Clinical indications of premenstrual disorders and subsequent risk of injury: a population-based cohort study in Sweden

We conducted a registry-based cohort study of 1,543,653 women who were born in Sweden from 1960 to 1990 and resided in Sweden in 1990, according to the Swedish Population and Housing Census. Using the unique personal identification numbers assigned to every resident, we cross-linked all women to the nationwide Causes of Death Register and Migration Register. All women were followed from January 1, 2001, or their 15 birthday (as 96% Swedish women would have had menarche by age 15 [20]), whichever occurred last, until death, emigration, bilateral oophorectomy, or hysterectomy, their 52nd birthday (the average age of menopause in Sweden [21]), or December 31, 2012, whichever occurred first. We excluded 71,346 women due to bilateral oophorectomy or hysterectomy (n=2698), emigration (n=64,689), or death (n=3,959) before cohort entry, leaving 1,472,379 women in the cohort (population analysis). The Swedish Multi-Generation Register consists of information on 97% of mothers and 95% of fathers of individuals born after 1932 and alive in 1961 [22]. By linking to the register, we identified 638,910 women who were full sisters (i.e., sharing the same biological parents) for the sibling analysis.

Clinical indications of PMD were identified using any primary or secondary clinical diagnosis of PMD from the Patient Register (625E in International Classification of Diseases (ICD) 9th revision (ICD-9) and N943 in ICD-10). The Patient Register has nationwide coverage on inpatient care from 1987 onward and includes information on more than 80% of specialist-based outpatient visits from 2001 onward with high validity (positive predicted value of 85–95% across diseases) [23]. All PMD diagnoses obtained from the Patient Register are considered provisional, as we could not confirm that all providers of PMD diagnoses ascertained from the Patient Register used two cycles of prospective daily symptom ratings for diagnosing [3], although it is required in the Swedish healthcare guidelines for PMDD in many regions [24]. The Patient Register does not cover diagnoses made in primary care, and 49.3% of mental health conditions are treated in primary care in Sweden [9]. Therefore, we also identified PMD by searching the Prescribed Drug Register for any PMD diagnosis or clear indication of PMD treatment in prescriptions of anti-depressants (the Anatomical Therapeutic Chemical (ATC) code: N06AB, N06AX, N06AA) and oral contraceptives (ATC code: G03A, G02B). We included only prescriptions explicitly made for PMD treatment, as indicated by PMD diagnosis/prescription specific to PMD. Diagnostic codes and keywords indicated in prescriptions are described in supplementary Table S1. The register collects information on drugs redeemed with a prescription from all pharmacies in Sweden from July 2005 onward, including drugs prescribed in primary care [25]. We defined the date of PMD diagnosis as either the date of clinical diagnosis or the date that a prescription for PMD medication was filled, whichever came first.

PMD were treated as a time-varying exposure. Women who had not received a diagnosis of PMD during follow-up contributed person-time to the reference group. Women with PMD contributed person-time to the reference group from the start of follow-up until the date of diagnosis and contributed to the PMD group thereafter.

By linking to the Patient Register and the Causes of Death Register, we identified the first injury resulting in healthcare visit (either as the primary or secondary diagnosis) or death (either as underlying or contributory cause) experienced by participants during follow-up. We also identified first injury event due to suicidal behavior (i.e., completed suicide and suicide attempt), accidents, assaults, and undetermined injury, where available information was insufficient to make a distinction between the aforementioned three subtypes, separately. Given our a priori hypotheses, we primarily focused on suicidal behavior and accidents in our main analysis. We further sub-grouped accidents into (1) falls, (2) transportation and accidents by other external factors, (3) accidents by natural forces or contact with animals or plants, (4) cutting or piercing, (5) poisoning, and (6) others (supplementary Table S1). Injuries that occurred before the start of the follow-up were defined as “history of injury” and not treated as outcomes; only the first injury occurring during the follow-up was registered as an outcome event.

The Patient Register covers >90% of hospital discharges nationwide for injury since 1987 and has a high validity (94.8%) for diagnosing injury [23]. Ascertainment of injury-related deaths in the Causes of Death Register is considered highly accurate [26] and complete [27].

Information about year of birth and region of residence was obtained from the Swedish Population and Housing Census in 1990, while information on the highest educational level was retrieved from the Swedish Education Register, which is updated through 2001. Comorbid psychiatric diagnoses were identified from the Patient Register from 1981 onward. PMD are commonly comorbid with psychiatric disorders, which may both mediate and confound the association between PMD and injury. We therefore defined psychiatric comorbidities for all women by identifying any diagnosis of psychiatric disorders (290-319 in ICD-8/9, F10-F90 in ICD-10) throughout follow-up from the Patient Register. Psychiatric comorbidities were then treated as a time-varying covariate.

First, we compared the distributions of demographic characteristics between the reference and PMD groups in both the population analysis and the sibling analysis. The characteristics of PMD patients identified via clinical diagnosis and through treatment indication were evaluated separately. Next, we calculated unadjusted incidence rates (number of events divided by accumulated person-years) of injury in both groups, separately.

In the population analysis, we used Cox proportional hazards regression (attained age as the underlying timescale) to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of injury among women with PMD, compared to the reference group. We accounted for the relatedness of individuals (i.e., sisters) using robust sandwich estimator of variance in the population analysis [28]. The proportional hazard (PH) assumption was tested by plotting the Schoenfeld residuals. Additionally, we conducted a sibling analysis using stratified Cox proportional hazards regression. The sibling analysis contrasts the rates within each set of full sister discordant on PMD diagnosis and inherently controls for unmeasured factors (e.g., shared genetic and familial environmental factors) shared between sisters [29].

We performed separate analyses for the 4 types of injury: (1) suicidal behavior, (2) accidents, (3) assaults, and (4) undetermined injury. We further conducted an analysis of accident subtypes.

We conducted a series of sensitivity analyses to assess the robustness of the PMD classification and assess the potential impact of misclassification. To assess potential differences in outcomes among PMD patients by diagnosis type, we differentiated between PMD identified by (1) clinical diagnosis, (2) treatment indication, and (3) clinical diagnosis and treatment indication. Although all diagnoses in the Patient Register are made by specialists with high validity [23], the clinical diagnosis of PMD has not been validated. To further test the validity of PMD diagnoses, we restricted the PMD group to patients with at least two consecutive PMD diagnoses appearing at least 28 days apart in the Patient Register. We also conducted a simulation analysis and calculated hazard ratios for injury in testing conditions where 10–80% of PMD diagnoses were false positives [30]. Finally, because use of antidepressants or hormonal contraceptives may be associated with different risk of suicidal behavior [31, 32], a sensitivity analysis was conducted by restricting our primary analysis to untreated PMD.

In all analyses, attained age was used as the underlying timescale. We adjusted for calendar year of birth, educational level, region of residence, and history of injury in Model 1. Psychiatric comorbidities were additionally adjusted for in a time-varying manner in Model 2. However, we only employed Model 2 for the analyses subsequent to the primary analysis, whenever applicable. All covariates were categorized (Table 1).

Because PMD has been associated with multiple psychiatric comorbidities [33], we repeated our primary analysis by separately adjusting for the number of psychiatric comorbidities and individual type of psychiatric comorbidities (e.g., substance abuse, schizophasia) in a time-varying way. Finally, as previous suicidal behavior is a strong predictor of suicidal behavior [34], we additionally adjusted for history of suicidal behavior when assessing the association between PMD and suicidal behavior.

A diagnosis of PMD may be delayed for years from symptom onset [35]. We therefore conducted an additional analysis to estimate the risk of injury preceding diagnosis among PMD patients (i.e., from cohort entry to the date of diagnosis), compared to women without PMD. Due to risk of a delay in PMD diagnosis, we also conducted a sensitivity analysis by excluding person-time preceding the diagnosis of PMD (i.e., from cohort entry to the date of diagnosis).

To shed light on the impact of psychiatric comorbidities, we performed an analysis stratified by psychiatric comorbidities by estimating HRs in the presence and absence of psychiatric comorbidities. To identify potential risk modifiers, we also performed stratified analyses for any injury by calendar year of birth, educational level, region of residency, and history of injury.

Data were prepared in SAS statistical software version 9.4 (SAS Institute, Cary, NC) and analyzed in Stata 15.1 (STATA, College Station, TX). The statistical significance was set at the nominal two-sided 5% level.

In total, we identified 18,628 women with a clinical indication of PMD, of which 7674 had identifiable full sisters. The average age at diagnosis was 36 years (standard deviation 7 years). Compared with the general female population, women with PMD were older, had higher educational attainment, and were more likely to have a history of injury and psychiatric comorbidities (Table 1) at baseline. Similar patterns were observed in the sibling analysis. PMD patients identified from the Patient Register were slightly different from those identified from the Prescribed Drug Register (Supplementary Table S2).

During the maximal follow-up of 12 years (mean 9.55 years), we identified a total of 22,636 women with injury due to suicidal behavior (216 in the PMD group), and 315,162 women with injury due to accidents (2191 in the PMD group) in the population analysis; of these, 9000 women with suicidal behavior (89 in the PMD group), and 132,900 women with accidents (895 in the PMD group) were included in the sibling analysis. After receiving a diagnosis of PMD, the cumulative incidence rate of suicidal behaviors and accidents rapidly increased over time and reached about 1.6% and 17%, respectively, by 12 years of follow-up, which were considerably higher than in the matched reference group (Fig. 1).

Compared to women without PMD, women with PMD were at increased risk of suicidal behavior (HR 1.65, 95% CI 1.43 to 1.89) and accidents (HR 1.28, 95% CI 1.23 to 1.34), in the population analysis. The sibling analysis gave similar estimates for accidents, though slightly lower estimates for suicidal behavior events with lower precision. Additional adjustment for psychiatric comorbidities minimally altered the associations with any injury and accidents in both population and sibling analyses. The association with suicidal behavior was considerably attenuated to non-significance in the sibling analysis (Table 2). For assaults and other undetermined injury, positive associations were observed in the population analysis but attenuated towards null in the sibling analysis (supplementary Table S3). We found elevated risks for all types of accidents among women with PMD in the population and similar associations in the sibling analysis (supplementary Table S4).

Results stayed substantially similar to the main analysis when (1) adjusting for the number of psychiatric disorders or type-specific psychiatric comorbidities separately (supplementary Tables S5, S6); (2) adjusting for history of suicidal behavior when assessing the risk of suicidal behavior (supplementary Table S7); (3) restricting to PMD patients who received one or two clinical diagnoses made by specialists, regardless of the prescribed medications (supplementary Table S8); or (4) excluding the person-time preceding the diagnosis among PMD patients (supplementary Table S9).

Women with PMD who were not using antidepressants or oral contraceptives, were at significantly higher risk of suicidal behavior and accidents, in both population and sibling analyses (supplementary Table S10). Simulation analysis indicated that the observed associations in the population analysis remained up to the point where 80% of PMD diagnoses would be false positives (supplementary Table S11).

The higher risk of suicidal behavior and accidents associated with PMD was strong within the first 2 years after PMD diagnosis, particularly the first year (HR 2.84, 95% CI 2.29 to 3.52 for suicidal behavior; HR 1.72, 95% CI 1.60 to 1.86 for accidents in the population analysis, Fig. 2). Relative risks declined somewhat by 2 years after PMD diagnosis (HR 1.43, 95% CI 1.12 to 1.64 for suicidal behavior and HR 1.07, 95% CI 1.01 to 1.14 for accidents in the population analysis). Similar results were observed in sibling analysis but not statistically significant for suicidal behavior due to small number of events. The risk of injury was already somewhat higher for PMD patients in the period pre-diagnosis (suicidal behavior: HR 1.67, 95% CI 1.50 to 1.86 in the population analysis; HR 1.43, 95% CI 0.96 to 2.14 in the sibling analysis; and accidents: HR 1.12, 95% CI 1.08 to 1.16 in the population analysis; HR 1.10, 95% CI 0.99 to 1.23 in the sibling analysis).

In a stratified analysis, an increased risk of accidents was noted for women with PMD with and without psychiatric comorbidities in the population analysis, with slightly stronger association for accidents for PMD patients without psychiatric comorbidities (P for interaction=0.007; Table 3). However, this pattern was not observed in the sibling analysis (P for interaction=0.783). Comparable associations were observed for suicidal behavior in both population and sibling analyses. An increased risk of any injury was observed for women with PMD regardless of history of injury. The association was stronger for PMD with a history of injury in the population analysis, although there was lack of precision in the sibling analysis. Finally, year of birth, educational level, and region of residency did not clearly modify the associations with injury risk (supplementary Table S12).

The major merit of our study is the large-scale population-based cohort with prospectively collected information on both PMD with clinical indications and injury, which minimizes bias due to selection and measurement. The sibling analysis further allows us to rigorously control for familial factors shared between full sisters, such as genetic factors and familial environment during childhood. However, our study also has limitations. First, the clinical diagnoses of PMD have not been validated in the Swedish Patient Register. The healthcare guidelines in most regions in Sweden, for instance the one in Stockholm [24], have specified that “the diagnosis must be confirmed by the woman making daily prospective evaluation of symptoms for at least two consecutive menstrual cycles”. The clinical guidelines are often well followed in the state-funded public healthcare system in Sweden. Furthermore, the quality for the diagnoses in the Patient Register has been considered fairly high in general [51] and for a range of psychiatric disorders [52‐55] and OB/GYN diseases [23, 56]. Moreover, our sensitivity analysis limited to PMD with two consecutive diagnoses 28 days apart have showed highly similar results to the primary population analysis, although we lacked power in the sibling analysis. In addition, our simulation analysis indicates that the observed association between PMD and injury in the population analysis would remain unless >80% of PMD diagnoses were incorrect. In addition, PMD identified only via treatment indication from the Prescribed Drug Register, and not clinical diagnosis, might represent less severe PMD. However, our sensitivity analysis restricted to PMD diagnosed in specialist care (as obtained from the Patient Register) yielded very similar results. Second, it might take years and many healthcare visits from the time of symptom onset to the receipt of a clinical diagnosis of PMD [35]. Thus, the date of PMD diagnosis in our data may not reflect either the timing of symptom onset or first clinical diagnosis, particularly for women who diagnosed before 2001 (i.e., when the Patient Register started to collect specialists-based outpatient diagnoses). This is supported by the higher proportion of PMD women with history of injury at cohort entry as well as the increased risk of injury before the date of diagnosis reported during the observation period. However, a sensitivity analysis excluding person-time preceding diagnosis among PMD patients yielded highly similar results. Third, the identified PMD individuals likely represent a patient group with most severe PMD symptomology, particularly those who felt obliged to seek help from healthcare and those who had more severe psychiatric comorbidities. Our findings may therefore not be generalized to individuals with milder premenstrual symptoms. Fourth, we lacked information on some potential confounding factors, e.g., childhood abuse [57, 58], smoking [59, 60], and had considerable missing data on educational level. However, we observed robust associations between PMD and accidents in the sibling analysis, which controls for factors shared between full sisters. Lastly, it is possible that women with other patterns of menstruation-related symptoms, such as premenstrual exacerbation of a pre-existing psychiatric disorder, are also at increased risk of injury. Future research is therefore needed for other menstrual cycle-related disorders [61].