scholarly journals Geospatial and Explanatory Models for Heart Failure Admissions, 2016 through 2018

2020 ◽  
Author(s):  
Clemens Scott Kruse ◽  
Bradley M. Beauvais ◽  
Matthew S. Brooks ◽  
Michael Mileski ◽  
Lawrence Fulton
Keyword(s):  
Heart Failure ◽  
The United States ◽  
Explanatory Models ◽  
Disease Rate ◽  
County Level ◽  
Unit Of Analysis ◽  
Hospital Unit ◽  
Admission Rates ◽  
Heart Failure Admission ◽  
Over Time

Abstract Background. About 5.7 million individuals in the United States have heart failure, and the disease was estimated to cost about $42.9 billion in 2020. This research provides geographical incidence models of this disease in the U.S. and explanatory models to account for hospitals’ number of heart failure DRGs using technical, workload, financial, geographical, and time-related variables. The research also provides updated financial and demand estimates based on inflationary pressures and disease rate increases. Understanding patterns is important to both policymakers and health administrators for cost control and planning. Methods. The number of diagnoses is forecast using regression (constrained and unconstrained) and ensemble (random forests, extra trees regressor, gradient boosting, and bagging) techniques at the hospital unit of analysis. Descriptive maps of heart failure diagnosis-related groups (DRGs) depict areas of high incidence. State and county level spatial and non-spatial regression models of heart failure admission rates are performed. Expenditure forecasts were calculated for 2016 through 2018. Results: The incidence of heart failure has increased over time with highest intensities in the East and center of the country; however, several Northern states (e.g., Minnesota) have seen large increases since 2016. The best predictive model for forecasting the number of diagnoses at the hospital unit of analysis was an extremely randomized tree ensemble (predictive R2 = 0.86 applied to a 20% unobserved test set.) The important variables in this model included workload metrics and hospital type. State level spatial lag models using 1st order Queen’s criteria were best at estimating heart failure admission rates (R2 =.816). At the county level, OLS was preferred over any GIS model based on a statistically insignificant Moran’s I and resultant R2; however, none of the traditional models performed well (R2=.169 for the OLS). Gradient boosted tree models were able to predict 36% of the total Sum of Squares; however, and the most important factors were facility workload, mean cash-on-hand of the hospitals in the county, and mean equity of those hospitals.. Online interactive maps at the state and county levels are provided. Conclusions. Heart failure and associated expenditures are increasing. Overall, the total cost of the three DRGs in the study has increased approximately $61 billion from 2016 through 2018 (average of two estimates). The increase in the more expensive DRG (DRG 291) has outpaced others with an associated increase of $92 billion in expenditures. With the increase in demand (linked to obesity and other factors) as well as the relatively steady-state supply of cardiologists over time, the costs are likely to balloon over the next decade. Models like the ones presented here that reliably forecast demand are needed to inform healthcare leaders.

scholarly journals Models for Heart Failure Admissions and Admission Rates, 2016 through 2018

Healthcare ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 22
Author(s):  
Clemens Scott Kruse ◽  
Bradley M. Beauvais ◽  
Matthew S. Brooks ◽  
Michael Mileski ◽  
Lawrence V. Fulton
Keyword(s):  
Heart Failure ◽  
The United States ◽  
Explanatory Models ◽  
Gradient Boosting ◽  
County Level ◽  
Unit Of Analysis ◽  
Hospital Unit ◽  
Admission Rates ◽  
Disease Costs ◽  
Heart Failure Admission

Background: Approximately 6.5 to 6.9 million individuals in the United States have heart failure, and the disease costs approximately $43.6 billion in 2020. This research provides geographical incidence and cost models of this disease in the U.S. and explanatory models to account for hospitals’ number of heart failure DRGs using technical, workload, financial, geographical, and time-related variables. Methods: The number of diagnoses is forecast using regression (constrained and unconstrained) and ensemble (random forests, extra trees regressor, gradient boosting, and bagging) techniques at the hospital unit of analysis. Descriptive maps of heart failure diagnostic-related groups (DRGs) depict areas of high incidence. State- and county-level spatial and non-spatial regression models of heart failure admission rates are performed. Expenditure forecasts are estimated. Results: The incidence of heart failure has increased over time with the highest intensities in the East and center of the country; however, several Northern states have seen large increases since 2016. The best predictive model for the number of diagnoses (hospital unit of analysis) was an extremely randomized tree ensemble (predictive R2 = 0.86). The important variables in this model included workload metrics and hospital type. State-level spatial lag models using first-order Queen criteria were best at estimating heart failure admission rates (R2 = 0.816). At the county level, OLS was preferred over any GIS model based on Moran’s I and resultant R2; however, none of the traditional models performed well (R2 = 0.169 for the OLS). Gradient-boosted tree models predicted 36% of the total sum of squares; the most important factors were facility workload, mean cash on hand of the hospitals in the county, and mean equity of those hospitals. Online interactive maps at the state and county levels are provided. Conclusions. Heart failure and associated expenditures are increasing. Costs of DRGs in the study increased $61 billion from 2016 through 2018. The increase in the more expensive DRG 291 outpaced others with an associated increase of $92 billion. With the increase in demand and steady-state supply of cardiologists, the costs are likely to balloon over the next decade. Models such as the ones presented here are needed to inform healthcare leaders.

scholarly journals Geospatial and Explanatory Models for Heart Failure Admissions, 2016 through 2018

2020 ◽  
Author(s):  
Clemens Scott Kruse ◽  
Bradley M. Beauvais ◽  
Matthew S. Brooks ◽  
Michael Mileski ◽  
Lawrence Fulton
Keyword(s):  
Heart Failure ◽  
Medical School ◽  
The United States ◽  
Explanatory Models ◽  
Disease Rate ◽  
Medicare Reimbursement ◽  
Hospital Type ◽  
Test Set ◽  
School Affiliation ◽  
Over Time

Abstract Background. About 5.7 million individuals in the United States have heart failure, and the disease was estimated to cost about $42.9 billion in 2020. This research provides geographical incidence models of this disease in the U.S. and explanatory models to account for hospitals’ number of heart failure DRGs using technical, workload, financial, geographical, and time-related variables. The research also provides updated financial and demand estimates based on inflationary pressures and disease rate increases. Understanding patterns is important to both policymakers and health administrators for cost control and planning. Methods. Maps of heart failure diagnosis-related groups (DRGs) from 2016 through 2018 depicted areas of high incidence as well as changes. Spatial regression identified no significant spatial correlations. Simple expenditure forecasts were calculated for 2016 through 2018. Linear, lasso, ridge, and Elastic Net models as well as ensembled tree regressors including were built on an 80% training set and evaluated on a 20% test set. Results: The incidence of heart failure has increased over time with highest intensities in the East and center of the country; however, several Northern states (e.g., Minnesota) have seen large increases in rates from 2016. The best traditional regression model explained 75% of the variability in the number of DRGs experienced by hospital using a small subset of variables including discharges, DRG type, percent Medicare reimbursement, hospital type, and medical school affiliation. The best ensembled tree models achieved R2 over .97 on the blinded test set and identified discharges, percent Medicare reimbursement, hospital acute days, affiliated physicians, staffed beds, employees, hospital type, emergency room visits, medical school affiliation, geographical location, and the number of surgeries as highly important predictors. Conclusions. Overall, the total cost of the three DRGs in the study has increased approximately $61 billion from 2016 through 2018 (average of two estimates). The increase in the more expensive DRG (DRG 291) has outpaced others with an associated increase of $92 billion in expenditures. With the increase in demand (linked to obesity and other factors) as well as the relatively steady-state supply of cardiologists over time, the costs are likely to balloon over the next decade.

scholarly journals Geospatial-Temporal, Explanatory, Demand, and Financial Models for Heart Failure

2020 ◽  
Author(s):  
Clemens Scott Kruse ◽  
Bradley M. Beauvais ◽  
Matthew S. Brooks ◽  
Michael Mileski ◽  
Lawrence Fulton
Keyword(s):  
Heart Failure ◽  
Medical School ◽  
The United States ◽  
Explanatory Models ◽  
Geographical Information ◽  
Medicare Reimbursement ◽  
Hospital Type ◽  
Test Set ◽  
School Affiliation ◽  
Over Time

Abstract Background About 5.7 million individuals in the United States have heart failure, and the disease was estimated to cost about $42.9 billion in 2020. This research provides geospatial-temporal incidence models of this disease in the U.S. and explanatory models to account for hospitals’ number of heart failure DRGs using technical, workload, financial, and geospatial-temporal variables. The research also provides updated financial and demand estimates based on inflationary pressures and disease rate increases. Understanding patterns is important to both policymakers and health administrators alike for cost control and planning. Methods Geographical Information Systems maps of heart failure diagnosis-related groups (DRGs) from 2016 through 2018 depicted areas of high incidence as well as changes. Simple expenditure forecasts were calculated for 2016 through 2018. Linear, lasso, ridge, and Elastic Net models as well as ensembled tree regressors including were built on an 80% training set and evaluated on a 20% test set. Results The incidence of heart failure has increased over time with highest intensities in the East and center of the country; however, several Northern states (e.g., Minnesota) have seen large increases in rates from 2016. The best traditional regression model explained 75% of the variability in the number of DRGs experienced by hospital using a small subset of variables including discharges, DRG type, percent Medicare reimbursement, hospital type, and medical school affiliation. The best ensembled tree models achieved R2 over .97 on the blinded test set and identified discharges, percent Medicare reimbursement, hospital acute days, affiliated physicians, staffed beds, employees, hospital type, emergency room visits, medical school affiliation, geographical location, and the number of surgeries as highly important predictors. Conclusions Overall, the total cost of the three DRGs in the study has increased approximately $61 billion from 2016 through 2018 (average of two estimates). The increase in the more expensive DRG (DRG 291) has outpaced others with an associated increase of $92 billion in expenditures. With the increase in demand (linked to obesity and other factors) as well as the relatively steady-state supply of cardiologists over time, the costs are likely to balloon over the next decade.

Abstract 439: Trajectory Analysis of Left Ventricular Dimensions From Biobank Data Uncovers Novel Genetic Associations

2020 ◽  
Vol 127 (Suppl_1) ◽  
Author(s):  
Tess Pottinger ◽  
Megan J Puckelwartz ◽  
Lorenzo L Pesce ◽  
Anthony Gacita ◽  
Isabella Salamone ◽  
...  
Keyword(s):  
Heart Failure ◽  
Longitudinal Data ◽  
Association Studies ◽  
The United States ◽  
Left Ventricular ◽  
Genome Wide Association Studies ◽  
Qtc Interval ◽  
Regulatory Regions ◽  
Genome Wide ◽  
Over Time

Background: Approximately 6 million adults in the United States have heart failure. The progression of heart failure is variable arising from differences in sex, age, genetic background including ancestry, and medication response. Many population-based genetic studies of heart failure have been cross-sectional in nature, failing to gain additional power from longitudinal analyses. As heart failure is known to change over time, using longitudinal data trajectories as a quantitative trait will increase power in genome wide association studies (GWAS). Methods: We used the electronic health record in a racially and ethnically diverse medical biobank from a single, metropolitan US center. We used whole genome data from 896 unrelated participants analyzed, including 494 who had at least 1 electrocardiogram and 324 who had more than 1 echocardiogram (average of 3 observations per person). A mixture model based semiparametric latent growth curve model was used to cluster outcome measures used for genome-wide analyses. Results: GWAS on the trajectory probability of QTc interval identified significant associations with variants in regulatory regions proximal to the WLS gene, which encodes Wntless, a Wnt ligand secretion mediator. WLS was previously associated with QTc and myocardial infarction, thus confirming the power of the method. GWAS on the trajectory probability of left ventricular diameter (LVIDd) identified significant associations with variants in regulatory regions near MYO10 , which encodes unconventional Myosin-10. MYO10 was previously associated with obesity and metabolic syndrome. Conclusions: This is the first study to show an association with variants in or near MYO10 and left ventricular dimension changes over time. Further, we found that using trajectory probabilities can provide increased power to find novel associations with longitudinal data. This reduces the need for larger cohorts, and increases yield from smaller, well-phenotyped cohorts, such as those found in biobanks. This approach should be useful in the study of rare diseases and underrepresented populations.

scholarly journals The evolution of infant mortality inequality in the United States, 1960–2016

2020 ◽  
Vol 6 (29) ◽  
pp. eaba5908
Author(s):  
Nick Turner ◽  
Kaveh Danesh ◽  
Kelsey Moran
Keyword(s):  
United States ◽  
Infant Mortality ◽  
The United States ◽  
Mortality Rates ◽  
County Level ◽  
Level Data ◽  
Antipoverty Programs ◽  
The Relationship ◽  
Over Time ◽  
Mortality Inequality

What is the relationship between infant mortality and poverty in the United States and how has it changed over time? We address this question by analyzing county-level data between 1960 and 2016. Our estimates suggest that level differences in mortality rates between the poorest and least poor counties decreased meaningfully between 1960 and 2000. Nearly three-quarters of the decrease occurred between 1960 and 1980, coincident with the introduction of antipoverty programs and improvements in medical care for infants. We estimate that declining inequality accounts for 18% of the national reduction in infant mortality between 1960 and 2000. However, we also find that level differences between the poorest and least poor counties remained constant between 2000 and 2016, suggesting an important role for policies that improve the health of infants in poor areas.

scholarly journals Trends in heart failure-related cardiovascular mortality in rural versus urban United States counties, 2011–2018: A cross-sectional study

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0246813
Author(s):  
Jacob B. Pierce ◽  
Nilay S. Shah ◽  
Lucia C. Petito ◽  
Lindsay Pool ◽  
Donald M. Lloyd-Jones ◽  
...  
Keyword(s):  
United States ◽  
Heart Failure ◽  
Cause Of Death ◽  
Negative Binomial ◽  
The United States ◽  
Mortality Rates ◽  
County Level ◽  
Physician Density ◽  
Rural And Urban ◽  
Related Mortality

Background Adults in rural counties in the United States (US) experience higher rates broadly of cardiovascular disease (CVD) compared with adults in urban counties. Mortality rates specifically due to heart failure (HF) have increased since 2011, but estimates of heterogeneity at the county-level in HF-related mortality have not been produced. The objectives of this study were 1) to quantify nationwide trends by rural-urban designation and 2) examine county-level factors associated with rural-urban differences in HF-related mortality rates. Methods and findings We queried CDC WONDER to identify HF deaths between 2011–2018 defined as CVD (I00-78) as the underlying cause of death and HF (I50) as a contributing cause of death. First, we calculated national age-adjusted mortality rates (AAMR) and examined trends stratified by rural-urban status (defined using 2013 NCHS Urban-Rural Classification Scheme), age (35–64 and 65–84 years), and race-sex subgroups per year. Second, we combined all deaths from 2011–2018 and estimated incidence rate ratios (IRR) in HF-related mortality for rural versus urban counties using multivariable negative binomial regression models with adjustment for demographic and socioeconomic characteristics, risk factor prevalence, and physician density. Between 2011–2018, 162,314 and 580,305 HF-related deaths occurred in rural and urban counties, respectively. AAMRs were consistently higher for residents in rural compared with urban counties (73.2 [95% CI: 72.2–74.2] vs. 57.2 [56.8–57.6] in 2018, respectively). The highest AAMR was observed in rural Black men (131.1 [123.3–138.9] in 2018) with greatest increases in HF-related mortality in those 35–64 years (+6.1%/year). The rural-urban IRR persisted among both younger (1.10 [1.04–1.16]) and older adults (1.04 [1.02–1.07]) after adjustment for county-level factors. Main limitations included lack of individual-level data and county dropout due to low event rates (<20). Conclusions Differences in county-level factors may account for a significant amount of the observed variation in HF-related mortality between rural and urban counties. Efforts to reduce the rural-urban disparity in HF-related mortality rates will likely require diverse public health and clinical interventions targeting the underlying causes of this disparity.

scholarly journals Changing Spatiotemporal Trends in County‐Level Heart Failure Death Rates in the United States, 1999 to 2018

2021 ◽  
Vol 10 (4) ◽  
Author(s):  
Adam S. Vaughan ◽  
Mary G. George ◽  
Sandra L. Jackson ◽  
Linda Schieb ◽  
Michele Casper
Keyword(s):  
United States ◽  
Heart Failure ◽  
Ethnic Group ◽  
The United States ◽  
Local Context ◽  
County Level ◽  
Age Group ◽  
Death Rates ◽  
Spatiotemporal Trends ◽  
Racial Ethnic

Background Amid recently rising heart failure (HF) death rates in the United States, we describe county‐level trends in HF mortality from 1999 to 2018 by racial/ethnic group and sex for ages 35 to 64 years and 65 years and older. Methods and Results Applying a hierarchical Bayesian model to National Vital Statistics data representing all US deaths, ages 35 years and older, we estimated annual age‐standardized county‐level HF death rates and percent change by age group, racial/ethnic group, and sex from 1999 through 2018. During 1999 to 2011, ~30% of counties experienced increasing HF death rates among adults ages 35 to 64 years. However, during 2011 to 2018, 86.9% (95% CI, 85.2–88.2) of counties experienced increasing mortality. Likewise, for ages 65 years and older, during 1999 to 2005 and 2005 to 2011, 27.8% (95% CI, 25.8–29.8) and 12.6% (95% CI, 11.2–13.9) of counties, respectively, experienced increasing mortality. However, during 2011 to 2018, most counties (67.4% [95% CI, 65.4–69.5]) experienced increasing mortality. These temporal patterns by age group held across racial/ethnic group and sex. Conclusions These results provide local context to previously documented recent national increases in HF death rates. Although county‐level declines were most common before 2011, some counties and demographic groups experienced increasing HF death rates during this period of national declines. However, recent county‐level increases were pervasive, occurring across counties, racial/ethnic group, and sex, particularly among ages 35 to 64 years. These spatiotemporal patterns highlight the need to identify and address underlying clinical risk factors and social determinants of health contributing to these increasing trends.

scholarly journals Trends in Mortality Due to Cancer in the United States by Age and County-Level Income, 1999–2015

2019 ◽  
Vol 111 (8) ◽  
pp. 863-866 ◽  
Author(s):  
Diana R Withrow ◽  
Amy Berrington de González ◽  
Susan Spillane ◽  
Neal D Freedman ◽  
Ana F Best ◽  
...  
Keyword(s):  
United States ◽  
Cancer Mortality ◽  
Death Certificate ◽  
The United States ◽  
Mortality Rates ◽  
Income Quintile ◽  
County Level ◽  
Younger Adults ◽  
Rate Ratios ◽  
Over Time

Abstract Disparities in cancer mortality by county-level income have increased. It is unclear whether these widening disparities have affected older and younger adults equally. National death certificate data were utilized to ascertain cancer deaths during 1999–2015. Average annual percent changes in mortality rates and mortality rate ratios (RRs) were estimated by county-level income quintile and age (25–64 vs ≥65 years). Among 25- to 64-year-olds, cancer mortality rates were 30% higher (RR = 1.30, 95% confidence interval [CI] = 1.29 to 1.31) in the lowest-vs the highest-income counties in 1999–2001 and 56% higher (RR = 1.56, 95% CI = 1.55 to 1.57) in 2013–2015; the disparities among those 65 years and older were smaller but also widened over time (RR1999–2001 = 1.04, 95% CI = 1.03 to 1.05; RR2013–2015 = 1.14, 95% CI = 1.13 to 1.14). Widening disparities occurred across cancer sites. If all counties had the mortality rates of the highest-income counties, 21.5% of cancer deaths among 25- to 64-year-olds and 7.3% of cancer deaths in those 65 years and older would have been avoided in 2015. These results highlight an ongoing need for equity-focused interventions, particularly among younger adults.

scholarly journals Federal Crop Insurance and the Disincentive to Adapt to Extreme Heat

2015 ◽  
Vol 105 (5) ◽  
pp. 262-266 ◽  
Author(s):  
Francis Annan ◽  
Wolfram Schlenker
Keyword(s):  
United States ◽  
Crop Insurance ◽  
The United States ◽  
Extreme Heat ◽  
County Level ◽  
Significant Progress ◽  
Insurance Program ◽  
Time Period ◽  
Potential Losses ◽  
Over Time

Despite significant progress in average yields, the sensitivity of corn and soybean yields to extreme heat has remained relatively constant over time. We combine county-level corn and soybeans yields in the United States from 1989-2013 with the fraction of the planting area that is insured under the federal crop insurance program, which expanded greatly over this time period as premium subsidies increased from 20 percent to 60 percent. Insured corn and soybeans are significantly more sensitive to extreme heat that uninsured crops. Insured farmers do not have the incentive to engage in costly adaptation as insurance compensates them for potential losses.

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