The NHI has been implemented in Taiwan since March 1995. It is a unique and universal health care system with a high coverage rate of > 99% of the Taiwan’s population. The Bureau of the NHI has contracts with all hospitals and with 93% of all medical settings. The reimbursement database of the NHI kept computerized records of disease diagnoses, medication prescriptions and clinical procedures and can be used for academic research if approved after ethics review. The present study was approved number 99274 by the Ethics Committee of the National Health Research Institutes. According to local regulations, the National Health Research Institutes de-identified the individuals in the database for the protection of privacy and the Ethics Committee approved the analyses of the database without the requirement of obtaining informed consent from the participants.
During the study period, the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) was used for disease diagnoses; and diabetes mellitus was coded as 250.XX and AF as 427.31.
Detailed description of the database has been reported in a previously published paper [16]. The unmatched cohort and the PS-matched cohort were enrolled according to the procedures shown in Fig. 1. At first, 423949 patients who were newly diagnosed of diabetes mellitus during 1999–2005 in the outpatient clinics and had been prescribed antidiabetic drugs for two or more times were identified. The following ineligible patients were then excluded: 1) metformin ever users who had been treated with other antidiabetic drugs before metformin was initiated (n = 183837); 2) type 1 diabetes mellitus (n = 2062), 3) patients who died before the start of follow-up (n = 65); 4) missing data (n = 358), 5) diagnosis of AF before the start of follow-up or within 6 months of diabetes diagnosis (n = 812), 6) diagnosis of any cancer before the start of follow-up or within 6 months of diabetes diagnosis (n = 26675), and 7) follow-up duration < 180 days (n = 15076). As a result, 173398 ever users and 21666 never users of metformin were identified as the unmatched cohort. PS was created from all characteristics listed in Table 1 plus the date of enrollment by logistic regression. A cohort of PS-matched pairs of 21647 ever users and 21647 never users (the matched cohort) was then created from the unmatched cohort by matching the PS using the Greedy 8◊1 digit match algorithm proposed by Parsons [17].
Table 1
Characteristics in never users and ever users of metformin in the unmatched and matched cohorts
Variable | Unmatched cohort | | Matched cohort |
Never users | | Ever users | | | Never users | | Ever users | |
(n = 21666) | | (n = 173398) | Standardized difference | | (n = 21647) | | (n = 21647) | Standardized difference |
n | % | | n | % | | | n | % | | n | % | |
Demographic data | | | | | | | | | | | | | |
Age* (years) | 68.77 | 13.25 | | 64.23 | 11.98 | -41.28 | | 68.76 | 13.25 | | 68.69 | 12.13 | 0.15 |
Sex (men) | 11793 | 54.43 | | 91570 | 52.81 | -3.51 | | 11781 | 54.42 | | 11902 | 54.98 | 1.09 |
Occupation | | | | | | | | | | | | | |
I | 7825 | 36.12 | | 64789 | 37.36 | | | 7818 | 36.12 | | 7989 | 36.91 | |
II | 3646 | 16.83 | | 36710 | 21.17 | 12.59 | | 3645 | 16.84 | | 3542 | 16.36 | -1.39 |
III | 5216 | 24.07 | | 39817 | 22.96 | -2.67 | | 5212 | 24.08 | | 5152 | 23.80 | -0.46 |
IV | 4979 | 22.98 | | 32082 | 18.50 | -12.94 | | 4972 | 22.97 | | 4964 | 22.93 | -0.01 |
Living region | | | | | | | | | | | | | |
Taipei | 7328 | 33.82 | | 54416 | 31.38 | | | 7318 | 33.81 | | 7358 | 33.99 | |
Northern | 2310 | 10.66 | | 20016 | 11.54 | 2.90 | | 2308 | 10.66 | | 2312 | 10.68 | 0.02 |
Central | 3769 | 17.40 | | 31678 | 18.27 | 2.12 | | 3767 | 17.40 | | 3757 | 17.36 | -0.17 |
Southern | 3729 | 17.21 | | 29840 | 17.21 | 0.39 | | 3725 | 17.21 | | 3684 | 17.02 | -0.28 |
Kao-Ping and Eastern | 4530 | 20.91 | | 37448 | 21.60 | 2.52 | | 4529 | 20.92 | | 4536 | 20.95 | 0.22 |
Major comorbidities | | | | | | | | | | | | | |
Hypertension | 18476 | 85.28 | | 144909 | 83.57 | -5.92 | | 18461 | 85.28 | | 18453 | 85.25 | 0.13 |
Dyslipidemia | 15127 | 69.82 | | 141918 | 81.85 | 32.52 | | 15123 | 69.86 | | 15238 | 70.39 | 1.18 |
Obesity | 518 | 2.39 | | 7364 | 4.25 | 11.03 | | 518 | 2.39 | | 524 | 2.42 | 0.11 |
Diabetes-related complications | | | | | | | | | | | | | |
Nephropathy | 8376 | 38.66 | | 50614 | 29.19 | -24.68 | | 8362 | 38.63 | | 8264 | 38.18 | -1.33 |
Eye diseases | 3741 | 17.27 | | 55119 | 31.79 | 35.85 | | 3740 | 17.28 | | 3657 | 16.89 | -1.43 |
Stroke | 8870 | 40.94 | | 58105 | 33.51 | -18.48 | | 8861 | 40.93 | | 8860 | 40.93 | 0.06 |
Ischemic heart disease | 11689 | 53.95 | | 84499 | 48.73 | -12.53 | | 11679 | 53.95 | | 11706 | 54.08 | 0.36 |
Peripheral arterial disease | 5678 | 26.21 | | 47465 | 27.37 | 2.10 | | 5672 | 26.20 | | 5763 | 26.62 | 0.78 |
Antidiabetic drugs | | | | | | | | | | | | | |
Insulin | 1891 | 8.73 | | 3953 | 2.28 | -34.85 | | 1877 | 8.67 | | 1649 | 7.62 | -5.81 |
Sulfonylurea | 15450 | 71.31 | | 123550 | 71.25 | 8.93 | | 15447 | 71.36 | | 16073 | 74.25 | 5.93 |
Meglitinide | 1958 | 9.04 | | 6930 | 4.00 | -22.82 | | 1952 | 9.02 | | 1924 | 8.89 | -0.71 |
Acarbose | 2443 | 11.28 | | 9223 | 5.32 | -20.85 | | 2438 | 11.26 | | 2558 | 11.82 | -0.13 |
Rosiglitazone | 618 | 2.85 | | 8158 | 4.70 | 10.80 | | 618 | 2.85 | | 618 | 2.85 | -0.79 |
Pioglitazone | 510 | 2.35 | | 4356 | 2.51 | 2.42 | | 509 | 2.35 | | 532 | 2.46 | -0.17 |
Commonly encountered comorbidities and potential risk factors of atrial fibrillation | | | | | | | |
Chronic obstructive pulmonary disease | 12054 | 55.64 | | 89210 | 51.45 | -10.90 | | 12039 | 55.62 | | 11904 | 54.99 | -1.12 |
Tobacco abuse | 507 | 2.34 | | 6439 | 3.71 | 8.81 | | 507 | 2.34 | | 473 | 2.19 | -1.07 |
Alcohol-related diagnoses | 1406 | 6.49 | | 11443 | 6.60 | -0.11 | | 1405 | 6.49 | | 1338 | 6.18 | -1.51 |
Cancer | 2254 | 10.40 | | 13711 | 7.91 | -9.64 | | 2249 | 10.39 | | 2260 | 10.44 | 0.20 |
Heart failure | 6237 | 28.79 | | 35109 | 20.25 | -24.13 | | 6229 | 28.78 | | 6145 | 28.39 | -0.96 |
Gout | 8921 | 41.18 | | 63552 | 36.65 | -10.62 | | 8915 | 41.18 | | 8930 | 41.25 | 0.32 |
Hyperthyroidism | 1030 | 4.75 | | 8637 | 4.98 | 1.31 | | 1029 | 4.75 | | 1046 | 4.83 | 0.42 |
Sleep apnea syndrome | 413 | 1.91 | | 3378 | 1.95 | 0.40 | | 412 | 1.90 | | 391 | 1.81 | -0.69 |
Commonly used medications in diabetes patients | | | | | | | | | | | | | |
Angiotensin-converting enzyme inhibitors/angiotensin receptor blockers | 15959 | 73.66 | | 130207 | 75.09 | 2.83 | | 15946 | 73.66 | | 15903 | 73.47 | -0.37 |
Calcium channel blockers | 14776 | 68.20 | | 109003 | 62.86 | -12.81 | | 14760 | 68.18 | | 14754 | 68.16 | 0.21 |
Beta-blockers | 15951 | 73.62 | | 119761 | 69.07 | -11.13 | | 15936 | 73.62 | | 15979 | 73.82 | 0.64 |
Statins | 11326 | 52.28 | | 112600 | 64.94 | 29.01 | | 11323 | 52.31 | | 11421 | 52.76 | 0.76 |
Fibrates | 7090 | 32.72 | | 73563 | 42.42 | 22.44 | | 7090 | 32.75 | | 7126 | 32.92 | 0.29 |
Aspirin | 13582 | 62.69 | | 111115 | 64.08 | 2.06 | | 13572 | 62.70 | | 13602 | 62.84 | 0.44 |
*Age is expressed as mean and standard deviation |
Refer to “Materials and Methods” for the classification of occupation |
The start of follow up was set on January 1, 2006 and all comorbidities and covariates were determined as a status/diagnosis at any time before the start of follow-up. Potential confounders included: 1) demographic data: age, sex, occupation and living region; 2) major comorbidities: hypertension (401–405), dyslipidemia (272.0-272.4) and obesity (278); 3) diabetes-related complications: nephropathy (580–589), eye diseases (250.5: diabetes with ophthalmic manifestations, 362.0: diabetic retinopathy, 369: blindness and low vision, 366.41: diabetic cataract, and 365.44: glaucoma associated with systemic syndromes), stroke (430–438), ischemic heart disease (410–414) and peripheral arterial disease (250.7, 785.4, 443.81 and 440–448); 4) antidiabetic drugs: insulin, sulfonylurea, meglitinide, acarbose, rosiglitazone and pioglitazone; 5) commonly encountered comorbidities and potential risk factors of AF: chronic obstructive pulmonary disease (a surrogate for smoking, 490–496), tobacco abuse (305.1, 649.0 and 989.84), alcohol-related diagnoses (291, 303, 535.3, 571.0-571.3 and 980.0), cancer (140–208), heart failure (398.91, 402.11, 402.91, 404.11, 404.13, 404.91, 404.93 and 428), gout (274), hyperthyroidism (242) and sleep apnea syndrome (327.2, 780.51, 780.53, 780.57); and 6) commonly used medications in diabetes patients: angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, calcium channel blockers, beta-blockers, statins, fibrates and aspirin. The accuracy of disease diagnoses in the NHI database has been studied previously. Agreements between claim data and medical records are moderate to substantial, with Kappa values range from 0.55 to 0.86 [18].
The classifications of living region and occupation were detailed elsewhere [19]. In brief, the living region was classified as Taipei, Northern, Central, Southern, and Kao-Ping/Eastern. Occupation was classified as class I (civil servants, teachers, employees of governmental or private businesses, professionals and technicians), class II (people without a specific employer, self-employed people or seamen), class III (farmers or fishermen) and class IV (low-income families supported by social welfare, or veterans).
Standardized difference was calculated according to the methods proposed by Austin and Stuart for each covariate as a test of balance diagnostic [20]. A value of > 10% is used as a cutoff for potential confounding from the variable.
Cumulative duration of metformin therapy in months was calculated and its tertiles were used for dose-response analyses. Incidence density of HAF was calculated for never users, ever users and users categorized according to the tertiles of cumulative duration of metformin therapy. The numerator of the incidence was the case number of newly diagnosed HAF as a primary diagnosis at the discharge of a hospitalization observed during follow-up. The denominator expressed in person-years was the follow-up time since January 1, 2006 until December 31, 2011, when a new diagnosis of HAF was made, or on the date of death or the last reimbursement record, whichever occurred first.
Kaplan-Meier curves for HAF-free probability were plotted for never users and ever users of metformin and for never users and users categorized according to the tertiles of cumulative duration of metformin therapy. Logrank test was used to test the significance in different subgroups of metformin exposure.
In main analyses, hazard ratios and their 95% confidence intervals were estimated by Cox proportional hazards model incorporated with the inverse probability of treatment weighting (IPTW) using the PS. This method was proposed by Austin for reducing the potential confounding from the differences in characteristics [21]. Models were created for ever users versus never users and for users in each tertile of cumulative duration of metformin therapy in comparison to never users.
For sensitivity analyses, the following models were created in the unmatched cohort by using both the IPTW method and the traditional Cox regression: 1) Excluding patients with irregular refill of metformin, based on two consecutive prescriptions of metformin spanning a period of > 4 months (the Bureau of NHI allows a maximum of 3 months at each time of drug prescription for chronic diseases and these patients with delayed refill of metformin for more than one month after a previous 3-month prescription might represent those with poor adherence); and 2) Excluding patients who happened to be treated with incretin-based therapies, i.e., dipeptidyl peptidase 4 inhibitors and glucagon-like peptide 1 receptor agonists, during follow-up (incretin-based therapies were not reimbursed by the Bureau of NHI in Taiwan until after 2009 and this analysis was aimed at excluding their potential impact).
Analyses were conducted using SAS statistical software, version 9.4 (SAS Institute, Cary, NC). P < 0.05 was considered statistically significant.