Study Design
We designed a population-based cohort study in Taiwan with 1:3 propensity score matching for the comparison group, with the longest follow-up being 12 years. The study was performed after obtaining approval from the Kuang Tien General Hospital Institutional Review Board (approval certificate: KTGH IRB-10428), and was conducted according to the Declaration of Helsinki.
Data Source
The National Health Insurance Research Database (NHIRD), a population-based and nationwide medical claims database, was adopted for the research. The analyses included a NHIRD-derived data set, the Longitudinal Health Insurance Database, with 1 million randomly recruited, insured individuals from the mother NHIRD database. We targeted the study population that had medical claim records from 1996 to 2008. The data set was de-identified on the levels of all insured participants and medical institutions prior to release for academic research purposes. The authors have previously conducted several population-based longitudinal studies using the NHIRD-derived data set on clinical epidemiology with study design and methodology similar to the current one.15
–
19
Target Population
The target population for this work was individuals aged 20 years or older with chronic insomnia taking sleep-inducing pills of their physician’s choice without any preexisting AID before the index date. The index date was defined as the date of accrual of the cases and the corresponding date for the comparators they were matched to. The beginning year 1996 in the data set was used as a wash-out period so that patients with pre-existing AID could be detected and excluded from the construction of the cohort. The exclusion criteria included records with missing data, age < 20 years, patients with a preexisting AID before the index date, individuals never prescribed with any hypnotics, and having < 2 medical claims over the first 3-month period. The exclusion criterion for the comparison group was subjects subsequently prescribed hypnotics during follow-up. The comparison group was formed after 1:3 propensity score matching using the perfect matching algorithm by sex, age, index date, smoking-related diagnoses, alcohol use disorder, morbid obesity, household insurance premium, and urbanization level (Fig. 1). The household insurance premiums of the participants for the index year were used as a proxy for household income, based on the fact that payroll-based premiums paid to the National Health Insurance are proportional to household income level.20
Diagnosis Criteria
According to the Diagnostic and Statistical Manual of Mental Disorders, fourth ed. (DSM-IV) classification for insomnia (International Classification of Disease, ninth edition, with clinical modifications, ICD-9-CM, code 780.52), the diagnostic criteria of chronic insomnia in adults is a predominant complaint of dissatisfaction with sleep quality or quantity, not attributable to the physiological effects of a substance, occurring at least 3 nights a week for 3 months or more, associated with at least one of the following symptoms: difficulty initiating sleep, difficulty maintaining sleep characterized by frequent awakenings or problems returning to sleep after awakenings, and early morning awakening with inability to return to sleep. Furthermore, this insomnia must have caused clinically significant distress or impairment in personal or social functioning in daily living. Individuals with chronic insomnia qualified for this study if they had physician-diagnosed insomnia and a prescription of sleep-inducing pills in two independent medical claims using the ICD-9-CM code 780.52 within a 3-month period. The index date was the last day of the third month observed.
Outcome Measures
The study endpoint was the development of a physician-diagnosed AID. In this study, the investigated AID and corresponding ICD-9-CM code(s) were: primary Sjögren’s syndrome (pSS; 710.2) without concurrent codes of any other AID; systemic lupus erythematosus (710.0); ankylosing spondylitis (720); polymyositis/dermatomyositis (710.3 and 710.4); multiple sclerosis (340); vasculitis (including hypersensitivity angiitis and anti-glomerular basement membrane antibody–mediated disease, 446.2 and 446.29); rheumatoid arthritis (714); Behçet’s disease (136.1); myasthenia gravis (358); systemic sclerosis (710.1); Guillain–Barré syndrome (357.0); Hashimoto’s thyroiditis (245.2); Graves’ disease (242 and 242.01), and uveitis (364.00 and 364.01). Three repeated claims with one of the above ICD-9-CM codes were required to confirm the presence of the outcome. The incidence of AID with person-year data are presented in the results. Figure 1 reviews the step-by-step process in the assembly of the case and the comparison cohorts and their follow-up.
Statistical Analysis
We used the SAS statistical package (SAS System for Windows, version 9.4; SAS Institute, Cary, NC, USA) to conduct all the statistical analyses required in this work. In this study, each patient (n = 22,944) was individually tracked for up to 12 years from their index date to identify those who were subsequently diagnosed as having AID. The age-, demographic-, and comorbidity-specific incidence density rates of AID were calculated in each subgroup in person-years. Both univariate and multivariate Cox proportional hazards regression models were conducted at α = 0.05 significance level to calculate the crude hazard ratio (HR) and mutually adjusted HR with their 95 % CI after controlling for all the selected sociodemographic and comorbidity variables. To satisfy the assumption of non-informative censoring, censoring of individual subjects was not related to the probability of occurrence of an event occurring; for example, continuation of follow-up in the cohort did not depend on a subject’s medical condition. We checked the survival curves in SAS using Proc Lifetest to ascertain that the hazard functions were proportional over time. We graphed log (−log[survival]) versus the log of the survival time to see if they resulted in parallel curves if the predictor was proportional. A table of the measured HRs for each type of AID using the multivariate Cox models was constructed after controlling for sex, age group, household insurance premium, urbanization level, smoking-related diagnoses, diabetes mellitus (DM), chronic kidney disease (CKD), liver cirrhosis, and depression. The Kaplan–Meier method and log-rank test were subsequently applied to examine the differences of the cumulative incidence for the AID between the chronic insomnia group and the comparison group. To control for type I errors, the Benjamini–Hochberg (BH) procedure was implemented to achieve false discovery rate (FDR) control.21 In short, the BH procedure is used to determine which p values to declare statistically significant when the p value for an individual hypothesis test is ≤d x і/n, where d represents the maximal FDR set as 10 % wherein case validation in the subsequent study procedure can be performed; і means the test of rank 1, 2, through і in ascending order of sorted p values; and n equals the total number of hypothesis tests, which was 15 in this work.22