Community, Hospital, and Patient Factors Contributing to Ethnoracial Disparities in Follow-Up After Psychiatric Hospitalization
Abstract
Objective:
This study used an ecosocial perspective to examine ethnoracial disparities in timely outpatient follow-up care after psychiatric hospitalization in a cohort of Medicaid recipients.
Methods:
This retrospective analysis used 2012–2013 New York State Medicaid claims data for 17,488 patients ages <65 years who were treated in hospital psychiatric units and discharged to the community. Claims data were linked to other administrative data sets capturing key social conditions and determinants of mental health for non-Latinx White (White hereafter), non-Latinx Black (Black), Latinx, non-Latinx Asian/Pacific Islander (Asian/Pacific Islander), non-Latinx American Indian or Native Alaskan (American Indian or Native Alaskan), and other ethnoracial groups. Regression models were used to estimate the variations in disparities in timely follow-up care that were attributable to community, organization (i.e., hospital), and individual patient characteristics.
Results:
Overall, 60.1% of patients attended an outpatient mental health visit within 30 days of discharge. Compared with the rate for White patients, the attendance rates were 9.5 percentage points lower for Black patients and 7.8 percentage points higher for Asian/Pacific Islander patients. No significant difference in attendance rates was found between Latinx and White patients. Community factors, specifically urban versus rural classification and county poverty status, accounted for the greatest variation in timely follow-up care in all comparisons.
Conclusions:
Efforts to increase connection to outpatient mental health follow-up care after psychiatric hospitalization should incorporate cultural and structural competencies to address social conditions and determinants of mental health that underly ethnoracial disparities.
HIGHLIGHTS
•
Among 17,488 Medicaid recipients treated in hospital psychiatric units, 60.1% attended outpatient mental health visits within 30 days of discharge.
•
Non-Latinx Asian/Pacific Islander patients had the highest rate of 30-day follow-up attendance (71.9%), followed by non-Latinx White (64.1%), Latinx (62.0%), non-Latinx Native American/Alaska Native (56.2%), and non-Latinx Black (54.6%) patients.
•
Community factors, specifically urban versus rural classification and county poverty status, accounted for the greatest amount of variation among ethnoracial groups in timely follow-up care.
•
Patients with higher levels of adverse social determinants require more complex discharge planning, and inpatient psychiatric teams can provide additional care transition and care management resources to these patients.
Timely follow-up with outpatient mental health care after hospital discharge is a widely recognized quality standard. Patients who successfully transition to outpatient mental health care have a decreased risk for hospital readmission, violence, homelessness, and criminal justice involvement (1–4). However, critical disparities exist; ethnoracially disadvantaged patients, especially Black patients, have lower rates of receiving outpatient treatment during the 30-day period after discharge, compared with White patients (5–10).
Inequities in access to outpatient mental health care are well documented. Compared with White patients, Black and Latinx patients are less likely to obtain mental health care (11–17), more likely to discontinue treatment (17–21), and more likely to experience worse treatment outcomes (5, 22–25). These findings underscore the role of social determinants of mental health (26). Ethnoracially disadvantaged groups are more likely to live in communities with higher poverty levels, fewer health care providers, and greater transportation barriers that diminish access to care (5, 8, 10). Additionally, health care providers may not be culturally competent or may have ethnoracial biases that impede patients’ willingness to engage in care (27). For an accurate understanding of inequalities in health care access and outcomes, studies must examine social determinants of mental health (28, 29).
In this study, we used an ecosocial perspective to examine ethnoracial disparities in follow-up after psychiatric hospitalization in a large cohort of Medicaid recipients. Ecosocial models acknowledge the potential for complex interactions among individual patient, organization (i.e., hospital), and community factors that influence health care outcomes (30, 31). We used a sequential three-level (community, hospital within the community, and individual patient within the hospital) ecosocial model and conceptualized ethnoracial status as a proxy for exposure to inequalities, such as systemic and structural racism, that may drive adverse health outcomes (32). Our aim was to estimate the variance in disparities that can be attributed to each of the three levels of the model. Sequential adjustment has been used to understand how characteristics across broad ecosocial levels interact to determine disparities (33).
Because community factors are present for all hospitals and patients in a community, we first estimated the impact of the community variables, such as county poverty and availability of providers, in predicting timely outpatient follow-up after discharge. We then estimated the impact of adding treatment organization (i.e., hospital) variables (such as size, case mix, and quality of discharge planning) to community variables in predicting timely outpatient follow-up care. Finally, we estimated the impact of adding patient characteristics to community and hospital variables in predicting timely outpatient follow-up after discharge. This sequential approach identified factors across three ecosocial levels that can be targets for interventions to eliminate disparities.
Methods
Data Sources and Study Population
Data were obtained from New York State (NYS) Medicaid claims, the 2012–2013 American Hospital Association Annual Survey (34), the 2012–2013 Health Resources and Human Services Administration Area Resource File (35), and a 2012–2013 NYS managed behavioral health care organization (MBHO) discharge file created during a quality assurance program in which NYS contracted with MBHOs in geographically distinct regions to review discharge planning practices for fee-for-service inpatient psychiatric admissions (36, 37). The MBHOs were not conducting utilization review or paying for inpatient care, because the study population consisted of Medicaid recipients who were excluded from managed care enrollment because they received Social Security Disability Insurance or had a serious mental illness (adults) or serious emotional disturbance (youths). Inclusion criteria were age <65 years, admitted to an inpatient psychiatric unit during 2012–2013 with a principal diagnosis of a mental illness, hospitalized <60 days, discharged to the community, continuously enrolled in Medicaid for the 60 days after discharge, and enrolled in Medicaid for at least 11 of the 12 months before the inpatient admission. Only the first-observed inpatient admission was included for patients who had more than one inpatient psychiatric admission during 2012–2013. Dually (Medicaid and Medicare) eligible patients were excluded because of a lack of available information on Medicare-financed service use. The study was approved and granted a waiver of individual consent by the NYS Psychiatric Institute Institutional Review Board.
Variables of Interest
Patient ethnoracial categories were extracted from NYS Medicaid enrollment files and included non-Latinx White (White hereafter), non-Latinx Black (Black), Latinx, non-Latinx Asian/Pacific Islander (Asian/Pacific Islander), non-Latinx Native American/Alaska Native (Native American/Alaska Native), other, and unknown. The other category included patients who reported that they belonged to more than one of the listed groups or did not belong to any of the listed groups. The unknown category consisted of patients who did not disclose their ethnoracial status when applying for Medicaid benefits. The primary outcome variables were attending an outpatient psychiatric service within 7 days or 30 days after discharge from the index hospital admission. An outpatient psychiatric service was defined as a Medicaid claim for a visit at a licensed mental health outpatient setting or any outpatient service (e.g., primary care or independent practitioner) with a primary diagnosis of a mental disorder.
Using our ecosocial model, we grouped covariates into community, hospital, and patient characteristics. Table 1 lists all covariates, selected on the basis of results from previous studies indicating associations with inpatient care transitions or treatment disengagement (6–8, 37–43). Two additional hospital-level variables were created to account for differences in patient populations served by each hospital, which may have affected individual hospital policies and procedures related to discharge planning. These “case-mix” variables included the percentage of psychiatric discharges with a substance use disorder diagnosis and the percentage of patients with two or more psychiatric hospitalizations during the study period. These variables were created by using information from the NYS Department of Health Statewide Planning and Research Cooperative System.
| Ecosocial level | Description | Data sourcea | Rationale for inclusion |
|---|---|---|---|
| Community | |||
| Poverty | County population in poverty (as defined by U.S. Census Bureau poverty thresholds) | HRSA Area Resource File | County poverty level, low income, and unemployment are associated with discontinuity of care (7, 38, 39). |
| Provider density | Mental health workers per 100,000 residents | HRSA Area Resource File | Greater number of psychiatrists per capita is associated with postdischarge follow-up (7); geographic distribution of providers and health facilities is associated with continuity of care (8). |
| Geographic area | Urban or rural classification | National Center for Health Statistics urban-rural classification scheme | Urban services are more likely to have lower engagement with services (6, 39). |
| Organization (hospital) | |||
| Size | Number of hospital beds | AHA Annual Survey | Larger hospitals have higher rates of successful care transitions (7). |
| Ownership | Public, private nonprofit, private for profit | AHA Annual Survey | Private nonprofit hospitals have higher rates of successful care transitions, compared with public and for-profit hospitals (7). |
| Payer status | Proportion of psychiatric discharges with Medicaid | AHA Annual Survey | Hospitals with greater proportion of Medicaid discharges have higher rates of successful care transitions (7). |
| Continuum of psychiatric services | Hospital provides outpatient psychiatric services | AHA Annual Survey | Hospitals providing outpatient psychiatric services have higher rates of successful care transitions (7). |
| Teaching status | Teaching hospital | AHA Annual Survey | Teaching hospitals have lower rates of postdischarge mortality and readmission (40). |
| Quality of discharge planning | Psychiatric discharges of patients for whom an outpatient aftercare appointment was made | MBHO discharge file | Timely inpatient-outpatient provider communication is associated with higher rates of successful care transitions (41). |
| Case mix: substance use disorders | Psychiatric discharges with a substance use disorder | NYS administrative data | Case-mix variable was created to account for complexity of population served by hospital. |
| Case mix: readmissions | Psychiatric readmission rate | NYS administrative data | Case-mix variable was created to account for complexity of population served by hospital. |
| Individual (patient) | |||
| Age | Age bands | NYS Medicaid claims data | Younger age is associated with poor continuity of care (38, 39). |
| Gender | Male or female | NYS Medicaid claims data | Male sex is associated with poor continuity of care (6, 38, 39). |
| Primary diagnosis | Primary diagnosis at discharge | NYS Medicaid claims data | Severity of illness is associated with continuing outpatient care (7, 39). |
| Substance use disorder diagnosis | Co-occurring substance use disorder diagnosis at discharge | NYS Medicaid claims data | Patients with co-occurring substance use disorders have significantly lower odds of receipt of regular outpatient care (6–8, 39). |
| Medical illness burden | General medical comorbid condition in previous 12 months assessed with Elixhauser Comorbidity Index (42) | NYS Medicaid claims data | Presence of chronic general medical condition is associated with successful care transitions (8) and greater likelihood of receiving regular outpatient care (7). |
| Engagement history | Four-level (high, partial, low, and none) variable related to engagement in outpatient psychiatric care during the 6 months before inpatient admission (43) | NYS Medicaid claims data | Lack of regular source of outpatient care is associated with discontinuation of treatment (38); preadmission outpatient visits are associated with postdischarge follow-up (6, 7). |
| Housing stability | Homeless at admission | MBHO discharge file | Homelessness is associated with discontinuity of care or treatment dropout (39). |
| Length of stay in hospital | Days hospitalized | NYS Medicaid claims data | Longer inpatient stay is associated with lower rate of successful care transitions (6). |
| Received discharge planning | Outpatient appointment made before discharge | MBHO discharge file | Timely inpatient-outpatient provider communication is associated with higher rates of successful care transitions (41); scheduling aftercare appointments is associated with higher rates of successful care transitions (37). |
a
AHA, American Hospital Association; HRSA, Health Resources and Services Administration; MBHO, managed behavioral health care organization; NYS, New York State.
Data Analysis
The percentages of patients attending outpatient mental health visits within 7 and 30 days postdischarge were estimated for each ethnoracial group and each category of the community, hospital, and patient characteristics. We first report unadjusted differences in proportions of outpatient attendance among categories of all variables. Sequential logistic regressions were used to estimate ethnoracial differences in rates of attending postdischarge outpatient appointments, while controlling for various ecosocial-level characteristics. In our ecosocial approach, model 1 included the ethnoracial group indicators as predictors and community covariates only, model 2 further added hospital variables, and model 3 added individual patient variables, thereby controlling for all three ecosocial levels. For each model, we report the adjusted difference in percentages (i.e., average marginal effects [AMEs]) in 7-day and 30-day outpatient visit attendance for each ethnoracial group, compared with the corresponding percentages for White patients.
The changes of the AME across models were calculated both incrementally and cumulatively. A positive percentage-point change indicated that the ecosocial-level covariates explained some of the ethnoracial differences, a negative change indicated that ethnoracial differences were masked (44, 45) because the covariates had not been considered. Generalized estimating equations were used for estimating changes in percentages to account for the correlation in the data due to nesting of patients within hospitals. Because of the small size of the Native American/Alaska Native group, only unadjusted associations are provided for that group. No p-value adjustments for multiple comparisons were made, given the exploratory nature of the study (46, 47). All analyses were performed with SAS, version 9.4, using the %Margins macro (48).
Results
Excluding 1,305 patients whose ethnoracial status was listed as unknown, the final analytic sample included 17,488 unique patients. Table 2 reports rates of attending outpatient visits after hospital discharge for each of the ethnoracial groups, along with unadjusted associations between attendance and community, hospital, and patient characteristics. For the entire sample, 39.3% and 60.1% of patients attended an outpatient visit within 7 and 30 days of discharge, respectively. Among Black patients, attendance was significantly lower, compared with White patients, whereas attendance for Latinx patients did not differ significantly from that of White patients. Compared with White patients, Asian/Pacific Islander patients had significantly higher attendance and Native American/Alaska Native and other patients had significantly lower attendance.
| Attended follow-up visit | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Within 7 days | Within 30 days | ||||||||||
| Characteristic | N | N | % | AME | 95% CI | p | N | % | AME | 95% CI | p |
| Total | 17,488 | 6,872 | 39.3 | 10,503 | 60.1 | ||||||
| Ethnoracial group | |||||||||||
| Non-Latinx White (reference) | 7,707 | 3,314 | 43.0 | 4,939 | 64.1 | ||||||
| Non-Latinx Black | 6,256 | 2,128 | 34.0 | −8.98 | −11.89, −6.08 | <.001 | 3,416 | 54.6 | −9.48 | −12.16, −6.80 | <.001 |
| Latinx | 2,052 | 845 | 41.2 | −1.82 | −4.83, 1.19 | .236 | 1,272 | 62.0 | −2.10 | −4.74, .55 | .121 |
| Non-Latinx Asian/Pacific Islander | 306 | 148 | 48.4 | 5.37 | −.02, 10.75 | .051 | 220 | 71.9 | 7.81 | 3.31, 12.31 | .001 |
| Non-Latinx Native American/Alaska Native | 187 | 65 | 34.8 | −8.24 | −15.51, −.97 | .026 | 105 | 56.2 | −7.93 | −.15, −.87 | .028 |
| Other | 980 | 372 | 38.0 | −5.04 | −9.08, −1.00 | .015 | 551 | 56.2 | −7.86 | −11.64, −4.08 | <.001 |
| Community characteristic | |||||||||||
| County population in povertyb | 17,332 | ||||||||||
| Low: <15% (reference) | 5,491 | 2,326 | 42.4 | 3,387 | 61.7 | ||||||
| Medium: 15%–19% | 6,717 | 2,649 | 39.4 | −2.92 | −6.30, .46 | .090 | 4,029 | 60.0 | −1.70 | −5.15, 1.74 | .333 |
| High: ≥20% | 5,124 | 1,848 | 36.1 | −6.29 | −10.87, −1.72 | .007 | 3,003 | 58.6 | −3.08 | −7.19, 1.04 | .143 |
| Mental health workers per 100,000 residents | 17,332 | ||||||||||
| Low: <67 (reference) | 1,331 | 611 | 45.9 | 901 | 67.7 | ||||||
| Medium: 67–166 | 9,899 | 3,887 | 39.3 | −6.64 | −12.37, −.91 | .023 | 5,961 | 60.2 | −7.48 | −12.26, −2.69 | .002 |
| High: ≥167 | 6,102 | 2,325 | 38.1 | −7.80 | −13.16, −2.45 | .004 | 3,557 | 58.3 | −9.40 | −14.15, −4.65 | .001 |
| Urban or rural classification | 17,332 | ||||||||||
| Large central metropolitan area (reference) | 9,653 | 3,541 | 36.7 | 5,484 | 56.8 | ||||||
| Large fringe metropolitan area | 3,003 | 1,174 | 39.1 | 2.41 | −.96, 5.78 | .161 | 1,816 | 60.5 | 3.66 | .11, 7.21 | .043 |
| Medium metropolitan area | 1,944 | 845 | 43.5 | 6.78 | .38, 13.19 | .038 | 1,258 | 64.7 | 7.90 | 2.15, 13.65 | .007 |
| Small metropolitan area | 989 | 453 | 45.8 | 9.12 | 3.18, 15.06 | .003 | 663 | 67.0 | 10.23 | 5.98, 14.48 | <.001 |
| Micropolitan | 1,353 | 602 | 44.5 | 7.81 | −.76, 16.38 | .074 | 938 | 69.3 | 12.52 | 7.71, 17.32 | <.001 |
| Noncore | 390 | 208 | 53.3 | 16.65 | 10.24, 23.06 | <.001 | 260 | 66.7 | 9.86 | 3.82, 15.89 | .001 |
| Organization (hospital) characteristic | |||||||||||
| N of hospital beds | 17,488 | ||||||||||
| Small: <100 (reference) | 684 | 341 | 49.9 | 471 | 68.9 | ||||||
| Medium: 100–499 | 9,262 | 3,699 | 39.9 | −9.92 | −24.22, 4.39 | .174 | 5,621 | 60.7 | −8.17 | −21.91, 5.57 | .244 |
| Large: ≥500 | 7,542 | 2,832 | 37.6 | −12.30 | −26.44, 1.84 | .088 | 4,411 | 58.5 | −10.37 | −23.99, 3.25 | .136 |
| Hospital ownership | 17,488 | ||||||||||
| Public (reference) | 4,860 | 1,776 | 36.5 | 2,825 | 58.1 | ||||||
| Private nonprofit | 11,052 | 4,325 | 39.1 | 2.59 | −1.87, 7.05 | .255 | 6,641 | 60.1 | 1.96 | −2.63, 6.55 | .402 |
| Private for profit | 1,576 | 771 | 48.9 | 12.38 | 5.22, 19.54 | .001 | 1,037 | 65.8 | 7.67 | −.02, 15.36 | .051 |
| Psychiatric discharges covered by Medicaid | 17,488 | ||||||||||
| Low: <49% (reference) | 3,250 | 1,469 | 45.2 | 2,074 | 63.8 | ||||||
| Medium: 49%–71% | 8,872 | 3,560 | 40.1 | −5.07 | −10.14, −.01 | .050 | 5,417 | 61.1 | −2.76 | −7.52, 2.00 | .256 |
| High: >71% | 5,366 | 1,843 | 34.4 | −10.85 | −16.5, −5.21 | .001 | 3,012 | 56.1 | −7.68 | −13.56, −1.81 | .010 |
| Hospital provides outpatient psychiatric services | 17,488 | ||||||||||
| No (reference) | 2,269 | 979 | 43.2 | 1,458 | 64.3 | ||||||
| Yes | 15,219 | 5,893 | 38.7 | −4.43 | −9.79, .94 | .106 | 9,045 | 59.4 | −4.83 | −9.97, .32 | .066 |
| Teaching hospital | 17,488 | ||||||||||
| No (reference) | 3,020 | 1,451 | 48.1 | 2,057 | 68.1 | ||||||
| Yes | 14,468 | 5,421 | 37.5 | −10.58 | −16.59, −4.56 | .001 | 8,446 | 58.4 | −9.74 | −14.63, −4.85 | <.001 |
| Psychiatric discharges for which outpatient appointment was made | 16,933 | ||||||||||
| Low: 40%–70% (reference) | 3,592 | 1,201 | 33.4 | 1,911 | 53.2 | ||||||
| Medium:71%–91% | 10,050 | 4,013 | 39.9 | 6.49 | 3.04, 9.95 | .001 | 6,081 | 60.5 | 7.31 | 3.04, 11.57 | .001 |
| High: over 91% | 3,291 | 1,494 | 45.4 | 11.96 | 6.63, 17.29 | <.001 | 2,200 | 66.9 | 13.65 | 8.61, 18.68 | <.001 |
| Psychiatric discharges with substance use disorder diagnosis | 17,488 | ||||||||||
| Low: <34% (reference) | 3,909 | 1,742 | 44.6 | 2,531 | 64.8 | ||||||
| Medium: 34%–60% | 9,887 | 3,796 | 38.4 | −6.17 | −11.79, −.55 | .031 | 5,795 | 58.6 | −6.14 | −11.07, −1.20 | .015 |
| High: >60% | 3,692 | 1,334 | 36.1 | −8.43 | −15.16, −1.70 | .014 | 2,177 | 59.0 | −5.78 | −11.18, −.38 | .036 |
| Psychiatric discharges with two or more past psychiatric discharges | 17,488 | ||||||||||
| Low: <24.5% (reference) | 3,659 | 1,758 | 48.1 | 2,420 | 66.1 | ||||||
| Medium: 24.5%–35% | 8,599 | 3,404 | 39.6 | −8.46 | −13.03, −3.89 | .001 | 5,231 | 60.8 | −5.31 | −9.94, −.67 | .025 |
| High: >35% | 5,230 | 1,710 | 32.7 | −15.35 | −20.35, −10.35 | <.001 | 2,852 | 54.5 | −11.61 | −17.17, −6.05 | <.001 |
| Individual (patient) characteristic | |||||||||||
| Outpatient appointment made | 16,646 | ||||||||||
| No (reference) | 3,389 | 773 | 22.8 | 1,337 | 39.5 | ||||||
| Yes | 13,257 | 6,004 | 45.3 | 22.48 | 18.84, 26.12 | <.001 | 8,944 | 67.5 | 28.02 | 23.34, 32.69 | <.001 |
| Previous engagement in care | 17,488 | ||||||||||
| Full (reference) | 7,461 | 3,961 | 53.1 | 6,049 | 81.1 | ||||||
| Partial | 1,844 | 846 | 45.9 | −7.21 | −10.23, −4.19 | <.001 | 1,254 | 68.0 | −13.07 | −15.86, −10.29 | <.001 |
| Inadequate | 3,374 | 1,116 | 33.1 | −20.01 | −22.37, −17.66 | <.001 | 1,738 | 51.5 | −29.56 | −31.75, −27.37 | <.001 |
| None | 4,809 | 949 | 19.7 | −33.36 | −36.16, −30.55 | <.001 | 1,462 | 30.4 | −50.67 | −53.06, −48.29 | <.001 |
| Age in years | 17,488 | ||||||||||
| 4–12 (reference) | 1,575 | 890 | 56.5 | 1,190 | 75.6 | ||||||
| 13–17 | 2,248 | 1,005 | 44.7 | −11.80 | −15.83, −7.78 | <.001 | 1,379 | 61.3 | −14.21 | −17.96, −10.46 | <.001 |
| 18–35 | 5,300 | 1,975 | 37.3 | −19.24 | −23.03, −15.45 | <.001 | 3,105 | 58.6 | −16.97 | −20.64, −13.30 | <.001 |
| 36–64 | 8,365 | 3,002 | 35.9 | −20.62 | −24.34, −16.90 | <.001 | 4,829 | 57.7 | −17.83 | −21.61, −14.05 | <.001 |
| Gender | 17,488 | ||||||||||
| Male (reference) | 9,461 | 3,470 | 36.7 | 5,404 | 57.1 | ||||||
| Female | 8,027 | 3,402 | 42.4 | 5.71 | 3.97, 7.44 | <.001 | 5,099 | 63.5 | 6.40 | 4.61, 8.20 | <.001 |
| Length of stay (days) | 17,488 | ||||||||||
| 0–4 (reference) | 2,600 | 920 | 35.4 | 1,437 | 55.3 | ||||||
| 5–14 | 8,909 | 3,521 | 39.5 | 4.14 | 1.82, 6.46 | .001 | 5,379 | 60.4 | 5.11 | 2.49, 7.73 | .001 |
| 15–30 | 4,518 | 1,881 | 41.6 | 6.25 | 2.45, 10.05 | .001 | 2,810 | 62.2 | 6.93 | 3.15, 10.70 | .001 |
| 31–60 | 1,461 | 550 | 37.7 | 2.26 | −1.30, 5.82 | .213 | 877 | 60.0 | 4.76 | .26, 9.26 | .038 |
| Homeless at admission | 17,488 | ||||||||||
| No (reference) | 15,356 | 6,281 | 40.9 | 9,539 | 62.1 | ||||||
| Yes | 1,258 | 272 | 21.6 | −19.28 | −22.32, −16.24 | <.001 | 456 | 36.3 | −25.87 | −30.17, −21.57 | <.001 |
| Primary diagnosis at discharge | 17,488 | ||||||||||
| Schizophrenia (reference) | 5,046 | 1,818 | 36.0 | 3,060 | 60.6 | ||||||
| Schizoaffective disorder | 1,810 | 722 | 40.0 | 3.86 | 1.16, 6.56 | .005 | 1,156 | 63.9 | 3.23 | .18, 6.27 | .038 |
| Bipolar disorder | 5,567 | 2,222 | 39.9 | 3.89 | .26, 7.51 | .036 | 3,278 | 58.9 | −1.76 | −5.44, 1.92 | .349 |
| Depressive disorder | 3,261 | 1,307 | 40.1 | 4.05 | 1.18, 6.92 | .006 | 1,896 | 58.1 | −2.50 | −5.52, .52 | .104 |
| Other mental disorder | 1,804 | 803 | 44.5 | 8.48 | 4.65, 12.32 | <.001 | 1,113 | 61.7 | 1.05 | −2.73, 4.84 | .586 |
| Co-occurring substance use diagnosis at discharge | 17,488 | ||||||||||
| No (reference) | 10,845 | 5,026 | 46.3 | 7,391 | 68.2 | ||||||
| Yes | 6,643 | 1,846 | 27.8 | −18.56 | −20.82, −16.29 | <.001 | 3,112 | 46.9 | −21.30 | −23.68, −18.93 | <.001 |
| N of general medical comorbid conditions in previous 12 months (nonbehavioral health) | 17,321 | ||||||||||
| 0 (reference) | 5,513 | 2,209 | 40.1 | 3,361 | 61.0 | ||||||
| 1–3 | 8,371 | 3,385 | 40.4 | .37 | −1.67, 2.40 | .723 | 5,121 | 61.1 | .21 | −1.92, 2.35 | .847 |
| ≥4 | 3,437 | 1,242 | 36.1 | −3.93 | −7.09, −.77 | .015 | 1,962 | 57.1 | −3.88 | −7.41, −.35 | .031 |
a
Row percentages are shown. The average marginal effect (AME) was estimated as the percentage-point difference between outpatient follow-up attendance for a given group and the reference group.
b
As defined by U.S. Census Bureau poverty thresholds.
Notable community, hospital, and patient characteristics significantly associated with 7-day or 30-day attendance included being discharged from large hospitals (associated with lower attendance, compared with small hospitals), being discharged from hospitals with higher proportions of patients (≥71.0%) for whom outpatient appointments were made before discharge (associated with increased attendance, compared with lower proportion [≤70.0%]), and being discharged from hospitals with higher proportions of patients (≥24.5%) with multiple admissions (associated with reduced attendance). Being fully engaged in care before admission increased attendance (compared with partial or no engagement) as did having an outpatient appointment scheduled during discharge planning, whereas being older (≥13 years), being homeless, or having a co-occurring substance use diagnosis reduced attendance (Table 2).
Table 3 summarizes ethnoracial differences in rates of attending outpatient visits within 7 and 30 days following discharge, with White patients as the reference group. The percentage of those differences attributable to each sequential ecosocial level is also shown across models 1–3. (To clarify ethnoracial group differences in covariates, a table in the online supplement to this article lists all variables stratified by ethnoracial group.)
| Unadjusted | Model 1: community | Model 2: community and hospital | Model 3: community, hospital, and patient | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | AME | 95% CI | AME | 95% CI | Difference (%) | AME | 95% CI | Difference (%) | AME | 95% CI | Difference (%) | |||
| Incremental | Cumulative | Incremental | Cumulative | Incremental | Cumulative | |||||||||
| 7-day follow-up | ||||||||||||||
| Non-Latinx Black (N=6,256) | −8.98 | −11.89, −6.08 | −5.85 | −7.96, −3.74 | 34.9 | 34.9 | −5.74 | −7.62, −3.85 | 1.2 | 36.1 | −3.82 | −5.94, −1.69 | 21.4 | 57.5 |
| Latinx (N=2,052) | −1.82 | −4.83, 1.19 | 1.42 | −1.22, 4.07 | 178.0 | 178.0 | 1.66 | −1.09, 4.41 | 13.2 | 191.2 | 1.29 | −1.27, 3.85 | −20.3 | 170.9 |
| Non-Latinx Asian/Pacific Islander (N=306) | 5.37 | −.02, 10.75 | 8.64 | 3.82, 13.46 | −60.9 | −60.9 | 10.11 | 4.92, 15.29 | −27.4 | −88.3 | 5.45 | −.30, 11.19 | 86.8 | −1.5 |
| Other (N=980) | −5.04 | −9.08, −1.00 | −1.26 | −4.45, 1.92 | 75.0 | 75.0 | −.63 | −3.83, 2.57 | 12.5 | 87.5 | 2.55 | −1.08, 6.19 | 63.1 | 150.6 |
| 30-day follow-up | ||||||||||||||
| Non-Latinx Black (N=6,256) | −9.48 | −12.16, −6.80 | −6.05 | −8.28, −3.83 | 36.1 | 36.1 | −5.88 | −8.05, −3.71 | 1.8 | 38.0 | −3.64 | −5.79, −1.48 | 23.6 | 61.6 |
| Latinx (N=2,052) | −2.10 | −4.74, .55 | 1.23 | −1.45, 3.91 | 158.6 | 158.6 | 1.57 | −1.21, 4.36 | 16.2 | 174.8 | 1.64 | −1.21, 4.49 | 3.3 | 178.1 |
| Non-Latinx Asian/Pacific Islander (N=306) | 7.81 | 3.31, 12.31 | 11.1 | 6.65, 15.55 | −42.1 | −42.1 | 12.03 | 7.47, 16.59 | −11.9 | −54.0 | 6.68 | 1.33, 12.04 | 68.5 | 14.5 |
| Other (N=980) | −7.86 | −11.64, −4.08 | −3.94 | −7.31, −.56 | 49.9 | 49.9 | −3.41 | −6.70, −.12 | 6.7 | 56.6 | −.80 | −4.55, 2.94 | 33.2 | 89.8 |
a
For all comparisons, the reference group was non-Latinx White patients. The AME was the percentage-point difference between outpatient follow-up attendance for a given group and the reference group. Model 1, all community covariates; model 2, community and hospital covariates; model 3, community, hospital, and patient covariates. Incremental difference is the difference (in %) compared with the previous model (e.g., model 1 compared with the unadjusted model, model 2 compared with model 1, and model 3 compared with model 2); cumulative difference is the difference (in %) compared with the unadjusted model.
Comparing Black and White Populations
For Black patients, community characteristics accounted for the greatest incremental variance in both 7- and 30-day outpatient attendance. When the analysis controlled for urban residence and residence in counties with high levels of poverty, both of which were more common for Black patients than for White patients (see table in the online supplement ), the variance in 7-day and 30-day attendance was reduced by 34.9% and 36.1%, respectively (Table 3). Adding hospital characteristics (model 2) accounted for only 1.2% and 1.8% of incremental variance for Black patients. By contrast, adding patient characteristics (model 3) accounted for an additional 21.4% and 23.6% of incremental variance in 7-day and 30-day aftercare attendance by Black patients, respectively. Patient characteristics that likely contributed to lower attendance among Black patients included lower rates of outpatient treatment participation before admission and higher rates of homelessness and co-occurring substance use disorders (see table in the online supplement ). The models that accounted for all measured variables (model 3) reduced the overall difference between Black and White patients in 7-day and 30-day aftercare attendance by 57.5% and 61.6%, respectively, with Black patients continuing to have a significantly lower probability of attending outpatient appointments, compared with White patients: 3.8 and 3.6 percentage points lower within 7 and 30 days, respectively.
Comparing Latinx and White Populations
Neither the unadjusted models nor any of the adjusted models comparing Latinx and White patients showed any significant differences in outpatient attendance (Table 3). When the analysis controlled for Latinx patients’ higher likelihood of living in large urban areas and counties with high poverty rates (see table in the online supplement ), we noted a trend toward increased outpatient attendance for Latinx patients, compared with White patients, but this difference was not statistically significant.
Comparing Asian/Pacific Islander and White Populations
Among Asian/Pacific Islanders, outpatient attendance was higher than among Whites within both 7 and 30 days after discharge (5.4 and 7.8 percentage points higher, respectively). In models 1 and 2, the mean differences in outpatient attendance increased and were statistically significant (Table 3), with the greatest increase seen after the analysis controlled for community characteristics. Like the other disadvantaged ethnoracial groups, Asian/Pacific Islanders were more likely to live in large urban areas or counties with high poverty levels (see table in the online supplement ). Controlling for these covariates further increased this group’s AME in attendance rate, compared with that of White patients. Model 3, which additionally controlled for patient factors, diminished this difference, although Asian/Pacific Islanders remained more likely than Whites to attend outpatient visits within 30 days. Patient characteristics that likely accounted for lowering the differences in outpatient attendance rates included older age, greater likelihood of outpatient care before admission, and lower rates of substance use and homelessness among Asian/Pacific Islanders, compared with Whites (see table in the online supplement ).
Comparing Other and White Populations
The “other” ethnoracial group was significantly less likely than the White group to attend outpatient visits within both 7 and 30 days. Controlling for community factors eliminated many of these disparities; after additionally controlling for hospital and patient characteristics, we observed that the other ethnoracial group tended to have higher attendance, compared with the White group, but this difference was not significant. The disparities appeared to be attributable to individuals in the other group being more likely than White patients to live in large urban areas and counties with high poverty rates, more likely to receive a psychotic disorder diagnosis, and less likely to have outpatient appointments scheduled before discharge (see table in the online supplement ).
Additional Analyses
To further examine the rationale for our sequential ecosocial-level modeling approach, we completed analyses comparing changes in AMEs when only single variable domains (community, hospital, or patient) were added to the unadjusted models (see tables in the online supplement ). For all ethnoracial groups in the models examining 7-day attendance, adjusting only for community variables accounted for more variation than did adjusting for either hospital or patient variables alone; the results were similar in the models examining 30-day attendance, except for the Asian/Pacific Islander group.
Discussion
In a cohort of Medicaid recipients ages <65 years who were treated in hospital psychiatric units and discharged to the community, we documented 7-day and 30-day outpatient attendance rates after discharge of 39.3% and 60.1%, respectively. Unadjusted analyses identified community, hospital, and patient characteristics that were associated with lower follow-up rates. These characteristics included patients not participating in mental health care before admission, not having an outpatient appointment scheduled during discharge planning, being discharged from hospitals located in counties with high poverty levels or a low density of providers, or being discharged from hospitals having public insurance or that treat higher proportions of patients with substance use disorders or who are homeless. Our findings are consistent with those of previous research documenting these common risk factors for unsuccessful care transitions (5–8, 10).
Our finding of lower rates of attending outpatient appointments among Black patients, compared with White patients, is consistent with results from previous studies of Medicaid adult populations (6, 7), Medicaid youth populations (8), and Medicare Advantage populations (9). Previous research identified only slightly lower rates of follow-up among Latinx patients, compared with White patients (7–9), and in our sample, Latinx patients had attendance rates similar to those of White patients. This finding suggests the need to consider alternative models for understanding the interrelationships between social determinants and continuity of care after discharge in Latinx populations (49). Our findings of higher follow-up attendance among Asian/Pacific Islanders are similar to those of Breslau et al. (9). We could not find comparable previous research on follow-up attendance among Native American/Alaska Native patients. The greatest disparity we observed involved Black patients, whose rate of attending outpatient visits within 30 days after discharge was 9.5 percentage points lower than that of White patients.
Community factors, specifically urban versus rural classification and county poverty status, accounted for the greatest amount of differences in follow-up attendance among the ethnoracial groups studied. Controlling for higher rates of living in large urban areas or in counties with high levels of poverty, which were more common for every ethnoracial group than for White patients, markedly diminished the disparities in follow-up attendance. In NYS, counties are local governmental units with specified authority and responsibility for mental health services. Variations in county mental health policies and services may therefore have contributed to this finding.
Additional control for hospital characteristics did not appreciably affect the findings on disparities. Adding patient characteristics in the final models had a differential impact across ethnoracial groups. For Black, Latinx, and the other ethnoracial groups, adding patient characteristics to the stepped models somewhat decreased disparities in follow-up attendance, but these three groups still remained less likely than the White group to attend outpatient appointments. The diminished differences were likely related to Black and other groups having higher rates of substance use and homelessness as well as poor prior engagement in outpatient mental health care before hospitalization—all factors that predict lower rates of attending outpatient appointments following discharge. Asian/Pacific Islanders, however, had lower rates of several of these risk factors, compared with Whites, possibly accounting for smaller differences in follow-up attendance between these two groups.
Our findings suggest that efforts to eliminate ethnoracial disparities in accessing mental health care after inpatient discharge will require providers to place more emphasis on social determinants of health. For example, policy makers could address ethnoracial disparities in follow-up care by increasing the allocation of social work services to underresourced communities. Hospital clinicians in underresourced areas could be trained to ask patients about social determinants to improve identification of these needs and referral of these patients to enhanced social services.
However unintentional, ethnoracial biases in clinical practices likely also contribute to lower rates of follow-up attendance. Providers should continue to emphasize cultural and structural competence training for staff (50), including training on implicit biases. To address structural barriers to care, providers must identify and acknowledge social determinants of mental health, such as the cohesiveness and level of safety of a patient’s neighborhood, housing stability, access to transportation, and demands on the patient to meet basic needs of family members. Patients with greater levels of adverse social determinants will require more complex discharge planning, including ensuring that outpatient appointments are scheduled and available at locations the patient can access. Our findings suggest that the need for such efforts is greatest in communities with fewer White individuals. Inpatient psychiatric teams cannot directly address inadequate neighborhood services or county poverty that may well be the sequelae of historically racist practices, such as redlining, but they can provide additional care transition and care management resources to patients in these communities.
The naturalistic design of this study limited inferences regarding causality related to unmeasured confounds. We measured a wide range of characteristics, and some decisions to classify covariates as community, hospital, or patient characteristics required interpretive judgment. We studied a Medicaid population, and our findings may not generalize to commercially insured populations. Misclassification of Latinx patients as White has been identified as a limiting factor in other studies using Medicaid claims, and sample size considerations prevented analysis at potentially more informative levels of geography, such as zip code or census tract. Some factors affecting care transitions may not have been included in our analyses. Our study cohort data were from 2012–2013, before health care reforms over the past decade that were meant to enhance continuity of care. For example, the Affordable Care Act and Medicaid expansion have reduced disparities in access to care and in health status, although disparities persist (51, 52). Of note, publicly reported national average 7-day and 30-day follow-up rates in the Healthcare Effectiveness Data and Information Set have not changed significantly over the previous 10 years (53), indicating an ongoing important quality gap. We also did not attempt to examine interaction effects between variables, such as discharge planning practices across urban versus rural areas. Future research should examine these processes and consider additional measures of social determinants of mental health, as well as factors such as the treatment alliance, mistrust, and cultural views of illness and care (54, 55). Such research may offer new and better tools to providers and may further diminish ethnoracial disparities in access to care.
Conclusions
The findings of this study indicate significant ethnoracial disparities in attendance of outpatient appointments after discharge from hospital psychiatric units. Compared with the 30-day attendance rate for White patients, the rate for Black patients was 9.5 percentage points lower and the rate for Asian/Pacific Islanders was 7.8 percentage points higher. Community characteristics, specifically urban versus rural residence and county poverty level, accounted for a greater amount of variation in ethnoracial disparities in postdischarge outpatient attendance than did hospital or patient factors. Providers and policy makers need to consider important structural and cultural factors that underly social determinants of mental health and affect health care access and outcomes.
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Information & Authors
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History
Received: 28 February 2022
Revision received: 22 October 2022
Accepted: 22 October 2022
Published online: 18 January 2023
Published in print: July 01, 2023
Keywords
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Competing Interests
The authors report no financial relationships with commercial interests. Dr. Dixon is Editor of the journal. Editor Emeritus Howard H. Goldman, M.D., M.P.H., was the decision editor on the manuscript.
Funding Information
This study was supported by grants R01 MH-106558 and P50 MH-115843 from NIMH.
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