Individual Probability Research Articles

Association between social cohesion and food insecurity among adults living in a healthcare region in southern Brazil

BackgroundFood insecurity (FI) has grown worldwide in recent years, especially in developing countries. Studies have shown that aspects of the social environment, such as social cohesion in the neighborhood, may be associated with FI; however, this topic has yet to be explored, in Brazil. This study aims to verify the association between aspects of the perceived social environment of the neighborhood (social cohesion) and FI.MethodsThis is a cross-sectional study with adults and elderly individuals (n = 400) residing in a healthcare region in Porto Alegre, Rio Grande do Sul (Brazil). The sample is selected proportionally from areas of higher and lower socioeconomic status, and data are collected through household interviews. The presence of FI is assessed using the Brazilian Food Insecurity Scale (short version). The perception of the neighborhood regarding social cohesion is obtained using a questionnaire validated for the Brazilian population. Poisson regression with robust variance was used to estimate the crude and adjusted prevalence ratios (PRs) and their respective 95% confidence intervals (95% CIs).ResultsIndividuals with more positive perceptions of social cohesion in the neighborhood are less likely to experience FI than those with less positive perceptions [PR = 0.73; 95% CI (0.55–0.97)], after adjustments.ConclusionsThe community social environment is independently associated with FI and may have characteristics that positively influence the probability of individuals experiencing FI. Interventions at the community level are necessary to address FI.

Development of a Multivariable PredictionModel to Estimate Probability of Sleep-Disordered-Breathing inPatientsWith AF: MOODS-AF.

Sleep-disordered breathing (SDB) is common in patients with atrial fibrillation (AF) and negatively impacts treatment outcomes. Optimal tools for AF patient selection for SDB testing are lacking. This study sought to develop and validate a prediction tool to detect patients who have AF with moderate-to-severe SDB. Prospectively collected data on 442 consecutive ambulatory patients with AF who were undergoing polysomnography were used as the derivation sample. Performance was externally validated on a test cohort of 409 patients. Significant SDB was defined as an apnea-hypopnea-index≥15/h. Multivariable logistic regression was used to construct a prediction model and calculate individual SDB probabilities. Significant SDB was present in 34% and 54% of patients in the derivation and validation cohorts, respectively. The prediction model comprised age, sex, body mass index (BMI), diabetes, and previous stroke or transient ischemic attack. Following calibration, the model had a good discrimination ability for significant SDB on external validation (C-statistic: 0.75; 95%CI: 0.71-0.80). A simplified composite score (MOODS, range 0-8) comprised male sex (1 point), overweight (BMI: 25-29.9kg/m2, 1 point) or obesity (BMI:≥30kg/m2, 3 points), diabetes (2 points), and stroke/transient ischemic attack (2 points) had good discrimination on external validation (C-statistic: 0.73; 95%CI: 0.68-0.77). As a rule-out or a rule-in test, a MOODS score of≤1 had a 100% sensitivity and score of≥5 had a 96% specificity for detecting significant SDB, respectively. The MOODS score provides an individualized and accurate probability of significant SDB in patients with AF. MOODS has the potential to aid clinical decision making and allow efficient resource allocation.

The effect of consumption inequality on subjective well-being: Evidence from China.

As an essential dimension of economic inequality, consumption inequality is tightly associated with public welfare. This study investigates the effect of consumption inequality on individuals' subjective well-being (SWB) in China using data from the 2014, 2018, and 2020 China Family Panel Studies. The findings indicate that consumption inequality has a significant negative impact on SWB. Specifically, for every unit increase in consumption inequality, the probability of individuals rating their SWB as "Happy" and "Very happy" decreases by 0.37% and 5.45% respectively. In addition, individuals' confidence about their future serves as an intermediary in the connection between consumption inequality and SWB. The investigation of heterogeneity evidences that the adverse impact of consumption inequality on SWB is more pronounced in terms of subsistence and development expenditures. Consumption inequality affects SWB more seriously among lower-income and urban residents. Overall, this study holds important implications for addressing economic inequality to bolster individuals' welfare.

Predicting individual patient and hospital-level discharge using machine learning

BackgroundAccurately predicting hospital discharge events could help improve patient flow and the efficiency of healthcare delivery. However, using machine learning and diverse electronic health record (EHR) data for this task remains incompletely explored.MethodsWe used EHR data from February-2017 to January-2020 from Oxfordshire, UK to predict hospital discharges in the next 24 h. We fitted separate extreme gradient boosting models for elective and emergency admissions, trained on the first two years of data and tested on the final year of data. We examined individual-level and hospital-level model performance and evaluated the impact of training data size and recency, prediction time, and performance in subgroups.ResultsOur models achieve AUROCs of 0.87 and 0.86, AUPRCs of 0.66 and 0.64, and F1 scores of 0.61 and 0.59 for elective and emergency admissions, respectively. These models outperform a logistic regression model using the same features and are substantially better than a baseline logistic regression model with more limited features. Notably, the relative performance increase from adding additional features is greater than the increase from using a sophisticated model. Aggregating individual probabilities, daily total discharge estimates are accurate with mean absolute errors of 8.9% (elective) and 4.9% (emergency). The most informative predictors include antibiotic prescriptions, medications, and hospital capacity factors. Performance remains robust across patient subgroups and different training strategies, but is lower in patients with longer admissions and those who died in hospital.ConclusionsOur findings highlight the potential of machine learning in optimising hospital patient flow and facilitating patient care and recovery.

Detailed-level modelling of influence spreading on complex networks

The progress in high-performance computing makes it increasingly possible to build detailed models to investigate spreading processes on complex networks. However, current studies have been lacking detailed computational methods to describe spreading processes in large complex networks. To fill this gap we present a new modelling approach for analysing influence spreading via individual nodes and links on various network structures. The proposed influence-spreading model uses a probability matrix to capture the spreading probability from one node to another in the network. This approach enables analysing network characteristics in a number of applications and spreading processes using metrics that are consistent with the quantities used to model the network structures. In addition, this study combines sub-models and offers a comprehensive look at different applications and metrics previously discussed in cases of social networks, community detection, and epidemic spreading. Here, we also note that the centrality measures based on the probability matrix are used to identify the most significant nodes in the network. Furthermore, the model can be expanded to include additional properties, such as introducing individual breakthrough probabilities for the nodes and specific temporal distributions for the links.

The uneven geography of the health system and its effect on the individual probability of death by COVID-19

The COVID-19 pandemic caused many life losses, which were not uniform across territories. Several factors can explain geographical differences, including the health and sanitary infrastructure and the economic performance. The present study seeks to determine the probability of death by COVID-19 of individuals by analyzing their individual and contextual characteristics related to their canton of residence with emphasis on the structure of the health system. Using combined data at the individual and contextual level, a logit multilevel model is estimated. The results show that cantonal differences explain 12.8–22.6% of the total variance of the individual probability to die. Cantons where people were more likely and less likely to die by stage were identified. Level 1 health care centers played an important role in reducing the probability of death by COVID-19 of individuals from the isolation stage to the vaccination stage.

Coordination failure in experimental banks of different sizes

We run a laboratory experiment to investigate how group size affects coordination in a bank-run game, in which participants choose simultaneously whether to withdraw or not and group members change over time. We find that bank size significantly affects the individual withdrawal probability, which is on average 12% higher in large than in small banks. In the initial round(s), all groups exhibit a similar withdrawal rate of about 40%; then, large and medium banks converge to the bank-run equilibrium, while small banks exhibit no systematic convergence. In all banks, experience and beliefs significantly affect the probability to withdraw and to experiment, i.e., to take in the current round the decision opposite to what was the best response in the previous one. We show that experimentation is a strategic choice, and interpret it as an attempt at promoting group convergence towards the efficient equilibrium.

Evolutionary dynamics of stochastic games in set-structured populations.

In structured populations, the ecology of games may vary over neighborhoods. The effect of the ecological variations on population dynamics remains largely unknown. We here incorporate the ecological variations into the set-structured populations to explore the coevolutionary dynamics of the ecology and cooperation. Individuals of a population are distributed over sets. Interactions occur in the form of evolutionary games. When two individuals share more common sets, they play the weak prisoner's dilemma. Otherwise, they play the strong prisoner's dilemma. Both the set memberships and the strategy update in the evolutionary process. Changes in set memberships hold sway over the games to be played, which, in turn, influences the performance of strategies. Combining evolutionary set theory and random walks on graphs, we derived the conditions for cooperation to be selected under the weak selection limit. We find that a denser set-structured population increases the probability of individuals participating in a weak prisoner's dilemma, and thereby promoting the spread of cooperation. Properly modulating the population structure and the payoff feedback can further lower the critical benefit-cost ratio required for cooperation to be selected. Our results may help better understand the effects of ecological variations in enhancing cooperative behavior in set-structured populations.

The relationship between mental health and employment: a systematic review of cohort studies

Abstract Background Knowledge on the bi-directional association between employment status and mental health is important, as it affects multiple dimensions of our daily lives, both at the individual and societal level. Given that the mental health status and employment situation of individuals are likely to change over time, there is an important need for longitudinal data to better understand causality patterns in this association and the underlying mechanisms. Methods A systematic review was carried out based on the PRISMA guidelines in 3 electronic databases: PubMed, Embase, and PsycInfo. Studies eligible for inclusion were cohort studies, involving the general population, and assessing the relationship between employment status and mental health longitudinally. Results In total, 3,733 studies were identified and 36 met the inclusion criteria. The eleven studies focusing on the social causation hypothesis highlighted the negative long-term impact of unemployment and unstable employment on mental health. Four studies observed mental health improvements upon re-entering employment, although limited and in more specific contexts. The fourteen studies focusing on the health selection hypothesis found a lower probability for individuals with mental health problems in finding and maintaining employment. Finally, the eleven studies assessing the two hypothesis simultaneously found that mental health problems were both a consequence of and risk factor for poor employment status, although some important moderating factors, such as gender differences, influence the direction and strength of the association. Conclusions This review underscores the bi-directional association between the employment status and mental health of individuals, highlighting the need to develop targeted prevention (e.g. mental health support in the workplace) and interventions strategies (e.g. supported employment programs) in both the mental health and labor market systems. Key messages • The bidirectional link between employment and mental health highlights the need for concurrent responses to break the vicious cycle between poor mental health and social exclusion. • To reduce the clinical and social burden of mental health problems, gender differences must be taken into account in mental health and employment support practices and policies.

External Validation of the fullPIERS Risk Prediction Model in a U.S. Cohort of Individuals with Preeclampsia.

To externally validate the fullPIERS risk prediction model in a cohort of pregnant individuals with preeclampsia in the United States. This was a retrospective study of individuals with preeclampsia who delivered at 22 weeks or greater from January 1, 2010, to December 31, 2020. The primary outcome was a composite of maternal mortality or other serious complications of preeclampsia occurring within 48 hours of admission. We calculated the probability of the composite outcome using the fullPIERS prediction model based on data available within 12 hours of admission including, gestational age, chest pain or dyspnea, serum creatinine levels, platelet count, aspartate transaminase levels, and oxygen saturation. We assessed the model performance using the area under the curve (AUC) of the receiver operating characteristic curve. The optimal cutoff point was determined using Liu's method. A calibration plot was used to evaluate the model's goodness-of-fit. Among 1,510 individuals with preeclampsia, 82 (5.4%) experienced the composite outcome within 48 hours. The fullPIERS model achieved an AUC of 0.80 (95% confidence interval: 0.75-0.86). The predicted probability for individuals with the composite outcome (median: 18.8%; interquartile range: 2.9-59.1) was significantly higher than those without the outcome (median: 0.9%; interquartile range: 0.4-2.7). The optimal cutoff point of 5.5% yielded a sensitivity of 70.7% (95% CI: 59.6-80.3), a specificity of 85% (95% CI: 82.7-86.5), a positive likelihood ratio of 4.6 (95% CI: 3.8-5.5), and an odds ratio of 13.3 (95% CI: 8.1-21.8). The calibration plot indicated that the model underestimated risk when the predicted probability was below 1% and overestimated risk when the predicted probability exceeded 5%. The fullPIERS model demonstrated good discrimination in this U.S. cohort of individuals with preeclampsia, suggesting it may be a useful tool for healthcare providers to identify individuals at risk for severe complications.

Sociodemographic predictors of the association between self-reported sleep duration and depression

Abstract Background This cross-sectional study is apart from one of the first and largest population-based prospective cohort studies in the UAE called the United Arab Emirates Healthy Future Study (UAEHFS). The aim of this study is to investigate the association between self-perceived sleep duration and self-perceived depression after adjustment for sociodemographic factors. Baseline line data from UAEHFS cohort participants were included in this cross-sectional study. Univariable and multivariable logistic regression models were fitted, with the binarized self-reported sleep hours as an outcome. The predictors were the self-reported PHQ-8, sociodemographic factors, and body mass index (BMI). Odds ratios with 95% confidence intervals (CI) were reported. Results Of 11,455 participants, 4,295 (37.5%) were included in the complete case analysis after omitting missing values. We applied a complete case analysis following omitting missing values. Participants’ median age was 32.0 years (Interquartile-Range: 24.0, 39.0). In total, 63.6% of the participants reported sleep duration of more than 7 hours. Statistically significant negative association was observed between the total PHQ-8 score and binarized self-reported sleep, OR = 0.961 (95% CI: 0.948, 0.974). For one unit increase in age and BMI, the odds ratio of reporting shorter sleep with OR of 0.979 (95% CI: 0.969, 0.990) and 0.987 (95% CI: 0.977, 0.998) respectively. Conclusions The study findings indicate a correlation between higher total PHQ-8 scores and an increased probability of individuals reporting shorter self-perceived sleep durations when considering some sociodemographic factors such as age, marital status and employment status. However, there was no difference between females and males in reporting self-perceived sleep duration among depressed individuals. Key messages • There is a correlation between higher total PHQ-8 scores and an increased probability of individuals reporting shorter self-perceived sleep durations. • Our results can help contribute to the knowledge based on current and potential population mental health in the UAE and Gulf Region.

Association between adjunctive rifampin and gentamicin use and outcomes for patients with staphylococcal prosthetic valve endocarditis: a propensity-score adjusted retrospective cohort study.

Although guidelines recommend adjunctive rifampin and gentamicin use for patients with staphylococcal prosthetic valve endocarditis (PVE), evidence behind the recommendation is limited and conflicting. We performed a retrospective cohort study of all patients with staphylococcal PVE within the Veterans Health Administration during 2003-2021. Patients were identified with diagnostic codes for prosthetic valves and positive blood cultures for Staphylococcus species and confirmed via manual chart reviews. The primary outcome was the composite of all-cause mortality or recurrence of staphylococcal PVE within one year from diagnosis. Inverse probability of treatment weighting (IPTW) was used to estimate the probability of individuals receiving rifampin using propensity scores. IPTW-adjusted multivariable Cox regression analysis was used to compare outcomes between patients who received rifampin and gentamicin, and those did not. Among 373 patients with staphylococcal PVE, 275 (73.7%) and 225 (60.3%) received at least one dose of rifampin and gentamicin, respectively. The incidence of staphylococcal PVE increased from 0.47 (2003-11) to 0.77 (2012-21) per 10,000 hospitalizations. Gentamicin use declined over time (70.1% in 2003-2011 to 54.8% in 2012-2021, p = 0.04) while rifampin use did not change significantly (76.1% in 2003-2011 to 72.4% in 2012-2021, p = 0.43). The composite outcome was observed in 209 (56.0%). Neither rifampin use (adjusted hazard ratio [HR] 0.77, 95% CI 0.48-1.24) and gentamicin use (adjusted HR 1.11, 95% CI 0.71-1.74) was associated with the composite outcome. No significant association was observed between adjunctive rifampin or gentamicin use and improved outcomes.

How do firms respond to state retirement plan mandates?

AbstractWe investigate how state “Auto‐IRA” mandates affect firm offerings of employer‐sponsored retirement plans (ESRPs). These policies require firms without ESRPs to facilitate automatic employee contributions to state‐created individual retirement accounts. We find that these policies increase an individual's probability of working for a firm with an ESRP by 6%–9% and of being included in the ESRP by 8%–13%. At the firm level, these policies increase the probability of offering an ESRP by 7%, the probability of establishing a new ESRP by 41%–44%, and the number of ESRP participants by 6 percent.

Decoding the Methylation Patterns of ABC Transporters in Colorectal Cancer

Colorectal cancer (CRC) is a significant global health concern, posing a major threat to morbidity and mortality rates. The molecular mechanisms that drive CRC, particularly the DNA methylation patterns in ATP-binding cassette (ABC) genes, have attracted considerable attention because of their potential roles in CRC drug resistance, initiation, and progression. This study aimed to investigate the methylation patterns of ABC genes in CRC using a high-throughput microarray technology. Microarray methylation data from CRC and adjacent normal tissues were subjected to preprocessing, differential methylation analysis, and correlation analysis using the MethSurv tool for a detailed study of methylation patterns. The study revealed global hypomethylation of 43 ABC transporters and two pseudogenes in CRC compared to normal tissues. Receiver operating characteristic curve analysis identified 369 CpG sites as potential biomarkers for CRC diagnosis, with area under the curve values ranging from acceptable to outstanding. Survival analysis demonstrated the correlation between DNA methylation of individual CpG sites and patient survival probability in colon and rectal adenocarcinoma datasets from The Cancer Genome Atlas. The study provides insights into the epigenetic landscape of ABC genes in CRC, highlighting their potential roles in drug resistance, disease initiation, and progression. The findings offer opportunities for developing innovative therapeutic approaches and improving patient outcomes in the fight against CRC. Further research is needed to validate these results and investigate the functional implications of ABC gene methylation in CRC pathogenesis and treatment response.

Assessing Codend Mesh Selectivity: Comparing Diamond and Square Mesh Codend in the Red Sea Shrimp Trawl Fishery of Saudi Arabia

This study assessed catch composition, size selectivity, and fishing efficiency of demersal trawls targeting penaeid shrimp species in the Red Sea. It first compared the currently used diamond mesh codends in two fishing areas, Al Qunfudhah and Jazan, and then compared alternative square mesh codends to diamond mesh codends in Jazan. A total of 33 valid hauls were conducted in 2023, yielding 10,869 kg of total catch. The results showed that the square mesh codend significantly improved size selectivity, particularly for Penaeus semisulcatus and Metapenaeus monoceros, with L50 (50% retention length) values closer to their size at first maturity. The fishing efficiency indicators revealed a reduced retention probability for undersized individuals with square mesh codends. Additionally, bycatch discard rates decreased, indicating potential benefits for ecosystem conservation. This study suggests incorporating square mesh codends into fishery management regulations to enhance size selectivity and reduce bycatch during Red Sea shrimp trawling. Establishing a legal minimum landing size requirement is recommended to complement these efforts and promote sustainable fishing practices.

Prediction model for early diagnosis of late-onset sepsis in preterm newborns.

Late-onset neonatal sepsis has a high mortality rate in premature infants. To date, no single test in the evaluation of neonatal sepsis has been demonstrated to be both sensitive and specific enough to assist in timely decision making. The aim of our study is to develop a predictive model that can be applied to all premature babies, using clinical and laboratory findings in premature babies, to recognize late-onset neonatal sepsis. 65 premature patients diagnosed with culture-proven late-onset neonatal sepsis and hospitalized in Dr. Behcet Uz Pediatric Diseases and Surgery Training and Research Hospital neonatal intensive care unit between January 2018 and December 2020, and 65 premature newborns of similar age and gender who did not have sepsis were included in the study retrospectively. In our study, feeding difficulties, worsening in clinical appearance and fever were found to be significant among clinical findings, while thrombocytopenia and high C-reactive protein among laboratory findings are the strongest data supporting late-onset neonatal sepsis. In multiple regression analysis, thrombocytopenia, mean platelet volume, C-reactive protein, lymphocyte count and feeding difficulties had the highest odds ratio (p < 0.05). By converting these data into a scoring system, a nomogram was created that can be easily used by all clinicians. In our study, we developed a scoring system that can be easily applied to all premature patients by evaluating the clinical and laboratory findings in late-onset neonatal sepsis. We think that it will help in recognizing late-onset neonatal sepsis and strengthening the treatment decision. Predicting the individual probability of sepsis in preterm newborns may provide benefits for uninfected newborns to be exposed to less antibiotics, not to be separated from mother and baby, and to reduce healthcare system expenditures. The nomogram can be used to assess the likelihood of sepsis and guide treatment decision.

Dynamics and backward bifurcations of SEI tuberculosis models in homogeneous and heterogeneous populations

The main difference between tuberculosis (TB) and other infectious diseases is that the transmission of the bacterium should be considered not only as the development of a primary infection, but also as exogenous reinfection or endogenous reactivation. Moreover, individuals in the population may have heterogeneous contact rates, which can be described by complex networks. To this end, we present two differential equation-based TB models in homogeneous and heterogeneous populations. The first model assumes that the number of contacts per unit time is constant for the whole population, whereas the second model considers the heterogeneous number of contacts per unit time for each individual. We derive the basic reproduction number of each model using the next-generation matrix method, and analyze the dynamical properties of each model in detail. We find that the two models undergo backward bifurcations and have the same threshold condition for backward bifurcation. From this threshold condition, we see that the reduced rate of exogenous reinfection of individuals plays an important role in causing the backward bifurcation. Interestingly, the second model allows the threshold condition for backward bifurcation to be independent of network parameters. Thus, unlike other infectious disease models on complex networks, in controlling the spread of tuberculosis among populations with different numbers of contacts, we only need to focus on disease parameters during treatment. Finally, numerical simulations more intuitively demonstrate the impact of parameter changes on the prevalence of tuberculosis and reveal the model's richer and more interesting dynamical properties, such as bistability. Sensitivity analysis indicates that the basic reproduction number is highly correlated with both the relapse rate of latent individuals progressing to active infection and the probability of healthy individuals becoming infected after contact with the pathogen. Therefore, enhancing the detection and treatment of latent cases and reducing contact between infected and uninfected individuals are the most crucial public health interventions.

Universal programmable unambiguous discrimination of two unknown states with two copies

We investigate the universal programmable unambiguous discrimination of two unknown quantum states with two copies. We first present the optimal positive-operator valued measure (POVM) elements that determine the individual success probabilities. In terms of the positivity of an inconclusive operator, we derive the average success probability, which is higher than the average success probability of discriminating two unknown states with one copy given in the reference [Bergou and Hillery, Phys. Rev. Lett. 94, 160501 (2005)].

Personal social network strengthens adherence to lifestyle changes in individuals with subjective cognitive decline.

Providing medical advice regarding lifestyle changes is currently the most effective intervention for delaying dementia onset among individuals with subjective cognitive decline (SCD). Adherence to such advice can be influenced by individual's social environment. We measured that impact within a Latinamerican population. We recruited 183 SCD individuals from a Memory Clinic, analyzed their health-related, and provided them with medical advice. We assessed personal network composition and its healthy habits. We evaluated adherence to medical advice 6 months later. The proportion of heavy drinkers in the network is a risk factor to reduce alcohol consumption (odds ratio [OR]=31.2, 95% confidence interval [CI] [3.73,301], p=0.002), poor diets in the network hinders improving diet (p<0.001 OR=74.1, 95% CI [14.7,471]), and sedentary people in the network make it difficult to start exercising (OR=4.92 95% CI [1.39,18.8], p=0.016). Personal networks have an inertial effect, as relationships engaged in an unhealthy habit lower the probability of individuals to quit that habit.

High chytrid prevalence and infection intensities in tadpoles of Mixophyes fleayi

Context The amphibian chytrid fungus Batrachochytrium dendrobatidis (Bd) has caused catastrophic biodiversity loss globally, but species and life stages within species respond differently to the pathogen. Although tadpoles are often reported to be less vulnerable to disease, they can constitute important infection reservoirs in ecosystems. Aims We aimed to describe Bd infection patterns of a long-lived tadpole in a species where post-metamorphic animals appear to exhibit limited mortality as a result of chytridiomycosis. We further investigated how oral dekeratinisation can be used as an indicator of infection. Methods We conducted surveys of tadpoles of Mixophyes fleayi (Fleay’s barred frog) over 2 years, at two rainforest streams on the east coast of Australia, to assess patterns in Bd infection prevalence and intensity. We developed an integrated hierarchical model propagating pathogen-detection errors and incorporating how Bd infections affect oral dekeratinisation. Key results We found that Bd infection prevalence was strongly associated with lower temperatures and a larger body size, consistent with Bd optimal thermal range and a cumulative risk of exposure for tadpoles. The individual probability of a tadpole being infected with Bd was estimated to be 0.58 [95% HPDI: 0.432, 0.713], the odds of which were approximately eight times greater than for adults at the same sites. Tadpoles infected with Bd were 113 [29, 293] times more likely to have oral dekeratinisation than were uninfected tadpoles, where uninfected individuals were estimated to have a 0.05 [95% HPDI: 0.011, 0.11] probability of having mouthpart loss. Conclusions Our results showed that M. fleayi tadpoles are more likely to be infected with Bd than are adults, suggesting that tadpoles could contribute to Bd maintenance in streams. We further showed that sites can be rapidly assessed for Bd by visually checking for oral dekeratinisation. Implications Long-lived tadpoles, in general, may contribute to Bd maintenance in ecosystems. We suggest continued exploration of Bd immunocompetence across amphibian life stages to further understand the vastly different infection patterns.