Search

Places of worship serve as a venue for both mass and routine gathering around the world, and therefore are associated with risk of large-scale SARS-CoV-2 transmission. However, such routine gatherings also offer an opportunity to distribute self-tests to members of the community to potentially help mitigate transmission and reduce broader community spread of SARS-CoV-2. Over the past four years, self-testing strategies have been an impactful tool for countries' response to the COVID-19 pandemic, especially early on to mitigate the spread when vaccination and treatment options were limited. We used an agent-based mathematical model to estimate the impact of various strategies of symptomatic and asymptomatic self-testing for a fixed percentage of weekly routine gatherings at places of worship on community transmission of SARS-CoV-2 in Brazil, Georgia, and Zambia. Testing strategies assessed included weekly and bi-weekly self-testing across varying levels of vaccine effectiveness, vaccine coverage, and reproductive numbers to simulate developing stages of the COVID-19 pandemic. Self-testing symptomatic people attending routine gatherings can cost-effectively reduce the spread of SARS-CoV-2 within places of worship and the community, resulting in incremental cost-effectiveness ratios of $69-$303 USD. This trend is especially true in contexts where population level attendance at such gatherings is high, demonstrating that a distribution approach is more impactful when a greater proportion of the population is reached. Asymptomatic self-testing of attendees at 100% of places of worship in a country results in the greatest percent of infections averted and is consistently cost-effective but remains costly. Budgetary needs for asymptomatic testing are expensive and likely unaffordable for lower-middle income countries (520-1550x greater than that of symptomatic testing alone), promoting that strategies to strengthen symptomatic testing should remain a higher priority., Competing Interests: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. The views expressed in this manuscript are those of the authors and do not necessarily represent the official position of the WHO., (Copyright: © 2024 Hansen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Implicit statistical learning, whereby predictable relationships between stimuli are detected without conscious awareness, is important for language acquisition. However, while this process is putatively implicit, it is often assessed using measures that require explicit reflection and conscious decision making. Here, we conducted three experiments combining an artificial grammar learning paradigm with a serial reaction time (SRT-AGL) task, to measure statistical learning of adjacent and nonadjacent dependencies implicitly, without conscious decision making. Participants viewed an array of six visual stimuli and were presented with a sequence of three auditory (nonsense words, Expt. 1; names of familiar objects, Expt. 2) or visual (abstract shapes, Expt. 3) cues and were asked to click on the corresponding visual stimulus as quickly as possible. In each experiment, the final stimulus in the sequence was predictable based on items earlier in the sequence. Faster responses to this predictable final stimulus compared to unpredictable stimuli would provide evidence of implicit statistical learning, without requiring explicit decision making or conscious reflection. Despite previous positive results (Christiansen et al. 2009 and Misyak et al. 2010) we saw little evidence of implicit statistical learning in any of the experiments, suggesting that in this case, these SRT-AGL tasks were not an effective measure implicit statistical learning., Competing Interests: This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2024 Jenkins et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective: To use routinely collected data, with the addition of geographic information and census data, to identify local hot spots of rates of reported tuberculosis cases., Design: Residential locations of tuberculosis cases identified from eight public health facilities in Lima, Peru (2013-2018) were linked to census data to calculate neighborhood-level annual case rates. Heat maps of tuberculosis case rates by neighborhood were created. Local indicators of spatial autocorrelation, Moran's I, were used to identify where in the study area spatial clusters and outliers of tuberculosis case rates were occurring. Age- and sex-stratified case rates were also assessed., Results: We identified reports of 1,295 TB cases across 74 neighborhoods during the five-year study period, for an average annual rate of 124.2 reported TB cases per 100,000 population. In evaluating case rates by individual neighborhood, we identified a median rate of reported cases of 123.6 and a range from 0 to 800 cases per 100,000 population. Individuals aged 15-44 years old and men had higher case rates than other age groups and women. Locations of both hot and cold spots overlapped across age- and gender-specific maps., Conclusions: There is significant geographic heterogeneity in rates of reported TB cases and evident hot and cold spots within the study area. Characterization of the spatial distribution of these rates and local hot spots may be one practical tool to inform the work of local coalitions to target TB interventions in their zones., Competing Interests: The authors have declared that no competing interests exist.

Purpose: To evaluate the diagnostic value of symptoms used by daycares and schools to screen children and adolescents for SARS-CoV-2 infection, we analyzed data from a primary care setting., Methods: This cohort study included all patients ≤17 years old who were evaluated at Providence Community Health Centers (PCHC; Providence, U.S.), for COVID-19 symptoms and/or exposure, and received SARS-CoV-2 polymerase chain reaction (PCR) testing between March-June 2020. Participants were identified from PCHC electronic medical records. For three age groups- 0-4, 5-11, and 12-17 years-we estimated the sensitivity, specificity, and area under the receiver operating curve (AUC) of individual symptoms and three symptom combinations: a case definition published by the Rhode Island Department of Health (RIDOH), and two novel combinations generated by different statistical approaches to maximize sensitivity, specificity, and AUC. We evaluated symptom combinations both with and without consideration of COVID-19 exposure. Myalgia, headache, sore throat, abdominal pain, nausea, anosmia, and ageusia were not assessed in 0-4 year-olds due to the lower reliability of these symptoms in this group., Results: Of 555 participants, 217 (39.1%) were SARS-CoV-2-infected. Fever was more common among 0-4 years-olds (p = 0.002); older children more frequently reported fatigue (p = 0.02). In children ≥5 years old, anosmia or ageusia had 94-98% specificity. In all ages, exposure history most accurately predicted infection. With respect to individual symptoms, cough most accurately predicted infection in <5 year-olds (AUC 0.69) and 12-17 year-olds (AUC 0.62), while headache was most accurate in 5-11 year-olds (AUC 0.62). In combination with exposure history, the novel symptom combinations generated statistically to maximize test characteristics had sensitivity >95% but specificity <30%. No symptom or symptom combination had AUC ≥0.70., Conclusions: Anosmia or ageusia in children ≥5 years old should raise providers' index of suspicion for COVID-19. However, our overall findings underscore the limited diagnostic value of symptoms., Competing Interests: The authors have declared that no competing interests exist.

Setting: Alcohol use increases the risk of tuberculosis (TB) disease and is associated with worse outcomes., Objective: To determine whether alcohol use affects TB severity at diagnosis in a high-burden setting., Design: Participants were smear-positive people living with TB (PLWTB) in India. Disease severity was assessed as 1) high versus low smear grade, 2) time to positivity (TTP) on liquid culture, 3) chest radiograph cavitation, and 4) percent lung affected. Alcohol use and being at-risk for alcohol use disorders (AUD) were assessed using the AUDIT-C. Univariable and multivariable analyses were conducted., Results: Of 1166 PLWTB, 691 (59.3%) were drinkers; of those, 518/691 (75.0%) were at-risk for AUD. Drinkers had more lung affected than non-drinkers (adjusted mean difference 10.8%, p<0.0001); this was not significant for those at-risk for AUD (adjusted mean difference 3.7%, p = 0.11). High smear grade (aOR 1.0, 95%CI: 0.7-1.4), cavitation (aOR 0.8, 95%CI 0.4-1.8), and TTP (mean difference 5.2 hours, p = 0.51) did not differ between drinkers and non-drinkers, nor between those at-risk and not at-risk for AUD., Conclusions: A large proportion of PLWTB were drinkers and were at-risk for AUD. Alcohol drinkers had more lung affected than non-drinkers. Studies are needed to explore mechanisms of this association., Competing Interests: The authors have declared that no competing interests exist.

Background: Predictive models can serve as early warning systems and can be used to forecast future risk of various infectious diseases. Conventionally, regression and time series models are used to forecast dengue incidence, using dengue surveillance (e.g., case counts) and weather data. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) methods are designed to work with a large number of predictors and thus offer an appealing alternative. Here, we compared the performance of ML algorithms with that of regression models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contribution of dengue surveillance and weather data on the predictive power of these models., Methods: We developed ML, regression, and time series models to forecast weekly dengue case counts and outbreaks in Iquitos, Peru; San Juan, Puerto Rico; and Singapore from 1990-2016. Forecasts were generated using available weekly dengue surveillance, and weather data. We evaluated the agreement between model forecasts and actual dengue observations using Mean Absolute Error and Matthew's Correlation Coefficient (MCC)., Results: For near term predictions of weekly case counts and when using surveillance data, ML models had 21% and 33% less error than regression and time series models respectively. However, using weather data only, ML models did not demonstrate a practical advantage. When forecasting weekly dengue outbreaks 12 weeks in advance, ML models achieved a maximum MCC of 0.61., Conclusions: Our results identified 2 scenarios when ML models are advantageous over regression model: 1) predicting dengue weekly case counts 4 weeks ahead when dengue surveillance data are available and 2) predicting weekly dengue outbreaks 12 weeks ahead when dengue surveillance data are unavailable. Given the advantages of ML models, dengue early warning systems may be improved by the inclusion of these models., Competing Interests: The authors have declared that no competing interests exist.

Household contact studies of tuberculosis (TB) are a common way to study disease transmission dynamics. However these studies lack a mechanism for accounting for community transmission, which is known to be significant, particularly in high burden settings. We illustrate a statistical approach for estimating both the correlates with transmission of TB in a household setting and the probability of community transmission using a modified Bayesian mixed-effects model. This is applied to two household contact studies in Vitória, Brazil from 2008-2013 and Kampala, Uganda from 1995-2004 that enrolled households with an individual that was recently diagnosed with pulmonary TB. We estimate the probability of community transmission to be higher in Uganda (ranging from 0.21 to 0.69, depending on HHC age and HIV status of the index case) than in Brazil (ranging from 0.13 for young children to 0.50 in adults). These estimates are consistent with a higher overall burden of disease in Uganda compared to Brazil. Our method also estimates an increasing risk of community-acquired TB with age of the household contact, consistent with existing literature. This approach is a useful way to integrate the role of the community in understanding TB disease transmission dynamics in household contact studies., Competing Interests: The authors have declared that no competing interests exist.

Background: South Africa has the highest tuberculosis incidence globally (781/100,000), with an estimated 4.3% of cases being rifampicin resistant (RR). Control and elimination strategies will require detailed spatial information to understand where drug-resistant tuberculosis exists and why it persists in those communities. We demonstrate a method to enable drug-resistant tuberculosis monitoring by identifying high-burden communities in the Western Cape Province using routinely collected laboratory data., Methods and Findings: We retrospectively identified cases of microbiologically confirmed tuberculosis and RR-tuberculosis from all biological samples submitted for tuberculosis testing (n = 2,219,891) to the Western Cape National Health Laboratory Services (NHLS) between January 1, 2008, and June 30, 2013. Because the NHLS database lacks unique patient identifiers, we performed a series of record-linking processes to match specimen records to individual patients. We counted an individual as having a single disease episode if their positive samples came from within two years of each other. Cases were aggregated by clinic location (n = 302) to estimate the percentage of tuberculosis cases with rifampicin resistance per clinic. We used inverse distance weighting (IDW) to produce heatmaps of the RR-tuberculosis percentage across the province. Regression was used to estimate annual changes in the RR-tuberculosis percentage by clinic, and estimated average size and direction of change was mapped. We identified 799,779 individuals who had specimens submitted from mappable clinics for testing, of whom 222,735 (27.8%) had microbiologically confirmed tuberculosis. The study population was 43% female, the median age was 36 years (IQR 27-44), and 10,255 (4.6%, 95% CI: 4.6-4.7) cases had documented rifampicin resistance. Among individuals with microbiologically confirmed tuberculosis, 8,947 (4.0%) had more than one disease episode during the study period. The percentage of tuberculosis cases with rifampicin resistance documented among these individuals was 11.4% (95% CI: 10.7-12.0). Overall, the percentage of tuberculosis cases that were RR-tuberculosis was spatially heterogeneous, ranging from 0% to 25% across the province. Our maps reveal significant yearly fluctuations in RR-tuberculosis percentages at several locations. Additionally, the directions of change over time in RR-tuberculosis percentage were not uniform. The main limitation of this study is the lack of unique patient identifiers in the NHLS database, rendering findings to be estimates reliant on the accuracy of the person-matching algorithm., Conclusions: Our maps reveal striking spatial and temporal heterogeneity in RR-tuberculosis percentages across this province. We demonstrate the potential to monitor RR-tuberculosis spatially and temporally with routinely collected laboratory data, enabling improved resource targeting and more rapid locally appropriate interventions., Competing Interests: MBM is a member of the Editorial Board of PLOS Medicine. The authors declare no other competing interests exist.

Background: Estimating mortality risk during TB treatment in HIV co-infected patients is challenging for health professionals, especially in a low TB prevalence population, due to the lack of a standardized prognostic system. The current study aimed to develop and validate a simple mortality prognostic scoring system for TB/HIV co-infected patients., Methods: Using data from the CDC's Tuberculosis Genotyping Information Management System of TB patients in Texas reported from 01/2010 through 12/2016, age ≥15 years, HIV(+), and outcome being "completed" or "died", we developed and internally validated a mortality prognostic score using multiple logistic regression. Model discrimination was determined by the area under the receiver operating characteristic (ROC) curve (AUC). The model's good calibration was determined by a non-significant Hosmer-Lemeshow's goodness of fit test., Results: Among the 450 patients included in the analysis, 57 (12.7%) died during TB treatment. The final prognostic score used six characteristics (age, residence in long-term care facility, meningeal TB, chest x-ray, culture positive, and culture not converted/unknown), which are routinely collected by TB programs. Prognostic scores were categorized into three groups that predicted mortality: low-risk (<20 points), medium-risk (20-25 points) and high-risk (>25 points). The model had good discrimination and calibration (AUC = 0.82; 0.80 in bootstrap validation), and a non-significant Hosmer-Lemeshow test p = 0.71., Conclusion: Our simple validated mortality prognostic scoring system can be a practical tool for health professionals in identifying TB/HIV co-infected patients with high mortality risk.

Background: Badgers are involved in the transmission to cattle of bovine tuberculosis (TB), a serious problem for the UK farming industry. Cross-sectional studies have shown an association between bite wounds and TB infection in badgers which may have implications for M. bovis transmission and control, although the sequence of these two events is unclear. Transmission during aggressive encounters could potentially reduce the effectiveness of policies which increase the average range of a badger and thus its opportunities for interaction with other social groups., Methods: Data were obtained on badgers captured during a long term study at Woodchester Park, UK (1998-2006). Many badgers had multiple observations. At each observation, the badger was assigned a "state" depending on presence of bite wounds and/or TB infection. Hence each badger had a "transition" from the previous state to the current state. We calculated the numbers of each type of transition and the time spent in each state. Transition rates were calculated for each transition category, dividing the number of such transitions by the total time at risk. We compared the rate of bite wound acquisition in infected badgers with that for uninfected badgers and the rate of positive M.bovis test results in bitten badgers with that in unbitten badgers., Results: The rate of bite wound acquisition in infected badgers (0.291 per year) was 2.09 (95% CI: 1.41, 3.08) times that in uninfected badgers (0.139 per year). The rate of positive M.bovis test results in bitten badgers (0.097 per year) was 2.45 (95% CI: 1.29, 4.65) times that in unbitten badgers (0.040 per year)., Conclusions: We found strong evidence of both potential sequences of events consistent with transmission via bite wounds and distinctive behaviour in infected badgers. The complex relationship between behaviour and infection must be considered when planning TB control strategies.

Background: Quantifying isoniazid resistant (INH-R) tuberculosis (TB) is important because isoniazid resistance reduces the probability of treatment success, may facilitate the spread of multidrug resistance, and may reduce the effectiveness of isoniazid preventive therapy (IPT)., Methodology/principal Findings: We used data reported to the World Health Organization between 1994-2009 to estimate the INH-R burden among new and retreatment TB cases. We assessed geographical and temporal variation in INH-R and reported levels in high HIV prevalence countries (≥2%) to understand implications for IPT. 131 settings reported INH-R data since 1994. A single global estimate of the percentage of incident TB cases with INH-R was deemed inappropriate due to particularly high levels in the Eastern European region where 44.9% (95% CI: 34.0%, 55.8%) of incident TB cases had INH-R. In all other regions combined, 13.9% (95% CI: 12.6%, 15.2%) of incident cases had INH-R with the lowest regional levels seen in West/Central Europe and Africa. Where trend data existed, we found examples of rising and falling burdens of INH-R. 40% of high HIV prevalence countries reported national data on INH-R and 7.3% (95% CI: 5.5%, 9.1%) of cases in these settings had INH-R., Conclusions/significance: Outside the Eastern European region, one in seven incident TB cases has INH-R, while this rises to nearly half within Eastern Europe. Many countries cannot assess trends in INH-R and the scarcity of data from high HIV prevalence areas limits insight into the implications for IPT. Further research is required to understand reasons for the observed time trends and to determine the effects of INH-R for control of TB.

Background: In the British Isles, control of cattle tuberculosis (TB) is hindered by persistent infection of wild badger (Meles meles) populations. A large-scale field trial--the Randomised Badger Culling Trial (RBCT)--previously showed that widespread badger culling produced modest reductions in cattle TB incidence during culling, which were offset by elevated TB risks for cattle on adjoining lands. Once culling was halted, beneficial effects inside culling areas increased, while detrimental effects on adjoining lands disappeared. However, a full assessment of the utility of badger culling requires information on the duration of culling effects., Methodology/principal Findings: We monitored cattle TB incidence in and around RBCT areas after culling ended. We found that benefits inside culled areas declined over time, and were no longer detectable by three years post-culling. On adjoining lands, a trend suggesting beneficial effects immediately after the end of culling was insignificant, and disappeared after 18 months post-culling. From completion of the first cull to the loss of detectable effects (an average five-year culling period plus 2.5 years post-culling), cattle TB incidence was 28.7% lower (95% confidence interval [CI] 20.7 to 35.8% lower) inside ten 100 km(2) culled areas than inside ten matched no-culling areas, and comparable (11.7% higher, 95% CI: 13.0% lower to 43.4% higher, p = 0.39) on lands