Longitudinal clustering of tuberculosis incidence and predictors for the time profiles: the impact of HIV

Determining the risk-factors for molecular clustering of drug-resistant tuberculosis in South Africa

BACKGROUND

Drug-resistant tuberculosis (DR-TB) epidemic is driven mainly by the effect of ongoing transmission. In high-burden settings such as South Africa (SA), considerable demographic and geographic heterogeneity in DR-TB transmission exists. Thus, a better understanding of risk-factors for clustering can help to prioritise resources to specifically targeted high-risk groups as well as areas that contribute disproportionately to transmission.

METHODS

The study analyzed potential risk-factors for recent transmission in SA, using data collected from a sentinel molecular surveillance of DR-TB, by comparing demographic, clinical and epidemiologic characteristics with clustering and cluster sizes. A genotypic cluster was defined as two or more patients having identical patterns by the two genotyping methods used. Clustering was used as a proxy for recent transmission. Descriptive statistics and multinomial logistic regression were used.

RESULT

The study identified 277 clusters, with cluster size ranging between 2 and 259 cases. The majority (81.6%) of the clusters were small (2-5 cases) with few large (11-25 cases) and very large (≥ 26 cases) clusters identified mainly in Western Cape (WC), Eastern Cape (EC) and Mpumalanga (MP). In a multivariable model, patients in clusters including 11-25 and ≥ 26 individuals were more likely to be infected by Beijing family, have XDR-TB, living in Nelson Mandela Metro in EC or Umgungunglovo in Kwa-Zulu Natal (KZN) provinces, and having history of imprisonment. Individuals belonging in a small genotypic cluster were more likely to infected with Rifampicin resistant TB (RR-TB) and more likely to reside in Frances Baard in Northern Cape (NC).

CONCLUSION

Sociodemographic, clinical and bacterial risk-factors influenced rate of Mycobacterium tuberculosis (M. tuberculosis) genotypic clustering. Hence, high-risk groups and hotspot areas for clustering in EC, WC, KZN and MP should be prioritized for targeted intervention to prevent ongoing DR-TB transmission.

A scoping review of the clinical application of machine learning in data-driven population segmentation analysis

OBJECTIVE

Data-driven population segmentation is commonly used in clinical settings to separate the heterogeneous population into multiple relatively homogenous groups with similar healthcare features. In recent years, machine learning (ML) based segmentation algorithms have garnered interest for their potential to speed up and improve algorithm development across many phenotypes and healthcare situations. This study evaluates ML-based segmentation with respect to (1) the populations applied, (2) the segmentation details, and (3) the outcome evaluations.

MATERIALS AND METHODS

MEDLINE, Embase, Web of Science, and Scopus were used following the PRISMA-ScR criteria. Peer-reviewed studies in the English language that used data-driven population segmentation analysis on structured data from January 2000 to October 2022 were included.

RESULTS

We identified 6077 articles and included 79 for the final analysis. Data-driven population segmentation analysis was employed in various clinical settings. K-means clustering is the most prevalent unsupervised ML paradigm. The most common settings were healthcare institutions. The most common targeted population was the general population.

DISCUSSION

Although all the studies did internal validation, only 11 papers (13.9%) did external validation, and 23 papers (29.1%) conducted methods comparison. The existing papers discussed little validating the robustness of ML modeling.

CONCLUSION

Existing ML applications on population segmentation need more evaluations regarding giving tailored, efficient integrated healthcare solutions compared to traditional segmentation analysis. Future ML applications in the field should emphasize methods' comparisons and external validation and investigate approaches to evaluate individual consistency using different methods.

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

BACKGROUND

Tuberculosis (TB) transmission often occurs within a household or community, leading to heterogeneous spatial patterns. However, apparent spatial clustering of TB could reflect ongoing transmission or co-location of risk factors and can vary considerably depending on the type of data available, the analysis methods employed and the dynamics of the underlying population. Thus, we aimed to review methodological approaches used in the spatial analysis of TB burden.

METHODS

We conducted a systematic literature search of spatial studies of TB published in English using Medline, Embase, PsycInfo, Scopus and Web of Science databases with no date restriction from inception to 15 February 2017. The protocol for this systematic review was prospectively registered with PROSPERO ( CRD42016036655 ).

RESULTS

We identified 168 eligible studies with spatial methods used to describe the spatial distribution (n = 154), spatial clusters (n = 73), predictors of spatial patterns (n = 64), the role of congregate settings (n = 3) and the household (n = 2) on TB transmission. Molecular techniques combined with geospatial methods were used by 25 studies to compare the role of transmission to reactivation as a driver of TB spatial distribution, finding that geospatial hotspots are not necessarily areas of recent transmission. Almost all studies used notification data for spatial analysis (161 of 168), although none accounted for undetected cases. The most common data visualisation technique was notification rate mapping, and the use of smoothing techniques was uncommon. Spatial clusters were identified using a range of methods, with the most commonly employed being Kulldorff's spatial scan statistic followed by local Moran's I and Getis and Ord's local Gi(d) tests. In the 11 papers that compared two such methods using a single dataset, the clustering patterns identified were often inconsistent. Classical regression models that did not account for spatial dependence were commonly used to predict spatial TB risk. In all included studies, TB showed a heterogeneous spatial pattern at each geographic resolution level examined.

CONCLUSIONS

A range of spatial analysis methodologies has been employed in divergent contexts, with all studies demonstrating significant heterogeneity in spatial TB distribution. Future studies are needed to define the optimal method for each context and should account for unreported cases when using notification data where possible. Future studies combining genotypic and geospatial techniques with epidemiologically linked cases have the potential to provide further insights and improve TB control.

Hepatic granulomas: a 17-year single tertiary centre experience

AIMS

Hepatic granulomas have an estimated prevalence of 5% in liver biopsies, with a wide range of aetiologies globally. Our aim was to assess the clinical relevance, presenting features and underlying aetiology in a non-transplant, tertiary referral centre from a western country.

METHODS AND RESULTS

This was a retrospective, single-centre review of clinical, laboratory and histological data including all adult patients for whom a liver biopsy was performed from January 1998 to December 2014. A total of 297 cases with hepatic granulomas were found in 9374 biopsies, but 57 were excluded from analysis either because they were lipogranulomas or the biopsy/aetiological work-up had not been performed at our institution. Overall, the most common aetiology was tuberculosis (35.8%), followed by primary biliary cholangitis (PBC) - 15.0%. In 30 patients (12.5%) granulomas were classified as idiopathic. From 1998 to June 2006 there were 147 granulomas in 5304 biopsies (2.8%), a frequency that did not change significantly compared to the period from July 2006 to December 2014 (93 granulomas in 4070 biopsies, 2.3%, P > 0.05). However, for the majority of cases (61.9%) there was a shift in granuloma aetiology during the former time-period that infectious diseases were responsible, whereas in the latter, autoimmune liver diseases (43%) were the main aetiology. In addition, while three cases of drug-induced granulomas were found from 1998 to June 2006, we report two cases in the second time-period.

CONCLUSIONS

Hepatic granulomas can result from various infectious and non-infectious diseases. During recent years, an epidemiological shift regarding granuloma aetiology was observed, from systemic infectious diseases to non-infectious, mainly immune-mediated primary liver disorders. With an appropriate work-up the aetiology can be identified in the vast majority of cases (~90%), rendering its histological identification and characterisation essential, as disease-specific therapies are becoming available.

Tuberculosis, social determinants and co-morbidities (including HIV)

The risk of exposure, progression to active tuberculosis (TB) and then to cure is a process affected by several risk factors. Along with well known risk factors such as human immunodeficiency virus (HIV), use of immunosuppressive drugs and being of young age, emerging risk factors such socio-economic and behavioral aspects play a significant role in increasing the susceptibility to infection, and unsuccessful treatment outcomes. This paper summarizes the effects of these socio-economic determinants and co-morbidities (including HIV) on TB infection and disease.