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

Spatial autocorrelation with environmental factors related to tuberculosis prevalence in Nepal, 2020-2023

BACKGROUND

Despite global efforts to reduce tuberculosis (TB) incidence, Nepal remains burdened by approximately 70,000 new cases annually, with an incidence rate of 229 per 100,000 people in 2022. This study investigated the geographic patterns of TB notifications in Nepal from fiscal year 2020 to 2023, focusing on environmental determinants such as land surface temperature (LST), urbanization, precipitation and cropland coverage.

METHODS

This study examined the spatial association between environmental factors and TB prevalence in Nepal at the district level, utilizing Geographic Information System (GIS) techniques, bivariate Local Indicators of Spatial Association (LISA) and spatial regression analyses. The tuberculosis prevalence data were obtained from the National Tuberculosis Control Center (NTCC) Nepal for the fiscal years (FY) 2020-2023.

RESULTS

Over the three fiscal years, high TB prevalence consistently clustered in districts such as Banke, Parsa, and Rautahat, while low prevalence areas included Mustang and Kaski. Significant positive spatial autocorrelation was found between environmental factors and TB prevalence. Moran's I values were as follows: for LST (day), 0.379, 0.424, and 0.423; for LST (night), 0.383, 0.420, and 0.425; for cropland coverage, 0.325, 0.339, and 0.373; for urbanization, 0.197, 0.245, and 0.246; and for precipitation, 0.222, 0.349, and 0.104 across FY 2020-2021, FY 2021-2022 and FY 2022-2023, respectively. Regression analyses, including Ordinary Least Squares (OLS), Spatial Lag Model (SLM), and Spatial Error Model (SEM), demonstrated that Land Surface Temperature Night (LSTN), urbanization, and precipitation significantly influenced TB prevalence, explaining up to 72.1% of the variance in FY 2021-2022 (R2: 0.721).

CONCLUSIONS

Environmental factors significantly influence the spatial distribution of TB in Nepal. This underscores the importance of integrating disease management strategies with environmental health policies in effectively addressing TB prevalence.

Spatial pattern of isoniazid-resistant tuberculosis and its associated factors among a population with migrants in China: a retrospective population-based study

Background

Isoniazid-resistant, rifampicin-susceptible tuberculosis (Hr-TB) globally exhibits a high prevalence and serves as a potential precursor to multidrug-resistant tuberculosis (MDR-TB). Recognizing the spatial distribution of Hr-TB and identifying associated factors can provide strategic entry points for interventions aimed at early detection of Hr-TB and prevention of its progression to MDR-TB. This study aims to analyze spatial patterns and identify socioeconomic, demographic, and healthcare factors associated with Hr-TB in Shanghai at the county level.

Method

We conducted a retrospective study utilizing data from TB patients with available Drug Susceptible Test (DST) results in Shanghai from 2010 to 2016. Spatial autocorrelation was explored using Global Moran's I and Getis-Ord G i ∗ statistics. A Bayesian hierarchical model with spatial effects was developed using the INLA package in R software to identify potential factors associated with Hr-TB at the county level.

Results

A total of 8,865 TB patients with DST were included in this analysis. Among 758 Hr-TB patients, 622 (82.06%) were new cases without any previous treatment history. The drug-resistant rate of Hr-TB among new TB cases in Shanghai stood at 7.20% (622/8014), while for previously treated cases, the rate was 15.98% (136/851). Hotspot areas of Hr-TB were predominantly situated in southwestern Shanghai. Factors positively associated with Hr-TB included the percentage of older adult individuals (RR = 3.93, 95% Crl:1.93-8.03), the percentage of internal migrants (RR = 1.35, 95% Crl:1.15-1.35), and the number of healthcare institutions per 100 population (RR = 1.17, 95% Crl:1.02-1.34).

Conclusion

We observed a spatial heterogeneity of Hr-TB in Shanghai, with hotspots in the Songjiang and Minhang districts. Based on the results of the models, the internal migrant population and older adult individuals in Shanghai may be contributing factors to the emergence of areas with high Hr-TB notification rates. Given these insights, we advocate for targeted interventions, especially in identified high-risk hotspots and high-risk areas.

Assessing heterogeneity of patient and health system delay among TB in a population with internal migrants in China

Backgrounds

The diagnostic delay of tuberculosis (TB) contributes to further transmission and impedes the implementation of the End TB Strategy. Therefore, we aimed to describe the characteristics of patient delay, health system delay, and total delay among TB patients in Shanghai, identify areas at high risk for delay, and explore the potential factors of long delay at individual and spatial levels.

Method

The study included TB patients among migrants and residents in Shanghai between January 2010 and December 2018. Patient and health system delays exceeding 14 days and total delays exceeding 28 days were defined as long delays. Time trends of long delays were evaluated by Joinpoint regression. Multivariable logistic regression analysis was employed to analyze influencing factors of long delays. Spatial analysis of delays was conducted using ArcGIS, and the hierarchical Bayesian spatial model was utilized to explore associated spatial factors.

Results

Overall, 61,050 TB patients were notified during the study period. Median patient, health system, and total delays were 12 days (IQR: 3-26), 9 days (IQR: 4-18), and 27 days (IQR: 15-43), respectively. Migrants, females, older adults, symptomatic visits to TB-designated facilities, and pathogen-positive were associated with longer patient delays, while pathogen-negative, active case findings and symptomatic visits to non-TB-designated facilities were associated with long health system delays (LHD). Spatial analysis revealed Chongming Island was a hotspot for patient delay, while western areas of Shanghai, with a high proportion of internal migrants and industrial parks, were at high risk for LHD. The application of rapid molecular diagnostic methods was associated with reduced health system delays.

Conclusion

Despite a relatively shorter diagnostic delay of TB than in the other regions in China, there was vital social-demographic and spatial heterogeneity in the occurrence of long delays in Shanghai. While the active case finding and rapid molecular diagnosis reduced the delay, novel targeted interventions are still required to address the challenges of TB diagnosis among both migrants and residents in this urban setting.