Characterizing social behavior relevant for tuberculosis transmission in four low- and middle-income countries
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Abstract
Background
Tuberculosis (TB) is caused by Mycobacterium tuberculosis ( Mtb ), a bacterium which is transmitted through the air. Close, sustained contact can lead to transmission of Mtb , but evidence also shows that transmission occurs also in community settings through more transient contact. However, social patterns that influence Mtb transmission, and locations that are most central to spread, are likely different by setting.
Methods
We use data from the GlobalMix study, which characterized social behavior across four countries, to evaluate differences in age-sex patterning and locations of contact across four moderate- to high-TB burden countries. Healthy individuals self-completed a survey about their daily person-to-person interactions and locations in which they spent time. To capture the Mtb exposure profile of participants, we calculated daily exposure-hours from household contacts, close non-household contacts, and casual contacts, stratified by contact age and sex, and weighted by country-, age-, and sex-specific tuberculosis incidence estimates.
Results
The most prominent shifts in the profile of exposure occur at entry to primary school (5-9 years) and early adulthood (20-29 years). Community locations varied in their relative importance as locations of transmission by country and age group, with school most important in India and Guatemala, transit more important in Pakistan, and others’ homes most important in Mozambique.
Conclusions
Our findings demonstrate that locations of community transmission are likely varied across settings, underscoring the importance of interventions designed specifically for the communities in which they will be implemented.
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This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17647831.
Summary and Overall Impression
This paper explores how daily contact patterns and social behaviors contribute to tuberculosis (TB) transmission in four low- and middle-income countries: India, Pakistan, Mozambique, and Guatemala. Using GlobalMix data, the authors analyze contact diaries to estimate exposure-hours from household, close, and casual interactions. They find that most exposure happens in community settings such as schools, public transit, and others' homes, and that exposure patterns vary by age, sex, and country.
Overall, this is a clear and well-written study that provides useful insight into TB transmission outside households. It uses a large multicountry dataset and presents …
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17647831.
Summary and Overall Impression
This paper explores how daily contact patterns and social behaviors contribute to tuberculosis (TB) transmission in four low- and middle-income countries: India, Pakistan, Mozambique, and Guatemala. Using GlobalMix data, the authors analyze contact diaries to estimate exposure-hours from household, close, and casual interactions. They find that most exposure happens in community settings such as schools, public transit, and others' homes, and that exposure patterns vary by age, sex, and country.
Overall, this is a clear and well-written study that provides useful insight into TB transmission outside households. It uses a large multicountry dataset and presents the results with good structure and visual clarity. The findings are relevant for public health interventions. However, the paper could better discuss data limitations and the possible influence of COVID-19 on contact behaviors.
Major Comments
1. Limitations of Self-Reported Contact Data: The study relies on self-reported contact diaries, which may introduce recall bias and underreport brief or forgotten interactions. This is especially important for casual contacts, which the study found to be the main exposure source. The authors should acknowledge this limitation more explicitly.
2. Impact of COVID-19: Since data collection occurred during or soon after the COVID-19 pandemic, contact patterns might not reflect typical behavior. Including a short note on how local restrictions or public behavior changes could have affected the results would strengthen the discussion.
3. Sample Restriction: The analysis includes only healthy participants, excluding individuals with active TB who may have systematically different contact behaviors. This limits generalizability to the population most relevant for transmission studies. The authors should explicitly discuss how restricting the sample to healthy individuals may bias the exposure-hour estimates, clarify the expected direction of bias, and explain how this assumption affects interpretation of their results.
4. Exposure Duration Cap: The exposure duration was capped at four hours. This could underestimate risk from long, close interactions. The authors should clearly describe how this cap may bias exposure-hour estimates and discuss its implications for interpretation.
5. Policy Implications: The conclusion could give clearer examples of how the results inform intervention design. For instance, the study's finding that schools and transport are key exposure sites could suggest ventilation improvements or school-based screening programs.
Minor Comments
- The abstract could include sample size for context.
- A short explanation of the "Q index" in the figure legends would help general readers.
- Some color scales in figures are difficult to compare between countries—standardizing them may help.
- Ensure consistent formatting of country names and references.
Strengths
- Large multicountry dataset covering diverse social and cultural contexts.
- Thoughtful use of exposure-hour weighting with national TB incidence data.
- Clear organization and logical flow of results.
- High public health relevance with potential for practical intervention design.
Overall Recommendation
This is a strong and well-presented study. The authors should expand the discussion of data limitations and clarify how pandemic conditions and sampling choices might affect the interpretation of contact patterns.
Recommendation: Minor Revision.
Competing interests
The author declares that they have no competing interests.
Use of Artificial Intelligence (AI)
The author declares that they did not use generative AI to come up with new ideas for their review.
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