Assessing The Capacity Of Local Social Services Agencies To Respond To Referrals From Health Care Providers

It is well established that having unmet social needs such as those related to housing, food, child care, safety, transportation, and financial resources is associated with a range of adverse health outcomes, including stress, sleep disturbances, physical and mental health problems, cognitive decline, and mortality.^1–5 Unmet social needs are also associated with lower likelihood of engaging in health-promoting behaviors, managing chronic diseases, keeping health care appointments, and using medication as prescribed, as well as with having lower self-rated health, fewer healthy days, and higher health care costs.^1,6–8

There is now great interest across the health care sector in assessing and addressing social needs in low-income patients and in determining the effects of social needs interventions on health improvement, costs, and utilization.^9–11 A recent review found that such interventions tended to have positive impacts on social needs, although evidence for health improvement was mixed—mainly because of the varying quality of some of the studies considered.¹² “Linkage interventions,” which aim to connect people to needed social services in their community through one-time referrals or ongoing case management, are currently the most common approaches for addressing social needs. Models for providing these referrals include clinic-based interactions, proactive outreach outside of clinical appointments, and community resource referral platforms.¹³ A 2017 survey of Medicaid managed care plans found that 91 percent reported assessing social needs among members and 93 percent linked members to social services.¹⁴

A key assumption of linkage interventions is that existing social services agencies have sufficient capacity to address the social needs in their communities. This is especially important because proactive and systematic screening will likely increase the number of people who seek assistance from the agencies. There have been no published data that describe social services system capacity—broadly defined as the amount of assistance a community can produce to address social needs—against which to check this assumption. This study provided the first such evidence, examining system capacity at the ZIP code level for fifty social needs in Missouri and comparing the pattern of results with similar data from Alabama, Connecticut, Iowa, Minnesota, Nebraska, and Nevada.

Our analyses sought to describe how system capacity varied by type of social need, geographically, and seasonally over a twelve-month period; integrate system capacity data with data on the prevalence of social needs to understand how the two intersect; and compare patterns of system capacity for a subset of social needs across seven states to assess their generalizability. We discuss the implications of these data for planning and delivering social needs interventions in health care.

Study Data And Methods

Data Source

All data analyzed in the study were collected by 211 helplines, which provide free information and referral services through more than 200 call centers covering all fifty states. Nationally, the helplines made over 12.8 million referrals in 2018.¹⁵ Most callers to 211 are seeking assistance with social needs such as housing, utility bill payment, and food.¹⁶ Callers are disproportionately low-income women with children.

Live operators answer all phone calls and record callers’ service requests using an extensive taxonomy of over 10,000 categories and subcategories.¹⁷ Requests are time- and date-stamped and linked to the caller’s ZIP code. The operators then provide up to three referrals to community agencies that address each social need the caller reports. In some instances, no referral can be provided because either there is no agency that both provides the needed service and serves the caller’s ZIP code or the agencies that do provide the service and serve the caller’s ZIP code have no resources available. In these instances, the caller’s request is coded by the operator as “unmet.” When the operator can provide a referral, the requests are coded as “met.” Because we could not link data back to individual callers, the study was not considered human subjects research, and no Institutional Review Board review was required.

Using the 2-1-1 Counts classification system,¹⁸ we collapsed the taxonomy of the more than 10,000 need categories into 13 top-level categories (housing and shelter, food, utilities, health care, mental health and addiction, employment and income, clothing and household, child care and parenting, government and legal, transportation, education, disaster, and other) and 115 subcategories. For example, the top-level category of housing and shelter contains subcategories for requests related to shelters, low-cost housing, home repair, rent assistance, mortgage assistance, and landlord-tenant issues. For analysis in this study, we selected fifty of these subcategories that represented a range of needs with higher and lower prevalence. Because not all 211 helplines code callers’ requests at the same level of detail (for example, some record “utilities” instead of specifying “electricity,” “gas,” or “water”), some helplines have data on fewer than fifty subcategories.

Data were collected for 2018. For Missouri the data included 181,418 service requests and covered all fifty social needs categories. The other states were Alabama (47,292 requests in forty-five categories), Connecticut (257,586 in forty-nine), Iowa (41,894 in forty-four), Minnesota (87,615 in forty-nine), Nebraska (17,825 in forty-three), and Nevada (77,983 in forty-seven). These states were selected because all of them coded callers’ social needs requests as met or unmet for the entire year and had sufficient call volume for us to calculate reasonably stable rates across a range of social needs.

Although 211 helpline data are easily the most timely, specific, and comprehensive on social needs service requests and unmet needs available, two limitations of these data must be acknowledged. First, they capture the needs of only those people who call 211. Thus, they almost certainly provide an underestimation of population risk, because some people with unmet social needs might not call 211, and even those who do call might enumerate only the most acute of the needs they are experiencing. Moreover, in many communities 211 is not the only helpline. For example, some communities also have specialized helpline services for certain social needs (such as crisis intervention and gambling addiction) or certain populations (for example, seniors and veterans).

Second, because each 211 helpline operates independently, there may be some variability in the coding of the calls they receive. For example, three of the seven states in our analysis did not distinguish between different types of utility bill assistance, whereas the remaining states coded separately for electric, gas, and water bills. We don’t know whether there is similar variability in how the helplines code “met” and “unmet” social needs service requests.

Variables

Our analyses focused on the prevalence of social needs and system capacity, by time and ZIP code.

Prevalence Of Social Needs:

For each of the fifty social needs, we summed the number of 211 helpline service requests received in 2018 in each state and converted these numbers to rates of requests per 100,000 population, using each state’s 2018 adult population. For Missouri we also created rates of requests for each ZIP code.

System Capacity:

We determined the total number of requests for assistance received by 211 helplines in 2018 for each of the fifty social needs in each state and—in Missouri only—in each ZIP code. We also determined the number of those requests that were classified by 211 helplines as “met.” We then calculated the quotient of the latter divided by the former separately for each social need. This yielded a system capacity score that represented the proportion of 2018 requests to which the local social services system theoretically had the capacity to respond. For example, if a 211 helpline received 100 requests for clothing in ZIP code 63112 and 80 of those were classified as met, the system capacity for clothing assistance in that ZIP code would be 80 percent. System capacity values therefore ranged from 0 percent (the lowest capacity) to 100 percent (the highest).

We strongly emphasize that system capacity as measured in this study is not the same as actually receiving assistance. When 211 helplines classify a request as “met” or “unmet,” that means only that a referral to a community organization could or could not, respectively, be provided. Thus, a system capacity score of 80 percent does not mean that 80 percent of people with a particular need received assistance. Research suggests that across all types of needs, just over one in three people will receive assistance.¹⁹ Many other people who contact service agencies find that there are no resources available or learn that they do not qualify for assistance. For example, people may be eligible to receive assistance for a given service only once per year, or resources may be available only to members of certain groups (for example, veterans or seniors). Our system capacity scores thus represent the upper limit of the proportion of callers who might receive assistance from a referral, and the proportion of callers who actually receive assistance is likely much, much lower.

For mapping purposes, we divided system capacity into four levels (0–25 percent, >25–50 percent, >50–75 percent, and >75–100 percent) to make patterns of geographic distribution easily recognizable.

Analyses

System capacity rates and numbers of requests for each social need are reported as descriptive data.

We used ArcGIS, version 10.5.1, to map system capacity in Missouri by ZIP code for each of the fifty social needs. We explored potential seasonality in the statewide system’s capacity by examining month-to-month changes during 2018 for each social need and plotting differences between the highest and lowest monthly scores.

To examine patterns of association between the prevalence of each social need and the system’s capacity to address it across Missouri, we plotted the fifty social needs along intersecting axes of prevalence and capacity.

Because several social needs were far more prevalent than others, the distribution of social needs requests was highly right skewed. We therefore used a natural log transformation on prevalence data to reduce skewness. The distribution of system capacity scores was moderately skewed in the opposite direction, so we used the square of system capacity to normalize the distribution. These transformations allowed us to plot the prevalence of need and system capacity for different social needs into visually clear and conceptually meaningful quadrants.

We then explored the consistency of these plot locations using 2018 data from 211 helplines in six other states to examine the relative position of social needs across states in a prevalence-by-capacity matrix.

Study Results

System Capacity Varied By Social Need

Exhibit 1 shows system capacity rates in Missouri for twenty-five of the fifty social needs, selected because each ranked highly within in its top-level category and received at least two requests per day, on average, in the state in 2018. System capacity was highest for needs such as food pantries (92 percent) and tax preparation (91 percent) and lowest for needs such as rent assistance (39 percent) and automobile assistance (26 percent).

System Capacity Varied By ZIP Code

For many social needs, system capacity varied considerably by ZIP code. This variability was mapped for the five most frequently requested social needs in Missouri during 2018: electric bill payments (exhibit 2); rent assistance (exhibit 3); and gas bill payments, low-cost housing, and food pantries (online appendix A).²⁰ We selected these because the more requests there were, the more stable system capacity estimates are at the ZIP code level (our analyses considered all 1,024 ZIP codes in Missouri that were ZIP code tabulation areas). For example, exhibit 2 shows that although system capacity was generally high for requests for assistance in paying electric bills, it was much lower in several regions of the state (the southeast and southwest) than in others.

SOURCE Authors’ analysis of information from 2-1-1 Counts.

SOURCE Authors’ analysis of information from 2-1-1 Counts.

Seasonality In System Capacity

Month-to-month system capacity was very stable for most of the twenty-five social needs described in exhibit 1—especially for several high-prevalence needs such as food pantries, electric and water bill assistance, job search, and shelters (exhibit 4). For example, the highest and lowest monthly levels of system capacity for food pantries were 93 percent and 89 percent, respectively, and comparable rates for electric bill assistance were 89 percent and 83 percent. Only four social needs had system capacity levels that varied widely by month: holiday meals (0 percent for the lowest monthly level and 80 percent for the highest), appliances (6 percent and 66 percent), tax preparation (46 percent and 96 percent), and financial assistance (43 percent and 79 percent). According to 211 helpline leaders, the first three of these reflect predictable and expected seasonal increases in demand because of Thanksgiving and Christmas, summer needs for window air conditioners and box fans, and tax season, respectively. It is unclear why the system capacity for financial assistance requests was higher in winter months and lower in summer months.

SOURCE Authors’ analysis of information from 2-1-1 Counts. NOTE The dots indicate the average annual capacity, and the error bars indicate the highest and lowest monthly system capacity rates.

Social Needs Prevalence By System Capacity Matrix

Exhibit 5 shows how the fifty social needs were distributed across a two-by-two matrix of social needs prevalence by system capacity in Missouri. Social needs in the upper-right quadrant (high prevalence/high capacity) included food pantries, electric bill payment, legal assistance, job search, and clothing. The lower-right quadrant (high prevalence/low capacity) included rent and mortgage assistance, home furnishings, appliances, public transportation, and automobile assistance.

SOURCE Authors’ analysis of information from 2-1-1 Counts and from the 2018 American Community Survey, US Census Bureau.

The lower-left quadrant (low prevalence/low capacity) included death-related and utility deposit assistance, and the upper-left quadrant (low prevalence/high capacity) included child care, home-delivered meals, and crisis intervention and suicide.

Consistency Of Matrix Locations Across Seven States

For thirty-two of the fifty social needs, matrix locations were similar across states. As examples, panels 1–3 of appendix B²⁰ show the distributions for food pantries, child care, and automobile assistance. Food pantry requests from the seven states were tightly clustered in the high-prevalence, high-capacity quadrant (panel 1); child care requests were less prevalent but still had relatively high capacity (panel 2); and automobile assistance requests were about as prevalent as child care requests but had much lower capacity (panel 3).

Other social needs that were similarly clustered across states included adult education, child and family law, clothing, dental care, eye care, gas utility payments, government, health insurance, help buying food, home-delivered meals, home repair or maintenance, job search, landlord-tenant issues, legal assistance, low-cost housing, medical equipment, medical providers, medical transportation, mental health facilities, money management, nursing homes and adult care, personal hygiene products, prescription medications, public transportation, shelters, soup kitchens, substance abuse and addiction, tax preparation, and thrift shops.

For other social needs, either prevalence was consistent across states while capacity varied or vice versa. For example, requests for rent assistance (panel 4 of appendix B)²⁰ were universally high in prevalence, but the capacity to address them varied by state, from a high of 94 percent to a low of 19 percent. Other social needs that shared this pattern included death-related expenses, home furnishings, medical expense assistance, mortgage assistance, phone utility payments, school supplies, seasonal or holiday issues, utility deposit assistance, and utility payment plans.

Social needs that varied in prevalence but had similar capacity included crisis intervention and suicide, electric utility payments, and mental health services. Finally, some social needs such as heating fuel (panel 5 of appendix B)²⁰ varied across states on both prevalence and capacity. Other needs with this profile included appliances, financial assistance, holiday meals, and water utility payments.

Discussion

This article presents the first published data on the capacity of local social services systems to respond to community members’ social needs. We found that system capacity can vary widely for different social needs, by state, and by ZIP codes within a state, which raises important questions about the possible limitations of linkage interventions that are now being widely used by health care organizations.

For example, housing and transportation are among the social needs recommended for screening in health care settings.^21,22 In our study, requests for assistance with these needs were common, but the capacity to respond to them was moderate or low in nearly all states.

Our findings align closely with those from longitudinal studies that tracked the outcomes of social needs referrals in vulnerable populations. Separate studies in Missouri and Oregon found that referrals for food-related needs were the most likely to be met, while referrals for housing-related needs were the least likely.^19,23 In our analyses, system capacity was high for food pantries and much lower for rent assistance, low-cost housing, appliances, and home furnishings.

Social needs interventions that unwittingly refer vulnerable patients to social services agencies with low capacity could create a lose-lose-lose scenario.

In a worst-case scenario, social needs interventions that unwittingly refer vulnerable patients to social services agencies with low capacity could create a lose-lose-lose scenario: Patients are sent seeking help that largely doesn’t exist; community agencies that are already stretched thin experience increased workloads, often as bearers of bad news to needy people; and health care professionals waste precious time, lose credibility, and don’t get the outcomes they seek. It’s not clear how likely this “triple blame” of health care and social needs might be. For example, research findings suggest that most patients think it is appropriate and important for their providers to screen for social needs, but many patients are not interested in receiving social needs referrals and have no expectation that their provider will solve their social needs problems.²⁴

Exhibit 5 and appendix B²⁰ introduce a proposed typology of social needs for health care action. We classified social needs into the four quadrants of the typology based on their prevalence and the system’s capacity to address them. Each quadrant can be labeled to reflect the broad types of action that might be most appropriate for social needs within it: screen and refer (for social needs with high prevalence/high capacity), increase capacity (high prevalence/low capacity), consolidate services (low prevalence/low capacity), and evaluate priorities (low prevalence/high capacity).

Social needs in the “screen and refer” quadrant included clothing, food pantries, gas utility payments, legal assistance, health insurance, medical providers, and substance abuse and addiction. At least in the seven states studied, they would appear to be good candidates for screen-and-refer linkage interventions, as they meet two key criteria for population screening: importance or prevalence and the availability of effective treatments.²⁵

Screening and referral interventions may be ill advised or inefficient for social needs classified in the other quadrants of the typology. Their effectiveness will be limited when system capacity is low, and population screening may be inefficient for very low-prevalence needs.

Social needs in these quadrants present a challenge to everyone who aims to address social determinants of health and individual social needs, not just those in the health care sector. For example, how should communities address the fact that there are high-prevalence needs such as rent assistance for which little or no help exists,^26,27 while resources are widely available to address needs that affect far fewer people? In many communities the process of allocating resources to address social needs could benefit greatly from such data. For example, studies evaluating community health needs assessments find that social determinants of health are often the least developed component, lacking even basic data to guide decisions.^28,29

What strategies might be needed to address social needs that don’t currently lend themselves to solution through linkage interventions? For social needs in the “increase capacity” quadrant (high prevalence/low capacity), health care organizations could lead or support efforts to strengthen the community response system. This might include becoming more involved in community decision making about how finite resources are allocated to different social needs, experimenting with new models such as “pay for success,”³⁰ or using Medicaid funding to address social needs.³¹

Social needs such as death-related services and utility deposit assistance appeared in the “consolidate services” quadrant (low prevalence/low capacity) for several states. The relative dearth of available services for these needs likely reflects lower demand for the services, at least in part. Services that address needs in this quadrant might be absorbed by high-functioning agencies with relevant core expertise that are looking to expand or partner to address unmet community needs.³² For example, a utility bill payment program could expand to provide utility deposit assistance.

For many needs in the “evaluate priorities” quadrant (low prevalence/high capacity), the issue is utilitarianism. It seems fair to ask whether community resources might be allocated differently to more closely align with demonstrated needs. This is one of the foundational strategies of Accountable Health Communities.³³ Organizations that fund community services (for example, United Ways, foundations, and philanthropies) could better use social needs data to prioritize funding for some needs over funding for others. Health care organizations should be engaged in these discussions at the community level and, where applicable, internally to ensure that their own philanthropic and community benefit investments address high-priority social needs. Because the prevalence of some social needs in this quadrant (for example, crisis intervention and suicide and medical transportation) will be underestimated if people seek help in addressing the need through channels other than 211 helplines (for example, crisis hotlines or their own health insurer or provider), analyses must be thorough and decisions cautious.

Another important finding with implications for health care is the geographic variability we observed in system capacity. Generally speaking, system capacity was lower in less densely populated regions of Missouri. It is well known within the social services sector that rural communities have fewer social services agencies and available resources.³⁴ This poses challenges to rural primary care providers who wish to address social needs using linkage interventions and suggests that health plans making policy decisions about social needs interventions at the state level should consider how those decisions might exacerbate difficulties already faced in some regions.

In some cases, regional variation may reflect important differences in community investment in certain social needs. For example, the system capacity map for rent assistance (exhibit 3) shows dramatic differences between the Kansas City and St. Louis metropolitan areas in Missouri. When shown this map, 211 helpline leaders examined their resource libraries and found that as of November 2019 the five largest counties in the St. Louis region had fifteen agencies that provided rent assistance, nine of which were out of resources. In contrast, at the same time, the four largest counties in the Kansas City region had thirty-seven agencies that provided rent assistance, only two of which were out of resources.

Some health care organizations may hedge against limited system capacity by assigning case managers to high-need or high-cost patients to increase the odds that those patients receive assistance. Although this might help the selected individuals, it could have the unintended consequence of creating tiers of privilege within the social services safety net, disproportionately allocating limited public resources to low-income people with the worst health problems, best health care, or best insurance.

As the evidence linking social needs and population health outcomes grows, it will be crucial to develop multisector collaborations and advocate for policies that expand social services resources at local, state, or federal levels, as well as to require the data-informed allocation of existing resources.^11,35 In Missouri, for example, the Missouri Foundation for Health has developed the Net Benefit, a nonpartisan campaign to build support for social safety-net services by educating Missourians about how meeting social needs can help improve population health.³⁶

We recognize that there are likely differences between people who call 211 and people who are screened in primary care settings. The former are actively seeking help with social needs, while most of the latter are not. Thus, the social needs experiences of 211 callers and their willingness to accept assistance could be different from those of a primary care patient population. We cannot infer that 211 data from Missouri (or the other six states included in our analyses) are representative of data from 211 helplines in other states, nor is it known whether the geographic or temporal distribution of the social needs reported in this article might be different for people who do not contact 211 for assistance or who contact a different helpline.

Conclusion

Current approaches to addressing social needs among patients with low socioeconomic status are likely undermined in many communities by limited capacity in the social services system. This may be especially problematic for certain high-prevalence needs, including many related to housing and transportation. Linkage approaches that rely on screening and referral to community agencies will need to be supplemented with alternative community-level strategies that use data from 211 helplines and other sources to prioritize and allocate resources among social needs, and develop and enact policy proposals to expand the availability of social needs resources.

ACKNOWLEDGMENTS

This research was supported by funding from the National Cancer Institute (Grant No. R01 CA 201429 and Data Integration Supplement) and the National Institute of Diabetes and Digestive and Kidney Diseases (Grant No. R01 DK 115916). Special thanks to the 211 helpline team members who collected and interpreted the data used in these analyses, especially Jennifer Miller, Robin Pokojski, and Cathy Vaisvil. Matthew Kreuter and Balaji Golla are codevelopers of 2-1-1 Counts, an open, public data source used in the study. Charlene Caburnay has financial ownership in Health Communication Impact, LLC, which develops and licenses 2-1-1 Counts to 211 helplines. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This is an open access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt, and build upon this work, for commercial use, provided the original work is properly cited. See https://creativecommons.org/licenses/by/4.0/.

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