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Primary versus specialty care outcomes for depressed outpatients managed with measurement-based care: results from STAR*D.

ABSTRACT: Whether the acute outcomes of major depressive disorder (MDD) treated in primary (PC) or specialty care (SC) settings are different is unknown.To compare the treatment and outcomes for depressed outpatients treated in primary versus specialty settings with citalopram in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study (www.star-d.org), a broadly inclusive effectiveness trial.Open clinical trial with citalopram for up to 14 weeks at 18 primary and 23 specialty sites. Participants received measurement-based care with 5 recommended treatment visits, manualized pharmacotherapy, ongoing support and guidance by a clinical research coordinator, the use of structured evaluation of depressive symptoms and side effects at each visit, and a centralized treatment monitoring and feedback system.A total of 2,876 previously established outpatients in primary (n = 1091) or specialty (n = 1785) with nonpsychotic depression who had at least 1 post-baseline measure.Remission (Hamilton Depression Rating Scale for Depression [Hamilton] or 16-item Quick Inventory of Depressive Symptomatology-Self-Rated [QIDS-SR(16)]); response (QIDS-SR(16)); time to first remission (QIDS-SR(16)). Remission rates by Hamilton (26.6% PC vs 28.0% SC, p = .40) and by QIDS-SR(16) (32.5% PC vs 33.1% SC, p = .78) and response rates by QIDS-SR(16) (45.7% PC vs 47.6% SC, p = .33) were not different. For those who reached remission or response at exit, the time to remission (6.2 weeks PC vs 6.9 weeks SC, p = .12) and to response (5.5 weeks PC vs 5.4 weeks SC, p = .97) did not differ by setting.Identical remission and response rates can be achieved in primary and specialty settings when identical care is provided.

SUBMITTER: Gaynes BN

PROVIDER: S-EPMC2324144 | biostudies-literature | 2008 May

REPOSITORIES: biostudies-literature

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Primary versus specialty care outcomes for depressed outpatients managed with measurement-based care: results from STAR*D.

Gaynes Bradley N BN Rush A John AJ Trivedi Madhukar H MH Wisniewski Stephen R SR Balasubramani G K GK McGrath Patrick J PJ Thase Michael E ME Klinkman Michael M Nierenberg Andrew A AA Yates William R WR Fava Maurizio M

Journal of general internal medicine 20080205 5

<h4>Background</h4>Whether the acute outcomes of major depressive disorder (MDD) treated in primary (PC) or specialty care (SC) settings are different is unknown.<h4>Objective</h4>To compare the treatment and outcomes for depressed outpatients treated in primary versus specialty settings with citalopram in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study (www.star-d.org), a broadly inclusive effectiveness trial.<h4>Design</h4>Open clinical trial with citalopram for up to ...[more]

PMID: 18247097

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Project description:ImportanceMedicaid and Children's Health Insurance Program cover almost 50% of children with special health care needs (CSHCN). CSHCN often require specialty services and have been increasingly enrolled in Medicaid managed care (MMC) plans, but there is a dearth of recent national studies on specialty care access among publicly insured children and particularly CSHCN.ObjectiveTo provide recent, nationwide evidence on the association of MMC penetration with specialty care access among publicly insured children, with a special focus on CSHCN.Design, setting, and participantsThis cross-sectional study used nationally representative data from the 2016 to 2019 National Survey of Children's Health to identify publicly insured children in 41 states that administered comprehensive managed care organizations for Medicaid. Data analysis was performed from May 2022 to March 2023.ExposureForm CMS-416 data were used to measure state-year level share of Medicaid-enrolled children who were covered by MMC (ie, MMC penetration).Main outcomes and measuresMeasures of specialty care access included whether, in the past year, the child had (1) any visit to non-mental health (MH) specialists, (2) any visit to MH professionals, and (3) any unmet health care needs and (4) whether the caregiver ever felt frustrated getting services for their child. Logistic regression models were used to examine the association of MMC penetration with specialty care access among all publicly insured children, and separately for CSHCN and non-CSHCN.ResultsAmong 20 029 publicly insured children, 7164 (35.8%) were CSHCN, 9537 (48.2%) were female, 4110 (37.2%) were caregiver-reported Hispanic, and 2812 (21.4%) were caregiver-reported non-Hispanic Black (all percentages are weighted). MMC was not associated with significant changes in any visit to non-MH specialists and unmet health care needs. In addition, MMC penetration was positively associated with caregiver frustration among all children (adjusted odds ratio, 1.23; 95% CI, 1.03-1.48; P = .02) and was negatively associated with any visit to MH professionals among CSHCN (adjusted odds ratio, 0.75; 95% CI, 0.58-0.98; P = .04).Conclusions and relevanceIn this cross-sectional study evaluating MMC and specialty care access for publicly insured children, increased MMC enrollment was not associated with improved specialty care access for publicly insured children, including CSHCN. MMC was associated with less access to specialties like MH and increased frustrations among caregivers seeking services for their children.

Project description:BackgroundStar Rating Assessment (SRA) was initiated in 2015 in Tanzania aiming at improving the quality of services provided in Primary Healthcare (PHC) facilities. Social accountability (SA) is among the 12 assessment areas of SRA tools. We aimed to assess the SA performance and its predictors among PHC facilities in Tanzania based on findings of a nationwide reassessment conducted in 2017/18.MethodsWe used the SRA database with results of 2017/2018 to perform a cross-sectional secondary data analysis on SA dataset. We used proportions to determine the performance of the following five SA indicators: functional committees/boards, display of information on available resources, addressing local concerns, health workers' engagement with local community, and involvement of community in facility planning process. A facility needed four indicators to be qualified as socially accountable. Adjusted odds ratios (AOR) with 95% confidence intervals (CI) were used to determine facilities characteristics associated with SA, namely location (urban or rural), ownership (private or public) and level of service (hospital, health centre or dispensary).ResultsWe included a total of 3,032 PHC facilities of which majority were dispensaries (86.4%), public-owned (76.3%), and located in rural areas (76.0%). On average, 30.4% of the facilities were socially accountable; 72.0% engaged with local communities; and 65.5% involved communities in facility planning process. Nevertheless, as few as 22.5% had functional Health Committees/Boards. A facility was likely to be socially-accountable if public-owned [AOR 5.92; CI: 4.48-7.82, p = 0.001], based in urban areas [AOR 1.25; 95% CI: 1.01-1.53, p = 0.038] or operates at a level higher than Dispensaries (Health centre or Hospital levels).ConclusionMost of the Tanzanian PHC facilities are not socially accountable and therefore much effort in improving the situation should be done. The efforts should target the lower-level facilities, private-owned and rural-based PHC facilities. Regional authorities must capacitate facility committees/boards and ensure guidelines on SA are followed.

Project description:BackgroundInsurance benefit features play a role in determining access to specialty mental health care. Previous research, primarily examining the effects of copayments, coinsurance, and deductibles in a fee-for-service setting, has concluded that specialty mental health use is highly sensitive to changes in financial requirements. Less is known about the effects of other benefit features and the effects of all of these features in a managed care environment.Aims of the studyDetermine whether increased generosity of three types of benefit features was associated with increases in specialty mental health use and expenditures in a managed care setting. Secondary analyses investigated whether these associations varied by income level.MethodsA first-differences design used linked claims, enrollment, and benefit data for 1,242,949 non-elderly adults (aged 18-64) with employer-sponsored insurance, before (2009) and after (2011) national behavioral health parity implementation. The data were provided by a large national managed behavioral health organization. Benefit design features included combined cost sharing from copayment and coinsurance, deductibles, the presence of annual use limits, cost sharing penalties associated with services used without getting required prior-authorization, and provider network. Outcomes included visits/days, total expenditures and patient out-of-pocket expenditures for individual psychotherapy and inpatient use, with separate values for in-network and out-of-network (OON) service use. Ordinary least squares regression was performed on change scores (2011 minus 2009 values) of all outcomes to implement the first-differences study design and normalize distributions of otherwise heavily skewed (towards zero) variables. Regressions stratified by higher income (>=USD75,000) and net worth (>=USD100,000) and lower income/net worth were also conducted.ResultsFor in-network individual psychotherapy, larger increases in cost sharing from copayment and coinsurance were modestly associated with larger decreases in use and total expenditures (beta_visits=--0.00008, p-value=0.030; beta_total expenditures=USD--0.00629, p-value=0.011), and elimination of treatment limits was associated with larger increases in use (beta=0.09637, p-value=0.002) and total expenditures (beta=USD6.57506, p-value=0.001). These results were observed among all enrollees of plans that covered in-network and out-of-network plans and among a sub-set of these enrollees who did not change plans between 2009 and 2011. Benefit features had fewer associations with inpatient care and OON services.DiscussionElimination of limits was associated with small average increases in in-network individual psychotherapy utilization and expenditures. Cost sharing sensitivities of individual psychotherapy visits to financial requirements reported here were small, and resembled previous findings based in a managed care setting, which were smaller than findings based on the fee-for-service settings. Cost sharing may not pose a practical barrier to specialty behavioral health for non-elderly adults with employer-sponsored managed care plans. However, the influence of cost sharing may vary by specific healthcare needs, something that should be explored in further research.

Project description:ImportanceDisparities in quality of care according to patient race and socioeconomic status persist in the US. Differential referral patterns to specialist physicians might be associated with observed disparities.ObjectiveTo examine whether differences exist between Black and White Medicare beneficiaries in the observed patterns of patient sharing between primary care physicians (PCPs) and physicians in the 6 specialties to which patients were most frequently referred.Design, setting, and participantsThis cross-sectional observational study of Black and White Medicare beneficiaries used claims data from 2009 to 2010 on 100% of traditional Medicare beneficiaries who were seen by PCPs and selected high-volume specialists in 12 health care markets with at least 10% of the population being Black. Statistical analyses were conducted from December 20, 2017, to September 30, 2020.ExposuresDifferences in patterns of patient sharing among Black and White patients.Main outcomes and measuresPrimary care physician and specialist degree (the number of other PCPs or specialists to whom each physician is connected) and strength (the number of shared patients per connection, overall, for Black patients and White patients and after equalizing the numbers of Black and White patients per PCP), as well as distance between PCP and patient and specialist zip code centroids.ResultsThe 12 selected markets ranged in size from Manhattan, New York (187 054 Black or White beneficiaries seen by at least 2 physicians within an episode of care; 9794 total physicians), to Tallahassee, Florida (44 644 Black or White beneficiaries seen by at least 2 physicians within an episode of care; 847 total physicians). The percentage of Black beneficiaries ranged from 11.5% (Huntsville, Alabama) to 46.8% (Chicago, Illinois). The mean PCP-specialist degree (number of specialists with whom a PCP shares patients) was lower for Black patients than for White patients. For instance, the mean PCP-cardiologist degree across all markets for White patients was 17.5 compared with 8.8 for Black patients. After sampling White patients to equalize the numbers of patients seen, the degree differences narrowed but were still not equivalent in many markets (eg, for all specialties in Baton Rouge, Louisiana: 4.5 for Black patients vs 5.7 for White patients). Specialist networks among White patients were much larger than those constructed based just on Black patients (eg, for cardiology across all markets: 135 for Black patients vs 330 for White patients), even after equalizing the numbers of patients seen per PCP (123 for Black patients vs 211 for White patients). The overall test for differences in referral patterns was statistically significant for all 6 specialties examined in 7 of the 12 markets and in 5 specialties for another 3.Conclusions and relevanceThis study suggests that differences exist in specialist referral patterns by race among Medicare beneficiaries. This is an observational study, and thus some differences might have resulted from patient-initiated visits to specialists.

Dataset Information

Primary versus specialty care outcomes for depressed outpatients managed with measurement-based care: results from STAR*D.

Publications

Primary versus specialty care outcomes for depressed outpatients managed with measurement-based care: results from STAR*D.

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