Prognostic models for short-term annual risk of severe complications and mortality in patients living with type 2 diabetes using a national medical claim database

OBJECTIVE

Prognostic models in patients living with diabetes allow physicians to estimate individual risk based on medical records and biological results. Clinical risk factors are not always all available to evaluate these models so that they may be complemented with models from claims databases. The objective of this study was to develop, validate and compare models predicting the annual risk of severe complications and mortality in patients living with type 2 diabetes (T2D) from a national claims data.

RESEARCH DESIGN AND METHODS

Adult patients with T2D were identified in a national medical claims database through their history of treatments or hospitalizations. Prognostic models were developed using logistic regression (LR), random forest (RF) and neural network (NN) to predict annual risk of outcome: severe cardiovascular (CV) complications, other severe T2D-related complications, and all-cause mortality. Risk factors included demographics, comorbidities, the adjusted Diabetes Severity and Comorbidity Index (aDSCI) and diabetes medications. Model performance was assessed using discrimination (C-statistics), balanced accuracy, sensibility and specificity.

RESULTS

A total of 22,708 patients with T2D were identified, with mean age of 68 years and average duration of T2D of 9.7 years. Age, aDSCI, disease duration, diabetes medications and chronic cardiovascular disease were the most important predictors for all outcomes. Discrimination with C-statistic ranged from 0.715 to 0.786 for severe CV complications, from 0.670 to 0.847 for other severe complications and from 0.814 to 0.860 for all-cause mortality, with RF having consistently the highest discrimination.

CONCLUSION

The proposed models reliably predict severe complications and mortality in patients with T2D, without requiring medical records or biological measures. These predictions could be used by payers to alert primary care providers and high-risk patients living with T2D.

Cholesterol and breast cancer risk: a cohort study using health insurance claims and health checkup databases

PURPOSE

This study aimed to investigate the association between serum cholesterol and triglyceride levels and breast cancer risk in Japanese women.

METHODS

We retrospectively evaluated the association between the levels of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TGs) and the incidence of breast cancer in a cohort study by using the health insurance claims and health checkup data from a database provided by JMDC Inc. We included 956,390 women who were insured between April 2008 and June 2019, identified breast cancer cases by using validated definitions, and estimated the risk of breast cancer by using multivariable Cox proportional hazards regression models adjusted for potential confounders.

RESULTS

During the 2,832,277 person-years observation period (median 2.4 years), 6284 participants were diagnosed with breast cancer. There was marginally significant association between LDL-C and breast cancer risk when comparing the highest and lowest quintiles and at the clinical cutoff values for diagnosing hyperlipidemia. HDL-C was not associated with breast cancer. However, when stratified by age groups (< 50 and ≥ 50), HDL-C was inversely associated with breast cancer risk in women over 50 years old. TG was not associated with breast cancer risk.

CONCLUSION

In this population, there was a modest association of LDL-C at the clinical cutoff values for diagnosing hyperlipidemia (140 mg/mL), and there were no associations of HDL-C and TG with breast cancer risk.

Effect of the Diabetic Nephropathy Aggravation Prevention Program on medical visit behavior in individuals under the municipal national health insurance

AIMS/INTRODUCTION

We aimed to clarify the effectiveness of the Diabetic Nephropathy Aggravation Prevention Program in Japan by comparing the diabetes-related medical visit behavior of individuals under the municipal national health insurance according to insurers' effort levels.

MATERIALS AND METHODS

We assessed changes in medical visit behavior according to insurers' effort levels, "Full Efforts," "Some Efforts" and "No Effort," using longitudinal data from the National Database of Health Insurance Claims and Specific Health Checkups before 2015 and after 2018 regarding the national health insurance programs in Japan. We analyzed the effect of the Diabetic Nephropathy Aggravation Prevention Program using a generalized linear mixed model for 208,388 participants with diabetes.

RESULTS

The additive effect on medical visit behavior was significantly higher for insurers with "Full Efforts" than for those with "No Effort;" the coefficient (log odds ratio) was 0.159 (95% confidence interval 0.063-0.256). The additive effects on medical visit behavior sizes for the people with hemoglobin A1c ≥7.0%, positive urinary protein and systolic blood pressure ≥140 mmHg were 0.508, 0.402 and 0.232, respectively, which were larger than the overall effect size (0.159) for insurers with "Full Efforts."

CONCLUSIONS

Our findings showed that insurer efforts had an additive effect on the increase in the number of medical visits, suggesting that this national program could reduce the number of end-stage renal failures or dialysis in Japan.

Prediction of long-term hospitalisation and all-cause mortality in patients with chronic heart failure on Dutch claims data: a machine learning approach

BACKGROUND

Accurately predicting which patients with chronic heart failure (CHF) are particularly vulnerable for adverse outcomes is of crucial importance to support clinical decision making. The goal of the current study was to examine the predictive value on long term heart failure (HF) hospitalisation and all-cause mortality in CHF patients, by exploring and exploiting machine learning (ML) and traditional statistical techniques on a Dutch health insurance claims database.

METHODS

Our study population consisted of 25,776 patients with a CHF diagnosis code between 2012 and 2014 and one year and three years follow-up HF hospitalisation (1446 and 3220 patients respectively) and all-cause mortality (2434 and 7882 patients respectively) were measured from 2015 to 2018. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated after modelling the data using Logistic Regression, Random Forest, Elastic Net regression and Neural Networks.

RESULTS

AUC rates ranged from 0.710 to 0.732 for 1-year HF hospitalisation, 0.705-0.733 for 3-years HF hospitalisation, 0.765-0.787 for 1-year mortality and 0.764-0.791 for 3-years mortality. Elastic Net performed best for all endpoints. Differences between techniques were small and only statistically significant between Elastic Net and Logistic Regression compared with Random Forest for 3-years HF hospitalisation.

CONCLUSION

In this study based on a health insurance claims database we found clear predictive value for predicting long-term HF hospitalisation and mortality of CHF patients by using ML techniques compared to traditional statistics.

Medical costs according to the stages of colorectal cancer: an analysis of health insurance claims in Hachioji, Japan

BACKGROUND

Although the effect of the early detection of colorectal cancer (CRC) on medical costs needs to be clarified, there are few reports on the actual medical costs of CRC patients in Japan. We aimed to identify medical costs according to CRC stage, using health insurance claims.

METHODS

This observational study included CRC patients who had received specific treatment for CRC, which was defined by the procedure code and the claim computer processing system code associated with the treatment of CRC. CRC patients who underwent endoscopic or radical surgical treatment were defined as the curable group and those with palliative treatment, including palliative chemotherapy, as the non-curable group. Total medical costs and medical costs of specific treatments for CRC for 3 years were measured using the claims held by Hachioji City from May 2014 to July 2019.

RESULTS

This study included 442 patients in the curable group, including 267 patients who underwent endoscopic treatment, and 175 patients who underwent radical surgical treatment, and 161 patients in the non-curable group. The mean (standard deviation) total medical costs in the curable and non-curable groups were 2,130 (2,494) and 8,279 (5,600) thousand Japanese Yen (JPY), respectively. The mean (standard deviation) medical costs for the specific treatment of CRC in the curable and non-curable groups were 408 (352) and 3,685 (3,479) thousand JPY, respectively.

CONCLUSIONS

We clarified the actual medical costs of CRC in curable and non-curable groups. These results suggest the effect of early detection of CRC in reducing medical costs.

Long-term prognosis of enteral feeding and parenteral nutrition in a population aged 75 years and older: a population-based cohort study

Background

Enteral feeding and parenteral nutrition (PN) using gastrostomy (GS) and a nasogastric tube feeding (NGT) and PN should be initiated for older patients based on their prognoses. This study aimed to investigate the long-term prognosis of patients aged ≥75 years who underwent enteral feeding via GS and NGT as well as PN.

Methods

A population-based cohort study was conducted using Japan’s universal health insurance claims in the Nara Prefecture. This study enrolled 3,548 patients aged ≥75 years who received GS (N=770), NGT (N=2,370), and PN (N=408) during hospital admissions between April 2014 and March 2016. The GS group was further categorized into secondary GS (N=400) with preceding NGT or PN within 365 days and primary GS (N=370) without preceding NGT or PN groups. In the secondary GS group, 356 (96%) patients received NGT (versus PN). The outcome was mortality within 730 days after receiving GS, NGT, and PN. Cox regression analyses in cases with or without malignant diseases, adjusted for sex, age, comorbidity, and hospital type, were performed to compare mortality in the groups.

Results

Of the 3,548 participants, 2,384 (67%) died within 730 days after the initiation of GS and NGT and PN. The 2-year mortality rates in the secondary GS, primary GS, NGT, and PN groups were 58%, 66%, 68%, and 83% in patients without malignancies and 67%, 71%, 74%, and 87% in those with malignancies, respectively. In the non-malignant group, Cox regression analysis revealed that secondary GS (hazard ratio (HR) = 0.43, 95% CI: 0.34–0.54), primary GS (HR = 0.51, 95% CI: 0.40–0.64), and NGT (HR = 0.71, 95% CI: 0.58-0.87) were statistically significantly associated with lower mortality compared with PN.

Conclusions

Approximately 58% to 87% patients aged ≥75 years died within 730 days after initiation of nutrition through GS, NGT, or PN. Patients with non-malignant diseases who received secondary GS exhibited better 2-year prognosis than those who received NGT or PN. Healthcare professionals should be aware of the effectiveness and limitations of enteral feeding and PN when considering their initiation.

Association of the ward pharmacy service with active implementation of therapeutic drug monitoring for vancomycin and teicoplanin-an epidemiological surveillance study using Japanese large health insurance claims database

Background

Ward pharmacists are required for the active implementation of therapeutic drug monitoring (TDM). This epidemiological study verified whether Japanese ward pharmacists contribute to improving the TDM implementation proportions of anti-methicillin-resistant Staphylococcus aureus (MRSA) agents using the large health insurance claims database.

Methods

The patients who received intravenous anti-MRSA agents from April 2012 to March 2017 were enrolled. We defined ward pharmacy service as the “drug management and guidance fee” and/or “inpatient pharmaceutical services premium”. In addition, implementation of TDM was identified by “the specific drug treatment management fee”. We compared the proportions of TDM implementation for vancomycin (VCM), teicoplanin (TEIC), and arbekacin (ABK) in the ward and non-ward pharmacy service groups. To avoid confounding, the propensity score method was employed. Moreover, the clinical variables affecting TDM implementation in each anti-MRSA agent were analyzed by using a multiple logistic regression model.

Results

The following number of patients were included in the study: VCM (n = 2138), TEIC (n = 596), and ABK (n = 142). After propensity score matching, the proportions of TDM implementation for VCM and TEIC were higher in the ward pharmacy service group than in the non-ward pharmacy service group (VCM: 69.2% vs 60.3%, TEIC: 51.4% vs 34.7%), while no significant difference was observed for ABK (21.2% vs 23.1%). As independent clinical variables affecting TDM implementation for VCM and TEIC, several clinical variables, including ward pharmacy services, were extracted. In contrast, no clinical variables were extracted for ABK.

Conclusions

We found that the ward pharmacy service is associated with the active implementation of TDM for anti-MRSA agents, such as VCM and TEIC.

Validation of claims data to identify death among aged persons utilizing enrollment data from health insurance unions

The identification of death is critical for epidemiological research. Despite recent developments in health insurance claims databases, the quality of death information in claims is not guaranteed because health insurance claims are collected primarily for reimbursement. We aimed to examine the usefulness and limitations of death information in claims data and to examine methods for improving the quality of death information for aged persons.

We used health insurance claims data and enrollment data (as the gold standard) from September 2012 through August 2015 for nondependent persons aged 65–74 years enrolled in Japanese workplace health insurance. Overall, 3,710,538 insured persons were registered in the database during the study period. We analyzed 45,441 eligible persons. Inpatient and outpatient deaths were identified from the discharge/disease status in the claims, with sensitivities of 94.3% and 47.4%, specificities of 98.5% and 99.9%, and PPVs of 96.3% and 95.7%, respectively, using enrollment data as the gold standard. For outpatients, death defined as a combination of disease status and charge data for terminal care still indicated low sensitivity (54.7%).

The validity of death information in inpatient claims was high, suggesting its potential usefulness for identifying death. However, given the low sensitivity for outpatient deaths, the use of death information obtained solely from records in outpatient claims is not recommended.

National burden of the pharmaceutical cost of wet compresses and its cost predictors: nationwide cross-sectional study in Japan

BACKGROUND

Although a high number of wet compresses are prescribed daily in medical institutions in Japan, our understanding of the national burden of the cost of wet compresses and the details regarding their prescription is far from complete. We investigated the national burden of the annual pharmaceutical cost of wet compresses prescribed in Japan and estimated the predictors of this cost using nationwide health insurance claims data.

METHODS

We extracted the records on wet compress products from summary table files obtained from the second version of the "NDB Open Data Japan" website and calculated the annual pharmaceutical cost of wet compresses by patients' 5-year age group, sex, and prefecture. We also conducted an ecological study treating each prefecture as an individual unit and multiple linear regression analyses using the age-standardized cost of wet compresses per resident as a dependent variable.

RESULTS

The annual pharmaceutical cost of wet compresses prescribed in Japan in fiscal year 2015 was 149.0 billion Japanese yen (1.18 billion euros; 1.33 billion USD). Multiple linear regression analyses showed that the number of orthopedists and rehabilitation physicians per 100,000 residents were significantly positively associated with the annual pharmaceutical cost of wet compresses per resident (P = 0.042 and P = 0.008, respectively).

CONCLUSIONS

The annual pharmaceutical cost of wet compresses prescribed in Japan has a considerable impact on the nation's limited healthcare resources. The number of orthopedists and rehabilitation physicians per 100,000 residents may be independent predictors of the wet compress cost in Japan.

Obesity, but not metabolic syndrome, as a risk factor for late-onset asthma in Japanese women

BACKGROUND

Several cross-sectional studies have suggested an association between obesity and asthma. However, few studies have investigated this relationship longitudinally, especially in middle-aged subjects. Although metabolic syndrome is a well-known risk factor for many non-communicable diseases, its contribution to asthma remains controversial.

METHODS

From 2008, specific health checkups for metabolic syndrome have been conducted throughout Japan. To seek relationships of obesity and metabolic syndrome with late-onset asthma in Japan, we analyzed data collected from health insurance claims and specific health checkups for metabolic syndrome at three large health insurance societies. Among subjects aged 40-64 years (n = 9888), multivariate logistic regression analyses were performed to investigate the relationships of obesity and metabolic syndrome in fiscal year 2012 (from April 2012 to March 2013) with the incidence of late-onset asthma in the following two years (from April 2013 to March 2015).

RESULTS

In women, BMI 25-29.9 kg/m² or ≥30 kg/m², waist circumference ≥90 cm, and waist-to-height ratio ≥0.5 were shown to be significant risk factors for asthma, with adjusted odds ratios (95% CI) of 1.92 (1.35-2.75), 2.24 (1.23-4.09), 1.89 (1.30-2.75), and 1.53 (1.15-2.03), respectively. Significance was retained even after adjustment for metabolic syndrome, and there were no significant relationships between metabolic syndrome itself and the incidence of asthma in men or women.

CONCLUSIONS

Only the obesity measures, not metabolic syndrome, were shown to be significant risk factors for the incidence of late-onset asthma but only in middle-aged Japanese women, and not in men.

Data regarding fracture incidence according to fracture site, month, and age group obtained from the large public health insurance claim database in Japan

The National Database of Health Insurance Claims and Specific Health Checkups of Japan includes all health insurance claims submitted in Japan and is considered representative of almost all health claims in Japan. Data regarding fracture incidence, based on the documented diagnoses in the claims and relevant procedure codes, were extracted from the National Database of Health Insurance Claims and Specific Health Checkups of Japan. This data paper includes fracture incidence according to fracture site, month, and age group for the population in Kanto area (Tokyo and surrounding areas), which consists of approximately 42 million people. These data provide supplementary material to be interpreted for the article "Variation in Fracture Risk by Season and Weather: A Comprehensive Analysis across Age and Fracture Site Using a National Database of Health Insurance Claims in Japan" Hayashi et al., and serve as one of the largest epidemiological datasets regarding seasonal differences in fracture incidence according to fracture site and age group.

Development of predictive risk models for major adverse cardiovascular events among patients with type 2 diabetes mellitus using health insurance claims data

Background

There exist several predictive risk models for cardiovascular disease (CVD), including some developed specifically for patients with type 2 diabetes mellitus (T2DM). However, the models developed for a diabetic population are based on information derived from medical records or laboratory results, which are not typically available to entities like payers or quality of care organizations. The objective of this study is to develop and validate models predicting the risk of cardiovascular events in patients with T2DM based on medical insurance claims data.

Methods

Patients with T2DM aged 50 years or older were identified from the Optum™ Integrated Real World Evidence Electronic Health Records and Claims de-identified database (10/01/2006–09/30/2016). Risk factors were assessed over a 12-month baseline period and cardiovascular events were monitored from the end of the baseline period until end of data availability, continuous enrollment, or death. Risk models were developed using logistic regressions separately for patients with and without prior CVD, and for each outcome: (1) major adverse cardiovascular events (MACE; i.e., non-fatal myocardial infarction, non-fatal stroke, CVD-related death); (2) any MACE, hospitalization for unstable angina, or hospitalization for congestive heart failure; (3) CVD-related death. Models were developed and validated on 70% and 30% of the sample, respectively. Model performance was assessed using C-statistics.

Results

A total of 181,619 patients were identified, including 136,544 (75.2%) without prior CVD and 45,075 (24.8%) with a history of CVD. Age, diabetes-related hospitalizations, prior CVD diagnoses and chronic pulmonary disease were the most important predictors across all models. C-statistics ranged from 0.70 to 0.81, indicating that the models performed well. The additional inclusion of risk factors derived from pharmacy claims (e.g., use of antihypertensive, and use of antihyperglycemic) or from medical records and laboratory measures (e.g., hemoglobin A1c, urine albumin to creatinine ratio) only marginally improved the performance of the models.

Conclusion

The claims-based models developed could reliably predict the risk of cardiovascular events in T2DM patients, without requiring pharmacy claims or laboratory measures. These models could be relevant for providers and payers and help implement approaches to prevent cardiovascular events in high-risk diabetic patients.

Electronic supplementary material

The online version of this article (10.1186/s12933-018-0759-z) contains supplementary material, which is available to authorized users.

Medical costs attributable to overweight and obesity in Japanese individuals

OBJECTIVE

We aimed to reveal the association between body mass index (BMI) and medical costs in the current Japanese population, and to estimate the population attributable fraction (PAF) of medical costs due to overweight and obesity.

METHODS

A generalized linear mixed model with log link function and gamma distribution was used to evaluate the association between BMI and medical costs in 34,537 beneficiaries of the National Health Insurance aged 40-69 years in Chiba City. Medical cost data were obtained from insurance claims submitted between April 2012 and March 2016. PAFs due to overweight (BMI ≥25.0 and <30.0kg/m²) and obesity (BMI ≥30.0kg/m²) were calculated.

RESULTS

Overweight and obesity were significant predictors of excessive medical costs in all age and sex groups. PAF due to overweight and obesity was estimated to be 9.62% (95% confidence interval, 8.52-10.73%). Additionally, PAFs in 40-59-year-old individuals (12.76% in men and 11.63% in women) were greater than those in 60-69-year-old subjects (6.55% in men and 7.80% in women) for both sexes.

CONCLUSIONS

In the Japanese population, overweight and obesity are an excessive financial burden with an estimated PAF of 9.62% of total medical costs.

Exploring the small-scale spatial distribution of hypertension and its association to area deprivation based on health insurance claims in Northeastern Germany

BACKGROUND

Hypertension is one of the most frequently diagnosed chronic conditions in Germany. Targeted prevention strategies and allocation of general practitioners where they are needed most are necessary to prevent severe complications arising from high blood pressure. However, data on chronic diseases in Germany are mostly available through survey data, which do not only underestimate the actual prevalence but are also only available on coarse spatial scales. The discussion of including area deprivation for planning of healthcare is still relatively young in Germany, although previous studies have shown that area deprivation is associated with adverse health outcomes, irrespective of individual characteristics. The aim of this study is therefore to analyze the spatial distribution of hypertension at very fine geographic scales and to assess location-specific associations between hypertension, socio-demographic population characteristics and area deprivation based on health insurance claims of the AOK Nordost.

METHODS

To visualize the spatial distribution of hypertension prevalence at very fine geographic scales, we used the conditional autoregressive Besag-York-Mollié (BYM) model. Geographically weighted regression modelling (GWR) was applied to analyze the location-specific association of hypertension to area deprivation and further socio-demographic population characteristics.

RESULTS

The sex- and age-adjusted prevalence of hypertension was 33.1% in 2012 and varied widely across northeastern Germany. The main risk factors for hypertension were proportions of insurants aged 45-64, 65 and older, area deprivation and proportion of persons commuting to work outside their residential municipality. The GWR model revealed important regional variations in the strength of the examined associations.

CONCLUSION

Area deprivation has only a significant and therefore direct influence in large parts of Mecklenburg-West Pomerania. However, the spatially varying strength of the association between demographic variables and hypertension indicates that there also exists an indirect effect of area deprivation on the prevalence of hypertension. It can therefore be expected that persons ageing in deprived areas will be at greater risk of hypertension, irrespective of their individual characteristics. The future planning and allocation of primary healthcare in northeastern Germany would therefore greatly benefit from considering the effect of area deprivation.

Analyzing health insurance claims on different timescales to predict days in hospital

Health insurers maintain large databases containing information on medical services utilized by claimants, often spanning several healthcare services and providers. Proper use of these databases could facilitate better clinical and administrative decisions. In these data sets, there exists many unequally spaced events, such as hospital visits. However, data mining of temporal data and point processes is still a developing research area and extracting useful information from such data series is a challenging task. In this paper, we developed a time series data mining approach to predict the number of days in hospital in the coming year for individuals from a general insured population based on their insurance claim data. In the proposed method, the data were windowed at four different timescales (bi-monthly, quarterly, half-yearly and yearly) to construct regularly spaced time series features extracted from such events, resulting in four associated prediction models. A comparison of these models indicates models using a half-yearly windowing scheme delivers the best performance on all three populations (the whole population, a senior sub-population and a non-senior sub-population). The superiority of the half-yearly model was found to be particularly pronounced in the senior sub-population. A bagged decision tree approach was able to predict 'no hospitalization' versus 'at least one day in hospital' with a Matthews correlation coefficient (MCC) of 0.426. This was significantly better than the corresponding yearly model, which achieved 0.375 for this group of customers. Further reducing the length of the analysis windows to three or two months did not produce further improvements.

A prospective randomized trial examining health care utilization in individuals using multiple smartphone-enabled biosensors

Background. Mobile health and digital medicine technologies are becoming increasingly used by individuals with common, chronic diseases to monitor their health. Numerous devices, sensors, and apps are available to patients and consumers-some of which have been shown to lead to improved health management and health outcomes. However, no randomized controlled trials have been conducted which examine health care costs, and most have failed to provide study participants with a truly comprehensive monitoring system. Methods. We conducted a prospective randomized controlled trial of adults who had submitted a 2012 health insurance claim associated with hypertension, diabetes, and/or cardiac arrhythmia. The intervention involved receipt of one or more mobile devices that corresponded to their condition(s) (hypertension: Withings Blood Pressure Monitor; diabetes: Sanofi iBGStar Blood Glucose Meter; arrhythmia: AliveCor Mobile ECG) and an iPhone with linked tracking applications for a period of 6 months; the control group received a standard disease management program. Moreover, intervention study participants received access to an online health management system which provided participants detailed device tracking information over the course of the study. This was a monitoring system designed by leveraging collaborations with device manufacturers, a connected health leader, health care provider, and employee wellness program-making it both unique and inclusive. We hypothesized that health resource utilization with respect to health insurance claims may be influenced by the monitoring intervention. We also examined health-self management. Results & Conclusions. There was little evidence of differences in health care costs or utilization as a result of the intervention. Furthermore, we found evidence that the control and intervention groups were equivalent with respect to most health care utilization outcomes. This result suggests there are not large short-term increases or decreases in health care costs or utilization associated with monitoring chronic health conditions using mobile health or digital medicine technologies. Among secondary outcomes there was some evidence of improvement in health self-management which was characterized by a decrease in the propensity to view health status as due to chance factors in the intervention group.