Census Variables Research Articles

Cardiogenic shock is more prevalent in racialised and newcomer populations and associated with higher mortality

Abstract Background The World Health Organization (WHO) has recognised social determinants of health (SDH) as non-medical factors that affect health outcomes. These SDH have a significant effect on health inequities, whereby more marginalised populations often have worse outcomes due to unfair and avoidable differences in health status. The Ontario Marginalization index (ON-Marg) is an area-level index derived from 42 census variables identifying differences in marginalisation, allowing identification of inequities between population groups and geographical areas. The ‘racialised and newcomer populations’ dimension of the ON-Marg characterises the proportions of recent immigrants and/or people belonging to ‘visible minority’ groups in geographical locales. These data can be used as a surrogate for individual patient data to investigate health outcomes related to this dimension. This study investigated the prevalence and mortality associated with racial marginalisation in patients admitted with cardiogenic shock at a quaternary cardiac referral centre. Methods A single-centre registry of CS admissions from 2014-2023 at a quaternary referral centre cardiac intensive care unit (CICU) in Ontario, Canada was studied. Patient postal codes were identified and mapped to ON-Marg ‘racialised and newcomer populations’ data using the Postal Code Conversion File. The ON-Marg data are categorised into equal quintiles, which was used to determine differences in distribution of CS cases and inpatient survival using the Chi-squared test and Kaplan-Meier methods. Only the index CS hospitalisation was included in the analysis. Results We identified 1513 patients, including 456 (30.1%) females, aged 60.2±16.1 years, with 333 (24.8%) due to acute myocardial infarction CS. The majority of patients were SCAI stage D (69.2%) with 488 (32.3%) dying and 186 (12.3%) receiving a heart transplant or durable ventricular assist device (VAD) before discharge. CS patients were more likely to be in the higher quintiles for marginalisation racialised and newcomer populations and these individuals had higher mortality (p=0.005, figure 1). In a survival analysis, with right-censoring for transplant or VAD, there were significant differences between marginalisation quintiles (figure 2). Conclusions Patients admitted with CS to a large cardiac centre were more likely to be from populations with higher levels of racialised and newcomer individuals. Increasing marginalisation was associated with higher mortality. This identifies a need for delineating whether these differences are aetiological or whether there are barriers in accessing high-quality care in a timely fashion. This could allow an improvement in outcomes for these patient groups. While census-based area-level marginalisation indices can be helpful for identifying possible barriers to equitable care, individualised patient-level data are needed to confirm these findings.

Health care disparities and bladder cancer mortality in the United States: A closer look at the social vulnerability index.

150 Background: Bladder cancer is a significant public health concern, with various factors influencing mortality rates. The Social Vulnerability Index (SVI) measures community resilience to external health stresses. This study investigates the association between bladder cancer mortality (BCM) and county-level SVI in the United States (U.S.). Methods: Mortality data from U.S. counties were obtained from the Centers for Disease Control (CDC) and Prevention Wide-Ranging Online Data for Epidemiologic Research (WONDER) database from 2015 to 2019 using the International Classification of Diseases-10th Edition (Code C67). The CDC and Agency for Toxic Substances and Disease Registry (ATSDR) county-level SVI was used as a composite measure of social vulnerability. The SVI includes several U.S. census variables grouped into four themes: Socioeconomic Status (SES), Household Characteristics, Racial and Ethnic Minority Status, and Housing Type and Transportation. Vulnerable SES variables include individuals with incomes below 150% of the federal poverty line, unemployment, those with housing cost burdens, and those without high school diplomas or health insurance. Vulnerable household variables include seniors over 65 years, children under 17 years, disabled civilians, single-parent households, and those with limited English proficiency. SVI Quartiles (Q1 being the least vulnerable and Q4 the most vulnerable) were compared to assess their impact on BCM. Negative binomial regression models were used to estimate Incidence Rate Ratios (IRRs) with 95% Confidence Intervals (CIs) using SAS software. Results: From 2015 to 2019, a total of 82,994 people died from bladder cancer, and the age-adjusted mortality rate per 1,000,000 was 42.4. A higher SVI in intermediate vulnerability quartiles was associated with a higher risk of BCM (Q2 and Q3: IRR = 1.06; 95% CI = 1.01-1.10; p = 0.009) compared to the least vulnerable quartile, Q1. Vulnerable SES had an increased risk of BCM (Q2 and Q3: IRR = 1.07; 95% CI = 1.03-1.12; p < 0.001) compared to Q1. BCM was also increased in individuals with vulnerable household characteristics (Q2 and Q3: IRR = 1.06; 95% CI = 1.02-1.10; p = 0.004) compared to Q1. Q4 was not significant in comparison to Q1 for the groups mentioned above. Interestingly, Racial and Ethnic Minority Status (Hispanic, Latino, African American, etc.) was associated with lower BCM in Q2 and Q3 (IRR = 0.88; 95% CI = 0.84-0.92; p < 0.001) and Q4 (IRR = 0.77; 95% CI = 0.73-0.81; p < 0.001) compared to Q1. No significant mortality differences were observed among vulnerable Housing Types and Transportation. Conclusions: Bladder cancer mortality is linked to vulnerable socioeconomic and household characteristics, while racial and ethnic minority status correlates with decreased risk. Future targeted public health interventions are needed to reduce bladder cancer mortality in vulnerable populations. Bladder cancer mortality and its association with social vulnerability quartiles. Bladder Cancer Mortality Characteristic IRR 95% CI p-value Social Vulnerability Index Q1 — — Q2&Q3 1.06 1.01, 1.10 0.009 Q4 0.98 0.93, 1.03 0.4 Socioeconomic Status Q1 — — Q2&Q3 1.07 1.03, 1.12 <0.001 Q4 1.00 0.95, 1.05 >0.9 Household Characteristics Q1 — — Q2&Q3 1.06 1.02, 1.10 0.004 Q4 1.05 1.00, 1.10 0.052 Racial & Ethnic Minority Status Q1 — — Q2&Q3 0.88 0.84, 0.92 <0.001 Q4 0.77 0.73, 0.81 <0.001 Housing Type & Transportation Q1 — — Q2&Q3 1.03 0.99, 1.08 0.2 Q4 0.98 0.93, 1.03 0.4 IRR = Incidence Rate Ratio, CI = Confidence Interval.

Prevalence of asymptomatic malaria at the communal level in Burkina Faso: an application of the small area estimation approach

BackgroundIn malaria-endemic countries, asymptomatic carriers of plasmodium represent an important reservoir for malaria transmission. Estimating the burden at a fine scale and identifying areas at high risk of asymptomatic carriage are important to guide malaria control strategies. This study aimed to estimate the prevalence of asymptomatic carriage at the communal level in Burkina Faso, the smallest geographical entity from which a local development policy can be driven.MethodsThe data used in this study came from several open sources: the 2018 Multiple Indicator Cluster Survey on Malaria and the 2019 general census of the population data and environmental. The analysis involved a total of 5489 children under 5 from the malaria survey and 293,715 children under 5 from the census. The Elbers Langjouw and Langjouw (ELL) approach is used to estimate the prevalence. This approach consists of including data from several sources (mainly census and survey data) in a statistical model to obtain predictive indicators at a sub-geographical level, which are not measured in the population census. The method achieves this by finding correlations between common census variables and survey data.FindingsThe findings suggest that the spatial distribution of the prevalence of asymptomatic carriage is very heterogeneous across the communes. It varies from a minimum of 5.1% (95% CI 3.6–6.5) in the commune of Bobo-Dioulasso to a maximum of 41.4% (95% CI 33.5–49.4) in the commune of Djigoué. Of the 341 communes, 208 (61%) had prevalences above the national average of 20.3% (95% CI 18.8–21.2).ContributionsThis analysis provided commune-level estimates of the prevalence of asymptomatic carriage of plasmodium in Burkina Faso. The results of this analysis should help to improve planning of malaria control at the communal level in Burkina Faso.

How An Academic Direct Primary Care Clinic Served Patients from Vulnerable Communities.

Direct primary care (DPC) critics are concerned that the periodic fee precludes participation from vulnerable populations. The purpose is to describe the demographics and appointments of a, now closed, academic DPC clinic and determine whether there are differences in vulnerability between census tracts with and without any clinic patients. We linked geocoded data from the DPC's electronic health record with the social vulnerability index (SVI). To characterize users, we described their age, sex, language, membership, diagnoses, and appointments. Descriptive statistics included frequencies, proportions or medians, and interquartile ranges. To determine differences in SVI, we calculated a localized SVI percentile within Harris County. A t test assuming equal variances and Mann-Whitney U Tests were used to assess differences in SVI and all other census variables, respectively, between those tracts with and without any clinic patients. We included 322 patients and 772 appointments. Patients were seen an average of 2.4 times and were predominantly female (58.4%). More than a third (37.3%) spoke Spanish. There was a mean of 3.68 ICD-10 codes per patient. Census tracts in which DPC patients lived had significantly higher SVI scores (ie, more vulnerable) than tracts where no DPC clinic patients resided (median, 0.60 vs 0.47, p-value < 0.05). This academic DPC clinic cared for individuals living in vulnerable census tracts relative to those tracts without any clinic patients. The clinic, unfortunately, closed due to multiple obstacles. Nevertheless, this finding counters the perception that DPC clinics primarily draw from affluent neighborhoods.

Comparison of geographical and individual deprivation index to assess the risk of Sars-CoV-2 infection and disease severity: a retrospective cohort study

BackgroundIt has been shown that COVID-19 affects people at socioeconomic disadvantage more strongly. Previous studies investigating the association between geographical deprivation and COVID-19 outcomes in Italy reported no differences in case-hospitalisation and case-fatality. The objective of this research was to compare the usefulness of the geographic and individual deprivation index (DI) in assessing the associations between individuals' deprivation and risk of Sars-CoV-2 infection and disease severity in the Apulia region from February to December 2020.MethodsThis was a retrospective cohort study. Participants included individuals tested for SARS-CoV-2 infection during the study period. The individual DI was calculated employing polychoric principal component analysis on four census variables. Multilevel logistic models were used to test associations between COVID-19 outcomes and individual DI, geographical DI, and their interaction.ResultsIn the study period, 139,807 individuals were tested for COVID-19 and 56,475 (43.5%) tested positive. Among those positive, 7902 (14.0%) have been hospitalised and 2215 (4.2%) died. During the first epidemic wave, according the analysis done with the individual DI, there was a significant inversely proportional trend between the DI and the risk of testing positive. No associations were found between COVID-19 outcomes and geographic DI. During the second wave, associations were found between COVID-19 outcomes and individual DI. No associations were found between the geographic DI and the risk of hospitalisation and death. During both waves, there were no association between COVID-19 outcomes and the interaction between individual and geographical DI.ConclusionsEvidence from this study shows that COVID-19 pandemic has been experienced unequally with a greater burden among the most disadvantaged communities. The results of this study remind us to be cautious about using geographical DI as a proxy of individual social disadvantage because may lead to inaccurate assessments. The geographical DI is often used due to a lack of individual data. However, on the determinants of health and health inequalities, monitoring has to have a central focus. Health inequalities monitoring provides evidence on who is being left behind and informs equity-oriented policies, programmes and practices. Future research and data collection should focus on improving surveillance systems by integrating individual measures of inequalities into national health information systems.

A multi-glimpse deep learning architecture to estimate socioeconomic census metrics in the context of extreme scope variance

Convolutional Neural Networks (CNNs) are leveraged for a wide range of satellite imagery information extraction tasks. However, for tasks which seek to estimate aggregated information across highly variable geographic extents, existing techniques are subject to critical limitations. We engage with a specific case study exploring this challenge: estimating census variables across 2358 Mexican municipalities, which range in scope from 2.21 km2 (˜74,000 30 m pixels) to 72,417.9 km2 (millions of pixels). Building on recent literature which has illustrated the capability of deep learning to extract socioeconomic information from satellite imagery, we specifically seek to establish baseline metrics of error that might be expected when estimating a range of census variables based on coarse-resolution (Landsat) satellite imagery alone. For each of 52 variables, we implement a multi-glimpse recurrent attention model, in which we parametrically determine subsets of each municipality to sample across iterative steps. Results of a five-fold validation indicate that nearly half of the tested variables (22) can be estimated with r2 values greater than 0.75. Results suggest considerable promise for the use of satellite imagery to estimate socioeconomic factors in both historic time periods for which surveys were not conducted, as well as contemporary inaccessible regions.

Top–down disaggregation of life expectancy up to municipal areas, using linear self-regressive spatial models

The paper aims to analyse a statistical procedure for the definition of territorial indicators, such as the biometric function of life expectancy of citizens of a territory e0, applying a methodology of Top–Down spatial disaggregation, using census data from 2011 in Italy. Through spatial regressions with the methodology proposed in 1971 by Chow and Lin with the use of ISTAT elaborations of annual mortality tables, which are structured from the national level to the regional level, up to the smallest details of the main level, as a dependent variable and predictors a number k of census variables plus accidents in regression models, life expectancy can also be defined at municipal levels (not elaborated by ISTAT) and even at sub-municipal levels (Census Area). The structure of the 2011 census was characterized by 152 variables, collected with CAPI and universal CAWI survey on all the survey units, throughout the national territory, divided into administrative areas of competence and 402,677 more granular areas in census sections. This structure represents a very relevant and useful information asset for applying a spatial disaggregation of indicators, based on three fundamental assumptions:Structural similarity, whereby the aggregate model and the disaggregate model are structurally similar, i.e., the relationships between the variables are valid both at the aggregate (Top) and at the disaggregate (Down) level, with the consequence that the regression parameters are the same in the two models.Error similarity: for spatially correlated errors they present the same structure at both aggregate (Top) and disaggregate (Down) levels, significantly testing for zero spatial correlation;Reliable indicators: the variables in the regression models are efficient predictors at both aggregates (Top) and disaggregate (Down) levels, estimable from model efficiency tests.As we see in the following, compared to others, the best predictors of the census and income variables show us a good interaction in terms of active regressors on the estimation variable.

Efeitos regionais heterogêneos do crédito rural sobre a produção agropecuária no Brasil

Abstract Access to rural credit has been persistently heterogeneous across Brazilian regions over time. This work aimed to estimate the regional effects of rural credit on agricultural production due to the heterogeneity observed in access to credit. Based on data from the 2017 Agricultural Census and climate variables, the effect of rural credit was estimated by standard regressions, combined with the entropy balancing technique. The results show positive and significant effects of rural credit on Brazilian agricultural production, even after controlling for observed covariates. This result was consistent even after balancing the covariates with regard to entropy. The effect of rural credit proved to be heterogeneous across Brazilian regions, being positive and significant for regions with greater access to rural credit and statistically null in less credit-intensive regions. Furthermore, estimates show that technical assistance is an important transmission mechanism of the rural credit effect.

SOCIOECONOMIC DISPARITIES IN PHYSICAL AND OCCUPATIONAL THERAPY HOME HEALTH CARE VISITS FOR PEOPLE WITH DEMENTIA

Abstract People living with Dementia (PLWD) utilize Home Health Care (HHC) services such as Physical Therapy (PT) and Occupational Therapy (OT), but little is known about service utilization patterns among PLWD from different socioeconomic backgrounds. We aimed to identify disparities in service utilization in PLWD in a HHC setting across socioeconomic backgrounds using a measure of geographic socioeconomic deprivation, the Area Deprivation Index (ADI). ADI is a composite measure of census variables describing socioeconomic disadvantage, reported as national percentile rank. This secondary analysis utilized data collected February 2017–May 2022 in a dementia symptom management clinical trial at a large, urban, Medicare certified nonprofit HHC agency in New Jersey. Demographic and service utilization data from 153 dyads of PLWD and caregivers was collected. PLWD’s area codes were used to calculate ADI. ADI national percentile was divided into quartiles, and PT and OT utilization for the highest quartile was compared to the three lowest quartiles using Poisson regression, consistent with previous literature. The highest quartile group was defined as ADI above the 36.5th percentile. ADI varied from 2nd to 73rd percentile, with a median of 24th percentile. Analysis controlled for pre-existing comorbidities using pre-trial Charlson Comorbidity Index scores, classified as high (&amp;gt;5), medium (3-4), or low (1-2). High ADI classification was associated with a 50.2% decrease in OT utilization (IRR=.498, CI=.348-.713), and 39.4% decrease in PT utilization (IRR=.606, CI=.485-.759). Further investigation of disparities in PT and OT utilization in HHC is needed, given the disproportionate burden of disease on socioeconomically distinct populations.

NEIGHBORHOOD SOCIOECONOMIC STATUS AND CHANGES IN COGNITIVE FUNCTION IN OLDER ADULTS: FINDINGS FROM HEALTH ABC STUDY

Abstract Neighborhood socioeconomic status (NSES), an overall marker of neighborhood condition, may influence multiple aspects of health and well-being in older adulthood, including cognitive function, though results regarding neighborhood and cognition have been largely cross-sectional. We examined the association between NSES and changes in cognitive function using 610 participants from the Pittsburgh site of the Health, Aging, and Body Composition (Health ABC) Study, a longitudinal cohort of community-dwelling, Black and white adults aged 70 and older. Changes in cognitive function were measured annually over a 6-year follow-up (2006-2012) using the Modified Mini-Mental State Examination (range: 0-100, 3 visits on average per person). Using baseline home address, NSES was calculated for census tracts by summing z-scores of six 2010 census variables representing wealth/income, education, and occupation, and divided into tertiles. Multivariate mixed-effect linear regression models assessed associations, adjusting for individual-level sociodemographic and health-related covariates. In the fully adjusted model, the lowest NSES tertile (vs. the highest) was not associated with baseline cognitive function (β: -0.99, 95% CI: -3.86, 1.87) but was associated with a faster rate of decline in cognitive function (β: -1.54, 95% CI: -2.67, -0.41). No significant differences in baseline cognitive function or rates of change were observed in the middle (vs. the highest) NSES tertile. There was no evidence of effect modification by race. The results indicate that exposure to adverse neighborhood socioeconomic environments may accelerate cognitive decline in late life. The findings add to existing literature concerning the role of environments in the context of aging.

Register-based census in Thailand: A case study in Chachoengsao province

The use of registers has been increasingly popular in the field of population census because of its advantages over the traditional census. While the traditional census requires a large amount of fieldwork and data collection, the registered-based census can rely on pre-existing administrative data. As a result, the register-based census can save both time and budget. Thailand explored a use of the register-based census in 2020. In this paper, the authors layout the methodology in an aspect of data preparation and integration as well as analyze data quality of the register-based census compared with the traditional census in Chachoengsao province, Thailand. In addition, we compared conceptual frameworks that are commonly used for a register-based census in several countries and the number of databases (a recent single database VS multiple databases) used to construct the register-based census. We found that using a conceptual framework that counts the number of populations based on the main census variables on a single recent database is better than using a framework that counts population who appears on many registers in term of overcoverage and data distribution regarding to sex. This provides the evidence of using one recent and complete database is sufficient for conducting the register-based census. The authors end up with recommendations for conducing the register-based census.

Neighborhood Disadvantage Increases Risk of Adverse Clinical Events in Children with Sickle Cell Disease

Background: Hydroxyurea is the most widely used pharmacologic agent to treat patients with sickle cell disease (SCD). Hydroxyurea treatment has well established effects of inducing expression of fetal hemoglobin (HbF) and a beneficial impact on clinical outcomes such as reduced number of vaso-occlusive (VOC) events, transfusions, and hospitalizations. In non-SCD populations, living in socio-economic disadvantage is a well-established factor for poor health outcomes. However, there is a paucity of data to describe how social stressors impact children with SCD. We hypothesized that socio-economic disadvantage impacts risk of adverse clinical outcomes in pediatric SCD patients even while on hydroxyurea therapy. Methods: We performed a retrospective chart review on 121 pediatric patients with severe SCD genotypes (homozygous HbS and sickle-beta 0 thalassemia) seen at Texas Children's Hospital (TCH) between 2010-2021. All patients were treated with hydroxyurea for a minimum of five years. Adherence to hydroxyurea was assessed through lab markers including absolute neutrophil count and reticulocyte counts. Patients not adherent were excluded from analysis. We examined the incidence of all clinical events for a total of 1026 patient years, including the number of office visits, ER visits, hospitalizations, transfusions, VOC, acute chest syndrome episodes (ACS), acute splenic sequestration (ASS), and febrile illness. We collected laboratory data including absolute neutrophil count, hemoglobin level, and HbF levels for each patient per year they were on hydroxyurea. We used the area deprivation index (ADI) to determine a score of neighborhood disadvantage amongst our cohort of patients. The ADI is a factor-based index which uses 17 US Census variables including poverty, education, and employment. We used individual residential addresses during our observation period to assign a geocode specific to each individual and obtain an ADI score. The ADI scores are ranked from 1-10, with higher ADI rankings reflecting a higher level of disadvantage. Results: All individuals with SCD had a robust response to hydroxyurea within their first year of initiating the drug (&amp;gt;20% HbF). We stratified our cohort based on their ADI scores into low ADI (1-3; n=42); medium ADI (4-6; n=42); and high ADI groups (7-10; n=37), where high ADI is the group with highest levels of disadvantage. All three groups had a similar average duration of hydroxyurea treatment. There were no significant differences in age, sex, or reason given for starting hydroxyurea between the ADI groups. When we examined hydroxyurea response, there was no significant difference in HbF levels at 1 year (23.8 vs. 24.06 %HbF in the low vs. high ADI groups) or after 5 years (19.6 vs. 18.6 % HbF in the low and high ADI groups). Individuals with high ADI did started hydroxyurea at a younger age (2.9 years vs. 4.9 years, p=0.02). We found that any individual living in an area with higher ADI had an increased incidence of adverse clinical events. We calculated the incidence of each clinical event per 100 patient years in our 3 ADI groups and used a conditional maximum likelihood estimate of rate ratio method to compare incidences (Table 1). We found that among individuals living in an area with a higher ADI (more deprived region), there was increased frequency of ER visits, hospitalizations, transfusions, VOC, and ASS. In particular, patients with the highest ADI had a 3 and 5 times higher rate of VOC and ASS, respectively (Table 1). Discussion: While we did not observe an association between ADI and HbF expression, our results show that higher neighborhood disadvantage is associated with poorer clinical outcomes among hydroxyurea-adherent pediatric patients with SCD who maintained a robust response to the drug (&amp;gt;20% HbF). All individuals in our study had a similar number of routine office visits at our clinic but any patient living in an area of high ADI had significantly more ER visits and hospitalizations for VOC and ASS events. While hydroxyurea is shown to reduce hospitalizations and VOC events in pediatric sickle cell patients, we show that neighborhood disadvantage can influence clinical outcomes even among patients who respond to hydroxyurea. More analysis remains in order to better characterize this association, and to understand additional strategies that can be used to reduce clinical events in pediatric patients of lower socioeconomic status.

Association between Individual versus Community-level Social Vulnerability and Neonatal Opioid Withdrawal Syndrome among Pregnant Individuals Receiving Buprenorphine for Opioid Use Disorder.

Individual patient-level measures of adverse social determinants of health are associated with neonatal opioid withdrawal syndrome (NOWS), but the relative impact of community-level adverse social determinants of health remains to be defined. We examined the association between community-level social vulnerability and NOWS among pregnant individuals receiving buprenorphine for opioid use disorder. We conducted a secondary analysis of an established cohort of pregnant individuals and their infants participating in a multidisciplinary prenatal/addiction care program from 2013 to 2021. Addresses were geocoded using ArcGIS and linked at the census tract to the Centers for Disease Control and Prevention 2018 Social Vulnerability Index (SVI), incorporating 15 census variables. The primary exposure was the SVI as a composite measure of community-level social vulnerability, and secondarily, individual scores for four thematic domains (socioeconomic status, household composition and disability, minority status and language, and housing type and transportation). The primary outcome was a clinical diagnosis of NOWS defined as withdrawal requiring pharmacological treatment following buprenorphine exposure. Among 703 pregnant individuals receiving buprenorphine, 39.8% (280/703) of infants were diagnosed with NOWS. Among our patinets, those who were nulliparous, had post-traumatic stress disorder, a term birth (≥ 37 weeks) and had a male infant were more likely to have an infant diagnosed with NOWS. Individuals with and without an infant diagnosed with NOWS had similarly high community-level social vulnerability per composite SVI scores (mean [standard deviation]: 0.6 [0.4-0.7] vs. 0.6 [0.4-0.7], p = 0.2]. In adjusted analyses, SVI, as a composite measure as well as the four domains, was not associated with NOWS diagnosis. Among pregnant persons receiving buprenorphine enrolled in a multidisciplinary prenatal and addition care program, while individual risk factors that measure adverse social determinants of health were associated with an NOWS diagnosis in the infant, community-level social vulnerability as measured by the SVI was not associated with the outcome. · Community-level SVI was not associated with neonatal opioid use disorder.. · Certain individual risk factors were identified as being associated with NOWS.. · Homogeneity of composite SVI scores may have led to lack of significant findings..

Bayesian kernel machine regression for count data: modelling the association between social vulnerability and COVID-19 deaths in South Carolina.

The COVID-19 pandemic created an unprecedented global health crisis. Recent studies suggest that socially vulnerable communities were disproportionately impacted, although findings are mixed. To quantify social vulnerability in the US, many studies rely on the Social Vulnerability Index (SVI), a county-level measure comprising 15 census variables. Typically, the SVI is modelled in an additive manner, which may obscure non-linear or interactive associations, further contributing to inconsistent findings. As a more robust alternative, we propose a negative binomial Bayesian kernel machine regression (BKMR) model to investigate dynamic associations between social vulnerability and COVID-19 death rates, thus extending BKMR to the count data setting. The model produces a 'vulnerability effect' that quantifies the impact of vulnerability on COVID-19 death rates in each county. The method can also identify the relative importance of various SVI variables and make future predictions as county vulnerability profiles evolve. To capture spatio-temporal heterogeneity, the model incorporates spatial effects, county-level covariates, and smooth temporal functions. For Bayesian computation, we propose a tractable data-augmented Gibbs sampler. We conduct a simulation study to highlight the approach and apply the method to a study of COVID-19 deaths in the US state of South Carolina during the 2021 calendar year.

Utility of the material community deprivation index as a metric to identify at-risk children for severe traumatic brain injury

OBJECTIVE Pediatric traumatic brain injury (TBI) is a significant cause of morbidity and mortality with lasting effects including neurological deficits and psychological comorbidities. Recent studies have shown that social determinants of health are key factors that impact clinical outcomes in other pediatric traumatic injuries, suggesting that these health disparities may have a significant impact on patients sustaining TBI as well. The purpose of this study was to retrospectively review a cohort of pediatric patients diagnosed with TBI and elucidate the relationships among socioeconomic deprivation, patient-specific demographics, and morbidity and mortality. METHODS The authors conducted a retrospective cross-sectional analysis of pediatric patients (≤ 18 years of age) treated for TBI at a level I pediatric trauma center between 2016 and 2020. Patients with concussion-related injuries without intracranial findings and those with nonaccidental trauma were excluded from the study. In addition to evaluating basic patient demographics, the authors geocoded patient addresses to allow identification of the patient’s home census tract using the material community deprivation index (MCDI). The MCDI is a unique composite index score created by the combination of six census variables and ranges from 0 to 1 in severity. RESULTS Of the 513 patients included in this study, 71 (13.8%) were diagnosed with severe TBI, 28 (5.5%) with moderate TBI, and 414 (80.7%) with mild TBI. Patients in quartile 4 (MCDI ≥ 0.45) were at a significantly higher risk of having a severe TBI than patients in quartile 1 (OR 2.29, 95% CI 1.1–4.71; p = 0.02). Black patients were more likely to have a firearm-related TBI (OR 3.74, 95% CI 2.01–8.7; p = 0.018) than non-Black patients. Patients who lived in a neighborhood with a lower MCDI were significantly more likely to be discharged home than those who lived in an area with a higher MCDI (OR 2.78, 95% CI 7.90–32.93; p &lt; 0.001). CONCLUSIONS This study demonstrated that inequities continue to exist within the pediatric TBI population and that the MCDI is a valuable tool to identify at-risk subpopulations. More specifically, patients who lived in a neighborhood with a higher MCDI were at higher risk of sustaining a severe TBI. By partnering with communities, families, and policymakers, healthcare providers could serve as advocates for these patients and work to minimize the social disparities that continue to exist.

The Influence of Patient Characteristics and Social Determinants of Health on Post-Operative Complications Following Achilles Tendon Ruptures

Category: Sports; Hindfoot Introduction/Purpose: While patient comorbidities are known to affect surgical complication rates after Achilles tendon rupture (ATR) repair, the correlation between social determinants of health (SDH) and postoperative complication rates is poorly understood. Two validated indices representative of SDH include the Area Deprivation Index (ADI), which ranks neighborhoods by social disadvantage, and the Social Vulnerability Index (SVI), which uses 16 US census variables to identify communities at risk before or after natural disasters. In this study, we aim to determine whether there is any correlation between patient demographics, SDH, and postoperative complication rates following surgical treatment of ATR. Methods: A retrospective chart review identified 521 patients who underwent surgical repair of an acute ATR between 2015 and 2021. Inclusion criteria included age ≥ 18 years, a minimum 30 day follow up, and patients who underwent acute repair within 28 days of ATR. Collected variables included patient demographics, time to surgery (TTS), injury characteristics, and postoperative complications sub-categorized into venous thromboembolism (VTE), re-rupture, surgical site infection (SSI), wound dehiscence, and sural nerve injury. SDH variables included age, race, smoking status, insurance status, level of education, and employment status. The ADI and SVI measurements of patient deprivation or vulnerability were also extracted from the data. A univariate regression test was performed to determine the correlation between each complication and each SDH indicator. Variables that showed significance (p &lt; 0.05) were then included in a multivariate regression to determine the correlation coefficients and significance. Results: Sixty-eight complications occurred in 59 patients (11.3%). Multivariate regression showed higher ADI was associated with VTE occurrence (OR = 0.39, 95% CI: 0.16 –0.91, p = 0.03). Female patients and open surgical approach correlated with decreased VTE occurrence (OR = 0.24, 95% CI: 0.09 – 0.66, p&lt; 0.01), (OR = 0.15, 95% CI: 0.04 – 0.55, p&lt; 0.01), respectively. Higher BMI was associated with VTE (OR = 1.62, 95% CI: 1.05 – 2.51, p=0.03) and re-rupture (OR = 3.86, 95% CI: 1.51 – 9.08, p&lt; 0.01). Women experienced decreased wound dehiscence rates (OR = 0.25, 95% CI: 0.09 – 0.69, p&lt; 0.01) and SSI (OR = 0.31, 95% CI: 0.11 – 0.93, p=0.04) compared to men. TTS correlated with sural nerve injuries (OR=2.09, 95% CI: 1.32 – 3.31, p&lt; 0.01). Conclusion: This study found that SDH, such as ADI, impacts complication rates after ATR repair, though the nature of this relationship remains unclear. Patient demographic and anthropometric factors such as gender and BMI also have an impact. Future studies should include a larger, more diverse sample population to better understand the impact of these factors on surgical outcomes.

SynthEco - A multi-layered digital ecosystem for analysing complex human behaviour in context

Introduction &amp; BackgroundHuman behaviour is multi-faceted and complex, with different dimensions interacting and impacting each other and individuals operating in an environmental context. In order to understand this behaviour better, the combination of data from different sources is useful to uncover some of those interactions and complexities. We present a multi-layered digital ecosystem based on a data platform providing statistically representative synthetic population derived from census data at different geo-spatial granularity, which we call SynthEco. This platform is enriched with individual data stemming from cohorts and cross-sectional surveys and geo-scanning of different layers of socio-environmental actors and conditions to create a complex digital ecosystem.&#x0D; Objectives &amp; ApproachThe objective of SynthEco is to allow for the analysis of behaviour, as well as health and wellbeing outcomes, through the integration of cohort and cross-sectional data into a geospatially anchored synthetic population embedded into environmental data which is forming the backdrop. We demonstrate the use of this platform on the example of Montreal, Canada. The synthetic population is first generated from census data using iterative proportional fitting, which allows for the creation of a population data set that is artificial yet statistically representative for a given geospatial granularity, such as a city. Each individual household is assigned a geospatial location, which allows for the consideration of their surrounding environment including enterprises or institutions such as schools, hospitals and the local food environment. Through fuzzy matching and statistical extrapolation, different cohort and cross-sectional survey data are then merged to individual records, in order to describe them in more detail. This includes health, as well as financial wellbeing or social environment descriptors.&#x0D; Relevance to Digital FootprintsThere are two important points made through the presented work in relation to Digital Footprints data: the first is the technical approach to merging multiple datasets describing different dimensions of interacting human characteristics and behaviour by anchoring them into a synthetic population through fuzzy record matching. The second is the consideration of a spatial dimension when describing human behaviour. This is especially important when describing behaviour within local environments, such as the interaction with local food outlets.&#x0D; ResultsRecent work in this context includes an analysis of the food environment in Montreal, Canada. It introduces a way of utilising the synthetic population to predict the healthfulness of their local environment in terms of healthy food outlets, as well as providing a platform for the analysis of food environment surveillance and intervention simulations. For this purpose, the healthfulness of different census tract regions in Montreal is calculated to identify food deserts, food swamps, as well as healthy areas as defined through the Modified Retail Food Environment Index. We test different machine learning approaches to then predict these healthfulness scores using census variables from the synthetic population in their respective census tract, achieving accuracy scores of around 0.53 to 0.60. This demonstrates that census data has some limited predictive power in explaining the healthiness of the local food environment, which could be especially relevant for situations in which no information on the retailers is available to local policy makers. Future work can extend this approach to also include further data describing the population, stemming from the integrated cohorts and survey data, which could improve the prediction accuracy or help in identifying areas of concern.&#x0D; Conclusions &amp; ImplicationsThe presented SynthEco platform views individuals as agents nested within modular systems of systems, trying to capture both internal systems and processes as well as environmental ones within which individuals are operating. The platform thus enables the application of computational systems modelling for the analysis of individual human behaviour in contexts. As demonstrated through the example of using SynthEco in the context of healthier food environments, the approach is especially relevant to practitioners and policy makers interested in local intervention strategies and identifying areas for targeted policy related to different dimensions of health and wellbeing.

Residential social vulnerability among healthcare personnel with and without severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection in Five US states, May-December 2020.

To characterize residential social vulnerability among healthcare personnel (HCP) and evaluate its association with severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection. Case-control study. This study analyzed data collected in May-December 2020 through sentinel and population-based surveillance in healthcare facilities in Colorado, Minnesota, New Mexico, New York, and Oregon. Data from 2,168 HCP (1,571 cases and 597 controls from the same facilities) were analyzed. HCP residential addresses were linked to the social vulnerability index (SVI) at the census tract level, which represents a ranking of community vulnerability to emergencies based on 15 US Census variables. The primary outcome was SARS-CoV-2 infection, confirmed by positive antigen or real-time reverse-transcriptase- polymerase chain reaction (RT-PCR) test on nasopharyngeal swab. Significant differences by SVI in participant characteristics were assessed using the Fisher exact test. Adjusted odds ratios (aOR) with 95% confidence intervals (CIs) for associations between case status and SVI, controlling for HCP role and patient care activities, were estimated using logistic regression. Significantly higher proportions of certified nursing assistants (48.0%) and medical assistants (44.1%) resided in high SVI census tracts, compared to registered nurses (15.9%) and physicians (11.6%). HCP cases were more likely than controls to live in high SVI census tracts (aOR, 1.76; 95% CI, 1.37-2.26). These findings suggest that residing in more socially vulnerable census tracts may be associated with SARS-CoV-2 infection risk among HCP and that residential vulnerability differs by HCP role. Efforts to safeguard the US healthcare workforce and advance health equity should address the social determinants that drive racial, ethnic, and socioeconomic health disparities.

629-P: Social Vulnerability Index and Recognition Status with the National Diabetes Prevention Program (National DPP)

Individuals in low socioeconomic status (SES) areas are disproportionately affected by diabetes and have poor lifestyle change outcomes. Organizations delivering the CDC National DPP lifestyle change program receive pending, preliminary, or full recognition status based on specific program criteria and outcomes. Full recognition is critical for organizations to sustain the program and receive reimbursements to cover costs, but organizations in low SES areas may face barriers to receiving it. A multinomial logit model was used to examine the association between the CDC social vulnerability index (SVI) and in-person full recognition organizations at the county level, adjusting for confounders (n=843). Using the National DPP registry, Census data, SVI, and CDC Diabetes Surveillance System, a three-level categorical dependent variable was created: counties without full recognition organizations, counties with at least one non-full recognition organization, and counties with all organizations with full recognition (reference group). The CDC created a four-level categorical SVI (Low, Low-Medium, Medium-High, High) using 16 Census variables to rank counties by social vulnerability: SES, Household Composition and Disability (e.g., age), Minority Status and Language, and Housing Type and Transportation (e.g., vehicle). Of the studied counties, 38.4% had no organizations with full recognition, 22.6% had at least one organization without full recognition, and 39.0% had all organizations with full recognition. Counties with higher SVI were more likely to be in counties without full recognition organizations (High vs. Low; OR=2.71, P &amp;lt;0.001). Counties with high SVI lack fully recognized programs, indicating barriers for organizations in high SVI areas to attain full recognition. This study highlighted the need to develop strategies to ensure organizations in high SVI areas can reach full recognition status to better serve at-risk populations. Disclosure T.Formagini: None. A.Rezwan: None. D.Rodriguez: None. B.Ng: None.

Association between neighbourhood composition, kindergarten educator-reported distance learning barriers, and return to school concerns during the first wave of the COVID-19 pandemic in Ontario, Canada.

Research to date has established that the COVID-19 pandemic has not impacted everyone equitably. Whether this unequitable impact was seen educationally with regards to educator reported barriers to distance learning, concerns and mental health is less clear. The objective of this study was to explore the association between the neighbourhood composition of the school and kindergarten educator-reported barriers and concerns regarding children's learning during the first wave of COVID-19 related school closures in Ontario, Canada. In the spring of 2020, we collected data from Ontario kindergarten educators (n = 2569; 74.2% kindergarten teachers, 25.8% early childhood educators; 97.6% female) using an online survey asking them about their experiences and challenges with online learning during the first round of school closures. We linked the educator responses to 2016 Canadian Census variables based on schools' postal codes. Bivariate correlations and Poisson regression analyses were used to determine if there was an association between neighbourhood composition and educator mental health, and the number of barriers and concerns reported by kindergarten educators. There were no significant findings with educator mental health and school neighbourhood characteristics. Educators who taught at schools in neighbourhoods with lower median income reported a greater number of barriers to online learning (e.g., parents/guardians not submitting assignments/providing updates on their child's learning) and concerns regarding the return to school in the fall of 2020 (e.g., students' readjustment to routines). There were no significant associations with educator reported barriers or concerns and any of the other Census neighbourhood variables (proportion of lone parent families, average household size, proportion of population that do no speak official language, proportion of population that are recent immigrants, or proportion of population ages 0-4). Overall, our study suggests that the neighbourhood composition of the children's school location did not exacerbate the potential negative learning experiences of kindergarten students and educators during the COVID-19 pandemic, although we did find that educators teaching in schools in lower-SES neighbourhoods reported more barriers to online learning during this time. Taken together, our study suggests that remediation efforts should be focused on individual kindergarten children and their families as opposed to school location.