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Risk for Disability and Poverty 

Among Central Asians in the 

United States 
 
 

 

Carlos Siordia1 & Athena K. 

Ramos2 

1Center for Aging and Population Studies, 

Graduate School of Public Health, 

University of Pittsburgh, PA; 2Center for 

Reducing Health Disparities, College of 

Public Health, University of Nebraska, NE 

 

 

 

 

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DOI 10.5195/cajgh.2015.220   |   http://cajgh.pitt.edu 

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SIORDIA 

 

 

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Central Asian Journal of Global Health 

Volume 4, No. 2 (2015)  |  ISSN   |  DOI 10.5195/cajgh.2015.220  |  http://cajgh.pitt.edu 

  

 

Abstract 

Understanding the disability-poverty relationship among minority groups within the United States (US) populations may help 

inform interventions aimed at reducing health disparities. Limited information exists on risk factors for disability and poverty 

among “Central Asians” (immigrants born in Kazakhstan, Uzbekistan, and other Central Asian regions of the former Soviet Union) 

in the US. The current cross-sectional analysis used information on 6,820 Central Asians to identify risk factors for disability and 

poverty. Data from the 2009-2013 Public Use Microdata Sample (PUMS) file from the American Community Survey (ACS) 

indicate that being married, non-Latino-white, and having higher levels of educational attainment are protective against disability 

and poverty. In contrast, older age, residing in the Middle Atlantic geographic division, and having limited English language ability 

are risk factors for both disability and poverty. Research should continue to develop risk profiles for understudied immigrant 

populations. Expanding knowledge on the well-being of Central Asians in the US may help impact public health interventions and 

inform health policies. 

Keywords: disability, poverty, American Community Survey, Public Use Microdata Sample, former USSR, 

Central Asia 

 

 
Risk for Disability and Poverty 
Among Central Asians in the United 
States 

Carlos Siordia1 & Athena K. Ramos2 
 

1Center for Aging and Population Studies, 
Graduate School of Public Health, University 
of Pittsburgh, PA; 2Center for Reducing 
Health Disparities, College of Public Health, 
University of Nebraska, NE 
 

Research 

In the United States (US) and across the world, 

having the ability to overcome psycho-social and 

physical barriers for independent living can influence 

quality of life.1 The differently abled (i.e., disabled) and 

economically deprived (i.e., poor) are at greater risk for 

adverse health.2 Although low-income minorities should 

not be defined as being “trapped in an inescapable cycle 

of poverty,”3 it should be noted that both the physically 

and economically disadvantaged individuals do face 

unique challenges for resisting exposures that may lead 

to disability.4 Unfortunately, the relationship between 

disability and poverty (i.e., the disability-poverty nexus) 

remains under-researched among smaller immigrant 

groups.5-7 This investigation fills a gap in the disability-

poverty nexus literature by focusing on the Central Asian 

population of the US.  

Although research on the disability-poverty 

nexus on minorities has been conducted,8-11 it is difficult 

to find peer-reviewed publications in scientific journals 

that focus on the group that may be called the Central 

Asian population of the US. Although explained more 

technically below, “Central Asians” in this investigation 

were selected based on place of birth. The US Census 

Bureau only defines “South Central Asians.”12 This is the 

first publication to define Central Asians as immigrants 

who report being born in Kazakhstan, Uzbekistan, former 

USSR, or other South Central Asia, not specified. The 

specific aim of this investigation was to identify risk 

factors for disability and poverty in the Central Asian 

immigrant population in the US. 

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Methods 

Data  

This cross-sectional analysis used data from the 

American Community Survey (ACS), 5-year (2009-

2013) Public Use Microdata Sample (PUMS) file. Data 

from the ACS is important for public health research 

because it may be used to influence policy aimed at 

providing services for disabled individuals. For example, 

ACS data influenced the distribution of US federal 

dollars to local governments in 2008 by affecting the 

distribution of $562.2 billion in grant funds.13 The use of 

publicly available ACS data does not require institutional 

review board approval. The same data source has been 

used before to delineate the prevalence of disability and 

poverty in other minority and immigrant populations in 

the US.14-16 ACS PUMS data may be one of the few data 

sources that has the capacity to provide information on 

the demographic, geographic, and health profiles of the 

Central Asian population in the US.  

Central Asians 

The sample of 6,820 Central Asians was 

selected using Place of Birth (POB). The US Census 

Bureau only provides an official definition of South 

Central Asia which includes places like India and Iran.12 

The analysis labeled US immigrants born in Kazakhstan, 

former USSR, Uzbekistan, and other South Central Asia, 

not specified as “Central Asians.” Although this data 

source does not allow researchers to identify immigrants 

who report being born in places like Kyrgyzstan, 

Tajikistan, and Turkmenistan, immigrants who were 

coded under the “other South Central Asia, not specified” 

category where included as Central Asians in the 

analysis. After extensive research, this appears to be the 

first analysis defining Central Asians as individuals born 

in Kazakhstan, former USSR, Uzbekistan, and other 

South Central Asia, not specified while using ACS data. 

While this grouping scheme is imperfect, limitations of 

the data source do not allow analysis by specific country 

of origin.17 As with people born in Kazakhstan, 

Uzbekistan, Kyrgyzstan, Tajikistan, and Turkmenistan, 

identifying immigrants in the US from recently 

transformed regions of the world is difficult.18 For 

simplicity, the selected sample is referred to as Central 

Asians. In addition to POB, individuals were only 

included in the sample if they resided within the 

contiguous US.  

Disability 

Central Asians were labeled as “disabled” if 

they reported a ‘yes’ to one or more of the following 

questions: Is this person deaf or does he/she have serious 

difficulty hearing?; Is this person blind or does he/she 

have serious difficulty seeing even when wearing 

glasses?; Because of a physical, mental, or emotional 

condition, does this person have serious difficulty 

concentrating, remembering, or making decisions?; Does 

this person have serious difficulty walking or climbing 

stairs?; Does this person have difficulty dressing or 

bathing?; Because of a physical, mental, or emotional 

condition, does this person have difficulty doing errands 

alone such as visiting a doctor’s office or shopping? 

Limitations and benefits of using these questions have 

been discussed in detail previously;2,8 however, 

responses to these questions may be utilized to identify 

perceived ability to perform physical functional tasks in 

daily living.9 It is important to note subjectively assessed 

physical function faces measurement bias because study 

participants are asked to self-evaluate their health and 

rely on their memory for reporting the data. In addition, 

in the ACS, the majority of individuals are assigned their 

disability status through a proxy-report.16 Despite these 

limitations, the ACS is the most reliable and largest data 

source for delineating the disability of hard-to-reach 

populations in the US.  

 

 

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SIORDIA 

 

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Poverty  

The ACS PUMS file contains the “income-to-

poverty ratio” (IPR) measure. The IPR shows at what 

‘level of poverty’ an individual is—e.g., being at or 

below a 100 IPR indicates that this person is in-poverty 

status. For example, in fiscal year 2013, the poverty 

threshold for a family of four was $23,550. Thus, all 

members of a family with a total household income of 

$23,550 would be assigned an IPR of 100 and be coded 

as being “in-poverty.” If the family reports a total 

household income of $11,775, each family member 

would be assigned an IPR of 50 and be coded as being in 

“deep-poverty.” If a family reports a total household 

income of $35,325, then each family member would be 

assigned an IPR of 150 and be coded as being “near-

poverty.” In this analysis, a person is classified to be in-

poverty if they have an IPR of 150 or below—an 

approach used to account for the fact that federal poverty 

thresholds do not account for geographical heterogeneity 

in cost-of-living. 

Statistical approach 

All data management and analysis was 

conducted using SAS® 9.3 software. Although 

population-weights are provided in the data, they were 

not used because generalizing information from 6,820 

individuals to about 149,473 Central Asian immigrants 

would result in population estimates with very large 

standard errors. That is, the 95% confidence limits 

around the population estimates would be large enough 

to render population-weighted estimates uninformative. 

Descriptive statistics for the analytic sample are 

presented. Non-population-weighted multivariable 

logistic regressions were used to model the likelihood of 

being disabled or in-poverty while adjusting for age, sex, 

marital status, race, ethnicity, naturalization status, 

ability to speak English, educational attainment, and 

geographic division of residence. Variables were coded 

using common approaches in socio-epidemiological 

research on poverty and disability.  

 

Results 

Participant characteristics 

Our sample was 54% female, 59% married, 

83% non-Latino-white, 60% US citizens (95.6% by 

naturalization, 4.4% by being born to parents with US 

citizenship), 18% lacked health insurance coverage, and 

24% were unable to speak English ‘well’ or ‘at all’ (Table 

1). Participants in this sample were aged 40.6±19.9, 36% 

reported high school education or below, and the 

majority (36%) of participants resided in the Middle 

Atlantic geographic division of the US. About 13% of the 

sample reported a disability and about 30% may be 

classified as being in-poverty (i.e., IPR < 150). The low 

level of disability in the sample may be partially 

explained by the plausibility that the selection process 

influences who emigrates from Central Asia 

 

Table 1. Demographics of 6,820 Central Asians living 

within the US 

 

Risk factors for disability 

Several factors were identified as risk factors for 

disability. For example, naturalized citizens were twice 

as likely to be disabled compared to those who report not 

being a citizen or naturalized (Table 2). Those who spoke 

English not well or not at all were over three times as 

likely to be disabled as those who spoke English very 

well or well. Each increase in age (by year) was 

associated with a 7% increase in the likelihood of being 

disabled. In addition, residing in the Middle Atlantic 

geographic division was associated with a 20% greater 

likelihood of being disabled than residing in other 

divisions. Education and marital status were associated 

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with lower odds of being disabled. For example, married 

Central Asians were 64% less likely to be disabled than 

non-married people. Each increase in educational 

attainment category was associated with a 7% decrease 

in the likelihood of being disabled.  

 

Table 2. Multivariable logistic regression predicting the 

likelihood of being disabled 

 

Risk factors for poverty 

The regression (Table 3) indicates that being 

married, non-Latino-white, and a naturalized citizen was 

associated with a 52%, 33%, and 56%, respectively, 

lower odds of being in-poverty. Each additional increase 

in educational attainment category was associated with a 

4% reduction in the likelihood of being in-poverty. In 

contrast, residing in the Middle Atlantic division reduced 

the odds of poverty by 16% while speaking English not 

well or not at all significantly increased the odds of 

poverty almost three-fold.  

 

Table 3. Multivariable logistic regression predicting 

likelihood of being in poverty 

 

Discussion 

High levels of educational attainment and being 

married were found to be consistently protective against 

disability and poverty. In contrast, older ages, residing in 

the Middle Atlantic geographic division, and speaking 

English not well or not at all were consistently found to 

be risk factors for disability and poverty. More complex 

is the fact that being a naturalized citizen is 

simultaneously a risk factor for disability and protective 

against being in-poverty. It may be said that a “Central 

Asian Immigrant Paradox” is found, where those at 

greater risk for economic disadvantage are also at lower 

risk for disability. This is paradoxical because for most 

non-immigrant groups, greater risk for poverty is 

frequently accompanied by greater risk for disability.19,20 

Future research should seek to disentangle plausible 

explanations for the Central Asian Immigrant Paradox.  

This study has some limitations including the 

inability to obtain and/or identify data from individuals 

from some of the countries within the region such as 

Kyrgyzstan, Tajikistan, and Turkmenistan as previously 

described. There is an inherent selection bias in 

participation in government administered surveys which 

may partially be affected by legal status. Furthermore, 

subjectively assessing disability through recall and self-

report may affect the validity of the results. It is possible 

that individuals born in Central Asia and who have the 

ability and the means to immigrate to the US come from 

family units with social and economic resources. The low 

prevalence of disability in the analytic sample may be 

partially explained by the fact that about two-thirds of the 

sample is below age 47 or due to ‘healthy immigrant 

selectivity.’9  

Objective measurements for disability are 

needed to supplement these results. Poverty is assessed 

using only economic measures. Additional measures for 

both poverty and health status are also needed to be able 

to assess the impact of disability on poverty and health 

status.  

The cross-sectional analysis provides empirical 

evidence on the understudied Central Asian population in 

the US. Findings from this analysis should be generalized 

with caution to the community-dwelling immigrants born 

in Kazakhstan, former USSR, Uzbekistan, other South 

Central Asia, not specified and who resided within the 

US mainland during the survey period. A multitude of 

challenges remain for intervening on the relationship 

between poverty and disability.21 Future research should 

be sensitive to poverty indicators focusing on practical 

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matters such as quantifying the risk for poverty among 

individuals related to someone with a disability.22 For 

immigrants residing in the US, like Central Asians, 

disability and poverty have the potential to become 

entangled with other forms of oppressions.23 Research 

should continue to explore if and how immigrants from 

Central Asia residing in the US retain their language, 

identity, health, family structures, and economic well-

being.   

 

Acknowledgement 

Supported by NIH grant number T32 

AG000181 and U01 AG023744 (AB Newman). 

 

References 

1. Dalal AK. Disability–poverty nexus psycho-social 

impediments to participatory development. Psychol Dev Soc. 

2010;22(2):409-437. 

2. Siordia C. Disability prevalence according to a class, race, 

and sex (CSR) hypothesis. J Racial Ethn Health Disparities. 

2015;2(3):303-310. 

3. Block P, Balcazar F, Keys C. From pathology to power 

rethinking race, poverty, and disability. J Disabil Policy Stud. 

2001;12(1):18-27. 

4. Braithwaite J, Mont D. Disability and poverty: A survey of 

World Bank poverty assessments and implications. ALTER. 

2009;3(3):219-232. 

5. Groce N, Kett M, Lang R, Trani JF. Disability and poverty: 

The need for a more nuanced understanding of implications for 

development policy and practice. Third World Quarterly. 

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6. Loeb M, Eide AH, Jelsma J, Toni MK, Maart S. Poverty and 

disability in eastern and western cape provinces, South Africa. 

Disabil Soc. 2008;23(4):311-321. 

7. Lamichhane K, Okubo T. The nexus between disability, 

education, and employment: Evidence from Nepal. Oxford 

Development Studies. 2014;42(3):439-453. 

8. Siordia C. Rates of allocation for disability items by mode 

in the American Community Survey. Issues in Social Science. 

2015;3(1):62-82. 

9. Siordia C. Sex-specific disability prevalence in immigrants 

from China, India, and Mexico and their US-born counterparts. 

IJHSR. 2015;5(4):267-279. 

10. Curtis KJ, Voss PR, Long DD. Spatial variation in poverty-

generating processes: Child poverty in the United States. Soc Sci 

Res. 2012;41(1):146-159. 

11. Siordia C. Disability estimates between same-sex and 

different-sex couples: Data from the 2009-2011 American 

Community Survey. Sex Disabil. 2015;33(1):107-121. 

12. Gryn T, Gambino C. The foreign born from Asia: 2011. 

American Community Survey Briefs. 2012. 

13. Reamer AD. Surveying for dollars: The role of the 

American Community Survey in the geographic distribution of 

federal funds. Washington D. C.: Metropolitan Policy Program at 

Brookings;2010. 

14. Siordia C, Le VD. Precision of disability estimates for 

Southeast Asians in American Community Survey 2008-2010 

microdata. CAJGH. 2013;1(2). 

15. Siordia C. A multilevel analysis of mobility disability in the 

United States population: Educational advantage diminishes as 

race-ethnicity poverty gap increases. Journal of Studies in Social 

Science. 2015;12(2):198-219. 

16. Siordia C. Proxy-reports in the ascertainment of disability 

prevalence with American Community Survey data. J Frailty 

Aging. 2014;3(4):238-246. 

17. Batalova J. Asian immigrants in the United States. 

Washington D. C.: Migration Policy Institute;2011. 

18. Mason PL, Matella A. Stigmatization and racial selection 

after September 11, 2001: Self-identity among Arab and Islamic 

Americans. IZA Journal of Migration. 2014;3:1-21. 

19. Palmer M. Disability and poverty: A conceptual review. J 

Disabil Policy Stud. 2011;21(4):210-218. 

20. Lustig DC, Strauser DR. Causal relationships between 

poverty and disability. Rehabilitation Counseling Bulletin. 

2007;50(4):194-202. 

21. Hansen H, Bourgois P, Drucker E. Pathologizing poverty: 

New forms of diagnosis, disability, and structural stigma under 

welfare reform. Soc Sci Med. 2014;103:76-83. 

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22. Fujiura GT. The political arithmetic of disability and the 

American family: A demographic perspective. Family Relations. 

2014;63(1):7-19. 

23. Goodley D. Dis/entangling critical disability studies. 

Disabil Soc. 2013;28(5):631-644. 

  

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Table 1. Demographics of 6,820 Central Asians living within the US 

Characteristic N (%) 

Disabled 884 (13) 

Female 3,667 (54) 

Married 3,990 (59) 

Non-Latino-White 5,630 (83) 

US citizen 4,089 (60) 

Has no health insurance coverage 1,204 (18) 

Speaks English not well/at all5 1,658 (24) 

  

Age group  

Age < 17 839 (12) 

Age 18-27 1,171 (17) 

Age 28-37 1,216 (18) 

Age 38-47 1,123 (16) 

Age 48-57 957 (14) 

Age > 58 1,514 (22) 

  

Education  

< 8th grade 1,026 (15) 

9th – 12th grade* 499 (7) 

High school  968 (14) 

Some College 1,319 (19) 

> Bachelor’s degree 3,008 (44) 

  

Poverty level  

Deep poverty (<50) 542 (8) 

In-poverty (51-100) 798 (12) 

Near poverty (101-150) 697 (10) 

Above near-poverty (151-200) 581 (9) 

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Out of poverty (>200) 4,202 (62) 

  

Location   

New England 448 (7) 

Middle Atlantic 2,453 (36) 

East North Central 571 (8) 

West North Central 203 (3) 

South Atlantic 950 (14) 

East South Central 97 (1) 

West South Central 295 (4) 

Mountain 431 (6) 

              Pacific 1,372 (20) 

  *Note. Participants in the 9th-12th grade education group did not complete high school. 

 

 

  

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Table 2. Multivariable logistic regression predicting the likelihood of being disabled  

  OR 95% CI 

Female 0.99 0.83, 1.18  

Married 0.37 0.30, 0.44 *** 

Non-Latino-White 0.84 0.62, 1.13  

Citizen or naturalized 2.01 1.60, 2.52 *** 

Speaks English not well/at all 3.25 2.67, 3.97 *** 

Age 1.07 1.07, 1.08 *** 

Education 0.93 0.92, 0.95 *** 

Resides in Middle Atlantic division 1.20 1.00, 1.43 * 

    *p<0.05,  **p<0.01,  ***p<0.001  

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Table 3. Multivariable logistic regression predicting likelihood of being in poverty  

  OR 95% CI 

Female 1.00 0.89, 1.12  

Married 0.48 0.42, 0.54 *** 

Non-Latino-White 0.67 0.57, 0.78 *** 

Citizen or naturalized 0.44 0.39, 0.50 *** 

Speaks English not well/at all 2.86 2.47, 3.30 *** 

Age 1.01 1.01, 1.02 *** 

Education 0.96 0.95, 0.97 *** 

Resides in Middle Atlantic division 1.16 1.03, 1.31 ** 

    *p<0.05,  **p<0.01,  ***p<0.001 

 

 

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