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Aim: To evaluate Medicaid spending and healthcare resource utilization (HRU) in treatment-resistant depression (TRD). Materials & methods: TRD beneficiaries were identified from Medicaid claims databases (January 2010–March 2017) and matched 1:1 with major depressive disorder (MDD) beneficiaries without TRD (non-TRD-MDD) and randomly selected patients without MDD (non-MDD). Differences in HRU and per-patient-per-year costs were reported in incidence rate ratios (IRRs) and cost differences (CDs), respectively. Results: TRD beneficiaries had higher HRU than 1:1 matched non-TRD-MDD (e.g., inpatient visits: IRR = 1.41) and non-MDD beneficiaries (N = 14,710 per cohort; e.g., inpatient visits: IRR = 3.42, p Conclusion: TRD is associated with higher HRU and costs versus non-TRD-MDD and non-MDD. TRD poses a significant burden to Medicaid.

Objective: To quantify the economic burden of postpartum depression (PPD) that accrues to commercially insured households in the year following childbirth. Methods: Administrative claims data from OptumHealth Care Solutions (2009-2016) were used to identify households that included women identified with PPD per the algorithm and propensity score–matched comparison households of women who were not identified with PPD or a history of depression after childbirth. Study outcomes included direct total all-cause medical and pharmaceutical costs during the first year following childbirth and number of outpatient visits at the household level stratified by household member. Results: Households affected by PPD as identified by the algorithm (N = 7,769) incurred 22% higher mean total all-cause medical and pharmaceutical spending than unaffected matched controls (N = 41,308) during the first year following childbirth ($36,049 versus $29,448, P 50%) of total all-cause spending. Mothers identified with PPD had significantly higher annual mean direct total all-cause medical and pharmaceutical spending than their matched controls without PPD ($19,611 versus $15,410, P Conclusions: Households affected by PPD as identified by the algorithm incurred higher mean total all-cause medical and pharmaceutical spending during the first year following childbirth than did their matched controls identified without PPD, but not all costs were attributable to maternal treatment for PPD. These findings contribute to a better understanding of the potential economic burden associated with PPD and demonstrated costs may extend beyond the mother to members of the household.

BackgroundPrevious studies have assessed the incremental economic burden of treatment-resistant depression (TRD) versus non-treatment-resistant major depressive disorder (i.e., non-TRD MDD) in commercially-insured and Medicaid-insured patients, but none have focused on Medicare-insured patients.ObjectiveTo assess healthcare resource utilization (HRU) and costs of patients with TRD versus non-TRD MDD or without major depressive disorder (MDD; i.e., non-MDD) in a Medicare-insured population.MethodsAdult patients were retrospectively identified from the Chronic Condition Warehouse de-identified 100% Medicare database (01/2010-12/2016). MDD was defined as ≥1 MDD diagnosis and ≥1 claim for an antidepressant. Patients initiated on a third antidepressant following two antidepressant treatment regimens of adequate dose and duration were considered to have TRD. The index date was defined as the date of the first antidepressant claim for the TRD and non-TRD MDD cohorts, and as a randomly imputed date for the non-MDD cohort. Patients with TRD were matched 1:1 to non-TRD MDD patients and randomly selected non-MDD patients based on propensity scores. Analyses were also performed for a subset of patients aged ≥65.ResultsOf 29,543 patients with MDD, 3,225 (10.9%) met the study definition of TRD; 157,611 were included in the non-MDD cohort. Matched patients with TRD and non-TRD MDD were, on average, 58.9 and 59.0 years old, respectively. The TRD cohort had higher per-patient-per-year (PPPY) HRU than the non-TRD MDD (e.g., inpatient visits: incidence rate ratio [IRR] = 1.36) and non-MDD cohorts (e.g., inpatient visits: IRR = 1.84, all PConclusionAmong Medicare-insured patients, those with TRD had higher HRU and costs compared to those with non-TRD MDD and non-MDD.

Objective Our objective was to assess healthcare resource utilization (HRU) and costs among patients with major depressive disorder (MDD) with and without treatment-resistant depression (TRD) and those without MDD in US Integrated Delivery Networks (IDNs). Methods This was a retrospective matched-cohort study. The Optum© Integrated Claims Electronic Health Record de-identified database was used to identify adult patients with TRD (January 2011–June 2017) across US IDNs. TRD patients were propensity score matched 1:1 with non-TRD MDD and non-MDD patients on demographics. Rates of HRU and costs were compared up to 2 years following the first antidepressant pharmacy claim (or randomly imputed date for non-MDD patients) using negative binomial and ordinary least squares regressions, respectively, with 95% confidence intervals (CIs) from nonparametric bootstraps (costs only) adjusted for baseline comorbidity index and costs. Results All 1582 TRD patients were matched to non-TRD MDD and non-MDD patients and evaluated. TRD patients were on average 46 years old, and 67% were female. Mean duration of observation was 20.1, 19.6, and 17.9 months in the TRD, non-TRD MDD, and non-MDD cohorts, respectively. Patients with TRD had significantly higher rates of HRU than did non-TRD MDD patients (inpatient visits 0.35 vs. 0.16 per patient per year [PPPY]; adjusted incidence rate ratio [IRR] 2.04 [95% CI 1.74–2.39]) and non-MDD patients (0.35 vs. 0.09 PPPY, adjusted IRR 3.05 [95% CI 2.54–3.66]). TRD patients incurred significantly higher costs PPPY than did non-TRD MDD patients ($US25,807 vs. 13,701, adjusted cost difference $US9479 [95% CI 7071–11,621]) and non-MDD patients ($US25,807 vs. 8500, adjusted cost difference $US11,433 [95% CI 8668–13,876]). Conclusions HRU and costs associated with TRD are significant in US IDNs. Electronic supplementary material The online version of this article (10.1007/s41669-019-0154-z) contains supplementary material, which is available to authorized users.

Combinations of healthcare claims data with additional datasets provide large and rich sources of information. The dimensionality and complexity of these combined datasets can be challenging to handle with standard statistical analyses. However, recent developments in artificial intelligence (AI) have led to algorithms and systems that are able to learn and extract complex patterns from such data. AI has already been applied successfully to such combined datasets, with applications such as improving the insurance claim processing pipeline and reducing estimation biases in retrospective studies. Nevertheless, there is still the potential to do much more. The identification of complex patterns within high dimensional datasets may find new predictors for early onset of diseases or lead to a more proactive offering of personalized preventive services. While there are potential risks and challenges associated with the use of AI, these are not insurmountable. As with the introduction of any innovation, it will be necessary to be thoughtful and responsible as we increasingly apply AI methods in healthcare.

[This corrects the article DOI: 10.1371/journal.pone.0223255.].

Purpose To compare the patient characteristics and the inter-temporal reporting patterns of adverse events (AEs) for atorvastatin (Lipitor®) and sibutramine (Meridia®) in social media (AskaPatient.com) versus the FDA Adverse Event Reporting System (FAERS). Methods We identified clinically important AEs associated with atorvastatin (muscle pain) and sibutramine (cardiovascular AEs), compared their patterns in social media postings versus FAERS and used Granger causality tests to assess whether social media postings were useful in forecasting FAERS reports. Results We analyzed 998 and 270 social media postings between 2001 and 2014, 69 003 and 7383 FAERS reports between 1997 and 2014 for atorvastatin and sibutramine, respectively. Social media reporters were younger (atorvastatin: 53.9 vs. 64.0 years, p

Objective: To understand factors driving the economic burden of major depressive disorder (MDD) patients with different treatment regimens, by evaluating the relationship between medical profiles and treatment costs. Abstract: Methods: Claims data for US privately insured employees (1999-2004) were analysed. Analysis included adult employees with ≥1 diagnosis of MDD and ≥1 prescription for specific antidepressants following a 6-month washout period. Patients were first classified into treatment pattern groups (switchers/discontinuers/maintainers/augmenters), then stratified into mutually exclusive treatment groups - nonstable, stable and intermediate - based on evidence of stability in treatment therapy. Rates of mental and physical co-morbidities, injuries/accidents, substance abuse and urgent care use were analysed across treatment pattern groups. Direct (medical/drug) costs were calculated per patient per year and disaggregated into depression- and non-depression-related components. A two-part multivariate model controlled for baseline characteristics. Costs were also estimated for patients with MDD only, patients with MDD and generalized anxiety disorder (GAD), and patients with MDD and any type of anxiety. Abstract: Results: Annual per patient adjusted costs (year 2005 values) were significantly lower among stable patients (&dollar;US6215) than among intermediate (&dollar;US7317) and nonstable patients (&dollar;US9948; p < 0.001). Stable patients also had lower depression- and non-depression-related costs. Patients with MDD and comorbid GAD or any type of anxiety had significantly higher costs than MDD-only patients. Abstract: Conclusions: Nonstability of treatment is associated with higher comorbidity rates, more urgent care use and higher total, depression- and non-depression-related direct costs. The stable group represents continuity of care and is associated with significant cost savings. Co-morbidities are associated with increased costs.

Major depressive disorder is the most common type of depression, affecting 6.6% of adults in the United States annually. Citalopram and escitalopram are common second-generation antidepressants used for the treatment of patients with this disorder. Because citalopram is available in generic forms that have lower acquisition costs compared with the branded escitalopram, some health plans may provide incentives to encourage the use of the generic option. Decisions based solely on drug acquisition costs may encourage the use of a therapy that is less cost-effective when treatment persistence, healthcare utilization, and overall costs are factored in.To compare, in a real-world setting, the treatment persistence, healthcare utilization, and overall costs of managing adult patients with major depressive disorder who are treated with escitalopram or citalopram.Administrative claims data (from January 1, 2003, to June 30, 2005) were analyzed for patients with major depressive disorder aged ≥18 years. Patients filled ≥1 prescriptions for citalopram or for escitalopram (first-fill time was defined as the index date) and had no second-generation antidepressant use during the 6-month preindex period. Treatment persistence, healthcare utilization, and healthcare costs were measured over the 6-month preindex and 6-month postindex periods and compared between patients treated with citalopram or escitalopram, using unadjusted and multivariate analyses.Patients receiving escitalopram (N = 10,465) were less likely to discontinue the treatment (hazard ratio 0.94; P = .005) and switch to another second-generation antidepressant (hazard ratio 0.83; P.001) than patients receiving citalopram (N = 4212). Patients receiving escitalopram were also less likely to have a hospital admission (odds ratio 0.88; P = .036) or an emergency department visit and had lower total healthcare costs (-$1174) and major depressive disorder-related costs (-$109; P.001) during the study period.Although the drug acquisition costs are lower for generic citalopram than for the brand-name escitalopram, patients treated with escitalopram had better treatment persistence, lower healthcare utilization, and lower overall costs compared with patients treated with citalopram over the study period. This may suggest that other considerations, in addition to acquisition cost, may need to be factored in to assess the cost-effectiveness of drug therapy.

Objective. This study determined the risk of serious hepatotoxicity resulting in hospitalizations among patients prescribed opioid/acetaminophen combinations. Methods. A retrospective cohort study using an insurance claims database was conducted. Adult patients with ≥1 claim for oxycodone/acetaminophen or hydrocodone/acetaminophen combinations were included (N = 1,228,356). A pre–post design was employed to compare serious hepatotoxicity risk before versus after initiation of opioid/acetaminophen combination. Serious hepatotoxicity risk between the opioid/acetaminophen group and a control group of opioid-alone users (N = 11,809) was also examined. Within the opioid/acetaminophen group, risk of hepatotoxicity-related hospitalizations pre- versus post-opioid/acetaminophen treatment was compared using the normal approximation with the binomial distribution. The incidence rate of hepatotoxicity-related hospitalizations for the opioid/acetaminophen group was compared with the opioid-alone group using multivariate Poisson regression adjusting for baseline differences between groups. Results. Of the opioid/acetaminophen cohort, hepatotoxicity-related hospitalization risk in the 6-month post-opioid/acetaminophen period was lower than that in the pre-period with a risk reduction of 1.2 per 10,000 (pre-period = 0.12%; 95% confidence interval [CI], 0.12 to 0.13; post-period = 0.11%; 95% CI, 0.11 to 0.12). In the 12-month period, risk increased in the post-period by 2.4 per 10,000 (pre-period = 0.14%; 95% CI, 0.14 to 0.15; post-period = 0.17%; 95% CI, 0.16 to 0.18). After adjusting for confounders, the opioid-alone group did not demonstrate a lower rate of hepatotoxicity-related hospitalizations than the opioid/acetaminophen group (incidence rate ratio of opioid-alone over opioid/acetaminophen = 2.9; 95% CI, 1.8 to 4.7). Conclusions. There is no population data-based evidence supporting elevated risk of hepatotoxicity-related hospitalization associated with opioid/acetaminophen combinations.

OBJECTIVES: The two major types of diabetes, Type I and Type II diabetes, can have life-threatening complications, which often lead to significant adverse economic consequences. This study investigates the extent to which Type I and Type II diabetes impose significant financial burdens on an employer METHODS: The data source is a rich administrative claims database from a national, Fortune 100 manufacturer. It includes all medical, pharmaceutical, and disability claims for employees, spouses, dependents, and retirees (n> 100,000). The diabetes research sample consists of individuals with at least two medical and/or disability claims for diabetes or at least one prescription drug claim for a hypoglycemic agent. Resource utilization by diabetes patients, who are identified as either Type I or Type II diabetics, is contrasted with that of matched samples from the employer's overall beneficiary population. RESULTS: Direct (medical and pharmaceutical) and indirect (disability and sporadic absenteeism) costs of Type I and Type II diabetes are analyzed. The total average per capita annual costs are $2,612 for the Matched Type I Control sample compared to $9,397 for Type I diabetics, and $3,432 for the Matched Type II Control sample compared to $7,639 for Type II diabetics. While Type I diabetics cost the employer more than Type II diabetics on average, the cost of Type II diabetes patients, who were prescribed insulin, exceeds the cost of Type I diabetes patients. Also, while the costs of both types of diabetes are relatively high, less than 50% of total medical costs for these patients are for the treatment of diabetes or related co-morbid conditions. CONCLUSIONS: Diabetes adds a significant financial burden on the employer. The resources used by both types of diabetics are substantial, not only for the treatment of diabetes, but also for the treatment of related co-morbid conditions, as well as other conditions.

There is growing recognition that bipolar disorder (BPD) has a spectrum of expression that is substantially more common than the 1% BP-I prevalence traditionally found in population surveys.To estimate the prevalence, correlates, and treatment patterns of bipolar spectrum disorder in the US population.Direct interviews.Households in the continental United States.A nationally representative sample of 9282 English-speaking adults (agedor=18 years).Version 3.0 of the World Health Organization's Composite International Diagnostic Interview, a fully structured lay-administered diagnostic interview, was used to assess DSM-IV lifetime and 12-month Axis I disorders. Subthreshold BPD was defined as recurrent hypomania without a major depressive episode or with fewer symptoms than required for threshold hypomania. Indicators of clinical severity included age at onset, chronicity, symptom severity, role impairment, comorbidity, and treatment.Lifetime (and 12-month) prevalence estimates are 1.0% (0.6%) for BP-I, 1.1% (0.8%) for BP-II, and 2.4% (1.4%) for subthreshold BPD. Most respondents with threshold and subthreshold BPD had lifetime comorbidity with other Axis I disorders, particularly anxiety disorders. Clinical severity and role impairment are greater for threshold than for subthreshold BPD and for BP-II than for BP-I episodes of major depression, but subthreshold cases still have moderate to severe clinical severity and role impairment. Although most people with BPD receive lifetime professional treatment for emotional problems, use of antimanic medication is uncommon, especially in general medical settings.This study presents the first prevalence estimates of the BPD spectrum in a probability sample of the United States. Subthreshold BPD is common, clinically significant, and underdetected in treatment settings. Inappropriate treatment of BPD is a serious problem in the US population. Explicit criteria are needed to define subthreshold BPD for future clinical and research purposes.

OBJECTIVES: Morbidity due to respiratory infections leads to significant adverse societal and economic consequences. This study investigates the extent to which treatment for respiratory infections imposes a financial burden on an employer, and documents variations in employer payments between specific respiratory infections. METHODS: The data source is a rich administrative claims database for a national, Fortune 100 manufacturer. It includes 1997 medical, pharmaceutical, and disability claims for employees, spouses, dependents, and retirees (n> 100,000) under age 65. The research sample consists of individual patients with one or more medical or disability claims for at least one of eleven respiratory infection condition. Resource utilization is contrasted with a 10 percent random sample of the employer's overall beneficiary population. RESULTS: Direct (medical and pharmaceutical) and indirect (disability and sporadic absenteeism) costs are analyzed. The average per capita annual costs are: for the entire employer population, $2,368; for all respiratory infections patients, $4,397; and for respiratory infections employees eligible for disability, $6,838. Total costs for respiratory infections patients are 1.8 times those for the typical beneficiary. Total costs are highest for patients with pneumonia ($11,544) and lowest for patients with acute tonsillitis and acute pharyngitis ($2,180). Medical and pharmaceutical treatment account for 65% of total costs for all respiratory infections employees, while the remaining 35% of costs are attributable to disability and sporadic absenteeism. CONCLUSIONS: Respiratory infections impose a significant financial burden on the employer. Resource utilization by respiratory infections patients is substantial, not only for the direct treatment of respiratory infections, but also for the treatment of co-morbid medical conditions of respiratory infections patients. These costs also vary considerably by type of respiratory infection. The study also shows that respiratory infections impose substantial indirect costs on employers from work loss associated with these infections.