Zip Code Areas Research Articles

SOCIODEMOGRAPHIC DISPARITIES IN CORONARY ARTERY CALCIUM SCREENING

ObjectivesThis study aimed to compare the demographic features and socioeconomic status of patients who underwent coronary artery calcium screening to that of their local population. BackgroundCoronary artery calcium scores provide important evidence of subclinical atherosclerosis. However, insurance often does not cover coronary artery calcium testing, which could exclude people of lower socioeconomic status. MethodsDemographic and occupational data were obtained for all patients referred for coronary artery calcium in a metropolitan area between October 2010 and August 2023. Household income and population reference information were taken from US Census Data and matched to zip code and regional metropolitan area. ResultsCoronary artery calcium tests were performed on 627 patients with a median age of 63 years and equal gender representation (52% female, 48% male). Patients were predominately White (77%) and English-speaking (98%), which is incongruent with local demographic data (p<0.001). Healthcare workers were the largest workforce (22%), followed by people involved in education (12%), which is higher than the local healthcare workforce of 10% (p<0.001). Those with graduate/professional degrees accounted for 32% of patients, significantly more than the local population (p<0.001). The average median annual income ($94,116) of patients who underwent CAC testing was greater than that of the metropolitan median income of $81,264 (p <0.001). ConclusionThe disproportionate distribution of coronary artery calcium screening favoring educated, affluent, White English speakers indicates that higher-income and healthcare personnel are more likely to receive testing. Disparities in coronary artery calcium testing, especially in minorities and non-English speaking individuals, should be further explored.

Estimating the risk and spatial spread of measles in populations with high MMR uptake: Using school-household networks to understand the 2013 to 2014 outbreak in the Netherlands.

Measles outbreaks are still routine, even in countries where vaccination coverage exceeds the guideline of 95%. Therefore, achieving ambitions for measles eradication will require understanding of how unvaccinated children interact with others who are unvaccinated. It is well established that schools and homes are key settings for both clustering of unvaccinated children and for transmission of infection. In this study, we evaluate the potential for contacts between unvaccinated children in these contexts to facilitate measles outbreaks with a focus on the Netherlands, where large outbreaks have been observed periodically since the introduction of mumps, measles and rubella (MMR). We created a network of all primary and secondary schools in the Netherlands based on the total number of household pairs between each school. A household pair are siblings from the same household who attend a different school. We parameterised the network with individual level administrative school and household data provided by the Dutch Ministry for Education and estimates of school level uptake of the MMR vaccine. We analysed the network to establish the relative strength of contact between schools and found that schools associated with low vaccine uptake are highly connected, aided by a differentiated school system in the Netherlands (Coleman homophily index (CHI) = 0.63). We simulated measles outbreaks on the network and evaluated the model against empirical measles data per postcode area from a large outbreak in 2013 (2,766 cases). We found that the network-based model could reproduce the observed size and spatial distribution of the historic outbreak much more clearly than the alternative models, with a case weighted receiver operating characteristic (ROC) sensitivity of 0.94, compared to 0.17 and 0.26 for models that do not account for specific network structure or school-level vaccine uptake, respectively. The key limitation of our framework is that it neglects transmission routes outside of school and household contexts. Our framework indicates that clustering of unvaccinated children in primary schools connected by unvaccinated children in related secondary schools lead to large, connected clusters of unvaccinated children. Using our approach, we could explain historical outbreaks on a spatial level. Our framework could be further developed to aid future outbreak response.

Socioeconomic factors and rural-urban differences in patients undergoing emergency laparotomy

Background: Emergency laparotomy (EL) is a common surgical procedure with high rates of mortality and complications. Socioeconomic circumstances and regional differences have an influence on the utilization of care and outcomes in many diagnostic groups, but there are only few studies focusing on their effect in EL population. The aim of this study was to examine the socioeconomic and regional differences in the rate of EL within one tertiary care hospital district. Methods: Retrospective single-center study of 573 patients who underwent EL in Oulu University Hospital between May 2015 and December 2017. The postal code area of each patient’s home address was used to determine the socioeconomic status and rurality of the location of residence. Results: The age adjusted rate of EL was higher in patients from low-income areas compared to patients from high-income areas (1.46 ((95% CI 1,27-1.64)) vs. 1.15 ((95% CI, 0.96-1.34))). The rate of EL was higher in rural areas compared to urban areas (1.29 (95% CI 1.17-1.41 vs. 1.42 (1.18-1.67) ). Peritonitis was more common in patients living in low-income areas. There were no differences in operation types or mortality between the groups. Conclusions: The study findings suggest that there are socioeconomic and regional differences in the need of EL. The patients living in low-income areas had a higher rate of EL and a higher rate of peritonitis. These differences cannot be explained by patient demographics or comorbidities alone.

Spatial and Socioeconomic Patterns of Mental Health and Healthcare Utilization in Cologne, Germany.

Children and adolescents are significantly tied to their family's socioeconomic position and living environment. Neighbourhood and the living environment have been identified as potential risk factors for mental disorders in this age group. The aim of the study was to investigate the distribution of mental and behavioural disorders (prevalence) and the provision of mental health services for children and adolescents aged 0-19 years in the city of Cologne. In particular, the study aimed to examine the association of these factors with area deprivation and the availability of mental health services covered by statutory health insurance. Finally, possible spatial variations in these aspects were analysed. Claims data of children and adolescents aged 0 to 19 years included in four statutory health insurance of the year 2021 were analysed. A deprivation index using data on the level of the ZIP code area was calculated. Analyses were carried out descriptively, using ordinary least squares (OLS) and geographically weighted regression (GWR). The prevalence of mental and behavioural disorders in children and adolescents varied across ZIP code areas, with higher rates in the northern, southern, and eastern parts of the city. The results indicated that the use of services by male children and adolescents with a prevalent diagnosis of mental and behavioural disorders was higher in areas with a higher density of healthcare providers. However, prevalence was on the whole lower in areas with a higher density of healthcare providers. In addition, the density of health care providers was higher in the city centre with comparatively lower deprivation. These results indicate inadequate access to care for children and young people outside the city centre. However, due to the heterogeneity of the population in these areas, this study provides only preliminary insights. Data with a finer geographic resolution are needed for further research in order to analyse the association further.

Livestock-associated spatial risk factors for human salmonellosis and campylobacteriosis.

Most human infections with non-typhoid Salmonella (NTS) or Campylobacter are zoonotic in nature and acquired though consumption of contaminated food of mainly animal origin. However, individuals may also acquire salmonellosis or campylobacteriosis through non-foodborne transmission pathways, such as those mediated by the environment. This emphasizes the need to consider both direct and indirect exposure to livestock sources as a possible transmission route for NTS and Campylobacter. Therefore, this study aimed at assessing whether salmonellosis and campylobacteriosis incidence is spatially associated with exposure to livestock (i.e. small ruminants, dairy cows, veal calves, laying hens, broiler chickens and pigs) in the Netherlands for the years 2007-2019 and 2014-2019 respectively. Risk factors (population-weighted number of animals) and their population attributable fractions were determined using a Poisson regression model with a log-link function fitted using integrated nested Laplace approximation. The analyses were performed for different hexagonal sizes (90, 50, 25 and 10 km2) and accounted for geographical coverage of the diagnostic laboratory catchment areas. Moreover, serological data were used to look into the possible effects of acquired immunity due to repeated exposure to the pathogen through the environment that would potentially hinder the analyses based on the incidence of reported cases. A linear mixed-effects model was then fitted where the postal code areas were included as a random effect. Livestock was not consistently significantly associated with acquiring salmonellosis or campylobacteriosis in the Netherlands. Results showed that living in livestock-rich areas in the Netherlands is not a consistently significant, spatially restricted risk factor for acquiring salmonellosis or campylobacteriosis, thereby supporting current knowledge that human infections with Salmonella and Campylobacter are mainly foodborne.

Planning, Implementation, and Evaluation of a Postpartum Nurse Home Visit Service to Improve Health Equity

ObjectiveTo describe how a college of nursing and urban academic medical center partnered with the local health department to plan, implement, and evaluate a universal nurse home visit service to improve health equity in the postpartum period. DesignEvidence-based practice. Setting/Local ProblemWide health disparity in rates of maternal and infant morbidity and mortality in Chicago, Illinois. PatientsAll patients who gave birth at the medical center and lived in Chicago. Intervention/MeasurementsA nurse home visit was offered after birth to all eligible patients beginning in March 2020. We used data from a Web-based platform to determine key performance indicators for the program and examined patient demographics to determine equitable delivery of the service for all visits provided in 2022. ResultsThere were 1,488 patients eligible for a home visit and 714 who received a home visit. The average contact rate was 76%, the scheduling rate was 63%, the completion rate for scheduled visits was 76%, and the population reach was 48%. Sixty-eight percent of families visited were from high-economic-hardship zip code areas of the city. Eighty-one percent of visits resulted in at least one referral to meet a family’s need, and 98% of patients surveyed rated their visit as “very helpful.” ConclusionThe successful implementation of this public–private partnership was due in part to an organizational culture that supports health equity initiatives, the inclusion of system-wide stakeholders, having a process in place to monitor outcomes, and hiring a diverse team of nurses who prioritize respectful patient-centered care.

Spatiotemporal analysis and forecasting of PV systems, battery storage, and EV charging diffusion in California: A graph network approach

Understanding the dynamics of clean energy adoption is crucial for shaping effective energy policies and strategies. To understand the spatiotemporal variability and bottom-up spontaneity of the diffusion of clean energy technologies, this study examined the spatiotemporal diffusion of Photovoltaic (PV) systems, Battery Energy Storage Systems (BESS), and Electric Vehicle Charging Stations (EVCS) across 1773 postcode areas in California over the past 35 years. The Bayesian change point detection algorithm (BCDA) was initially utilized to identify temporal change points in the evolution of these technologies, revealing their diffusion trends over time. Then, the Louvain community detection algorithm (LCDA) was employed to elucidate spatial diffusion patterns across regions, offering insights into the geographic proliferation of clean energy solutions. Furthermore, to forecast future distributions, the spatiotemporal graph convolutional network (STGCN) model was applied, adeptly capturing the multi-stage, nonlinear characteristics of clean energy diffusion marked by significant spatiotemporal interactions. With historical data and spatial proximity, the STGCN model demonstrated fine forecasting precision (R^2 ≥ 0.78). This study validates the effectiveness of a graph network-based approach for analyzing the diffusion of clean energy technologies. It recommends policies tailored to account for technological maturity, geographic disparities, and diffusion stages, aiming for equitable and sustainable clean energy development in the region.

Understanding uptake in demand-side broadband subsidy programs: The affordable connectivity program case

The Affordable Connectivity Program (ACP) represents a historic investment by the federal government to address the digital divide. Its goals are both to increase the rate of broadband adoption and make it easier for low-income households to sustain service. But what have been the drivers of ACP enrollment? We know a great deal about trends in adoption and the geography of enrollment among eligible households. But those metrics – especially a focus on enrollment rates among eligible households – are only so useful in explaining ACP sign-ups.This invites analysis that investigates the drivers of ACP enrollment in a comprehensive way, which this paper proposes to do. The paper hypothesizes that ACP enrollment decisions are not solely individual (i.e., that a household's enrollment decision is structured only by income level), but also influenced by community-wide considerations, such as housing costs, share of occupied houses, presence of anchor institutions such as public libraries, and population density (i.e., whether a place is urban or rural). The paper develops a regression model that predicts ACP enrollment rates among eligible households at the 5-digit zip code geography as a function of the variables discussed above, as well as others such as the racial and ethnic make-up of a zip code area. The analysis also controls for existing levels of broadband subscriptions and computer ownership in a given area. The difference between predicted and actual ACP enrollment rates at the 5-digit zip code level is proposed as a metric of ACP performance. These findings are supplemented with a geographically weighted regression (GWR) model that allows the relationship between each explanatory variable and ACP enrollment to vary by locality. This allows for analyzing and visualizing how these relationships vary across space.The empirical findings show that high levels of economic distress are a strong determinant of ACP enrollment in a given area, but social and community-wide indicators are important as well. The GWR results demonstrate that important differences in these relationships exist across geographies. Understanding spatial variations in ACP performance can help policymakers and other stakeholders better target resources to address the digital divide.

The WERA cancer center matrix: Strategic management of patient access to precision oncology in a large and mostly rural area of Germany

PurposeProviding patient access to precision oncology (PO) is a major challenge of clinical oncologists. Here, we provide an easily transferable model from strategic management science to assess the outreach of a cancer center. MethodsAs members of the German WERA alliance, the cancer centers in Würzburg, Erlangen, Regensburg and Augsburg merged care data regarding their geographical impact. Specifically, we examined the provenance of patients from WERA´s molecular tumor boards (MTBs) between 2020 and 2022 (n = 2243). As second dimension, we added the provenance of patients receiving general cancer care by WERA. Clustering our catchment area along these two dimensions set up a four-quadrant matrix consisting of postal code areas with referrals towards WERA. These areas were re-identified on a map of the Federal State of Bavaria. ResultsThe WERA matrix overlooked an active screening area of 821 postal code areas – representing about 50 % of Bavaria´s spatial expansion and more than six million inhabitants. The WERA matrix identified regions successfully connected to our outreach structures in terms of subsidiarity – with general cancer care mainly performed locally but PO performed in collaboration with WERA. We also detected postal code areas with a potential PO backlog – characterized by high levels of cancer care performed by WERA and low levels or no MTB representation. ConclusionsThe WERA matrix provided a transparent portfolio of postal code areas, which helped assessing the geographical impact of our PO program. We believe that its intuitive principle can easily be transferred to other cancer centers.

Identification of factors associated with opioid-related and hepatitis C virus-related hospitalisations at the ZIP code area level in the USA: an ecological and modelling study

Opioid overdose and related diseases remain a growing public health crisis in the USA. Identifying sociostructural and other contextual factors associated with adverse health outcomes is needed to improve prediction models to inform policy and interventions. We aimed to identify high-risk communities for targeted delivery of screening and prevention interventions for opioid use disorder and hepatitis C virus (HCV). In this ecological and modelling study, we fit mixed-effects negative binomial regression models to identify factors associated with, and predict, opioid-related and HCV-related hospitalisations for ZIP code tabulation areas (ZCTAs) in South Carolina, USA. All individuals aged 18 years or older living in South Carolina from Jan 1, 2016, to Dec 31, 2021, were included. Data on opioid-related and HCV-related hospitalisations, as well as data on additional individual-level variables, were collected from medical claims records, which were obtained from the South Carolina Revenue and Fiscal Affairs Office. Demographic and socioeconomic variables were obtained from the United States Census Bureau (American Community Survey, 2021) with additional structural health-care barrier data obtained from South Carolina's Center for Rural and Primary Health Care, and the American Hospital Directory. Between Jan 1, 2016, and Dec 31, 2021, 41 691 individuals were hospitalised for opioid misuse and 26 860 were hospitalised for HCV. There were a median of 80 (IQR 24-213) opioid-related hospitalisations and 61 (21-196) HCV-related hospitalisations per ZCTA. A standard deviation increase in ZCTA-level uninsured rate (relative risk 1·24 [95% CI 1·17-1·31]), poverty rate (1·24 [1·17-1·31]), mortality (1·18 [1·12-1·25]), and social vulnerability index (1·17 [1·10-1·24]) was significantly associated with increased combined opioid-related and HCV-related hospitalisation rates. A standard deviation increase in ZCTA-level income (0·79 [0·75-0·84]) and unemployment rate (0·87 [0·82-0·93]) was significantly associated with decreased combined opioid-related and HCV-related hospitalisations. Using 2016-20 hospitalisations as training data, our models predicted ZCTA-level opioid-related hospitalisations in 2021 with a median of 80·4% (IQR 66·8-91·1) accuracy and HCV-related hospitalisations in 2021 with a median of 75·2% (61·2-87·7) accuracy. Several underserved high-risk ZCTAs were identified for delivery of targeted interventions. Our results suggest that individuals from economically disadvantaged and medically under-resourced communities are more likely to have an opioid-related or HCV-related hospitalisation. In conjunction with hospitalisation forecasts, our results could be used to identify and prioritise high-risk, underserved communities for delivery of field-level interventions. South Carolina Center for Rural and Primary Healthcare, National Institute on Drug Abuse, and National Library of Medicine.

Text Messages With Financial Incentives for Men With Obesity

Effective weight loss interventions are needed for men with obesity. To determine whether an intervention that combined text messaging with financial incentives attained significant weight loss at the 12-month follow-up compared with the control group and whether an intervention of text messaging alone attained significant weight loss at the 12-month follow-up compared with the control group. An assessor-blinded randomized clinical trial conducted in Belfast, Bristol, and Glasgow areas in the UK. A total of 585 men with body mass index (BMI) of 30 or more were enrolled between July 2021 and May 2022. Final follow-up occurred June 2023. Participants were randomly assigned to 12 months of behavioral focused text messages combined with financial incentives (n = 196), 12 months of behavioral focused text messages alone (n= 194), or a waiting list (control group; n= 195). The financial incentive consisted of a monetary reward that was lost if weight loss targets were not met. All participants received weight management information and a pedometer at baseline. The 2 primary comparisons were the 12-month comparison of within-participant weight change between the text messaging with financial incentive group and the control group and the comparison between the text messaging alone group and the control group (minimum clinically important difference, 3%). The P value defined for statistical significance was P < .025 for each comparison. Of the 585 men (mean [SD] age, 50.7 [13.3] years; mean weight, 118.5 [19.9] kg; mean BMI, 37.7 [5.7]; 525 [90%] White), 227 (39%) lived in postal code areas with lower socioeconomic status, and 426 (73%) completed the 12-month follow-up. At the 12-month follow-up, compared with the control group, the mean percent weight change was significantly greater in the text messaging with financial incentive group (mean difference, -3.2%; 97.5% CI, -4.6% to -1.9%; P < .001) but was not significantly greater in the text messaging alone group (mean difference, -1.4%; 97.5% CI, -2.9% to 0.0, P = .05). The mean (SD) weight changes were -5.7 (7.4) kg for the text messaging with financial incentives group, -3.0 (7.5) kg for the text messaging alone group, and -1.5 (6.6) kg for the control group. The 12-month mean (SD) percentage weight changes from baseline were -4.8% (6.1%) for the text messaging with financial incentives group, -2.7% (6.3%) for text messaging alone group, and -1.3% (5.5%) for the control group. Of 366 adverse events reported, the most common were infections (83 [23%]). Of the 23 serious adverse events (6.3%), 12 (52%) occurred in the text messaging with financial incentives group, 5 (22%) in the texts messaging alone group, and 6 (26%) in the control group. None were considered related to participating in a trial group. Among men with obesity, an intervention with text messaging with financial incentive significantly improved weight loss compared with a control group, whereas text messaging alone was not significantly better than the control condition. These findings support text messaging combined with financial incentives to attain weight loss in men with obesity. isrctn.org Identifier: ISRCTN91974895.

Positive relationship between bird diversity and human mental health: an analysis of repeated cross-sectional data

An increasing body of research has examined the link between biodiversity of birds and human mental health, but most studies only use cross-sectional data. Few studies have used longitudinal or repeated cross-sectional data to investigate the mental health benefits of bird diversity. The aim of this study is to analyse the relationship between bird diversity and mental health at the national level using a unique repeated cross-sectional dataset. I used repeated cross-sectional health data from the German National Cohort health study, collected between March, 2014, and September, 2019, and annual bird citizen science data to investigate the effects of bird-diversity exposure on mental health. Mental health was measured using the summary score of the Patient Health Questionnaire depression module 9 (SumPHQ) and the Short Form Health Survey-12 Mental Health Component Scale. As a proxy for bird diversity, I created a unique indicator called reporting-rate richness and combined it with the health data. Reporting-rate richness measures the number of bird species within postcode areas across Germany in probabilities while accounting for variation in survey efforts. Alternative indicators of bird diversity, such as bird-species richness or abundance, were also calculated. Associations between bird diversity and mental health were estimated using linear regression with region and time fixed effects, adjusted for a range of sociodemographic and environmental confounders and spatial autocorrelation. Interaction terms between income levels and reporting-rate richness were also analysed to examine the moderating effect of socioeconomic status. I did the analyses for an unbalanced (n=176 362) and balanced (n=125 423) dataset, with the balanced dataset comprising only regions (postcode areas) in which health data were available for each year. The linear fixed-effects regression analysis indicated a significant negative association between reporting-rate richness and SumPHQ, as observed in both the unbalanced dataset (β -0·02, p=0·017) and the balanced dataset (β -0·03, p=0·0037). Similarly, regression results with both datasets showed a positive relationship between reporting-rate richness and Mental Health Component Scale (MCS; unbalanced β 0·02, p=0·0086; balanced β 0·03, p=0·0018). The moderator analyses revealed a significant influence of socioeconomic status on the relationship between reporting-rate richness and mental health. The robustness of these findings was confirmed through sensitivity analyses. The results suggest that a greater likelihood of having many different bird species in a person's area of residence might positively contribute to mental health, especially for people with lower socioeconomic status. These findings could have implications for biodiversity conservation and health policy decisions, as governments are facing challenges such as global biodiversity loss and growing public mental health problems. None.

Practice variation in surgical treatment for lumbar degenerative disc disease: exploring regional and hospital factors influencing surgical rates

The presence of significant, unwarranted variation in treatment suggests that clinical decision making also depends on where patients live instead of what they need and prefer. Historically, high practice variation in surgical treatment for lumbar degenerative disc disease (LDDD) has been documented. This study aimed to investigate current regional variation in surgical treatment for sciatica resulting from LDDD. We conducted a retrospective, cross-sectional analysis of all Dutch adults (>18 years) between 2016 and 2019. Demographic data from Statistics Netherlands were merged with a nationwide claims database, covering over 99% of the population. Inclusion criteria comprised LDDD diagnosis codes and relevant surgical codes. Practice variation was assessed at the level of postal code areas and hospital service areas (HSAs). Multivariable logistic regression analysis was employed to identify variables associated with surgical treatment. Among the 119,148 hospital visitors with LDDD, 14,840 underwent surgical treatment. Practice variation for laminectomies and discectomies showed less than two-fold variation in both postal code and HSAs. However, instrumented fusion surgery demonstrated a five-fold variation in postal code areas and three-fold variation in HSAs. Predictors of receiving surgical treatment included opioid prescription and patient referral status. Gender differences were observed, with males more likely to undergo laminectomy or discectomy, and females more likely to receive instrumented fusion surgery. Our study revealed low variation rates for discectomies and laminectomies, while indicating a high variation rate for instrumented fusion surgery in LDDD patients. High-quality research is needed on the extent of guideline implementation and its influence on practice variation.

Visualizing the Influence of New Public Transport Infrastructure on Travel Times

Due to the global climate change and increasing traffic volumes in cities, a shift from individual to public and multimodal transport is aspired. Travel time is one of the most important aspects for many people when choosing their mode of transportation. This leads to the requirement that changes in travel times have to be considered when planning new public transport infrastructure. This research paper presents and compares different techniques for visualizing the impact of new lines in existing public transport networks on travel times. The general approach of simulating timetable data and calculating intermodal travel times considering public transport and walking is being applied to two current infrastructure projects in the city of Bonn and the surrounding region. The created maps generally aim to visualize the spread in travel times between existing and extended transportation networks discretized by different spatial units such as rectangles or postal code areas. In comparison to other common methods which typically require two maps for two different scenarios (e.g. in case of isochrones), our approach gives the opportunity to combine all relevant information within one map. It is also shown how to apply bivariate choropleth maps for displaying travel times and how to visualize improvements in the accessibility of multiple target points of interest at once.

Outpatient geriatric health care in the German federal state of Mecklenburg-Western Pomerania: a population-based spatial analysis of claims data

BackgroundDue to unidentified geriatric needs, elderly patients have a higher risk for developing chronic conditions and acute medical complications. Early geriatric screenings and assessments help to identify geriatric needs. Holistic and coordinated therapeutic approaches addressing those needs maintain the independence of elderly patients and avoid adverse effects. General practitioners are important for the timely identification of geriatric needs. The aims of this study are to examine the spatial distribution of the utilization of outpatient geriatric services in the very rural Federal State of Mecklenburg-Western Pomerania in the Northeast of Germany and to identify regional disparities.MethodsGeographical analysis and cartographic visualization of the spatial distribution of outpatient geriatric services of patients who are eligible to receive basic geriatric care (BGC) or specialized geriatric care (SGC) were carried out. Claims data of the Association of Statutory Health Insurance Physicians in Mecklenburg-Western Pomerania were analysed on the level of postcode areas for the quarter periods between 01/2014 and 04/2017. A Moran’s I analysis was carried out to identify clusters of utilization rates.ResultsOf all patients who were eligible for BGC in 2017, 58.3% (n = 129,283/221,654) received at least one BCG service. 77.2% (n = 73,442/95,171) of the patients who were eligible for SGC, received any geriatric service (BGC or SGC). 0.4% (n = 414/95,171) of the patients eligible for SGC, received SGC services. Among the postcode areas in the study region, the proportion of patients who received a basic geriatric assessment ranged from 3.4 to 86.7%. Several regions with statistically significant Clusters of utilization rates were identified.ConclusionsThe widely varying utilization rates and the local segregation of high and low rates indicate that the provision of outpatient geriatric care may depend to a large extent on local structures (e.g., multiprofessional, integrated networks or innovative projects or initiatives). The great overall variation in the provision of BGC services implicates that the identification of geriatric needs in GPs’ practices should be more standardized. In order to reduce regional disparities in the provision of BGC and SGC services, innovative solutions and a promotion of specialized geriatric networks or healthcare providers are necessary.

Insights Into Eye Care Accessibility: Geospatial Distribution of Eye Care Providers and Socioeconomic Factors by ZIP Code.

In the United States, the ZIP Code has long been used to collect geospatial data revealing disparities in social determinants of health. This cross-sectional study examines the distribution of eye care access in association with local socioeconomic factors at a ZIP Code level. Data from the 2020 Centers of Medicare and Medicaid Services and American Community Survey were used to examine locations of 47,949 providers (17,631 ophthalmologists and 30,318 optometrists) and corresponding local socioeconomic variables (education, employment, and income). Multivariable zero-inflated negative binomial regression was used to model eye care provider count per capita in each ZIP Code area with socioeconomic factors as independent covariates. For every 1% increase in percentage of population over 25 years with a bachelor's degree or higher, the expected number of providers increases by 4.4% (incidence rate ratio [IRR] = 1.044; 95% confidence interval [CI], 1.041-1.046; P < 0.001). For every 1% increase in percentage unemployment, the expected number of providers decreases by 2.7% (IRR = 0.973; 95% CI, 0.964-0.983; P < 0.001). However, for every $1000 increase in median household income, the expected number of providers decreases by 1.6% (IRR = 0.984; 95% CI, 0.983-0.986; P < 0.001). Disparities in access exist in areas of lower employment and educational attainment, as both have positive correlations with eye care provider access. Conversely, areas of greater median household income have lower access to providers. This research contributes to a greater field studying social determinants of health and may inform public health strategies on allocation of providers to improve equitable access to vision care.

Investigating the spatiotemporal associations between meteorological conditions and air pollution in the federal state Baden-Württemberg (Germany)

When analyzing health data in relation to environmental stressors, it is crucial to identify which variables to include in the statistical model to exclude dependencies among the variables. Four meteorological parameters: temperature, ultraviolet radiation, precipitation, and vapor pressure and four outdoor air pollution parameters: ozone (O3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{O}_3$$\\end{document}), nitrogen dioxide (NO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{NO}_2$$\\end{document}), particulate matter (PM2.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PM_{2.5}$$\\end{document}, PM10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PM_{10}$$\\end{document}) were studied on a daily basis for Baden-Württemberg (Germany). This federal state covers urban and rural compartments including mountainous and river areas. A temporal and spatial analysis of the internal relationships was performed among the variables using (a) cross-correlations, both on the grand ensemble of data as well as within subsets, and (b) the Local Indications of Spatial Association (LISA) method. Meteorological and air pollution variables were strongly correlated within and among themselves in time and space. We found a strong interaction between nitrogen dioxide and ozone, with correlation coefficients varying over time. The coefficients ranged from negative correlations in January (−0.84), April (−0.47), and October (−0.54) to a positive correlation in July (0.45). The cross-correlation plot showed a noticeable change in the correlation direction for O3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{O}_3$$\\end{document} and NO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{NO}_2$$\\end{document}. Spatially, NO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{NO}_2$$\\end{document}, PM2.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PM_{2.5}$$\\end{document}, and PM10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PM_{10}$$\\end{document} concentrations were significantly higher in urban than rural regions. For O3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{O}_3$$\\end{document}, this effect was reversed. A LISA analysis confirmed distinct hot and cold spots of environmental stressors. This work examined and quantified the spatio-temporal relationship between air pollution and meteorological conditions and recommended which variables to prioritize for future health impact analyses. The results found are in line with the underlying physico-chemical atmospheric processes. It also identified postal code areas with dominant environmental stressors for further studies.

Capacity Planning of Virtual Wards for Frail and Elderly Patients.

This paper investigates the planning of virtual ward (VW) capacity including the remote monitoring of frail and elderly patients. The main objective is to optimize VW hub locations across a region in the United Kingdom. Furthermore, assigning the optimal number of clinicians to different regions needs to be considered. We develop a mathematical model that minimizes the setup and travel costs of VW hubs and staff. Our experimental analysis evaluates different levels of demand considering postcode areas within different Trusts, also known as Health Boards, in the National Health Service (NHS). Furthermore, our experiments provide insights into how many hub locations should be deployed and staffed. This can be used to individually find the number of remote monitors and clinicians for each facility as well as the system overall.

Evaluating completion rates of COVID-19 contact tracing surveys in New York City

ImportanceContact tracing is the process of identifying people who have recently been in contact with someone diagnosed with an infectious disease. During an outbreak, data collected from contact tracing can inform interventions to reduce the spread of infectious diseases. Understanding factors associated with completion rates of contact tracing surveys can help design improved interview protocols for ongoing and future programs.ObjectiveTo identify factors associated with completion rates of COVID-19 contact tracing surveys in New York City (NYC) and evaluate the utility of a predictive model to improve completion rates, we analyze laboratory-confirmed and probable COVID-19 cases and their self-reported contacts in NYC from October 1st 2020 to May 10th 2021.MethodsWe analyzed 742,807 case investigation calls made during the study period. Using a log-binomial regression model, we examined the impact of age, time of day of phone call, and zip code-level demographic and socioeconomic factors on interview completion rates. We further developed a random forest model to predict the best phone call time and performed a counterfactual analysis to evaluate the change of completion rates if the predicative model were used.ResultsThe percentage of contact tracing surveys that were completed was 79.4%, with substantial variations across ZIP code areas. Using a log-binomial regression model, we found that the age of index case (an individual who has tested positive through PCR or antigen testing and is thus subjected to a case investigation) had a significant effect on the completion of case investigation – compared with young adults (the reference group,24 years old < age < = 65 years old), the completion rate for seniors (age > 65 years old) were lower by 12.1% (95%CI: 11.1% – 13.3%), and the completion rate for youth group (age < = 24 years old) were lower by 1.6% (95%CI: 0.6% –2.6%). In addition, phone calls made from 6 to 9 pm had a 4.1% (95% CI: 1.8% – 6.3%) higher completion rate compared with the reference group of phone calls attempted from 12 and 3 pm. We further used a random forest algorithm to assess its potential utility for selecting the time of day of phone call. In counterfactual simulations, the overall completion rate in NYC was marginally improved by 1.2%; however, certain ZIP code areas had improvements up to 7.8%.ConclusionThese findings suggest that age and time of day of phone call were associated with completion rates of case investigations. It is possible to develop predictive models to estimate better phone call time for improving completion rates in certain communities.

Bovine mortality: the utility of two data sources for the provision of population-level surveillance intelligence.

The use of existing data to provide surveillance intelligence is widely advocated but often presents considerable challenges. Two data sources could be used as proxies for the mortality experienced by the Scottish cattle population: deaths recorded in the mandatory register [Cattle Tracing System (CTS)] and fallen stock collections by the National Fallen Stock Company (NSFCo) with a nationwide voluntary membership. Data for the period 2011-2016 were described and compared to establish their strengths and limitations. Similarities and differences in their temporal, seasonal and spatial patterns were examined overall, at postcode area level and for different age groups. Temporal aberration detection algorithms (TADA) were fitted. Broadly, similar patterns were observed in the two datasets; however, there were some notable differences. The observed seasonal, annual and spatial patterns match expectations, given knowledge of Scottish cattle production systems. The registry data provide more comprehensive coverage of all areas of Scotland, while collections data provide a more comprehensive measure of the mortality experienced in 0-1-month-old calves. Consequently, estimates of early calf mortality and their impact on the livestock sector made using CTS, or successor registers, will be under-estimates. This may apply to other registry-based systems. Fitted TADA detected points of deviations from expected norms some of which coincided in the two datasets; one with a known external event that caused increased mortality. We have demonstrated that both data sources do have the potential to be utilized to provide measures of mortality in the Scottish cattle population that could inform surveillance activities. While neither is perfect, they are complementary. Each has strengths and weaknesses, so ideally, a system where they are analyzed and interpreted in parallel would optimize the information obtained for surveillance purposes for epidemiologists, risk managers, animal health policy-makers and the wider livestock industry sector. This study provides a foundation on which to build an operational system. Further development will require improvements in the timeliness of data availability and further investment of resources.