Characterization of geographic mobility among participants in facility- and community-based tuberculosis case finding in urban Uganda

Air pollution and mobility patterns in two Ugandan cities during COVID-19 mobility restrictions suggest the validity of air quality data as a measure for human mobility

We explored the viability of using air quality as an alternative to aggregated location data from mobile phones in the two most populated cities in Uganda. We accessed air quality and Google mobility data collected from 15th February 2020 to 10th June 2021 and augmented them with mobility restrictions implemented during the COVID-19 lockdown. We determined whether air quality data depicted similar patterns to mobility data before, during, and after the lockdown and determined associations between air quality and mobility by computing Pearson correlation coefficients ([Formula: see text]), conducting multivariable regression with associated confidence intervals (CIs), and visualized the relationships using scatter plots. Residential mobility increased with the stringency of restrictions while both non-residential mobility and air pollution decreased with the stringency of restrictions. In Kampala, PM2.5 was positively correlated with non-residential mobility and negatively correlated with residential mobility. Only correlations between PM2.5 and movement in work and residential places were statistically significant in Wakiso. After controlling for stringency in restrictions, air quality in Kampala was independently correlated with movement in retail and recreation (- 0.55; 95% CI =  - 1.01- - 0.10), parks (0.29; 95% CI = 0.03-0.54), transit stations (0.29; 95% CI = 0.16-0.42), work (- 0.25; 95% CI =  - 0.43- - 0.08), and residential places (- 1.02; 95% CI =  - 1.4- - 0.64). For Wakiso, only the correlation between air quality and residential mobility was statistically significant (- 0.99; 95% CI =  - 1.34- - 0.65). These findings suggest that air quality is linked to mobility and thus could be used by public health programs in monitoring movement patterns and the spread of infectious diseases without compromising on individuals' privacy.

Connecting the dots: understanding how human mobility shapes TB epidemics

Tuberculosis (TB) remains a leading infectious cause of death worldwide. Reducing TB infections and TB-related deaths rests ultimately on stopping forward transmission from infectious to susceptible individuals. Critical to this effort is understanding how human host mobility shapes the transmission and dispersal of new or existing strains of Mycobacterium tuberculosis (Mtb). Important questions remain unanswered. What kinds of mobility, over what temporal and spatial scales, facilitate TB transmission? How do human mobility patterns influence the dispersal of novel Mtb strains, including emergent drug-resistant strains? This review summarizes the current state of knowledge on mobility and TB epidemic dynamics, using examples from three topic areas, including inference of genetic and spatial clustering of infections, delineating source-sink dynamics, and mapping the dispersal of novel TB strains, to examine scientific questions and methodological issues within this topic. We also review new data sources for measuring human mobility, including mobile phone-associated movement data, and discuss important limitations on their use in TB epidemiology.

Geospatial analysis of reported activity locations to identify sites for tuberculosis screening

Mobile screening units can help close tuberculosis case detection gaps. Placing screening units where people at high risk for undiagnosed tuberculosis preferentially spend time could make screening more resource-effective. We conducted a case–control study in Lima, Peru to identify locations where people with tuberculosis were more likely to spend time than community controls. We surveyed participants about activity locations over the past 6 months. We used density-based clustering to assess how patient and control activity locations differed, and logistic regression to compare location-based exposures. We included 109 tuberculosis patients and 79 controls. In density-based clustering analysis, the two groups had similar patterns of living locations, but their work locations clustered in distinct areas. Both groups were similarly likely to use public transit, but patients predominantly used buses and were less likely to use rapid transit (adjusted odds ratio [aOR] 0.31, 95% confidence interval [CI] 0.10–0.96) or taxis (aOR 0.42, 95% CI 0.21–0.85). Patients were more likely to have spent time in prison (aOR 11.55, 95% CI 1.48–90.13). Placing mobile screening units at bus terminals serving locations where tuberculosis patients have worked and within and around prisons could help reach people with undiagnosed tuberculosis.

Tuberculosis contact tracing yield and associated factors in Uganda

BACKGROUND

The yield of tuberculosis (TB) contact tracing is historically low in Uganda. We determined factors associated with a positive contact tracing yield at an urban public TB clinic in Kampala, Uganda.

METHODS

We reviewed contact tracing registers of index TB cases registered between 2015 and 2020 at Kitebi Health Center, a primary level facility. Contacts who had symptoms of TB were designated as having presumptive TB. A contact investigation that yielded a new TB case was designated as a positive yield. We used logistic regression to determine factors associated with a positive yield of contact tracing.

RESULTS

Of 778 index TB cases, 455 (58.5%) had a contact investigation conducted. Index cases with a telephone contact in the unit TB register (adjusted odds ratio (aOR) 1.66, 95% CI 1.02-1.97, p = 0.036) were more likely to have a contact investigation conducted than those who did not. Of 1350 contacts, 105 (7.8%) had presumptive TB. Of these, 73 (69.5%) were further evaluated for active TB and 29 contacts had active TB. The contact tracing yield for active TB was therefore 2.1% (29/1,350). The odds of a positive yield increased tenfold with each additional presumptive contact evaluated for active TB (aOR 10.1, 95% CI 2.95-34.66, p < 0.001). Also, retreatment index TB cases were more likely to yield a positive contact (aOR 7.69 95% CI 2.08-25.00, p = 0.002) than to new cases.

CONCLUSION

TB contact tracing should aim to evaluate all contacts with presumptive TB and contacts of retreatment cases to maximise the yield of contact tracing.

Sociological variety and the transmission efficiency of Mycobacterium tuberculosis: a secondary analysis of qualitative and quantitative data from 15 communities in Zambia

OBJECTIVES

Selected Zambian communities formed part of a cluster randomised trial: the Zambia and South Africa TB and AIDS Reduction study (ZAMSTAR). There was wide variability in the prevalence of Mycobacterium tuberculosis infection and tuberculosis (TB) disease across these communities. We sought to clarify whether specific communities could have been more/less vulnerable to M. tuberculosis transmission as a result of sociological variety relevant to transmission efficiency.

DESIGN

We conducted a mixed methods secondary analysis using existing data sets. First, we analysed qualitative data to categorise and synthesise patterns of socio-spatial engagement across communities. Second, we compared emergent sociological variables with a measure of transmission efficiency: the ratio of the annual risk of infection to TB prevalence.

SETTING

ZAMSTAR communities in urban and peri-urban Zambia, spanning five provinces.

PARTICIPANTS

Fifteen communities, each served by a health facility offering TB treatment to a population of at least 25 000. TB notification rates were at least 400 per 100 000 per annum and HIV seroprevalence was estimated to be high.

RESULTS

Crowding, movement, livelihoods and participation in recreational activity differed across communities. Based on 12 socio-spatial indicators, communities were qualitatively classified as more/less spatially crowded and as more/less socially 'open' to contact with others, with implications for the presumptive risk of M. tuberculosis transmission. For example, watching video shows in poorly ventilated structures posed a presumptive risk in more socially open communities, while outdoor farming and/or fishing were particularly widespread in communities with lower transmission measures.

CONCLUSIONS

A dual dynamic of 'social permeability' and crowding appeared relevant to disparities in M. tuberculosis transmission efficiency. To reduce transmission, certain socio-spatial aspects could be adjusted (eg, increasing ventilation on transport), while more structural aspects are less malleable (eg, reliance on public transport). We recommend integrating community level typologies with genome sequencing techniques to further explore the significance of 'social permeability'.

TRIAL REGISTRATION NUMBER

ISRCTN36729271.