Health Datasets Research Articles

Impact of sleep problems on the cardiometabolic risks: an integrated epidemiological and metabolomics study

BackgroundWe investigated the association between sleep problems and cardiometabolic risks and the potential linking effect of metabolites and metabolic pathways based on multi-layered research, including observational, mendelian randomization (MR), and metabolomics analysis.MethodsA cross-sectional analysis of the 2015–2018 National Health and Nutrition Examination Survey (NHANES) dataset was conducted to identify the association between sleep problems and cardiometabolic risks. A subsequent MR study based on genetic data was performed to explore the causal correlation of significant associations in the NHANES study. The underlying alteration of metabolism was explored by constructing zebrafish models and wide-targeted metabolomics analysis.ResultsThe cross-sectional analysis of the NHANES database revealed a significant association of snoring with obesity [OR = 2.65, 95% confidence intervals (CI): 1.87, 3.74]; sleep apnea with hypertension (OR = 1.68, 95% CI: 1.14, 2.48) and obesity (OR = 1.44, 95% CI: 1.05, 1.96); trouble sleeping with hypertension (OR = 1.84, 95% CI: 1.18, 2.86), obesity (OR = 1.56, 95% CI: 1.07, 2.26), and type 2 diabetes (T2DM) (OR = 1.52, 95% CI: 1.02, 2.25). MR analysis verified the causal relationship between genetically proxied sleep apnea or snoring and obesity. The decreased activity levels and altered expression levels of six circadian genes (bmal1b, cry1aa, cry1ab, clock1a, per1b, per2) were identified in the zebrafish of the sleep disorder group. Multiple metabolites related to disturbed glucose metabolism (e.g., 20-HETE), lipid metabolism (e.g., inosine), and vascular-related metabolites (e.g., riboflavin) were finally identified, indicating the latent effect of metabolism.ConclusionsThis study identified the chain of sleep-circadian rhythm-metabolism-cardiometabolic risks. These findings can promote improved prevention implementation and therapeutic strategies.

Comparison of Outcomes Between ST-Segment Elevation and Non-ST-Segment Elevation Myocardial Infarctions Based on Left Ventricular Ejection Fraction

Background: This study was conducted to address the lack of reports comparing the clinical outcomes of non-ST-segment elevation myocardial infarction (NSTEMI) and STEMI based on left ventricular ejection fraction (LVEF). Methods: A total of 9854 patients from the Korea Acute Myocardial Infarction Registry-National Institute of Health dataset were classified into three LVEF categories: heart failure (HF) with reduced ejection fraction (EF) (HFrEF, n = 1250), HF with mildly reduced EF (HFmrEF, n = 2383), and HF with preserved EF (HFpEF, n = 6221). Each group was further divided into NSTEMI and STEMI groups. The primary clinical outcome was the incidence of patient-oriented composite outcomes, defined as all-cause death, recurrent myocardial infarction, any repeat coronary revascularization, hospitalization for HF, and stroke. Results: Following adjustment, in-hospital mortality rates were comparable between the NSTEMI and STEMI groups in the HFrEF and HFmrEF groups. However, 3-year mortality rates were higher in the NSTEMI group. In contrast, in the HFpEF group, the STEMI group had higher rates of in-hospital all-cause death (p = 0.001) and cardiac death (p < 0.001) compared to the NSTEMI group, which was associated with increased 3-year all-cause death (p = 0.026) and cardiac death (p < 0.001) in the STEMI group. When in-hospital mortality was excluded, no difference in 3-year mortality rates was observed between the NSTEMI and STEMI groups in the HFpEF group. Conclusions: In-hospital mortality and 3-year outcomes varied across LVEF groups. Therefore, comparing NSTEMI and STEMI based on LVEF provides valuable insights into the differences in patient outcomes.

Equity of access to pathological diagnosis and bronchoscopy for lung cancer in Aotearoa New Zealand.

Māori are less likely to survive their lung cancer once diagnosed, but it remains unclear whether this is partially driven by poorer access to best-practice diagnostic services. We examined all lung cancer registrations in Aotearoa New Zealand between 2007-2019 (n=27,869) linked to national administrative health datasets and further stratified by ethnicity, tumour type and stage of disease. Using descriptive and regression analyses, we compared ethnic groups in terms of the basis of diagnosis (e.g., histology, cytology), receipt of bronchoscopy and travel distance and time to access bronchoscopy. We found no differences in access to a pathological diagnosis between ethnic groups regardless of cancer type or stage. We found that Māori within the cohort were marginally more likely to access bronchoscopy than the majority European group; however, we also found that Māori had lower odds of living close to the location of their bronchoscopy, and correspondingly higher odds of living 100-200km (adjusted odds ratio [adj. OR] 1.46, 95% confidence interval [CI] 1.26-1.69) or more than 200km away (1.36, 95% CI 1.15-1.61) than Europeans. Interventions that aim to further support Māori to overcome the systematic and cumulative disadvantages in access to cancer care should be broadly supported and resourced.

Association Between Composite Dietary Antioxidant Index and Endometriosis from NHANES 2001-2006: A Cross-Sectional Study.

To evaluate the association between Composite Dietary Antioxidant Index (CDAI) and the risk of endometriosis in American women. The study adopted a cross-sectional design, incorporating 3862 women aged over 20 years, selected from the National Health and Nutrition Examination Survey (NHANES) dataset from 2001 to 2006. Six dietary antioxidants were taken into account in calculating CDAI. Endometriosis was determined based on self-report. To evaluate the association between CDAI and the risk of endometriosis, we employed models with multivariable logistic variables. For subgroup assessment in relation to CDAI, a stratified multivariate logistic regression model was utilized. Among all participants, 273 participants (7.1%) were found to exhibit endometriosis. The preliminary analysis showed a reverse association between CDAI and the likelihood of endometriosis (odds ratio [OR] = 0.95; 95% confidence interval [CI]: 0.92~0.98). Upon full adjustment within the multivariable logistic regression, the ORs (95% CI) for endometriosis prevalence per unit increase in CDAI were estimated to be 0.96 (0.93~1). When the CDAI levels were divided into quartiles, it was found that the ORs for endometriosis with CDAI levels in Q2 (-2.131-0.023), Q3 (0.023-2.650), and Q4 (2.650-42.854) were 0.74 (0.52, 1.05), 0.76 (0.53, 1.1), and 0.53 (0.36, 0.79), respectively, compared to those with CDAI levels in Q1 (-7.151--2.131). We evaluated the association between CDAI and endometriosis using subgroups stratified by age, race/ethnicity, education level, body mass index (BMI), oral contraceptive, and menopausal status, revealing a substantial negative relationship. In this cross-sectional study, increasing CDAI was proportionally associated with a reduced risk of endometriosis among American women, suggesting a diet high in antioxidants may play an important role in reducing the risk of endometriosis. The findings of NHANES data spanning 2001 to 2006 suggest that promoting antioxidant-rich diets could be an important prevention strategy for endometriosis.

Daily and Peak Monitor Independent Movement Summary (MIMS) Values Associated With Metabolic Syndrome: NHANES 2011-12 and 2013-14.

The U.S. National Health and Nutrition Examination Survey (NHANES) physical activity monitor datasets for 2011-12 and 2013-14 were released in late 2020. To date, there has been limited interpretation of these nationally representative wrist-worn accelerometer data (summarized and reported in Monitor Independent Movement Summary [MIMS] units) and their relationships with health-related outcomes. This study examined the associations between free-living DailyMIMS (volume), Peak 1MIMS and Peak 30MIMS (intensity), and risk of Metabolic Syndrome (MetS). Data from adults (N=3787; 18-80+ years) in the 2011-12 and 2013-14 NHANES cycles with health examination and accelerometry data were included. Accelerometer data were processed into Peak 1MIMS and Peak 30MIMS (MIMS/min), and DailyMIMS (MIMS/day). Design-based generalized linear and logistic regressions, and a sample-weighted decision tree, were used to examine associations between MIMS variables and MetS risk factors. Lower Peak 1MIMS, Peak 30MIMS, and DailyMIMS were observed for every one-unit increase in the number of risk factors -3.9 [95% CI: -4.3, -3.4] and -2.3 [-2.6, -2.1] MIMS/min, (-672.1 [-772.7, -571.5] MIMS/day, respectively, all p< 0.001). The Decision Tree classified individuals ≥ 46.5 years with a DailyMIMS ≥ 12 245 MIMS/day and a Peak 30MIMS < 45.1 MIMS/min as having MetS (≥ 3 risk factors). Individuals < 46.5 years with a Peak 1MIMS ≥ 62.9 MIMS/min were classified with 0 risk factors. Higher DailyMIMS and PeakMIMS were associated with an absence of MetS risk factors, with a progressive decline as the number of risk factors increased. These findings may be considered as preliminary benchmarks for DailyMIMS and PeakMIMS associated with cardiometabolic risk.

Variation in Commercial Prices for Thyroidectomy and Parathyroidectomy at US Hospitals

BackgroundThe 2021 Hospital Price Transparency Rule mandated hospitals to publicly disclose their service prices to improve competition and lower healthcare costs. Our aim was to characterize commercial price variation for thyroidectomy and parathyroidectomy. MethodsWe performed a national cross-sectional study of hospital price variation in 2022 and 2023 using the Turquoise Health dataset. Our main outcomes were within- and across-hospital 90th-to-10th percentile commercial price ratios and a high commercial-to-Medicare (1.5) price ratio. We performed logistic regressions to identify hospital factors associated with a high commercial-to-Medicare price ratio. ResultsFor 16,794 unique commercial rates across 564 facilities, within-hospital price ratios ranged from 2.0 to 2.4, and across-hospital price ratios ranged from 2.7 to 4.1. High market concentration and five-star hospital rating were associated with high commercial-to-Medicare price ratios compared to low market concentration and three-star hospital rating, respectively. ConclusionsNotable variation exists within and across hospitals signaling facilities have negotiated different payments from insurance companies for the same service. Quality may be a modifiable factor to increase hospital revenue and improve care for patients.

Association between systemic immune-inflammatory index and systemic inflammatory response index with body mass index in children and adolescents: a population-based study based on the National Health and Nutrition Examination Survey 2017-2020.

The Systemic Immune-Inflammatory Index (SII) and Systemic Inflammatory Response Index (SIRI) are novel composite inflammatory markers. Previous studies suggest that obesity in individuals correlates with persistently low levels of chronic inflammation. This study aims to explore the association between SII and SIRI and Body Mass Index (BMI) among children and adolescents. A cross-sectional survey was conducted using the National Health and Nutrition Examination Survey (NHANES) dataset from 2 consecutive cycles from 2017-2020. Multivariate linear regression models were employed to examine the linear relationships between BMI and SII and SIRI. Non-linear associations were explored using smooth curve fitting and threshold effect analysis. A total of 2980 children and adolescents aged 6-19 years were included in this population-based study. In the population description of body mass index categories, we found progressively higher levels of SII and SIRI, notably peaking among obese children (SII mean ± SD: 528.83 ± 285.46; SIRI mean ± SD: 1.12 ± 0.79). Weighted multivariate linear regression confirmed a significant positive association between BMI and both inflammatory indices (P < 0.0001). Subgroup analyses revealed consistent correlations across gender divisions and highlighted a non-linear relationship between BMI and SII. SII and SIRI are positively associated with BMI in children and adolescents, indicating their potential as markers for assessing systemic inflammation in pediatric obesity. Further large-scale prospective studies are required to substantiate these findings.

Association between stroke and relative fat mass: a cross-sectional study based on NHANES

BackgroundThis study was aimed at investigating the correlation between the occurrence of stroke and relative fat mass (RFM), a novel metric for determining total body fat.MethodsThis cross-sectional study employed the National Health and Nutrition Examination Survey (NHANES) dataset, which encompassed the years 2005 to 2018 to assess the independent relationship between RFM and stroke. Moreover, multinomial logistic regression, subgroup analysis, smooth curve fitting, and interaction testing were also used.ResultsThis study included 35,842 participants and 1,267 (3.53%) of them were diagnosed with stroke. Fully adjusted Models showed that RFM was positively associated with stroke (odds ratio [OR] = 1.02; 95% confidence interval [CI] = 1.01–1.03). The odds of having a stroke in quartile 4 were significantly elevated by 44%, compared to quartile 1 (OR = 1.44,95%CI:1.09–1.90). In addition, a subgroup analysis also demonstrated that age and BMI significantly impacted the association between RFM and stroke (P for interaction<0.01).ConclusionsElevated RFM is associated with increased odds of stroke, suggesting that RFM may have potential value in the prevention and management of stroke.

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.

EXPRESS: Association of exposure to non-persistent 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 metabolic syndrome (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 non-persistent 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 lower 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 non-persistent 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.