Comparison of clinical outcomes in elderly patients undergoing TAVR- a nationwide analysis

Introduction Transcatheter aortic valve replacement (TAVR) is a less invasive alternative to traditional surgical aortic valve replacement (SAVR) that has been increasingly utilized in the management of aortic stenosis. Several studies have compared the outcomes of TAVR to SAVR, and studies have also compared the clinical outcomes in the elderly population. However, the comparison in outcomes of TAVR between patients more than 80 years and less than 80 years old has not been well characterized. Therefore, in this study, we sought to assess the hospital outcomes and major adverse events of TAVR in patients ≥80 years old compared to those <80 years. Methods We performed a retrospective observational study using the National Inpatient Sample for the year 2018. Using the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10) procedure codes we identified patients who underwent TAVR. We further divided these patients into two cohorts based on age being ≥80 years and <80 years old. The primary outcomes were the comparison of in-hospital mortality and major adverse events (MAE) in patients with TAVR procedure stratified based on age. Secondary analysis included sub-groups analysis of both the cohorts and comparing those with and without MAE as well as comparison of those with MAE only in both cohorts. Results We identified 63,630 patients who underwent TAVR procedures from January 1 to December 31, 2018. Among them, 35, 115(55%) were ≥80 years and 28,515(45%) were <80 years of age. There was no difference in the in-hospital mortality rate (1.6% vs. 1.1%, p=0.89) and rates of MAE (23.8 vs 23.4, p=0.49) between ≥80 and <80year patients. Anemia (aOR-2.12 vs. aOR-1.93), Liver disease (aOR-1.57 vs aOR-1.48), CKD (aOR-1.34 vs. aOR-1.68), history of stroke (aOR-1.54 vs. aOR-1.46), and a higher number of comorbidities were independently associated with higher odds of MAE in both groups. Among patients ≥80, increasing age was also associated with higher MAE (aOR-1.03). In patients who had MAE, those < 80 years had higher comorbidities compared to those ≥80 years (Charlson category ≥3 - 74.5 vs 67%, p<0.001). More patients of age ≥80 years old also belonged to zip-codes with higher median income (p<0.001). On multivariate analysis of patients with MAE on both cohorts, there was no significant difference in in-hospital mortality rate (p=0.65) and length of stay (p=0.12) but total hospital charges were higher for patients less than 80 years of age (283,618 vs 300,624$, p=0.04). However, patients ≥80 years had a higher rate of pacemaker insertion compared to those < 80 years (25.1 vs 24.4%, p=0.008). Conclusion This study shows that in patients undergoing TAVR, the in-hospital mortality and MAE were not statistically significant between those aged ≥80 years and < 80 years. However, among subjects who experienced MAE, those < 80 years had a higher proportion of comorbidities than those ≥80 years of age. Our study also shows that for those above 80 years of age undergoing TAVR, the odds of MAE increases by 3% for each year on increasing age.

For the few, not the many: local economic conditions constrain the large-scale management of invasive mosquitoes

Invasive mosquitoes are an emerging ecological and sanitary issue. Many factors have been suggested as drivers or barriers to their control, still no study quantified their influence over mosquito management by local authorities, nor their interplay with local economic conditions.We assessed how multiple environmental, sanitary, and socio-economic factors affected the engagement of municipalities in Italy (n = 7,679) in actions against Aedes albopictus, an invasive mosquito affecting human health and well-being, between 2000 and 2020.Municipalities are more prone to manage A. albopictus if more urbanized, in lowlands, with long infestation periods and close to outbreaks of Chikungunya, for which A. albopictus is a competent vector. Moreover, these variables were more strongly associated with management in municipalities with a high median income, and thus more economic resources. Only 25.5% of Italian municipalities approved regulations for managing A. albopictus, and very few of them were in Southern Italy, the most deprived area of the country.Our findings indicate that local economic conditions moderate the effect of other drivers of mosquito control and ultimately can lead to better management of A. albopictus. Thus, to ensure social justice, existing policies for managing the impacts of invasive vectors should explicitly address territorial inequalities by providing policymakers with adequate economic means.

Tornadoes, poverty and race in the USA: A five-decade analysis

PurposeThe study aims to analyze the roles of poverty and African American status in terms of vulnerability to tornado damages and barriers to recovery afterward.Design/methodology/approachUsing five decades of county-level data on tornadoes, the authors test whether economic damages from tornadoes are correlated with vulnerability (proxied by poverty and African American status) and wealth (proxied by median income and educational attainment), controlling for tornado risk. A multinomial logistic difference-in-difference (DID) estimator is used to analyze long-run effects of tornadoes in terms of displacement (reduced proportions of the poor and African Americans), abandonment (increased proportions of those groups) and neither or both.FindingsControlling for tornado risk, poverty and African American status are linked to greater tornado damages, as is wealth. Absent tornadoes, displacement and abandonment are both more likely to occur in urban settings and communities with high levels of vulnerability, while abandonment is more likely to occur in wealthy communities, consistent with on-going forces of segregation. Tornado damages significantly increase abandonment in vulnerable communities, thereby increasing the prevalence of poor African Americans in those communities. Therefore, the authors conclude that tornadoes contribute to on-going processes generating inequality by poverty/race.Originality/valueThe current paper is the first study connecting tornado damages to race and poverty. It is also the first study finding that tornadoes contribute to long-term processes of segregation and inequality.

What a difference a year makes: A snapshot of the trends in vaccinations, hospitalizations, and mortality rates for SARS- CoV-2 across the state of Indiana

Background: Many efforts both scientifically and politically helped reduced the spread of SARS-CoV-2. In December of 2020, vaccinations were authorized for distribution. It is important to understand demographical correlations to COVID-19 acute illness rates and whether COVID-19 vaccinations significantly reduced these rates. Project Methods: This study focused on data from seventeen counties in Indiana. This information was used to determine if there were correlations between demographics and COVID-19 illness rates. County demographics were obtained from the United States Census Bureau. COVID-19 hospitalization and mortality were collected from the Regenstrief Institute and the Indiana State Department of Health respectively. Linear regression analyses were performed to determine if there were significant correlations between demographics and rates COVID-19 illness. T-test analyses assuming unequal variances were performed in order to determine if there has been a significant reduction in COVID-19 illness. Results: The results of this study revealed that the percentage of the population over the age of 65, with a bachelor’s degree, disabled under age 65, and the median income (r values: 0.729, 0.701, 0.661, and 0.533 respectively) are significantly correlated to the mortality rate. The percentage of the population over the age of 65 and with a bachelor’s degree (r values: 0.565 and 0.524 respectively) are significantly correlated to the hospitalization rate. When comparing the COVID-19 acute illness rates for each county from 07/27/20 until 02/01/21 to the rates after 02/01/21 until late- June of 2021, each county had significant decrease in the hospitalization and mortality rate after February 1, 2021. Potential Impact: The result of this study suggests that vaccinating residents was a significant factor in the 50% or higher reduction in COVID-19 hospitalization and mortality rates. These findings emphasize the importance of COVID-19 vaccinations to protect Americans from COVID-19 severe illness.

Geographic footprints of life expectancy inequalities in the state of Geneva, Switzerland

Though Switzerland has one of the highest life expectancies in the world, this global indicator may mask significant disparities at a local level. The present study used a spatial cluster detection approach based on individual death records to investigate the geographical footprint of life expectancy inequalities in the state of Geneva, Switzerland. Individual-level mortality data (n = 22,751) were obtained from Geneva’s official death notices (2009–2016). We measured life expectancy inequalities using the years of potential life lost or gained (YPLLG) metric, defined as the difference between an individual’s age at death and their life expectancy at birth. We assessed the spatial dependence of YPLLG across the state of Geneva using spatial autocorrelation statistics (Local Moran’s I). To ensure the robustness of the patterns discovered, we ran the analyses for ten random subsets of 10,000 individuals taken from the 22,751 deceased. We also repeated the spatial analysis for YPLLG before and after controlling for individual-level and neighborhood-level covariates. The results showed that YPLLG was not randomly distributed across the state of Geneva. The ten random subsets revealed no significant difference with the geographic footprint of YPLLG and the population characteristics within Local Moran cluster types, suggesting robustness for the observed spatial structure. The proportion of women, the proportion of Swiss, the neighborhood median income, and the neighborhood median age were all significantly lower for populations in low YPLLG clusters when compared to populations in high YPLLG clusters. After controlling for individual-level and neighborhood-level covariates, we observed a reduction of 43% and 39% in the size of low and high YPLLG clusters, respectively. To our knowledge, this is the first study in Switzerland using spatial cluster detection methods to investigate inequalities in life expectancy at a local scale and based on individual data. We identified clear geographic footprints of YPLLG, which may support further investigations and guide future public health interventions at the local level.

Predictors of Food Insecurity Before and During COVID-19 Among Middle-Aged and Older Adults

The indirect impact of COVID-19 on food security of middle aged and older adults is not well understood. This study examines changes in risk factors for food security from 2018-2020 in a population-based sample. Using data from the Health and Retirement Study (2018 and 2020 waves), we utilized generalized estimating equations (GEE) with repeated measures to examine factors associated with food insecurity among US adults aged 50 and older (n=3170) before COVID-19 and since COVID-19. The prevalence of food insecurity doubled from 2018 (4.83%) to 2020 (9.54%). In multivariate analyses, the population-averaged odds of experiencing food insecurity was 81% higher in 2020 compared to 2018. Other factors significantly associated with higher odds of food insecurity included being female (OR: 1.29), Black (OR: 1.46), lowest quintile for wealth (OR: 1.82), not working due to a disability (OR: 3.29), renting (OR: 2.04), greater IADL limitations (OR: 1.32), and greater number of chronic illness comorbidities (OR: 1.14). Factors significantly associated with lower odds of food insecurity included older age (65-74: OR: 0.73; 75+: OR: 0.56) and being above the median income level (OR: 0.47). Partnership status, education level, and ADL limitations were not significantly associated with the population-averaged odds of experiencing food insecurity. This study identified factors related to food insecurity among a community-dwelling sample of middle aged and older adults in the U.S. Future research should examine the impact of policies and intervention strategies to address the disproportionate impact of COVID-19 on populations at increased risk of experiencing food insecurity.

The Role of the California Tier System in Controlling Population Mobility During the COVID-19 Pandemic

Policies to restrict population mobility are a commonly used strategy to limit the transmission of contagious diseases. Among measures implemented during the COVID-19 pandemic were dynamic stay-at-home orders informed by real-time, regional-level data. California was the only state in the U.S. to implement this novel approach; however, the effectiveness of California’s four-tier system on population mobility has not been quantified. Utilizing data from mobile devices and county-level demographic data, we evaluated the impact of policy changes on population mobility and explored whether demographic characteristics explained variability in responsiveness to policy changes. For each Californian county, we calculated the proportion of people staying home and the average number of daily trips taken per 100 persons, across different trip distances and compared this to pre-COVID-19 levels. We found that overall mobility decreased when counties moved to a more restrictive tier and increased when moving to a less restrictive tier, as the policy intended. When placed in a more restrictive tier, the greatest decrease in mobility was observed for shorter and medium-range trips, while there was an unexpected increase in the longer trips. The mobility response varied by geographic region, as well as county-level median income, gross domestic product, the prevalence of farms, and recent election results. This analysis provides evidence of the effectiveness of the tier-based system in decreasing overall population mobility to ultimately reduce COVID-19 transmission. Results demonstrate that economic and political indicators drive important variability in such patterns across counties.

Racial and Socioeconomic Disparities in Out-Of-Hospital Cardiac Arrest Outcomes: Artificial Intelligence-Augmented Propensity Score and Geospatial Cohort Analysis of 3,952 Patients

Introduction. Social disparities in out-of-hospital cardiac arrest (OHCA) outcomes are preventable, costly, and unjust. We sought to perform the first large artificial intelligence- (AI-) guided statistical and geographic information system (GIS) analysis of a multiyear and multisite cohort for OHCA outcomes (incidence and poor neurological disposition). Method. We conducted a retrospective cohort analysis of a prospectively collected multicenter dataset of adult patients who sequentially presented to Houston metro area hospitals from 01/01/07-01/01/16. Then AI-based machine learning (backward propagation neural network) augmented multivariable regression and GIS heat mapping were performed. Results. Of 3,952 OHCA patients across 38 hospitals, African Americans were the most likely to suffer OHCA despite representing a significantly lower percentage of the population (42.6 versus 22.8%; p < 0.001 ). Compared to Caucasians, they were significantly more likely to have poor neurological disposition (OR 2.21, 95%CI 1.25–3.92; p = 0.006 ) and be discharged to a facility instead of home (OR 1.39, 95%CI 1.05–1.85; p = 0.023 ). Compared to the safety net hospital system primarily serving poorer African Americans, the university hospital serving primarily higher income commercially and Medicare insured patients had the lowest odds of death (OR 0.45, p < 0.001 ). Each additional $10,000 above median household income was associated with a decrease in the total number of cardiac arrests per zip code by 2.86 (95%CI -4.26- -1.46; p < 0.001 ); zip codes with a median income above $54,600 versus the federal poverty level had 14.62 fewer arrests ( p < 0.001 ). GIS maps showed convergence of the greater density of poor neurologic outcome cases and greater density of poorer African American residences. Conclusion. This large, longitudinal AI-guided analysis statistically and geographically identifies racial and socioeconomic disparities in OHCA outcomes in a way that may allow targeted medical and public health coordinated efforts to improve clinical, cost, and social equity outcomes.

Small area estimation of socioeconomic indicators for sampled and unsampled domains

Socioeconomic indicators play a crucial role in monitoring political actions over time and across regions. Income-based indicators such as the median income of sub-populations can provide information on the impact of measures, e.g., on poverty reduction. Regional information is usually published on an aggregated level. Due to small sample sizes, these regional aggregates are often associated with large standard errors or are missing if the region is unsampled or the estimate is simply not published. For example, if the median income of Hispanic or Latino Americans from the American Community Survey is of interest, some county-year combinations are not available. Therefore, a comparison of different counties or time-points is partly not possible. We propose a new predictor based on small area estimation techniques for aggregated data and bivariate modeling. This predictor provides empirical best predictions for the partially unavailable county-year combinations. We provide an analytical approximation to the mean squared error. The theoretical findings are backed up by a large-scale simulation study. Finally, we return to the problem of estimating the county-year estimates for the median income of Hispanic or Latino Americans and externally validate the estimates.

Disparities in Obstetric Readmissions: A Multistate Analysis, 2007–2014

Background Hospital readmissions are generally higher among racial-ethnic minorities and patients of lower socioeconomic status. However, this has not been widely studied in obstetrics. Objective The aim of the study is to determine 30-day postpartum readmission rates by patient-level social determinants of health: race ethnicity, primary insurance payer, and median income, independently and as effect modifiers. Study Design Using state inpatient databases from the health care cost and utilization project from 2007 to 2014, we queried all deliveries. To produce accurate estimates of the effects of parturients' social determinants of health on readmission odds while controlling for confounders, generalized linear mixed models (GLMMs) were used. Additional models were generated with interaction terms to highlight any associations and their effect on the outcome. Adjusted odds ratios (aOR) with 95% confidence intervals are reported. Results There were 5,129,867 deliveries with 79,260 (1.5%) 30-day readmissions. Of these, 947 (1.2%) were missing race ethnicity. Black and Hispanic patients were more likely to be readmitted within 30 days of delivery, as compared with White patients (p < 0.001 and p < 0.05, respectively). Patients with government insurance were more likely to be readmitted than those with private insurance (p < 0.001). Patients living in the second quartile of median income were also more likely to be readmitted than those living in other quartiles (p < 0.05). Using GLMMs, we observed that Black patients with Medicare were significantly more likely to get readmitted as compared with White patients with private insurance (aOR 2.78, 95% CI 2.50–3.09, p < 0.001). Similarly, Black patients living in the fourth (richest) quartile of median income were more likely to get readmitted, even when compared with White patients living in the first (poorest) quartile of median income (aOR 1.48, 95% CI 1.40–1.57, p < 0.001). Conclusion Significant racial-ethnic disparities in obstetric readmissions were observed, particularly in Black patients with government insurance and even in Black patients living in the richest quartile of median income. Key Points