BMC Research Notes

The PREDICT breast prognostic and treatment benefit model has undergone several revisions since its first release. The most recent version (v3.1) was developed using a data set of 35,474 cases diagnosed between 2000 and 2017 in a single region of England. PREDICT breast provides predicted outcomes at 5, 10 and 15 years, but most clinicians use the 10-year outcomes for decision making. The purpose of this study was to reparameterize the model using a larger data set from across the UK and to compare the performance of v4.0 with that of v3.1. There were 172,208 eligible cases randomly split 50:50 into model development and validation data sets. Cox proportional hazards models were derived for estrogen receptor negative and estrogen receptor positive cancer for breast cancer specific mortality with a third model for non-breast cancer mortality. In cases with at least five years follow-up and censored at ten years, the model was well-calibrated with a less than 5% difference between observed and predicted breast cancer deaths. Model discrimination was also good with AUCs in the validation data of 0.735 and 0.794 for ER negative and ER positive cases respectively. Calibration and discrimination were slightly improved compared to PREDICT breast v3.1.

BackgroundAmerican adults delay dental care more than any other healthcare service. Unfortunately, the COVID-19 pandemic may have stalled efforts to address dental service delays. Early evidence has suggested substantial declines in dental service visits in the early phase of the pandemic, however our study is among the first to measure within-person changes from 2019 to 2020 and conduct subgroup analyses to examine if changing dental patterns were mediated by exposure to the pandemic, risk of adverse COVID-19 outcomes, or dental insurance. MethodsWe analyzed a National Health Interview Survey panel of individuals initially surveyed in 2019, with subsequent follow up in 2020. The outcomes included dental service access measures and the interval of a most recent dental visit. By constructing a probability weighted linear regression model with fixed-effects, we estimated the average within-person change from 2019 to 2020. Robust standard errors were clustered within each respondent. ResultsOverall, adults in 2020 were 4.6%-points less likely to visit the dentist compared to 2019 (p < 0.001). Significantly higher declines were found in Northeast/West regions compared to Midwest/South. We find no evidence that declining dental services in 2020 were associated with more chronic diseases, older age, or lack of dental insurance coverage. Adults did not report more financial or non-financial access barriers to dental care in 2020 compared to 2019. ConclusionsThe long-term effects of the COVID-19 pandemic on delayed dental care warrants continued monitoring as policymakers aim to mitigate the pandemics negative consequences on oral health equity.

PurposeGuided by the intersectionality framework, we examined the differential in breast cancer care experience across population subgroups in England. MethodsSecondary data analysis using the 2017/2018 English National Cancer Patient Experience Survey (NCPES). We applied disaggregated descriptive statistics (mean, standard errors, 95% confidence interval) to analyse 26,030 responses from female breast cancer patients to a question relating to overall care experience categorised by age, ethnicity, and sexual orientation in their intersection with deprivation status. We then applied multivariable logistic regression (odds ratios, 95% confidence intervals) to ascertain the relationship of reporting a positive care experience adjusting for patient, clinical, and trust-level factors. ResultsPoorer breast cancer care experience was mostly reported by the most deprived younger and minoritised ethnic groups. Similar findings were observed in adjusted multivariable analyses. Younger respondents were less likely than older patients to rate their care favourably. Pakistani, Indian, Chinese, and Black African women were less likely than White British women to rate their care favourably. Respondents from the most socioeconomic deprived backgrounds were less likely than the most affluent ones to rate their care favourably. ConclusionThere is evidence of inequity in overall cancer care experience among female breast cancer patients in England, particularly among women living at the specific intersection of age, ethnicity and socioeconomic position. Future research is necessary to understand the mechanisms underlying breast cancer inequities. Policymakers, commissioners, and providers should consider the existence of multiple forms of marginalization to inform improvement initiatives targeting patients at higher risk of vulnerability.

BackgroundThe increasing demand for health care resources requires measures to evaluate the impact of cancer control approaches. A cancer simulation model can help integrate new knowledge to inform clinical and policy decisions. OncoSim-Breast is a breast cancer simulation model. This paper aims to describe the key assumptions in the OncoSim-Breast model and how well it reproduces more recent breast cancer trends and the observed effects in a randomized screening trial. MethodsThe OncoSim-Breast model simulates the onset, growth and spread of invasive and ductal carcinoma in situ tumours. The model combines Canadian cancer incidence, mortality, screening program and cost data to project population-level outcomes. Users can change the model input to answer specific policy questions. Here we report three validation exercises. First, we compared the models projected breast cancer incidence and stage distributions with the observed data in the Canadian Cancer Registry. Second, we compared OncoSims projected breast cancer mortality with the Vital Statistics. Third, we replicated the UK Age trial to compare the models projections with the trials observed screening effects. ResultsOncoSim-Breasts projected incidence, mortality and stage distribution of breast cancer were close to the observed data in the Canadian Cancer Registry and the Vital Statistics. OncoSim-Breast also reproduced the breast cancer screening effects observed in the UK Age trial. InterpretationOncoSim-Breasts ability to reproduce the observed population-level breast cancer trends and the screening effects in a randomized trial increases the confidence of using its results to inform policy decisions related to early detection of breast cancer.

ObjectivesThis umbrella review evaluates systematic reviews and meta-analyses for salivary biomarker concentration and activity in caries-affected versus caries-free children. MethodsA comprehensive literature search identified relevant reviews, which were systematically selected using PRISMA guidelines, assessed qualitatively with AMSTAR 2, and analyzed quantitatively using RevMan software. Certainty of evidence was evaluated via the GRADE assessment tool. ResultsOf 609 identified articles, three reviews were included for quantitative analysis. AMSTAR 2 assessments rated three reviews as high quality and one as low quality. Meta-analysis findings showed that for salivary secretory immunoglobulin-A concentration with a mean of 65.54, with a 2.24 higher concentration (0.59 to 3.89 higher) in caries-affected children; carbonic anhydrase-VI concentration with a mean of 2.18, with a 0.92 lower concentration (2.21 lower to 0.38 higher) in caries-affected children; and carbonic anhydrase-VI activity with a mean of 3698.30, with a 2.89 higher activity level (1.24 to 4.54 higher) in caries-affected children. Heterogeneity was low for carbonic anhydrase, high for Salivary secretory immunoglobulin-A, and publication bias risk was low. The GRADE assessment indicated moderate confidence in evidence suggesting slight differences in Salivary secretory immunoglobulin-A and carbonic anhydrase-VI levels in caries-affected children. ConclusionsCaries-affected children under age nine exhibit higher salivary secretory immunoglobulin-A concentration and carbonic anhydrase-VI activity but lower carbonic anhydrase-VI concentration. Current evidence suggests that screening with these three salivary biomarker tests are likely to benefit and unlikely to harm children. Widespread clinical application remains limited until U.S. commercial laboratories provide standardized saliva-based testing for salivary secretory immunoglobulin-A and carbonic anhydrase-VI.

IntroductionIndia has been under four phases of a national lockdown from March 25 to May 31 in response to the COVID-19 pandemic. Unmasking the state-wise variation in the effect of the nationwide lockdown on the progression of the pandemic could inform dynamic policy interventions towards containment and mitigation. MethodsUsing data on confirmed COVID-19 cases across 20 states that accounted for more than 99% of the cumulative case counts in India till May 31, 2020, we illustrate the masking of state-level trends and highlight the variations across states by presenting evaluative evidence on some aspects of the COVID-19 outbreak: case-fatality rates, doubling times of cases, effective reproduction numbers, and the scale of testing. ResultsThe estimated effective reproduction number R for India was 3.36 (95% confidence interval (CI): [3.03, 3.71]) on March 24, whereas the average of estimates from May 25 - May 31 stands at 1.27 (95% CI: [1.26, 1.28]). Similarly, the estimated doubling time across India was at 3.56 days on March 24, and the past 7-day average for the same on May 31 is 14.37 days. The average daily number of tests have increased from 1,717 (March 19-25) to 131,772 (May 25-31) with an estimated testing shortfall of 4.58 million tests nationally by May 31. However, various states exhibit substantial departures from these national patterns. ConclusionsPatterns of change over lockdown periods indicate the lockdown has been effective in slowing the spread of the virus nationally. The COVID-19 outbreak in India displays large state-level variations and identifying these variations can help in both understanding the dynamics of the pandemic and formulating effective public health interventions. Our framework offers a holistic assessment of the pandemic across Indian states and union territories along with a set of interactive visualization tools that are daily updated at covind19.org.

BackgroundWe aimed to compare various common approaches for handling missing vital status or follow-up time. As a case study for application of these methods, we estimated incidence of metachronous contralateral breast cancer (CBC). MethodsFor 1980-2016, incidence of metachronous CBC with follow-up through 2024 was estimated using Poisson regression with overdispersion, by age at incidence, year of incidence, histology and follow-up period. Missing follow-up time was ignored in the naive approach, simulated once using the average hazard derived from published Swiss cancer registry data, or multiply imputed using 3 different imputation models. Results24,612 women aged 20-84 had unilateral breast cancer between 1980 and 2016 in the Swiss cantons of Zurich and Zug. Of those, 5% (n=1264) were lost to follow-up. Over 291463 person-years, 1145 contralateral breast malignancies were diagnosed, corresponding to 393 per 100000 person-years (95% CI 353 to 438). Incidence rates have been decreasing over time to 238 (171 to 333) for the incidence period 2010-2016. The same overall pattern was observed regardless of how we handled missing follow-up times. However, using a single imputation generally produced lower incidence rates compared to the naive approach, with multiple imputation giving higher estimates. The most complex multiple imputation model gave incidence estimates that were very similar to those from the naive approach. ConclusionDifferent methods to handle missing follow-up times yielded similar results: that CBC incidence has declined in recent decades. Multiple imputation is likely an appropriate method to handle missing follow-up data, enabling researchers to include all eligible individuals in the analysis.

BackgroundSchool-based caries prevention can increase access to critical dental services and reduce oral health inequities. However, little is known regarding the incidence of dental caries in children participating in school caries prevention, and caries diagnosis is often interval censored. MethodsIn this paper, we used data from a longitudinal, school-based, randomized clinical trial of minimally invasive treatments for dental caries to estimate the per-visit incidence rate and compare the hazard of dental caries in children receiving either silver diamine fluoride or glass ionomer dental sealants. To account for interval censoring, we used semiparametric transformation models for univariate failure time data and imputed the caries incidence using G-imputation. ResultsThere were 3040 children that met inclusion criteria for analysis, 1516 (49.9%) of which were randomly assigned to receive silver diamine fluoride and 1524 (50.1%) assigned to receive glass ionomer dental sealants. There were no differences in the hazard of caries between treatments (HR = 0.98, 95% CI = 0.73, 1.24), while children with caries at baseline had a significant increase in the hazard of new caries (HR = 2.54, 95% CI = 2.26, 2.83) compared to those that were caries-free. The per-visit caries incidence ranged from 4.8 to 11.1 per 1000 person-years and increased with each successive study observation. ConclusionsSchool-based caries prevention can positively affect caries incidence, and results can be used to inform future program design and implementation.

BackgroundEvidence-based non-surgical interventions to halt the progression of dental caries, the most prevalent noncommunicable disease in the world, include atraumatic restorative treatment (ART) and silver diamine fluoride (SDF). Data are needed on their effectiveness when used in school caries prevention programs. MethodsIn this school-based, cluster-randomized pragmatic trial conducted from February 1, 2018 to June 1, 2023, 48 primary schools in New York City were randomly assigned to receive either silver diamine fluoride or atraumatic restorations for untreated caries on any mesial, occlusal, distal, buccal, and lingual surface of permanent molars, premolars, and primary molars. All children then received fluoride varnish. Children were treated by either dental hygienists, pediatric dentists, or medical nurses (SDF group only). Dental caries was diagnosed as any lesion scoring either 5 or 6 on the ICDAS scale. The primary outcome was the caries control rate. ResultsA total of 7418 children were enrolled in the trial, of which 1668 (861 in the SDF group, 807 in the ART group) presented with treatable dental caries and completed at least one follow-up observation. The total surface-level failure in the SDF group was 38.3%, compared to 45.5% in the ART group. There were 2167 failures observed in SDF participants over 1372 person-years, compared to 2116 ART failures over 1291 person-years, yielding an incidence rate ratio of 0.96 (95% CI = 0.86, 1.03). At the subject level, 45.5% of SDF recipients experienced at least one surface failure, compared to 53.3% of ART recipients. There were no significant differences in the risk of recurrent surface failure between treatments (HR = 0.92, 95% CI = 0.82, 1.04). ConclusionsIn this four-year pragmatic trial of school-based utilization of minimally invasive interventions for dental caries, similar control rates were observed in children receiving either SDF or ART. These results support the use of secondary preventive therapies for school dental programs. Author rolesRRR conceptualization, formal analysis, methodology, funding acquisition, supervision, writing-original draft; AG: formal analysis; QX: formal analysis; TJBG: data curation, investigation.

BackgroundSchool-based caries prevention using silver diamine fluoride (SDF) has been shown to effectively prevent and control dental caries. To better inform program design and implementation, this paper estimated transition probabilities for dental caries in a school SDF program. MethodsThe CariedAway project was a pragmatic, cluster-randomized trial of school-based caries prevention interventions conducted in predominately low-income minority children. For children in CariedAway receiving SDF, transition probabilities were computed between sound, carious, and arrested states for 6-year molars using multistate Markov models. Subject-level transition probabilities over one- and two-year periods were then calculated by aggregating states of all 6-year molars and first and second bicuspids. ResultsA total of 7418 children were enrolled in CariedAway, of which 1352 met inclusion criteria for this study. Of eligible participants, the baseline prevalence of untreated decay was 29% and the prevalence of dental sealants was 8%. The probability of transitioning between sound and carious states in 6-year molars ranged from 0.0022 to 0.0074. At the subject-level, the sound to carious transition probabilities were 0.07 and 0.12 after one and two years, respectively. Once in a fully arrested state, the probability of remaining arrested was 0.72 and 0.60 after one and two years. ConclusionsThe overall probabilities of teeth remaining in diseased-free or arrested states was high after receiving silver diamine fluoride, although multiple applications might be needed for consistent caries arrest.

Breast cancer remains a significant concern worldwide, with a rising incidence in Indonesia. This study aims to evaluate the applicability of risk-based screening approaches in the Indonesian demographic through a case-control study involving 305 women. We developed a personalized breast cancer risk assessment workflow that integrates multiple risk factors, including clinical (Gail) and polygenic (Mavaddat) risk predictions, into a consolidated risk category. By evaluating the area under the receiver operating characteristic curve (AUC) of each single-factor risk model, we demonstrate that they retain their predictive accuracy in the Indonesian context (AUC for clinical risk: 0.67 [0.61,0.74]; AUC for genetic risk: 0.67 [0.61,0.73]). Notably, our combined risk approach enhanced the AUC to 0.70 [0.64,0.76], highlighting the advantages of a multifaceted model. Our findings demonstrate for the first time the applicability of the Mavaddat and Gail models to Indonesian populations, and show that within this demographic, combined risk models provide a superior predictive framework compared to single-factor approaches.

Agricultural injuries remain a significant occupational hazard, causing substantial human and economic losses worldwide. This study investigates the prediction of agricultural injury severity using both linear and ensemble machine learning (ML) models and applies explainable AI (XAI) techniques to understand the contribution of input features. Data from AgInjuryNews (2015-2024) was preprocessed to extract relevant attributes such as location, time, age, and safety measures. The dataset comprised 2,421 incidents categorized as fatal or non-fatal. Various ML models, including Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB), were trained and evaluated using standard performance metrics. Ensemble models demonstrated superior accuracy and recall compared to linear models, with XGBoost achieving a recall of 100% for fatal injuries. However, all models faced challenges in predicting non-fatal injuries due to class imbalance. SHAP analysis provided insights into feature importance, with age, gender, location, and time emerging as the most influential predictors across models. This research highlights the effectiveness of ensemble ML models in injury prediction while emphasizing the need for balanced datasets and XAI techniques for actionable insights. The findings have practical implications for enhancing agricultural safety and guiding policy interventions. HighlightsO_LIThis study analyzed 2,421 agricultural injury incidents from AgInjuryNews (2015- 2024) and utilized machine learning models to predict injury severity, focusing on both fatal and non-fatal outcomes. C_LIO_LIEnsemble models, such as XGBoost and Random Forest, outperformed linear models in accuracy and recall, especially in predicting fatal injuries, although challenges in non-fatal predictions due to class imbalance were observed. C_LIO_LIKey predictors identified through SHAP analysis included age, gender, location, and time, providing interpretable insights into the factors influencing injury severity. C_LIO_LIThe integration of explainable AI (XAI) enhanced the transparency of machine learning predictions, enabling stakeholders to prioritize targeted safety interventions effectively. C_LIO_LIThis research highlights the potential of combining ensemble ML models with XAI techniques to improve agricultural safety practices and provides a foundation for addressing data challenges in future studies. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/25321769v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@dc19a8org.highwire.dtl.DTLVardef@189646org.highwire.dtl.DTLVardef@31d6f2org.highwire.dtl.DTLVardef@16b01f_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundCurrent guidelines for low-risk chest pain patients recommend obtaining serial ECGs and serial measurements of cardiac troponin between 6 and 12 hours. As a result, the majority of patients require prolonged assessment before safe discharge. There is a need to identify these patients promptly to help in reducing the time to provide the treatment as well as reduce the burden over the ED. Present study was done with the objective of estimating the incidence of thirty-day Major Averse Cardiac Event (MACE) in patients presenting to emergency department with low-risk chest pain, and to compare the Thrombolysis In Myocardial Infarction (TIMI), HEART and Emergency Department Assessment of Chest Pain Score (EDACS) Score in patients with low-risk chest pain. MethodsPresent study was descriptive follow up study done at a tertiary care hospital (Fortis Memorial Research Institute, in Gurugram, Haryana, India. Study was conducted from Jan 2018 to Jan 2019. All the patient reporting with low-risk chest pain during study period were recruited in the study. Semi-structured interview schedule was used for the data collection. Outcome variable was MACE (Major adverse cardiac event) event in 30 days. ResultsTotal 156 participants were included in the study. Mean age of participants was 44.1 years. Out of 156 participants, 10 (6.4%) reported MACE in 30 days of presentation. We found that HEART and EDACS score had incidence of MACE less than 2% in their low-risk groups and TIMI score had incidence of MACE >2% in its low-risk group. ConclusionEDACS and HEART score can be used in the Emergency department to identify the low-risk chest pain patients. This could help in early identification and save time and other resources. What is already known on this topicCurrent guidelines for low-risk chest pain patients recommend obtaining serial ECGs and serial measurements of (non-high sensitivity) cardiac troponin between 6 and 12 hours after patient presentation to the ED. As a result, the majority of patients require prolonged assessment before safe discharge. Prolonged assessment leads to increased health care costs and ED crowding, which has been shown to lead to increased adverse events in patients with both acute and non-acute coronary syndrome-related chest pain. The efficient identification of low-risk patients who can be safely discharged after rapid assessment in the ED remains an important issue. Risk assessment scores have been developed for chest pain, among these few are TIMI score, Heart score, and EDACS score. What this study addsOverall incidence of 30-day MACE was less 10% among the patients presenting to emergency department of FMRI Gurugram, Haryana with low-risk chest pain. HEART, and EDACS scores performed better in identifying the low-risk category than the TIMI score. Among these EDACS was the best, with none of the participants in low-risk category having 30-day MACE. How this study might affect research, practice or policyEDACS and HEART score can be used in the Emergency department to identify the low-risk chest pain patients. This could help in early identification and save time and other resources.

BackgroundHyperhidrosis (HH) is characterized by excessive sweating beyond thermoregulatory requirements and can significantly impair quality of life. Although oxybutynin has become an effective first-line therapy, video-assisted thoracic sympathectomy (VATS) remains the only definitive option for refractory cases. The availability of nationwide public and private healthcare datasets in Brazil enables a comprehensive assessment of national procedural trends on a continental scale. MethodsWe performed a retrospective nationwide analysis of all VATS procedures for HH conducted in Brazil between 2015 and 2023. Data were extracted from publicly available governmental databases covering both the public and private healthcare systems. Procedure rates were evaluated by year, geographic region, sex, and age group. Temporal patterns and differences between healthcare sectors were analyzed. ResultsA total of 26,980 VATS procedures were performed during the study period, with the private sector accounting for 75.25% of surgeries. Regionally, the Southeast (48.28%) and South (29.51%)--the most economically developed areas of the country--concentrated the majority of procedures. Women represented 64.5% of operated patients, and most surgeries were performed in individuals aged 15-39 years (82.74%). The mean cost per procedure in the private system was 4.2 times higher than in the public system (US$1,351.94 vs. US$351.27, p < 0.001), and the private sector accounted for more than 90% of total national expenditures. The overall mortality rate was 0.048%, with no significant difference between healthcare sectors. ConclusionsThis study provides the first nationwide evaluation of VATS performed for HH across Brazils public and private systems. Based on 26,980 procedures carried out between 2015 and 2023, most surgeries occurred in the private sector and predominantly involved women and young adults aged 15-39 years. Although mortality was low and similar across systems, procedure costs were substantially higher in the private sector, which accounted for the vast majority of national expenditures.

BackgroundTamoxifen is a cornerstone of endocrine treatment for hormone receptor-positive breast cancer, reducing recurrence and breast cancer-specific mortality. However, its use is associated with a small, yet clinically relevant, increase in uterine cancer. As diagnosis of this cancer remains symptom-triggered, it is essential for patients to be aware of this risk and report symptoms promptly for optimal outcomes. We therefore assessed risk awareness among breast cancer survivors while exploring their attitudes towards potential future endometrial surveillance strategies. MethodsOver a 10-month period, a web-based survey was conducted among breast cancer survivors with/without tamoxifen treatment. The mixed-format questionnaire included closed-ended questions and optional free-text comments. Quantitative data were summarized descriptively and analyzed statistically; qualitative responses were reviewed thematically to contextualize survey findings. ResultsOf 163 respondents, 154 breast cancer survivors were included in the analysis, 128 of whom had received tamoxifen. Among tamoxifen-associated participants, 60% reported insufficient awareness of the associated uterine cancer risk, and half expressed uncertainty about the adequacy of the current symptom-triggered endometrial evaluation. Despite this, acceptance of tamoxifen therapy was high; only one patient declined treatment over concerns about side effects. Almost all participants (96%) were willing to adopt endometrial surveillance methods, if developed and validated. ConclusionAs evaluation of tamoxifen-associated uterine pathology is symptom-triggered, our data highlight the need for improved and standardized risk communication to promote timely symptom recognition, reporting, and diagnostic evaluation. Moreover, our findings support incorporating patient-reported preferences into the development of future endometrial detection strategies to improve survivorship care.

ImportanceIndia has taken strong and early public health measures for arresting the spread of the COVID-19 epidemic. With only 536 COVID-19 cases and 11 fatalities, India - a democracy of 1.34 billion people - took the historic decision of a 21-day national lockdown on March 25. The lockdown was further extended to May 3rd, soon after the analysis of this paper was completed. ObjectiveTo study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 cases in India compared to other less severe non-pharmaceutical interventions using epidemiological forecasting models and Bayesian estimation algorithms; to compare effects of hypothetical durations of lockdown from an epidemiological perspective; to study alternative explanations for slower growth rate of the virus outbreak in India, including exploring the association of the number of cases and average monthly temperature; and finally, to outline the pivotal role of reliable and transparent data, reproducible data science methods, tools and products as we reopen the country and prepare for a post lock-down phase of the pandemic. Design, Setting, and ParticipantsWe use the daily data on the number of COVID-19 cases, of recovered and of deaths from March 1 until April 7, 2020 from the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Additionally, we use COVID-19 incidence counts data from Kaggle and the monthly average temperature of major cities across the world from Wikipedia. Main Outcome and MeasuresThe current time-series data on daily proportions of cases and removed (recovered and death combined) from India are analyzed using an extended version of the standard SIR (susceptible, infected, and removed) model. The eSIR model incorporates time-varying transmission rates that help us predict the effect of lockdown compared to other hypothetical interventions on the number of cases at future time points. A Markov Chain Monte Carlo implementation of this model provided predicted proportions of the cases at future time points along with credible intervals (CI). ResultsOur predicted cumulative number of COVID-19 cases in India on April 30 assuming a 1-week delay in peoples adherence to a 21-day lockdown (March 25 - April 14) and a gradual, moderate resumption of daily activities after April 14 is 9,181 with upper 95% CI of 72,245. In comparison, the predicted cumulative number of cases under "no intervention" and "social distancing and travel bans without lockdown" are 358 thousand and 46 thousand (upper 95% CI of nearly 2.3 million and 0.3 million) respectively. An effective lockdown can prevent roughly 343 thousand (upper 95% CI 1.8 million) and 2.4 million (upper 95% CI 38.4 million) COVID-19 cases nationwide compared to social distancing alone by May 15 and June 15, respectively. When comparing a 21-day lockdown with a hypothetical lockdown of longer duration, we find that 28-, 42-, and 56-day lockdowns can approximately prevent 238 thousand (upper 95% CI 2.3 million), 622 thousand (upper 95% CI 4.3 million), 781 thousand (upper 95% CI 4.6 million) cases by June 15, respectively. We find some suggestive evidence that the COVID-19 incidence rates worldwide are negatively associated with temperature in a crude unadjusted analysis with Pearson correlation estimates [95% confidence interval] between average monthly temperature and total monthly incidence around the world being -0.185 [-0.548, 0.236] for January, -0.110 [-0.362, 0.157] for February, and -0.173 [-0.314, -0.026] for March. Conclusions and RelevanceThe lockdown, if implemented correctly in the end, has a high chance of reducing the total number of COVID-19 cases in the short term, and buy India invaluable time to prepare its healthcare and disease monitoring system. Our analysis shows we need to have some measures of suppression in place after the lockdown for the best outcome. We cannot heavily rely on the hypothetical prevention governed by meteorological factors such as temperature based on current evidence. From an epidemiological perspective, a longer lockdown between 42-56 days is preferable. However, the lockdown comes at a tremendous price to social and economic health through a contagion process not dissimilar to that of the coronavirus itself. Data can play a defining role as we design post-lockdown testing, reopening and resource allocation strategies. SoftwareOur contribution to data science includes an interactive and dynamic app (covind19.org) with short- and long-term projections updated daily that can help inform policy and practice related to COVID-19 in India. Anyone can visualize the observed data for India and create predictions under hypothetical scenarios with quantification of uncertainties. We make our prediction codes freely available (https://github.com/umich-cphds/cov-ind-19) for reproducible science and for other COVID-19 affected countries to use them for their prediction and data visualization work.

BackgroundIndias official death totals from the COVID pandemic are widely regarded as under-reports. MethodsWe quantified all-cause excess mortality in India, comparing deaths during the peak of the first and second COVID waves (Jul-Dec 2020 and April-June 2021) with month wise deaths in 2015-19 from three sources: Civil Registration System (CRS) mortality reports from 15 states or cities with 37% of Indias population; deaths in 0.2 million health facilities; and a representative survey of 0.14 million adults about COVID deaths. ResultsDuring the first viral wave, the median excess mortality compared to CRS baseline was 22% and 41%, respectively, in included states and cities, rising to 46% and 85% during the second wave. In settings with 10 or more months of data across the two waves, the median excess mortality was 32% and 37% for states and cities, respectively. Deaths in health facilities showed a 27% excess mortality from July 2020-May 2021, reaching 120% during April-May 2021. The national survey found 3.5% of adults reported a COVID death in their household in April-June 2021, approximately doubling the 3.2% expected overall deaths. The national survey showed 29-32% excess deaths from June 1, 2020 to June 27, 2021, most of which were likely to be COVID. This translates to 3.1-3.4 million COVID deaths (including 2.5-2.8 million during April-June 2021). National extrapolations from health facility and CRS data suggest 2.7-3.3 million deaths during the year. ConclusionsIndias COVID death rate may be about 7-8 times higher than the officially reported 290/million population.

Many popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures and other non-pharmaceutical interventions. We study how five epidemiological models forecast and assess the course of the pandemic in India: a baseline model, an extended SIR (eSIR) model, two extended SEIR (SAPHIRE and SEIR-fansy) models, and a semi-mechanistic Bayesian hierarchical model (ICM). Using COVID-19 data for India from March 15 to June 18 to train the models, we generate predictions from each of the five models from June 19 to July 18. To compare prediction accuracy with respect to reported cumulative and active case counts and cumulative death counts, we compute the symmetric mean absolute prediction error (SMAPE) for each of the five models. For active case counts, SMAPE values are 0.72 (SEIR-fansy) and 33.83 (eSIR). For cumulative case counts, SMAPE values are 1.76 (baseline) 23.10 (eSIR), 2.07 (SAPHIRE) and 3.20 (SEIR-fansy). For cumulative death counts, the SMAPE values are 7.13 (SEIR-fansy) and 26.30 (eSIR). For cumulative cases and deaths, we compute Pearsons and Lins correlation coefficients to investigate how well the projected and observed reported COVID-counts agree. Three models (SAPHIRE, SEIR-fansy and ICM) return total (sum of reported and unreported) counts as well. We compute underreporting factors as of June 30 and note that the SEIR-fansy model reports the highest underreporting factor for active cases (6.10) and cumulative deaths (3.62), while the SAPHIRE model reports the highest underreporting factor for cumulative cases (27.79).

ObjectiveThis systematic review update synthesized recent evidence on the benefits and harms of breast cancer screening in women aged [&ge;] 40 years and aims to inform the Canadian Task Force on Preventive Health Cares (CTFPHC) guideline update. MethodsWe searched Ovid MEDLINE(R) ALL, Embase Classic+Embase, and Cochrane Central Register of Controlled Trials to update our searches to July 8, 2023. Search results for observational studies were limited to publication dates from 2014 to capture more relevant studies. Screening was performed independently and in duplicate by the review team. To expedite the screening process, machine learning was used to prioritize relevant references. Critical health outcomes, as outlined by the CTFPHC, included breast cancer and all-cause mortality, treatment-related morbidity, and overdiagnosis. Randomized controlled trials (RCTs), non/quasi RCTs, and observational studies were included. Data extraction and quality assessment were performed by one reviewer and verified by another. Risk of bias was assessed using the Cochrane Risk of Bias 2.0 tool for RCTs and the Joanna Briggs Institute (JBI) checklists for non-randomized and observational studies. When deemed appropriate, studies were pooled via random-effects models. The overall certainty of the evidence was assessed following GRADE guidance. ResultsThree new papers reporting on existing RCT trial data and 26 observational studies were included. No new RCTs were identified in this update. No study reported results by ethnicity, race, proportion of study population with dense breasts, or socioeconomic status. For breast cancer mortality, RCT data from the prior review reported a significant relative reduction in the risk of breast cancer mortality with screening mammography for a general population of 15% (RR 0.85 95% CI 0.78 to 0.93). In this review update, the breast cancer mortality relative risk reduction based on RCT data remained the same, and absolute effects by age decade over 10 years were 0.27 fewer deaths per 1,000 in those aged 40 to 49; 0.50 fewer deaths per 1,000 in those aged 50 to 59; 0.65 fewer deaths per 1,000 in those aged 60 to 69; and 0.92 fewer deaths per 1,000 in those aged 70 to 74. For observational data, the relative mortality risk reduction ranged from 29% to 62%. Absolute effects from breast cancer mortality over 10 years ranged from 0.79 to 0.94 fewer deaths per 1,000 in those aged 40 to 49; 1.45 to 1.72 fewer deaths per 1,000 in those aged 50 to 59; 1.89 to 2.24 fewer deaths per 1,000 in those aged 60 to 69; and 2.68 to 3.17 fewer deaths per 1,000 in those aged 70 to 74. For all-cause mortality, RCT data from the prior review reported a non-significant relative reduction in the risk of all-cause mortality of screening mammography for a general population of 1% (RR 0.99, 95% CI 0.98 to 1.00). In this review update, the absolute effects for all-cause mortality over 10 years by age decade were 0.13 fewer deaths per 1,000 in those aged 40 to 49; 0.31 fewer deaths per 1,000 in those aged 50 to 59; 0.71 fewer deaths per 1,000 in those aged 60 to 69; and 1.41 fewer deaths per 1,000 in those aged 70 to 74. No observational data were found for all-cause mortality. For overdiagnosis, this review update found the absolute effects for RCT data (range of follow-up between 9 and 15 years) to be 1.95 more invasive and in situ cancers per 1,000, or 1 more invasive cancer per 1,000, for those aged 40 to 49 and 1.93 more invasive and in situ cancers per 1,000, or 1.18 more invasive cancers per 1,000, for those aged 50 to 59. A sensitivity analysis removing high risk of bias studies found 1.57 more invasive and in situ cancers, or 0.49 more invasive cancers, per 1,000 for those aged 40 to 49 and 3.95 more invasive and in situ cancers per 1,000, or 2.81 more invasive cancers per 1,000, in those aged 50 to 59. For observational data, one report (follow-up for 13 years) found 0.34 more invasive and in situ cancers per 1,000 in those aged 50 to 69. Overall, the GRADE certainty of evidence was assessed as low or very low, suggesting that the evidence is very uncertain about the effect of screening for breast cancer on the outcomes evaluated in this review. ConclusionsThis systematic review update did not identify any new trials comparing breast cancer screening to no screening. Although 26 new observational studies were identified, the overall quality of evidence remains generally low or very low. Future research initiatives should prioritize studying screening in higher risk populations such as those from different ages, racial or ethnic groups, with dense breasts, or family history. RegistrationProtocol available on the Open Science Framework: https://osf.io/xngsu/

IntroductionBreast cancer remains a prevalent disease in women worldwide. Though significant advancements in the standard of care for breast cancer have contributed to improved patient survival and quality of life, a breast cancer diagnosis and subsequent treatment interventions have a long-lasting impact on patients lived experiences. A high-quality healthcare system uses a patient-centred approach to healthcare, with patient engagement being a central pillar in the delivery of patient-centred care. However, the disconnect between patients and researchers can translate into research lacking real-world relevance to patient health needs. Here, we report a patient and stakeholder engagement workshop series that was conceptualized with the goal of promoting dialogue between patients with breast cancer, breast cancer researchers and the clinician involved in their care. We present the collaborative learning process and emerging opportunities from this patient engagement workshop series as a community-academic partnership. MethodWe report on a three-part storytelling workshop, with the scope of the workshops including topics related to raising awareness of the patient lived experience following a breast cancer diagnosis, breast cancer research activities undertaken by researchers, and the approach used by multidisciplinary healthcare teams in the management of breast cancer using storytelling as a tool. We used an iterative approach to cohort trust and relationship building, narrative development, and the use of multiple media formats to capture patient stories. This included the use of object memories, storytelling prompt cards and open-mic audio format to capture patient stories from diagnosis to treatment, and remission. Results20 patients shared their stories with key themes emerging from the qualitative analysis of audio recordings. For many, this was the first time they had spoken about their breast cancer experience beyond family and friends. Emerging themes included common public misconceptions about a breast cancer diagnosis, the importance of self-advocacy in patient decision making about treatment, and the complex emotional journey experienced by patients diagnosed with breast cancer. The group-based storytelling approach provided collective empowerment to share personal experiences and connect meaningfully across the peer community. ConclusionWhile a breast cancer diagnosis can be overwhelming from a physical, social, emotional and cognitive perspective, storytelling as a patient engagement approach can build patient trust in researchers, ensuring that as key stakeholders they are involved in the process of research. Understanding the patient perspective of a breast cancer diagnosis and subsequent experiences can support healthcare professionals in developing an empathetic approach to sharing information, and involving patients in shared decision making about their healthcare.