Characterizing the treatment patterns, medication burden, and patient demographics of older adults with major depressive disorder treated with antidepressants with or without selected comorbidities

OBJECTIVE

Evaluate clinical characteristics, comorbidity burden, major depressive disorder (MDD)-related healthcare resource utilization (HCRU), medication burden, and antidepressant treatment (ADT) patterns among older adults with MDD with and without selected comorbidities.

METHODS

Using Komodo's Healthcare Map claims data (1/1/2016-9/30/2022), patients with MDD (≥65 years) treated with ADTs were assessed 24 months preceding (baseline) and 12 months following (follow-up) first observed ADT prescription fill (index). Patients were separated into cohorts of those with ≥1 of 5 selected comorbidities and those without. Clinical characteristics, comorbidities, and MDD-related HCRU were assessed during baseline; treatment patterns were assessed during follow-up. Baseline and follow-up all-cause and comorbidity-specific medication burdens (mean prescription claims/month) were determined.

RESULTS

Among the total cohort (N = 417,643), 97.1% had ≥1 of 5 selected comorbidities: hypertension (80.3%), hyperlipidemia (75.4%), diabetes (54.2%), anxiety disorder (39.0%), and chronic obstructive pulmonary disorder (19.5%). Baseline and follow-up all-cause medication burdens per month were 3.8 and 4.5 for patients with selected comorbidities and 1.7 and 2.3 for those without. During baseline, most patients (96.0% with selected comorbidities, 96.2% without) had ≥1 outpatient visit, and a numerically higher percentage of those with vs. without selected comorbidities had MDD-related emergency room (13.9% vs. 6.0%) and inpatient (13.5% vs. 4.1%) visits. The majority of both cohorts (61.0% with selected comorbidities, 59.5% without) underwent treatment pattern changes.

CONCLUSION

This study highlights the medication burden and ADT patterns in older adults with MDD, assessing these outcomes among patients with and without comorbidities. Numerically higher medication burdens among those with selected comorbidities suggests future studies could investigate the impact of comorbidities on MDD-related care.

Association between adjuvant radiation treatment and breast cancer-specific mortality among older women with comorbidity burden: A comparative effectiveness analysis of SEER-MHOS

BACKGROUND

The National Comprehensive Cancer Network suggested that older women with low-risk breast cancer (LRBC; i.e., early-stage, node-negative, and estrogen receptor-positive) could omit adjuvant radiation treatment (RT) after breast-conserving surgery (BCS) if they were treated with hormone therapy. However, the association between RT omission and breast cancer-specific mortality among older women with comorbidity is not fully known.

METHODS

1105 older women (≥65 years) with LRBC in 1998-2012 were queried from the Surveillance, Epidemiology, and End Results-Medicare Health Outcomes Survey data resource and were followed up through July 2018. Latent class analysis was performed to identify comorbidity burden classes. A propensity score-based inverse probability of treatment weighting (IPTW) was applied to Cox regression models to obtain subdistribution hazard ratios (HRs) and 95% CI for cancer-specific mortality considering other causes of death as competing risks, overall and separately by comorbidity burden class.

RESULTS

Three comorbidity burden (low, moderate, and high) groups were identified. A total of 318 deaths (47 cancer-related) occurred. The IPTW-adjusted Cox regression analysis showed that RT omission was not associated with short-term, 5- and 10-year cancer-specific death (p = 0.202 and p = 0.536, respectively), regardless of comorbidity burden. However, RT omission could increase the risk of long-term cancer-specific death in women with low comorbidity burden (HR = 1.98, 95% CI = 1.17, 3.33), which warrants further study.

CONCLUSIONS

Omission of RT after BCS is not associated with an increased risk of cancer-specific death and is deemed a reasonable treatment option for older women with moderate to high comorbidity burden.

Is Age the Most Important Risk Factor in COVID-19 Patients? The Relevance of Comorbidity Burden: A Retrospective Analysis of 10,551 Hospitalizations

Purpose

To assess the contribution of age and comorbidity to the risk of critical illness in hospitalized COVID-19 patients using increasingly exhaustive tools for measuring comorbidity burden.

Patients and Methods

We assessed the effect of age and comorbidity burden in a retrospective, multicenter cohort of patients hospitalized due to COVID-19 in Catalonia (North-East Spain) between March 1, 2020, and January 31, 2022. Vaccinated individuals and those admitted within the first of the six COVID-19 epidemic waves were excluded from the primary analysis but were included in secondary analyses. The primary outcome was critical illness, defined as the need for invasive mechanical ventilation, transfer to the intensive care unit (ICU), or in-hospital death. Explanatory variables included age, sex, and four summary measures of comorbidity burden on admission extracted from three indices: the Charlson index (17 diagnostic group codes), the Elixhauser index and count (31 diagnostic group codes), and the Queralt DxS index (3145 diagnostic group codes). All models were adjusted by wave and center. The proportion of the effect of age attributable to comorbidity burden was assessed using a causal mediation analysis.

Results

The primary analysis included 10,551 hospitalizations due to COVID-19; of them, 3632 (34.4%) experienced critical illness. The frequency of critical illness increased with age and comorbidity burden on admission, irrespective of the measure used. In multivariate analyses, the effect size of age decreased with the number of diagnoses considered to estimate comorbidity burden. When adjusting for the Queralt DxS index, age showed a minimal contribution to critical illness; according to the causal mediation analysis, comorbidity burden on admission explained the 98.2% (95% CI 84.1-117.1%) of the observed effect of age on critical illness.

Conclusion

Comorbidity burden (when measured exhaustively) explains better than chronological age the increased risk of critical illness observed in patients hospitalized with COVID-19.

The Effect of Menopausal Status, Age, and Human Immunodeficiency Virus (HIV) on Non-AIDS Comorbidity Burden Among US Women

Menopause may impact the earlier onset of aging-related comorbidities among women with versus without human immunodeficiency virus (HIV). We found that menopausal status, age, and HIV were independently associated with higher comorbidity burden, and that HIV impacted burden most in the pre-/perimenopausal phases.

Influence of Comorbidity Burden, Socioeconomic Status, and Race and Ethnicity on Survival Disparities in Patients With Cancer

PURPOSE

The purpose of this study was to assess the association of comorbidity burden with overall survival, accounting for racial/ethnic and socioeconomic differences in patients with cancer.

METHODS

In this retrospective cohort study, patients newly diagnosed with cancer between 2010 and 2018 were identified from a large health plan in southern California. Cancer registry data were linked with electronic health records (EHR). Comorbidity burden was defined by the Elixhauser comorbidity index (ECI). Patients were followed through December 2019 to assess all-cause mortality. Association of comorbidity burden with all-cause mortality was evaluated using Cox proportional hazards model. Crude and adjusted hazard ratio (HR, 95%CI) were determined.

RESULTS

Of 153,270 patients included in the analysis, 29% died during the ensuing 10-year follow-up. Nearly 49% were patients of color, and 32% had an ECI > 4. After adjusting for age, sex, race/ethnicity, cancer stage, smoking status, insurance payor, medical center, year of cancer diagnosis, and cancer treatments, we observed a trend demonstrating higher mortality risk by decreasing socioeconomic status (SES) (P-trend<.05). Compared to patients in the highest SES quintile, patients in the lowest, second lowest, middle, and second highest quintiles had 25%, 21%, 18%, and 11% higher risk of mortality, respectively [(HR, 95%CI): 1.25 (1.21-1.29), 1.21 (1.18-1.25), 1.18 (1.15-1.22), and 1.11 (1.07-1.14), respectively]. When we additionally adjusted for ECI, the adjusted HRs for SES were slightly attenuated; however, the trend persisted. Patients with higher comorbidity burden had higher mortality risk compared to patients with ECI score = 0 in the adjusted model [(HR, 95%CI): 1.22 (1.17-1.28), 1.48 (1.42-1.55), 1.80 (1.72-1.89), 2.24 (2.14-2.34), and 3.39 (3.25-3.53) for ECI = 1, 2, 3, 4, and >5, respectively].

CONCLUSIONS

Comorbidity burden affects overall survival in cancer patients irrespective of racial/ethnic and SES differences. Reducing comorbidity burden can reduce some, but not all, of the mortality risk associated with lower SES.

Cognitive and Physiologic Reserve Independently Relate to Superior Neurocognitive Abilities in Adults Aging With HIV

BACKGROUND

To investigate joint contributions of cognitive and physiologic reserve to neurocognitive SuperAging in older persons with HIV (PWH).

METHODS

Participants included 396 older PWH (age range: 50-69 years) who completed cross-sectional neuropsychological and neuromedical evaluations. Using published criteria, participants exhibiting global neurocognition within normative expectations of healthy 25-year-olds were classified as SuperAgers (SA; n = 57). Cognitively normal (CN; n = 172) and impaired (n = 167) participants were classified with chronological age-based norms. Cognitive reserve was operationalized with an estimate of premorbid verbal intelligence, and physiologic reserve was operationalized with a cumulative index of 39 general and HIV-specific health variables. Analysis of variance with confirmatory multinomial logistic regression examined linear and quadratic effects of cognitive and physiologic reserve on SA status, adjusting for chronological age, depression, and race/ethnicity.

RESULTS

Univariably, SA exhibited significantly higher cognitive and physiologic reserve compared with CN and cognitively impaired ( d s ≥ 0.38, p s < 0.05). Both reserve factors independently predicted SA status in multinomial logistic regression; higher physiologic reserve predicted linear increases in odds of SA, and higher cognitive reserve predicted a quadratic "J-shaped" change in odds of SA compared with CN (ie, odds of SA > CN only above 35th percentile of cognitive reserve).

CONCLUSIONS

Each reserve factor uniquely related to SA status, which supports the construct validity of our SA criteria and suggests cognitive and physiologic reserve reflect nonoverlapping pathways of neuroprotection in HIV. Incorporation of proxy markers of reserve in clinical practice may improve characterization of age-related cognitive risk and resilience among older PWH, even among PWH without overt neurocognitive impairment.

Associations of weight loss with obesity-related comorbidities in a large integrated health system

AIMS

To determine the health outcomes associated with weight loss in individuals with obesity, and to better understand the relationship between disease burden (disease burden; ie, prior comorbidities, healthcare utilization) and weight loss in individuals with obesity by analysing electronic health records (EHRs).

MATERIALS AND METHODS

We conducted a case-control study using deidentified EHR-derived information from 204 921 patients seen at the Cleveland Clinic between 2000 and 2018. Patients were aged ≥20 years with body mass index ≥30 kg/m² and had ≥7 weight measurements, over ≥3 years. Thirty outcomes were investigated, including chronic and acute diseases, as well as psychological and metabolic disorders. Weight change was investigated 3, 5 and 10 years prior to an event.

RESULTS

Weight loss was associated with reduced incidence of many outcomes (eg, type 2 diabetes, nonalcoholic steatohepatitis/nonalcoholic fatty liver disease, obstructive sleep apnoea, hypertension; P < 0.05). Weight loss >10% was associated with increased incidence of certain outcomes including stroke and substance abuse. However, many outcomes that increased with weight loss were attenuated by disease burden adjustments.

CONCLUSIONS

This study provides the most comprehensive real-world evaluation of the health impacts of weight change to date. After comorbidity burden and healthcare utilization adjustments, weight loss was associated with an overall reduction in risk of many adverse outcomes.

Comparison of Indexes to Measure Comorbidity Burden and Predict All-Cause Mortality in Rheumatoid Arthritis

Objectives: To examine the comorbidity burden in patients with rheumatoid arthritis (RA) patients using a nationwide population-based cohort by assessing the Charlson Comorbidity Index (CCI), Elixhauser Comorbidity Index (ECI), Multimorbidity Index (MMI), and Rheumatic Disease Comorbidity Index (RDCI) scores and to investigate their predictive ability for all-cause mortality. Methods: We identified 24,767 RA patients diagnosed from 1998 to 2008 in Taiwan and followed up until 31 December 2013. The incidence of comorbidities was estimated in three periods (before, during, and after the diagnostic period). The incidence rate ratios were calculated by comparing during vs. before and after vs. before the diagnostic period. One- and 5-year mortality rates were calculated and discriminated by low and high-score groups and modified models for each index. Results: The mean score at diagnosis was 0.8 in CCI, 2.8 in ECI, 0.7 in MMI, and 1.3 in RDCI, and annual percentage changes are 11.0%, 11.3%, 9.7%, and 6.8%, respectively. The incidence of any increase in the comorbidity index was significantly higher in the periods of “during” and “after” the RA diagnosis (incidence rate ratios for different indexes: 1.33–2.77). The mortality rate significantly differed between the high and low-score groups measured by each index (adjusted hazard ratios: 2.5–4.3 for different indexes). CCI was slightly better in the prediction of 1- and 5-year mortality rates. Conclusions: Comorbidities are common before and after RA diagnosis, and the rate of accumulation accelerates after RA diagnosis. All four comorbidity indexes are useful to measure the temporal changes and to predict mortality.

Incident Non-AIDS Comorbidity Burden Among Women With or at Risk for Human Immunodeficiency Virus in the United States

BACKGROUND

Human immunodeficiency virus (HIV) infection may accelerate development of aging-related non-AIDS comorbidities (NACMs). The incidence of NACMs is poorly characterized among women living with HIV (WLWH).

METHODS

WLWH and HIV-seronegative participants followed in the Women's Interagency HIV Study (WIHS) through 2009 (when >80% of WLWH used antiretroviral therapy) or onward were included, with outcomes measured through 31 March 2018. Sociodemographics, clinical covariates, and prevalent NACM were determined at enrollment. We used Poisson regression models to determine incident NACM burden (number of NACMs accrued through most recent WIHS visit out of 10 total NACMs assessed) by HIV serostatus and age.

RESULTS

There were 3129 participants (2239 WLWH, 890 HIV seronegative) with 36 589 person-years of follow-up. At enrollment, median age was 37 years, 65% were black, and 47% currently smoked. In fully adjusted analyses, WLWH had a higher incident NACM rate compared with HIV-seronegative women (incidence rate ratio, 1.36 [95% confidence interval (CI), 1.02-1.81]). Incident NACM burden was higher among WLWH vs HIV-seronegative women in most age strata (HIV × age interaction: P = .0438), and women <25 years old had the greatest incidence rate ratio by HIV serostatus at 1.48 (95% CI, 1.19-1.84) compared with those in older age groups. Incident NACM burden was associated with traditional comorbidity risk factors but not HIV-specific indices.

CONCLUSIONS

Incident NACM burden was higher among WLWH than HIV-seronegative women. This difference was most dramatic among women aged <25 years, a group for whom routine comorbidity screening is not prioritized. Established non-HIV comorbidity risk factors were significantly associated with incident NACM burden. More data are needed to inform best practices for NACM screening, prevention, and management among WLWH, particularly young women.

The Prevalence and Burden of Non-AIDS Comorbidities Among Women Living With or at Risk for Human Immunodeficiency Virus Infection in the United States

BACKGROUND

The prevalence and burden of age-related non-AIDS comorbidities (NACMs) are poorly characterized among women living with HIV (WLWH).

METHODS

Virologically suppressed WLWH and HIV-seronegative participants followed in the Women's Interagency HIV Study (WIHS) through at least 2009 (when >80% of WLWH used antiretroviral therapy) were included, with outcomes measured through 31 March 2018. Covariates, NACM number, and prevalence were summarized at most recent WIHS visit. We used linear regression models to determine NACM burden by HIV serostatus and age.

RESULTS

Among 3232 women (2309 WLWH, 923 HIV-seronegative) with median observation of 15.3 years, median age and body mass index (BMI) were 50 years and 30 kg/m2, respectively; 65% were black; 70% ever used cigarettes. WLWH had a higher mean NACM number than HIV-seronegative women (3.6 vs 3.0, P < .0001) and higher prevalence of psychiatric illness, dyslipidemia, non-AIDS cancer, kidney, liver, and bone disease (all P < .01). Prevalent hypertension, diabetes, and cardiovascular and lung disease did not differ by HIV serostatus. Estimated NACM burden was higher among WLWH versus HIV-seronegative women in those aged 40-49 (P < .0001) and ≥60 years (P = .0009) (HIV × age interaction, P = .0978). In adjusted analyses, NACM burden was associated with HIV, age, race, income, BMI, alcohol abstinence, cigarette, and crack/cocaine use; in WLWH, additional HIV-specific indices were not associated, aside from recent abacavir use.

CONCLUSIONS

Overall, NACM burden was high in the cohort, but higher in WLWH and in certain age groups. Non-HIV traditional risk factors were significantly associated with NACM burden in WLWH and should be prioritized in clinical guidelines for screening and intervention to mitigate comorbidity burden in this high-risk population.

Pre-existing Comorbidity Burden and Patient Perceived Stroke Impact

BACKGROUND

Pre-existing comorbidities can compromise recovery post-stroke. However, the association between comorbidity burden and patient-rated perceived impact has not been systematically investigated. To date, only observer-rated outcome measures of function, disability, and dependence have been used, despite the complexity of the impact of stroke on an individual.

AIM

Our aim was to explore the association between comorbidity burden and patient-rated perceived impact and overall recovery, within the first-year post-stroke, after adjusting for stroke severity, age, and sex.

METHODS

The sample comprised 177 stroke survivors from 18 hospitals throughout Australia and New Zealand. Comorbidity burden was calculated using the Charlson Comorbidity Index. Perceived impact and recovery were measured by the Stroke Impact Scale index and Stroke Impact Scale overall recovery scale. Quantile regression models were applied to investigate the association between comorbidity burden and perceived impact and recovery.

RESULTS

Significant negative associations between the Charlson Comorbidity Index and the Stroke Impact Scale index were found at three months. At the .25 quantile, a one-point increase on the Charlson Comorbidity Index was associated with 6.80-points decrease on the Stroke Impact Scale index (95%CI: -11.26, -2.34; p = .003). At the median and .75 quantile, a one-point increase on the Charlson Comorbidity Index was associated, respectively, with 3.58-points decrease (95%CI: -5.62, -1.54; p = .001) and 1.76-points decrease (95%CI: -2.80, -0.73; p = .001) on the Stroke Impact Scale index. At 12 months, at the .25 and .75 quantiles, a one-point increase on the Charlson Comorbidity Index was associated, respectively, with 6.47-points decrease (95%CI: -11.05, -1.89; p = .006) and 1.26-points decrease (95%CI: -2.11, -0.42; p = .004) on the Stroke Impact Scale index. For the Stroke Impact Scale overall recovery measure, significant negative associations were found only at the median at three months and at the .75 quantile at 12 months.

CONCLUSION

Comorbidity burden is independently associated with patient-rated perceived impact within the first-year post-stroke. The addition of patient-rated impact measures in personalized rehabilitation may enhance the use of conventional observer-rated outcome measures.

Comorbidity Burden in Adults With Autism Spectrum Disorders and Intellectual Disabilities-A Report From the EFAAR (Frailty Assessment in Ageing Adults With Autism Spectrum and Intellectual Disabilities) Study

Background: Autism spectrum disorder (ASD) is an early-onset and lifelong neurodevelopmental condition frequently associated with intellectual disability (ID). Although emerging studies suggest that ASD is associated with premature ageing and various medical comorbidities, as described for ID, data are scarce. Objectives: To determine the comorbidity burden and its association with distinct clinical presentation in terms of ASD severity, adaptive skills, level of autonomy, and drug exposure in a well-phenotyped sample of individuals with ASD-ID-the EFAAR (Frailty Assessment in Ageing Adults with Autism Spectrum and Intellectual Disabilities) cohort. Methods: A total of 63 adults with ASD-ID, with a mean age of 42.9 ± 15.1 years, were recruited from 2015 to 2017 from nine specialized institutions. They underwent detailed clinical examinations, including screening for comorbidities, ASD severity [Childhood Autism Rating Scale (CARS)], adaptive functioning [Vineland Adaptive Behavior Scale II (VABS-II)], autonomy [activities of daily living (ADLs)], and drug use [polypharmacy and the Drug Burden Index (DBI)]. The comorbidity burden was evaluated using the Cumulative Illness Rating Scale (CIRS-G) and its sub-scores [the severity index (CIRS-SI) and severe comorbidity (CIRS-SC)]. Results: We found a large range of comorbidities, including gastrointestinal disorders and mental and neurological diseases. Overall, 25% of our ASD-ID sample had chronic kidney disease with the associated increased cardiovascular risk factors. The comorbidity burden was high (mean CIRS-G total score of 10.6 ± 4.8), comparable with that observed among patients older than those in our population hospitalized in geriatric departments. Furthermore, the comorbidity burden positively correlated with age, decreased autonomy, and polypharmacy. Conclusion: The severity of the comorbidity burden associated with premature ageing in adults with ASD and ID highlight their crucial need of personalized medical care.

Patient-Reported Outcome Instruments for Physical Symptoms Among Patients Receiving Maintenance Dialysis: A Systematic Review

BACKGROUND

Patients with end-stage renal disease (ESRD) receiving dialysis have poor health-related quality of life. Physical symptoms are highly prevalent among dialysis-dependent patients and play important roles in health-related quality of life. A range of symptom assessment tools have been used in dialysis-dependent patients, but there has been no previous systematic assessment of the existing symptom measures' content, validity, and reliability.

STUDY DESIGN

Systematic review of the literature.

SETTINGS & POPULATION

Patients with ESRD on maintenance dialysis therapy.

SELECTION CRITERIA FOR STUDIES

Instruments with 3 or more physical symptoms previously used in dialysis-dependent patients and evidence of validity or reliability testing.

INTERVENTION

Patient-reported physical symptom assessment instrument.

OUTCOMES

Instrument symptom-related content, validity, and reliability.

RESULTS

From 3,148 screened abstracts, 89 full-text articles were eligible for review. After article exclusion and further article identification by reference reviews, 58 articles on 23 symptom assessment instruments with documented reliability or validity testing were identified. Of the assessment instruments, 43.5% were generic and 56.5% were ESRD specific. Symptoms most frequently assessed were fatigue, shortness of breath, insomnia, nausea and vomiting, and appetite. Instruments varied widely in respondent time burden, recall period, and symptom attributes. Few instruments considered recall periods less than 2 weeks and few assessed a range of symptom attributes. Psychometric testing was completed for congruent validity (70%), known-group validity (25%), responsiveness (30%), internal consistency (78%), and test-retest reliability (65%). Content validity was assessed in dialysis populations in 57% of the 23 instruments.

LIMITATIONS

Consideration of physical symptoms only and exclusion of single symptom-focused instruments.

CONCLUSIONS

The number of available instruments focused exclusively on physical symptoms in dialysis patients is limited. Few symptom-containing instruments have short recall periods, assess diverse symptom attributes, and have undergone comprehensive psychometric testing. Improved symptom-focused assessment tools are needed to improve symptom evaluation and symptom responsiveness to intervention among dialysis-dependent patients.

The clinical and economic burden of newly diagnosed Alzheimer's disease in a medicare advantage population

BACKGROUND/RATIONALE

Alzheimer's disease (AD) represents a serious public health issue affecting approximately 5.4 million individuals in the United States and is projected to affect up to 16 million by 2050. This study examined health care resource utilization (HCRU), costs, and comorbidity burden immediately preceding new diagnosis of AD and 2 years after diagnosis.

METHODS

This study utilized a claims-based, retrospective cohort design. Medicare Advantage members newly diagnosed with AD (n = 3374) were compared to matched non-AD controls (n = 6748). All patients with AD were required to have 12 months of continuous enrollment prior to AD diagnosis (International Classification of Diseases, Clinical Modification [ICD-9] 331.0), during which time no diagnosis of AD, a related dementia, or an AD medication was observed. Non-AD controls demonstrated no diagnosis of AD, a related dementia, or a prescription claim for an AD medication treatment during their health plan enrollment. Medical and pharmacy claims data were used to measure HCRU, costs, and comorbidity burden over a period of 36 months (12 months pre-diagnosis and 24 months post-diagnosis).

RESULTS

The HCRU and costs were greater for AD members during the year prior to diagnosis and during postdiagnosis years 1 and 2 compared to controls. The AD members also displayed greater comorbidity than their non-AD counterparts during postdiagnosis years 1 and 2, as measured by 2 different comorbidity indices.

CONCLUSIONS

Members newly diagnosed with AD demonstrated greater HCRU, health care costs, and comorbidity burden compared to matched non-AD controls.