scholarly journals Hospital-Based Back Surgery: Geospatial-Temporal, Explanatory, and Predictive Models (Preprint)

2019 ◽  
Author(s):  
Lawrence Fulton ◽  
Clemens Scott Kruse
Keyword(s):  
Time Series ◽  
Random Forests ◽  
Geographical Information Systems ◽  
The United States ◽  
Practice Variation ◽  
Geographical Information ◽  
Federal State ◽  
Test Set ◽  
Back Surgery ◽  
Over Time

BACKGROUND Hospital-based back surgery in the United States increased by 60% from January 2012 to December 2017, yet the supply of neurosurgeons remained relatively constant. During this time, adult obesity grew by 5%. OBJECTIVE This study aimed to evaluate the demand and associated costs for hospital-based back surgery by geolocation over time to evaluate provider practice variation. The study then leveraged hierarchical time series to generate tight demand forecasts on an unobserved test set. Finally, explanatory financial, technical, workload, geographical, and temporal factors as well as state-level obesity rates were investigated as predictors for the demand for hospital-based back surgery. METHODS Hospital data from January 2012 to December 2017 were used to generate geospatial-temporal maps and a video of the Current Procedural Terminology codes beginning with the digit 63 claims. Hierarchical time series modeling provided forecasts for each state, the census regions, and the nation for an unobserved test set and then again for the out-years of 2018 and 2019. Stepwise regression, lasso regression, ridge regression, elastic net, and gradient-boosted random forests were built on a training set and evaluated on a test set to evaluate variables important to explaining the demand for hospital-based back surgery. RESULTS Widespread, unexplained practice variation over time was seen using geographical information systems (GIS) multimedia mapping. Hierarchical time series provided accurate forecasts on a blind dataset and suggested a 6.52% (from 497,325 procedures in 2017 to 529,777 in 2018) growth of hospital-based back surgery in 2018 (529,777 and up to 13.00% by 2019 [from 497,325 procedures in 2017 to 563,023 procedures in 2019]). The increase in payments by 2019 are estimated to be US $323.9 million. Extreme gradient-boosted random forests beat constrained and unconstrained regression models on a 20% unobserved test set and suggested that obesity is one of the most important factors in explaining the increase in demand for hospital-based back surgery. CONCLUSIONS Practice variation and obesity are factors to consider when estimating demand for hospital-based back surgery. Federal, state, and local planners should evaluate demand-side and supply-side interventions for this emerging problem.

scholarly journals Hospital-Based Back Surgery: Geospatial-Temporal, Explanatory, and Predictive Models

10.2196/14609 ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. e14609 ◽  
Author(s):  
Lawrence Fulton ◽  
Clemens Scott Kruse
Keyword(s):  
Time Series ◽  
Random Forests ◽  
Geographical Information Systems ◽  
The United States ◽  
Practice Variation ◽  
Geographical Information ◽  
Federal State ◽  
Test Set ◽  
Back Surgery ◽  
Over Time

Background Hospital-based back surgery in the United States increased by 60% from January 2012 to December 2017, yet the supply of neurosurgeons remained relatively constant. During this time, adult obesity grew by 5%. Objective This study aimed to evaluate the demand and associated costs for hospital-based back surgery by geolocation over time to evaluate provider practice variation. The study then leveraged hierarchical time series to generate tight demand forecasts on an unobserved test set. Finally, explanatory financial, technical, workload, geographical, and temporal factors as well as state-level obesity rates were investigated as predictors for the demand for hospital-based back surgery. Methods Hospital data from January 2012 to December 2017 were used to generate geospatial-temporal maps and a video of the Current Procedural Terminology codes beginning with the digit 63 claims. Hierarchical time series modeling provided forecasts for each state, the census regions, and the nation for an unobserved test set and then again for the out-years of 2018 and 2019. Stepwise regression, lasso regression, ridge regression, elastic net, and gradient-boosted random forests were built on a training set and evaluated on a test set to evaluate variables important to explaining the demand for hospital-based back surgery. Results Widespread, unexplained practice variation over time was seen using geographical information systems (GIS) multimedia mapping. Hierarchical time series provided accurate forecasts on a blind dataset and suggested a 6.52% (from 497,325 procedures in 2017 to 529,777 in 2018) growth of hospital-based back surgery in 2018 (529,777 and up to 13.00% by 2019 [from 497,325 procedures in 2017 to 563,023 procedures in 2019]). The increase in payments by 2019 are estimated to be US $323.9 million. Extreme gradient-boosted random forests beat constrained and unconstrained regression models on a 20% unobserved test set and suggested that obesity is one of the most important factors in explaining the increase in demand for hospital-based back surgery. Conclusions Practice variation and obesity are factors to consider when estimating demand for hospital-based back surgery. Federal, state, and local planners should evaluate demand-side and supply-side interventions for this emerging problem.

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.

Protected areas review: McMurdo Sound, Ross Sea

Polar Record ◽  
1994 ◽  
Vol 30 (174) ◽  
pp. 189-192 ◽  
Author(s):  
Colin M. Harris
Keyword(s):  
Protected Areas ◽  
Geographical Information Systems ◽  
Process Management ◽  
Ross Sea ◽  
The United States ◽  
Geographical Information ◽  
Management Plans ◽  
Antarctic Treaty ◽  
Field Information ◽  
The Antarctic

AbstractAs a result of new provisions in the Protocol on Environmental Protection to the Antarctic Treaty a number of countries are reviewing the management plans for protected areas in Antarctica. The United States and New Zealand have initiated a review of the 15 existing sites in the Ross Sea region, using an independent party, the International Centre for Antarctic Information and Research, to facilitate and coordinate the process. Management provisions are being revised to comply with the Protocol, and improved maps for the sites are being prepared using Geographical Information Systems. Visits in 1993/94 gathered field information, and thus far two sites have had new plans drafted: these are proceeding through the international review process. Input and comment is invited from interested parties with experience in these areas.

scholarly journals Interrupted Time-Series for the Evaluation of Affordable Care Act Impact on Stereotactic Radiosurgery for the Treatment of Brain Metastases

2021 ◽  
Author(s):  
Hind Beydoun ◽  
Shuyan Huang ◽  
May Beydoun ◽  
Shaker Eid ◽  
Alan Zonderman
Keyword(s):  
United States ◽  
Time Series ◽  
Radiation Therapy ◽  
Brain Metastases ◽  
Stereotactic Radiosurgery ◽  
Affordable Care Act ◽  
Interrupted Time Series ◽  
The United States ◽  
Over Time ◽  
Hospital Characteristics

Abstract Background: The 2010 Affordable Care Act aimed at reducing healthcare costs, improving healthcare quality and expanding health insurance coverage among uninsured individuals in the United States. We examined trends in utilization of radiation therapies and stereotactic radiosurgery before and after its implementation among U.S. adults hospitalized with brain metastasis.Methods: Interrupted time-series analyses of data on 383934 2005-2014 Nationwide Inpatient Sample hospitalizations were performed, whereby yearly and quarterly cross-sectional data were evaluated and Affordable Care Act implementation was considered the main exposure variable, stratifying by patient and hospital characteristics. Results: We observed consistently declining trends in radiation therapy over time and post-Affordable Care Act status with variability in level of utilization among specific sub-groups. Stereotactic radiosurgery prevalence increased over time among Hispanics, elective admissions, Midwestern hospitals, non-teaching hospitals and hospitals with medium bed size. Post-Affordable Care Act was associated with increased stereotactic radiosurgery prevalence among African-Americans, non-elective and weekend admissions, with changes in slope in the context of weekend admissions and hospitals with large bed size. Conclusions: Whereas hospitalized adults in the United States utilized less radiation therapy and slightly more stereotactic radiosurgery over the ten-year period, utilization levels and trends were not consistent among distinct sub-groups defined by patient and hospital characteristics, with some traditionally underserved populations more likely to receive healthcare services post-Affordable Care Act implementation. The Affordable Care Act may be helpful at reducing the need for radiation therapy and closing the gap in access to technological advances such as stereotactic radiosurgery for treating brain metastases.

Monitoring and Evaluation

2019 ◽  
pp. 299-327
Author(s):  
Bruce Mitchell
Keyword(s):  
Environmental Management ◽  
European Union ◽  
Geographical Information Systems ◽  
Monitoring And Evaluation ◽  
The United States ◽  
Geographical Information ◽  
The European Union ◽  
Lake Chad ◽  
Lake Chad Basin ◽  
Environmental Audits

To improve implementation of policies and plans for resource and environmental management, systematic monitoring and evaluation are essential. In this chapter attention is given first to characteristics, opportunities, and limitations with regard to monitoring and evaluation. Then, three kinds of monitoring and evaluation are examined: environmental auditing; state of environment reports; and geomatics, or Geographical Information Systems (GIS)-based, monitoring and assessments. Detailed case studies cover environmental audits of a mine in Alaska and a tourism resort in Greece; state of environment reports in the European Union, Saskatchewan in Canada, and North Carolina in the United States; and GIS-based monitoring and assessment of wetlands in India and hotspots in the Lake Chad basin in Africa. Tung Fung’s guest statement explains how GIS has been used to monitor and evaluate environmental conditions in Hong Kong.

A Literature Review on Rail Transport of Hazmat Release Risk Analysis

2020 ◽  
Author(s):  
Zheyong Bian ◽  
Xiang Liu
Keyword(s):  
Risk Analysis ◽  
Geographical Information Systems ◽  
Hazardous Materials ◽  
The United States ◽  
Rail Transport ◽  
Geographical Information ◽  
Rolling Stock ◽  
Transportation Risk ◽  
Hazmat Transportation ◽  
Train Length

Abstract Rail plays an important role in hazmat transportation, transporting over two million carloads of hazardous materials (hazmat) in the United States annually. Compared with a truck trailer carrying a single hazmat car, a train has much more severe consequence of hazmat release due to carrying multiple connected hazmat cars (e.g., 50 to 120 flammable liquid cars). It is of high priority for the government and railroad companies to enhance the railroad hazmat transportation safety since the train accidents can cause severe railroad hazmat release incidents. Based on the data provided by Federal Railroad Administration (FRA) of the U.S. Department of Transportation (U.S.DOT), there are over 300 accident causes, including infrastructure failure defects, rolling stock failures, human errors, weather conditions, etc. It is significant to understand the relationship between hazmat transportation risk and accident cause to provide guidance for developing, evaluating, and prioritizing accident prevention strategies, thereby mitigating hazmat transportation risk. Therefore, this paper reviews the literature on rail transport of hazmat release risk analysis in order to capture the event chain leading to hazmat release, possible risk factors, and the state of the art on existing risk analysis methodologies. We reviewed the related references based on a five-step process: (1) train accident occurrence, (2) number of cars derailed, (3) number of hazardous material cars derailed, (4) number of hazmat cars releasing, and (5) release consequences. First, many severe hazmat release incidents are caused by train accidents, particularly train derailments. Prior research found that over 70% of freight train mainline derailments were caused by either infrastructure defects or rolling stock failures. Possible strategies for reducing the probability of train accidents include the prevention of track defects, equipment condition monitoring to reduce in-service failures, and the use of more advanced train control technologies to reduce human error. Second, number of cars derailed is an important factor causing hazmat releasing. Based on the reviewed literature, the total number of cars derailed depends on accident cause, speed, train length, and point of derailment. Third, the literature implied that the total number of hazmat cars derailed is related to train length, number of hazmat cars and non-hazmat cars in a train, and their placement. Fourth, the number of hazmat cars releasing contents is influenced by hazardous materials car safety design, accident speed, etc. Finally, the consequences of a release can be measured by different metrics, such as property damage, environmental impact, traffic delay, or the affected population. Geographical information systems (GIS) can be used for consequence analysis integrated with other databases such as census and rail network data.

Geoinformation mapping in economic and social geography

2018 ◽  
Author(s):  
Анна Молочко ◽  
Anna Molochko ◽  
Дмитрий Хворостухин ◽  
Dmitriy Hvorostuhin
Keyword(s):  
Higher Education ◽  
Environmental Management ◽  
Information Systems ◽  
Land Management ◽  
Geographical Information Systems ◽  
Social Geography ◽  
Geographical Information ◽  
Federal State ◽  
Educational Standard ◽  
State Educational

In the tutorial, the technology of creating digital cartographic works using the tools of geographical information systems (GIS) is considered; the technique of designing, compiling and designing General geographic, as well as a series of thematic maps of analytical and complex types is presented; the cartographic methods of image used most often in economic and social geography and their implementation through a block of geo-analysis and modeling of modern applied desktop GIS are considered in detail. Corresponds to the Federal state educational standard of higher education of the last generation. It is intended for students studying in the areas of bachelor and master 05.03.02 and 05.04.02 "Geography", 05.03.03 "Cartography and Geoinformatics", 05.04.06 "Ecology and environmental management", 21.03.02 "land Management and cadastres", as well as for novice GIS users.

scholarly journals County-level longitudinal clustering of COVID-19 mortality to incidence ratio in the United States

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nasim Vahabi ◽  
Masoud Salehi ◽  
Julio D. Duarte ◽  
Abolfazl Mollalo ◽  
George Michailidis
Keyword(s):  
Risk Factors ◽  
Great Plains ◽  
The United States ◽  
Federal State ◽  
Similar Data ◽  
County Level ◽  
Model Based Clustering ◽  
Growth Mixture ◽  
Potential Risk Factors ◽  
Over Time

AbstractAs of November 12, 2020, the mortality to incidence ratio (MIR) of COVID-19 was 5.8% in the US. A longitudinal model-based clustering system on the disease trajectories over time was used to identify “vulnerable” clusters of counties that would benefit from allocating additional resources by federal, state and county policymakers. County-level COVID-19 cases and deaths, together with a set of potential risk factors were collected for 3050 U.S. counties during the 1st wave of COVID-19 (Mar25–Jun3, 2020), followed by similar data for 1344 counties (in the “sunbelt” region of the country) during the 2nd wave (Jun4–Sep2, 2020), and finally for 1055 counties located broadly in the great plains region of the country during the 3rd wave (Sep3–Nov12, 2020). We used growth mixture models to identify clusters of counties exhibiting similar COVID-19 MIR growth trajectories and risk-factors over time. The analysis identifies “more vulnerable” clusters during the 1st, 2nd and 3rd waves of COVID-19. Further, tuberculosis (OR 1.3–2.1–3.2), drug use disorder (OR 1.1), hepatitis (OR 13.1), HIV/AIDS (OR 2.3), cardiomyopathy and myocarditis (OR 1.3), diabetes (OR 1.2), mesothelioma (OR 9.3) were significantly associated with increased odds of being in a more vulnerable cluster. Heart complications and cancer were the main risk factors increasing the COVID-19 MIR (range 0.08–0.52% MIR↑). We identified “more vulnerable” county-clusters exhibiting the highest COVID-19 MIR trajectories, indicating that enhancing the capacity and access to healthcare resources would be key to successfully manage COVID-19 in these clusters. These findings provide insights for public health policymakers on the groups of people and locations they need to pay particular attention while managing the COVID-19 epidemic.

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.

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