Insurance status and continuity for young adults with first-episode psychosis

Abstract

Objective:

Insurance status and insurance continuity may affect access to care and quality of care. We characterized patterns of insurance status and changes in insurance status over a one-year period among people with first-episode psychosis, comparing insurance patterns with adults of similar age in the general population.

Methods:

Longitudinal data on insurance status and predictors of insurance status among adults with first-episode psychosis were obtained from RAISE-ETP study participants with complete 1-year data (N=288). The frequencies of insurance status and transitions are presented and bivariate comparisons were used to assess the impact of the comprehensive coordinated care intervention in RAISE-ETP on insurance changes. These data were compared with contemporaneous longitudinal data in the 2011 Medical Expenditures Panel Study.

Results:

The RAISE-ETP experimental intervention did not significantly change insurance status (p=.066). At baseline, levels of uninsurance (47%) and public insurance (31%) were higher among the RAISE-ETP participants than among the general population in a similar age group (29% and 13% respectively). Insurance transitions were common among people with first-episode psychosis, although 79% of those with public insurance at baseline also had public insurance at one year. 60% of studied RAISE-ETP participants had an uninsurance spell during the year studied.

Conclusions:

Compared to a national sample, people with first-episode psychosis were more likely to use public insurance, but still had high persistence of 12-month uninsurance. That over half of the RAISE-ETP participants had an uninsurance spell suggests more research on whether these spells affect treatment continuity and medication adherence is needed.

1. Introduction

Evidence from the US and other countries has accumulated that shows that people with psychosis can have significantly improved outcomes when they receive timely and comprehensive care during the first episode of psychosis, which typically occurs before age 30.^1,2 Yet, new treatment approaches can only be valuable if people have access to comprehensive services, and insurance status may potentially determine access to optimally effective care for first-episode psychosis (FEP).¹ Uninsurance has been associated with a lower likelihood of receiving mental health care prior to a first hospital admission for psychosis,³ and with significantly longer durations of untreated psychosis.⁴

Type of insurance coverage and insurance continuity may also matter. Patterns of outpatient treatment prior to a first hospital admission for psychosis differ between private and public insurance.³ Features of insurance plans such as levels of cost-sharing and restrictiveness of drug formulary design can impair access to effective treatment for psychosis.^5–7 Additionally, certain elements of evidence-based care for early psychosis (e.g., supported employment) are infrequently covered by private insurance.⁸ Different mental health providers accept different types of insurance,⁹ so changes in insurance coverage can also affect the likelihood of experiencing unmet needs for schizophrenia care.¹⁰ In general, greater continuity in health insurance coverage is correlated with better access to care,¹¹ and insurance disruptions are associated with reductions in office-based services.¹²

Several factors influence insurance status during young adulthood when psychosis typically emerges. The Affordable Care Act (ACA) allows people to stay on their parent’s private insurance plan until age 26, at which point young adults face a transition point for losing private insurance.¹³ Medicaid is another important source of insurance for teenagers, but child eligibility ends at age 18 or 21 depending on the state, and adult eligibility varies broadly. Emerging psychosis could complicate insurance status for young adults. Symptoms may interfere with the ability to hold a job or maintain student status, which could jeopardize one’s employment-related insurance. Young adults with psychosis could also access public insurance by applying for Supplemental Security Income (SSI) or Social Security Disability Income (SSDI). SSI generally gives Medicaid eligibility and in most states is linked with automatic Medicaid eligibility. However, emerging psychosis may not meet the disability severity or chronicity criteria for SSI eligibility. SSDI only grants Medicare eligibility after a 12–24 month waiting period.

Little is known about patterns of insurance status and insurance transitions among young adults with emerging psychosis who are beginning treatment, partly, because it is difficult to definitively identify emerging psychosis in available administrative or claims data sources. One study looked at insurance transitions for 48 participants in a trial of early psychosis treatment in Connecticut, finding that insurance disruptions were common during the first 12 months of the study.¹⁴ In this paper we use data from the landmark RAISE-ETP trial to describe insurance coverage and insurance transitions for young adults newly diagnosed with FEP. The RAISE-ETP trial is unique in that data come from multiple sites and the inclusion requirements for the study ensure that the participants were newly diagnosed during a first episode of psychosis and are receiving their initial or early treatment. With the exception of supported employment and education, the trial did not fund any treatments and thus pressure to obtain or maintain insurance coverage was important in much the same way as in usual care.

Our objective in this secondary analysis was to describe the distribution of insurance status (i.e., private, public, or uninsured) at program entry in the RAISE-ETP sample, compare this distribution to nationally-representative data of similar-aged young adults, and to identify clinical and socidemographic correlates of insurance status and transitions.

Methods

Data

The main data for our analyses come from the RAISE-ETP study, a cluster-randomized clinical trial that found that comprehensive coordinated care led to significant improvements in quality of life, psychosis symptoms, treatment engagement, and participation in education or work activities.^15–17 The RAISE-ETP study included 404 patients who received care from 34 non-academic community mental health centers located in 21 states across the U.S. The study included people age 15–51 who met criteria for a psychotic illness in no more than one discrete psychotic episode.¹⁸ Participants had less than six months of cumulative exposure to antipsychotics, indicating close proximity to first treatment. The average age of participants at the start of the study was 23 years old. Study enrollment occurred between July 2010-July-2012, and data collection continued through July 2014.¹⁸ Written informed consent was obtained from adult participants and from legal guardians of participants under age 18. The institutional review boards of the coordinating center and of each site approved the study. An NIMH data and safety monitoring board provided oversight, and there is additional IRB approval for secondary analyses from the VA Connecticut Healthcare System. The RAISE-ETP study largely occurred before most major ACA provisions (Medicaid expansions and insurance exchanges) went into effect in 2014. An advantage of these data is that participants were interviewed at baseline and then regularly over the two year study period. Self-reported health insurance status was documented at baseline and then quarterly through the two year study period. Participants were asked about their health insurance status and whether they were currently uninsured, or whether they were covered by private health insurance, Medicaid, Medicare, SCHIP, or the VA.

We used the baseline RAISE-ETP information along with the quarterly assessment information for only the first 12 months of the study to minimize missing assessment data that became more of a problem after the first 12 months of the study. We limit the analysis to the 288 participants with non-missing information on baseline insurance status, and with non-missing insurance status at the 12 month, 15 month, or 18 month assessment. We used the 12-month assessment data for the 253 participants who had it available. For those with missing 12-month insurance data, we used the 15-month data (15 participants), and if the 15 month data were missing we used the 18 month data (18 participants). The RAISE-ETP participants who were excluded from our analytic sample were similar in terms of demographics but have somewhat longer average duration of untreated psychosis, worse symptoms and quality of life, and are more likely to be in the RAISE-ETP comparison group (Appendix table 1).

To simplify the description of insurance transitions, we created three mutually-exclusive insurance status categories for both the baseline measurement and the 12-month measurement: private insurance, public insurance, or uninsured. Participants with private insurance were coded as having private insurance, regardless of whether they had another type of insurance at the same time. Only 5 RAISE-ETP participants reported both private and public insurance at baseline. Participants who reported having Medicaid, SCHIP, or Medicare (but no private insurance) were coded as having public insurance. Participants that did not report any type of private or public insurance were coded as uninsured.

We also sought to assess the degree to which insurance status and stability is similar between the RAISE-ETP sample and the general population of young adults in the U.S. To make this comparison, we used the 2011 Medical Expenditures Panel Study (MEPS) data. The MEPS is a nationally representative survey of the U.S. civilian noninstitutionalized population conducted by the Agency for Healthcare Research and Quality. Data are collected through an overlapping panel design. Each year a new panel of households is selected and data for each panel are collected in five rounds of interviews that occur over two and one-half years.¹⁹ Respondents are asked about insurance status on a monthly basis over those five interviews, although we only considered whether there was a change between January and December of 2011. In order to match the age and sex characteristics of the RAISE-ETP sample, we reweighted the MEPS data. Each unique year-of-age and sex group was weighted by the number of times the age-sex cell appears in RAISE-ETP sample. To calculate the within age-sex group mean of insurance variables we used the MEPS sampling weights so that estimates are nationally-representative.

Analyses

First, we described the patterns of insurance status at baseline. Since we hoped to pool data from the RAISE-ETP study arms in subsequent analyses, we first evaluated the association of treatment condition with the proportion of participants who obtained or changed insurance status between initial treatment and the following 12-months. We used a Cochran-Mantel-Haenszel test to evaluate the significance of differences between treatment groups in insurance status at 12 months net of baseline status.

Second, we described the patterns of insurance status at baseline and the 12 month measurements in both the RAISE and the MEPS data. We then described insurance changes over the 12 month period. We also examined changes in disability program (SSI and SSDI) status over the 12 month period because of disability programs’ importance for public insurance eligibility.

Third, we assessed the independent correlates of baseline insurance status and predictors of transitions. We estimated a multivariate multinomial logistic regression model to assess correlates of baseline insurance status. We further estimated multivariate logistic regression models of the likelihood of losing insurance conditional on having it at the time of study entry, and having any gap in coverage over 12 months. These models include the following sociodemographic variables that were measured at baseline: age (15–17, 18–23, 24–25, 26–28, 29–32, 33 and older), gender, race and ethnicity (non-Hispanic white, non-Hispanic Black, Hispanic or Latino, other), mother’s completed education (some or completed grade school, some high school, completed high school, some college or higher), whether currently employed or in school, and whether currently receiving disability benefits. These models also included three clinical predictor variables: duration of untreated psychosis (above or below the median of 74 days), the Positive and Negative Symptoms Scale (PANSS, higher=worse symptoms), and the Quality of Life Scale (QLS, higher=better quality of life).

2. Results

Impact of Coordinated Specialty Care

Comparison of insurance status by treatment group from baseline to 12 month assessment showed no significant difference in the changes in insurance status by treatment group (Figure 1) (Cochran-Mantel-Haenszel Statistics (p=.066)). The treatment and control groups in the RAISE-ETP data were thus pooled in subsequent analyses.

Figure 1. Insurance Status at Baseline and 12-Months, by RAISE-ETP Experimental Group.

Notes: RAISE-ETP data for respondents with baseline and 12-month follow-up insurance status data (details in text). Cochran-Mantel-Haenszel test for whether the Community Care and NAVIGATE groups had the same pattern of insurance changes between baseline and 12-month follow-up had p=.066.

Among the RAISE-ETP sample (Table 1) at baseline, 64 people (22%) had private insurance, 90 people (31%) had public insurance, and 134 people (47%) were uninsured. In comparison, the MEPS sample of 18–28 year olds had much higher levels of private insurance (55%), and lower levels of both public insurance (16%) and uninsurance (30%) at baseline compared to the RAISE-ETP sample (Figure 2).

Table 1.

Characteristics of RAISE-ETP Sample at Baseline

	Overall N=288		Private Insurance at Baseline N=64 (22%)		Public Insurance at Baseline N=90 (31%)		Uninsured at Baseline N=134 (47%)		p-value for difference across baseline insurance status
	N	Col %	N	Col %	N	Col %	N	Col %
Sociodemographic Characteristics
Age									<0.01
15–17	17	6%	4	6%	8	9%	5	4%
18–23	156	54%	48	75%	45	50%	63	47%
24–25	35	12%	4	6%	11	12%	20	15%
26–28	29	10%	1	2%	7	8%	21	16%
29–32	32	11%	3	5%	11	12%	18	13%
33 or above	19	7%	4	6%	8	9%	7	5%
Gender									0.07
Male	215	75%	54	84%	61	68%	100	75%
Female	73	25%	10	16%	29	32%	34	25%
Race/Ethnicity									<0.01
Non-Hispanic White	133	46%	39	61%	43	48%	51	38%
Non-Hispanic Black	93	32%	13	20%	34	38%	46	34%
Hispanic/Latino	49	17%	6	9%	10	11%	33	25%
Other	13	5%	6	9%	3	3%	4	3%
Mother’s education									<0.01
Some college or higher	125	43%	48	75%	24	27%	53	40%
Completed high school	76	26%	12	19%	33	37%	30	22%
Some high school	38	13%	2	3%	14	16%	22	16%
Some or completed grade school	50	17%	2	3%	19	21%	29	22%
Whether a student or working									<0.01
No	199	69%	31	48%	69	77%	99	74%
Yes	89	31%	33	52%	21	23%	35	26%
Disability at baseline									<0.01
No	260	91%	61	95%	69	77%	130	98%
Yes	27	9%	3	5%	21	23%	3	2%
Clinical Characteristics
DUP									<0.01
DUP < 74	148	52%	43	67%	37	42%	68	51%
DUP >= 74	139	48%	21	33%	52	58%	66	49%
PANSS: Mean (SD)	76.05	(14.72)	72.55	(15.20)	75.20	(13.46)	78.29	(15.00)	0.03
QLS: Mean (SD)	53.14	(19.28)	60.47	(17.58)	55.59	(20.20)	48.01	(18.07)	<0.01
Experimental Group									0.17
Community Care	119	41%	20	31%	41	46%	58	43%
NAVIGATE	169	59%	44	69%	49	54%	76	57%

Notes: Data from the RAISE-ETP respondents with baseline and 12-month follow-up insurance status data (details in text). Possible scores of the Positive and Negative Symptoms Scale (PANSS) range from 30 to 210, with higher scores indicating worse symptoms. Possible scores of the Quality of Life Scale (QLS) range from 0 to 126, with higher scores indicating better quality of life.

Figure 2. Insurance at Baseline and 12-Months in RAISE-ETP and MEPS Samples.

Source: RAISE-ETP data and 2011 MEPS data. MEPS data are re-weighted to match the age and sex distribution of the RAISE-ETP data. Each column label percentage is the percent of the baseline sample in each insurance category.

Among the RAISE-ETP sample, 9.4% received disability benefits at baseline, and this proportion was higher for people who were uninsured at baseline (Table 1). Those with private insurance at baseline were more likely to have a shorter duration of untreated psychosis at baseline that people with public insurance or uninsured. Non-Hispanic white people and people with higher mother’s educational attainment were more likely to have private insurance at baseline than other racial and ethnic groups and people with lower educational attainment. Being a student or currently employed was also associated with a higher likelihood of having private insurance at baseline. People with private insurance at baseline had lower duration of untreated psychosis than people with public insurance or no insurance. Schizophrenia symptoms and quality of life measures at baseline were better for people with private insurance, and worse for people who were uninsured.

Insurance Stability

Among those in the RAISE data who had private insurance at baseline, 70% still had private insurance 12 months later, while 19% had public coverage 12 months later and 11% were uninsured 12 months later (Figure 2). There was more stability among those who had public insurance at baseline, with 79% still having public insurance 12 months later. Among those who started without insurance, nearly half gained public coverage and nearly half remained uninsured 12 months later.

In comparison, among the similarly-aged MEPS sample, 93% of those who started the year with private insurance still had private insurance at the end of the year, while transitions to both public insurance and uninsurance were both infrequent and less common than in the RAISE-ETP sample (Figure 2). Among those in the MEPS sample with public insurance at baseline, 66% also had public insurance 12 months later. However, the persistence of insurance status was very different from the RAISE-ETP sample for the MEPS sample that started the year without insurance. In contrast to the more than 50% who remained uninsured in the RAISE-ETP sample, 77% of the smaller proportion of the MEPS sample that started the year uninsured were also uninsured at the end of the year.

In Figure 3, we describe insurance transitions in the RAISE-ETP sample in greater detail. Overall, 52% of the sample experienced no changes in their insurance status over the 12 months, while 31.3% experienced one insurance change and 17% experienced two or more insurance changes over the 12 month period. Sixty percent of the entire sample experienced uninsurance at some point over the 12 month study period. Among those who were insured at baseline, 25% had some spell of uninsurance over the next 12 months.

Figure 3. Insurance and Disability Transitions in RAISE-ETP Sample Data Collection Points.

Notes: Data from the RAISE-ETP sample.

Among those who did not have Medicaid at baseline, 42.5% gained Medicaid over the next 12 months and kept it once they obtained it, 7.5% gained Medicaid but did not keep it, and 50% remained without Medicaid. Some people gained disability benefits during the 12-month period. Among the 91% of the sample that did not have disability benefits at baseline, only 32% gained disability benefits over the 12-month period.

Predictors of insurance status and insurance changes

Few individual characteristics predicted baseline insurance status or changes in insurance status. Multivariate analysis of being uninsured at baseline (Table 2) showed that people age 24–28 were more likely to be uninsured at baseline, and people ages 26–32 were less likely to have private insurance at baseline, compared to people age 18–23. Hispanics/Latinos were less likely to have private and public insurance at baseline, compared to non-Hispanic whites. Higher mother’s educational attainment and currently in school or working, i.e. higher functioning, were both correlated with a higher likelihood of private insurance and a lower likelihood of public insurance at baseline. A longer DUP was correlated with higher likelihood of having public insurance. Higher QLS scores and receiving disability benefits were both correlated with a higher likelihood of public insurance and a lower likelihood of uninsurance at baseline.

Table 2.

Predictors of Insurance Status at Baseline in RAISE-ETP

	Incremental Change in the Probability of Private Insurance	95% CI		P-Value	Incremental Change in the Probability of Public Insurance	95% CI		P-Value	Incremental Change in the Probability of Uninsurance	95% CI		P-Value
NAVIGATE group	0.06	−0.03	0.15	0.161	0.03	−0.07	0.13	0.544	−0.10	−0.20	0.01	0.083
Age
15 to 17	−0.17	−0.29	−0.04	0.009	0.24	−0.01	0.49	0.058	−0.07	−0.31	0.17	0.551
18 to 23 (ref.)
24 to 25	−0.13	−0.26	0.01	0.076	−0.08	−0.21	0.06	0.276	0.20	0.03	0.37	0.019
26 to 28	−0.24	−0.34	−0.14	<.001	−0.08	−0.23	0.08	0.334	0.32	0.15	0.48	<.001
29 to 32	−0.15	−0.29	−0.01	0.048	−0.05	−0.19	0.10	0.554	0.19	0.14	0.37	0.034
33 and above	0.08	−0.14	0.29	0.494	−0.13	−0.28	0.03	0.115	0.05	−0.17	0.27	0.657
Female	−0.09	−0.18	0.01	0.078	0.02	−0.10	0.13	0.781	0.07	−0.06	0.20	0.278
Male (ref.)
Non-Hisp Black	−0.04	−0.15	0.06	0.422	−0.003	−0.12	0.11	0.957	0.05	−0.08	0.17	0.476
Hispanic/Latino	−0.12	−0.23	−0.01	0.036	−0.12	−0.26	0.01	0.072	0.24	0.09	0.39	0.002
Other	0.22	−0.03	0.46	0.077	−0.16	−0.36	0.04	0.113	−0.06	−0.30	0.19	0.648
Non-Hisp White (ref.)
Mother’s Education
Some college or higher	0.26	0.15	0.37	<.001	−0.28	−0.44	−0.13	<0.001	0.02	−0.13	0.18	0.758
Completed high school	0.10	−0.01	0.21	0.084	−0.02	−0.19	0.15	0.795	−0.07	−0.24	0.09	0.375
Some high school or grade school	0.04	−0.10	0.18	0.604	−0.18	−0.37	0.01	0.070	0.138	−0.06	0.33	0.169
No school or unknown (ref)
Whether a student or working	0.15	0.06	0.24	0.001	−0.16	−0.28	−0.04	0.008	0.01	−0.12	0.13	0.907
DUP	−0.06	−0.15	0.02	0.148	0.14	0.04	0.23	0.007	−0.07	−0.17	0.04	0.231
PANSS	−0.001	−0.005	0.002	0.466	0.001	−0.003	0.005	0.782	0.001	−0.004	0.005	0.760
QLS	0.001	−0.002	0.004	0.408	0.01	0.002	0.01	0.001	−0.007	−0.01	−0.003	<.001
Disability at baseline	0.01	−0.16	0.18	0.922	0.45	0.30	0.61	<.001	−0.46	−0.68	−0.24	<.001
N	286
Pseudo R2	0.273

Notes: Data from the RAISE-ETP respondents with baseline and 12-month follow-up insurance status data (details in text). Incremental changes in the probability of baseline uninsurance and the associated confidence intervals and p-values were calculated from multinomial logistic regression estimates using the Margins command in Stata Version 15.

Among those with any insurance at baseline, only age significantly predicted the likelihood of having any period of uninsurance over the next 12 months. Those age 24–25 at baseline were more likely to have any uninsurance than 18–23 year olds, and those age 29 or older at baseline were less likely to have any uninsurance than 18–23 year olds (Appendix Table 2). Longer DUP was marginally significantly associated with a higher likelihood of having any uninsurance. Being age 24–28, Hispanic/Latino ethnicity, a lower baseline quality of life score, and not receiving Disability were associated with a higher likelihood of having any period of uninsurance over the study period among the full sample (Appendix Table 3).

3. Discussion

Receiving coordinated comprehensive care in the RAISE-ETP trial did not lead to statistically significant changes in insurance status over a 12-month period. Similar to prior research,¹⁴ we find that many people experience changes in their insurance status in the 12 months subsequent to using services for first-episode psychosis. People with FEP have higher levels of uninsurance and public insurance than the general population, and higher rates of transitioning to public insurance over a year after starting either with private insurance or uninsured. Additionally, compared to a sample of 325 in the OnTrack study,²⁰ we find much higher levels of uninsurance in the RAISE-ETP sample. This may reflect the RAISE-ETP’s sampling from public mental health clinics where patients are disproportionately from lower socioeconomic backgrounds.

Prior research has found that the type of coverage may affect the type and amount of mental health services received.^3–8 Private insurance status was relatively persistent among RAISE-ETP participants, but private insurance may not cover the comprehensive set of services that represent the standard of care for early intervention. Public insurance, and Medicaid in particular, may offer better coverage of early intervention services than private insurance.²¹ We find that once people in the RAISE-ETP study gained Medicaid, they were very likely to stay on Medicaid for the remainder of the year. This continuity may reflect the important role that disability programs like SSI play in providing Medicaid eligibility.

We find that people age 24–25 at the study baseline was more likely to lose insurance coverage over the 12-month period. This could reflect the role of the ACA’s dependent coverage mandate that allowed young adults to stay on their parents’ private insurance plan until they turned 26 and went into effect in September 2010, which was only three months after the start of the RAISE-ETP study. Hispanics/Latinos were more likely to be uninsured at baseline than other racial/ethnic groups, reflecting high rates of uninsurance nationally for Hispanics/Latinos.²² We also find that people with FEP who have more-highly educated mothers are more likely to have private insurance and less likely to have public insurance at baseline. More-educated parents may be more likely to have private insurance, or may have more resources to help obtain private insurance through other means. The relatively few predictors of insurance status and changes suggests that the mechanisms through which people with FEP obtain and maintain insurance coverage may not be sensitive to those who have the greatest needs. Disability benefit status is an important mechanism for obtaining public insurance in this population, particularly SSI which is linked to Medicaid enrollment in most states rather than SSDI which confers Medicare eligibility only after a waiting period. Rosenheck and colleagues found that only 10% of the RAISE-ETP participants received disability benefits (either SSI or SSDI) at baseline, but by Month 24 of the study more than 50% received disability benefits.¹⁷ Prior research on the RAISE-ETP sample shows that longer DUP, greater positive symptoms, greater education, and currently in school or working are all associated with a lower likelihood of starting federal disability benefits.

This study has several strengths. It is difficult to identify people experiencing a first episode of psychosis and their insurance status from administrative claims data or from survey data. The RAISE-ETP study included a relatively large sample of people from 34 research sites across 21 states experiencing first-episode psychosis, and collected longitudinal information on the sample.

The study also has several important limitations. First is that self-reported insurance status may contain inaccuracies. A second limitation is that the RAISE-ETP data are not nationally representative of the population of people with FEP. RAISE-ETP included people who received care at non-academic community mental health centers, but excluded other settings where psychosis services are delivered. The data also do not capture people with FEP who have not initiated services. Third, although our longitudinal data offer insight into insurance changes over time, they are point-in-time estimates at several points and do not capture all potential insurance transitions. Fourth, we considered 15- or 18-month responses for RAISE-ETP study participants for whom 12-month data were not available. That allowed for a few additional months for insurance transitions, which is substantially longer in relative terms. Results were similar after limiting to RAISE-ETP participants with 12-month data (Appendix Figures 1–3). Additionally, results were robust to dropping the five observations that had both private and public insurance at baseline. Fifth, although insurance status may be an important determinant of clinical engagement and outcomes for people with FEP, we do not examine this association here.

Our results have several implications. Expanding insurance coverage for young adults may significantly benefit people experiencing FEP, as that population has substantially higher levels of uninsurance compared to the general population in the same age group. Insurance expansions may reduce insurance discontinuities for people experiencing FEP, as the large majority of people with insurance at the start of the RAISE-ETP study still had insurance 12 months later. Nearly half of people with FEP who were uninsured at baseline remained uninsured 12 months later, and nearly half of people with FEP who were uninsured at baseline gained public insurance over the next 12 months, which is largely due to qualifying for disability benefits. Some observers have advocated for policies which delink access to public insurance for people with FEP from the disability application and approval process.^23,24 For example, some states have used Medicaid Section 1115 waivers to expand coverage to people with mental health or substance use disorders without first qualifying as disabled.²⁵ States could use this mechanism to target coverage expansions to people with psychosis diagnoses, which may improve access to services with demonstrated effectiveness. Other approaches at the federal level to expand coverage for people with FEP could include allowing Medicaid programs to expand eligibility to people with FEP without requiring a waiver, similar to the Breast and Cervical Cancer Prevention and Treatment Act. Finally, more and better data that follow people experiencing FEP are needed to understand how public policy affects trajectories of insurance, service use, and symptoms.

Highlights:

Insurance status and continuity may facilitate evidence-based comprehensive services for first-episode psychosis.

Longitudinal data on insurance status from the RAISE-ETP were used to document patterns of insurance status and transitions, and were compared with data on the general population of young adults.

Compared to those of similar ages in the general population, young adults with first-episode psychosis have higher rates of uninsurance and public insurance, along with more insurance transitions.