Usefulness and caveats of real-world data for research on hypertension and its association with cardiovascular or renal disease in Japan

Abstract

The role of real-world data, collected from clinical practice rather than clinical trials, has become increasingly important for investigating real-life situations, such as treatment effects. In Japan, evidence on hypertension, cardiovascular diseases, and kidney diseases using real-world data is increasing. These studies are mainly based on "the insurer-based real-world data" collected as electronic records, including data from health check-ups and medical claims such as JMDC database, DeSC database, the Japan Health Insurance Association (JHIA) database, or National Databases of Health Insurance Claims and Specific Health Checkups (NDB). Based on the insurer-based real-world data, traditional but finely stratified associations between hypertension and cardiovascular or kidney diseases can be explored. The insurer-based real-world data are also useful for pharmacoepidemiological studies that capture the distribution and trends of drug prescriptions; combined with annual health check-up data, the effectiveness of drugs can also be examined. Despite the usefulness of insurer-based real-world data collected as electronic records from a wide range of populations, we must be cautious about several points, including issues regarding population uncertainty, the validity of cardiovascular outcomes, the accuracy of blood pressure, traceability, and biases, such as indication and immortal biases. While a large sample size is considered a strength of real-world data, we must keep in mind that it does not overcome the problem of systematic error. This review discusses the usefulness and pitfalls of insurer-based real-world data in Japan through recent examples of Japanese research on hypertension and its association with cardiovascular or kidney disease.

Keywords: Blood pressure; Cardiovascular diseases; Citizen science; Hypertension; Kidney diseases.

Fig. 1

Major health insurance type and outline of the system in Japan. *Insurers receive claims/health screening data through employers or other related organizations. The data is provided to researchers as secondary data for use under appropriate contracts among related organizations

Fig. 2

Adjusted hazard ratios (HRs) of CKD according to SBP and DBP. The HRs were adjusted for age, sex, body mass index <18.5 kg/m² and ≥25 kg/m², current smoking status, alcohol consumption, diabetes mellitus, dyslipidemia, and estimated glomerular filtration rate (eGFR) at baseline. The group with systolic/diastolic blood pressure (SBP/DBP) < 120/ ≥ 100 mmHg (n = 6) was excluded from the analysis due to the limited number of participants and events. SBP, systolic blood pressure; DBP, diastolic blood pressure; CKD chronic kidney disease, HR hazard ratio, CI confidence interval. This figure was copied from a corresponding article (Suenaga et al. [32])

Fig. 3

Blood pressure and eGFR change relative to the baseline. ‘Pre’ indicates the pretreatment visit, i.e., baseline. eGFR, estimated glomerular filtration rate. This figure was copied from a corresponding article with permission (Satoh et al. [15]). Use of the material in any format is prohibited without written permission from the publisher, Wolters Kluwer Health, Inc. Please contact permissions@lww.com for further details. The data were based on hypertensive participants (n = 10,151), dihydropyridine calcium channel blocker (dCCB) users (n = 5078), and angiotensin II receptor blocker (ARB) users (n = 5073)

Fig. 4

Apparent relative risks and positive predictive values according to specificity and incidence rate in a simulation. The y-axis is for the relative risk (RR) and positive predictive value (PPV). The curve is based on a power function

Fig. 5

Apparent relative risk according to sensitivity in the unexposed group in a simulation. The results indicate the apparent relative risk under the assumption that the sensitivity differed between the exposed and unexposed groups, whereas the specificity is 1.0 (perfect) in both groups. The apparent relative risk was not affected by the incidence rate or specificity. The curve is based on a rational function