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

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

BACKGROUND

Individuals with a history of tuberculosis (TB) disease are at higher risk of developing a subsequent episode than those without. Considering the role of social and environmental factors in tuberculosis, we assessed neighbourhood-level risk factors associated with recurrent tuberculosis in Cape Town, South Africa.

METHODS

This cohort consisted of patients who completed treatment for their first drug-sensitive TB episode between 2003 and 2015. Addresses were geocoded at neighbourhood level. Data on neighbourhood-level factors were obtained from the Census 2011 (household size, population density) and the City of Cape Town (Socio-Economic Index). Neighbourhood-level TB burden was calculated annually by dividing the number of notified TB episodes by the population in that neighbourhood. Multilevel survival analysis was performed with the outcome recurrent TB, defined as a second episode of TB, and controlling for individual-level risk factors (age, gender and time since first episode in years). Follow-up ended at the second episode, or on 31 December 2015, whichever came first.

RESULTS

The study included 173 421 patients from 700 neighbourhoods. Higher Socio-Economic Index was associated with a lower risk of recurrence compared with average Socio-Economic Index. An increased risk was found for higher household size and TB burden, with an increase of 20% for every additional person in mean household size and 10% for every additional TB episode/100 inhabitants. No association was found with population density.

CONCLUSION

Recurrent TB was associated with increased household size and TB burden at neighbourhood level. These findings could be used to target TB screening activities.

Epidemic investigations within an arm's reach - role of google maps during an epidemic outbreak

Epidemics such as novel Coronavirus 2019 (COVID-19) can be contained and the rate of infection reduced by public health measures such as epidemiologic inquiries and social distancing. Epidemiologic inquiry requires resources and time which may not be available or reduced when the outbreak is excessive. We evaluated the use of Google Maps Timeline (GMTL) for creating spatial epidemiologic timelines. The study compares locations, routes, and means of transport between GMTL and user recall for 17 suitable users who were recruited during March 2020. They were interviewed about their timeline using the Timeline Follow-Back (TLFB) method which was then compared to their GMTL and discrepancies between both methods were addressed. Interviewer conclusions were divided into categories: (1) participant recalled, (2) no recall (until shown). Categories were subdivided by GMTL accuracy: [a] GMTL accurate, [b] GMTL inaccurate, [c] GMTL data missing. A total of 362 locations were compared. Participants recalled 322 (88.95% SD = 8.55) locations compared with 40 (11.05%, SD = 2.05) locations not recalled. There were 304 locations found accurate on GMTL (83.98%, SD = 9.49), 29 (8.01%, SD = 1.11) inaccurate locations, and 29 (8.01%, SD = 0.54) missing locations. The total discrepancy between GMTL and TLFB recall was 95 cases (26.24%, SD = 3.25). Despite variations between users, Google Maps with GMTL technology may be useful in identifying potentially exposed individuals in a pandemic. It is especially useful when resources are limited. Further research is required with a larger number of users who are undergoing a real epidemiologic investigation to corroborate findings and establish further recommendations.