Health Datasets Research Articles

6.E. Practice session: Using European environmental data in public health: datasets and applications

Abstract The European Environment Agency collects, quality-assures and quality-checks data on environment, climate and sustainability. It does so through its network and other institutional partners across 38 European countries. Most of these validated environmental data are publicly available and many can be used by practitioners to assess environmental risks to the health of their communities, as well as for designing and evaluating policies’ effectiveness. The objective of this workshop is to provide public health practitioners and scientists with an overview of the EEA data streams of public health relevance, including on air pollution, noise, water pollution, industrial emissions and climate impacts, among others. It will also highlight existing examples of local and national assessments and communication products based on these data, as well as demonstrating how specifically data on air pollution can be used for a quick health impact evaluation. The format of the workshop will be a practice session of 60 minutes with four presentations, the last being a live example demonstration, and an ample session of questions and answers. Key messages • The EEA collects and publishes large, validated datasets of public health from across 38 member countries, including on air and water pollution, industrial emissions, noise and climate change. • These data can be directly used by practitioners to assess environmental risks to the health of their communities, and to design and evaluate policies, with publicly available models and tools.

Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review.

The rapid advancement of digital technologies, particularly in big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and deep learning (DL), is reshaping the global health care system, including in Bangladesh. The increased adoption of these technologies in health care delivery within Bangladesh has sparked their integration into health care and public health research, resulting in a noticeable surge in related studies. However, a critical gap exists, as there is a lack of comprehensive evidence regarding the research landscape; regulatory challenges; use cases; and the application and adoption of BDA, AI, ML, and DL in the health care system of Bangladesh. This gap impedes the attainment of optimal results. As Bangladesh is a leading implementer of digital technologies, bridging this gap is urgent for the effective use of these advancing technologies. This scoping review aims to collate (1) the existing research in Bangladesh's health care system, using the aforementioned technologies and synthesizing their findings, and (2) the limitations faced by researchers in integrating the aforementioned technologies into health care research. MEDLINE (via PubMed), IEEE Xplore, Scopus, and Embase databases were searched to identify published research articles between January 1, 2000, and September 10, 2023, meeting the following inclusion criteria: (1) any study using any of the BDA, AI, ML, and DL technologies and health care and public health datasets for predicting health issues and forecasting any kind of outbreak; (2) studies primarily focusing on health care and public health issues in Bangladesh; and (3) original research articles published in peer-reviewed journals and conference proceedings written in English. With the initial search, we identified 1653 studies. Following the inclusion and exclusion criteria and full-text review, 4.66% (77/1653) of the articles were finally included in this review. There was a substantial increase in studies over the last 5 years (2017-2023). Among the 77 studies, the majority (n=65, 84%) used ML models. A smaller proportion of studies incorporated AI (4/77, 5%), DL (7/77, 9%), and BDA (1/77, 1%) technologies. Among the reviewed articles, 52% (40/77) relied on primary data, while the remaining 48% (37/77) used secondary data. The primary research areas of focus were infectious diseases (15/77, 19%), noncommunicable diseases (23/77, 30%), child health (11/77, 14%), and mental health (9/77, 12%). This scoping review highlights remarkable progress in leveraging BDA, AI, ML, and DL within Bangladesh's health care system. The observed surge in studies over the last 5 years underscores the increasing significance of AI and related technologies in health care research. Notably, most (65/77, 84%) studies focused on ML models, unveiling opportunities for advancements in predictive modeling. This review encapsulates the current state of technological integration and propels us into a promising era for the future of digital Bangladesh.

Association of exposure to nonpersistent endocrine disruptors with sex hormones and metabolic health in US females.

Endocrine disruptive chemicals (EDCs) are considered as the potential attributes for the increasing trend in obesity and metabolic syndrome (MS) through disruption of sex hormones, particularly in women. We evaluated the association of understudied EDC compounds with total testosterone (TT), sex hormone-binding globulin (SHBG), obesity, and MS. A population-based cross-sectional study was conducted using the National Health and Nutrition Examination Survey datasets collected during the years 2013-2016. Women of age ≥15 years with urinary measurements of nonpersistent EDCs, including bisphenol, triclosan, triclocarban, dichlorophenol, and paraben compounds were included in this study. Data were analyzed using the modified Poisson models to estimate the adjusted relative risk (RR) and 95% confidence interval (CI). The associations were also validated by considering TT and SHBG concentrations as the outcomes. The study included 1974 women with 11% high TT, 10.5% low SHBG, 40% obesity, and 46.2% MS. A medium to high exposure to bisphenol-A (RR = 1.64; 95% CI: 1.14, 2.35, p = 0.009), bisphenol-F (RR = 1.83; 95% CI: 1.35, 2.49, p < 0.001), bisphenol-S (RR = 1.83; 95% CI: 1.35, 2.49, p = 0.041) and 2, 4- dichlorophenol (RR = 1.61; 95% CI: 1.06, 2.45, p = 0.026) were associated with low SHBG but not with high TT. In addition, high exposure to triclosan was also inversely associated with SHBG concentrations (regression coefficient = -0.09; 95% CI: -0.15, -0.02, p = 0.013). However, these EDCs were found to be associated with SHBG, obesity, and MS according to menopausal status. High exposure to certain nonpersistent EDCs was associated with low SHBG, obesity, and MS according to menopausal status.

Contraceptive Use and Its Associations With Social Determinants of Health Among Young Adults.

Nonuse and inconsistent use of contraception in young adults can result in undesired pregnancies. Prior research on contraceptive use has primarily focused on individual influences such as attitudes, beliefs, and skills related to contraceptive use, with little consideration of the social and economic determinants. This study explored domains from the Healthy People 2030 Social Determinants of Health (SDH) framework and examined their cross-sectional associations with young adults' self-reported contraceptive use (ie, any use in the past year and consistent use of contraception). Eleven measures related to 5 SDH domains were extracted from the Add Health dataset collected from 2001 to 2002 (Wave III). The final sample included 11,172 youth with a mean age of 21.88 years (SD: 1.84) and was 53.8% female. Measures associated with the past year's use of contraceptives included non-poverty status, employment, housing instability, high school graduation, enrollment in higher education, English language spoken at home, experience of civic participation, and access to primary care. For consistent use of contraception, associated measures were participants' non-poverty status, employment, high school graduation, enrollment in higher education, English language spoken at home, experience of civic participation, incarceration, and access to health care. Interventions that address social determinants of health can potentially promote contraceptive use.

Cardiac events among a cohort of 17,389 patients receiving cancer chemotherapy: short and long term implications

BackgroundThe association between cardiovascular disease and carcinogenesis is bidirectional and well-established. Furthermore, cancer treatment improves overall patient survival, potentially at the cost of incremental and fatal cardiovascular disease (CVD).AimTo evaluate (a) In a real-world cohort, the proportion of patients offered cancer chemotherapy who have antecedent CVD (CVDA); (b) The rates of patient admission with subsequent development of CVD (CVDS) requiring hospital admission post assignment to chemotherapy; (c) The impact of CVDA and CVDS on mortality rates relative to those seen in patients without overt CVD (CVD−) and (d) The time course of mortality in CVD− versus CVDS patients.MethodsRetrospective analysis was performed in deidentified linked health data sets. Correlates of mortality were evaluated by Cox proportional hazards evaluation. Relative and absolute time-variability of CVD as a primary cause of death were determined.ResultsOf the total 17,389 patients, there were 2,159 with CVDA. Over a median follow-up time of 4.6 years, CVDS admissions (n = 8,529) occurred more commonly in the presence of CVDA (70.0% vs. 46.1%, p < 0.001), and more than 50% of CVDS cases occurred in the first 12 months of follow-up. The 5-year mortality rates were 71.5% for CVDA, 64.7% for CVDS, and 40.8% for CVD− (p < 0.001). Development of CVDS was associated with a substantially increased risk of mortality in the next 12 months. The development of CVDs was also associated with an increased risk of cardiovascular, as against non-cardiovascular, mortality (7.1% vs. 1.6%, p < 0.001).ConclusionsApproximately 50% of patients assigned to cancer chemotherapy developed CVDS, heralding a particularly high risk of mortality over the next 12 months. Both CVDA and CVDS are associated with substantial increases in mortality rates relative to those in CVD− patients. This increased risk merits close individual monitoring.

Performance of the Classical Model in Feature Selection Across Varying Database Sizes of Healthcare Data

Machine learning is increasingly being applied to medical research, particularly in selecting predictive modelling variables. By identifying relevant variables, researchers can improve model accuracy and reliability, leading to better clinical decisions and reduced overfitting. Efficient utilization of resources and the validity of medical research findings depend on selecting the right variables. However, few studies compare the performance of classical and modern methods for selecting characteristics in health datasets, highlighting the need for a critical evaluation to choose the most suitable approach. We analysed the performance of six different variable selection methods, which includes stepwise, forward and backward selection using p-value and AIC, LASSO, and Elastic Net. Health-related surveillance data on behaviors, health status, and medical service usage were used across ten databases, with sizes ranging from 10% to 100%, maintaining consistent outcome proportions. Varying database sizes were utilized to assess their impact on prediction models, as they can significantly influence accuracy, overfitting, generalizability, statistical power, parameter estimation reliability, computational complexity, and variable selection. The stepwise and backward AIC model showed the highest accuracy with an Area under the ROC Curve (AUC) of 0.889. Despite its sparsity, the Lasso and Elastic Net model also performed well. The study also found that binary variables were considered more crucial by the Lasso and Elastic Net model. Importantly, the significance of variables remained consistent across different database sizes. The study shows that no major variations in results between the fitness metric of the model and the number of variables in stepwise and backward p-value models, irrespective of the database's size. LASSO and Elastic Net models surpassed other models throughout various database sizes, and with fewer variables.

Association between visceral adiposity index and bowel habits and inflammatory bowel disease: a cross-sectional study

Obesity has become a global public health problem, and its relationship with gastrointestinal diseases has become a major concern. The visceral adiposity index (VAI) is a novel index to assess the distribution and content of visceral fat, and this study aimed to investigate the association between VAI and bowel habits (chronic diarrhea, chronic constipation) and inflammatory bowel disease (IBD). The 2005–2010 National Health and Nutrition Examination Survey (NHANES) dataset was used for the cross-sectional survey. Bowel habits and IBD were defined by self-report. Multiple logistic regression models were used to test the linear association of VAI with bowel habits and IBD. Fitted smoothed curves and threshold effects analyses were used to characterize nonlinear relationships. This cross-sectional study included 10,391 adults (≥ 20 years). After adjusting for covariates, there was a significant negative association between VAI and chronic constipation (OR [95% CI]: 0.97 [0.95, 1.00]) but no significant association with IBD (OR [95% CI]: 0.97 [0.87, 1.07]). Additionally, there was a nonlinear association between VAI and chronic diarrhea with a breakpoint of 3.08, with a positive correlation between the two on the left side of the breakpoint and no statistical significance on the right side. Subgroup analyses and interaction tests showed that maintaining sleep health was associated with a low risk of chronic constipation. Elevated VAI levels were negatively associated with chronic constipation, and elevated levels were positively associated with chronic diarrhea at VAI < 3.08. This reminds us that maintaining moderate levels of visceral fat may prevent the onset of chronic constipation and circumvent the risk of chronic diarrhea. Notably, maintaining healthy sleep may play a positive role in reducing chronic constipation.

Unique Pattern of Missed Opportunities for Congenital Syphilis Prevention in Alabama Compared to National Trends

Abstract Background Missed opportunities to prevent congenital syphilis include no syphilis testing during prenatal care, late prenatal care, inadequate treatment, no prenatal care, late identification, and unidentified missed opportunities. Cases of unidentified missed opportunities are those identified as newborns with congenital syphilis born to mothers that received adequate treatment thirty days before delivery. Missed opportunities in Alabama may differ from national trends. Comparing rates of missed opportunities in Alabama to national trends may help inform Alabama physicians of the most effective intervention points for our state. Methods Data on congenital syphilis cases and rates of missed opportunities from 2018-2021 were collected from the Alabama Department of Public Health and CDC STI datasets. The occurrence of missed opportunity types in Alabama was determined and compared to national trends. Results No timely prenatal care or syphilis testing was the greatest missed opportunity nationally at 37.58%. However, the most prevalent missed opportunity in Alabama was late identification of seroconversion at 44.44% in comparison to 14.22% nationally. Second most common missed opportunity in Alabama was no timely prenatal care or syphilis testing (divided into no prenatal care at 14.81% and late prenatal care at 6.17%). Additionally, evidence of congenital syphilis despite maternal treatment was the least common missed opportunity nationally (3.75%) but made up 25% of missed opportunities in Alabama in 2021. Conclusion The incidence of late identification of seroconversion in Alabama is three times the national average, which emphasizes the need for targeted interventions to address this disparity. Furthermore, congenital syphilis despite maternal treatment in Alabama is six times the national rate, suggesting a need for further investigation into other missed opportunities.

Association between the neutrophil-to-lymphocyte ratio and cancer in adults from NHANES 2005–2018: a cross-sectional study

Inflammation plays a crucial role in cancer development. The neutrophil-to-lymphocyte ratio (NLR), a measure of inflammation, is obtained from a complete blood count. However, little is known about the association between NLR and cancer in the general adult population in the United States. This study aimed to evaluate whether NLR is associated with cancer in American adults. This retrospective cross-sectional study included 28,016 adult participants from the National Health and Nutrition Examination Survey (NHANES) dataset spanning 2005 to 2018. Data on demographics (age, sex, race, marital status, Poverty-Income Ratio, education level), lifestyle factors (smoking, alcohol consumption, body mass index), medical conditions (hypertension, diabetes, cardiovascular disease), and laboratory parameters (hemoglobin, platelet count, alanine aminotransferase, creatinine, albumin, and lactate dehydrogenase), were collected. Logistic regression analysis was used to investigate the research objectives. Of the total 28,016 participants, 2639 had cancer. The mean age was 49.6 ± 17.6 years, and 50% were male. A positive association between NLR and cancer risk was observed after multivariate adjustment (OR = 1.20, 95% confidence interval (CI) = 1.05–1.36, p = 0.006). Similar patterns were observed in subgroup analyses (all p-values for interaction > 0.05). A higher NLR was directly correlated with an increased risk of developing cancer in adults.

Enabling data linkages for rare diseases in a resilient environment with the SERDIF framework

Environmental factors amplified by climate change contribute significantly to the global burden of disease, disproportionately impacting vulnerable populations, such as individuals with rare diseases. Researchers require innovative, dynamic data linkage methods to enable the development of risk prediction models, particularly for diseases like vasculitis with unknown aetiology but potential environmental triggers. In response, we present the Semantic Environmental and Rare Disease Data Integration Framework (SERDIF). SERDIF was evaluated with researchers studying climate-related health hazards of vasculitis disease activity across European countries (NP1 = 10, NP2 = 17, NP3 = 23). Usability metrics consistently improved, indicating SERDIF’s effectiveness in linking complex environmental and health datasets. Furthermore, SERDIF-enabled epidemiologists to study environmental factors in a pregnancy cohort in Lombardy, showcasing its versatility beyond rare diseases. This framework offers for the first time a user-friendly, FAIR-compliant design for environment-health data linkage with export capabilities enabling data analysis to mitigate health risks posed by climate change.

Health care transition rates and associated factors for adolescents with asthma

BackgroundAdolescents and young adults (AYAs) with asthma, face increased risks during healthcare transition (HCT) from pediatric to adult care. Despite guidelines advocating for more HCT preparedness, this does not consistently occur in clinical practice. The rates of transition preparation exposure in adolescents with asthma are unknown. Objective(s)Our goal was to understand the rates of HCT exposure among adolescents with asthma in the United States along with predictive characteristics associated with receiving HCT exposure using data from a nationally representative survey. MethodsWe studied adolescents aged 12-17 years old with asthma in the 2020-2021 National Survey of Children’s Health (NSCH) dataset. Associations were explored between sociodemographic, health-related and provider practice-related variables and HCT exposure through univariate analysis and multivariable logistic regression. ResultsOnly 19% of adolescents with asthma from this cohort met criteria indicating they received HCT exposure. In our multivariable analysis, older age, female gender, having a provider actively work with the child to make positive choices about health, a written care plan addressing transition, routine preventive care visits, and a caregiver had someone discuss about health insurance into adulthood were associated with higher odds of HCT exposure. Hispanic ethnicity, lack of insurance, and living in a metropolitan area were associated with lower odds of receiving transitional care preparation but were not significant in the multivariable model. ConclusionsOur findings underscore the need to improve transitional care preparation for adolescents with asthma, with attention needed to address disparities based on sociodemographic factors, including healthcare access.

The provenance of Apple Health data: A timeline of update history

Fitness tracking smart watches are becoming more prevalent in investigations and the need to understand and document their forensic potential and limitations is important for practitioners and researchers. Such fitness devices have undergone several hardware and software upgrades, changing the way they operate and evolving as more sophisticated pieces of technology. One example is the Apple Watch, working in conjunction with the Apple iPhone, to measure and record a vast amount of health information in the Apple Health database, healthdb_secure.sqlite. Over time, an end user will update their devices, but their health data, uniquely, carries over from one device to the next. In this paper, we investigate and analyse the hardware and software provenance of a real 5+ year Apple Health dataset to determine changes, patterns and anomalies over time. This provenance investigation provides insights in the form of (1) a timeline, representing the dataset's history of device and firmware updates that can be used in the context of investigation validation, (2) anomaly detection and, (3) insights into cyber hygiene. Analysis of the non-health data recorded in the health database arguably provides just as much insightful information as the health data itself.

Associations between sensory impairment and functional limitations among older Chinese adults: mediating roles of social isolation and cognition

BackgroundThe high prevalence of sensory impairment and functional limitations in older adults is a significant concern, yet there is limited understanding of the relationship between these two conditions. Therefore, the objective of this study was to investigate the pathways connecting sensory impairment and functional limitations by examining serial multiple mediating effects of social isolation and cognition in older adults.MethodsUsing the China Health and Retirement Longitudinal Study dataset, a sample of 4871 older adults was selected. The study variables included sensory impairment, functional limitations, social isolation and cognition, and other covariates. A hierarchical multiple linear regression model was used to assess the association between sensory impairment and functional limitations. Mediation analysis was conducted to explore the sequential multiple mediating effects of social isolation and cognitive function in the relationship between sensory impairment and functional limitations.ResultsOur findings revealed a significant and positive association between sensory impairment and functional limitations (B = 0.615, 95% CI: 0.397, 0.834). After adjusting for social isolation and cognitive function, the impact of sensory impairment on functional limitations accounted for 87.19% of the total effect. Additionally, approximately 12.81% of the significant relationship between dual sensory impairment and functional limitations was mediated by social isolation and cognitive function. A serial multiple mediating pathway (sensory impairment → social isolation → cognition → functional limitations) was identified, with a coefficient of 0.013 (95% CI: 0.006, 0.020).ConclusionsOur study provides evidence for the mediating effects of social isolation and cognition in the relationship between sensory impairment and functional limitations. Given the prevalence of functional limitations among older adults with sensory impairment, it is crucial to consider social isolation and cognitive function in efforts to reduce the burden of disability care. Future validation of these findings through longitudinal studies is necessary.

Development of a predictive model for the U-shaped relationship between the triglyceride glycemic index and depression using machine learning (NHANES 2009–2018)

BackgroundAt present, the relationship between depression and the triglyceride glycemic (TyG) index remains a topic of debate. This study sought to elucidate the relationship between depression and the TyG index to create a predictive model that would help doctors diagnose patients. MethodsWe conducted a cross-sectional study utilizing the National Health and Nutrition Examination Survey (NHANES) dataset, which comprises data from 2009 to 2018. The analysis involved 11,222 adults with a Patient Health Questionnaire-9 (PHQ-9) score of 5 or higher, indicating the presence of depression. As part of the analysis, multiple regression models were used to test whether a linear relationship existed between the TyG index and depression. A threshold effects analysis was used to generate smoothed curves and detect nonlinear correlations. Additionally, the Least Absolute Shrinkage and Selection Operator (LASSO) regression were employed to identify the key risk factors associated with depression. The factors identified were then used to construct the risk prediction nomogram. Finally, Receiver Operating Characteristic (ROC) curves were used to evaluate the discriminative performance of the model. ResultsMultivariable linear regression analysis indicated a strong positive correlation between depression and the TyG index (β: 0.38, 95 % CI: 0.16–0.60, p = 0.0008). A U-shaped relationship with an inflection point was observed at a TyG index of 8.16. The nomogram model, constructed using risk factors identified by LASSO, exhibited a significant predictive value (AUC = 0.888). ConclusionsThe results of this investigation point to a U-shaped association between depression risk and the TyG index among Americans. Those with a TyG index of over 8.16 are significantly more likely to develop depression. These results suggest a possible causal relationship and emphasize the importance of monitoring the TyG index in depression risk assessment.

The role of body mass index in the association between serum vitamin D and non-alcoholic fatty liver disease: A mediation analysis

BackgroundNon-alcoholic fatty liver disease (NAFLD) is a classic disease of liver injury, and studies have confirmed that nutrients can influence NAFLD. Findings on serum vitamin D (VD) and NAFLD are conflicting and the mechanisms are not yet clear. This study aimed to determine the association between the two and the mediating role mediated by body mass index (BMI). MethodsA cross-sectional study (N = 2609) was conducted using the 2017–2018 National Health and Nutrition Examination Survey (NHANES) dataset. Weighted multivariate logistic regression models estimated OR and 95 % CIs between serum VD and NAFLD. The mediation analysis was performed for BMI. ResultsSerum VD was statistically significant in subjects with and without NAFLD (P = 0.026). Adequate serum VD was found to be a protective factor for NAFLD after adjusting for covariates (OR (95 %CI) of 0.717 (0.553,0.929)). After mediation analysis, it was found that BMI mediated 34.07 % of the effect of serum VD on NAFLD. ConclusionThere is an association between serum vitamin D and NAFLD and sufficient VD was protective factor for NAFLD. BMI partly mediated the association between serum VD and NAFLD.

Predicting the risk of diabetes complications using machine learning and social administrative data in a country with ethnic inequities in health: Aotearoa New Zealand

BackgroundIn the age of big data, linked social and administrative health data in combination with machine learning (ML) is being increasingly used to improve prediction in chronic disease, e.g., cardiovascular diseases (CVD). In this study we aimed to apply ML methods on extensive national-level health and social administrative datasets to assess the utility of these for predicting future diabetes complications, including by ethnicity.MethodsFive ML models were used to predict CVD events among all people with known diabetes in the population of New Zealand, utilizing nationwide individual-level administrative data.ResultsThe Xgboost ML model had the best predictive power for predicting CVD events three years into the future among the population with diabetes (N = 145,600). The optimization procedure also found limited improvement in prediction by ethnicity (using area under the receiver operating curve, [AUC]). The results indicated no trade-off between model predictive performance and equity gap of prediction by ethnicity (that is improving model prediction and reducing performance gaps by ethnicity can be achieved simultaneously). The list of variables of importance was different among different models/ethnic groups, for example: age, deprivation (neighborhood-level), having had a hospitalization event, and the number of years living with diabetes.Discussion and conclusionsWe provide further evidence that ML with administrative health data can be used for meaningful future prediction of health outcomes. As such, it could be utilized to inform health planning and healthcare resource allocation for diabetes management and the prevention of CVD events. Our results may suggest limited scope for developing prediction models by ethnic group and that the major ways to reduce inequitable health outcomes is probably via improved delivery of prevention and management to those groups with diabetes at highest need.

Estimating the effect of inclisiran on hypercholesterolemia and primary prevention of cardiovascular disease: the NHANES 1999–2018 study

BackgroundHypercholesterolemia has been identified as an independent predictor of cardiovascular disease (CVD). Inclisiran, an innovative small interfering RNA agent, is anticipated to result in a notable reduction of approximately 50% in low-density lipoprotein cholesterol (LDL-C) levels. Given its transformative impact, this study scrutinized the eligibility of the US population for inclisiran treatment and evaluated its potential effects on hypercholesterolemia and the primary prevention of CVD.MethodsThis study applied the eligibility criteria from the ORION 10 and 11 trials to the 1999–2018 National Health and Nutrition Examination Survey (NHANES) dataset to estimate the size of the eligible population for atherosclerotic cardiovascular disease (ASCVD) and ASCVD-risk equivalents. Utilizing the reduction in LDL-C levels from ORION 10, this study predicted the impact of inclisiran on LDL-C levels among ASCVD patients. Similarly, leveraging the changes in lipid levels from ORION 11, this study predicted inclisiran’s effect on the 10-year change in CVD risk and preventable CVD events in the ASCVD-risk equivalents population, employing the Framingham CVD Risk Score.ResultsThe study identified 579 ASCVD patients (5 million) and 382 ASCVD-risk equivalents (2.66 million) who met the eligibility criteria from ORION 10 and 11. Among the ASCVD population, 3.5 million (70.2%) would achieve a ≥ 50% reduction in LDL-C levels after treatment. Furthermore, 4.6 million (91.3%) would achieve LDL-C levels < 70 mg/dL, and 3.8 million (75%) would achieve LDL-C levels < 55 mg/dL after treatment. For the ASCVD-risk equivalents population, the estimated 10-year CVD risk would decrease from 25.3 to 17.7%, an absolute reduction of 7.6% and a relative reduction of 30% following inclisiran treatment, potentially preventing 202,353 CVD events over a decade, including 138,084 coronary heart disease cases, 37,351 strokes, and 23,894 congestive heart failure cases.ConclusionsInclisiran has the potential to substantially reduce the prevalence of hypercholesterolemia and prevent nearly 200,000 CVD events in eligible US adults.

Impact of symptom-to-balloon time in patients with non-ST-segment elevation myocardial infarction and complex lesions.

Considering the limited data regarding clinical outcomes of patients with non-ST-segment on the ECG elevation myocardial infarction (NSTEMI), this study compared the outcomes of patients undergoing percutaneous coronary intervention with newer-generation drug-eluting stents stratified by the presence/absence of complex lesions and symptom-to-balloon time (SBT; <48 h or ≥48 h). We enrolled 4373 patients with NSTEMI from the Korea Acute Myocardial Infarction Registry-National Institute of Health dataset and stratified them into the complex group (2106 patients; SBT < 48 h, n = 1365; SBT ≥48 h, n = 741) and the noncomplex group (2267 patients; SBT < 48 h, n = 1573; SBT ≥48 h, n = 694). The primary outcome was the 3-year all-cause mortality rate. The secondary outcomes were any major adverse cardiac events (MACE), including cardiac death (CD), recurrent myocardial infarction, and stroke. The incidence of all-cause mortality (adjusted hazard ratio, 0.656; P = 0.009), CD ( P = 0.037), and MACE ( P = 0.047) in the complex group and of stroke in the noncomplex group ( P = 0.020) were significantly lower in patients with SBT < 48 h than in those with SBT ≥48 h. Among patients with SBT < 48 h, the stroke incidence ( P = 0.019) was higher in the complex group than in the noncomplex group, while among patients with SBT ≥48 h, the MACE incidence ( P = 0.011) was higher in the former than in the latter. SBT reduction effectively decreased the 3-year mortality in patients with NSTEMI in the complex group compared with the noncomplex group.

National stakeholder consultation on how to measure care home residents' quality of life.

The Developing research resources And minimum data set for Care Homes' Adoption and use or DACHA study aims to create a prototype minimum data set combining residents' information recorded by care homes with their data held in health and social care data sets. The DACHA minimum data set will contain information on quality of life. Internationally and in the UK, there is no consensus on collecting information on quality of life in a standardised format equivalent to the consensus for health measures. This paper describes an online consultation with stakeholder representatives about how to measure the quality of life of residents in UK older-adult care homes, for inclusion in the DACHA minimum data set. We drew on principles of the Delphi technique, identifying participants knowledgeable about living, working in and visiting care homes, and preference scoring. We used a bespoke online research engagement platform (Thiscovery, www.thiscovery.org, Cambridge, UK) to engage the participants. Participants included care home staff and managers, old age specialists (clinical/research), commissioners/providers/regulators, primary care professionals, relatives/family carers of care home residents. The consultation is complementary to DACHA's research and patient and public involvement and engagement activities, which have involved people living in care homes; thus, care home residents were not included in this consultation. The first round asked 30 participants to rank the most important principles and domains to consider when measuring quality of life in care homes. Responses to round 1 informed the selection of quality of life measures that round 2 (September 2022) participants were asked to report their familiarity with and confidence in a range of outcome measures all of which met the criteria identified as important in round 1. Recruitment was extended in round 2, and 72 individuals participated. Based on the rankings and the qualitative feedback in round 2, we included four of the shortlisted quality of life outcome measures in DACHA's prototype minimum data set for care homes. The qualitative feedback suggested a shared understanding across the different representative groups about the strengths and limitations of the selected measures. This work makes an important contribution, understanding the opportunities that quality of life measures pose for different stakeholder groups as regular users of care home resident data. In future DACHA work, interviews and focus groups will collect further data about the perceptions of care home staff who completed measures during the pilot study and about the usefulness of the data collected via these measures. The quality-of-life section of the DACHA minimum data set can contribute to informing similar care home data sets internationally. This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme as award number NIHR127234.

Effects of community water fluoridation on child dental caries in remote Northern Territory, Australia: a difference-in-difference analysis.

Community water fluoridation (CWF) is a cost-effective intervention to reduce dental caries at population level. This Australian study used a difference-in-difference (DiD) analysis to measure dental caries in children exposed to CWF in the Northern Territory (NT), Australia. Oral health data obtained from the NT Department of Health contained 64 399 person-year observations from 2008 to 2020, totalling 24 546 children aged 1-17 years. Drinking water quality data for fluoride levels, held by the Power and Water Corporation, were obtained for 50 remote communities and linked to the oral health dataset. The DiD analysis used a treatment group and two control groups to compare the effects of CWF on dental caries outcomes in children, measured using the decayed, missing and filled teeth (dmft/DMFT) index. The treatment group consisted of records from children residing in five remote NT communities that implemented CWF in 2014.The control 1 group included records of children residing in communities with naturally occurring fluoride in drinking water supplies at levels at or above the Department of Health policy threshold of 0.5 mg/L. The control 2 group included records of children residing in communities with naturally occurring fluoride in drinking water supplies below the level recommended by the Department of Health policy (<0.5 mg/L). The data were grouped into time periods prior to the inception of CWF in five remote communities in 2014 (pre-intervention) and after 2014 (post-intervention). Our results demonstrated that dental caries was significantly decreased for children in the treatment group following the implementation of CWF at a greater magnitude than both control groups for the same time period. Overall, children assigned to the treatment group exhibited a decline in the number of teeth affected by caries by an average of 0.28 (p=0.001). Notably, children of ages 7-10 years and 11-17 years experienced significantly greater post-intervention declines in average dmft/DMFT, by 0.32 (p=0.051) and 0.40 (p=0.012) fewer affected teeth respectively. While dental caries disproportionately impacts Aboriginal children in remote and very remote NT, it is clear that CWF produces population-level reductions in overall dental caries for these populations. Additionally, our study demonstrates the application of the DiD method in a public health policy evaluation. Our findings suggest that the longstanding policy position of the NT Department of Health on CWF has supported improvements in oral health among child populations that experience high levels of dental caries in remote NT communities.