Neighbourhood risk factors of recurrent tuberculosis in Cape Town: a cohort study using geocoded notification data

Molemans, Marjan; Leth, Frank van; McKelly, David; Wood, Robin; Hermans, Sabine

doi:10.1136/jech-2022-219622

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…Data from 2016 onwards are collected in a different system and format and are managed by a separate organisation. Another limitation is related to the geocoding process, which shortcomings are also discussed in our previous paper using the same register [31]. The study excluded some neighbourhoods due to incomplete or inaccurate data, which may have resulted in a biased sample.…”

Section: Discussionmentioning

confidence: 99%

“…Looking at framework of Lönnroth et al [41], which we adapted for a study on neighbourhood factors and recurrent TB [31], we see socioeconomic status as an upstream determinant, which affects downstream factors. The downstream factors of neighbourhood TB burden, average household size affect contact with infectious droplets, while gender and age affect impaired host defence.…”

Section: Discussionmentioning

confidence: 99%

“…In previous work, we used probabilistic linkage to identify the first and recurrent episodes for the same individual [27] In a subsequent study, we geocoded the dataset to the neighbourhood of residence. The geocoding process was explained elsewhere [31]; but consisted of combining Stata 16.1 (StataCorp) and ArcGIS (ESRI, Redlands, California, USA) with a series of systematic corrections.…”

Section: Methodsmentioning

confidence: 99%

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

Molemans,

Kayaert,

Olislagers

et al. 2024

Tropical Med Int Health

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ObjectivesAlthough 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).MethodsAll 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.ResultsThe 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.ConclusionNeighbourhood 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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Molemans,

Kayaert,

Olislagers

et al. 2024

Tropical Med Int Health

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Accessibility of TB diagnostic services at primary healthcare clinics in the eThekwini district, South Africa: a geospatial analysis

Dlangalala,

Musekiwa,

Mckelly

et al. 2024

BMJ Open

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BackgroundImproving geographic access can aid in managing tuberculosis (TB) by enabling early diagnosis and treatment initiation. Although geospatial techniques have been used to map the transmission patterns of drug-resistant TB in South Africa, fewer studies have investigated the accessibility of TB diagnostic services. This study evaluated the accessibility of TB diagnostic services and disease distribution in the eThekwini district of South Africa.MethodsIn this cross-sectional study, population data for 2021 were disaggregated into smaller analysis units and then re-aggregated through the dasymetric mapping technique. Data on notified TB patients, including Global Positioning System coordinates of clinics, were obtained from the District of Health Information System, exported to ArcGIS 10.8.2 and used to calculate distances to the nearest clinics and hospitals.Results92% of the population (3 730 494 people) in eThekwini could access TB diagnostic services within 5 km. Patients travelled an average distance of 4.7 km (range: 0.1–26.9 km). TB diagnostic services were highly accessible in the Northern and Central regions and moderately accessible in the predominately rural Western and Southern regions. The smallest population of eThekwini resides in rural areas; however, 40.7% of its residents live >5 km from a diagnosing facility, with patients in the South having to travel up to 44.5 km. TB incidence was higher in the predominately rural West and South regions compared with the Central and North regions which are mainly comprised of urban and suburban areas. Our findings also showed that 98.4% of the clinics in eThekwini were located within 30 km of a hospital at an average distance of 9.6 km within the district. However, the distribution of these hospitals does not demonstrate equitable access as the majority are located within the Central region, and fewer are found in the other three regions of eThekwini.ConclusionsAddressing the disparities in access to TB diagnostic services is required in the eThekwini district. Leveraging the existing mobile health clinics can assist with this, particularly, in rural areas with inadequate access. Additionally, active-case finding should be intensified in these regions since they had a higher TB burden per population. Prioritising interventions in these areas is crucial for reducing the impact of the disease on affected communities.

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Association between TB delay and TB treatment outcomes in HIV-TB co-infected patients: a study based on the multilevel propensity score method

Liao,

Hu,

et al. 2024

BMC Infect Dis

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Background HIV-tuberculosis (HIV-TB) co-infection is a significant public health concern worldwide. TB delay, consisting of patient delay, diagnostic delay, treatment delay, increases the risk of adverse anti-TB treatment (ATT) outcomes. Except for individual level variables, differences in regional levels have been shown to impact the ATT outcomes. However, few studies appropriately considered possible individual and regional level confounding variables. In this study, we aimed to assess the association of TB delay on treatment outcomes in HIV-TB co-infected patients in Liangshan Yi Autonomous Prefecture (Liangshan Prefecture) of China, using a causal inference framework while taking into account individual and regional level factors. Methods We conducted a study to analyze data from 2068 patients with HIV-TB co-infection in Liangshan Prefecture from 2019 to 2022. To address potential confounding bias, we used a causal directed acyclic graph (DAG) to select appropriate confounding variables. Further, we controlled for these confounders through multilevel propensity score and inverse probability weighting (IPW). Results The successful rate of ATT for patients with HIV-TB co-infection in Liangshan Prefecture was 91.2%. Total delay (OR = 1.411, 95% CI: 1.015, 1.962), diagnostic delay (OR = 1.778, 95% CI: 1.261, 2.508), treatment delay (OR = 1.749, 95% CI: 1.146, 2.668) and health system delay (OR = 1.480 95% CI: (1.035, 2.118) were identified as risk factors for successful ATT outcome. Sensitivity analysis demonstrated the robustness of these findings. Conclusions HIV-TB co-infection prevention and control policy in Liangshan Prefecture should prioritize early treatment for diagnosed HIV-TB co-infected patients. It is urgent to improve the health system in Liangshan Prefecture to reduce delays in diagnosis and treatment.

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Neighbourhood risk factors of recurrent tuberculosis in Cape Town: a cohort study using geocoded notification data

Cited by 3 publications

References 25 publications

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

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

Accessibility of TB diagnostic services at primary healthcare clinics in the eThekwini district, South Africa: a geospatial analysis

Association between TB delay and TB treatment outcomes in HIV-TB co-infected patients: a study based on the multilevel propensity score method

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