Citations

AbstractBackgroundSome studies have reported associations between prenatal use of venlafaxine, a serotonin‐norepinephrine reuptake inhibitor used for depressive and anxiety disorders, and some birth defects. We described the prevalence of venlafaxine prescription claims among privately insured women of reproductive age and pregnant women.MethodsVenlafaxine prescription claims were examined using the IBM MarketScan Commercial Databases. We included women of reproductive age (15–44 years) who had ≤45 days of lapsed enrollment during the calendar year of interest (2011–2016) in a non‐capitated healthcare plan sponsored by a large, self‐insured employer with prescription drug coverage and no mental health service carve‐out. Annual cohorts of pregnant women were identified among eligible women of reproductive age via pregnancy diagnosis and procedure codes. Venlafaxine prescriptions were identified via National Drug Codes in outpatient pharmacy claims and we estimated the annual proportion of women with venlafaxine claims by pregnancy trimester (pregnant women only), age, and Census division.ResultsEach year during 2011–2016, approximately 1.2% of eligible reproductive‐aged and 0.3% of eligible pregnant women filled a venlafaxine prescription. Among pregnant women, the proportion with venlafaxine claims was highest during the first trimester and decreased during the second and third trimesters. Small temporal increases in venlafaxine claims were observed for reproductive‐aged and pregnant women, with the largest among women aged 15–19 years.ConclusionsVenlafaxine prescription claims were low among women of reproductive age and pregnant women during 2011–2016, with some increasing use over time among women aged 15–19 years.

Objectives To describe the percentile distribution of multimorbidity across age by sex, race and ethnicity, and to demonstrate the utility of multimorbidity percentiles to predict mortality. Design Population-based descriptive study and cohort study. Setting Olmsted County, Minnesota (USA). Participants We used the medical records-linkage system of the Rochester Epidemiology Project (REP; http://www.rochesterproject.org ) to identify all residents of Olmsted County, Minnesota who reached one or more birthdays between 1 January 2005 and 31 December 2014 (10 years). Methods For each person, we obtained the count of chronic conditions (out of 20 conditions) present on each birthday by extracting all of the diagnostic codes received in the 5 years before the index birthday from the electronic indexes of the REP. To compare each person’s count to peers of same age, the counts were transformed into percentiles of the total population and displayed graphically across age by sex, race and ethnicity. In addition, quintiles 1, 2, 4 and 5 were compared with quintile 3 (reference) to predict the risk of death at 1 year, 5 years and through end of follow-up using time-to-event analyses. Follow-up was passive using the REP. Results We identified 238 010 persons who experienced a total of 1 458 094 birthdays during the study period (median of 6 birthdays per person; IQR 3–10). The percentiles of multimorbidity across age did not vary noticeably by sex, race or ethnicity. In general, there was an increased risk of mortality at 1 and 5 years for quintiles 4 and 5 of multimorbidity. The risk of mortality for quintile 5 was greater for younger age groups and for women. Conclusions The assignment of multimorbidity percentiles to persons in a population may be a simple and intuitive tool to assess relative health status, and to predict short-term mortality, especially in younger persons and in women.

Objective: To determine whether a bi-directional relationship exists between depression and HF within a single population of individuals receiving primary care services, using longitudinal electronic health records (EHRs). Methods: This retrospective cohort study utilized EHRs for adults who received primary care services within a large healthcare system in 2006. Validated EHR-based algorithms identified 10,649 people with depression (depression cohort) and 5,911 people with HF (HF cohort) between January 1, 2006 and December 31, 2018. Each person with depression or HF was matched 1:1 with an unaffected referent on age, sex, and outpatient service use. Each cohort (with their matched referents) was followed up electronically to identify newly diagnosed HF (in the depression cohort) and depression (in the HF cohort) that occurred after the index diagnosis of depression or HF, respectively. The risks of these outcomes were compared (vs. referents) using marginal Cox proportional hazard models adjusted for 16 comorbid chronic conditions. Results: 2,024 occurrences of newly diagnosed HF were observed in the depression cohort and 944 occurrences of newly diagnosed depression were observed in the HF cohort over approximately 4–6 years of follow-up. People with depression had significantly increased risk for developing newly diagnosed HF (HR 2.08, 95% CI 1.89–2.28) and people with HF had a significantly increased risk of newly diagnosed depression (HR 1.34, 95% CI 1.17–1.54) after adjusting for all 16 comorbid chronic conditions. Conclusion: These results provide evidence of a bi-directional relationship between depression and HF independently of age, sex, and multimorbidity from chronic illnesses.

Abstract Objective: We studied eight mental health conditions diagnosed before bilateral oophorectomy performed for nonmalignant indications. Methods: We identified 1,653 premenopausal women who underwent bilateral oophorectomy for a nonmalignant indication in Olmsted County, Minnesota, during a 20-year period (1988-2007). Each woman was matched by age (±1 year) to one population-based control who had not undergone bilateral oophorectomy before the index date (age range: 21-49 years). Both cases and controls were identified using the records-linkage system of the Rochester Epidemiology Project (REP http://www.rochesterproject.org). For eight mental health conditions, we calculated odds ratios (ORs) and their 95% confidence intervals (95% CIs) adjusted for race, education, and income using conditional logistic regression. Results: Pre-existing mood disorders, anxiety disorders, and somatoform disorders were associated with increased risk of bilateral oophorectomy in overall analyses. These associations were also significant in women ≤45 years of age at index date. Personality disorders were associated with increased risk only in overall analyses and adjustment disorders only in women 46 to 49 years of age. Some of the associations were significantly different across strata by age at index date and by indication. There was also a linear trend of increasing adjusted ORs from 1.55 (95% CI 1.31-1.83) for one mental health condition to 2.19 (95% CI 1.40-3.41) for three or more conditions (trend P < 0.001). Conclusions: We identified several mental health conditions that were associated with bilateral oophorectomy for nonmalignant indications. Awareness of these associations may guide women and physicians in future decision-making and limit unindicated bilateral oophorectomies. Video Summary: http://links.lww.com/MENO/A458.

We set out to determine whether machine learning–based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied 1,030 white outpatients with MDD treated with citalopram/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN‐AMPS; n = 398), Sequenced Treatment Alternatives to Relieve Depression (STAR*D; n = 467), and International SSRI Pharmacogenomics Consortium (ISPC; n = 165) trials. A genomewide association study for PGRN‐AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified single nucleotide polymorphisms (SNPs) in DEFB1, ERICH3, AHR, and TSPAN5 that we tested as predictors. Supervised machine‐learning methods trained using SNPs and total baseline depression scores predicted remission and response at 8 weeks with area under the receiver operating curve (AUC) > 0.7 (P < 0.04) in PGRN‐AMPS patients, with comparable prediction accuracies > 69% (P ≤ 0.07) in STAR*D and ISPC. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI therapy response using total baseline depression severity combined with pharmacogenomic biomarkers.

AbstractThe purpose of this study was to estimate the extent of potential antidepressant overprescribing in a geographically defined U.S. population, and to determine the indications and factors that account for it. We conducted a cohort study of new antidepressant prescriptions for elderly residents of Olmsted County, Minnesota, 2005‐2012, using the Rochester Epidemiology Project medical records‐linkage system. Indications for antidepressants were abstracted from health records for all cohort members. Potential antidepressant overprescribing was defined based on regulatory approval, the level of evidence identified from a standardized drug information database, and multidisciplinary expert review. Predictors of potential antidepressant overprescribing were investigated using logistic regression models, stratified by general antidepressant indication (general medical indication, specific psychiatric diagnosis, and non‐specific psychiatric symptoms). Potential antidepressant overprescribing occurred in 24% of 3199 incident antidepressant prescriptions during the study period, and involved primarily newer antidepressants that were prescribed for non‐specific psychiatric symptoms and subthreshold diagnoses. Potential antidepressant overprescribing was associated with nursing home residence, having a higher number of comorbid medical conditions and outpatient prescribers, taking more concomitant medications, having greater use of urgent or acute care services in the year preceding the index antidepressant prescription, and being prescribed antidepressants via telephone, e‐mail, or patient portal. In conclusion, potential antidepressant overprescribing occurred in elderly persons and involved mainly newer antidepressants used for non‐specific psychiatric symptoms and subthreshold diagnoses, and was associated with indicators of higher clinical complexity or severity and with prescribing without face‐to‐face patient contact.