Objective
To compare cost estimates for hospital stays calculated using diagnosis‐related group (DRG) weights to actual Medicare payments.
Data Sources/Study Setting
Medicare MedPAR files and DRG ...tables linked to participant data from the Study of Osteoporotic Fractures (SOF) from 1992 through 2010. Participants were women age 65 and older recruited in three metropolitan and one rural area of the United States.
Study Design
Costs were estimated using DRG payment weights for 1,397 hospital stays for 795 SOF participants for 1 year following a hip fracture. Medicare cost estimates included Medicare and secondary insurer payments, and copay and deductible amounts.
Principal Findings
The mean (SD) of inpatient DRG‐based cost estimates per person‐year were $16,268 ($10,058) compared with $19,937 ($15,531) for MedPAR payments. The correlation between DRG‐based estimates and MedPAR payments was 0.71, and 51 percent of hospital stays were in different quintiles when costs were calculated based on DRG weights compared with MedPAR payments.
Conclusions
DRG‐based cost estimates of hospital stays differ significantly from Medicare payments, which are adjusted by Medicare for facility and local geographic characteristics. DRG‐based cost estimates may be preferable for analyses when facility and local geographic variation could bias assessment of associations between patient characteristics and costs.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
To develop and validate an algorithm to estimate probability of ever smoking using administrative claims.
Using population-based samples of Medicare-aged individuals (121,278 Behavioral Risk Factor ...Surveillance System survey respondents and 207,885 Medicare beneficiaries), we developed a logistic regression model to predict probability of ever smoking from demographic and claims data. We applied the model in 1,657,266 additional Medicare beneficiaries and calculated area under the receiver operating characteristic curve (AUC) using presence or absence of a tobacco-specific diagnosis or procedure code as our “gold standard.” We used these “gold standard” and lung/laryngeal cancer codes to over-ride predicted probability as 100%. We calculated Spearman’s rho between probability from this full algorithm and smoking assessed in prior Parkinson disease studies, by substituting our observed and prior (“true”) smoking-Parkinson disease odds ratios into the attenuation equation.
The predictive model contained 23 variables, including basic demographics, high alcohol consumption, asthma, cardiovascular disease and associated risk factors, selected cancers, and indicators of routine medical usage. The AUC was 67.6% (95% confidence interval 67.5%−67.7%) comparing smoking probability to tobacco-specific diagnosis or procedure codes. Spearman’s rho for the full algorithm was 0.82.
Ever smoking might be approximated in administrative data for use as a continuous, probabilistic variable in epidemiologic analyses.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Obesity is a risk factor for aggravation of and mortality from coronavirus disease 2019 (COVID-19). We aimed to investigate the relationship between COVID-19 and Body Mass Index (BMI) in the Japanese ...population.
We used administrative claims data from an advanced treatment hospital in Japan and extracted data from patients hospitalized for COVID-19. The exposure variable was BMI measured at the time of admission, and the study outcomes were progression to critical illness and death. Analyses were performed for each age group.
Overall, 58,944 patients met the inclusion criteria. The risk of critical illness increased monotonically with higher BMI. In contrast, the relationship between BMI and mortality follows a J-shaped curve; being underweight and obese are risk factors for mortality. When stratified by age, similar trends were observed for both critical illness and mortality.
A higher BMI is a risk factor for the progression of COVID-19 severity, whereas both lower and higher BMIs are risk factors for mortality in the Japanese population.
The ex-Gratia Compensation Scheme was introduced by the Government of Indonesia in 1994 to provide compensation, which is not covered by any prior legislation, for all its workers suffering from ...injuries and illnesses due to work. This study explores a key aspect of insurance policies in Indonesia, e.g., ex gratia payment to the insured individual by their insurance company. This study argues that several issues still exist in the insurance business in Indonesia that require a change in policies and regulations by the government for the welfare of the Indonesian people, especially the vulnerable. This paper seeks to address the question of how the ex gratia rule can be used to solve insurance claim disputes. In other words, are insurance claims always approved by insurance companies under the ex gratia rule? This study reveals that despite more than 10 years of implementation, the ex gratia claims filing is still minimal and unpopular. This is mainly due to the population's unawareness of the provisions of ex gratia and its benefits.
When distributing the benefits produced by social cooperation, Rawls’s difference principle targets a specific group (i.e., the least advantaged group) and requires its expectations to be maximized. ...One natural worry is whether the practical application of the difference principle comes with a significant cost to other groups in society. Rawls was quite aware of this potential worry and gave his earnest efforts to respond to it. His solution comes from his notions of chain connection and close-knitness. Rawls’s claim was that whenever society satisfies both chain connection and close-knitness, the practical implementation of the difference principle will (a) always lead to strict Pareto improvements, and, as a result, (b) the final state will be Pareto optimal. In this article, it will be shown that under close scrutiny neither of these claims holds even when society is both chain connected and close-knit.
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IZUM, KILJ, NUK, ODKLJ, PILJ, PNG, SAZU, SIK, UL, UM, UPUK
This article addresses widespread concerns about the reliability and strength of many causal claims made in management research. We first critically review the three prevalent forms of theorizing ...used to identify causal relationships in this field, i.e., propositional, configurational, and process approaches to causation. Highlighting the strengths and limitations of these approaches, we show that while no single approach is sufficient by itself as the basis for robust causal claims, researchers can nonetheless enhance and strengthen claims significantly by combining approaches and thus subjecting them to multiple criteria for drawing robust inferences. We emphasize the risks of continuing with narrow monolithic approaches, using examples of weak claims to show how these could have been strengthened (or abandoned) if the researchers had followed our proposed model of causal triangulation. Finally, we elucidate the practical benefits for management researchers and stakeholders in society of adopting this theoretically pluralistic approach to causation.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Working groups have called for linkages of existing and diverse databases to improve quality measurement in palliative and end-of-life (EOL) care, but limited data are available on the challenges of ...using different data sources to measure such care.
To assess concordance of data obtained from different sources in a novel linkage of death certificates, electronic health records (EHRs), cancer registry data, and insurance claims for patients who died with cancer.
We joined a database of Washington State death certificates and EHR to a data repository of commercial health plan enrollment and claims files linked to registry records from Puget Sound Cancer Surveillance System. We assessed care in the last month including hospitalizations, intensive care unit (ICU) admissions, emergency department visits, imaging scans, radiation, and hospice, plus chemotherapy in the last 14 days. We used a Chi-squared test to compare differences between health care in EHR and claims.
Records of hospitalization, ICU use, and emergency department use were 33%, 15%, and 33% lower in EHR versus claims, respectively. Radiation, hospice, and imaging were 6%, 14%, and 28% lower, respectively, in EHR, but chemotherapy was 4% higher than that in claims. These differences were statistically different for hospice (P < 0.02), hospitalization, ICU, ER, and imaging (all P < 0.01) but not radiation (P = 0.12) or chemotherapy (P = 0.29).
We found substantial variation between EHR and claims for EOL health-care use. Reliance on EHR will miss some health-care use, while claims will not capture the complex clinical details in EHR that can help define the quality of palliative care and EOL health-care utilization.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
Although claims data are widely used in medical research, their ability to identify persons' health-related conditions has not been fully justified. We assessed the validity of claims-based ...algorithms (CBAs) for identifying people with common chronic conditions in a large population using annual health screening results as the gold standard.
Using a longitudinal claims database (n = 523,267) combined with annual health screening results, we defined the people with hypertension, diabetes, and/or dyslipidemia by applying health screening results as their gold standard and compared them against various CBAs.
By using diagnostic and medication code-based CBAs, sensitivity and specificity were 74.5% (95% confidence interval CI, 74.2%–74.8%) and 98.2% (98.2%–98.3%) for hypertension, 78.6% (77.3%–79.8%) and 99.6% (99.5%–99.6%) for diabetes, and 34.5% (34.2%–34.7%) and 97.2% (97.2%–97.3%) for dyslipidemia, respectively. Sensitivity did not decrease substantially for hypertension (65.2% 95% CI, 64.9%–65.5%) and diabetes (73.0% 71.7%–74.2%) when we used the same CBAs without limiting to primary care settings.
We used regularly collected data to obtain CBA association measures, which are applicable to a wide range of populations. Our framework can be a basis of the validity assessment of CBAs for identifying persons' health-related conditions with regularly collected data.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPUK, ZRSKP
Large hail events are typically infrequent, with significant time gaps between occurrences at specific locations. However, when these events do happen, they can cause rapid and substantial economic ...losses within a matter of minutes. Therefore, it is crucial to have the ability to accurately observe and understand hail phenomena to improve the mitigation of this impact. While in situ observations are accurate, they are limited in number for an individual storm. Weather radars, on the other hand, provide a larger observation footprint, but current radar-derived hail size estimates exhibit low accuracy due to horizontal advection of hailstones as they fall, the variability of hail size distributions (HSDs), complex scattering and attenuation, and mixed hydrometeor types. In this paper, we propose a new radar-derived hail product developed using a large dataset of hail damage insurance claims and radar observations. We use these datasets coupled with environmental information to calculate a hail damage estimate (HDE) using a deep neural network approach aiming to quantify hail impact, with a critical success index of 0.88 and a coefficient of determination against observed damage of 0.79. Furthermore, we compared HDE to a popular hail size product (MESH), allowing us to identify meteorological conditions that are associated with biases on MESH. Environments with relatively low specific humidity, high CAPE and CIN, low wind speeds aloft, and southerly winds at the ground are associated with a negative MESH bias, potentially due to differences in HSD, hail hardness, or mixed hydrometeors. In contrast, environments with low CAPE, high CIN, and relatively high specific humidity aloft are associated with a positive MESH bias.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Current research on flooding risk often focuses on understanding hazards, de-emphasizing the complex pathways of exposure and vulnerability. We investigated the use of both hydrologic and social ...demographic data for flood exposure mapping with Random Forest (RF) regression and classification algorithms trained to predict both parcel- and tract-level flood insurance claims within New York State, US. Topographic characteristics best described flood claim frequency, but RF prediction skill was improved at both spatial scales when socioeconomic data was incorporated. Substantial improvements occurred at the tract-level when the percentage of minority residents, housing stock value and age, and the political dissimilarity index of voting precincts were used to predict insurance claims. Census tracts with higher numbers of claims and greater densities of low-lying tax parcels tended to have low proportions of minority residents, newer houses, and less political similarity to state level government. We compared this data-driven approach and a physically-based pluvial flood routing model for prediction of the spatial extents of flooding claims in two nearby catchments of differing land use. The floodplain we defined with physically based modeling agreed well with existing federal flood insurance rate maps, but underestimated the spatial extents of historical claim generating areas. In contrast, RF classification incorporating hydrologic and socioeconomic demographic data likely overestimated the flood-exposed areas. Our research indicates that quantitative incorporation of social data can improve flooding exposure estimates.
•We examine historical flooding insurance claims across New York State.•Flooding claims are related to both topography and social demographics.•Percentage of minority residents and political dissimilarity were predictive of insurance claims.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
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