The influence of primary care quality on nursing home admissions in a multimorbid population with and without dementia in Germany: a retrospective cohort study using health insurance claims data

Background Multimorbidity poses a challenge for high quality primary care provision for nursing care-dependent people with (PWD) and without (PWOD) dementia. Evidence on the association of primary care quality of multimorbid PWD and PWOD with the event of a nursing home admission (NHA) is missing. This study aimed to investigate the contribution of individual quality of primary care for chronic diseases in multimorbid care-dependent PWD and PWOD on the duration of ongoing residence at home before the occurrence of NHA. Methods We conducted a retrospective cohort study among elderly care-dependent PWD and PWOD in Germany for six combinations of chronic diseases using statutory health insurance claims data (2007–2016). Primary care quality was measured by 21 process and outcome indicators for hypertension, diabetes, depression, chronic obstructive pulmonary disease and heart failure. The primary outcome was time to NHA after initial onset of care-dependency. Multivariable Cox proportional hazard models were used to compare the time-to-event between PWD and PWOD. Results Among 5876 PWD and 12,837 PWOD 5130 NHA occurred. With the highest proportion of NHA for PWD with hypertension and depression and for PWOD with hypertension, diabetes and depression. Average duration until NHA ranged from 6.5 to 8.9 quarters for PWD and from 9.6 to 13.5 quarters for PWOD. Adjusted analyses show consistent associations of the quality of diabetes care with the duration of remaining in one’s own home regardless of the presence of dementia. Process indicators assessing guideline-fidelity are associated with remaining in one’s home longer, while indicators assessing complications, such as emergency inpatient treatment (HR = 2.67, 95% CI 1.99–3.60 PWD; HR = 2.81, 95% CI 2.28–3.47 PWOD) or lower-limb amputation (HR = 3.10, 95% CI 1.78–5.55 PWD; HR = 2.81, 95% CI 1.94–4.08 PWOD) in PWD and PWOD with hypertension and diabetes, increase the risk of NHA. Conclusions The quality of primary care provided to care-dependent multimorbid PWD and POWD, influences the time individuals spend living in their own homes after onset of care-dependency before a NHA. Health care professionals should consider possibilities and barriers of guideline-based, coordinated care for multimorbid care-dependent people. Further research on quality indicator sets that acknowledge the complexity of care for multimorbid elderly populations is needed.

Automobile Fatal Accident and Insurance Claim Analysis Through Artificial Neural Network

This chapter presents a thorough descriptive analysis of automobile fatal accident and insurance claims data. Major components of the artificial neural network (ANN) are discussed, and parameters are investigated and carefully selected to ensure an efficient model construction. A prediction model is constructed through ANN as well as generalized linear model (GLM) for model comparison purposes. The authors conclude that ANN performs better than GLM in predicting data for automobile fatalities data but does not outperform for the insurance claims data because automobile fatalities data has a more complex data structure than the insurance claims data.

Risk of Glaucoma Associated with Components of Metabolic Disease in Taiwan: A Nationwide Population-Based Study

Purpose: This retrospective cohort study was conducted to determine the glaucoma risk associated with metabolic disease (MetD) using insurance claims data of Taiwan. Methods: From the database, we identified patients with newly diagnosed hypertension, diabetes and/or hyperlipidemia from the years 2000 to 2002 as the MetD cohort (N = 42,036) and an age-gender-diagnosis-date matched control cohort without MetD with a two-fold sample size than that of the MetD cohort. Both cohorts were followed until the development of glaucoma, death, or withdrawal, until December 31, 2013. The incidence of glaucoma, and the Cox method estimated hazard ratio (HR) of glaucoma were calculated. Results showed that the incidence of glaucoma was two-fold higher in the MetD cohort than in the controls (1.99 versus 0.99 per 1000 person-years), with an adjusted HR of 1.66 (95% CI: 1.50–1.85). The glaucoma incidence was higher in patients with diabetes than those with hypertension and hyperlipidemia (2.38 versus 1.95 and 1.72 per 1000 person-years, respectively). The incidence increased to 5.67 per 1000 person-years in patients with all three comorbidities, with an aHR of 4.95 (95% CI: 2.35–10.40). We also found higher incidence rates of primary open-angle glaucoma and primary angle-closure glaucoma with aHRs of 2.03 and 1.44, respectively. It was concluded that glaucoma risk increased with the number of MetD. Health providers need to monitor patients with MetD to prevent glaucoma.

IMPROVING AUTOMOBILE INSURANCE CLAIMS FREQUENCY PREDICTION WITH TELEMATICS CAR DRIVING DATA

Novel navigation applications provide a driving behavior score for each finished trip to promote safe driving, which is mainly based on experts’ domain knowledge. In this paper, with automobile insurance claims data and associated telematics car driving data, we propose a supervised driving risk scoring neural network model. This one-dimensional convolutional neural network takes time series of individual car driving trips as input and returns a risk score in the unit range of (0,1). By incorporating credibility average risk score of each driver, the classical Poisson generalized linear model for automobile insurance claims frequency prediction can be improved significantly. Hence, compared with non-telematics-based insurers, telematics-based insurers can discover more heterogeneity in their portfolio and attract safer drivers with premiums discounts.

Outcomes and costs of ureteroscopy, extracorporeal shockwave lithotripsy, and percutaneous nephrolithotomy for the treatment of urolithiasis: an analysis based on health insurance claims data in Germany

Purpose Comparisons of ureteroscopy (URS), extracorporeal shockwave lithotripsy (SWL), and percutaneous nephrolithotomy (PCNL) for urolithiasis considering long-term health and economic outcomes based on claims data are rare. Our aim was to analyze URS, SWL, and PCNL regarding complications within 30 days, re-intervention, healthcare costs, and sick leave days within 12 months, and to investigate inpatient and outpatient SWL treatment as the latter was introduced in Germany in 2011. Methods This retrospective cohort study based on German health insurance claims data included 164,203 urolithiasis cases in 2008–2016. We investigated the number of complications within 30 days, as well as time to re-intervention, number of sick leave days and hospital and ambulatory health care costs within a 12-month follow-up period. We applied negative binomial, Cox proportional hazard, gamma and two-part models and adjusted for patient variables. Results Compared to URS cases, SWL and PCNL had fewer 30-day complications, time to re-intervention within 12 months was decreased for SWL and PCNL, SWL and PCNL were correlated with a higher number of sick leave days, and SWL and particularly PCNL were associated with higher costs. SWL outpatients had fewer complications, re-interventions and lower costs than inpatients. This study was limited by the available information in claims data. Conclusion URS cases showed benefits in terms of fewer re-interventions, fewer sick leave days, and lower healthcare costs. Only regarding complications, SWL was superior. This emphasizes URS as the most frequent treatment choice. Furthermore, SWL outpatients showed less costs, fewer complications, and re-interventions than inpatients.

A Strategy to Identify Event Specific Hospitalizations in Large Health Claims Databases

Background Health insurance claims data offer a unique opportunity to study disease distribution on a large scale. Challenges arise in the process of accurately analyzing these raw data. One important challenge to overcome is the accurate classification of study outcomes. For example, using claims data, there is no clear way of classifying hospitalizations due to a specific event. This is because of the inherent disjointedness and lack of context that typically come with raw claims data. Methods In this paper, we propose a framework for classifying hospitalizations due to a specific event. Results We then test this framework in a health insurance claims database with approximately 4 million US adults who tested positive with COVID-19 between March and December 2020. Our claims specific COVID-19 related hospitalizations proportion is then compared to nationally reported rates from the Centers for Disease Control by age and sex. Conclusions The proposed methodology is a rigorous way to define event specific hospitalizations in claims data. This methodology can be extended to many different types of events and used on a variety of different types of claims databases.

Asymmetric Density for Risk Claim-Size Data: Prediction and Bimodal Data Applications

A new, flexible claim-size Chen density is derived for modeling asymmetric data (negative and positive) with different types of kurtosis (leptokurtic, mesokurtic and platykurtic). The new function is used for modeling bimodal asymmetric medical data, water resource bimodal asymmetric data and asymmetric negatively skewed insurance-claims payment triangle data. The new density accommodates the “symmetric”, “unimodal right skewed”, “unimodal left skewed”, “bimodal right skewed” and “bimodal left skewed” densities. The new hazard function can be “decreasing–constant–increasing (bathtub)”, “monotonically increasing”, “upside down constant–increasing”, “monotonically decreasing”, “J shape” and “upside down”. Four risk indicators are analyzed under insurance-claims payment triangle data using the proposed distribution. Since the insurance-claims data are a quarterly time series, we analyzed them using the autoregressive regression model AR(1). Future insurance-claims forecasting is very important for insurance companies to avoid uncertainty about big losses that may be produced from future claims.