Changing US Support for Public Health Data Use Through Pandemic and Political Turmoil.

Policy Points This study examines the impact of several world-changing events in 2020, such as the pandemic and widespread racism protests, on the US population's comfort with the use of identifiable data for public health. Before the 2020 election, there was no significant difference between Democrats and Republicans. However, African Americans exhibited a decrease in comfort that was different from other subgroups. Our findings suggest that the public remained supportive of public health data activities through the pandemic and the turmoil of 2020 election cycle relative to other data use. However, support among African Americans for public health data use experienced a unique decline compared to other demographic groups. CONTEXT: Recent legislative privacy efforts have not included special provisions for public health data use. Although past studies documented support for public health data use, several global events in 2020 have raised awareness and concern about privacy and data use. This study aims to understand whether the events of 2020 affected US privacy preferences on secondary uses of identifiable data, focusing on public health and research uses. METHODS: We deployed two online surveys-in February and November 2020-on data privacy attitudes and preferences using a choice-based-conjoint analysis. Participants received different data-use scenario pairs-varied by the type of data, user, and purpose-and selected scenarios based on their comfort. A hierarchical Bayes regression model simulated population preferences. FINDINGS: There were 1,373 responses. There was no statistically significant difference in the population's data preferences between February and November, each showing the highest comfort with population health and research data activities and the lowest with profit-driven activities. Most subgroups' data preferences were comparable with the population's preferences, except African Americans who showed significant decreases in comfort with population health and research. CONCLUSIONS: Despite world-changing events, including a pandemic, we found bipartisan public support for using identifiable data for public health and research. The decreasing support among African Americans could relate to the increased awareness of systemic racism, its harms, and persistent disparities. The US population's preferences support including legal provisions that permit public health and research data use in US laws, which are currently lacking specific public health use permissions.

Agreement between a web collaborative dataset and an administrative dataset to assess the retail food environment in Mexico.

BACKGROUND: Latin American countries are often limited in the availability of food outlet data. There is a need to use online search engines that allow the identification of food outlets and assess their agreement with field observations. We aimed to assess the agreement in the density of food outlets provided by a web collaborative data (Google) against the density obtained from an administrative registry. We also determined whether the agreement differed by type of food outlet and by area-level socioeconomic deprivation. METHODS: In this cross-sectional study, we analyzed 1,693 census tracts from the municipalities of Hermosillo, Leon, Oaxaca de Juarez, and Tlalpan. The Google service was used to develop a tool for the automatic acquisition of food outlet data. To assess agreement, we compared food outlet densities obtained with Google against those registered in the National Statistical Directory of Economic Units (DENUE). Continuous densities were assessed using Bland-Altman plots and concordance correlation coefficient (CCC), while agreement across tertiles of density was estimated using weighted kappa. RESULTS: The CCC indicated a strong correlation between Google and DENUE in the overall sample (0.75); by food outlet, most of the correlations were from negligible (0.08) to moderate (0.58). The CCC showed a weaker correlation as deprivation increased. Weighted kappa indicated substantial agreement between Google and DENUE across all census tracts (0.64). By type of food outlet, the weighted kappa showed substantial agreement for restaurants (0.69) and specialty food stores (0.68); the agreement was moderate for convenience stores/small food retail stores (0.49) and fair for candy/ice cream stores (0.30). Weighted kappa indicated substantial agreement in low-deprivation areas (0.63); in very high-deprivation areas, the agreement was moderate (0.42). CONCLUSIONS: Google could be useful in assessing fixed food outlet densities as a categorical indicator, especially for some establishments, like specialty food stores and restaurants. The data could also be informative of the availability of fixed food outlets, particularly in less deprived areas.

Impact of a complex health services intervention in long-term care nursing homes on 3-year overall survival: results from the CoCare study.

BACKGROUND: The Coordinated medical Care (CoCare) project aimed to improve the quality of medical care in nursing homes by optimizing collaboration between nurses and physicians. We analyze the impact of the CoCare intervention on overall survival. METHODS: The effect of time-varying treatment on 3-year overall survival was analyzed with treatment as time-varying covariate within the entire cohort. To reduce bias due to non-random assignment to treatment groups, regression adjustment was applied. Therefore, age, sex, and level of care were used as potential confounders. RESULTS: The study population consisted of 8,893 nursing home residents (NHRs), of which 1,330 participated in the CoCare intervention. The three-year overall survival was 49.8% in the entire cohort. NHRs receiving the intervention were associated with a higher survival probability compared to NHRs of the control group. In a univariable cox model with time-dependent treatment, the intervention was associated with a hazard ratio of 0.70 [95%CI 0.56-0.87, p = 0.002]. After adjustment for age, sex and level of care, the hazard ratio increased to 0.82 but was still significant [95%CI 0.71-0.96, p = 0.011]. CONCLUSION: The analysis shows that optimizing collaboration between nurses and physicians leads to better survival of NHRs in Germany. This adds to the already published favorable cost-benefit ratio of the CoCare intervention and shows that a routine implementation of optimized collaboration between nurses and physicians is highly recommended.

COVID-19-associated costs and mortality in Germany: an incidence-based analysis from a payer's perspective.

BACKGROUND: This study aims to estimate average COVID-19-associated healthcare costs per capita in Germany from a payer perspective. In addition, insights into COVID-19-associated mortality should be gained. METHODS: For this purpose, a retrospective longitudinal analysis using health insurance claims data was performed. Patients affected by COVID-19 in Q1/2021 (investigation group (IG)) were compared to a matched non-COVID-19 control group (CG) (1:1 propensity score matching (PSM)). Mean values of healthcare costs in 2020 and 2021 were computed for both groups and then separated by age and by development of Post-COVID-19 Syndrome (PCS). Group differences were examined using Mann-Whitney U test (α = 0.05). Difference-in-Differences approach (DiD) was used to estimate average cost effects of COVID-19 in 2021. Concerning mortality, the number of deaths in 2021 was compared between IG and CG using χ2 test of independence. RESULTS: A total of 8,014 insurants were included (n = 4,007 per group; n = 536 per group examining PCS patients only). Total healthcare costs varied a lot in the sample, were comparable between IG and CG in 2020, but were significantly higher in the IG in 2021 (DiD estimate = € 1,063 (in total); € 3,242 (PCS group)). This was more pronounced in the older age groups. High hospital costs of a minority of patients were the most influential driver of COVID-19-associated healthcare costs. Mortality was more than doubled in the IG (tripled in patients aged ≥ 60). CONCLUSIONS: COVID-19 is associated with significantly increased healthcare costs and mortality, especially in older age groups. The additional development of PCS further increases the costs of COVID-19.

Investigating Patient Use and Experience of Online Appointment Booking in Primary Care: Mixed Methods Study.

BACKGROUND: Online appointment booking is a commonly used tool in several industries. There is limited evidence about the benefits and challenges of using online appointment booking in health care settings. Potential benefits include convenience and the ability to track appointments, although some groups of patients may find it harder to engage with online appointment booking. We sought to understand how patients in England used and experienced online appointment booking. OBJECTIVE: This study aims to describe and compare the characteristics of patients in relation to their use of online appointment booking in general practice and investigate patients' views regarding online appointment booking arrangements. METHODS: This was a mixed methods study set in English general practice comprising a retrospective analysis of the General Practice Patient Survey (GPPS) and semistructured interviews with patients. Data used in the retrospective analysis comprised responses to the 2018 and 2019 GPPS analyzed using mixed-effects logistic regression. Semistructured interviews with purposively sampled patients from 11 general practices in England explored experiences of and views on online appointment booking. Framework analysis was used to allow for comparison with the findings of the retrospective analysis. RESULTS: The retrospective analysis included 1,327,693 GPPS responders (2018-2019 combined). We conducted 43 interviews with patients with a variety of experiences and awareness of online appointment booking; of these 43 patients, 6 (14%) were from ethnic minority groups. In the retrospective analysis, more patients were aware that online appointment booking was available (581,224/1,288,341, 45.11%) than had experience using it (203,184/1,301,694, 15.61%). There were deprivation gradients for awareness and use and a substantial decline in both awareness and use in patients aged >75 years. For interview participants, age and life stage were factors influencing experiences and perceptions, working patients valued convenience, and older patients preferred to use the telephone. Patients with long-term conditions were more aware of (odds ratio [OR] 1.43, 95% CI 1.41-1.44) and more likely to use (OR 1.65, 95% CI 1.63-1.67) online appointment booking. Interview participants with long-term conditions described online appointment booking as useful for routine nonurgent appointments. Patients in deprived areas were clustered in practices with low awareness and use of online appointment booking among GPPS respondents (OR for use 0.65, 95% CI 0.64-0.67). Other key findings included the influence of the availability of appointments online and differences in the registration process for accessing online booking. CONCLUSIONS: Whether and how patients engage with online appointment booking is influenced by the practice with which they are registered, whether they live with long-term conditions, and their deprivation status. These factors should be considered in designing and implementing online appointment booking and have implications for patient engagement with the wider range of online services offered in general practice.

Exploring Relations Between Unique Patient Characteristics and Virtual Reality Immersion Level on Anxiety and Pain in Patients Undergoing Venipuncture: Secondary Analysis of a Randomized Control Trial.

BACKGROUND: Virtual reality (VR) is a well-researched digital intervention that has been used for managing acute pain and anxiety in pediatric patients undergoing various medical procedures. This study focuses on investigating the role of unique patient characteristics and VR immersion level on the effectiveness of VR for managing pediatric pain and anxiety during venipuncture. OBJECTIVE: The purpose of this study is to determine how specific patient characteristics and level of immersion during a VR intervention impact anxiety and pain levels for pediatric patients undergoing venipuncture procedures. METHODS: This study is a secondary data analysis of 2 combined, previously published randomized control trials on 252 pediatric patients aged 10-21 years observed at Children's Hospital Los Angeles from April 12, 2017, to July 24, 2019. One randomized clinical trial was conducted in 3 clinical environments examining peripheral intravenous catheter placement (radiology and an infusion center) and blood draw (phlebotomy). Conditional process analysis was used to conduct moderation and mediation analyses to assess the impact of immersion level during the VR intervention. RESULTS: Significant moderation was found between the level of immersion and anxiety sensitivity when predicting postprocedural anxiety (P=.01). Patients exhibiting the highest anxiety sensitivity within the standard of care yielded a 1.9 (95% CI 0.9-2.8; P<.001)-point elevation in postprocedural anxiety relative to individuals with high immersion levels. No other significant factors were found to mediate or moderate the effect of immersion on either postprocedural anxiety or pain. CONCLUSIONS: VR is most effective for patients with higher anxiety sensitivity who report feeling highly immersed. Age, location of the procedure, and gender of the patient were not found to significantly impact VR's success in managing levels of postprocedural pain or anxiety, suggesting that immersive VR may be a beneficial intervention for a broad pediatric population. TRIAL REGISTRATION: ClinicalTrials.gov NCT04268901; https://clinicaltrials.gov/study/NCT04268901.

Patterns and factors associated with dental service utilization among insured people: a data mining approach.

BACKGROUND: Insurance databases contain valuable information related to the use of dental services. This data is instrumental in decision-making processes, enhancing risk assessment, and predicting outcomes. The objective of this study was to identify patterns and factors influencing the utilization of dental services among complementary insured individuals, employing a data mining methodology. METHODS: A secondary data analysis was conducted using a dental insurance dataset from Iran in 2022. The Cross-Industry Standard Process for Data Mining (CRISP-DM) was employed as a data mining approach for knowledge extraction from the database. The utilization of dental services was the outcome of interest, and independent variables were chosen based on the available information in the insurance dataset. Dental services were categorized into nine groups: diagnostic, preventive, periodontal, restorative, endodontic, prosthetic, implant, extraction/surgical, and orthodontic procedures. The independent variables included age, gender, family size, insurance history, franchise, insurance limit, and policyholder. A multinomial logistic regression model was utilized to investigate the factors associated with dental care utilization. All analyses were conducted using RapidMiner Version 2020. RESULTS: The analysis encompassed a total of 654,418 records, corresponding to 118,268 insured individuals. Predominantly, restorative treatments were the most utilized services, accounting for approximately 38% of all services, followed by diagnostic (18.35%) and endodontic (13.3%) care. Individuals aged between 36 and 60 years had the highest rate of utilization for any dental services. Additionally, families comprising three to four members, individuals with a one-year insurance history, people contracted with a 20% franchise, individuals with a high insurance limit, and insured individuals with a small policyholder, exhibited the highest rate of service usage compared to their counterparts. The regression model revealed that all independent variables were significantly associated with the use of dental services. However, the patterns of association varied among different service categories. CONCLUSIONS: Restorative treatments emerged as the most frequently used dental services among insured individuals, followed by diagnostic and endodontic procedures. The pattern of service utilization was influenced by the characteristics of the insured individuals and attributes related to their insurance.

The impact of the COVID-19 pandemic on administrative eating disorder prevalence in the outpatient sector and on severity of anorexia nervosa.

The COVID-19 pandemic appears to have had a considerable impact on the mental health of children and adolescents, particularly regarding eating disorders. However, it remains unclear whether the pandemic affected only the frequency or also the severity of eating disorders. We examined potential pandemic-related changes in the administrative prevalence of eating disorders in the outpatient sector compared with other mental disorders using German statutory health insurance data for the age group 10 to 16 years. We also examined disorder severity of anorexia nervosa using data from the multicenter German Registry of Children and Adolescents with Anorexia Nervosa in the same age group. Our results showed a marked increase in the administrative prevalence of eating disorders (based on documented diagnoses) in the outpatient sector among girls but not among boys. A similar pattern was found for internalizing disorders, whereas the administrative prevalences of externalizing disorders decreased. Regarding the severity of anorexia nervosa among inpatients, we found no pandemic-related changes in body mass index standard deviation score at admission, body weight loss before admission, psychiatric comorbidities and psychopharmacological medication. Given the administrative prevalence increase in the outpatient sector, the lack of impact of the pandemic on the inpatient sector may also be partly due to a shift in healthcare utilization towards outpatient services during the pandemic. Thus, the higher number of children and adolescents requiring specialized and timely outpatient care may be a major concern under pandemic conditions.

Children and young people's reported contact with professional services for mental health concerns: a secondary data analysis.

Children and young people's mental health services have been under increasing pressure following COVID-19. Understanding, for which channels help is sought from, will highlight services needing support. This study aims to explore the professional services that parents of children, and young people get help from when they have a concern for the child's/their mental health. Secondary analysis of data is taken from Mental Health of Children and Young People in England Survey, 2017. 7608 reports of mental health-related contact with professional services from parents of 5-16 year-olds and self-reports from young people aged 17-19 were available. Service contact was reported by Diagnostic and Statistical Manual of Mental Disorders (DSM-V) diagnosis, age, gender and ethnicity. Less than two-thirds of children and young people with a DSM-V diagnosis (63.5% (95% CI 58.6-68.1) aged 5-10, and 64.0% (95% CI 59.4-68.4) aged 11-16) reported contact with any professional services. The figure was lower for those aged 17-19; 50.1% (95% CI 42.8-58.2), p = 0.005. Children and young people aged 5-16 from Black (11.7%; 95% CI 2.4-41.4), Asian (55.1%; 95% CI 34.7-73.9) and Mixed (46.0%; 95% CI 32.4-60.3) ethnic groups reported less contact with professional services compared to those from the White group (66.9%; 95% CI 63.5-70.2). Patterns of service access during the three main educational stages aid with understanding service need during childhood. These lower levels of reported service access for young people aged 17-19 with a DSM-V diagnosis and those in ethnic minority groups demand further investigation.

Advanced Data Processing of Pancreatic Cancer Data Integrating Ontologies and Machine Learning Techniques to Create Holistic Health Records.

The modern healthcare landscape is overwhelmed by data derived from heterogeneous IoT data sources and Electronic Health Record (EHR) systems. Based on the advancements in data science and Machine Learning (ML), an improved ability to integrate and process the so-called primary and secondary data fosters the provision of real-time and personalized decisions. In that direction, an innovative mechanism for processing and integrating health-related data is introduced in this article. It describes the details of the mechanism and its internal subcomponents and workflows, together with the results from its utilization, validation, and evaluation in a real-world scenario. It also highlights the potential derived from the integration of primary and secondary data into Holistic Health Records (HHRs) and from the utilization of advanced ML-based and Semantic Web techniques to improve the quality, reliability, and interoperability of the examined data. The viability of this approach is evaluated through heterogeneous healthcare datasets pertaining to personalized risk identification and monitoring related to pancreatic cancer. The key outcomes and innovations of this mechanism are the introduction of the HHRs, which facilitate the capturing of all health determinants in a harmonized way, and a holistic data ingestion mechanism for advanced data processing and analysis.

Attitudes on data reuse among internal medicine residents.

Background: NYU Langone Health offers a collaborative research block for PGY3 Primary Care residents that employs a secondary data analysis methodology. As discussions of data reuse and secondary data analysis have grown in the data library literature, we sought to understand what attitudes internal medicine residents at a large urban academic medical center had around secondary data analysis. This case report describes a novel survey on resident attitudes around data sharing. Methods: We surveyed internal medicine residents in three tracks: Primary Care (PC), Categorical, and Clinician-Investigator (CI) tracks as part of a larger pilot study on implementation of a research block. All three tracks are in our institution's internal medicine program. In discussions with residency directors and the chief resident, the term "secondary data analysis" was chosen over "data reuse" due to this being more familiar to clinicians, but examples were given to define the concept. Results: We surveyed a population of 162 residents, and 67 residents responded, representing a 41.36% response rate. Strong majorities of residents exhibited positive views of secondary data analysis. Moreover, in our sample, those with exposure to secondary data analysis research opined that secondary data analysis takes less time and is less difficult to conduct compared to the other residents without curricular exposure to secondary analysis. Discussion: The survey reflects that residents believe secondary data analysis is worthwhile and this highlights opportunities for data librarians. As current residents matriculate into professional roles as clinicians, educators, and researchers, libraries have an opportunity to bolster support for data curation and education.

Minimum clinically important difference and substantial clinical benefit in patients with chronic temporomandibular disorders.

BACKGROUND: Research on temporomandibular disorder (TMD) responsiveness is scarce and limited regarding patients' representativeness. OBJECTIVE(S): This study aimed to estimate minimum clinically important difference (MCID) and substantial clinical benefit (SCB) among a large and diverse patient population regarding sex and age. METHODS: In this study, 162 patients participated from five hospitals. MCID and SCB in pain, functional disability and quality of life were examined with anchor-based methods. Patients' global impression of change was used as the anchor. Area under the curve (AUC) values were determined for testing accuracy. Changes from baseline and coefficient of variation by responsiveness status were calculated to explain the results of accuracy. RESULTS: SCB was estimated to be 2.18 for the numeric rating scale (NRS) for pain (AUC: 0.80 [95% CI: 0.72-0.88]) in all patients and 2.50 in women (AUC: 0.81 [95% CI: 0.71-0.89]). The estimated SCB of NRS for discomfort (1.50) and Jaw Functional Limitation Scale for mastication (1.35) had wide CIs for AUCs. Likewise, the estimated MCIDs of NRS for pain (0.80) and NRS for discomfort (1.50) had wide CIs for AUCs. Among non-responders who did not achieve the MCID of NRS for pain, the coefficient of variation was very high for all outcomes other than the NRS for pain. CONCLUSION: This study investigated the responsiveness of patients with TMD using a large and diverse patient sample. SCB in pain decrease can be used to assess the responsiveness of patients with TMD. Composite outcomes should be developed to estimate MCID.

The influence of spirituality on caregiver burden and quality of life in older adult informal caregivers.

BACKGROUND: Caregiver burden (CB) reduces quality of life (QOL) and causes poor health outcomes. Spirituality impacts this relationship. AIMS: To determine prevalence of CB and investigate relationships among CB, spirituality, and QOL in older U.S. adult informal caregivers (n = 754). METHODS: This was a cross-sectional, descriptive secondary analysis of data from the 2020 Health and Retirement Study using GLM and SEM. RESULTS: Caregiver mean age was 65.93 (SD=8.37). Caregivers were primarily female (n = 456, 54.0%), White (n = 500, 79.5%), and married (n = 469, 65.3%). Most caregivers had moderate CB (n = 369, 49.8%). Black caregivers who were spiritual (p=.031) and caregivers with a high school diploma/GED who were spiritual (p=.021) had lower CB. Lower CB was correlated with higher QOL (p=< 0.001). SEM depicting an influencing effect of spirituality revealed good model fit (NFI=0.988; IFI=0.993; TLI=0.983; PCFI=0.397, RMSEA=0.043; χ2=9.577, p=.048, DF=4) CONCLUSIONS: Fostering spirituality in older adult caregivers could reduce CB and improve QOL.

[Reference evaluations for estimating prevalence, incidence, and mortality of public health relevant diseases based on routine data]. / Referenzauswertungen für die Schätzung von Prävalenz, Inzidenz und Mortalität Public-Health-relevanter Erkrankungen auf Basis von Routinedaten.

The routine data of all statutorily insured persons according to the Data Transparency Regulation (DaTraV data) represent a promising data source for the recurrent and timely surveillance of non-communicable diseases (NCDs) in Germany. Thereby, it has become apparent that there is a high demand for reference evaluations that enable quick and regularly repeatable analyses on important NCDs. Against this background, ReFern-01 was initiated, a joint project of the Robert Koch Institute (RKI) and the Federal Institute for Drugs and Medical Devices (BfArM). In collaboration with experts from the field of secondary data analysis and healthcare research, reference evaluations for estimating prevalence, incidence, and mortality for important public health-relevant diseases were developed. First, 11 central NCDs were selected by means of an online survey, and initial case definitions were created in conjunction with a literature review. These were then discussed and agreed upon in a virtual workshop. The created reference evaluations (analysis scripts) allow a standardized estimation of the mentioned epidemiological figures, which are comparable over time and regionally. In addition to providing the results, the scripts will be available at the BfArM for further analysis. Provided that remote access to the analysis of the DaTraV data is available in the future, the results of the ReFern project can strengthen the surveillance of NCDs and support public health actors, for example, in the planning and implementation of health promotion and prevention measures at the federal, state, county, and local levels.

[Establishment of the new "Health Data Lab" to provide data for science]. / Aufbau des neuen "Forschungsdatenzentrums Gesundheit" zur Datenbereitstellung für die Wissenschaft.

The analysis of real-world data (RWD) has become increasingly important in health research in recent years. With the BfArM Health Data Lab (HDL), which is currently being set up, researchers will in future be able to gain access to routine data from the statutory health insurance of around 74 million people in Germany. Data from electronic patient records can also be made available for research prospectively. In doing so, the Health Data Lab guarantees the highest data protection and IT security standards. The digital application process, the provision of data in secure processing environments as well as the features supporting the analyses such as catalogues of coding systems, a point-and-click analysis tool and predefined standard analyses increase user-friendliness for researchers. The use of the extensive health data accessible at HDL will open a wide range of future possibilities for improving the health system and the quality of care. This article begins by highlighting the advantages of the HDL and outlining the opportunities that the RWD offers for research in healthcare and for the population. The structure and central aspects of the HDL are explained afterwards. An outlook on the opportunities of linking different data is given. What the application and data usage processes at the HDL will look like is illustrated using the example of fictitious possibilities for analysing long COVID based on the routine data available at the HDL in the future.

Leveraging Existing Datasets to Advance Family Caregiving Research: Opportunities to Measure What Matters.

More than 17.7 million people in the U.S. care for older adults. Analyzing population datasets can increase our understanding of the needs of family caregivers of older adults. We reviewed 14 U.S. population-based datasets (2003-2023) including older adults' and caregivers' data to assess inclusion and measurement of 8 caregiving science domains, with a focus on whether measures were validated and/or unique variables were used. Challenges exist related to survey design, sampling, and measurement. Findings highlight the need for consistent data collection by researchers, state, tribal, local, and federal programs, for improved utility of population-based datasets for caregiving and aging research.

Cancer Screening Rates Among Asian Americans: A Cross-Sectional Secondary Data Analysis Study.

PURPOSE: This study aims 1) to investigate recent cancer screening rates among Asian Americans and 2) to test the relationship between race/ethnicity and cancer screening rates. METHODS: This is a cross-sectional secondary data analysis study using data from the 2019 National Health Interview Survey. The screening rates of prostate cancer, colorectal cancer, cervical cancer, and breast cancer among non-Hispanic (NH) Asian Americans, Hispanics, NH Whites, NH African Americans, and NH American Indian and Alaska Natives (AIAN) were analyzed in July 2022. The variables were recoded and analyzed using descriptive analysis and chi-square test. The SPSS version 27 software was used. RESULTS: Descriptive analysis showed a general low screening rate of cancers among Asian Americans, which ranged from 40.5% to 67.5%. The chi-square test suggested significant associations between race/ethnicity and the screening rates of colorectal cancer (P = .002), cervical cancer (P < .01), and breast cancer (P = .021), but not the prostate cancer (P = .472). CONCLUSION: Necessary intervention programs should be designed to increase the uptake rates of cancer screening among Asian Americans.

Inter-Hospital Transfer of Patients With Ruptured Abdominal Aortic Aneurysm in Switzerland.

OBJECTIVE: To analyse the association between inter-hospital transfer and hospital mortality in patients with ruptured abdominal aortic aneurysms (rAAA) in Switzerland. METHODS: Secondary data analysis of case related hospital discharge data from the Swiss Federal Statistical Office for the years 2009 - 2018. All cases with rAAA as primary or secondary diagnosis were included. Cases with rAAA as a secondary diagnosis without surgical treatment and cases that had been transferred to another hospital without surgical treatment at the referring hospital were excluded. Logistic regression models for hospital mortality were constructed with age, sex, type of admission, van Walraven comorbidity score, type of treatment, insurance class, hospital level, and year of treatment as independent variables. RESULTS: A total of 1 798 cases with rAAA were treated either surgically (62.5%) or palliatively (37.5%) in Switzerland from 1 January 2009 to 31 December 2018. Of these cases, 72.9% were treated directly (surgically or palliatively) at the hospital of first presentation, whereas 27.1% of all cases with rAAA were transferred between hospitals. The overall crude hospital mortality was 50.3%; being 23.1% in the surgically treated cohort and 95.7% in the palliatively treated cohort. Inter-hospital transfer was associated with better survival compared with patients who were admitted directly (OR 0.52; 95% CI 0.36 - 0.75; p < .001). Treatment in major hospitals was associated with significantly higher mortality rate compared with university hospitals (OR 1.98; 1.41 - 2.79; p < .001). There was no evidence of an association between open repair and hospital mortality (OR 1.06; 0.77 - 1.48; p = .72) compared with endovascular repair. CONCLUSION: In a healthcare system such as Switzerland's with a highly specialised rescue chain, transfer of haemodynamically stable patients with rAAA is probably safe. In this setting, centralised medical care might outweigh the potential disadvantages of a short delay due to patient transfer. Further studies are needed to address potential confounding factors such as haemodynamic and anatomical features.

Primary and secondary data in emergency medicine health services research - a comparative analysis in a regional research network on multimorbid patients.

BACKGROUND: This analysis addresses the characteristics of two emergency department (ED) patient populations defined by three model diseases (hip fractures, respiratory, and cardiac symptoms) making use of survey (primary) and routine (secondary) data from hospital information systems (HIS). Our aims were to identify potential systematic inconsistencies between both data samples and implications of their use for future ED-based health services research. METHODS: The research network EMANET prospectively collected primary data (n=1442) from 2017-2019 and routine data from 2016 (n=9329) of eight EDs in a major German city. Patient populations were characterized using socio-structural (age, gender) and health- and care-related variables (triage, transport to ED, case and discharge type, multi-morbidity). Statistical comparisons between descriptive results of primary and secondary data samples for each variable were conducted using binomial test, chi-square goodness-of-fit test, or one-sample t-test according to scale level. RESULTS: Differences in distributions of patient characteristics were found in nearly all variables in all three disease populations, especially with regard to transport to ED, discharge type and prevalence of multi-morbidity. Recruitment conditions (e.g., patient non-response), project-specific inclusion criteria (e.g., age and case type restrictions) as well as documentation routines and practices of data production (e.g., coding of diagnoses) affected the composition of primary patient samples. Time restrictions of recruitment procedures did not generate meaningful differences regarding the distribution of characteristics in primary and secondary data samples. CONCLUSIONS: Primary and secondary data types maintain their advantages and shortcomings in the context of emergency medicine health services research. However, differences in the distribution of selected variables are rather small. The identification and classification of these effects for data interpretation as well as the establishment of monitoring systems in the data collection process are pivotal. TRIAL REGISTRATION: DRKS00011930 (EMACROSS), DRKS00014273 (EMAAGE), NCT03188861 (EMASPOT).

Disparities in Drinking Water Manganese Concentrations in Domestic Wells and Community Water Systems in the Central Valley, CA, USA.

Over 1.3 million Californians rely on unmonitored domestic wells. Existing probability estimates of groundwater Mn concentrations, population estimates, and sociodemographic data were integrated with spatial data delineating domestic well communities (DWCs) to predict the probability of high Mn concentrations in extracted groundwater within DWCs in California's Central Valley. Additional Mn concentration data of water delivered by community water systems (CWSs) were used to estimate Mn in public water supply. We estimate that 0.4% of the DWC population (2342 users) rely on groundwater with predicted Mn > 300 µg L-1. In CWSs, 2.4% of the population (904 users) served by small CWSs and 0.4% of the population (3072 users) served by medium CWS relied on drinking water with mean point-of-entry Mn concentration >300 µg L-1. Small CWSs were less likely to report Mn concentrations relative to large CWSs, yet a higher percentage of small CWSs exceed regulatory standards relative to larger systems. Modeled calculations do not reveal differences in estimated Mn concentration between groundwater from current regional domestic well depth and 33 m deeper. These analyses demonstrate the need for additional well-monitoring programs that evaluate Mn and increased access to point-of-use treatment for domestic well users disproportionately burdened by associated costs of water treatment.