About

Helen E Jenkins is an Associate Professor in the Department of Biostatistics at Boston University School of Public Health. She holds an MSc in Biostatistics from the London School of Hygiene and Tropical Medicine and a PhD in Infectious Disease Epidemiology from Imperial College, London. Her career focuses on quantitative analysis of infectious disease data with the aim of influencing policy and decision-making to reduce morbidity and mortality caused by infectious diseases. Her research includes extensive modeling of tuberculosis in various settings to address public health questions, such as spatial heterogeneity of drug-resistant tuberculosis and the epidemiology of pediatric TB. Jenkins has analyzed surveillance and lab data from multiple countries, including Moldova, Georgia, Ukraine, South Africa, and Peru, to identify disease hot-spots and inform resource allocation. She has contributed to global TB burden estimates, notably publishing work that the WHO has incorporated into their official statistics. Additionally, her work on bovine TB in the UK and poliomyelitis surveillance has directly impacted policy and advanced scientific understanding. Jenkins is also the Co-Director of the Summer Institute of Biostatistics and Data Science, a program dedicated to introducing undergraduates to biostatistics.

Research topics

• Medicine
• Geography
• Political Science
• Sociology
• Medical education
• Nursing
• Public economics
• Development economics
• Biology
• Ecology
• Economic growth
• Medical emergency
• Telecommunications
• Engineering
• Economics
• Business
• Internal medicine
• Socioeconomics
• Virology
• Law

Selected publications

• Social Vulnerability Modifies the Effects of Geographic Proximity on Engagement in Latent Tuberculosis Infection Care in a United States Safety Net Healthcare Network

  Open Forum Infectious Diseases · 2025-06-10 · 1 citations

  articleOpen access

  Background: Latent tuberculosis (TB) infection care often requires engagement with multiple teams in several locations throughout the diagnostic and treatment steps of the TB infection care cascade. The intersecting effects of geographic proximity and social drivers on care cascade retention have not been well examined. Methods: We conducted a retrospective cohort study of patients with a positive TB infection test between 2018-2019 within a health system in Boston, Massachusetts. The primary outcome was attendance at a TB clinic after a referral was placed. The primary exposure was geographic proximity, as measured by travel time by car. We assessed effect modification of proximity by Social Vulnerability Index (SVI), a composite measure of census tract social drivers. Results: We identified 1677 patients with positive TB infection tests; 1208 (72%) were referred to a TB clinic, of whom 748 (62%) completed referral. Longer travel times were associated with lower odds of referral completion (furthest vs nearest quartiles: adjusted odds ratio, 0.76 [95% confidence interval, .71-.82]). SVI significantly modified the effects of proximity: Increasing travel time was associated with decreasing probability of clinic attendance for patients in lower-vulnerability census tracts but had minimal effect on clinic attendance among patients in higher vulnerability census tracts. Conclusions: Additional support is needed for individuals referred to TB clinics that require long travel times to attend. Support should also account for other social drivers affecting care access for those living near TB clinics.

  PublisherOA PDFDOI

• The risk of rifampicin-resistant TB after drug-susceptible TB treatment

  IJTLD OPEN · 2025-03-01

  articleOpen accessSenior author
  PublisherDOI

• Network Analysis of Pairwise Relative Tuberculosis Transmission Probabilities in Lima, Peru

  medRxiv · 2025-11-19

  preprintOpen accessSenior author

  Background: Identifying transmission events is important in understanding infectious disease dynamics. Such events are typically unobservable, particularly in diseases with long serial intervals such as tuberculosis (TB). We apply network techniques to identify transmission clusters and features shared within clusters. Methods: We estimate directed pairwise transmission probabilities via an existing iterative algorithm that employs a modified Naïve Bayes classifier to incorporate demographic, clinical, and genetic data and use these probabilities to create a network. We explore noise reduction techniques to trim low probability edges. We apply clustering algorithms to group together individuals with TB based on edges informed by transmission probabilities. We apply our framework to simulated data and assess how the clustering algorithms captured the simulated clusters. We then apply this approach to data from a cohort study in Lima, Peru and examine the homogeneity of the clusters using a binary entropy measure. Results: We find cluster performance to be consistent across all edge trimming scenarios and clustering methods. We find high levels of entropy for age, sex, socioeconomic status, and individuals who work outside the house and use public transit, indicating these variables are heterogenous across clusters. Conclusions: We demonstrate approaches to analyze estimated directed pairwise transmission probabilities with network techniques. The approach is consistent across network construction and clustering methods. This method can be applied to any disease outbreak to understand its dynamics.

  PublisherOA PDFDOI

• Data-driven targets for reducing the global burden of TB

  IJTLD OPEN · 2025-06-01 · 1 citations

  articleOpen access

  BACKGROUND: The proportion of persons with infectious TB that need to be cured to reduce prevalence is an important but not well characterized target for TB control. METHODS: We compared infectious TB prevalence from countries with two population-based surveys since 2000, accounting for persons receiving curative treatment and those dying or undergoing natural recovery. Annual incidence was estimated as the proportion of prevalence that, when applied to each year over the interval between the two surveys, yielded the observed second survey prevalence. We then determined the relationship between the proportion of people with TB cured and the change in prevalence in each of the years covered by the surveys. RESULTS: Achieving a decline in prevalence required curing at least 20% of those with infectious TB. None of the countries studied reached the 11% annual decline in prevalence required to yield the END TB goal of a 90% decrease in prevalence over 20 years; this would require diagnosing and curing 35-40% of people with prevalent TB each year. CONCLUSIONS: These results provide targets for achieving the goal of a 90% reduction in TB and indicate that active case finding will be required to reach these targets.

  PublisherDOI

• Adapting Back-calculation Methods to Estimate the Incidence of Tuberculosis

  Epidemiology · 2025-12-05

  articleOpen accessCorresponding

  BACKGROUND: Despite being the leading cause of death, the global tuberculosis (TB) burden is ill-defined. Existing methods to estimate incidence are time and/or resource-intensive and often inaccurate. Back-calculation was developed to estimate HIV incidence by considering reported cases to be a convolution of the disease duration and the incidence of new cases. New estimates of TB natural history parameters allow us to develop Bayesian back-calculation methods for TB to assign case notification data to the time point of onset of disease. METHODS: Recorded counts of TB cases are underestimates of the true burden of disease, so we include a multiplier derived from prevalence to notification ratios to account for underreporting. We assume a Poisson distribution for notifications and incidence and use a penalized-likelihood before smooth estimates. We estimate sex-stratified TB incidence for Vietnam, Cambodia, and the Philippines via Markov chain Monte Carlo. RESULTS: Annual estimated TB incidence was, on average 19% greater than recorded notifications. TB incidence among males was on average 3.8% higher than females in Vietnam, 1.3% in Cambodia, and 2.5% higher in the Philippines. CONCLUSIONS: These estimates account for the delay between bacteriologically positive subclinical disease and notification and, as such, may be more temporally accurate than existing methods.

  PublisherOA PDFDOI

• Xpert MTB/RIF Ultra ‘trace’: considerations for diagnostics

  The International Journal of Tuberculosis and Lung Disease · 2025-05-30

  articleOpen access
  PublisherOA PDFDOI

• Risk of rifampicin resistance emergence after incomplete first-line tuberculosis treatment

  European Respiratory Journal · 2025-07-01

  letterOpen accessSenior author

  Tuberculosis (TB) treatment is lengthy and causes side-effects, making treatment completion challenging. Some patients are “lost to follow-up” (LTFU) before completing treatment. Patients sometimes subsequently return to care if symptoms motivate them, or if health systems and/or personal issues that caused LTFU are resolved. Case–control studies have established that drug-susceptible TB treatment and incomplete adherence were risk factors for relapse with drug-resistant TB, but its frequency and the lengths of incomplete treatment that pose the greatest risk are unknown [1, 2]. We aimed to estimate the risk of recurrent TB, and specifically rifampicin-resistant TB (RIF-R), after rifampicin-susceptible TB (RS-TB) treatment and how these risks vary depending on previous RS-TB treatment length.

  PublisherOA PDFDOI

• Adaptive bandit algorithms increase efficiency of mobile tuberculosis screening programs

  Scientific Reports · 2025-12-08

  articleOpen access

  Community-based tuberculosis screening using mobile X-ray units can effectively increase case detection rates by reducing barriers to accessing services. This study evaluated the multi-armed bandit (MAB) framework, a machine learning approach, for optimizing mobile screening locations. Using simulations, we compared two MAB algorithms-Exp3 and LinUCB-with strategies based on historical case rates and random placement. The MAB algorithms continually updated site selection based on observed screening yields, and LinUCB additionally incorporated local socioeconomic indicators associated with tuberculosis rates. Over three years, assuming two mobile units serving 95 sites in Lima, Peru, 1,000 simulations demonstrated the MAB algorithms significantly reduced the average number of screenings needed to detect one individual with tuberculosis: 112 (standard deviation [SD]: 10) for Exp3 and 79 (SD: 12) for LinUCB, versus 152 (SD: 11) for random placement and 143 (SD: 11) for historic case-rate-driven placement. LinUCB performed best, achieving a 20% increase in detection efficiency by week 16 and 50% by week 40 compared to case-rate-driven placement. Overall, both MAB algorithms improved tuberculosis screening yields, emphasizing the value of data-driven approaches for optimizing mobile screening interventions. Incorporating adaptive models into screening programs may enhance targeting efficiency and offers a promising direction for policymakers and implementers seeking to optimize resource allocation in high-burden setting.

  PublisherOA PDFDOI

• Outcomes for people with TB by disease severity at presentation

  The International Journal of Tuberculosis and Lung Disease · 2024-03-01 · 3 citations

  articleOpen accessSenior author

  <sec id="st1"><title>BACKGROUND</title>There is substantial heterogeneity in disease presentation for individuals with TB disease, which may correlate with disease outcomes. We estimated disease outcomes by disease severity at presentation among individuals with TB during the pre-chemotherapy era.</sec><sec id="st2"><title>METHODS</title>We extracted data on people with TB enrolled between 1917 and 1948 in the USA, stratified by three disease severity categories at presentation using the U.S. National Tuberculosis Association diagnostic criteria. These criteria were based largely on radiographic findings ("minimal", "moderately advanced", and "far advanced"). We used Bayesian parametric survival analysis to model the survival distribution overall, and by disease severity and Bayesian logistic regression to estimate the severity-level specific natural recovery odds within 3 years.</sec><sec id="st3"><title>RESULTS</title>People with minimal TB at presentation had a 2% (95% CrI 0-11%) probability of TB death within 5 years vs. 40% (95% CrI 15-68) for those with far advanced disease. Individuals with minimal disease had 13.62 times the odds (95% CrI 9.87-19.10) of natural recovery within 3 years vs. those with far advanced disease.</sec><sec id="st4"><title>CONCLUSION</title>Mortality and natural recovery vary by disease severity at presentation. This supports continued work to evaluate individualized (e.g., shortened or longer) regimens based on disease severity at presentation, identified using radiography.</sec>.

  PublisherOA PDFDOI

• The impact of the COVID-19 pandemic on TB notifications in Ukraine in 2020

  IJTLD OPEN · 2024-06-01 · 1 citations

  articleOpen accessSenior author

  BACKGROUND: We assessed the impact of the COVID-19 pandemic on TB notifications in Ukraine, stratified by multiple subgroups. DESIGN/METHODS: We analyzed data from Ukraine's National TB Program from January 2015 to December 2020 using interrupted time series models. We compared observed cases to counterfactual estimated cases had the pandemic not occurred and estimated trends through December 2020 nationally and by various demographics. We compared the proportions of individuals who underwent drug susceptibility testing (DST) in February 2020 and April 2020 to assess the pandemic impact on drug resistance testing. RESULTS: In April 2020, there were 39% (95% CI 36-42) fewer TB notifications than the estimated counterfactual (3,060 estimated; 95% CI 2,918-3,202; 1,872 observed). We observed a greater decrease in notifications among refugees/migrants compared with non-refugees/migrants (64%, 95% CI 60-67 vs. 39%, 95% CI 36-42), and individuals aged <15 years compared with those aged ≥15 years (60%, 95% CI 57-64 vs. 38%, 95% CI 36-41). We also observed a decrease in the proportion of individuals receiving DST for several drugs. CONCLUSIONS: These findings underscore the challenges to TB prevention and care during disruption and may be generalizable to the current wartime situation, especially considering the substantial increase in refugees within and leaving Ukraine.

  PublisherDOI

Recent grants

• Tracking tuberculosis patients in Ukraine to understand pathways through care and the impact of geographic movements

  NIH · $165k · 2021–2024

• Evaluating the tuberculosis epidemic in South Africa using a novel ten-year cohort

  NIH · $1.6M · 2020–2026

• Understanding spatial heterogeneity of drug resistant tuberculosis

  NIH · $627k · 2013–2019

Frequent coauthors

• Richard I. Lindley

  University of Sydney

  911 shared

• Yi Wang

  Weatherford College

  740 shared

• Thompson Robinson

  British Heart Foundation

  677 shared

• Joanna M. Wardlaw

  University of Edinburgh

  624 shared

• Lidan Wang

  Jiangsu University

  555 shared

• Hui Wang

  Xian Mental Health Center

  555 shared

• Longfei Wu

  Capital Medical University

  555 shared

• Craig S. Anderson

  491 shared