Development and Validation of Claims-based Algorithms for Identifying Hospitalized Patients With COVID-19 and Their Severity in 2020 and 2021

BACKGROUND

This study aimed to develop and validate claims-based algorithms for identifying hospitalized patients with coronavirus disease 2019 (COVID-19) and disease severity.

METHODS

We used claims data including all patients at the National Center for Global and Medicine Hospital between January 1, 2020, and December 31, 2021. The claims-based algorithms for three statuses with COVID-19 (hospitalizations, moderate or higher status, and severe status) were developed using diagnosis codes (International Classification of Diseases, 10th revision code: U07.1, B34.2) and relevant medical procedure code. True cases were determined using the COVID-19 inpatient registry and electronic health records. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each algorithm at 6-month intervals.

RESULTS

Of the 75,711 total patients, the number of true cases was 1,192 for hospitalizations, 622 for moderate or higher status, and 55 for severe status. The diagnosis code-only algorithm for hospitalization had sensitivities 90.4% to 94.9% and PPVs 9.3% to 19.4%. Among the algorithms consisting of both diagnosis codes and procedure codes, high sensitivity and PPV were observed during the following periods: 93.9% and 97.1% for hospitalization (January-June 2021), 90.4% and 87.5% for moderate or higher status (July-December 2021), and 92.3% and 85.7% for severe status (July-December 2020), respectively. Almost all algorithms had specificities and NPVs of approximately 99%.

CONCLUSION

The diagnosis code-only algorithm for COVID-19 hospitalization showed low validity throughout the study period. The algorithms for hospitalizations, moderate or higher status, and severe status with COVID-19, consisting of both diagnosis codes and procedure codes, showed high validity in some periods.

Self-controlled Case Series Study for Acute Kidney Injury after Starting Proton Pump Inhibitors or Potassium-Competitive Acid Blocker in Patients with Cancer Using a Large Claims Database

To investigate the risk of acute kidney injury (AKI) in patients with cancer following the initiation of proton pump inhibitors (PPIs) and potassium-competitive acid blocker (PCAB), considering sex and anti-cancer drug use. We conducted a self-controlled case-series study using the Japan Medical Data Center claims data from 12422 patients with cancer who were prescribed PPIs or PCAB between January 2017 and December 2019. Considering the timing of PPI or PCAB, control period (days -120 to -1), risk period 1 (days 0 to +30), and risk period 2 (days +31 to +365) were defined. To assess the incidence rate ratio (IRR) and 95% confidence interval (CI) as the risk ratio, we adjusted for anti-cancer drugs to assess the risk of AKI. Additionally, we also examined sex differences to identify the risk of AKI. AKI was observed in risk period 1 [2.05 (1.12-3.72), p = 0.0192], but a slight reduction was noted in risk period 2 [0.60 (0.36-1.00), p = 0.0481]. A sex-specific increase in the risk of AKI was observed only in males during risk period 1 [2.18 (1.10-4.32), p = 0.0260], with a reduction in risk period 2 [0.48 (0.26-0.89), p = 0.0200]. We identified an increased risk of AKI in patients with cancer starting PPIs or PCAB particularly in males within 30 d after PPI or PCAB initiation, emphasizing the need for vigilant monitoring and management of AKI in this patient population.

Status of screening and preventive efforts against diabetic kidney disease between 2013 and 2018: analysis using an administrative database from Kyoto-city, Japan

Background

Japan has the second highest prevalence of dialysis use in the world. Approximately 40% of patients who begin dialysis have diabetic kidney disease (DKD). Local governments and medical facilities are required to provide preventive measures against worsening diabetes mellitus (DM). However, the percentage of patients with DM who receive such screening or interventions for DKD is unclear. This study aimed to reveal to what extent screening for DKD and preventive measures against worsening DKD are performed in patients with DM, using an administrative database in a municipality.

Methods

This was a cross-sectional study that used the Kyoto-city's administrative medical and long-term care database. Patients with a diagnosis of DM and receiving antidiabetic medication between 2013 and 2018 were defined as patients with DM and included. Patients with DKD were defined as those diagnosed with diabetic nephropathy or those with chronic kidney disease. We described the characteristics of patients with DM, diabetic complications, and extent of DKD screenings and preventive efforts against worsening of DM by fiscal year.

Results

Across fiscal years, 25.8% to 27.5% of patient with DM had DKD. More than 3% of patients were on dialysis due DM in each fiscal year; approximately 15% started receiving dialysis that year. The percentage of patients who were regularly prescribed antidiabetic medication and received glycosylated hemoglobin testing ranged from 64.0% to 67.2% and from 30.6% to 36.5%, respectively. Urine microalbuminuria testing at least once a year occurred in 9.3% to 10.0%. The percentage of patients who received nutritional guidance ranged from 19.0% to 21.0%. Approximately 1% of patients received guidance for preventing DM from progressing to a disease that requires dialysis each fiscal year.

Conclusion

This study from Japan, where a super-aging society has developed, using an administrative database in a municipality covering most of the elderly population clearly demonstrated an evidence-practice gap in efforts to prevent worsening of DKD. Strengthening cooperation between government and medical facilities and support for providing preventive measures against DKD are urgently needed.

Validation studies of Japanese administrative health care data: A scoping review

PURPOSE

Large-scale administrative health care databases are increasingly being utilized for research. However, there has not been much literature that validated administrative data in Japan; a previous review identified six validation studies published between 2011 and 2017. We conducted a literature review of studies that assessed the validity of Japanese administrative health care data.

METHODS

We searched for studies published by March 2022 that compared individual-level administrative data with a reference standard from another data source, as well as studies that validated administrative data using other data within the same database. The eligible studies were also summarized based on characteristics which included data types, settings, reference standard used, numbers of patients, and conditions validated.

RESULTS

There were 36 eligible studies, including 29 that used external reference standard and seven that validated administrative data using other data within the same database. Chart review was the reference standard in 21 studies (range of the numbers of patients, 72-1674; 11 studies conducted in single institutions and nine studies in 2-5 institutions). Five studies used a disease registry as the reference standard. Diagnoses of cardiovascular diseases, cancer, and diabetes were frequently evaluated.

CONCLUSIONS

Validation studies are being conducted at an increasing rate in Japan, although most of them are small scale. Further large-scale comprehensive validation studies are necessary to effectively utilize the databases for research.

Survival and medical costs of non-small cell lung cancer patients according to the first-line treatment: An observational study using the Kyoto City Integrated Database

BACKGROUND

Lung cancer is the primary cause of cancer mortality and non-small cell lung cancer (NSCLC) accounts for the majority of lung cancer cases. New drug treatments have been developed since 2010 but there are concerns about the increase in medical costs. This study aimed to compare survival and medical costs among patients with NSCLC according to their initial treatment to estimate the impact of early NSCLC detection.

METHODS

Patients with primary NSCLC who filed insurance claims between April 2013 and March 2019 were identified using the Kyoto City Integrated Database. Patients were divided into two groups depending on their initial treatment: the resection group and drug or radiation group. The survival and medical costs were calculated.

RESULTS

A total of 2609 patients with primary NSCLC were identified. Among them, 1035 patients underwent resection. The 5-year survival was 75% for the resection group while below 25% for the drug or radiation group. At 6 months of survival, the median cumulative total cost was 2409 thousand yen (interquartile range [IQR] 1947-4012 thousand yen) in the resection group and 2951 thousand yen (IQR 1600-4706 thousand yen) in the drug or radiation group. At 4 years of survival, the cumulative median total cost was 5257 thousand yen (IQR 3808-8243 thousand yen) in the resection group and 10 202 thousand yen (IQR 4845-20 450 thousand yen) in the drug or radiation group.

CONCLUSIONS

As a first-line therapy in newly diagnosed patients with NSCLC, surgical resection is associated with longer survival and lower medical costs than pharmacotherapy or radiotherapy.

Validation of algorithms to identify colorectal cancer patients from administrative claims data of a Japanese hospital

BACKGROUND

Administrative claims data are a valuable source for clinical studies; however, the use of validated algorithms to identify patients is essential to minimize bias. We evaluated the validity of diagnostic coding algorithms for identifying patients with colorectal cancer from a hospital's administrative claims data.

METHODS

This validation study used administrative claims data from a Japanese university hospital between April 2017 and March 2019. We developed diagnostic coding algorithms, basically based on the International Classification of Disease (ICD) 10th codes of C18-20 and Japanese disease codes, to identify patients with colorectal cancer. For random samples of patients identified using our algorithms, case ascertainment was performed using chart review as the gold standard. The positive predictive value (PPV) was calculated to evaluate the accuracy of the algorithms.

RESULTS

Of 249 random samples of patients identified as having colorectal cancer by our coding algorithms, 215 were confirmed cases, yielding a PPV of 86.3% (95% confidence interval [CI], 81.5-90.1%). When the diagnostic codes were restricted to site-specific (right colon, left colon, transverse colon, or rectum) cancer codes, 94 of the 100 random samples were true cases of colorectal cancer. Consequently, the PPV increased to 94.0% (95% CI, 87.2-97.4%).

CONCLUSION

Our diagnostic coding algorithms based on ICD-10 codes and Japanese disease codes were highly accurate in detecting patients with colorectal cancer from this hospital's claims data. The exclusive use of site-specific cancer codes further improved the PPV from 86.3 to 94.0%, suggesting their desirability in identifying these patients more precisely.

Validation of Algorithms to Identify Bone Metastases Using Administrative Claims Data in a Japanese Hospital

BACKGROUND

Validated coding algorithms are essential to generate high-quality, real-world evidence from claims data studies.

OBJECTIVE

We aimed to evaluate the validity of the algorithms to identify patients with bone metastases using claims data from a Japanese hospital.

PATIENTS AND METHODS

This study used administrative claims data and electronic medical records at Juntendo University Hospital from April 2017 to March 2019. We developed two candidate claims-based algorithms to detect bone metastases, one based on diagnosis codes alone (Algorithm 1) and the other based on the combination of diagnosis and imaging test codes (Algorithm 2). Of the patients identified by Algorithm 1, 100 patients were randomly sampled. Among these 100 patients, 88 patients met the conditions of Algorithm 2; further, 12 additional patients were randomly sampled from those identified by Algorithm 2, thus obtaining a total of 100 patients for Algorithm 2. They were evaluated for their true diagnosis using the patient chart review as the gold standard. The positive predictive value (PPV) was calculated to assess the accuracy of each algorithm.

RESULTS

For Algorithm 1, 82 patients were analyzed after excluding 18 patients without diagnostic imaging reports. Of these, 69 patients were true positive by chart review, resulting in a PPV of 84.1% (95% confidence interval (CI) 74.5-90.6). For Algorithm 2, 92 patients were analyzed after excluding eight patients whose diagnoses were not judged by chart review. Of these, 76 patients were confirmed positive by chart review, yielding a PPV of 82.6% (95% CI 73.4-89.1).

CONCLUSION

Both claims-based algorithms yielded high PPVs of approximately 85%, with no improvement in PPV by adding imaging test conditions. The diagnosis code-based algorithm is sufficient and valid for identifying bone metastases in this Japanese hospital.

Accuracy of algorithms to identify patients with a diagnosis of major cancers and cancer-related adverse events in an administrative database: a validation study in an acute care hospital in Japan

OBJECTIVES

Validation studies in oncology are limited in Japan. This study was conducted to evaluate the accuracy of diagnosis and adverse event (AE) definitions for specific cancers in a Japanese health administrative real-world database (RWD).

DESIGN AND SETTING

Retrospective observational validation study to assess the diagnostic accuracy of electronic medical records (EMRs) and claim coding regarding oncology diagnosis and AEs based on medical record review in the RWD. The sensitivity and positive predictive value (PPV) with 95% CIs were calculated.

PARTICIPANTS

The validation cohort included patients with lung (n=2257), breast (n=1121), colorectal (n=1773), ovarian (n=216) and bladder (n=575) cancer who visited the hospital between January 2014 and December 2018, and those with prostate cancer (n=3491) visiting between January 2009 and December 2018, who were identified using EMRs.

OUTCOMES

Key outcomes included primary diagnosis, deaths and AEs.

RESULTS

For primary diagnosis, sensitivity and PPV for the respective cancers were as follows: lung, 100.0% (96.6 to 100.0) and 81.0% (74.9 to 86.2); breast, 100.0% (96.3 to 100.0) and 74.0% (67.3 to 79.9); colorectal, 100.0% (96.6 to 100.0) and 80.5% (74.3 to 85.8); ovarian, 89.8% (77.8 to 96.6) and 75.9% (62.8 to 86.1); bladder, 78.6% (63.2 to 89.7) and 67.3% (52.5 to 0.1); prostate, 100.0% (93.2 to 100.0) and 79.0% (69.7 to 86.5). Sensitivity and PPV for death were as follows: lung, 97.0% (84.2 to 99.9) and 100.0% (84.2 to 100.0); breast, 100.0% (1.3 to 100.0) and 100.0% (1.3 to 100.0); colorectal, 100.0% (28.4 to 100.0) and 100.0% (28.4 to 100.0); ovarian, 100.0% (35.9 to 100.0) and 100.0% (35.9 to 100.0); bladder, 100.0% (9.4-100.0) and 100.0% (9.4 to 100.0); prostate, 75.0% (19.4 to 99.4) and 100.0% (19.4 to 100.0). Overall, PPV tended to be low, with the definition based on International Classification of Diseases, 10th revision alone for AEs.

CONCLUSION

Diagnostic accuracy was not so high, and therefore needs to be further investigated.

TRIAL REGISTRATION NUMBER

University Hospital Medical Information Network (UMIN) Clinical Trials Registry (UMIN000039345).

Validity of Diagnostic Algorithms for Inflammatory Bowel Disease in Japanese Hospital Claims Data

Inflammatory bowel disease (IBD) diagnoses are increasing in Japan. Some patients have symptoms that are difficult to control, and further research on IBD is needed. Claims databases, which have a large sample size, can be useful for IBD research. However, it is unclear whether the International Classification of Diseases, Tenth Revision (ICD-10) codes alone can correctly identify IBD. We aimed to develop algorithms to identify IBD in claims databases. We used claims data from the Department of Gastroenterology, Tohoku University Hospital from 1 January 2016 to 31 December 2020. We developed 11 algorithms by combining the ICD-10 code, prescription drug, and workup information. We had access to the database which contains all the information for Crohn’s disease and ulcerative colitis patients who visited our department, and we used it as the gold standard. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value for each algorithm. We enrolled 19,384 patients, and among them, 1012 IBD patients were identified in the gold standard database. Among 11 algorithms, Algorithm 4 (ICD-10 code and ≥1 prescription drugs) showed a strong performance (PPV, 94.8%; sensitivity, 75.6%). The combination of an ICD-10 code and prescription drugs may be useful for identifying IBD among claims data.

Temporal Trend in an Initial Treatment, Survival, and Medical Costs Among Patients With Lung Cancer Between 2013 and 2018 in Kyoto City, Japan

OBJECTIVES

This study aimed to identify the variation of treatment contents and outcomes and economic burden of lung cancer among the elderly population in Japan.

METHODS

New-onset primary lung cancer from April 2013 to March 2019 were identified by using the Kyoto City administrative database for National Health Insurance and Advanced Elderly Medical Service System. Patient characteristics, initial treatment, medical costs, and deaths were analyzed. Continuous variables were calculated using standard descriptive statistical methods.

RESULTS

A total of 4845 people who were diagnosed as having lung cancer and received any treatment between 2013 and 2018 were included in the study. The average age of patients was 73 to 74 years for a 6-year study period. The proportion of patients who received surgery, drug therapy, and radiation therapy as initial treatment was 31% to 42%, 36% to 44%, and 21% to 24%, respectively. Healthcare costs increased between fiscal year (FY) 2014 and FY 2018, with a particularly significant increase of 340 million for drug therapy, whereas the mortality rate in <2-year follow-up decreased from 42.7% in FY 2013 to 368% in FY 2016.

CONCLUSIONS

This cross-sectional study demonstrated that the improvement in the survival rate and proportion of surgery as an initial treatment was increased whereas drug therapy decreased and medical costs increased among patients with lung cancer over time. Based on these results, it is necessary to implement sustainable healthcare measures with a consideration of cost-effectiveness.

Context and Considerations for Use of Two Japanese Real-World Databases in Japan: Medical Data Vision and Japanese Medical Data Center

In Japan, an increasing interest in real-world evidence for hypothesis generation and decision-making has emerged in order to overcome limitations and restrictions of clinical trials. We sought to characterize the context and concrete considerations of when to use Medical Data Vision (MDV) and JMDC databases, the main Japanese real-world data (RWD) sources accessible by pharmaceutical companies. Use cases for these databases, and related issues and considerations, were identified and summarized based on a literature search and experience-based knowledge. Studies conducted using MDV or JMDC were mostly descriptive in nature, or explored potential risk factors by evaluating associations with a target outcome. Considerations such as variable ascertainment at different time points, including issues relating to treatment identification and missing data, were highlighted for these two databases. Although several issues were commonly shared (e.g., only month of event occurrence reported), some database-specific issues were also identified and need to be accounted for. In conclusion, MDV and JMDC present limitations that are relatively typical of RWD sources, though some of them are unique to Japan, such as the identification of event occurrence and the inability to track patients visiting different healthcare settings. Addressing study design and careful result interpretation with respect to the specificities and uniqueness of the Japanese healthcare system is of particular importance. This aspect is especially relevant with respect to the growing global interest of conducting RWD studies in Japan.

Use of diagnostic likelihood ratio of outcome to evaluate misclassification bias in the planning of database studies

Background

The diagnostic likelihood ratio (DLR) and its utility are well-known in the field of medical diagnostic testing. However, its use has been limited in the context of an outcome validation study. We considered that wider recognition of the utility of DLR would enhance the practices surrounding database studies. This is particularly timely and important since the use of healthcare-related databases for pharmacoepidemiology research has greatly expanded in recent years. In this paper, we aimed to advance the use of DLR, focusing on the planning of a new database study.

Methods

Theoretical frameworks were developed for an outcome validation study and a comparative cohort database study; these two were combined to form the overall relationship. Graphical presentations based on these relationships were used to examine the implications of validation study results on the planning of a database study. Additionally, novel uses of graphical presentations were explored using some examples.

Results

Positive DLR was identified as a pivotal parameter that connects the expected positive-predictive value (PPV) with the disease prevalence in the planned database study, where the positive DLR is equal to sensitivity/(1-specificity). Moreover, positive DLR emerged as a pivotal parameter that links the expected risk ratio with the disease risk of the control group in the planned database study. In one example, graphical presentations based on these relationships provided a transparent and informative summary of multiple validation study results. In another example, the potential use of a graphical presentation was demonstrated in selecting a range of positive DLR values that best represented the relevant validation studies.

Conclusions

Inclusion of the DLR in the results section of a validation study would benefit potential users of the study results. Furthermore, investigators planning a database study can utilize the DLR to their benefit. Wider recognition of the full utility of the DLR in the context of a validation study would contribute meaningfully to the promotion of good practice in planning, conducting, analyzing, and interpreting database studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01757-1.

Validation Study of Algorithms to Identify Malignant Tumors and Serious Infections in a Japanese Administrative Healthcare Database

BACKGROUND

This retrospective observational study validated case-finding algorithms for malignant tumors and serious infections in a Japanese administrative healthcare database.

METHODS

Random samples of possible cases of each disease (January 2015-January 2018) from two hospitals participating in the Medical Data Vision Co., Ltd. (MDV) database were identified using combinations of ICD-10 diagnostic codes and other procedural/billing codes. For each disease, two physicians identified true cases among the random samples of possible cases by medical record review; a third physician made the final decision in cases where the two physicians disagreed. The accuracy of case-finding algorithms was assessed using positive predictive value (PPV) and sensitivity.

RESULTS

There were 2,940 possible cases of malignant tumor; 180 were randomly selected and 108 were identified as true cases after medical record review. One case-finding algorithm gave a high PPV (64.1%) without substantial loss in sensitivity (90.7%) and included ICD-10 codes for malignancy and photographing/imaging. There were 3,559 possible cases of serious infection; 200 were randomly selected and 167 were identified as true cases after medical record review. Two case-finding algorithms gave a high PPV (85.6%) with no loss in sensitivity (100%). Both case-finding algorithms included the relevant diagnostic code and immunological infection test/other related test and, of these, one also included pathological diagnosis within 1 month of hospitalization.

CONCLUSIONS

The case-finding algorithms in this study showed good PPV and sensitivity for identification of cases of malignant tumors and serious infections from an administrative healthcare database in Japan.

Regional Inequality in Dental Care Utilization in Japan: An Ecological Study Using the National Database of Health Insurance Claims

BACKGROUND

This study examined regional inequalities in dental care utilization in Japan and the association of dental care utilization with socioeconomic factors.

METHODS

Using the Fourth National Database of Health Insurance Claims and Specific Health Checkups of Japan Open data, this ecological study analyzed 216 million pieces of aggregated data from April 2017 to March 2018. Nine indicators of dental care utilization were used: outpatient visits, outreach services, cavity fillings, pulpectomies, dental calculus removals, periodontal surgeries, tooth extractions, dental bridges, and dentures. Standardized claim ratios (SCRs) for these indicators were calculated for Japan's 47 prefectures, which were divided into three groups based on the number of dental clinics per population, average income per capita, and the proportion of university enrollments. Associations of the dental care utilization with dental supply and regional socioeconomic factors were examined.

FINDINGS

The ratios of maximum to minimum of SCRs were 1·4 for outpatient visits, 19·3 for outreach services, and 17·6 for periodontal surgeries. Dental supply was positively associated with outpatient visits, outreach services, dental calculus removal, and periodontal surgeries. Regional average income and educational level were positively associated with dental calculus removals, and negatively associated with pulpectomies, tooth extractions, dental bridges, and dentures.

INTERPRETATION

In Japan, regional inequalities in dental care utilization exist for periodontal care and outreach services but are smaller for urgent and substantial dental care. Regional income and educational levels appear to have influence on dental care utilization.

FUNDING

Ministry of Health, Labour and Welfare of Japan (H31-19FA1001).

Accuracy of Japanese claims data in identifying diabetes-related complications

PURPOSE

To evaluate the accuracy of various claims-based definitions of diabetes-related complications (coronary artery disease [CAD], heart failure, cerebrovascular disease and dialysis).

METHODS

We evaluated data on 1379 inpatients who received care at the Niigata University Medical & Dental Hospital in September 2018. Manual electronic medical chart reviews were conducted for all patients with regard to diabetes-related complications and were used as the gold standard. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of each claims-based definition associated with diabetes-related complications based on Diagnosis Procedure Combination (DPC), International Classification of Diseases, Tenth Revision (ICD-10) codes, procedure codes and medication codes were calculated.

RESULTS

DPC-based definitions had higher sensitivity, specificity, and PPV than ICD-10 code definitions for CAD and cerebrovascular disease, with sensitivity of 0.963-1.000 and 0.905-0.952, specificity of 1.000 and 1.000, and PPV of 1.000 and 1.000, respectively. Sensitivity, specificity, and PPV were high using procedure codes for CAD and dialysis, with sensitivity of 0.963 and 1.000, specificity of 1.000 and 1.000, and PPV of 1.000 and 1.000, respectively. DPC and/or ICD-10 codes + medication were better for heart failure than the ICD-10 code definition, with sensitivity of 0.933, specificity of 1.000, and PPV of 1.000. The PPVs were lower than 60% for all diabetes-related complications using ICD-10 codes only.

CONCLUSION

The DPC-based definitions for CAD and cerebrovascular disease, procedure codes for CAD and dialysis, and DPC or ICD-10 codes with medication codes for heart failure could accurately identify these diabetes-related complications from claims databases.

Validation of novel identification algorithms for major adverse cardiovascular events in a Japanese claims database

Predicting clinical outcomes can be difficult, particularly for life‐threatening events with a low incidence that require numerous clinical cases. Our aim was to develop and validate novel algorithms to identify major adverse cardiovascular events (MACEs) from claims databases. We developed algorithms based on the data available in the claims database International Classification of Diseases, Tenth Revision (ICD‐10), drug prescriptions, and medical procedures. We also employed data from the claims database of Jichi Medical University Hospital, Japan, for the period between October 2012 and September 2014. In total, we randomly extracted 100 potential acute myocardial infarction cases and 200 potential stroke cases (ischemic and hemorrhagic stroke were analyzed separately) based on ICD‐10 diagnosis. An independent committee reviewed the corresponding clinical data to provide definitive diagnoses for the extracted cases. We then assessed the algorithms’ accuracy using positive predictive values (PPVs) and apparent sensitivities. The PPVs of acute myocardial infarction, ischemic stroke, and hemorrhagic stroke were low only by diagnosis (81.6% [95% CI 72.5–88.7]; 31.0% [95% CI 22.8–40.3]; and 45.5% [95% CI 34.1–57.2], respectively); however, the PPVs were elevated after adding the prescription and procedure data (87.0% [95% CI 78.3–93.1]; 44.4% [95% CI 32.7–56.6]; and 46.1% [95% CI 34.5–57.9], respectively). When we added event‐specific prescription and procedure data to the algorithms, the PPVs for each event increased to 70%–98%, with apparent sensitivities exceeding 50%. Algorithms that rely on ICD‐10 diagnosis in combination with data on specific drugs and medical procedures appear to be valid for identifying MACEs in Japanese claims databases.

Fracture risk increased by concurrent use of central nervous system agents in older people: Nationwide case-crossover study

BACKGROUND

Multiple medication use among older patients is reported to increase fracture risk. But this association is unclear in different subgroups and has not been confirmed by a case-crossover study, which can eliminate measurable and unmeasurable time-invariant confounders.

OBJECTIVE

To estimate the fragility fracture risk associated with concurrent use of multiple central nervous system (CNS) agents in older patients using a case-crossover design.

METHODS

This study targeted almost all patients aged ≥65 years in Japan who incurred fragility fractures from May 2013 to September 2014, based on the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB Japan). Conditional logistic regression analysis estimated the risk of fragility fracture associated with the daily number of CNS agents, including subgroup analyses stratified by sex, age, and fracture location.

RESULTS

For 446,101 patients, the adjusted odds ratios (ORs) of fragility fracture increased almost linearly with number of CNS agents; 0, 0-1, 1-2, 2-3, 3-4, 4-5, and >5: OR reference, 1.21 (95% confidence interval, 1.18-1.23), 1.40 (1.35-1.46), 1.58 (1.49-1.67), 1.89 (1.74-2.05), 1.80 (1.60-2.03), and 1.90 (1.61-2.23; trend p < 0.001), respectively. A similar trend was observed for several subgroups, especially in males and those aged ≥85 years, showing marked linearity.

CONCLUSIONS

The increased risk of fragility fracture associated with the use of multiple CNS agents was robust in older people in Japan.

Fragility Fractures in Older People in Japan Based on the National Health Insurance Claims Database

Fragility fractures associated with age-related bone loss are of urgent concern worldwide because they reduce QOL and pose financial burdens for health care services. Currently, national data in Japan are limited. This study provides quantitative data for older patients throughout Japan who, although otherwise relatively healthy, sustained fragility fractures and were hospitalized for them. The National Database of Health Insurance Claims and Specific Health Checkups of Japan was accessed to target patients aged 65 years or older who sustained fractures between May 2013 and September 2014 and were not hospitalized for at least 13 months prior to fracture. We investigated whether the first fracture sustained was fragility related at any of four locations (proximal humerus, distal radius, vertebra, or femoral neck) and whether it necessitated hospitalization. Fragility fractures were identified in 490138 of 1188754 patients (41.2%, 345980 patients/year; 1 : 4 male-to-female ratio). Regardless of gender, vertebral fractures were most common across the age cohorts studied (43286 males and 162767 females/year), and femoral neck fractures increased markedly with increased patient age. Approximately 80% of patients with femoral neck fractures were hospitalized (62.3% of males, 71.1% of females) compared with up to 10.4% of patients with other fragility fractures. Data provided in this study can be used as a baseline for evaluating the health economy and establishing health policy in Japan.