s I statistic and the Getis–Ord statistic. Poisson regression models were developed with a conditional autoregressive (CAR) prior structure, and with posterior parameters estimated using a Bayesian approach with Markov chain Monte Carlo (MCMC) simulation. A total of 323,340 TB cases were reported to the Hunan TB Control Institute from 2013 to 2018. The mean age of patients was 51.7 years (SD + 17.6 years). The majority of the patients were male (72.6%, n = 234,682) and had pulmonary TB (97.5%, n = 315,350). Of 319,825 TB patients with registered treatment outcomes, 306,107 (95.7%) patients had a successful treatment outcome. The annual incidence of TB decreased over time from 85.5 per 100,000 population in 2013 to 76.9 per 100,000 population in 2018. TB case numbers have shown seasonal variation, with the highest number of cases reported during the end of spring and the beginning of summer. Spatial clustering of TB incidence was observed at the county level, with hotspot areas detected in the west part of Hunan Province. The spatial clustering of TB incidence was significantly associated with low sunshine exposure (RR: 0.86; 95% CrI: 0.74, 0.96) and a low prevalence of contraceptive use (RR: 0.88; 95% CrI: 0.79, 0.98). Substantial spatial clustering and seasonality of TB incidence were observed in Hunan Province, with spatial patterns associated with environmental and health care factors. This research suggests that interventions could be more efficiently targeted at locations and times of the year with the highest transmission risk.

Increasing smear positive tuberculosis detection using a clinical score - A stepped wedge multicenter trial from Africa

BACKGROUND

The Bandim TBscore is a clinical score that predicts treatment outcome in Tuberculosis (TB) patients and proved useful as an indicator of which healthcare-seeking adults to refer for sputum smear microcopy. We aimed to test in a randomized trial if the TBscore could be used to enhance the detection of smear positive (SP) TB.

METHODS

We carried out a stepped wedge cluster-randomized trial at six health centers in Bissau, Guinea-Bissau, and Gondar, Ethiopia. The primary outcome was diagnostic yield for SP TB. Secondary outcomes were successful treatment and effect on overall 12 months mortality. The study was registered at the Pan African Clinical Trials Registry (PACTR201611001838365).

RESULTS

We included 3571 adults. Overall, there was no effect of the intervention on SP PTB detected (OR 1.39 (95%CI 0.75 - 2.56). Analysis stratified by country, showed that the TBscore increased case detection in Gondar (OR 4.05 (95%CI 1.67 - 9.85)) but no effect was found in Bissau (OR 0.47 (95%CI 0.22 - 1.05)) where take-up was much lower. Overall mortality decreased during the intervention (HR 0.31 (95%CI 0.13-0.72)).

CONCLUSION

Using the TBscore for triage before smear microscopy may improve case detection and decrease mortality if there is sufficient laboratory capacity to increase sputum smears.

Identifying geographical heterogeneity of pulmonary tuberculosis in southern Ethiopia: a method to identify clustering for targeted interventions

Background

Previous studies from Ethiopia detected disease clustering using broader geographic settings, but limited information exists on the spatial distribution of the disease using residential locations. An assessment of predictors of spatial variations of TB at community level could fill the knowledge gaps, and helps in devising tailored interventions to improve TB control.

Objective

To assess the pattern of spatial distribution of pulmonary tuberculosis (PTB) based on geographic locations of individual cases in the Dale district and Yirga Alem town in southern Ethiopia.

Methods

The socio-demographic characteristics of PTB cases were collected using a structured questionnaire, and spatial information was collected using geographic position systems. We carried out Getis and Ord (Gi*) statistics and scan statistics to explore the pattern of spatial clusters of PTB cases, and geographically weighted regression (GWR) was used to assess the spatial heterogeneities in relationship between predictor variables and PTB case notification rates (CNRs).

Results

The distribution of PTB varied by enumeration areas within the kebeles, and we identified areas with significant hotspots in various areas ineach year. In GWR analysis, the disease distribution showed a geographic heterogeneity (non-stationarity) in relation to physical access (distance to TB control facilities) and population density (AICc = 5591, R² = 0.3359, adjusted R² = 0.2671). The model explained 27% of the variability in PTB CNRs (local R² ranged from 0.0002–0.4248 between enumeration areas). The GWR analysis showed that areas with high PTB CNRs had better physical accessibility to TB control facilities and high population density. The effect of physical access on PTB CNRs changed after the coverage of TB control facilities was improved.

Conclusion

We report a varying distribution of PTB in small and different areas over 10 years. Spatial and temporal analysis of disease distribution can be used to identify areas with a high burden of disease and predictors of clustering, which helps in making policy decisions and devising targeted interventions.

Spatial-temporal clustering of notified pulmonary tuberculosis and its predictors in East Gojjam Zone, Northwest Ethiopia

BACKGROUND

Tuberculosis (TB) remains a key health menace in Ethiopia and its districts. This study aimed to assess the spatial-temporal clustering of notified pulmonary TB (PTB) cases in East Gojjam Zone, Northwest Ethiopia.

METHODS

A retrospective study was conducted among all PTB cases reported from 2013-2019. Case notification rates (CNRs) of PTB cases at Kebele (the lowest administrative unit), woreda, and zone levels were estimated. The PTB clustering was done using global Moran's I statistics on Arc GIS 10.6. We used Kulldorff SaTScan 9.6 with a discrete Poisson model to identify statistically significant spatial-temporal clustering of PTB cases at Kebele level. Similarly, a negative binomial regression analysis was used to identify factors associated with the incidence of PTB cases at kebele level.

RESULTS

A total of 5340 (52%) smear-positive and 4928 (48%) smear-negative PTB cases were analyzed. The overall mean CNR of PTB cases at zone, woreda and Kebele levels were 58(47-69), 82(56-204), and 69(36-347) per 100,000 population, respectively. The purely spatial cluster analysis identified eight most likely clusters (one for overall and one per year for seven reporting years) and 47 secondary clusters. Similarly, the space-time scan analysis identified one most likely and seven secondary clusters. The purely temporal analysis also detected one most likely cluster from 2013-2015. Rural residence, distance from the nearest health facility, and poor TB service readiness were factors (p-value <0.05) to PTB incidence at kebele level.

CONCLUSION

The distribution of PTB cases was clustered. The PTB CNR was low and showed a decreasing trend during the reporting periods. Rural residence, distance from the health facilities, and poor facility readiness were factors of PTB incidence. Improving accessibility and readiness of health facilities mainly to rural and hotspot areas is vital to increase case detection and reduce TB transmission.

Spatial patterns of tuberculosis and HIV co-infection in Ethiopia

BACKGROUND

Tuberculosis (TB) and human immunodeficiency virus (HIV) are the leading causes of infectious-disease-related deaths in Ethiopia, but little is known about their spatial distribution across the country. The aim of this study was to investigate the spatial patterns of TB and HIV co-infection in Ethiopia at the district level.

METHODS

We conducted an ecological study using TB and HIV data reported from all regions of Ethiopia through the national Health Management Information System (HMIS), between June 2015 and June 2017. Spatial clustering was assessed using Moran's I statistic and Getis-Ord statistic. Spatial binomial regression models were constructed separately for the prevalence of TB among people living with HIV and for the prevalence of HIV among TB patients, with and without spatial components using a Bayesian approach.

RESULTS

A total of 1,830,880 HIV and 192,359 TB patients were included in the analysis. The prevalence of HIV among TB patients was 7.34%; hotspots were observed in districts located in Amhara, Afar, and Gambela regions, and cold spots were observed in Oromiya and Southern Nations, Nationalities, and People (SNNP) regions. The prevalence of TB among people living with HIV varied from 0.7% in Oromia region to 14.5% in Afar region. Hotspots of TB prevalence among people living with HIV were observed in districts located in Gambela, Afar, Somali, and Oromiya regions; whereas the cold spots were observed in districts located in Amhara and Tigray regions. The ecological-level factors associated with the prevalence of TB among people living with HIV were low wealth index (OR: 1.49; 95% CrI: 1.05, 2.05), low adult literacy rate (OR: 0.67; 95% CrI: 0.46, 0.94), and distance to an international border (OR: 0.61; 95% CrI: 0.40, 0.91). The factors associated with the prevalence of HIV among TB patients were poor health care access (OR: 0.76; 95% CrI: 0.59, 0.95), low wealth index (OR: 1.31; 95% CrI: 1.01, 1.67), and low adult literacy rate (OR: 1.37; 95% CrI: 1.03, 1.78).

CONCLUSION

Our study provides evidence for geographic clustering of TB/HIV co-infection in Ethiopia. Health care access, proximity to international borders, and demographic factors such as low wealth index and adult literacy were significantly associated with the prevalence of TB/HIV co-infection.