BMJ Open. Validity assessment of self-reported medication use by comparing to pharmacy insurance claims

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1 Validity assessment of self-reported medication use by comparing to pharmacy insurance claims Journal: BMJ Open Manuscript ID: bmjopen Article Type: Research Date Submitted by the Author: -Jul-0 Complete List of Authors: Fujita, Misuzu; Chiba University, Department of Public Health Sato, Yasunori; Chiba University, Department of Global Clinical Research; Chiba University Hospital, Clinical Research Center Nagashima, Kengo; Chiba University, Department of Global Clinical Research; Chiba University Hospital, Clinical Research Center Takahashi, Sho; Chiba University Hospital, Clinical Research Center Hata, Akira; Chiba University, Department of Public Health <b>primary Subject Heading</b>: Health services research Secondary Subject Heading: Diabetes and endocrinology Keywords: Hypertension < CARDIOLOGY, General diabetes < DIABETES & ENDOCRINOLOGY, Lipid disorders < DIABETES & ENDOCRINOLOGY, MEDICAL HISTORY

2 Page of BMJ Open Validity assessment of self-reported medication use by comparing to pharmacy insurance claims Misuzu Fujita, Yasunori Sato,, Kengo Nagashima,, Sho Takahashi, Akira Hata Chiba University, Department of Public Health, Chiba City, Chiba, Japan Chiba University, Department of Global Clinical Research, Chiba City, Chiba, Japan Chiba University Hospital, Clinical Research Center, Chiba City, Chiba, Japan Keywords: sensitivity and specificity, validation studies, questionnaire, drug utilization, insurance Corresponding author: Misuzu Fujita Chiba University, Department of Public Health -- Inohana, Chuo-ku, Chiba 0-, Japan address: [email protected] Telephone: +---0

3 Page of ABSTRACT Objectives: In Japan, an annual health check-up and health promotion guidance program was established in 00 in accordance with the Act on Assurance of Medical Care for the Elderly. A self-reported questionnaire for medication use is a required item in this program and has been used widely, but its validity has not been assessed. The aim of this study is to evaluate the validity of this questionnaire by comparing self-reported usage to pharmacy insurance claims. Setting: This is a population-based validation study. Self-reported medication use for hypertension, diabetes, and dyslipidemia are evaluated measurements. Data of pharmacy insurance claims is the reference standard. Participants: The subjects of this study were, beneficiaries of the National Health Insurance of Chiba City. Primary and secondary outcome measures: Sensitivity, specificity, and Kappa statistics of the self-reported medication use questionnaire for predicting actual prescriptions during one month (that of the check-up) and three months (that of the check-up and the previous two) were calculated. Results: Sensitivity and specificity scores of questionnaire data for predicting insurance claims covering three months were.% (% confidence interval (CI):.-.) and.% (% CI:.0-.) for hypertension,.% (% CI:.-.0) and.% (% CI:.-.) for diabetes, and.% (% CI:.-.) and.0% (% CI: 0.-.) for dyslipidemia. Corresponding kappa statistics were 0.% (% CI: 0.-.),.% (% CI:.-.), and.% (% CI:.-0.). The specificity was significantly higher for questionnaire data covering three months compared to data covering one month for all three disease conditions. Conclusion: Self-reported questionnaire data for medication use were of sufficiently high validity for further analyses. Item responses are an accurate representation of actual prescriptions, particularly those covering three months.

4 Page of BMJ Open Strengths and limitations of this study A self-reported questionnaire on medication use is a required item in the annual health check-up system of Japan, however, no validity assessment of this questionnaire has been performed. We evaluated the validation of this questionnaire by comparing self-reported usage to pharmacy insurance claims, a record that is free of recall bias and regarded as the gold standard. Population-based large sample was used. Specificity might be under-estimated due to incompleteness of pharmacy insurance claims data.

5 Page of INTRODUCTION Self-reported questionnaires have been employed to determine drug usage in many epidemiological studies. - However, the accuracy of the information obtained by the questionnaire is limited by recall bias. - A substantial amount of inaccurate data could result in misclassification bias, leading to incorrect estimates of disease risk and/or prevalence. medication use. Nevertheless, few studies have evaluated the validity of self-reported - In Japan, an annual health check-up and health promotion guidance program in accordance with the Act on Assurance of Medical Care for the Elderly enforced by the Ministry of Health, Labour and Welfare (MHLW) was initiated in April 00. Medical insurers are obliged to provide this program to all their beneficiaries aged 0 to years. During the period from 00 to 0, roughly million people utilized the program. Individuals with metabolic syndrome are a primary target of this program. A self-reported questionnaire of medication use for hypertension, hyperglycemia, and hypercholesterolemia is one of the required items and the obtained data are used to identify individuals with metabolic syndrome in need of further guidance. In addition, untreated individuals with laboratory data strongly indicating hypertension, diabetes, and/or dyslipidemia are recommended to see a doctor. Data from this annual health check-up and guidance have been collected in the National Database since fiscal year (FY) 00 by MHLW and are now available to public health researchers. The aim of this study was to evaluate the validity of the self-reported questionnaire for use of drugs for hypertension, diabetes, and dyslipidemia by comparing self-reported usage to pharmacy insurance claims, a record that is free of recall bias and regarded as the gold standard. METHODS Subjects The subjects of this study were beneficiaries of National Health Insurance (NHI) of Chiba City. Japan has a universal health care insurance system that covers all citizens. There are two types of coverage for individuals younger than years, Employees Health Insurance and NHI; the latter is managed by municipalities and covers the self-employed, farmers, retirees, and the unemployed. The subjects in this study

6 Page of BMJ Open consisted of,0 beneficiaries aged 0 years who received a health check-up from May, 0 to February, 0. Of these individuals, with missing data were excluded, for a final total of, beneficiaries (, men and,0 women). Health check-up data and pharmacy insurance claim data were integrated for comparison using the values of household number, birth month, and sex. Ethics statement Consent was not obtained from subjects because this study was performed using only existing data. To ensure anonymity, personal identifiers (e.g., name, address, and telephone number) were removed from the records, date of birth was changed to the st of the month, and personal number and household number administered by Chiba City Hall were converted to random numbers prior to release. The Research Ethics Committee of the Graduate School of Medicine, Chiba University approved this study. The study was conducted in accordance with the Declaration of Helsinki. Definition of self-reported medication users The self-reported questionnaire, which is required in the health check-up, includes the following item. Are you currently taking the following medications?. Medication for hypertension (yes or no). Insulin injection or oral medication for hyperglycemia (yes or no). Medication for hypercholesterolemia (yes or no) A subject who answers yes is defined as a self-reported user of drugs to treat hypertension, diabetes, and/or dyslipidemia. Definition of true medication users True medication users were determined by pharmacy claims submitted from April 0 to March 0. Unfortunately, pharmacy insurance claims include only prescriptions dispensed outside hospitals. Nonetheless, pharmacy insurance claims are available for.% of all prescriptions filled in Chiba Prefecture during FY 0. Generic names of medications for the three conditions are listed in Supplementary Files

7 Page of to. For detecting appropriate drugs, the Database of Drugs in Japan was used. Codes of the Anatomical Therapeutic Chemical Classification System (ATC codes) provided by the World Health Organization have not been assigned to every drug used in Japan, but we list as many as possible in the Supplementary Files. Initially, we used two different definitions for the true medication users detected by pharmacy insurance claims: one for subjects prescribed during the same month as the health check-up (one month); and the other for subjects prescribed during the same month as the check-up or in the previous two months (three months). In Japan, the law limiting prescriptions to two weeks was repealed in April 00 to allow long-term prescriptions (with some exceptions). Thus, even if the subjects did not receive a prescribed medication during a survey month, they might have already received during the previous month. To overcome this possible omission error, we decided to analyze the month of the check-up and the month of the check-up plus the previous two months separately. Equivalent household income Individual annual income from January to December, 0 was obtained from tax records at Chiba City Hall. Number of people per household was obtained by counting the persons with the same household number. People per household included persons insured by NHI in Chiba City and other householders regardless of whether he or she was a beneficiary. Household income was calculated by adding the incomes of all household members as mentioned above. An equivalent household income was calculated as household income divided by the square of the number of household members. Statistical analysis The proportions of the individual medication users as determined by the self-reported questionnaire and by pharmacy insurance claims were compared by McNemar s test. For assessing the validity of self-reported medication use for hypertension, diabetes, and dyslipidemia, medication use as detected by pharmacy insurance claims was assumed to be accurate (as the gold standard). Sensitivity was calculated as the proportion of subjects with self-reported medication use among the subjects with pharmacy claims, and specificity was calculated as the proportion of subjects without self-reported medication use among the subjects without pharmacy claims. In addition, kappa

8 Page of BMJ Open statistics were also calculated for each medical condition. The kappa statistic measure of agreement is scaled to be zero when the agreement is what would be expected by chance and to when there is perfect agreement. Landis and Koch defined values of as slight, as fair, as moderate, as substantial, and as almost perfect agreement. 0 All comparisons were planned and all tests two-tailed. A p-value < 0.0 was considered statistically significant. All statistical analyses were performed using the STATA software package (Stata Corp., College Station, TX). RESULTS Subject demographics, clinical characteristics, and medication use as determined by the self-reported questionnaire and insurance claims are shown in Table. Means and standard deviations of age and body mass index were. ±. years and. ±. kg/m. Median equivalent income was,000 JPY (as of June 0, 0, USD was the equivalent of. JPY). The proportions of subjects prescribed medications for hypertension, diabetes, and dyslipidemia as indicated by insurance claims during the check-up month (0.% for hypertension,.0% for diabetes, and.% for dyslipidemia) and within three months (.0% for hypertension,.0% for diabetes, and.% for dyslipidemia) were all significantly lower than as indicated from self-reports (.% for hypertension,.% for diabetes, and.% for dyslipidemia). Table presents the results of the validity analysis of self-reported medication use in all subjects. In general, the self-reported questionnaire predicted actual prescription (i.e., according to pharmacy insurance claims) with high sensitivity and specificity for both the month of check-up and for three months. Specificity was uniformly higher for predicting prescription within months for all three drug classes. Kappa values were also higher for predicting prescription within months compared to one month. Thus, the self-reported questionnaire more accurately represented medication use for three months that for one month, the only exception being sensitivity for hypertension medication use (p<0.0). Only results for predicting actual prescriptions over three months were used for subgroup analyses (Table ). Analyses of subgroups divided by sex, age range, and income are shown in Table. In all subgroups, sensitivity and specificity were >0% and kappa statistic >0%. Thus, the validity of the self-report questionnaire was high regardless of sex, age, and income.

9 Page of DISCUSSION The self-reported medication use questionnaire for the annual health check-up program overseen by the Japanese MHLW was found to have high validity for predicting actual prescriptions for drugs used to treat hypertension, diabetes, and dyslipidemia. The national database compiled from these self-reports is thus a reliable tool for epidemiological studies of hypertension, diabetes, and dyslipidemia in Japan. One drawback of the pharmacy claims dataset used as the comparator is that it does not cover prescriptions filled by hospital pharmacies, which accounted for.% of all prescription in FY 0. Indeed, the proportions of subjects with actual pharmacy claims were significantly lower than the proportions based on self-reports for all three conditions. Accordingly, we should consider how this drawback influences the accuracy of the sensitivity and specificity values calculated. For calculation of sensitivity, we determined the proportion of subjects self-reporting use only among those with external (outside of hospital) prescriptions. However, if the responses to the self-reported questionnaire by subjects with external prescriptions and those with in-hospital prescriptions are assumed to be the same, the sensitivity should be close to the true figure. On the other hand, for calculation of specificity, we used the number of subjects without actual prescription claims as the denominator, which includes those actually prescribed in-hospital, making the value lower than the true data. Despite this influence, however, values of specificity and sensitivity were satisfactory (>0%) for all three diseases. To date, only two studies to our knowledge have conducted a validity assessment of self-reported medication use for hypertension, diabetes, and/or dyslipidemia. One assessed self-reported drug use for hypertension and diabetes in, subjects of different ethnicities in British Columbia, Canada. 0 This study found high specificity for hypertension (% 00% in each ethnicity) and diabetes (% 00% in each ethnicity), but relatively low sensitivity for hypertension (0% % in each ethnicity). Here, the two-step methodology may have influenced the result. In the first step, only those subjects who answered Yes to Do you have this condition? were extracted. Then, the extracted subjects were asked In the past months, have you taken any medicine for this condition? It is known that this two-step method increases specificity but

10 Page of BMJ Open decreases sensitivity. In contrast, the self-reported questionnaire item for medication use analyzed in the present study asks subjects a single question for each condition ( Are you currently taking the following medications? ) and high validity was observed, indicating that this mode of questioning is appropriate and reliable. The second study from Washington State reported high validity for self-reported medication use for hypertension and dyslipidemia (statins) in 0 participants of a population-based, case-control study of breast cancer in women aged years. Sensitivity in cases and controls were % and % for hypertensive medication and % and % for statins. Specificity was % and % for hypertensive medication and % and % for statins. This result is quite similar to ours. In contrast to medication for hypertension, diabetes, and dyslipidemia, these studies reported lower sensitivity of self-reported medication use for asthma (% % in each ethnicity 0 ) and depression (% and % in cases and controls ), suggesting that the validity of self-reported medication use depends on the specific medical condition. So et al. suggested that the difference in self-report validity among medication types is likely related to the frequency of use. 0 For example, self-report validity for medications used to treat acute symptoms, such as asthma, tends to be lower than that for medications taken routinely for chronic conditions such as hypertension, diabetes, and dyslipidemia. Further studies are needed to confirm whether this is also the case for the health check-up self-report questionnaire. We found that validity of the self-reported questionnaire was higher (more accurately reflected medication use) during three months than during one month. Patients in Japan can now obtain longer-term prescriptions (after the law limiting prescriptions to two weeks was repealed). Thus, we assumed that many patients currently taking medications for hypertension, diabetes, and/or dyslipidemia would have prescriptions filled in previous months before the annual check-up and simultaneous completion of the self-reported questionnaire. In general, prescriptions for chronic diseases are renewed at most every three months, so there should be relatively few answering YES but with no recent claims over this period. Even so, to control for the influence of longer-term prescriptions, we evaluated the validity for predicting actual prescription over ten months in 0, subjects who received the health check-up from December st, 0 to February th, 0 (data not shown). The kappa (% CI) values for hypertension,

11 Page 0 of diabetes, and dyslipidemia medication use were.% (0.%.%),.% (.0%.%), and 0.% (.%.%), respectively, similar to or even slightly higher than the values for three months. Thus, we suggest that the self-reported questionnaire is better for predicting medium- and long-term medication use than short-term use. Conclusion We found that the self-reported questionnaire of medication use for hypertension, diabetes, and dyslipidemia that is conducted as part of the annual health check-up in Japan s health guidance program is a valid measure of true medication use. Accuracy appears better for predicting prescriptions filled within three months compared to one month.

12 Page of BMJ Open ACKNOWLEDGMENTS We greatly appreciate the invaluable help and support from staff of Chiba City Hall, Shinji Tada, Atsushi Nakamura, Yukiko Ishikawa, Isamu Nagata, and Shinsuke Mizuma, who provided all data analyzed in this study. CONTRIBUTORS MF was responsible for the study conception, design, analysis, interpretation, and drafting of the article. MF and AH acquired the data. AH, YS, KN, and ST assisted with study conception and design. YS, KN, and ST assisted with statistical analysis. All authors contributed to critical revisions of the manuscript and approved the final manuscript. COMPETING INTERESTS None. FUNDING This work was supported by JSPS KAKENHI Grant Number 0 and the Chiba Foundation for Health Promotion & Disease Prevention. ETHICS APPROVAL The Research Ethics Committee of the Graduate School of Medicine, Chiba University approved this study (No. ). PROVENANCE AND PEER REVIEW Not commissioned; externally peer reviewed. DATA SHARING STATEMENT Data were obtained from a third party (Chiba City Hall). We made a contract with Chiba City Hall prohibiting us for disclosing any data provided without written consent. To request the data, please contact Misuzu Fujita (Affiliation: Department of Public Health, Chiba University Graduate School of Medicine. [email protected]).

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15 Page of Table Demographic and clinical characteristics, including medication use as determined by the self-reported questionnaire and insurance claims Mean Median p-value (standard deviation) ( th th percentile) Number, Sex (men) N (%), (0.) Age year. (.) ( ) Body mass index kg/m. (.). (0..) Equivalent income Ten thousand yen () ( ) Self-reported medication user Hypertension N (%), (.) Diabetes N (%),00 (.) Dyslipidemia N (%), (.) The subjects prescribed during one month Hypertension N (%),0 (0.) <0.00 Diabetes N (%),0 (.0) <0.00 Dyslipidemia N (%), (.) <0.00 The subjects prescribed during three month Hypertension N (%), (.0) <0.00 Diabetes N (%), (.0) <0.00 Dyslipidemia N (%), (.) <0.00 Number (%) shown. Subjects prescribed drugs during the same month as the health check-up according to insurance claims. Subjects prescribed drugs during the month of check-up or the previous two months according to insurance claims.

16 Page of BMJ Open Comparison of the proportions of medication users as determined by insurance claims versus that from the self-reported questionnaire, using McNemar s test. As of June 0, 0, USD was equivalent to. JPY.

17 Page of Table. Validity of self-reported medication use Predicting actual prescription during one month Predicting actual prescription during three months Hypertension Diabetes Dyslipidemia Hypertension p-value Diabetes p-value Dyslipidemia p-value True positive N 0,,0,,,, True negative N,,,,, 0, False positive N,0,,,,0 False negative N,00,0, Sensitivity (% CI) %. (.0.) Specificity %. (% CI) (..) Kappa statistic % 0. (% CI) (..) % CI: % confidence interval The check-up month. The check-up month and previous two months. One month vs. three months (chi-square test).. (0..). (..). (..). (.0.). (..). (.0 0.). (..). (.0.) 0. (0..) 0.00 < (..0). (..). (..) (..).0 <0.00 <0.00 (0..). - - (. 0.)

18 Page of BMJ Open Table. Validity of self-reported medication among subgroups for predicting actual prescription during three months Hypertension Sex Age Income Men Women 0 years years < median median True positive N,,00, 0,0,, True negative N,0,,,00,, False positive N,,,0,,, False negative N Sensitivity (% CI) %. (.-.).0 (.-.). (0.0-.). (.-.). (.-.). (.-.) Specificity (%CI) %. (.-.). (.-.). (.-.).0 (.-.). (.-.). (.-.) Kappa statistic (%CI) %. (.-0.).0 (.0-.). (.-.). (.-.) 0. (.-.). (0.0-.) Diabetes True positive N,,,,0 True negative N 0,,0,,,, False positive N 0 False negative N 0 Sensitivity (% CI) %. (0.0-.). (.-.). (0.-.). (0.-.). (.-.). (.-.) Specificity (%CI) %. (.-.0).0 (.-.). (.-.). (.-.). (.-.). (.-.) Kappa statistic (%CI) %. (.-.).0 (.-.). (.0-0.0). (.-.). (.-.). (.-.) Dyslipidemia True positive N,,,,0,,0 True negative N,,,0, 0, 0,0 False positive N,0,,0,0,

19 Page of False negative N 0,0 Sensitivity (% CI) %. (0.-.). (.-.). (.-.0). (.-.). (.-.). (.-.) Specificity (%CI) %. (.-.). (.0-.). (.-.). (.-.) 0. (0.-.0). (.0-.) Kappa statistic (%CI) %.0 (.-0.). (.-0.). (.-.). (.-.). (.-0.) 0. (.-.) % CI: % confidence interval Median of equivalent income was,000 JPY (as of June 0, 0, USD was the equivalent to. JPY).

20 Page of BMJ Open Supplementary File. Drugs for hypertension Therapeutic category of drugs in Japan Generic names ATC codes : : Antiarrhythmic acebutolol hydrochloride C0AB0 Cardiovascular agents arotinolol hydrochloride - agents : Diuretics benzylhydrochlorothiazide - furosemide C0CA0 mefruside C0BA0 spironolactone C0DA0 triamterene C0DB0 : Antihypertensives trichlormethiazide alacepril - aliskiren fumarate amosulalol hydrochloride - aranidipine - azelnidipine - azilsartan - azilsartan amlodipine besilate - barnidipine hydrochloride benazepril hydrochloride C0AA0 C0XA0 C0CA C0AA0 benzylhydrochlorothiazide reserpine combined C0AA C0LA bevantolol hydrochloride candesartan cilexetil amlodipine besilate candesartan cilexetil hydrochlorothiazide captopril carvedilol celiprolol hydrochloride cilazapril hydrate cilnidipine C0AB0 C0DB0 C0DA0 C0AA0 C0AG0 C0AB0 C0AA0 C0CA clonidine hydrochloride C0AC0 N0CX0 S0EA0 delapril hydrochloride doxazosin mesilate efonidipine hydrochloride ethanolate - eplerenone felodipine C0AA C0CA0 C0DA0 C0CA0 guanabenz acetate - hydralazine hydrochloride C0DB0 imidapril hydrochloride C0AA indapamide C0BA irbesartan C0CA0 irbesartan amlodipine besilate - irbesartan trichlormethiazide - labetalol hydrochloride C0AG0

21 Page 0 of losartan potassium C0CA0 losartan potassium hydrochlorothiazide - manidipine hydrochloride C0CA methyldopa hydrate C0AB0 meticrane C0BA0 nicardipine hydrochloride C0CA0 nilvadipine C0CA0 olmesartan medoxomil C0CA0 olmesartan medoxomil azelnidipine - perindopril erbumine C0AA0 prazosin hydrochloride quinapril hydrochloride sodium nitroprusside hydrate telmisartan telmisartan amlodipine besilate - telmisartan hydrochlorothiazide - temocapril hydrochloride terazosin hydrochloride hydrate trandolapril tripamide - valsartan valsartan amlodipine besilate - valsartan cilnidipine - valsartan hydrochlorothiazide - reserpine C0CA0 C0AA0 C0DD0 C0CA0 C0AA G0CA0 C0AA0 C0CA0 C0AA0 betaxolol hydrochloride C0AB0 S0ED0 bunazosin hydrochloride - nipradilol - carteolol hydrochloride C0AA S0ED0 atenolol bisoprolol metoprolol tartrate nadolol pindolol propranolol hydrochloride hydrochlorothiazide candesartan cilexetil enalapril maleate lisinopril hydrate nifedipine C0AB0 C0AB0 C0AB0 C0AA C0AA0 C0AA0 C0AA0 C0CA0 C0AA0 C0AA0 C0CA0 nitroglycerin C0DA0 C0AE0 urapidil C0CA0

22 Page of BMJ Open : Vasodilators amlodipine besilate C0CA0 benidipine hydrochloride diltiazem hydrochloride - nisoldipine nitrendipine : Miscellaneous amlodipine besilate atorvastatin calcium hydrate dihydroergotoxine mesilate C0CA C0CA0 C0CA0 - C0AE0

23 Page of Supplementary File. Drugs for diabetes Therapeutic category of drugs in Japan Generic names ATC codes : Hormones : exenatide - Miscellaneous insulin aspart (genetical recombination) A0AB0 A0AD0 insulin degludec (genetical recombination) A0AE0 insulin detemir (genetical recombination) A0AE0 : Other : agents Antidiabetic affecting agents metabolism insulin glargine (genetical recombination) insulin glulisine (genetical recombination) A0AE0 A0AB0 insulin human (genetical recombination) A0AB0 A0AC0 A0AD0 A0AE0 A0AF0 insulin lispro (genetical recombination) A0AB0 A0AC0 A0AD0 liraglutide (genetical recombination) lixisenatide mecasermin (genetical recombination) acarbose acetohexamide alogliptin benzoate alogliptin benzoate pioglitazone hydrochloride anagliptin - buformin hydrochloride canagliflozin hydrate chlorpropamide dapagliflozin propylene glycolate hydrate A0BX0 A0BX0 H0AC0 A0BF0 A0BB A0BH0 A0BD0 A0BA0 A0BX A0BB0 A0BX0 glibenclamide A0BB0 A0BB0 gliclazide A0BB0 A0BB0 glimepiride A0BB glyclopyramide - ipragliflozin L-proline - linagliptin A0BH0 luseogliflozin hydrate - metformin hydrochloride A0BA0 miglitol A0BF0 mitiglinide calcium hydrate A0BX0 mitiglinide calcium hydrate voglibose - nateglinide A0BX0 pioglitazone hydrochloride A0BG0

24 Page of BMJ Open pioglitazone hydrochloride glimepiride A0BD0 pioglitazone hydrochloride metformin hydrochloride A0BD0 repaglinide A0BX0 saxagliptin hydrate A0BH0 sitagliptin phosphate hydrate A0BH0 teneligliptin hydrobromide hydrate - tofogliflozin hydrate - tolbutamide A0BB0 vildagliptin voglibose A0BH0 A0BF0

25 Page of Supplementary File. Drugs for dyslipidemia Therapeutic category of drugs in Japan Generic names ATC codes : Cardiovascular : Hyperlipidemia gamma oryzanol - agents agents atorvastatin calcium hydrate C0AA0 bezafibrate C0AB0 clinofibrate - clofibrate C0AB0 colestimide V0AE0 dextran sulfate sodium sulfur - : Vitamins : Vitamin B preparations elastase ES - ezetimibe fenofibrate fluvastatin sodium niceritrol nicomol - omega--acid ethyl esters - pitavastatin calcium hydrate - pravastatin sodium probucol rosuvastatin calcium simvastatin ethyl icosapentate - polyenephosphatidyl choline - colestyramine C0AX0 C0AB0 C0AA0 C0AD0 C0AA0 C0AX0 C0AA0 C0AA0 C0AC0 : Miscellaneous amlodipine besilate atorvastatin calcium hydrate C0BX0 tocopherol nicotinate pantethine riboflavin butyrate - AHA0 AHA

26 Page of BMJ Open STARD checklist for reporting of studies of diagnostic accuracy (version January 00) Section and Topic Item On page # # TITLE/ABSTRACT/ Identify the article as a study of diagnostic accuracy (recommend MeSH, KEYWORDS heading 'sensitivity and specificity'). INTRODUCTION State the research questions or study aims, such as estimating diagnostic accuracy or comparing accuracy between tests or across participant groups. METHODS Participants The study population: The inclusion and exclusion criteria, setting and, locations where data were collected. Participant recruitment: Was recruitment based on presenting symptoms,, results from previous tests, or the fact that the participants had received the index tests or the reference standard? Participant sampling: Was the study population a consecutive series of, participants defined by the selection criteria in item and? If not, specify how participants were further selected. Data collection: Was data collection planned before the index test and, reference standard were performed (prospective study) or after (retrospective study)? Test methods The reference standard and its rationale., Technical specifications of material and methods involved including how, and when measurements were taken, and/or cite references for index tests and reference standard. Definition of and rationale for the units, cut-offs and/or categories of the, results of the index tests and the reference standard. 0 The number, training and expertise of the persons executing and reading none the index tests and the reference standard. Whether or not the readers of the index tests and reference standard were blind (masked) to the results of the other test and describe any other clinical information available to the readers. Statistical methods Methods for calculating or comparing measures of diagnostic accuracy,, and the statistical methods used to quantify uncertainty (e.g. % confidence intervals). Methods for calculating test reproducibility, if done. none RESULTS Participants When study was performed, including beginning and end dates of, recruitment. Clinical and demographic characteristics of the study population (at least information on age, gender, spectrum of presenting symptoms). The number of participants satisfying the criteria for inclusion who did or, did not undergo the index tests and/or the reference standard; describe why participants failed to undergo either test (a flow diagram is strongly recommended). Test results Time-interval between the index tests and the reference standard, and, any treatment administered in between. Distribution of severity of disease (define criteria) in those with the target condition; other diagnoses in participants without the target condition. A cross tabulation of the results of the index tests (including - indeterminate and missing results) by the results of the reference standard; for continuous results, the distribution of the test results by the results of the reference standard. 0 Any adverse events from performing the index tests or the reference none standard. Estimates Estimates of diagnostic accuracy and measures of statistical uncertainty (e.g. % confidence intervals). How indeterminate results, missing data and outliers of the index tests, were handled. Estimates of variability of diagnostic accuracy between subgroups of, participants, readers or centers, if done. Estimates of test reproducibility, if done. none DISCUSSION Discuss the clinical applicability of the study findings. 0

27 Validity assessment of self-reported medication use by comparing to pharmacy insurance claims Journal: BMJ Open Manuscript ID bmjopen r Article Type: Research Date Submitted by the Author: -Sep-0 Complete List of Authors: Fujita, Misuzu; Chiba University, Department of Public Health Sato, Yasunori; Chiba University, Department of Global Clinical Research; Chiba University Hospital, Clinical Research Center Nagashima, Kengo; Chiba University, Department of Global Clinical Research; Chiba University Hospital, Clinical Research Center Takahashi, Sho; Chiba University Hospital, Clinical Research Center Hata, Akira; Chiba University, Department of Public Health <b>primary Subject Heading</b>: Health services research Secondary Subject Heading: Research methods, Epidemiology Keywords: Hypertension < CARDIOLOGY, General diabetes < DIABETES & ENDOCRINOLOGY, Lipid disorders < DIABETES & ENDOCRINOLOGY, MEDICAL HISTORY, EPIDEMIOLOGY

28 Page of BMJ Open Validity assessment of self-reported medication use by comparing to pharmacy insurance claims Misuzu Fujita, Yasunori Sato,, Kengo Nagashima,, Sho Takahashi, Akira Hata Chiba University, Department of Public Health, Chiba City, Chiba, Japan Chiba University, Department of Global Clinical Research, Chiba City, Chiba, Japan Chiba University Hospital, Clinical Research Center, Chiba City, Chiba, Japan Corresponding author: Misuzu Fujita Chiba University, Department of Public Health -- Inohana, Chuo-ku, Chiba 0-, Japan address: [email protected] Telephone: +---0

29 Page of ABSTRACT Objectives: In Japan, an annual health check-up and health promotion guidance program was established in 00 in accordance with the Act on Assurance of Medical Care for the Elderly. A self-reported questionnaire on medication use is a required item in this program and has been used widely, but its validity has not been assessed. The aim of this study was to evaluate the validity of this questionnaire by comparing self-reported usage to pharmacy insurance claims. Setting: This is a population-based validation study. Self-reported medication use for hypertension, diabetes, and dyslipidemia are the evaluated measurements. Data on pharmacy insurance claims are used as a reference standard. Participants: Subjects were, beneficiaries of the National Health Insurance of Chiba City. Primary and secondary outcome measures: Sensitivity, specificity, and kappa statistics of the self-reported medication use questionnaire for predicting actual prescriptions during one month (that of the check-up) and three months (that of the check-up and the previous two) were calculated. Results: Sensitivity and specificity scores of questionnaire data for predicting insurance claims covering three months were, respectively,.% (% confidence interval (CI):.-.) and.% (% CI:.0-.) for hypertension,.% (% CI:.-.0) and.% (% CI:.-.) for diabetes, and.% (% CI:.-.) and.0% (% CI: 0.-.) for dyslipidemia. Corresponding kappa statistics were 0.% (% CI: 0.-.),.% (% CI:.-.), and.% (% CI:.-0.). The specificity was significantly higher for questionnaire data covering three months compared to data covering one month for all three conditions. Conclusion: Self-reported questionnaire data on medication use had sufficiently high validity for further analyses. Item responses showed close agreement with actual prescriptions, particularly those covering three months.

30 Page of BMJ Open Strengths and limitations of this study We evaluated for the first time the validity of a self-reported questionnaire on medication use in the annual health check-up system of Japan by comparing self-reported usage to pharmacy insurance claims, a record that is free of recall bias and regarded as a gold standard. A large population-based sample was used. Specificity might be underestimated due to incomplete data on pharmacy insurance claims.

31 Page of INTRODUCTION Self-reported questionnaires have been employed to determine drug usage in many epidemiological studies. - However, the accuracy of the information obtained by such questionnaires is limited by recall bias. - A substantial amount of inaccurate data could result in misclassification bias, leading to incorrect estimates of disease risk and/or prevalence. To date, a few studies have evaluated the validity of self-reported medication use but the results have been inconsistent, with some finding high validity 0 and others finding relatively low validity. This inconsistency could result from differences in data collection method, type of drug, age and/or nationality of the target population, and healthcare system. In Japan, an annual health check-up and health promotion guidance program was started in April 00 in accordance with the Act on Assurance of Medical Care for the Elderly by the Ministry of Health, Labour and Welfare (MHLW). Medical insurers are obliged to provide this program to all their beneficiaries aged 0 to years. During the period from 00 to 0, a total of around million people utilized the program. The program mainly targets individuals with metabolic syndrome. A self-reported questionnaire on medication use for hypertension, hyperglycemia, and hypercholesterolemia is one of the required items, and the collected data are used to identify individuals in need of further guidance. When a recipient of a health check-up answers yes to the question on medication use, he or she is automatically excluded from the target population for the health guidance program. Consequently, misclassification of medication use can lead to recipients with metabolic syndrome losing the opportunity to receive appropriate guidance. In addition, the questionnaire has been used to detect untreated individuals so that they can be advised to see a doctor when their laboratory data strongly indicate hypertension, diabetes, and/or dyslipidemia. If all health insurance claims were computerized and integrated with health check-up data, a self-reported questionnaire would no longer be necessary for the public health researchers. In fact, the Japan National Database (NDB) project led by the MHLW was started for this purpose. At the moment, however, the linkage rate between health insurance claims and health check-up data in the NDB is very low, meaning that researchers must use self-reported questionnaire data as an alternative. Thus, validation of the data is crucial for both practical and research applications.

32 Page of BMJ Open The aim of this study was to evaluate the validity of the self-reported questionnaire on the use of drugs for hypertension, diabetes, and dyslipidemia that is conducted as part of the annual health check-up in Japan s health guidance program. To do this, we compared self-reported usage to pharmacy insurance claims, a record that is free of recall bias and regarded as a gold standard. METHODS Subjects The subjects of this study were beneficiaries of National Health Insurance (NHI) of Chiba City. Japan has a universal health care insurance system that covers all citizens. There are two types of coverage for individuals younger than years, Employees Health Insurance and NHI; the latter is managed by municipalities and covers the self-employed, farmers, retirees, and the unemployed. The subjects in this study consisted of,0 beneficiaries aged 0 years who received a health check-up from May, 0 to February, 0. Of these individuals, with missing data were excluded, for a final total of, beneficiaries (, men and,0 women). Health check-up data and pharmacy insurance claims data were integrated for comparison using the values of household number, birth month, and sex. Ethics statement Consent was not obtained from subjects because this study was performed using only existing data. To ensure anonymity, personal identifiers (e.g., name, address, and telephone number) were removed from the records, date of birth was changed to the st of the month, and personal number and household number administered by Chiba City Hall were converted to random numbers prior to release. The Research Ethics Committee of the Graduate School of Medicine, Chiba University approved this study. The study was conducted in accordance with the Declaration of Helsinki. Definition of self-reported medication users The self-reported questionnaire, which is required in the health check-up, includes the following item. Are you currently taking the following medications?

33 Page of Medication for hypertension (yes or no). Insulin injection or oral medication for hyperglycemia (yes or no). Medication for hypercholesterolemia (yes or no) A subject who answers yes is defined as a self-reported user of drugs to treat hypertension, diabetes, and/or dyslipidemia. Definition of true medication users True medication users were determined by pharmacy claims submitted from April 0 to March 0. Unfortunately, pharmacy insurance claims provided by Chiba City for this study include only prescriptions dispensed outside hospitals. Nonetheless, pharmacy insurance claims are available for.% of all prescriptions filled in Chiba Prefecture during FY 0. Although the pharmacy claim data in this study were not perfect, they were the best data currently available for determining medication users in our study. Thus, we used the obtained pharmacy claim data as a tentative gold standard. Generic names of medications for the three conditions are listed in Supplementary Files to. For detecting appropriate drugs, the Database of Drugs in Japan was used. Codes of the Anatomical Therapeutic Chemical Classification System (ATC codes) provided by the World Health Organization 0 have not been assigned to every drug used in Japan, but we list as many as possible in the Supplementary Files. Initially, we used two different definitions for the true medication users detected by pharmacy insurance claims: one for subjects prescribed during the same month as the health check-up (one month); and the other for subjects prescribed during the same month as the check-up or in the previous two months (three months). In Japan, the law limiting prescriptions to two weeks was repealed in April 00 to allow long-term prescriptions (with some exceptions). Thus, even if the subjects did not receive a prescribed medication during a survey month, they might have already received during the previous month. To overcome this possible omission error, we decided to analyze the month of the check-up and the month of the check-up plus the previous two months separately. Equivalent household income

34 Page of BMJ Open Individual annual income from January to December, 0 was obtained from tax records at Chiba City Hall. Number of people per household was obtained by counting the persons with the same household number. People per household included persons insured by NHI in Chiba City and other householders regardless of whether he or she was a beneficiary. Household income was calculated by summing the incomes of all household members, as mentioned above. An equivalent household income was calculated as household income divided by the square of the number of household members. Statistical analysis The proportions of the individual medication users as determined by the self-reported questionnaire and by pharmacy insurance claims were compared by McNemar s test. For assessing the validity of self-reported medication use for hypertension, diabetes, and dyslipidemia, medication use as detected by pharmacy insurance claims was assumed to be accurate (as the gold standard). Sensitivity was calculated as the proportion of subjects with self-reported medication use among the subjects with pharmacy claims, and specificity was calculated as the proportion of subjects without self-reported medication use among the subjects without pharmacy claims. In addition, kappa statistics were also calculated for each medical condition. The kappa statistic measure of agreement is scaled to be zero when the agreement is what would be expected by chance and to when there is perfect agreement. Landis and Koch defined values of as slight, as fair, as moderate, as substantial, and as almost perfect agreement. All comparisons were planned and all tests were two-tailed. A p-value < 0.0 was considered statistically significant. All statistical analyses were performed using the STATA software package (Stata Corp., College Station, TX). RESULTS Subject demographics, clinical characteristics, and medication use as determined by the self-reported questionnaire and insurance claims are shown in Table. Means and standard deviations of age and body mass index were. ±. years and. ±. kg/m. Median equivalent income was,0,000 JPY (as of June 0, 0, USD was

35 Page of equivalent to. JPY). The proportions of subjects prescribed medications for hypertension, diabetes, and dyslipidemia as indicated by insurance claims during the check-up month (0.% for hypertension,.0% for diabetes, and.% for dyslipidemia) and within three months (.0% for hypertension,.0% for diabetes, and.% for dyslipidemia) were all significantly lower than as indicated from self-reports (.% for hypertension,.% for diabetes, and.% for dyslipidemia). Table presents the results of the validity analysis of self-reported medication use in all subjects. In general, the self-reported questionnaire predicted actual prescriptions (i.e., according to pharmacy insurance claims) with high sensitivity and specificity for both the month of check-up and for three months. Specificity was uniformly higher for predicting prescriptions within three months for all three drug classes. Kappa values were also higher for predicting prescriptions within three months compared to one month. Thus, the self-reported questionnaire more accurately represented medication use for three months that for one month, the only exception being sensitivity for hypertension medication use (p<0.0). Only results for predicting actual prescriptions over three months were used for subgroup analyses (Table ). Analyses of subgroups divided by sex, age range, and income are shown in Table. In all subgroups, sensitivity and specificity were >0% and kappa statistic >0%. Thus, the validity of the self-report questionnaire was high regardless of sex, age, and income. DISCUSSION The self-reported medication use questionnaire for the annual health check-up program overseen by the Japanese MHLW was found to have high validity for predicting actual prescriptions for drugs used to treat hypertension, diabetes, and dyslipidemia. Although pharmacy insurance claims data inherently include comprehensive prescription information, making the data suitable as a gold standard, only prescriptions dispensed outside hospitals were available in this study, which accounted for.% of all prescriptions in FY 0. Indeed, the proportions of subjects with actual pharmacy claims were significantly lower than the proportions based on self-reports for all three conditions. Accordingly, we should consider how this drawback influences the accuracy of the sensitivity and specificity values calculated. For calculation of sensitivity, we determined the proportion of subjects self-reporting use

36 Page of BMJ Open only among those with external (outside of hospital) prescriptions. However, if the responses to the self-reported questionnaire by subjects with external prescriptions and those with in-hospital prescriptions are assumed to be the same, the sensitivity should be close to the true figure. On the other hand, for calculation of specificity, we used the number of subjects without actual prescription claims as the denominator, which includes those actually prescribed in-hospital, making the value lower than the true data. Despite this influence, however, values of specificity and sensitivity were satisfactory (>0%) for all three diseases. To date, only two studies to our knowledge have conducted a validity assessment of self-reported medication use for hypertension, diabetes, and/or dyslipidemia. One assessed self-reported drug use for hypertension and diabetes in, subjects of different ethnicities in British Columbia, Canada. 0 This study found high specificity for hypertension (% 00% in each ethnicity) and diabetes (% 00% in each ethnicity), but relatively low sensitivity for hypertension (0% % in each ethnicity). Here, the two-step methodology may have influenced the result. In the first step, only those subjects who answered Yes to Do you have this condition? were extracted. Then, the extracted subjects were asked In the past months, have you taken any medicine for this condition? It is known that this two-step method increases specificity but decreases sensitivity. In contrast, the self-reported questionnaire item for medication use analyzed in the present study asks subjects a single question for each condition ( Are you currently taking the following medications? ) and high validity was observed, indicating that this mode of questioning is appropriate and reliable. The second study from Washington State reported high validity for self-reported medication use for hypertension and dyslipidemia (statins) in 0 participants of a population-based, case-control study of breast cancer in women aged years. Sensitivity in cases and controls were % and % for hypertensive medication and % and % for statins. Specificity was % and % for hypertensive medication and % and % for statins. This result is quite similar to ours. In contrast to medication for hypertension, diabetes, and dyslipidemia, these studies reported lower sensitivity of self-reported medication use for asthma (% % in each ethnicity 0 ) and depression (% and % in cases and controls ), suggesting that the validity of self-reported medication use depends on the specific medical condition. So et

37 Page 0 of al. suggested that the difference in self-report validity among medication types is likely related to the frequency of use. 0 For example, self-report validity for medications used to treat acute symptoms, such as asthma, tends to be lower than that for medications taken routinely for chronic conditions such as hypertension, diabetes, and dyslipidemia. Further studies are needed to confirm whether this is also the case for the health check-up self-report questionnaire. We found that validity of the self-reported questionnaire was higher (more accurately reflected medication use) during three months than during one month. Patients in Japan can now obtain longer-term prescriptions (after the law limiting prescriptions to two weeks was repealed). Thus, we assumed that many patients currently taking medications for hypertension, diabetes, and/or dyslipidemia would have prescriptions filled in previous months before the annual check-up and simultaneous completion of the self-reported questionnaire. In general, prescriptions for chronic diseases are renewed at most every three months, so there should be relatively few answering yes but with no recent claims over this period. Even so, to control for the influence of longer-term prescriptions, we evaluated the validity for predicting actual prescriptions over ten months in 0, subjects who received the health check-up from December st, 0 to February th, 0 (data not shown). The kappa (% CI) values for hypertension, diabetes, and dyslipidemia medication use were.% (0..),.% (.0.), and 0.% (..), respectively, similar to or even slightly higher than the values for three months. Thus, we suggest that the self-reported questionnaire is better for predicting medium- and long-term medication use than short-term use. We found high validity of the self-reported questionnaire in this study, indicating that both healthcare workers and public health researchers can use these data for practical and research purposes. For example, these data can be used in research toward the NDB s goal of replacing self-reported data once integration of data between annual health check-ups and insurance claims becomes reliable. Conclusion We found that the self-reported questionnaire of medication use for hypertension, diabetes, and dyslipidemia that is conducted as part of the annual health check-up in Japan s health guidance program is a valid measure of true medication use. Accuracy appears better for predicting prescriptions filled within three months compared to one 0

38 Page of BMJ Open month.

39 Page of ACKNOWLEDGMENTS We greatly appreciate the invaluable help and support from staff of Chiba City Hall, Shinji Tada, Atsushi Nakamura, Yukiko Ishikawa, Isamu Nagata, and Shinsuke Mizuma, who provided all data analyzed in this study. CONTRIBUTORS MF was responsible for the study conception, design, analysis, interpretation, and drafting of the article. MF and AH acquired the data. AH, YS, KN, and ST assisted with study conception and design. YS, KN, and ST assisted with statistical analysis. All authors contributed to critical revisions of the manuscript and approved the final manuscript. COMPETING INTERESTS No, there are no competing interests. FUNDING This work was supported by JSPS KAKENHI Grant Number 0 and the Chiba Foundation for Health Promotion & Disease Prevention. ETHICS APPROVAL The Research Ethics Committee of the Graduate School of Medicine, Chiba University approved this study (No. ). PROVENANCE AND PEER REVIEW Not commissioned; externally peer reviewed. DATA SHARING STATEMENT Additional data is available by ing the corresponding author ( [email protected] ).

40 Page of BMJ Open REFERENCES. Iser BP, Malta DC, Duncan BB, et al. Prevalence, correlates, and description of self-reported diabetes in brazilian capitals - results from a telephone survey. PLoS One 0;:e00.. Depp CA, Strassnig M, Mausbach BT, et al. Association of obesity and treated hypertension and diabetes with cognitive ability in bipolar disorder and schizophrenia. Bipolar Disord 0;:-.. Rawlings AM, Sharrett AR, Schneider AL, et al. Diabetes in midlife and cognitive change over 0 years: a cohort study. Ann Intern Med 0;:-.. Boudreau DM, Daling JR, Malone KE, et al. A validation study of patient interview data and pharmacy records for antihypertensive, statin, and antidepressant medication use among older women. Am J Epidemiol 00;:0-.. Reijneveld SA. The cross-cultural validity of self-reported use of health care: a comparison of survey and registration data. J Clin Epidemiol 000;:-.. Bellon JA, Lardelli P, Luna JD, et al. Validity of self reported utilisation of primary health care services in an urban population in Spain. J Epidemiol Community Health 000;:-.. Yu ST, Chang HY, Lin MC, et al. Agreement between self-reported and health insurance claims on utilization of health care: A population study. J Clin Epidemiol 00;:-.. Peersman W, Pasteels I, Cambier D, et al. Validity of self-reported utilization of physician services: a population study. Eur J Public Health 0;:-.. Skurtveit S, Selmer R, Tverdal A, et al. The validity of self-reported prescription medication use among adolescents varied by therapeutic class. J Clin Epidemiol 00;:-. 0. So L, Morgan SG, Quan H. Does concordance between survey responses and administrative records differ by ethnicity for prescription medication? J Popul Ther Clin Pharmacol 0;:e-.. Uiters E, van Dijk L, Deville W, et al. Ethnic minorities and prescription medication; concordance between self-reports and medical records. BMC Health Serv Res 00;:.. Klungel OH, de Boer A, Paes AH, et al. Influence of question structure on the recall

41 Page of of self-reported drug use. J Clin Epidemiol 000;:-.. Reijneveld SA, Stronks K. The validity of self-reported use of health care across socioeconomic strata: a comparison of survey and registration data. Int J Epidemiol 00;0:0-.. Gordis L. Epidemiology fourth edition. Philadelphia: Saunders, 00:-0.. Ministry of Health Labour and Welfare. Specific Health checkups and Specific Health Guidance. Secondary Specific Health checkups and Specific Health Guidance (accessed June 0).. Okamoto E. Linkage rate between data from health checks and health insurance claims in the Japan National Database. J Epidemiol 0;:-.. Ministry of Health Labour and Welfare. Annual Health, Labour and Welfare Report 0-0, Health and Medical Services. Secondary Annual Health, Labour and Welfare Report 0-0, Health and Medical Services (accessed June 0).. Japan Pharmaceutical Association. separation of medical practice and drug dispensing. Secondary separation of medical practice and drug dispensing (accessed June 0).. Jiho I. Drugs in Japan. Tokyo: Jiho, Inc., WHO. Guidelines for ATC classification and DDD assignment 0. Secondary Guidelines for ATC classification and DDD assignment uidelines+for+atc+classification% (accessed June 0).. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics ;:-.. Onda M, Kamei M, Kono K. Impacts on the quality of medication service for outpatietns and management of medical institutions by deregulation of drug prescription term. Japanese Journal of Health Economics and Policy 00;:-.

42 Page of BMJ Open Table Demographic and clinical characteristics, including medication use as determined by the self-reported questionnaire and insurance claims Mean Median p-value (standard deviation) ( th th percentile) Number, Sex (men) N (%), (0.) Age year. (.) ( ) Body mass index kg/m. (.). (0..) Equivalent income Ten thousand yen () ( ) Self-reported medication user Hypertension N (%), (.) Diabetes N (%),00 (.) Dyslipidemia N (%), (.) The subjects prescribed during one month Hypertension N (%),0 (0.) <0.00 Diabetes N (%),0 (.0) <0.00 Dyslipidemia N (%), (.) <0.00 The subjects prescribed during three month Hypertension N (%), (.0) <0.00 Diabetes N (%), (.0) <0.00 Dyslipidemia N (%), (.) <0.00 Number (%) shown. Subjects prescribed drugs during the same month as the health check-up according to insurance claims. Subjects prescribed drugs during the month of check-up or the previous two months according to insurance claims.

43 Page of Comparison of the proportions of medication users as determined by insurance claims versus that from the self-reported questionnaire, using McNemar s test. As of June 0, 0, USD was equivalent to. JPY.

44 Page of BMJ Open Table. Validity of self-reported medication use Predicting actual prescriptions during one month Predicting actual prescriptions during three months Hypertension Diabetes Dyslipidemia Hypertension p-value Diabetes p-value Dyslipidemia p-value True positive N 0,,0,,,, True negative N,,,,, 0, False positive N,0,,,,0 False negative N,00,0, Sensitivity (% CI) %. (.0.) Specificity %. (% CI) (..) Kappa statistic % 0. (% CI) (..) % CI: % confidence interval The check-up month. The check-up month and previous two months. One month vs. three months (chi-square test).. (0..). (..). (..). (.0.). (..). (.0 0.). (..). (.0.) 0. (0..) 0.00 < (..0). (..). (..) (..) 0. < (0..) < (. 0.) -

45 Page of Table. Validity of self-reported medication among subgroups for predicting actual prescriptions during three months Hypertension Sex Age Income Men Women 0 years years < median median True positive N,,00, 0,0,, True negative N,0,,,00,, False positive N,,,0,,, False negative N Sensitivity (% CI) %. (.-.).0 (.-.). (0.0-.). (.-.). (.-.). (.-.) Specificity (%CI) %. (.-.). (.-.). (.-.).0 (.-.). (.-.). (.-.) Kappa statistic (%CI) %. (.-0.).0 (.0-.). (.-.). (.-.) 0. (.-.). (0.0-.) Diabetes True positive N,,,,0 True negative N 0,,0,,,, False positive N 0 False negative N 0 Sensitivity (% CI) %. (0.0-.). (.-.). (0.-.). (0.-.). (.-.). (.-.) Specificity (%CI) %. (.-.0).0 (.-.). (.-.). (.-.). (.-.). (.-.) Kappa statistic (%CI) %. (.-.).0 (.-.). (.0-0.0). (.-.). (.-.). (.-.) Dyslipidemia True positive N,,,,0,,0 True negative N,,,0, 0, 0,0 False positive N,0,,0,0,

46 Page of BMJ Open False negative N 0,0 Sensitivity (% CI) %. (0.-.). (.-.). (.-.0). (.-.). (.-.). (.-.) Specificity (%CI) %. (.-.). (.0-.). (.-.). (.-.) 0. (0.-.0). (.0-.) Kappa statistic (%CI) %.0 (.-0.). (.-0.). (.-.). (.-.). (.-0.) 0. (.-.) % CI: % confidence interval Median equivalent income was,0,000 JPY (as of June 0, 0, USD was equivalent to. JPY).

47 Page 0 of Supplementary File. Drugs for hypertension Therapeutic category of drugs in Japan Generic names ATC codes : : Antiarrhythmic acebutolol hydrochloride C0AB0 Cardiovascular agents arotinolol hydrochloride - agents : Diuretics benzylhydrochlorothiazide - furosemide C0CA0 mefruside C0BA0 spironolactone C0DA0 triamterene C0DB0 : Antihypertensives trichlormethiazide alacepril - aliskiren fumarate amosulalol hydrochloride - aranidipine - azelnidipine - azilsartan - azilsartan amlodipine besilate - barnidipine hydrochloride benazepril hydrochloride C0AA0 C0XA0 C0CA C0AA0 benzylhydrochlorothiazide reserpine combined C0AA C0LA bevantolol hydrochloride candesartan cilexetil amlodipine besilate candesartan cilexetil hydrochlorothiazide captopril carvedilol celiprolol hydrochloride cilazapril hydrate cilnidipine C0AB0 C0DB0 C0DA0 C0AA0 C0AG0 C0AB0 C0AA0 C0CA clonidine hydrochloride C0AC0 N0CX0 S0EA0 delapril hydrochloride doxazosin mesilate efonidipine hydrochloride ethanolate - eplerenone felodipine C0AA C0CA0 C0DA0 C0CA0 guanabenz acetate - hydralazine hydrochloride C0DB0 imidapril hydrochloride C0AA indapamide C0BA irbesartan C0CA0 irbesartan amlodipine besilate - irbesartan trichlormethiazide - labetalol hydrochloride C0AG0

48 Page of BMJ Open losartan potassium C0CA0 losartan potassium hydrochlorothiazide - manidipine hydrochloride C0CA methyldopa hydrate C0AB0 meticrane C0BA0 nicardipine hydrochloride C0CA0 nilvadipine C0CA0 olmesartan medoxomil C0CA0 olmesartan medoxomil azelnidipine - perindopril erbumine C0AA0 prazosin hydrochloride quinapril hydrochloride sodium nitroprusside hydrate telmisartan telmisartan amlodipine besilate - telmisartan hydrochlorothiazide - temocapril hydrochloride terazosin hydrochloride hydrate trandolapril tripamide - valsartan valsartan amlodipine besilate - valsartan cilnidipine - valsartan hydrochlorothiazide - reserpine C0CA0 C0AA0 C0DD0 C0CA0 C0AA G0CA0 C0AA0 C0CA0 C0AA0 betaxolol hydrochloride C0AB0 S0ED0 bunazosin hydrochloride - nipradilol - carteolol hydrochloride C0AA S0ED0 atenolol bisoprolol metoprolol tartrate nadolol pindolol propranolol hydrochloride hydrochlorothiazide candesartan cilexetil enalapril maleate lisinopril hydrate nifedipine C0AB0 C0AB0 C0AB0 C0AA C0AA0 C0AA0 C0AA0 C0CA0 C0AA0 C0AA0 C0CA0 nitroglycerin C0DA0 C0AE0 urapidil C0CA0

49 Page of : Vasodilators amlodipine besilate C0CA0 benidipine hydrochloride diltiazem hydrochloride - nisoldipine nitrendipine : Miscellaneous amlodipine besilate atorvastatin calcium hydrate dihydroergotoxine mesilate C0CA C0CA0 C0CA0 - C0AE0

50 Page of BMJ Open Supplementary File. Drugs for diabetes Therapeutic category of drugs in Japan Generic names ATC codes : Hormones : exenatide - Miscellaneous insulin aspart (genetical recombination) A0AB0 A0AD0 insulin degludec (genetical recombination) A0AE0 insulin detemir (genetical recombination) A0AE0 : Other : agents Antidiabetic affecting agents metabolism insulin glargine (genetical recombination) insulin glulisine (genetical recombination) A0AE0 A0AB0 insulin human (genetical recombination) A0AB0 A0AC0 A0AD0 A0AE0 A0AF0 insulin lispro (genetical recombination) A0AB0 A0AC0 A0AD0 liraglutide (genetical recombination) lixisenatide mecasermin (genetical recombination) acarbose acetohexamide alogliptin benzoate alogliptin benzoate pioglitazone hydrochloride anagliptin - buformin hydrochloride canagliflozin hydrate chlorpropamide dapagliflozin propylene glycolate hydrate A0BX0 A0BX0 H0AC0 A0BF0 A0BB A0BH0 A0BD0 A0BA0 A0BX A0BB0 A0BX0 glibenclamide A0BB0 A0BB0 gliclazide A0BB0 A0BB0 glimepiride A0BB glyclopyramide - ipragliflozin L-proline - linagliptin A0BH0 luseogliflozin hydrate - metformin hydrochloride A0BA0 miglitol A0BF0 mitiglinide calcium hydrate A0BX0 mitiglinide calcium hydrate voglibose - nateglinide A0BX0 pioglitazone hydrochloride A0BG0

51 Page of pioglitazone hydrochloride glimepiride A0BD0 pioglitazone hydrochloride metformin hydrochloride A0BD0 repaglinide A0BX0 saxagliptin hydrate A0BH0 sitagliptin phosphate hydrate A0BH0 teneligliptin hydrobromide hydrate - tofogliflozin hydrate - tolbutamide A0BB0 vildagliptin voglibose A0BH0 A0BF0

52 Page of BMJ Open Supplementary File. Drugs for dyslipidemia Therapeutic category of drugs in Japan Generic names ATC codes : Cardiovascular : Hyperlipidemia gamma oryzanol - agents agents atorvastatin calcium hydrate C0AA0 bezafibrate C0AB0 clinofibrate - clofibrate C0AB0 colestimide V0AE0 dextran sulfate sodium sulfur - : Vitamins : Vitamin B preparations elastase ES - ezetimibe fenofibrate fluvastatin sodium niceritrol nicomol - omega--acid ethyl esters - pitavastatin calcium hydrate - pravastatin sodium probucol rosuvastatin calcium simvastatin ethyl icosapentate - polyenephosphatidyl choline - colestyramine C0AX0 C0AB0 C0AA0 C0AD0 C0AA0 C0AX0 C0AA0 C0AA0 C0AC0 : Miscellaneous amlodipine besilate atorvastatin calcium hydrate C0BX0 tocopherol nicotinate pantethine riboflavin butyrate - AHA0 AHA

53 Page of STARD checklist for reporting of studies of diagnostic accuracy (version January 00) Section and Topic Item On page # # TITLE/ABSTRACT/ Identify the article as a study of diagnostic accuracy (recommend MeSH KEYWORDS heading 'sensitivity and specificity'). INTRODUCTION State the research questions or study aims, such as estimating diagnostic accuracy or comparing accuracy between tests or across participant groups. METHODS Participants The study population: The inclusion and exclusion criteria, setting and locations where data were collected. Participant recruitment: Was recruitment based on presenting symptoms, results from previous tests, or the fact that the participants had received the index tests or the reference standard? Participant sampling: Was the study population a consecutive series of participants defined by the selection criteria in item and? If not, specify how participants were further selected. Data collection: Was data collection planned before the index test and, reference standard were performed (prospective study) or after (retrospective study)? Test methods The reference standard and its rationale., Technical specifications of material and methods involved including how, and when measurements were taken, and/or cite references for index tests and reference standard. Definition of and rationale for the units, cut-offs and/or categories of the, results of the index tests and the reference standard. 0 The number, training and expertise of the persons executing and reading none the index tests and the reference standard. Whether or not the readers of the index tests and reference standard were blind (masked) to the results of the other test and describe any other clinical information available to the readers. Statistical methods Methods for calculating or comparing measures of diagnostic accuracy, and the statistical methods used to quantify uncertainty (e.g. % confidence intervals). Methods for calculating test reproducibility, if done. none RESULTS Participants When study was performed, including beginning and end dates of recruitment. Clinical and demographic characteristics of the study population (at least information on age, gender, spectrum of presenting symptoms). The number of participants satisfying the criteria for inclusion who did or, did not undergo the index tests and/or the reference standard; describe why participants failed to undergo either test (a flow diagram is strongly recommended). Test results Time-interval between the index tests and the reference standard, and, any treatment administered in between. Distribution of severity of disease (define criteria) in those with the target condition; other diagnoses in participants without the target condition. A cross tabulation of the results of the index tests (including - indeterminate and missing results) by the results of the reference standard; for continuous results, the distribution of the test results by the results of the reference standard. 0 Any adverse events from performing the index tests or the reference none standard. Estimates Estimates of diagnostic accuracy and measures of statistical uncertainty - (e.g. % confidence intervals). How indeterminate results, missing data and outliers of the index tests, were handled. Estimates of variability of diagnostic accuracy between subgroups of, participants, readers or centers, if done. Estimates of test reproducibility, if done. none DISCUSSION Discuss the clinical applicability of the study findings. 0