Social Isolation and Population Density: Racial and Ethnic Differences Among Older Adults

Abstract

This study uses secondary data analysis to assess the relationship between social isolation (SI) and population density in the US, as well as any moderating influence that race/ethnicity may have on that relationship. A recent consensus on the measure of SI is that it focuses on the objective absence of social connections, whereas loneliness refers to subjective assessments of one’s social connections. Therefore, while the original study measured both objective and subjective SI may be innovative, it may also be overly ambitious. SI puts older people at risk for health problems, including an increased chance of dying. The AARP Foundation gathered the initial convenience sample, which included 8149 senior citizens. The study determined population density using self-reported zip codes, measured as persons per square mile, and divided the results into tertiles. Linear mixed models were used to investigate the moderating role of race/ethnicity between population density and SI. The findings revealed that greater population density was associated with less SI for individuals residing in zip codes with a higher percentage of the same race/ethnicity, but more SI for those in zip codes with a lower percentage of the same race/ethnicity. These results suggest that race/ethnicity should be considered in future studies or when developing policies and interventions to address SI among older adults in high-population-density areas. For example, when policymakers aim to address SI in a community, they may want to collect data based on zip codes and create targeted interventions for specific racial/ethnic groups within those zip code areas.

• What do we already know about this topic?

• Existing research has shown that older adults who experience social isolation (SI) can result in adverse health consequences. Studies have also suggested that factors like race/ethnicity influence the experience of SI. Nevertheless, the existing literature is constrained in its examination of the relationships among SI, race/ethnicity, and population density.

• How does your research contribute to the field?

• No other research has examined the association between decreased isolation and the density of older individuals in zip codes where most people share a common racial or cultural identity. This study aims to close this information gap on the subject.

• What are your research’s implications toward theory, practice, or policy?

• To comprehend the relationship among SI, social density, and race/ethnicity, we require theoretical frameworks like the concept of structural racism. Health professionals or policymakers should consider not only population density and racial/ethnic demographics, but also historical context, social structure, disparities, and cultural factors when addressing SI.

Introduction

In 2021, about 55 million Americans were aged 65 and older, representing 17% of the total U.S. population. ¹ This number is expected to reach 83.7 million by 2050. ² Aging is characterized by psychological, physical, and social changes that may lead to declined health and increase vulnerability to chronic conditions and impaired functioning. ³ Research shows that social isolation impacts anywhere from 24% to 50% of these older adults, a rate that may increase as adults age.^4,5 Numerous negative health outcomes related to social isolation have been repeatedly demonstrated in the literature.^6,7 Specifically, social isolation leads to increased risk for all-cause mortality, hospitalization, anxiety, depression, poor quality of life, and falls.^8-10 In fact, individuals who experience social isolation have a 29% increased risk for premature death. ¹¹ Considering the negative consequences of social isolation, it is important to study the risk factors of social isolation among older adults aged ≥65 years. ¹² Evidence is clear that socioeconomic status, social factors, and disconnection from family can lead to an increased risk of social isolation.^13-15 Moreover, Ibrahim et al ¹⁶ found that social isolation in older adults is impacted by where they live. Lai et al found that higher density of detached housing was negatively associated with social isolation, while density of flats was positively associated with the outcome. Effects of density can lead to mental and physical health risk factors of stress, infection, pollution, noise, traffic congestion, and crime due to space deprivation, lack of green space, and more people.^17,18 Especially, after the COVID-19 pandemic, people seriously consider the issues and effects of social isolation and density in many aspects of society. ¹⁸ However, there is a paucity of literature that attempts to understand upstream thinking regarding crucial aspects of how social isolation connects to the living situation of older adults. Thus, a better understanding of older adults’ living situations is important as where older adults live (dense or sparse population areas) may be associated with social isolation. Understanding this association would grant researchers information to combat social isolation at its root and help to improve the quality of life of older adults.

Despite social isolation and health equity having been growing public concerns negatively associated with physical and mental health in the US, the current evidence is mixed among older adults. In 2019, AARP Kansas presented findings on potential factors contributing to social isolation and how these factors impact older individuals based on gender, race and ethnicity, and other demographic variables. ¹⁹ Cudjoe et al reported that African Americans were less likely to report social isolation compared to Whites. Byrne et al ²⁰ showed that Black older adults had greater levels of social isolation compared to White older adults. Miyawaki ²¹ found that there was no association between physical health and isolation among Hispanic elderly individuals. However, most evidence indicates that Black and Hispanic older adults experience higher rates of social isolation in comparison to White older adults.^22,23

Structural racism is defined as a system in which public policies, institutional practices, and cultural representations to perpetuate racial group inequity. ²⁴ When considering explanations for race and ethnicity on social isolation based on the zip code system for our research, structural racism explains some populations are more susceptible to isolation than others because their social standing and position in society makes them more prone to experiencing isolation. ²⁴ Historically, redlining was used throughout the US in the 1930s to keep black people restricted to certain areas of a community that lacked social and economic resources. ²⁵ Some people believe that even now invisible redlining exists. Compared to white people, Black and Latino individuals in the US are more likely to live in neighborhoods that lack healthy food options, green spaces, and places to exercise safely, and have more pollution. ²⁶ The structural inequities and racism experienced by people of color on a daily basis led to social isolation, stress, poor physical and mental health, and chronic conditions sooner than among white people. ²⁷ However, we have very little evidence about where people of color adults may feel isolated from who they live with based on zip code system.

We need more research into the relationship between population density and the interaction of race and ethnicity which relate to social isolation. Knowing where these isolated older adults whether the same race and ethnicity or not are located may find targeted interventions. Researchers need a better understanding of this phenomenon and more effective ways to intervene. The purpose of this study is to examine the relationship between population density and social isolation when older adults live surrounded by the same race and ethnicity.

Research Design

Dataset and Study Population

This study is a secondary data analysis. A recent consensus on the measure of social isolation is that it focuses on the objective absence of social connections, whereas loneliness refers to subjective assessments of one’s social connections. ²⁸ However, when authors from the original article developed the SIS, they explained that social isolation was not frequently assessed in clinical settings because of the lack of conceptual clarity on the concept. ²⁹ They addressed that some people believed the concept of social isolation evolved from simply including the objective aspect of very small social networks to include both the objective and subjective components due to the complexity of the concept of social isolation. Unfortunately, recent data is not available to see how zip code-level racial mix has changed from 2012 to the most recent period. According to the US Census Bureau, ³⁰ throughout the decade from 2010 to 2020, there was a rise in diversity of about 8.6% and urban areas experienced growth of about 9% among older adults. The data was collected by the AARP Foundation at selected Tax-Aide sites between March 7 and April 30, 2012. The AARP Foundation Tax-Aide has been helping low-to-moderate-income taxpayers increase discretionary income via tax service assistance, ensuring that they receive the maximum amount of applicable tax credits and deductions. The AARP Foundation Tax-Aide is freely available to taxpayers with low-to-moderate income, with special consideration for those aged 60 and older. The AARP Foundation Tax-Aide typically asks the taxpayers using this service to complete a satisfaction survey about the assistance they received. For the year 2012, a social isolation and relationships questionnaire for the development of the newly constructed Social Isolation Scale (SIS) ²⁹  was placed in the standard satisfaction survey. The AARP Foundation Tax-Aide survey also collects demographic information, including gender, ethnicity, race, age, marital status, and household income. In addition to the AARP Foundation Tax-Aide survey, data was analyzed from zip-code.com. The original data was collected by the AARP. IRB (# 03518) was approved for this study by Quinnipiac University. This study includes information regarding population by zip code, including age, gender, income, race, and ethnicity. In this database, race and ethnicity are reported together as White, Black, Hispanic, and Asian.

Sample and Procedures

A total of 15 535 respondents in the original data returned questionnaires to the AARP Foundation via postage-paid envelopes from 624 sites spanning 44 states. Study inclusion criteria comprised being at least 60 years old, completing the SIS questions in the survey, and providing the zip code in the survey. This left 8149 participants from 1821 zip code regions for analysis. Using self-reported zip codes from that survey as well as the SIS data, the researchers used zip-code.com data to determine population density for each zip code along with aggregate demographic data at the zip code level.

Study Measures

Social isolation in the original study was measured by the recently developed SIS. ²⁹ The SIS includes 6-items asking respondents to indicate frequency of contact with family, friends, and neighbors, and to rate their social activities and relationships. The first set of the scale asks respondents to, “Think about your family, friends, or neighbors . . .” and respond to 3 questions: (a) How many of them do you see face-to-face at least once a month? (b) How many of them do you communicate with on a personal level by phone or electronically at least once a month?, and (c) How many of them do you feel close to on a personal level? The second set of questions asks respondents, “Thinking about the relationships you have with individuals or groups you are a part of, please rate how much you agree or disagree with the following statements.” Participants are asked to rate their level of agreement with the following statements: (a) Overall, I feel that my relationships are fulfilling, (b) I feel like I just don’t belong, and (c) I feel that I spend enough time involved in social activities. The scale was scored such that higher scores are reflective of greater social isolation.

The survey measures ethnicity with 1 yes or no question, “Are you Hispanic or Latino?” The survey measures race with the question, “Would you say you are (select all that apply): White, African American, Asian/Pacific Islander, Native American, Other.” Due to differences in reporting race and ethnicity between the databases, analysis that included population density information required the creation of a new race and ethnicity variable from the AARP Foundation data to match the zip-code.com data. Respondents who reported their ethnicity as Hispanic were labeled as Hispanic for the variable race and ethnicity and their reported race was not additionally considered. Respondents who reported their race as White were labeled White. Respondents who reported their race as African American were labeled Black. Respondents who reported their race as Asian/Pacific Islander were labeled Asian. Respondents who reported their race as Native American, Other, or selected multiple (n = 319) were grouped as other. The population density was obtained from the zip-code.com data and reported as people per square mile (ppsm). Based on Census bureau data, entitled Core-Based Statistical Data (CBSA), we grouped the data into 3 density categories in tertiles with metropolitan at 50 000 ppsm, micropolitan at 10 000 to 49 999 ppsm, and finally not micropolitan or metropolitan defined as <10 000 ppsm. The proportion of persons residing in a zip code area by race and ethnicity was calculated from information given on the total number of persons and number of persons who identified as White, Black, Hispanic, and Asian in that zip code. The predictor is demographic data such as age, gender, marriage status and density, etc. and the outcome is social isolation. Additionally, we examine a moderator effect with race and ethnicity.

Analytic Plan

Descriptive statistics included means with standard deviations with all the above demographic variables. A linear mixed model was used to examine if population density, measured at the level of zip code, was predictive of social isolation. To control possible confounding, person-level demographic variables were incorporated into the model and included gender, age, household income, marital status, race, and ethnicity. The fixed effects were the population density and demographic variables, while the intercept was random and varied zip codes. To reduce the impact of outliers and better approximate a linear relationship with social isolation (SIS score), a Box-Cox transformation of population density was performed, and the square root transformation was deemed best.

The main analysis included investigating the association between population density and social isolation and then to analyzing the association of moderator effect by the percentage of the population that was of the same race and ethnicity. A variable was created that indicated the percentage of the population that was of the same race and ethnicity as the individual. For example, if a White and Black persons lived in the same zip code and the percentage of respondents in that zip code who were White and Black was 50% and 20% respectively, then the White individual would have a value of 50 and the Black individual a value of 20 for the variable. The interaction with population density tested the modifying effect. This was followed by a three-way interaction with race and ethnicity (all lower terms included) to test if the two-way interaction varied by race and ethnicity. The data preparation and analyses were conducted using the software IBM SPSS Statistics 27.

Results

Table 1 shows the demographic characteristics of the study participants. Approximately two-thirds were female (67.3%), and most were White (82.8%). The average age was 72.1 and ranged from 60 to 100. Less than half (39.3%) were married or living together, with the remaining either widowed, divorced, separated, or never married. Roughly half of the participants had a household income between $10,000 and $30,000. Table 2 shows the aggregate characteristics for the 1821 zip codes included in the study. Most (65.7%) were located in the south and had a Core Based Statistical Area (CBSA) classification of metropolitan (70.8%). The number of sampled individuals per zip code in the survey varied from 1 to 162, with an average of 5.2 per zip code. The average population size of a zip code was more than 16,000 people and varied from less than 100 to over 100,000. The average percentage of individuals who self-identified as White varied from 1.4% to 100%, and the range was almost as great for the percentage of individuals who self-identified as Black, Hispanic, and Asian. Population density varied from 0.4 residents per square mile to nearly 97 000 per square mile, with an average of over 1500 per square mile.

Table 1.

Individual Demographic Characteristics by Race/Ethnicity.

	Total	African-American	Hispanic	Asian/Pacific Islander	Other	White
	n = 8149	n = 1014	n = 184	n = 60	n = 266	n = 6625
Gender
Female	5481 (67%)	722 (71%)	119 (65%)	36 (60%)	142 (53%)	4462 (67%)
Male	2668 (33%)	292 (29%)	65 (35%)	24 (40%)	124 (47%)	2163 (33%)
Age
Mean ± SD	72.1 ± 7.9	69.5 ± 7.6	70.3 ± 7.9	69.2 ± 6.9	71.2 ± 8.0	72.6 ± 7.9
Min, max	60, 100	60, 97	60, 94	60, 92	60, 95	60, 100
Marital status
Married/LIVING TOGETHER	3203 (39%)	316 (31%)	70 (38%)	28 (47%)	112 (42%)	2673 (40%)
Other	4946 (61%)	698 (69%)	114 (62%)	32 (53%)	154 (58%)	3952 (60%)
Household income
Less than $10 000	522 (6%)	91 (9%)	27 (15%)	13 (22%)	26 (10%)	365 (6%)
$10 000-$19 999	2012 (25%)	245 (24%)	47 (26%)	10 (17%)	79 (30%)	1631 (25%)
$20 000-$29 999	2288 (28%)	272 (27%)	39 (21%)	12 (20%)	64 (24%)	1901 (29%)
$30 000-$39 999	1422 (17%)	172 (17%)	31 (17%)	9 (15%)	41 (15%)	1169 (18%)
$40 000-$49 999	857 (11%)	96 (10%)	14 (8%)	4 (7%)	24 (9%)	719 (11%)
$50 000 or more	1048 (13%)	138 (14%)	26 (14%)	12 (20%)	32 (12%)	840 (13%)

Table 2.

Zip Code Characteristics (N = 1821).

	Frequency	Percent
Region
Midwest	432	23.7
Northeast	95	5.2
South	1196	65.7
West	98	5.4
CBSA
Micropolitan	257	16.3
Metropolitan	1116	70.8
Not Politan	179	11.4
Missing	24	1.5
	Mean ± SD	Min, Max
Individuals sampled in zip code	5.17 ± 9.3	1, 162
Population	16 470 ± 15 020	89, 103 689
Persons per household	2.47 ± 0.27	1.25, 4.60
Median age	39.0 ± 5.8	19.5, 73.5
Percent Female	50.7 ± 2.3	14.8, 58.5
Percent White	81.2 ± 21.3	1.4, 100
Percent Black	13.7 ± 19.6	0.1, 98.5
Percent Hispanic	6.8 ± 11.3	0.1, 89.2
Percent Asian	4.5 ± 5.8	0.3, 97.0
Persons per square mile	1515 ± 5779	0.4, 96 770

The average social isolation score was 6.3 (SD = 4.5) and ranged from 0 (lowest possible score) to 24 (highest possible score). As shown in Table 3 in the unadjusted linear mixed model, the more densely populated the zip code area, the greater the isolation (b = 0.004, P = .053). Table 3 also shows the results from the adjusted linear mixed model. Males reported higher levels of isolation (P < .001) compared to women. Individuals with higher incomes and those being married and living together reported less isolation (P < .001). Race was significant as those who identified as African American or Other reported higher levels of social isolation compared to those who identified as White (P < .001). Population density was not statistically significant (b = 0.002, P = .471). The intra-class correlation (ICC = .3%) indicates almost all the variance in social isolation scores can be attributed to differences between individuals, and not to differences between zip code regions due to the sampling bias in the original data. A subsequent model resulted in no significant interaction effects between population density and ethnicity (P = .447) or population density and race (P = .105).

Table 3.

Linear Mixed Models Results for Population Density Predicting SIS Score.

	Unadjusted model			Adjusted model
Effect	Coefficient	Std error	P-value	Coefficient	Std error	P-value
Intercept	6.17	0.081	<.001	7.30	0.482	<.001
Population density	0.004	0.002	.053	0.002	0.002	.471
Male				1.55	0.113	<.001
Age				0.002	0.006	.718
Married/living together				−0.571	0.117	<.001
Household income				−0.485	0.037	<.001
Race/Ethnicity
White				Reference		<.001
African American				0.570	0.156	<.001
Hispanic				0.528	0.332	.113
Asian/Pacific Islander				0.466	0.588	.428
Other				0.821	0.279	.003
Variance components
Residual	19.77	0.32	ICC = .004	18.62	0.30	ICC = .003
Intercept (zip code)	0.09	0.07		0.03	0.06

In Table 4, a model controlling demographic variables resulted in a significant effect for the variable “percentage same race and ethnicity” (b = −0.010, P = .002), such that the higher the percentage, the less isolated the individual felt. Next, the interaction with population density was added to the model and it was statistically significant (b = −0.0002, P = .044), such that the more densely populated the zip code, the stronger the effect. Figure 1 shows the estimated isolation score for low (1 SD below mean), medium (mean), and high (1 SD above mean) levels of percentage same race and ethnicity. For persons residing in zip codes with a high percentage of the same race and ethnicity, a greater population density reduces isolation, while for persons living in zip codes with a low percentage of the same race and ethnicity, population density increases isolation. This model was followed with a model that added a three-way interaction effect between population density, percentage of same race and ethnicity, and race and ethnicity to test if the two-way interaction differed by race and ethnicity. It was not statistically significant (P = .188), suggesting the two-way interaction effect was consistent across race and ethnicity. Table 5 shows the model parameter estimates by race and ethnicity and the coefficients for the two-way interaction between the percentage same race and ethnicity and population density are of the same sign and similar magnitude across the four groups. The sign is negative as found in the earlier model when the groups were analyzed together and varied in magnitude from −0.002 for Blacks to −0.009 for Asians with a significant effect in the White population (b = −.003, P = .027) because of the larger sample size.

Table 4.

Linear Mixed Models Results for Interaction between Population Density and Percentage Same Race/Ethnicity.

	Conditional main effects			With interaction effect
Effect	Coefficient	Std error	P-value	Coefficient	Std Error	P-value
Intercept	8.05	0.56	<0.001	7.67	0.59	<0.001
Male	1.56	0.12	<0.001	1.56	0.12	<0.001
Age	0.003	0.006	0.680	0.003	0.006	0.660
Married/living together	−0.558	0.119	<0.001	−0.563	0.118	<0.001
Household income	−0.478	0.037	<0.001	−0.476	0.037	<0.001
Race/ethnicity
White	Reference
African	0.151	0.203	0.786	Reference	0.203	0.745
American	−0.070	0.385	0.458	0.135	0.385	0.506
Hispanic	−0.221	0.627	0.856	−0.054	0.632	0.889
Asian/Pacific Islander			0.725	−0.389		0.538
Population density	0.001	0.002	0.734	0.012	0.006	0.045
Percent same race	−0.009	0.003	0.002	−0.004	0.004	0.252
Pop density × Percent same race				−0.0002	0.0001	0.044

Figure 1.

Social isolation in percentage of same race and ethnicity.

Table 5.

Population Density and Percentage Same Race/Ethnicity Effects by Racial/Ethnic Group. ^a

Effect	Coefficient	Std error	P-value
White
Population density	0.03298	0.01445	.023
Percentage White	0.00520	0.00705	.461
%White × Pop density	−0.00039	0.00018	.027
Black
Population density	0.00849	0.00781	.278
Percentage Black	−.00251	0.00820	.760
%Black × Pop density	−0.00017	0.00013	.216
Hispanic
Population density	0.02485	0.02330	.289
Percentage Hispanic	−.00822	0.02384	.731
%Hispanic × Pop density	−0.00075	0.00082	.361
Asian
Population density	0.06992	0.05157	.182
Percentage Asian	0.47178	0.34465	.178
%Asian × Pop density	−0.00854	0.00617	.174

^aControlling for gender, age, married/living together, and household income.

Discussion

Our study findings showed that the associations between population density and social isolation were mixed. Those who lived in more densely populated areas had higher levels of social isolation than those in less densely populated areas, yet this was not statistically significant in the adjusted model. One explanation of such findings can be that the study participants were not representative of US older adults as the data have very little clustering, which is why the ICC is .002. The sample is selective, in that it is mostly low-to-mid income people and predominantly White adults and does not include older adults from less than micro areas (less than 50 000 ppsm).

We found that race was significantly associated with social isolation; ethnicity, however, was not. Specifically, older adults who identified as White adults had lower average Social Isolation Scores than older adults who identified as African American or Other adults, but were not statistically significantly different from older adults who identified as Asian/Pacific Islander adults. Alcaraz et al ³¹  found that white males and females were more likely to fall into the least isolated category compared to black males and females. Kannan and Veazie reported that Black Americans reported higher levels of social isolation and lower levels of overall social engagement than other racial groups. However, according to a 2020 study by Cudjoe, Black, and Hispanic elderly were less likely than white older adults to be socially isolated or to experience severe social isolation. When the percentage of people of the same race and ethnicity increased in a zip code and the population density also increased, social isolation within that racial and ethnic group decreased. Put simply, if there are more people with the same racial and ethnic identity in more densely populated areas, like cities, people within that same group feel less isolated. Conversely, this effect is diminished in less densely populated areas like rural communities. These results need to be considered in the context of the sample used, in which 82% of respondents identified as White, 12.4% of respondents identified as African American, 0.8% of respondents identified as Asian/Pacific Islander and 3.9% of respondents identified as Other.

Individuals in the study identified as male were more socially isolated than individuals who identified as female. Umberson et al ³² reported that men are more socially isolated than women for most of their life, and this gender gap is even larger for the never-married and those with disturbed relationships. Lim et al ³³ also found that men were more likely than women to suffer from social isolation.

Understanding structural racism could help to partially explain our findings. In this study, when there are more specific people (Whites) in the immediate area (A structured system or society), they create their own privilege or advantage based on their race and ethnicity. So, they feel less socially isolated. However, it’s hard to conclude that Black and Hispanic participants did not feel isolated when compared to neighbors with less similar racial ethnicities, given that only 16.3% of participants were Black and Hispanic. Thus, future studies are critically needed to fully understand social isolation in the older population with a more representative US sample.

There are some limitations of this study. First, the measure of social isolation with the SIS in the original study can be innovative, but it can be over-ambitious. Thus, combining both objective and subjective measures of social isolation with the SIS in the study can also be problematic. Second, the lack of information regarding household size (beyond marital status), the respondent’s primary language (particularly important for Hispanic and Asian-American older adults, and individual mobility (types of transportation) restricts the extent of policy implications due to the secondary data analysis in the study. Third, the participants were present to take this survey only due to a need for help in filing their tax returns, which was a service designed for low-to-moderate-income taxpayers. Furthermore, as this survey was only distributed to individuals who went to an AARP Foundation Tax-Aid event, this may lead to a self-selection bias. The availability of transportation to get to the AARP Foundation Tax-Aid site to obtain this help with filing suggests that these older adults may have had the opportunity for social engagements that others absent would not have. Additionally, those in extreme social isolation may not know about community programming; therefore, they may be underrepresented in this study. As discussed earlier, people who identified as White are disproportionally represented in the study, as are people living in the Midwest and the South. People living in metropolitan areas are also over-represented. Another limitation of this study was that the survey was conducted before the COVID-19 outbreak, and the participants may change of population density in consideration of the pandemic. We need more and further research on the matter of density on social isolation in older adults.

Conclusion

Future research should expand on the findings and limitations of from this study. A data set that is more representative of the population—geographic and demographic—would be more generalizable. Additionally, race and ethnicity need to be clearly and consistently defined across platforms and datasets. Considering that there will be new Census data available from 2020, future research could use those demographic questions as a model. Additionally, the “Silent Generation” had a different lived experience than the “Baby Boomers.” These generational differences, their implications for social isolation, and generation-specific interventions are all areas for future research. Finally, the impacts of gender and relationship status, household size, transportation, and primary language should be further explored in order to target interventions.

Policy makers need to acknowledge and confront structural and interpersonal racism in communities. When policymakers identify the issues of social isolation among community people, they may want to collect data on the issues based on Zip codes and create supportive interventions for certain race/ethnicity groups within zip code levels. Supportive interventions can be group therapy or individual counseling, remote support through telephone or online platforms, educational programs focusing on improving social skills or facilitating occasions for social engagement. Additionally, even in the absence of medical care needs, hiring a home caregiver can combat social isolation for older adults. To target interventions to the older adults, aged ≥65 years, in density areas such as urban areas at most risk for social isolation, community health practitioners need to conduct regular screenings. Those who screen positive for social isolation based on zip code levels should be referred for services to address the negative physical and mental health outcomes that come with being socially isolated. Interventions need to be identified or developed that target the preferences of older adults. There is no singular intervention that will meet the needs of everyone in this population. Options include individual or group therapy activities based on interests, race and ethnicity, gender, etc., which would require additional research to test their efficacy.