Community employment,facility-based work,and day activities for working age people with intellectual and developmental disability

Abstract

BACKGROUND:

Individuals with Intellectual and Developmental Disability (IDD) participate in a variety of day/employment activities including community-based activities and work and facility-based activities and work. These different activities have important implications for community inclusion and economic independence.

OBJECTIVE:

The purpose of this study is to use the National Core Indicators-In person Survey (NCI-IPS) to explore the prevalence of day/employment activities for adult service users with IDD and to determine what personal and environmental factors are related to each type of activity.

METHODS:

The data analyzed in this study come from the 2018-19 National Core Indicators (NCI), a survey of adult services users with IDD in the U.S. We used descriptive statistics and multinominal logistic regression to explore how the various covariates differentially relate to the four day/employment activity types.

RESULTS:

A variety of personal and environmental characteristics were associated with each type of activity. Characteristics related to equity (e.g., race and gender) and support needs (e.g., mental health) were associated with less community-based work.

CONCLUSIONS:

This study was the first, to our knowledge, to describe the complexity of day/employment activities individuals with IDD experience in terms of prevalence and overlap. Further, this study provides evidence that certain profiles are leading to different experiences and that services do impact service users employment opportunities.

Keywords

Employment IDD disability services national core indicators home and community based services (HCBS)dacility work

1 Introduction

Adults with Intellectual and Developmental Disability (IDD) who receive services participate in a variety of day/employment activities. These include community-based work, facility-based work, facility-based activities, and community-based activities. Community-based work refers to paid activities that take place in an integrated setting, whereas facility-based work refers to paid activities that occur in a segregated setting where most or all people have disabilities. Facility-based activities are day activities that take place in a segregated setting that may include unpaid “work-like” activities. Finally, community-based activities are day activities that take place in a community/integrated setting (Winsor et al., 2019.) It is important to consider the frequency and variety of activities services users with IDD participate in, and to recognize that individuals may take part in more than one type (Stancliffe et al., 2018).

Competitive, integrated, community-based employment is a key pathway to avoid the extremes of poverty. Independent of such economic concerns, employment is often an important life goal (Nord et al., 2013). The current study explores the different patterns of participation in day/employment activities of people with IDD. Further, we explore the individual and environmental factors associated with opportunities to participate in community-based employment in comparison to other activities.

1.1 Federal policy and community-based employment

In 2014, the Centers for Medicare and Medicaid Services (CMS) in the U.S. Department of Health and Human Services issued a Final Rule into the federal register (Riesen & Synder, 2019). The Final Rule stipulates that states must ensure that services funded by CMS are delivered in the most integrated setting to meet their obligations under the Americans with Disabilities Act and the Supreme Court decision in Olmstead v. L.C., 527 U.S. 581 (1999). Compliance with the HCBS Final Rule would certainly be improved by increasing community-based employment of HCBS service users with IDD (The Arc, 2014; Riesen & Synder, 2019).

Employment First represents a more targeted U.S. initiative to increase community-based employment. This approach affirms that all individuals with IDD (a) are capable of performing work in typical integrated employment settings; (b) should receive, as a matter of state policy, employment-related services and supports as a priority over other facility-based and non-work day services; and (c) should be paid at minimum or prevailing wage rates (Kiernan et al., 2011; Rogan & Rinne, 2011). Employment First policies seek to prioritize resources towards training, assistance, and residential supports that enable such employment (Moseley, 2009).

These policies clearly favor community-based employment as opposed to “work-like” experiences or other unpaid activities. In the sections which follow, we review previous research findings on participation rates by adults with IDD in the four different types of work/activities described earlier. These previous estimates mainly focus on each type separately, with little attention given to the notable minority of individuals who use more than one type (Stancliffe et al., 2018). In addition, we examine personal characteristics and other factors known to be associated with participation in each work/activity type to identify who has access to each type. Interventions such as work experience, intended to improve access, are beyond the scope of the current descriptive study.

1.2 Community-based employment

Despite the policies we have mentioned, community employment rates for adults with IDD in the U.S. ranged between 11% -19% from 2008 to 2016 (Bush & Tassé, 2017; Domin & Butterworth, 2013; Nord et al., 2018). In comparison, the overall employment rate in the U.S. in 2018 was estimated at 82% (Blank & Edwards, 2019). While estimates vary and can be calculated via different methods, there remains a clear gap between those with IDD and the general population when it comes to community-based employment.

Factors related to community-based employment have been explored in previous research. These include age, the severity of an individual’s disability, funding availability, level of choice-making abilities, communication method, and living situation, among others. Younger individuals with less severe levels of ID have higher chances of community employment (Bush & Tassé, 2017; Nord et al., 2018; Stancliffe et al., 2018). More severe levels of ID, communication challenges, and mobility impairments significantly reduced the odds of employment (Nord et al., 2018; Stancliffe et al., 2019). Additionally, increased reasons (mental health or behavioral diagnoses) for psychotropic medication use correlated with lower likelihood of paid employment (Bush & Tassé, 2017).

Participation in community employment also varied by living situation. Adults living independently in either their own home or apartment had the highest community-based employment rate (32.2%), compared to those in foster care or host home settings (20.2%), living with relatives or parents (19.8%), in group homes or agency-operated apartment programs (16.7%). Individuals living in specialized institutional settings such as ICF/IID, nursing facilities, or other institutional settings experienced the lowest rate of community-based employment rate of 8.8% (Hiersteiner & Butterworth, 2018).

In terms of targeted services, individuals with community employment goals were much more likely to be individually employed in a community-based setting (Nord et al., 2018; Stancliffe et al., 2018). Other factors such as authentic work experience, state of residence, parental expectation, etc. also are related to community-based employment opportunities (Nord et al., 2013), but are out of the purview of this study. The data we analyze does not allow for considering such factors, thus we focus on a variety of personal and environmental characteristics.

1.3 Facility-based work and facility-based activities

Facility-based (segregated) day activities (e.g., sheltered workshop or work activity center) is often accompanied by job-related supports and supervision. These settings may provide paid employment only, or a mix of paid work and unpaid activities. Further, facility-based activity settings (e.g., day habilitation) typically offer unpaid activities only. Estimates of participation rates by U.S. adults with IDD in paid facility-based work were 25.2% in 2010-2011 and 20.5% in 2016-2017, and for unpaid facility-based activities were 24.1% in 2010-2011 and 38.2% in 2016-2017 (Domin & Butterworth, 2013; Hiersteiner & Butterworth, 2018). These estimates came from two different data sets so may not necessarily indicate change over time.

Participation in facility-based work and activities varied by residential type, where individuals living in institutional settings and group residential settings had the greatest percentage who participated in facility-based activities or work. Respectively for unpaid facility-based activities and paid facility-based work, participation rates between 2016-17 for each residence type were: (a) ICF/IID, nursing facilities, or other institutional settings (50.7%, 26.1%) (b) group residential settings (47.3%, 24.8%) (c) own home (21.8%, 18.7%), (d) parents/relatives’ home (34.6%, 16.9%), and (e) foster care or host homes (21.8%, 16.0%) (Hiersteiner & Butterworth, 2018).

Other factors related to paid facility-based work include age, communication methods and mobility. Facility based-work was less common in the youngest and oldest adult age groups and more common in middle age, whereas prevalence of facility-based activities increased consistently with age (Stancliffe et al., 2018). Individuals who communicated verbally were more likely to participate in paid facility-based work compared to those who communicated through gestures (Nord et al., 2018). Further, individuals who did not require mobility aids or assistance also had a higher likelihood of employment in facility-based settings (Nord et al., 2018; Stancliffe et al., 2019).

1.4 Community-based (non-work) activities

Services focusing on accessing community-based activities do not include employment or employment-related activities (Winsor et al., 2019) but may encompass post-secondary education, volunteering, recreation, psychosocial skill development, and other daily living activities (Domin & Butterworth, 2013; Sanford et al., 2011). Estimated participation rates in community-based non-work activities vary depending on how non-work is defined. However, some national surveys estimated that participation rates for adults with IDD ranged from 15.2% to 23.7% (Domin & Butterworth, 2013; Hiersteiner & Butterworth, 2018). Further, data suggest that 32% of individuals accessed community participation services in 2016 compared to 18.7% in 1999 (Winsor et al., 2019).

1.5 No employment or day activities

Substantial numbers of adults with IDD do not participate in any of the four types of work or activity services described above. Stancliffe et al. (2018) reported that 26.5% of adults were in this no-formal-activity category in 2014-15. Their situation is also important, but because our focus is on participation in work/activities, such individuals are out of scope for the current study.

1.6 Research questions and purpose

The purpose of this study is to use the National Core Indicators-In Person Survey (NCI-IPS) data to explore the prevalence of the four day/employment activities for adult service users with IDD. Further, the study was designed to determine what personal and environmental factors are related to having access to day/employment activities compared to community-based employment. This approach allows for a focus on the important goal of community-based employment while exploring which specific characteristics appear to relate those other day/employment activities.

This study addresses the following research questions:

What are the different prevalence rates for day/employment activities of service users with IDD, including the degree to which people participate in multiple activity types?

What personal and environmental characteristics are associated with participation in community-based employment compared to other types of day/employment activities?

2 Methods

2.1 Sample

The data we analyzed came from the 2018-19 National Core Indicators (NCI), an outcomes measurement program that provides an annual national and state picture of the lives of adults with IDD in the U.S. Participating states sample at random or in a stratified random fashion a minimum of 400 adults with IDD receiving publicly-funded long-term services and supports. However, sampling strategies may vary by state (see Appendix B in the 2018-2019 IPS Final Report for a complete listing of strategies (NCI, 2019)).

NCI-IPS participants are aged 18 or older and receive at least one service from their U.S. state IDD system as well as case management. Participants in 2018-2019 came from 37 states (AL, AR, AZ, CO, CT, DE, FL, GA, HI, IN, KS, KY, ME, MI, MN, MO, NC, NE, NH, NJ, NV, NY, OH, OK, OR, PA, RI, SC, SD, TN, TX, UT, VA, VT, WA, WI WY). Recruited samples averaged 518 per state and ranged from 316 (RI) to 2,327 (TX). We only assessed those of working age who were 65 and younger, and who participated in on or more types of day/employment activities, leaving a total analytic sample of 14,208.

2.2 Instrument

The National Core Indicators In-Person Survey (NCI-IPS) was developed by the National Association of State Directors of Developmental Disabilities Services (NASDDDS) and the Human Services Research Institute (HSRI) (Bradley & Moseley, 2007). The NCI-IPS survey is used to collect cross-sectional data on service users with IDD and includes three main sections. All data analyzed in this study came from the Background Information Section which includes questions related to personal demographics, disability diagnoses, health, mobility, communication, residence type and size, employment, and behavioral supports. These items are typically completed by agency staff or case managers/service coordinators based on agency records but may also be completed by the individual or family members.

2.3 Dependent variables

Our analyses focused on four types of day/employment activities as outcomes of interest. These categories are not mutually exclusive (a person may engage in one, several or all of these activities). All items asked about activities undertaken in the last two weeks.

Community-based work was coded as yes if the person engaged in a paid individual or group job in the community or a job at a community business that primarily hires people with disabilities.

Paid facility-based work was coded as yes if the person performed paid work in a facility-based setting.

Unpaid community-based activity was coded as yes if the person participated in unpaid community activities.

Unpaid facility-based activity was coded as yes if the person performed unpaid activities in a facility-based setting.

2.4 Independent variables

We included 11 independent variables in our analysis, selected based on previous research (Houseworth et al., 2018; Hiersteiner & Butterworth, 2018; Nord et al, 2018) to fully explore this important issue. These were divided into personal and environmental characteristics.

2.4.1 Personal factors

Race/ethnicity is a single item with multiple categories. We reduced these categories to white (referent), African-American, Hispanic/Latino, and other (i.e., Asian, Pacific-Islander, and all remaining identifications).

Level of intellectual disability (ID) is a single item that categorizes intellectual disability into mild, moderate, severe, and profound levels. Those categorized as unknown/other or with no ID diagnosis were excluded from analysis. Mild ID was the referent category.

Age was categorized as young (<22 years old), middle (between 22 and 40 years old), and older (>40 years old). Middle was the referent category.

Autism indicates whether a participant has an autism diagnosis or not. No autism diagnosis was the referent category.

Verbal expression specifies the participant’s usual means of communication. We recoded the five categories into verbal communication and other. Verbal was the referent category.

Gender was coded male (referent) and female.

Behavior needs was based on a series of three items assessing whether the participant needed support for various behaviors: self-injurious, disruptive, or destructive. We collapsed these items into no behavior needs and any/all needs. No behavioral supports needed served as the referent category.

Mental health diagnosis was based on three types of mental health conditions diagnosed: anxiety, psychosis, or other mental illness. Presence of any or all of these categories was coded yes, while absence of all three was coded no. No mental health diagnosis served as the referent category.

Mobility needs was based on those who need assistance to move around with aids or a wheelchair or who are non-ambulatory and always need assistance. Those who move around without aids were the referent category.

2.4.2 Environmental factors

Residential setting included multiple categories. We collapsed them into those living in agency-owned/operated settings with 16+ residents, those living in agencies with 4–16 residents, those living in agencies with 1–3 residents, those living with family/hosts, and those living independently (own home/apartment). Family/host home served as referent category.

Metro category categorizes each participants’ primary residence into urban, micropolitan, small town, or rural. Small town and rural were collapsed into a single category. Urban was the referent category.

2.5 Weighting

All analyses, both descriptive and inferential, were weighted by state. NCI-ACS 2018-19 weights were calculated using each state’s number of valid surveys and its total survey-eligible population, so that each state’s weighted contribution is proportional to its adult service population (National Core Indicators, n.d.-a). States randomly select service users to be surveyed. Most states sample from all adult service users, so a single state weight is applied to all participants from that state. Four states (IN, MN, TX, and WI) oversampled some service-user subpopulations, so separate weights were calculated for each subpopulation within that state. State weights varied from 0.235 (NV) to 3.466 (NY). All analyses used these weights.

Table 1 shows the frequencies for all independent variables. This number differs from the 14, 208 reported elsewhere due to listwise deletion (incomplete data across individuals) in order to reflect frequencies used in our final model.

Table 1
Frequency summary of covariates

Personal characteristics

Frequency Percent (%)

Race/Ethnicity

White (referent) 7702 72.7

Black 1818 17.2

Hispanic 606 5.7

Other 469 4.4

Level of Intellectual Disability

Mild (referent) 5644 53.3

Moderate 3152 29.7

Severe 1172 11.1

Profound 627 5.9

Age

Young 288 2.7

Middle (referent) 5111 48.2

Older 5196 49.1

Autism

Not present (referent) 8470 79.9

Present 2216 20.1

Verbal Expression

Non-verbal 1802 27.0

Verbal (referent) 8794 83.0

Gender

Male (referent) 6161 58.1

Female 4435 41.9

Behavior Needs

Not present (referent) 6133 57.9

Present 4463 42.1

Mental Health Diagnosis

Not present (referent) 5826 55.0

Present 4770 45.0

Mobility Needs

Mobile without assistance (referent) 8666 81.8

Not mobile/Need assistance 1930 18.2

Environmental characteristics

Frequency Valid Percent (%)

Residential Setting

Family home (referent) 4924 46.4

Agency 16+ residents 239 2.3

Agency 4–16 residents 2279 21.5

Agency 1–3 residents 1231 11.6

Independent home or apartment 1923 18.2

Metro Category

Urban (referent) 8307 78.4

Micropolitical 1570 14.8

Small town/Rural 719 6.8

Personal characteristics
	Frequency	Percent (%)
Race/Ethnicity
White (referent)	7702	72.7
Black	1818	17.2
Hispanic	606	5.7
Other	469	4.4
Level of Intellectual Disability
Mild (referent)	5644	53.3
Moderate	3152	29.7
Severe	1172	11.1
Profound	627	5.9
Age
Young	288	2.7
Middle (referent)	5111	48.2
Older	5196	49.1
Autism
Not present (referent)	8470	79.9
Present	2216	20.1
Verbal Expression
Non-verbal	1802	27.0
Verbal (referent)	8794	83.0
Gender
Male (referent)	6161	58.1
Female	4435	41.9
Behavior Needs
Not present (referent)	6133	57.9
Present	4463	42.1
Mental Health Diagnosis
Not present (referent)	5826	55.0
Present	4770	45.0
Mobility Needs
Mobile without assistance (referent)	8666	81.8
Not mobile/Need assistance	1930	18.2
Environmental characteristics
	Frequency	Valid Percent (%)
Residential Setting
Family home (referent)	4924	46.4
Agency 16+ residents	239	2.3
Agency 4–16 residents	2279	21.5
Agency 1–3 residents	1231	11.6
Independent home or apartment	1923	18.2
Metro Category
Urban (referent)	8307	78.4
Micropolitical	1570	14.8
Small town/Rural	719	6.8

Note. Skewness and kurtosis for all variables was within acceptable ranges (between –2 and 2 and –7 and 7, respectively). N = 10,595 for all due to listwise deletion.

2.6 Analyses

Descriptive statistics were run on all independent and dependent variables. To further explore the prevalence of day/employment experiences of service users with IDD, we used a frequency overlap analysis. We used multinominal logistic regression to explore how the various covariates differentially relate to the four day/employment categories.

To proceed with our regression analyses, day/employment categories and their overlap needed to be collapsed into distinct categories. We used a 4-step hierarchical recoding approach that favored employment over unpaid activities and community-based over facility-based experiences. First, community-based employment was prioritized. If an individual had such experience in the last two weeks, they were coded into community-based employment. This effectively collapsed those individuals who may also have engaged in other day/employment activities into the community-based employment category. Subsequently, paid facility work was prioritized. Except for those who also engaged in community-based employment, those involved in paid facility work were collapsed into this category regardless of their other day activities. Next, unpaid community activities were prioritized. Except for those who engage in community-based employment or paid facility work, those involved in unpaid community activities were coded as such, regardless of involvement in facility-based activities. Finally, those participating only in unpaid facility activities (not engaged in community-based work, paid facility-based work, or community-based activities) were coded in this category. As noted, those with no employment of day activities were excluded from all analyses. This approach allowed our analyses to compare the characteristics of those with other types of activities to those with opportunities for community-based work. Next, by placing unpaid facility activities last, our analyses are able to pinpoint factors related to a group that did not participate in community-based activities or paid work.

Multinomial logistic regression is an appropriate approach for categorically-exclusive dependent data. It utilizes maximum likelihood estimation, an improvement over the least squares approach when multiple types of variables are included with various distributional characteristics; and it controls for the increased risk of Type I error when assessing multiple outcomes. This regression approach also allows for the assessment of multiple outcomes and how different factors differentially related to each. IBM SPSS version 26 was used to conduct the analyses (IBM Corp., 2019.)

3 Results

3.1 Variety of participation in day/employment activities

Our first research question was what are the different prevalence rates for day/employment activities of service users with IDD, including the degree to which people participate in multiple activity types?

Figure 1 presents a picture of the complexity of day/employment experiences of the individuals who receive IDD services. Numbers in non-overlapping segments of Fig. 1 (such as the 1879 in the Community Activity circle) represent the number of people who only engage in that one activity type. Numbers that overlap two circles (such as 480 in the Community Activity and Community Work circles) represent the number of people who engage in those two activities only, and so forth. The most common single day/employment service category was unpaid facility activity. However, as Fig. 1 shows, there are people who engage in more than one type of activity, with some individuals being involved in three or all four types of day/employment experiences. Overall, 10,999 (77.4%) participants took part in one type of employment or activity, with the most frequent being unpaid facility-based activities (n = 4920). A further 2,778 (19.6%) participated in two types –the most common combination was community activity and facility-based activity (n = 1186). Three hundred ninety six participants (2.7%) took part in three different types, with the largest number using community work, community activity, and facility activity (n = 181). Just 35 (0.2%) were involved in all four types of employment or activity.

Fig. 1

Visual depiction of overlap across day/employment activities (N = 14,208). Note. Figure not to scale. Numbers refers to those only engaging in the single, double, triple, or quadruple category (categories) indicated.

3.2 Characteristics associated with type of day/employment activities

Our second research question was: what personal and environmental characteristics are associated with participation in community-based employment compared to other types of day/employment activities?Table 2 shows the frequencies created by the hierarchical recoding approach described in the Analyses section for our main outcome variables. The largest of these mutually-exclusive groups was facility-based activity, followed by community-based work, facility-based work, and community-based activity.

Table 2
Frequency of hierarchically recoded day/employment activities into mutually-exclusive groups

Frequency Valid Percent (%)

Day/Employment Activities

Community Work 3842 (2875) 27.0 (27.1)

Facility Work 2381 (1870) 16.8 (17.7)

Community Activity 3066 (2468) 21.6 (23.3)

Facility Activity 4919 (3382) 34.6 (31.9)

Total 14208 (10595) 100.0 (100.0)

	Frequency	Valid Percent (%)
Day/Employment Activities
Community Work	3842 (2875)	27.0 (27.1)
Facility Work	2381 (1870)	16.8 (17.7)
Community Activity	3066 (2468)	21.6 (23.3)
Facility Activity	4919 (3382)	34.6 (31.9)
Total	14208 (10595)	100.0 (100.0)

Note. Numbers in parentheses represent those used in the final regression analysis due to listwise deletion.

Multinominal logistic regression was used to explore how the various covariates differentially related to the four day/employment categories. Community-based work was chosen as the referent category for the outcome variables in order to interpret results in terms of the absence of such opportunities. Listwise deletion reduced the sample from 14,208 to 10,595. Table 3 shows a summary of significant covariates used in the analyses and their effects. See the Appendix for results of the full model broken down by each outcome. Note that the results are in reference to those with community-based work opportunities and to each independent variable’s referent group, thus should be interpreted as such.

Table 3

Summary of significant results by day/employment activity\\ with community-based employment as the referent Group

3.2.1 Personal characteristics

Non-white race was associated with higher participation in day/employment activities other than community-based employment. To clarify how to interpret the results of a multinomial logistic regression model with categorical variables (such as race), consider that compared to white individuals (the referent category for race), individuals of other races (African-American, Hispanic/Latino, Asian and others) were more likely to engage in activities other than community-based employment. Conversely, this means that white individuals were more likely to be associated with community-based employment. More specifically, people of Hispanic ethnicity or other races were more likely to participate in any unpaid day activities while people of African-American race were more likely to participate in facility-based work or activities

Compared to those with mild ID, more severe levels of intellectual disability were consistently associated with participation in all types of day/employment activities compared to community-based work. As shown by the effect-size shading in Table 3, this pattern appears to strengthen as levels of ID become more severe and trend towards unpaid facility-based activities. Older and younger age (i.e., people 40–65 and younger than 21 years) were also associated with less community-based work. Female gender was associated with higher participation in all types of day/employment activities compared to community-based work. Autism and behavior needs were not significantly related to facility-based work but were related to facility-based activities and community-based activities. Being non-verbal, having a mental health diagnosis, and being not independently mobile were significant related to all other activities than community-based work.

3.2.2 Environmental characteristics

Compared to those living with family, most residential settings were associated with participation in activities other than community-based work. In particular, living in an agency-operated setting of any size (with larger effect sizes for settings with a higher number of residents with IDD) was associated with more facility-based work or activities. Living in an independent home was associated with more community-based work, as shown by the negative associations presented in Table 3 with facility-based work, and with unpaid activities, whether community- or facility-based. Next, those living in rural/small town or micropolitan (i.e., suburban) settings were generally less likely to participate in other day/employment activities than community-based employment.

4 Discussion

4.1 Variety in day/employment experiences

With four employment/activity types, there are four categories for individuals engaged in one type, six possible combinations of two types, four combinations of three types, and one combination of all four types, yielding 15 different combinations of activity/employment. Figure 1 shows that all 15 combinations were represented in our data, clearly depicting the wide variety of experiences that a sizeable minority of service users with IDD are having that cross two, three, or even four day/employment categories. Individuals with IDD receive other services (e.g., residential, in-home, transportation) and may have multiple service providers, while others direct their own services.

The majority of participants (n = 7985, 56.2%), all of whom were of working age (18–65) experienced only facility or community-based non-work. Redirecting their services toward community-based employment to give them even a few hours of experience may help clarify their desire and support needs to engage in more such employment. The majority of those already participating in community-based employment (2552 of 3842, 66.4%) were not involved in other types of day/employment services. But for the 33.6% who participated in community employment and other day/employment services, consolidation of services into community-based employment by working more hours in those positions, if they so desire, would further strengthen the benefits of such employment.

Over one-quarter (27%) of our sample engaged in community-based paid employment (with 18% participating only in this activity), slightly higher than previously found (Bush & Tassé, 2017; Butterworth et al., 2015; Domin & Butterworth, 2013; Giordano & Bradley, 2018; Nord et al., 2018). This group is of particular interest for policies such as Employment First. However, the largest category in our coding scheme was still unpaid, facility-based activities, with 7250 (51.0%) individuals involved in these activities, of whom most (4920) only participated in facility-based activities. These results continue to highlight the strong tendency for the service system to support segregated and unpaid activities even though other options exist that allow individuals with IDD to participate in the community. Such findings clearly run counter to the policy imperatives of the HCBS Final Rule and of Employment First.

4.2 Social inequities and community-based work among adults with IDD

Generally, we found those identified as a race other than white were less likely to participate in community-based work. Similarly, people identified as female were less likely to engage in community-based work and more likely to participate in unpaid facility work. These findings mirror trends in the general population (U.S. Bureau of Labor Statistics, 2022) and given the lower employment rates found in general for adults with IDD suggest an even greater lack of opportunities due to the intersection of race, gender, and disability. Such results suggest the need for more attention to be focused on these issues.

4.3 Support needs and community-based work among adults with IDD

Similar to results related to social inequities, we found a tendency for those with more support needs to engage in activities other than community-based work, particularly unpaid facility-based activities. Consistent with previous findings (Nord et al., 2018), those with more severe levels of ID (a proxy for support needs) were more likely to be engage in activities other than community-based employment. Particularly, as the degree of ID was more severe, there was an increased likelihood of engaging in facility-based and unpaid activities. The same finding generally held for other indicators of support needs (autism diagnoses, mental health diagnoses, non-verbal communication, and mobility) which also reinforces previous findings (Bush & Tassé, 2017; Nord et al., 2018).

These results have both practical and ethical implications, as this tendency may be placing persons with higher support needs in s with little track record in moving people into community-based employment. For example, a scoping review on employment outcomes for youth and adults with autism found that such facilities (sheltered workshops) geared toward job training or practice were not identified as a path to competitive employment (Schall et al., 2020). Further, these facilities can be exempt from federal and state minimum wage laws when using employees to complete tasks that are economically productive.

4.4 Age

Next, there was a tendency for both those under the age of 22 and over the age of 40 to be engaged in day/employment activities of any type other than community-based work. It is likely that adults under the age of 22 are pursuing further educational or job training programs, and may not yet be seeking employment, particularly because they are still eligible for additional educational transition services funding. According to National Longitudinal Transition Study-2 (NLTS2) data, 55% of young adults with IDD participated in post-secondary education of some kind after high school (Sanford et al., 2011). Regarding older adults (in our case those over 40), consistent with our findings of more segregated and unpaid activities in older age, Stancliffe et al. (2018, 2019) found an increasingly smaller percentage of people with IDD middle aged and older in community-based employment and concluded that there was “an even greater reliance on facility-based services for older participants” (Stancliffe et al., 2018).

4.5 Metropolitan, micropolitan, and rural/small town settings

It is interesting to find that rural/small town and suburban town settings were more likely to provide community work than urban settings. One might assume urban settings would provide better community-based work opportunities due to job density, public transportation options, and other such resources. These findings remain a potentially important consideration in terms of community-based employment and services supporting it.

4.6 Living arrangement and group day/employment settings

Regarding residential setting, similar to previous findings (Hiersteiner & Butterworth, 2018), our results associated living in congregate settings (compared to family homes) with more engagement in facility-based activities, whereas living independently involved fewer unpaid activities and more community employment. Earning more money through paid community work may facilitate living independently. Families (the group all other residential settings were compared to) may be providing important natural supports that enable community-based employment but that are less available for those living in congregate settings.

Further, congregate settings may tend to deindividualize services such that facility-based work is more often supported as it is administratively and logistically convenient (e.g., transporting a group of residents to a single destination). Such block treatment is one common feature of institutional practices (Pratt et al., 1980) where individual service users are treated as a group. Such practices also occur in congregate community settings, such as group homes (Stancliffe et al., 2022). A common example of block treatment is all residents of a group home having to go to the same community outing (e.g., bowling) regardless of their preferred activity. The strong residential-setting effect found in this study may be related to the tendency for group homes, especially larger ones, to engage in block treatment, even with day programming. By contrast, the small number of residents with IDD in family homes and independent homes likely made it easier to support individualized options such as community employment.

4.7 Limitations

This study was cross-sectional, thus causal relationships cannot be inferred. It is unclear whether community-based employment may change the odds of living in certain residential settings or vice versa. Next, data are limited to working age (18–65) service users with IDD in the sample collected. Results may not generalize to the entire population, especially people with IDD not receiving IDD services. Further, this study did not examine whether young adults with IDD were engaged in post-secondary options (e.g., university, community college, community-based training program or apprenticeship, etc.). Nor did we include participants of any age with no formal day/employment activities at all. Finally, our categorization system to define primary day/community employment activity did not include an objective measure of primary (e.g., hours spent in each) for those engaging in multiple activities. This limitation is due to the unavailability of reliable data but does suggest caution when interpreting our results.

4.8 Future directions

This study excluded those with no day/employment activity. Because they are not engaged in any formal out-of-home day/employment activities at all, they are at risk for the most isolation from the community and should be considered as a focus of future research. One issue to examine is whether the lack of engagement with day/employment activities is the individuals’ choice, a result of service provision or funding shortfalls, or due to other factors. Future research should also explore the relationship between residential settings and employment/day services to examine in detail how they interrelate and to determine which supports are needed in each type of setting to encourage community-based employment. Further, how these services are integrated with other programs (e.g., vocational rehabilitation) outside the HCBS system should also be explored. Such research could help explain some of the findings in this study. Finally, future research should track changes in day/employment activities over time, with a particular focus on how policy changes nationally and/or within states impact community-based employment opportunities for people with IDD.

5 Conclusion

This study was the first, to our knowledge, to describe the complexity of day/employment activities individuals with IDD experience in terms of prevalence and overlap and to determine what personal and environmental factors were associated with those activities. This study demonstrated a clear need to consider and address the intersection of societal inequities related to race, gender and disability. Given each factor alone has implications for community-based employment opportunities, individuals within the disability service system who are also members of groups with inequities beyond disability status may need more nuanced consideration when it comes to efforts to increase community-based employment.

Similarly, this study shows the pivotal role a range of specific support needs play in opportunities to work in the community. Those with more significant needs related to issues such as mobility and mental health may be at greater risk of placement in a facility-based setting. Our findings can help target policy and practice interventions at individuals at higher risk of exclusion from community employment. In addition, specific workplace accommodations and therapeutic interventions for particular issues (e.g., mobility impairment) should be evaluated for their success in enabling community employment.

Even though supports for community-based employment service of people with IDD are an economically beneficial investment for society (Taylor et al., 2021), in common with previous research, our findings show a disappointing level of such employment. We hope that the factors we identified associated with what service users with IDD primarily do for employment or day activities, may provide avenues for future intervention and for policies at the state and federal level to be adjusted to better support community-based employment among this group.

Footnotes

Acknowledgments

The authors thank the National Association of State Directors of Developmental Disabilities Services (NASDDDS) and the Human Services Research Institute (HSRI) for granting access to the NCI-IPS 2018-19 data.

Conflict of interest

All authors declare that they have no competing interests.

Ethics approval

The University of Minnesota’s institutional review board (IRB) reviewed this research and granted a waiver of ongoing IRB review and approval.

Funding

The development of this article was supported by Grant #90RTCP0003 to the Research and Training Center for Community Living from the National Institute on Disability Independent Living and Rehabilitation Research, U.S. Department of Health and Human Services. Grantees undertaking projects under government sponsorship are encouraged to express freely their findings and conclusions. Points of view or opinions do not therefore necessarily represent official NIDILRR policy.

Informed consent

This study used secondary data for analysis, therefore informed consent was not required.

Appendix

Multinominal logistic regression results for day/employment activities by category

	B	Std. Error	Wald	df	Sig.	OR	95% Confidence Interval for OR
							Lower bound	Upper bound
Unpaid facility
Personal characteristics
Intercept	–10.427	0.073	380.556	1	0.000
Race/Ethnicity
African American^*	0.580	0.078	54.655	1	0.000	1.785	1.531	2.082
Hispanic/Latino^*	0.883	0.121	52.938	1	0.000	2.419	1.907	3.069
Other	0.088	0.146	0.361	1	0.548	1.092	0.820	1.454
White (referent)	0	.	.	0	.	.	.	.
Level of Intellectual Disability
Moderate^*	0.963	0.066	215.480	1	0.000	2.619	2.303	2.978
Severe^*	2.097	0.144	211.106	1	0.000	8.141	6.135	10.803
Profound^*	2.407	0.282	72.768	1	0.000	11.103	6.386	19.304
Mild (referent)	0	.	.	0	.	.	.	.
Age
Young <22	0.046	0.182	0.064	1	0.801	1.047	0.733	1.495
Older >40^*	0.512	0.063	66.522	1	0.000	1.669	1.475	1.887
Middle (referent)	0	.	.	0	.	.	.	.
Autism
Present^*	0.343	0.077	19.816	1	0.000	1.409	1.211	1.638
Not present (referent)	0	.	.	0	.	.	.	.
Verbal Expression
Non-verbal/assistance devices^*	1.161	0.115	101.079	1	0.000	3.193	2.546	4.003
Verbal (referent)	0	.	.	0	.	.	.	.
Gender
Female^*	0.399	0.059	45.412	1	0.000	1.490	1.327	1.673
Male (referent)	0	.	.	0	.	.	.	.
Behavior Needs
Present^*	0.400	0.066	36.164	1	0.000	1.491	1.309	1.699
Not present (referent)	0	.	.	0	.	.	.	.
Mental Health Diagnosis
Present^*	0.158	0.065	5.956	1	0.015	1.171	1.032	1.330
Not present (referent)	0	.	.	0	.	.	.	.
Mobility
Not mobile/need assistance^*	1.171	0.092	161.003	1	0.000	3.226	2.692	3.866
Mobile (referent)	0	.	.	0	.	.	.	.
Environmental characteristics
Residential Setting
Agency 16+ Residents^*	1.443	0.436	10.958	1	0.001	4.235	1.802	9.954
Agency 4–16 Residents^*	0.303	0.082	13.561	1	0.000	1.354	1.152	1.591
Agency 1–3^* residents	0.378	0.099	14.654	1	0.000	1.459	1.202	1.770
Independent home or apartment^*	–0.825	0.084	97.605	1	0.000	0.438	0.372	0.516
Family/Host home	0	.	.	0	.	.	.	.
Metro Category
Rural/Small town^*	–0.499	0.119	17.535	1	0.000	0.607	0.481	0.767
Micropolitan^*	–0.470	0.083	32.226	1	0.000	0.625	0.532	0.735
Metropolitan (referent)	0	.	.	0	.	.	.	.
Unpaid Community Activity
Personal characteristics
Intercept	–1.429	0.075	364.970	1	0.000
Race/Ethnicity
African American^*	0.366	0.082	19.720	1	0.000	1.442	1.227	1.695
Hispanic/Latino	0.208	0.138	2.290	1	0.130	1.232	0.940	1.613
Other^*	0.634	0.134	22.202	1	0.000	1.885	1.448	2.453
White (referent)	0	.	.	0	.	.	.	.
Level of Intellectual Disability
Moderate^*	0.763	0.068	125.855	1	0.000	2.145	1.877	2.451
Severe^*	1.694	0.149	128.659	1	0.000	5.441	4.060	7.292
Profound^*	1.994	0.288	48.029	1	0.000	7.342	4.178	12.902
Mild (referent)	0	.	.	0	.	.	.	.
Age
Young <22^*	0.757	0.160	22.384	1	0.000	2.132	1.558	2.917
Older >40^*	0.462	0.065	50.576	1	0.000	1.587	1.397	1.802
Middle (referent)	0	.	.	0	.	.	.	.
Autism
Present^*	0.324	0.078	17.159	1	0.000	1.383	1.186	1.613
Not present (referent)	0	.	.	0	.	.	.	.
Verbal Expression
Non-verbal/ assistance devices^*	1.150	0.118	95.261	1	0.000	3.159	2.507	3.979
Verbal (referent)	0	.	.	0	.	.	.	.
Gender
Female^*	0.413	0.061	46.010	1	0.000	1.512	1.342	1.703
Male (referent)	0	.	.	0	.	.	.	.
Behavior Needs
Present^*	0.550	0.068	65.647	1	0.000	1.734	1.518	1.981
Not present (referent)	0	.	.	0	.	.	.	.
Mental Health Diagnosis
Present^*	0.182	0.067	7.496	1	0.006	1.200	1.053	1.368
Not present (referent)	0	.	.	0	.	.	.	.
Mobility
Not mobile/need assistance^*	0.894	0.097	85.638	1	0.000	2.444	2.023	2.954
Mobile (referent)	0	.	.	0	.	.	.	.
Environmental characteristics
Residential Setting
Agency 16+ Residents	–0.090	0.482	0.034	1	0.853	0.914	0.355	2.353
Agency 4–16 residents	–0.108	0.087	1.550	1	0.213	0.897	0.756	1.064
Agency 1–3 residents	0.033	0.104	0.099	1	0.753	1.033	0.843	1.266
Independent home or apartment^*	–0.661	0.082	65.819	1	0.000	0.516	0.440	0.606
Family/Host home	0	.	.	0	.	.	.	.
Metro Category
Rural/Small town	–0.164	0.116	2.003	1	0.157	0.849	0.677	1.065
Micropolitan^*	–0.239	0.083	8.293	1	0.004	0.787	0.669	0.926
Metropolitan (referent)	0	.	.	0	.	.	.	.
Paid Facility
Personal characteristics
Intercept	–1.297	0.077	280.931	1	0.000
Race/Ethnicity
African American^*	0.245	0.087	8.025	1	0.005	1.278	1.078	1.514
Hispanic/Latino	–0.212	0.164	1.687	1	0.194	0.809	0.587	1.114
Other	–0.333	0.179	3.459	1	0.063	0.717	0.505	1.018
White (referent)	0	.	.	0	.	.	.	.
Level of Intellectual Disability
Moderate^*	0.648	0.071	84.173	1	0.000	1.912	1.665	2.196
Severe^*	1.070	0.165	42.245	1	0.000	2.915	2.111	4.026
Profound^*	1.120	0.312	12.891	1	0.000	3.065	1.663	5.649
Mild (referent)	0	.	.	0	.	.	.	.
Age
Young <22^*	–0.807	0.282	8.163	1	0.004	0.446	0.257	0.776
Older >40^*	0.449	0.067	45.307	1	0.000	1.567	1.375	1.786
Middle (referent)	0	0
Autism
Present	–0.076	0.089	0.732	1	0.392	0.926	0.778	1.104
Not present (referent)	0	.	.	10	.	.	.	.
Verbal Expression
Non-verbal/ assistance devices^*	0.601	0.134	20.132	1	0.000	1.824	1.403	2.371
Verbal (referent)	0	.	.	0	.	.	.	.
Gender
Female^*	0.254	0.064	16.018	1	0.000	1.290	1.139	1.461
Male (referent)	0	.	.	0	.	.	.	.
Behavior Needs
Present	0.052	0.072	0.527	1	0.468	1.054	0.915	1.214
Not present (referent)	0	.	.	0	.	.	.	.
Mental Health Diagnosis
Present^*	0.200	0.069	8.456	1	0.004	1.222	1.067	1.398
Not present (referent)	0	.	.	0	.	.	.	.
Mobility
Not mobile/need assistance^*	0.501	0.104	23.231	1	0.000	1.650	1.346	2.023
Mobile (referent)	0	.	.	0	.	.	.	.
Environmental characteristics
Residential Setting
Agency 16+ residents^*	2.537	0.433	34.384	1	0.000	12.640	5.414	29.511
Agency 4–16 residents^*	0.416	0.090	21.419	1	0.000	1.516	1.271	1.808
Agency 1–3 residents^*	0.289	0.110	6.858	1	0.009	1.335	1.075	1.658
Independent home or apartment^*	–0.202	0.081	6.132	1	0.013	0.817	0.697	0.959
Family/Host home	0	.	.	0	.	.	.	.
Metro Category
Rural/Small town	0.170	0.111	2.339	1	0.126	1.186	0.953	1.475
Micropolitan	0.060	0.081	0.544	1	0.461	1.062	0.905	1.245
Metropolitan (referent)	0	.	.	0	.	.	.	.

Note. Outcome referent category was Community-based employment. Final model fit: –2 log likelihood = 12908.260, χ= 3022.884, df = 60, p < 0.000.Pseudo R-Square:Cox and Snell = 0.248, Nagelkerke = 0.266. ^*=significant at p < 0.05. N = 10595.

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