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Am J Respir Crit Care Med
Am J Respir Crit Care Med
ajrccm
American Journal of Respiratory and Critical Care Medicine
1073-449X
1535-4970
American Thoracic Society

202405-0958LE
10.1164/rccm.202405-0958LE
Correspondence
Pairs or Paradoxes: Questioning Assumptions in Tuberculosis Transmission Research
https://orcid.org/0000-0003-3171-7672
Lee Wen-Chung
Institute of Health Data Analytics and Statistics and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
Correspondence and requests for reprints should be addressed to Wen-Chung Lee, M.D., Ph.D., Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Room 536, No. 17, Xuzhou Road, Taipei 100, Taiwan. Email: wenchung@ntu.edu.tw.
17 7 2024
15 9 2024
17 7 2024
210 6 849850
Copyright © 2024 by the American Thoracic Society
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0. For commercial usage and reprints, please e-mail Diane Gern (dgern@thoracic.org).

National Science and Technology Council 10.13039/501100020950 MOST 111-2314-B-002-089-MY3
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pmcTo the Editor:

I commend Trevisi and colleagues for their study (1), which significantly advances our understanding of tuberculosis (TB) transmission dynamics by integrating whole-genome sequencing with comprehensive clinical and sociodemographic analyses. The study’s innovative design and analytical framework strategically use patient pairing, using a stringent criterion based on genetic distance, specifically, pairs differentiated by three or fewer SNPs as indicating a direct transmission (DT) event. Although concerns exist that a three-SNP cutoff might not completely encompass all true DT events, this approach significantly enriches the exploration of TB transmission dynamics. The comparative analysis between DT and non-DT pairs provides a detailed perspective on the factors influencing TB spread, crucial for devising effective public health interventions.

Despite these strengths, the use of “patient pairs” as the unit of analysis introduces substantial challenges for the analysis and interpretation of results and may lead to bias. Although modern computational power can handle the large dataset, the underlying methodology warrants closer scrutiny. Specifically, the comparative analysis between DT pairs and non-DT pairs, whether using contingency table analysis or logistic regression (2), relies on the assumption that these “pairs” are independent and identically distributed (i.i.d.). This assumption is robust for individual patients but problematic for patient pairs, where a single patient may appear in multiple pairs, thus violating the required independence. This fundamental flaw seriously compromises the reliability of any inferences that are made, including hypothesis testing (P values) and confidence intervals. For those uncertain about the severity of violating the i.i.d. assumption, consider this: Trevisi and colleagues analyzed 3,168,903 patient pairs as though they represent more than 3 million individual subjects, although the actual number of distinct individuals involved is fewer than 3,000. Clearly, inflating the sample size so drastically without appropriate adjustments is an unmistakable recipe for bias.

Beyond the i.i.d. issue, the study presents additional problems that merit attention. The non-DT pairs in this study actually encompass two distinct types, though they are not easily distinguishable: pairs in which one patient transmits TB to the other indirectly (indirect transmission pairs) and pairs in which there is no transmission link between the two whatsoever (no transmission [NT] pairs). A comparison between DT and indirect transmission pairs is logical. However, one might question the implications of comparing DT and NT pairs. The patients in an NT pair might reside in separate areas of Peru, making transmission between them impossible if one were to acquire TB; these are not the at-risk pairs for DT. Using these for comparison violates the principle of studying an at-risk population in epidemiology (3), much like conducting a case-control study of prostate cancer but using women as the control group. Furthermore, although the study is commendable for separately accounting for each covariate—age, sex, incarceration status, smoking, drinking, and others—for both the transmitter and the recipient in a pair, this detailed approach prompts a pertinent question for NT pairs: who should be designated as transmitter and who as recipient?

In conclusion, although Trevisi and colleagues’ use of patient pairs and detailed covariate accounting marks a notable advance (1), the study’s methodological issues demand immediate and thorough review. There is an urgent need to develop models that accurately account for the nonindependence of subjects in transmission studies and establish clear guidelines for control group selection in infectious disease research. Addressing these challenges will require robust collaboration between epidemiologists and biostatisticians to enhance the accuracy and reliability of future epidemiological research.

Supported by National Science and Technology Council in Taiwan grant MOST 111-2314-B-002-089-MY3.

Originally Published in Press as DOI: 10.1164/rccm.202405-0958LE on July 17, 2024

Author disclosures are available with the text of this letter at www.atsjournals.org.
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References

1. Trevisi L Brooks MB Becerra MC Calderón RI Contreras CC Galea JT et al. Who transmits tuberculosis to whom: a cross-sectional analysis of a cohort study in Lima, Peru Am J Respir Crit Care Med 2024 210 222 233 38416532
2. Pagano M Gauvreau K Mattie H Principles of biostatistics 3rd ed Boca Raton, FL CRC Press 2022
3. Lash TL VanderWeele TJ Haneuse S Rothman KJ Modern epidemiology 4th ed Philadelphia Wolters Kluwer 2021
