Exploratory and spatial analysis of disability among older Asian Indians

Applied Geography 113 (2019) 102099

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Exploratory and spatial analysis of disability among older Asian Indians Andy Sharma University of Maryland at College Park, Institute for Governmental Service and Research, 4321 Hartwick Road, College Park, MD, 20742, USA

A R T I C L E I N F O

A B S T R A C T

Keywords: Population health Older minority adults Disability Functional limitations Minority health Asian indians

Asian Indians are becoming a larger share of the total U.S. population and represent nearly 20% of the Asian American subgroup. However, they are often understudied in disability research. To overcome this gap, the present paper utilized the 2012–2016 American Community Survey to conduct an exploratory and spatial analysis of disability for older Asian Indians (i.e., 60 þ years of age). Results from the logit analyses revealed an increased likelihood for any disability at older ages with an odds ratio of 1.08. Meanwhile, male [OR 0.63–0.81, 95% CI], currently married [OR 0.66–0.88, 95% CI], Medicare recipients [OR 0.39–0.56, 95% CI], individuals with private insurance [OR 0.42–0.58, 95% CI], and those with higher levels of education exhibited a reduced probability for having any disability. Subsequent regression analyses with state-level variables (i.e., California, Illinois, New Jersey, and New York) resulted in similar estimates. The analyses with metropolitan-level variables revealed the Illinois region (Chicago-Naperville area) exhibited a greater likelihood while the northern region of California (San Jose-Sunnyvale-Santa Clara and Oakland-Hayward areas) exhibited a lower likelihood for any disability. An additional semi-nonparametric model, which relaxed the assumption of a logistic distribution of the error terms, produced similar results. Further exploratory spatial analyses were conducted for the two sta­ tistically significant areas: Illinois and northern California. Results showed a high concentration of disability in the Bloomingdale, Schaumburg, Wayne, and Winfield Townships (Chicago-Naperville metropolitan division) and in Fremont and Union Cities (San Jose-Sunnyvale-Santa Clara and Oakland-Hayward areas). Results from this study can better inform community health workers about the socio-economic factors related to disability and which areas to target for health and wellness interventions to improve functional mobility.

1. Introduction Asian Indians in the United States (U.S.) are one of the fastestgrowing minority groups and will become the largest immigrant group in the nation by 2050 (Pew, 2017A, 2017B). As with other immigrant and minority subgroups, Asian Indians also present health challenges and research in this area can assist public health experts as the U.S. older adult population becomes larger and more diverse. Although Asian In­ dians have many attributes associated with better health (i.e., college educated, high median income, health service access), they do not necessarily maintain a robust profile and may have poorer outcomes relative to other immigrant groups (Hastings et al., 2015; Ngo-Metzger, Legedza, & Phillips, 2004). For example, a well-established body of research has documented a greater occurrence of cardiovascular disease and diabetes for Asian Indians with dyslipidemia and abdominal fat deposition as key contributors (Hastings et al., 2015; Islam et al., 2013; Misra & Shrivastava, 2013; O’Keefe, DiNicolantonio, Patil, Helzberg, & Lavie, 2016). A higher incidence of mortality from strokes has also been

observed among older Asian Indians despite lower rates of alcohol and tobacco use relative to other immigrant subgroups (Hastings et al., 2015; Qureshi et al., 2014; Wu & Blazer, 2015). Other scholars have investi­ gated mental health and found high rates for anxiety and depression among older Asian Indians (Methikalam, Wang, Slaney, & Yeung, 2015; Ritenour, Rodriguez, & Wilson-Frederick, 2017). Although health scholars continue to offer greater insight into the well-being of Asian Indians, the literature has been sparse with respect to functional limi­ tations and disability (Islam et al., 2010; Mutchler, Prakash, & Burr, 2007; Sharma, 2018; Singh & Lin, 2013). This literature has also esti­ mated disability by examining Asian American subgroups relative to one another. That is, comparing disability status in relation to Chinese, Filipino, Japanese, or non-Hispanic White (i.e., omitted or reference group). Lastly, most of the existing research has not accounted for the geographic distribution of this population. Therefore, the purpose of this study was to overcome such a gap by investigating the demographic, economic, health insurance, and immigrant attributes associated with disability for older Asian Indians by accounting for the geographic

E-mail address: [email protected]. https://doi.org/10.1016/j.apgeog.2019.102099 Received 6 December 2018; Received in revised form 24 September 2019; Accepted 16 October 2019 Available online 19 November 2019 0143-6228/© 2019 Elsevier Ltd. All rights reserved.

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Applied Geography 113 (2019) 102099

concentration of this subgroup in California, Illinois, New Jersey, and New York.

were constructed for two large groups: (a) children under 18 and (b) adults 18 and older. Another limitation with previous studies on Asian Indians and disability status relates to better capturing the full range of socioeconomic factors, particularly education. SES has been found to play an important role in minority health in the U.S. and older adults with high SES have experienced lower levels of disability (Markides & Gerst, 2011). As such, models which encompass the full range of education, income, and poverty status offer better insight into functional mobility. Given the important role of higher education in the Asian Indian cul­ tural/value system, models which delineate education into greater cat­ egories can help public health scholars better understand the education gradient for health outcomes (Yee, DeBaryshe, Yuen, Kim, & McCubbin, 2007). However, previous studies have modelled education with a limited number of categories (i.e., less than high school, high school, and college) or as a continuous variable (Fuller-Thomson, Brennenstuhl, & Hurd, 2011; Huang et al., 2011; Mutchler et al., 2007; Singh & Lin, 2013). This present study overcomes this limitation by extending edu­ cation to include college as well as a professional degree while including health insurance status employing both a standard logistic and a more advanced semi-nonparametric model.

1.1. Importance of examining disability Disability status, much like cognitive decline, cardiovascular disease, diabetes, and other health-related conditions due to aging, is an important aspect of wellness. And as the U.S. older adult population continues to become more diverse, improving population health re­ quires greater awareness of physical well-being. To expound, older adults with disabilities and physical limitations maintain a lower quality of life (Musalek & Kirchengast, 2017; Szanton et al., 2011). Disabled older adults are unable to participate in physical activities which require dexterity or strength. Golf, tennis, and other recreational sports may not be played. Older adults with limitations are also restricted in social ac­ tivities, such as dancing or group walking (Rosso, Taylor, Tabb, & Michael, 2013). Furthermore, older adults with limitations are at higher risk for entry into long-term care and will require some type of care­ giving (Kaye, Harrington, & LaPlante, 2010; Sharma, 2017, 2018). And the costs associated with care can be excessive (Dai, Roberto, Tom, Gentry, & Stuart, 2017). For individuals reaching age 65 in 2015–2019 with chronic disability, the average expenditure for long-term services and supports approximated $73,000 per year (Favreault & Dey, 2015). By understanding which characteristics are associated with disability for older Asian Indians, community health professionals can devise in­ terventions which improve the mobility and quality of life for this fast-growing group and, possibly, delay entry into long-term care. A longer period with minimal disability may also defer some of the high costs associated with formal caregiving.

2. Methods 2.1. Study population and area U.S. census estimates show the Asian alone population increased from 10.2 million to nearly 14.7 million from 2000 to 2010 (Hoeffel, Rastogi, Kim, & Hasan, 2012; Pew 2017A, pp. 2017–2019). By 2020, the Asian population will approach 26 million and the growth of the Asian alone population will approximate 115% from 2013 to 2050 while Af­ rican American will estimate 40% and Hispanic (all races) will be nearly 87%. Asian Indians accounted for a good part of the Asian population growth and comprise one of the largest subgroups at approximately 20% of the total (Table 1). From 2010 to 2016, the Asian Indian population increased by 30% and approximated four million (Pew 2017A, B, pp. 2017–2019). Based on current U.S. Census projections, the Asian Indian population will continue to grow rapidly for the next decade. Although the Asian Indian population has increased across the nation during the past two decades, they continue to be geographically concentrated in a few areas. Nearly 50% of the total population resides in four states scattered across the country: California, Illinois, New Jersey, and New York (Pew 2017A, B, pp. 2017–2019).

1.2. Previous research To date, the literature on Asian Indian disability status has been limited due to small samples sizes in health surveys (Islam et al., 2010). To overcome this, most scholars have utilized the decennial census. However, these studies have largely been comparative in nature (i.e., examining disability by referring to another subgroup). For example, an early study by Cho and Hummer (2001) employed the Integrated Public Use Microdata Series (IPUMS) from the 1990 U.S. Census to better un­ derstand disability differentials across 15 Asian American groups. Although a comprehensive analysis, the models compared disability relative to a reference subgroup. As such, understanding the association between different demographic and economic characteristics and func­ tional mobility for only Asian Indians was limited because conclusions were drawn by referring to Japanese Americans. While an important contribution, this study has limited applicability today. Besides being outdated, it did not control for various types of health insurance because these variables were not present in the dataset. Mutchler et al. (2007) employed the 5% IPUMS from the 2000 U.S. Census to better understand the demography of disability for older Asians. Although this time period covered growth of the Asian American population, these scholars also compared disability relative to non-Hispanic Whites. Additionally, they were unable to account for health insurance. Health insurance status serves an important variable because studies suggest it is associated with a lower risk for disability for older adults (August & Sorkin, 2010; Dunlop, Song, Manheim, Daviglus, & Chang, 2007; Miller, Kirk, Kaiser, & Glos, 2014). Singh and Lin (2013) employed the 2008–2010 ACS (i.e., three-year ACS) to estimate both disability and health insurance rates for non-Hispanic Blacks, non-Hispanic Whites, Mexicans, Puerto Ricans, Cubans, Pacific Islander, and 12 Asian American subgroups. Although their analyses utilized more recent data, they did not control for health insurance when estimating disability status and kept the two, separate models (i.e., [1] disability status and [2] health insurance) identical in terms of the independent variables. Additionally, any conclusions about disability were drawn by referring to non-Hispanic Whites (i.e., omitted group). Since the focus of their study was not on older adults, the models

Table 1 Asian Indian population in U.S. by Year.

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Applied Geography 113 (2019) 102099

2.2. Data

2.5. Dependent variable

This study utilized the 2012–2016 American Community Survey, which is also referred to as the five-year integrated public-use microdata or IPUMS ACS sample (Ruggles, Genadek, Goeken, Grover, & Sobek, 2018). The ACS is a major project of the U.S. Census Bureau and has been in use since 2005. It is an annual survey and provides a wealth of demographic, economic, and health information about the national population. The five-year ACS data offered advantages relevant to this research. First, a large sample size (i.e., 5-in-100 national random sample) with high response and coverage rates (i.e., more than 95%). Second, self-reported data about racial/ethnic identity and ancestry. Third, publicly available geographic identifiers and geographies smaller than the state-level. Lastly, a robust set of disability indicators.

The ACS measures three types of disabilities: mobility, self-care, and physical difficulty. Mobility refers to difficulty performing independent living tasks/basic activities outside the home; self-care applies to health conditions which impact bathing, dressing, or moving inside the home; and physical corresponds to conditions which substantially limit lifting, climbing stairs, or walking. For these measures, the ACS asks re­ spondents to report only if the condition has existed for at least six months. Given that many respondents report having more than one type of disability, these measures were re-coded into a binary variable where the presence of any disability was denoted as ¼ 1 and absence of any disability as ¼ 0. This also permitted a larger sample size for the any disability group. 2.6. Independent variables

2.3. Sample selection by ancestry

The independent variables corresponded to (a) demographic char­ acteristics, (b) economic factors, (c) types of health insurance, and (d) immigrant attributes. Demographic variables included age, gender with female ¼ 0 and male ¼ 1, currently married ¼ 1, family-size, number of children, and education. Education was re-categorized as less than high school, high school, some college, college, and professional degree with high school as the reference group. Economic attributes were labor force participation (as yes/no), total family income (measured in U.S. dollars), and poverty status. The poverty status was available as a digit code and the ACS formulated it by examining total income for the person and then dividing this dollar amount by the appropriate poverty threshold from the previous year. This value was then multiplied by 100 to obtain the status. Types of health insurance coverage involved Medicare and pri­ vate health insurance. Medicare was health care coverage for adults at least 65 years of age and private insurance was a health care plan offered through employment or obtained by the individual. Medicaid, which offers health insurance for individuals with disabilities, children, and families below the poverty line, was examined. However, it was not added in the final model due to a very small number of Asian Indians reporting use (i.e., fewer than 50 observations for many states). Lastly, immigrant attributes related to (a) the number of years the individual resided in the U.S., (b) citizenship status where not a U.S. citizen ¼ 1, and (c) limited ability to speak English. This last variable was recoded where ¼ 1 indicated speaking ability as “not well” or “not at all” and ¼ 0 as “very well” or “well.” The variables for demographic, economic, health, and immigrant attributes were selected based on current and previous research (Cho & Hummer, 2001; Fuller-Thomson et al., 2011; Huang et al., 2011; Melvin, Hummer, Elo, & Mehta, 2014; Mutchler et al., 2007; Sharma, 2018).

Although race can be used to identify Asian Indians, this research utilized ancestry. Ancestry refers to “a person’s ethnic origin or descent, ‘roots,’ or heritage, or the place of birth of the person or the person’s parents or ancestors before their arrival in the United States” (Britting­ ham & De la Cruz, 2004). There were three reasons why ancestry was selected: (1) it permits local and state health care agencies to tailor programs based on language and culture, which may not be accurately reflected by race, (2) ancestry can be used to determine which subgroups receive medical services under the Public Health Service Act, and (3) questions about ancestry may be easier for respondents to understand and self-report as compared to questions on ethnicity (Cohn, 2015). After selecting cases based on ancestry, the initial study sample con­ sisted of 18,634 Asian Indians at least 60 years of age. After removing institutionalized individuals (n ¼ 875), the sample reduced to 17,759. After removing individuals reporting non-applicable or N/A for citi­ zenship status (n ¼ 356), N/A for education (n ¼ 1,096), and residence outside the U.S. for the previous year (n ¼ 575), the final study sample consisted of 15,732 individuals (weighted n ¼ 325,315). 2.4. Sample selection by state and region Utilizing the two-digit Federal Information Processing Standards (FIPS) code, the following states were selected for the state-level anal­ ysis: California, Illinois, New Jersey, and New York. As mentioned under the Study population and area section, these states maintain a large population of Asian Indians (Pew 2017A, B, pp. 2017–2019). While a state-level analysis can provide greater insight into disability, geographic concentration of this subgroup in large, urban centers prompted a more detailed analysis at the metropolitan statistical area (MSA) and metropolitan division level.1 This was accomplished by uti­ lizing Public Use Microdata Areas (PUMA) for the above-mentioned states. PUMA is a geographic unit and represents places with at least 100,000 residents. PUMAs do not cross state boundaries and are the smallest geographic level available in the public microdata. By aggre­ gating select PUMAs, the following MSA and metropolitan divisions were approximated: San Jose-Sunnyvale-Santa Clara and Oakland-Hayward MSAs for Northern California and Los Angeles-Long Beach-Santa Ana MSA for Southern California (two areas given Cal­ ifornia’s large size); Chicago-Naperville metropolitan division for Illi­ nois; Newark metropolitan division for New Jersey; and New York City (primarily Brooklyn) for New York (Jones, 2012; Pew 2017A, B, pp. 2017–2019; see Appendix for further explanation). PUMA was also the feature class for the additional spatial analysis.

2.7. Analyses The main analyses consisted of descriptive and inferential statistics employing logit regression. For the regression framework, the logit model was well-suited given the binary nature of the dependent vari­ able. This was represented with the following equations: Y* ¼ Xβ þ ε � 1 if ðY * > 0Þ Y¼ 0 otherwise

(1) (2)

Y* signified the latent function, which was determined by a vector of exogenous variables, X, and the error term, ε. Although Y* was latent, it could be related from Y, which was the observed counterpart. Addi­ tionally, ε was assumed to follow a standardized logistic distribution. � 1; any disability More specifically, values for Y ¼ while the matrix Xβ 0; no disability denoted demographic, economic, types of health insurance, and immi­ grant attributes. This model was first formulated at the national-level

1 MSA are defined by the U.S. Office of Management and Budget (OMB) and consist of contiguous urban clusters of high population density with substantial economic integration.

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(Model #1). It was then extended to include state-level variables. That is, Model #2 added dummy variables to represent each of the four states with a high number of older Asian Indians: California, Illinois, New Jersey, and New York. Model #3 built upon this by adding indicator variables at the MSA level. That is, dummy variables to represent metropolitan statistical areas and metropolitan divisions with a high number of older Asian Indians. As previously stated, nearly 50% of the Asian Indian population resides in large urban centers throughout Cal­ ifornia, Illinois, New Jersey, and New York. Given the exploratory na­ ture of this study, the logit analysis was supplemented with a seminonparametric (SNP) regression with Model #4. The SNP framework relaxed the assumption of a standardized logistic distribution for the error terms and offered the possibility of obtaining consistent estimates (see Appendix for further explanation). To ensure proper design-based analyses, all three survey components were utilized: cluster and strata for proper variance estimation and person weights to account for sam­ pling. All analyses were performed using Stata version 15 (StataCorp., 2017). The spatial analysis of disability consisted of (a) choropleth map and (b) hot spot analysis employing optimized Getis-Ord Gi* using ArcGIS 10.6 Pro (ESRI, 2018). For the choropleth map, standard deviation (SD) was used as to minimize subjectivity because the categorizes were based on deviations from the disability mean as opposed to any artificial data categorization such as equal intervals, quantiles, or manual intervals. The choropleth map was utilized to show variation in disability across the MSA while optimized Getis-Ord Gi* was employed to measure spatial clustering by including the value of the location when comparing nearby features. That is, Gi* was used to detect patterns in spatial data (Ord, 1995). This measure provided an index of spatial autocorrelation to discern high or low concentrations for disability. It was calculated as: Pn P X n wi;j j¼1 wi;j xj ffiffiffiffiffiffiffiffiffij¼1 ffiffiffiffiffiffiffi� ffiffiffiffiffiiffiffi G*i ¼ sffihffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi� (3) 2 P P n

S

n

n

w2 j¼1 i;j

j¼1

where xj was the attribute value for feature j (or the disability count by PUMA), wi;j was the spatial weight between i and j, and n was the total number of features. A positive Gi* provides evidence to support High-High clustering while a negative Gi* provides evidence to support Low-Low clustering. As a measure, Gi* is quite versatile and has been applied in a wide range of fields: agricultural economics, environmental studies, land use and development, public health, and transportation studies. For this study, the maps pertain to Illinois and northern California. With respect to the Illinois analysis (Chicago-Naperville division), 53 valid features were evaluated with an optimal fixed distance band with peak clustering at 29,268 m. This is important to note because the spatial analysis required at least 30 PUMA features to produce reliable estimates (Mitchell, 2005). Another consideration when using Gi* was the application of the false discovery rate (FDR) correction to deal with multiple testing and spatial dependency. This advanced procedure identified significant clusters while preserving a low number of false positives. By employing this procedure, the aspect of spatial dependency which exists for local pattern analysis was not artificially inflated (Coustasse, Bae, Arvidson, & Singh, 2008). For the Illinois analysis, no outliers were identified and 23 output features were statistically sig­ nificant based on the FDR correction. For the California analysis (San Jose-Sunnyvale-Santa Clara and Oakland-Hayward MSAs), 33 valid features were evaluated with an optimal fixed distance band with peak clustering at 13,238 m. This analysis also applied the false discovery rate (FDR) correction to deal with multiple testing and spatial dependency. No outliers were identified and two output features were statistically significant based on the FDR correction. 3. Results 3.1. Sample characteristics Table 2 provides mean descriptive measures for the no disability and any disability groups, as well as the total sample. The overall theme can

wi;j

n 1

Table 2 Mean characteristics for no disability and any disability for total sample for older Asian Indians. Variables

No disability

Any disability

Total sample

Mean

Mean

Mean

SD

Any disability Demographic Age Male Female Currently married Family-size Number of children at home Less than high school education High school Some college College Professional degree Economic In labor force Family total income Poverty status Health insurance Medicare Private health insurance Immigrant attributes Not a U.S. citizen Number of years in U.S. Limited ability to speak English State California Illinois New Jersey New York

0

1

18%

39%

67.75 56% 44% 81% 3.20 0.54 16% 13% 13% 25% 34%

73.62 38% 62% 60% 3.84 0.72 39% 16% 12% 17% 16%

68.82 53% 47% 77% 3.31 0.57 20% 13% 13% 24% 30%

7.04 50% 50% 42% 1.84 0.70 40% 34% 33% 43% 46%

44% $ 148,097 390

10% $ 122,322 334

37% $143,430.90 380

48% $ 149,805.50 151

22% 61%

22% 34%

22% 56%

41% 50%

18% 4.44 6%

29% 4.85 6%

20% 4.52 6%

40% 11.32 24%

19% 6% 10% 15%

23% 8% 9% 17%

20% 7% 10% 15%

40% 25% 30% 36%

N¼

13,096

2,636

15,732

4

Min.

Max.

60

95

1 0

18 7

$ (13,788) 1

$ 2,186,200 501

0

91

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Applied Geography 113 (2019) 102099

be stated as a larger percentage of Asian Indians with a disability were nearly six years older, female, not currently married, maintained less than high school education, did not have private medical insurance, and were not U.S. citizens while those without a disability reflected a higher percentage of currently married, in the labor force, and having earned a college or professional degree. One interesting observation was both groups approximated 4.5 years in the U.S. Another noteworthy char­ acteristic was no significant difference in limited ability to speak English (6% for each group). As for the total sample, the mean age was 69 years, nearly equal proportion men/women, 77% currently married, over half with a college or professional degree, total family income of $143,000, and 20% for not having status as a U.S citizen. Lastly, 20% resided in California, 7% in Illinois, 10% in New Jersey, and 15% in New York. That is, nearly 52% of the total older Asian Indian population was concentrated in these four states. Table 3 provides mean descriptive measures by state. Illinois had the highest disability at 22% while New Jersey had the lowest at 16%. Il­ linois also had the highest percentage of college or professional degree while New York had the lowest (58% vs 42%, respectively) and the highest percentage for private medical insurance while California had the lowest (60% vs 51%, respectively). At 15%, Illinois also had the lowest amount for not having status as a U.S. citizen while California had the highest at 24%. Although differences existed between states, there were also similarities. For example, the mean age approximated 69 years, approximately half were men/women, nearly 76% were currently married, approximately one-third remained in the labor force, and the average number of years in the U.S. was just under five years. Lastly, 30% resided in the San Jose-Sunnyvale-Santa Clara and OaklandHayward MSAs and 29% resided in the Los Angeles-Long Beach-Santa Ana MSA for California; 85% resided in the Chicago-Naperville division for Illinois; 81% resided in the Newark division for New Jersey; and 66% resided in the New York City area for New York.

Table 3 Mean characteristics for any disability for older Asian Indians by the four states. Variables Any disability Demographic Age Male Female Currently married Family-size Number of children at home Less than high school education High school Some college College Professional degree Economic In labor force Family total income Poverty status Health insurance Medicare Private health insurance Immigrant attributes Not a U.S. citizen Number of years in U.S. Limited ability to speak English Metropolitan areas San JoseSunnyvale-Santa Clara and OaklandHayward (CA) Los Angeles-Long Beach-Santa Ana (CA) Chicago-Naperville (IL) Newark (NJ) New York City Area (NY) N¼

3.2. Regression and spatial analyses Table 4 provides odds ratio estimates for demographic, economic, types of health insurance, and immigrant attributes from the logit regression. With an estimate of 1.08 (p < .01) in Model #1 (far left col­ umn), each unit increase in age was associated with a higher probability for any disability. Meanwhile, male (0.72, p < .01) and currently married (0.76, p < .01) reflected a lower odds ratio. A greater likelihood was also found for number of children at home with an odds ratio of 1.31 (p < .01). Relative to the high school reference group, an education gradient seemed present: 1.22 (p < .05) for less than high school education, 0.73 (p < .01) for college education, and 0.62 (p < .01) for professional degree. That is, estimates suggested a higher likelihood for those with lower levels of ed­ ucation and a lower likelihood for those with higher levels of education. Participation in the labor force was associated with lower odds (0.34, p < .01). No association with practical significance was found for the poverty status variable. With an estimate of 0.47 (p < .01) for Medicare and 0.50 (p < .01) for private health insurance, the types of health insur­ ance were associated with a decreased probability for disability. Model #2 (middle column) extended Model #1 by adding state-level variables. Overall, the estimates remained robust and the same conclu­ sions could be drawn. With respect to the state variables, California estimated 1.05, Illinois was 1.51, New Jersey was 0.92, and New York was 1.07. However, only Illinois was significant with p < .01. As such, older Asian Indians in Illinois reflected greater odds for any disability. Model #3 (last column) extended Model #2 by adding MSA variables. Once again, the estimates remained stable in size and significance. For California, the San Jose-Sunnyvale-Santa Clara and Oakland-Hayward MSAs estimated 0.74 (p < .05) while the Los Angeles-Long BeachSanta Ana MSA estimated 1.11. As such, older Asian Indians in the northern region of California reflected lower odds for any disability. For Illinois, the Chicago-Naperville division approximated 1.45 (p < .01). As with the previous model, older Asian Indians in this large urban center

California

Illinois

New Jersey

New York

Mean

Mean

Mean

Mean

21%

22%

16%

20%

69.35 52% 48% 76% 3.66 0.65

68.96 50% 50% 79% 3.32 0.55

68.74 52% 48% 78% 3.52 0.65

68.29 53% 47% 76% 3.31 0.66

23%

17%

20%

27%

13% 13% 25% 26%

10% 14% 26% 32%

13% 13% 27% 27%

19% 13% 19% 23%

32% $ 148,136 375

37% $ 149,727 394

39% $ 151,948 398

39% $ 132,320 361

22% 51%

19% 60%

20% 58%

19% 54%

24% 4.64

15% 4.75

21% 4.68

19% 4.30

6%

9%

7%

5%

30%

29% 85% 81% 2,994

1,068

1,664

66% 2,358

reflected greater odds for any disability. For New Jersey, the Newark division estimated 0.89 but was not significant. Lastly, the New York City area for New York approximated 0.98 but was not significant. Table 5 provides estimates from the SNP analysis. Since this formu­ lation does not model log-odds, an odds ratio is not possible (i.e., the estimates are not exponentiated). The same estimates continued to remain significant. For example, age now estimated 0.06 (p < 0.01). Meanwhile, male approximated 0.23 (p < .01) and currently married estimated 0.18 (p < .01). The education variables again suggested the presence of a gradient: compared to the high school reference group, less than high school approximated 0.14, college was nearly 0.23, and professional degree equaled 0.39. Once again, no association with practical significance was found for poverty status. However, the health insurance variables remained practically and statistically significant. As for the MSA variables, San Jose-Sunnyvale-Santa Clara and OaklandHayward MSAs now approximated 0.14 (p < .10) while ChicagoNaperville estimated 0.23 (p < .01). In sum, the logit and SNP resulted in similar conclusions. Spatial analyses were conducted for the two statistically significant areas: Illinois and northern California. Fig. 1, Panel A shows disability was concentrated north and west of downtown Chicago, wherein these areas were at least 1.5 SD above zero (i.e., at least 43% above the mean). With a 2.5 SD above zero, DuPage County Northwest and Cook County North far 5

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Applied Geography 113 (2019) 102099

Table 4 Logit estimates for any disability for all three models: (1) National only, (2) National with the four states, and (3) National with region specific for older Asian Indians. Variables

Demographic Age Male Currently married Family-size Number of children at home Less than high school education Some college College Professional degree Economic In labor force Family total income Poverty status Health insurance Medicare Private health insurance Immigrant attributes Not a U.S. citizen Number of years in U.S. Limited ability to speak English State California

(1) National

(3) National with Metropolitan-level

1.084*** (0.005) 0.715*** (0.045) 0.762*** (0.056) 0.978 (0.023) 1.312*** (0.080) 1.217** (0.118) 1.013 (0.109) 0.726*** (0.071) 0.624*** (0.065)

1.085*** (0.005) 0.716*** (0.045) 0.759*** (0.056) 0.977 (0.023) 1.313*** (0.080) 1.219** (0.118) 1.009 (0.110) 0.723*** (0.072) 0.622*** (0.065)

1.085*** (0.005) 0.717*** (0.045) 0.759*** (0.056) 0.978 (0.023) 1.320*** (0.080) 1.221** (0.118) 1.013 (0.110) 0.727*** (0.071) 0.621*** (0.065)

Male

0.341*** (0.034) 1.000 (0.000) 1.000* (0.000)

0.342*** (0.034) 1.000 (0.000) 1.000* (0.000)

0.340*** (0.034) 1.000 (0.000) 1.000* (0.000)

Poverty status

0.466*** (0.041) 0.495*** (0.040)

0.470*** (0.042) 0.497*** (0.040)

0.468*** (0.041) 0.494*** (0.040)

Immigrant attributes Not a U.S. citizen

0.992 (0.090) 1.002 (0.003) 1.009 (0.139)

1.001 (0.091) 1.002 (0.003) 0.995 (0.137)

1.013 (0.092) 1.002 (0.003) 0.994 (0.137)

Limited ability to speak English

Demographic Age

Currently married Family-size Number of children at home Less than high school education Some college College Professional degree Economic In labor force

Health insurance Medicare Private health insurance

Number of years in U.S.

Metropolitan areas San Jose-Sunnyvale-Santa Clara and OaklandHayward (CA) Los Angeles-Long Beach-Santa Ana (CA) Chicago-Naperville (IL)

1.048 (0.089) 1.509*** (0.193) 0.918 (0.095) 1.071 (0.098)

New Jersey New York Metropolitan areas San Jose-Sunnyvale-Santa Clara and OaklandHayward (CA) Los Angeles-Long BeachSanta Ana (CA) Chicago-Naperville (IL)

Newark (NJ) New York City Area (NY) Constant V1

0.743** (0.098)

Newark (NJ) New York City Area (NY)

Observations Log likelihood AIC BIC

Variables

(2) National with Statelevel

Illinois

Constant

Table 5 SNP estimates for any disability: (4) National with region specific for older Asian Indians.

0.008*** (0.004) 15,732 5749 11,536 11,674

0.008*** (0.003) 15,732 5741 11,526 11,695

V2 V3

1.108 (0.146) 1.453*** (0.197) 0.885 (0.098) 0.984 (0.103) 0.008*** (0.004) 15,732 5740 11,528 11,704

V4 Observations Log likelihood AIC BIC

(4) National with Metropolitanlevel 0.060*** (0.003) 0.230*** (0.040) 0.184*** (0.042) 0.029** (0.013) 0.197*** (0.035) 0.140*** (0.052) 0.010 (0.062) 0.232*** (0.058) 0.394*** (0.063) 0.985*** (0.149) 0.000*** (0.000) 0.513*** (0.053) 0.537*** (0.052) 0.022 (0.045) 0.001 (0.002) 0.057 (0.086) 0.137* (0.074) 1.071 (0.074) 0.234*** (0.070) 0.048 (0.067) 0.001 (0.058) 2.733 0.518*** (0.150) 0.050 (0.147) 0.204*** (0.029) 0.041* (0.023) 15,732 5730 11,511 11,702

Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.10. Reference group: High school education. Family total income omitted due to failure to converge.

exceeded the mean. The spatial analyses also revealed areas that were at least 31% below the mean. These areas were nearly 35,000 m outside Chicago proper (e.g., Will County, Cook County southeast and Cook County south central). Panel B shows results from the optimized Getis-Ord Gi*. Clusters of high disability (denoted in red) were north and west of Chicago while clusters of low disability (denoted in blue) were south and

Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.10. Reference group: High school education.

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east of Chicago. Specifically, Bloomingdale, Wayne, and Winfield Town­ ships in DuPage County and Schaumburg Township in Cook County Northwest were identified as hot spots for clustering at a 99% confidence interval. Fig. 2, Panel A shows disability was concentrated in Fremont City and Union City, wherein these areas were at least 1.5 SD above zero (i.e., at least 43% above the mean). Panel B shows results from the optimized Getis-Ord Gi*. Clusters of high disability (denoted in red) were north of San Jose. Specifically, Alameda County Southwest and Alameda County South Central were identified as hot spots for clustering at a 99% confidence interval. Please see state maps for Illinois (Figure 1: Panel C) and California (Figure 2: Panel C) in the Appendix.

as one continuous variable (Mehta, Sudharsanan, & Elo, 2013; Whitfield, 2013). For example, Coustasse, Bae, Arvidson, and Singh (2008) found lower levels of disability for higher-educated Asian older adults but categorized education into two broad groups: (a) less than high school and (b) high school or above. Meanwhile, Fuller-Thomson et al. (2011) modelled education as a continuous variable and did not find a statisti­ cally significant effect for Asian Indians. Given that many Asian Indians have specialized and professional degrees, categorizing education into distinct levels may offer additional insight into the disability and health gradient (Zimmerman, Woolf, & Haley, 2015, pp. 347–384). This is particularly important given that Lee (2011) uncovered differential ef­ fects by education for Asians with respect to disability status. The present study also found evidence to support this. For example, individuals with less than high school education had 20% increased probability for any disability while those with a college education approximated 25% decreased probability and those with a professional degree reflected nearly 35% decreased probability for any disability. Given such robust findings, additional research with the older Asian Indian population may provide insight into some of the mechanisms (e.g., ability to better cope, more effective management of life transitions) associated with a longer disability-free state due to higher levels of education (Bengtsson & Gupta, 2017). Future research utilizing a qualitative approach may provide insight into how older Asian Indians with advanced education view wellness and manage functional limitations.

4. Discussion 4.1. General overview This exploratory research is among a small group to examine disability for one of fastest-growing minority populations in the U.S. by utilizing the most recent five-year ACS. This study found 18% of older Asian Indians suffered from any disability and additional years were consequential for developing physical, mobility, and self-care limita­ tions [1.07 OR for logit and 0.06 for SNP]. For older adults, disability status can adversely impact the ability to walk and climb stairs. Should these limitations result in greater time spent in sedentary behavior, then older adults will progressively become weaker and eventually encounter difficulty with independent living tasks, such as dressing and grooming (Dunlop et al., 2015). Another noteworthy finding was a higher per­ centage of older Asian Indians with conditions which substantially limit mobility and independent tasks were not currently married. While 81% of those without any disability were currently married, this estimate reduced to 60% for those with a disability. Given the important role of spousal support in caregiving, these individuals will receive less assis­ tance and support from a primary caregiver (Grossman & Webb, 2016). For older Asian Indians with limited family involvement and a small social support system, the inability to manage limitations can also result in frailty and a continued decline in overall physical and mental health (Cornwell & Laumann, 2015; Diwan, 2008). Spousal assistance with tasks and the division of labor within the household may delay the onset of limitations. This was evident with the logit [0.76 OR] and SNP [-0.18] analyses for the currently married estimate. That is, a reduced likelihood of any disability for those in a marital state. Health insurance status was practically and statistically significant in both the logit and SNP analyses. Older Asian Indians with private health insurance were less likely to have any disability. This also applied for those receiving Medicare. More specifically, older individuals with health insurance coverage reflected nearly a 40% decreased probability for any disability. This finding can be useful for improving population health and quality of life outcomes for older adults. Based on a special report from the American Association of Retired Persons or AARP regarding the health care needs of older Asian Americans and Pacific Islanders (Montenegro, 2015), Asian Indians have low levels of health insurance coverage when compared to other Asian subgroups. Older Asian Indians with limited or no coverage may also rely upon home treatments and remedies. This lack of adequate coverage could exacer­ bate disability and place older adults at greater risk for developing other health conditions (Kail, 2015; Porell & Miltiades, 2001). While studies have found mixed quantitative results with respect to health insurance status and health, many studies offer qualitative consistency with respect to a positive relation between the two (Hadley, 2003; Levy & Meltzer, 2008). As such, finding ways to increase health insurance coverage for a group with already low rates can promote better long-term health. Another notable finding was the presence of an education gradient. In all models, older Asian Indians with higher levels of education had a much lower likelihood of having any disability. Although other scholars have observed this pattern, some have not been able to delineate edu­ cation into multiple categories while others have formulated education

4.2. Geographic considerations Asian Indians represent a large share of the Asian American popu­ lation. However, they are geographically concentrated in select areas throughout the U.S. Nearly 50% of the total population resides in large metropolitan centers scattered across the nation: New York City, Chi­ cago, San Jose, and Los Angeles. When this was accounted by utilizing PUMA, the analyses uncovered important patterns. For one, the overall estimate for California (Model #2) suggested the possibility of a higher likelihood for any disability. However, the MSA level analysis (Model #3) revealed San Jose-Sunnyvale-Santa Clara and Oakland-Hayward exhibited a reduced likelihood while Los Angeles-Long Beach-Santa Ana may exhibit a greater likelihood for any disability. This was an interesting finding because one area was associated with greater odds while another area within the same state reflected lower odds even after accounting for demographic, economic, health insurance, and immi­ grant attributes. Another example was Illinois. The MSA level analysis continued to suggest much greater odds for disability for the ChicagoNaperville area. The New York analysis is also worth mentioning briefly. The state level analysis suggested a higher likelihood but the MSA level analysis revealed the possibility of a slightly reduced likeli­ hood. Both the California and New York results warrant greater inquiry. 4.3. Role for public health professionals Asian Indians remain as distinct subgroups within their own com­ munities due to different customs and traditions (Kalavar & Van Willigen, 2005; Misra, 2012, pp. 226–230). These characteristics, combined with a strong preference to retain cultural practices and traditions, could adversely impact participation in the health care system (Coustasse, Bae, Arvidson, & Singh, 2008; Jang, Yoon, Park, Rhee, & Chiriboga, 2018). In such instances, community health care professionals can play a vital role by reaching disabled older Asian Indians with limited social capital (Hooper & Groves, 2017). This research has the potential to assist com­ munity health professionals by providing PUMAs with disability clusters because such a pattern was different from those which depict the overall spatial distribution of the Asian Indian population in Illinois. Utilizing the 2010 Census, Wei (2015) conducted a demographic and spatial analysis of various Asian American groups in the Chicago area. The dot density and graduated colors maps revealed a high concentration of Asian In­ dians in Buffalo Grove, Hoffman Estates, Schaumburg, Lincolnwood, 7

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Skokie, and Devon Avenue. Except for Schaumburg, this present analysis found disability clusters in other areas. Specifically, (a) Bloomingdale, (b) Wayne, and (c) Winfield Townships in DuPage County Northwest represent areas where a sizeable portion of Asian Indians with a disability reside. As Fig. 1: Panel A shows, these areas were at least 2.5 SD above zero while Panel B shows high clustering in DuPage County. This infor­ mation can be utilized by local agencies to develop targeted interventions which address mobility issues for this older age group. This also applies for Northern California. Although Asian Indians in the San Jose-Sunnyvale-Santa Clara and Oakland-Hayward MSAs had lower odds for any disability, that does not translate to this region being disability-free. Specifically, (a) Union City and (b) Fremont City repre­ sented areas that were at least 1.5 SD above zero with clustering in Alameda County (Fig. 2: Panels A and B). Community health pro­ fessionals can target specific neighborhoods in these counties with the goal of improving physical functioning. As with Illinois, these disability clusters in California do not entirely overlap with areas that have historically maintained a high spatial concentration of Asian Indians (e.g., San Francisco proper or San Mateo).

to a lack of health measures. Having information about body mass index (BMI), number of chronic conditions, depression, alcohol consumption, and tobacco use would enrich the models by accounting for important covariates associated with personal and self-care limitations. Although Sharma (2018) found only a weak association between BMI and func­ tional limitations, measures which account for health characteristics would offer confirmatory support into how demographic, economic, and health attributes impact mobility for an understudied population. Third, the motivation for this exploratory study was to address gaps in Asian American health and disability. This study did not attempt to model a causal relationship and should not be interpreted in such a manner. 5. Conclusion This study examined associations between demographic, economic, health insurance, and immigrant attributes with disabilities for older Asian Indians in the U.S. and in California, Illinois, New Jersey, and New York. One insight was the strong negative association between health insurance and disability. That is, older Asian Indians with Medicare or private coverage had a lower probability for any disability. If improve­ ments are to be made in population health, then ensuring older Asian Indians obtain Medicare is a practical policy objective since this group has a high uninsured rate (Montenegro, 2015; Moy, Greenberg, & Borsky, 2008). Another insight was the presence of a strong education gradient in all four models. Irrespective of geographic location, older Asian Indians with higher levels of education reflected a reduced probability for any

4.4. Limitations This study has some limitations. As with most surveys, the disability measures were self-reported. Although the ACS defines disabilities as conditions which last six months or longer, there may be over or under reporting and this could bias the results. Another limitation corresponded

Fig. 1. Panel A. Disability for Chicago-Naperville metropolitan division. The map represents hot–cold rendering where RED denotes areas of high disability while BLUE denotes very low. The mean disability was 410 (range of 5 to 2139). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) Panel B. Hot/cold rendering of disability clusters for Chicago-Naperville metropolitan division. The map represents hot spot analysis where RED denotes areas of high disability while BLUE denotes very low. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

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Applied Geography 113 (2019) 102099

Fig. 2. Panel A. Disability for San Jose-Sunnyvale-Santa Clara and Oakland-Hayward metropolitan region. The map represents hot–cold rendering where RED denotes areas of high disability while BLUE denotes very low. The mean disability was 547 (range of 0–2469). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) Panel B. Hot/cold rendering of disability clusters for San Jose-Sunnyvale-Santa Clara and Oakland-Hayward metropolitan region. The map represents hot spot analysis where RED denotes areas of high disability while BLUE denotes very low. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

disability. As previously mentioned, why education has such a positive impact requires greater inquiry. Another interesting finding was the minimal to no association of the poverty status. While public health scholars have observed a positive relationship between poverty and worse health for minority populations, this was not apparent in the present study. Finally, number of years in U.S. (i.e., duration) and English proficiency were not statistically or practically significant. This was a surprising finding given a well-established body of literature suggesting otherwise. Perhaps, these factors play a smaller role because Asian In­ dians families may undertake a more active group role when discussing health care needs with providers. As Bhattacharya and Shibusawa (2009) note, community health and social workers will need to involve family members when engaging with treatment plans for older Asian Indians. This study also offered spatial insight into where Asian Indians with a disability reside. Community health workers can initiate interventions to maintain and improve mobility near Bloomingdale, Wayne, and

Winfield townships. These findings can also be utilized by the Illinois Department of Aging in fulfilling its mission of helping older adults live independently. Finally, this study provided new directions for future research. The geographic distribution of disability requires further exploration and one promising extension is a mixed-methods analysis of how other contextual factors (e.g., housing and neighborhood charac­ teristics) impact Asian Indian health and functional limitations. In this case, future research could examine how older adults interact with space and how geography can promote (or hamper) mobility and other healthrelated quality of life outcomes (Hall & Wilton, 2017). Acknowledgment I would like to thank three anonymous reviewers for helpful com­ ments. I would also like to thank Dr. Bagchi-Sen for offering suggestions.

Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.apgeog.2019.102099. Appendix 1. Further explanation regarding the formulation of MSAs from PUMAs The MSAs and metropolitan divisions were determined by examining the distribution of the Asian Indian population by state and PUMA from the 2012–2016 ACS data. That is, obtaining counts for the total number of older Asian Indians for each state and then ranking the list of PUMAs from high to low. Next, these values were compared with other studies which focused on the geographic distribution of Asian Indians in the U.S. For example, Bhardwaj and Rao (1990, pp. 197–217) found Asians Indians were primarily concentrated in major functional urban regions in [1] California, [2] Illinois, [3] New York, and [4] New Jersey (i.e., Queens, Newark, Chicago, and West Coast MSAs). These findings parallel another study which found a high concentration of Asian Indians in Queens County, NY, Cook County, IL, and Santa Clara County, CA (Berry, Berry, & Henderson, 2002). A more recent study commissioned by the Office of Economic Development of San Jose, CA provided additional basis for the MSAs used in this present analysis. The Population Dynamics Research Group in the Sol Price School of Public Policy at the University of Southern California analyzed past and current census data and found Asian Americans represented less than 8% of the racial composition in Santa Clara County in 1980 (Pitkin & Myers, 9

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2015). By 2010, this estimate increased to 33% and the number of Asian Indians increased from 70,159 to 117,596 from the previous decade (Springer, 2011). The Asian American population is projected to reach 40% by 2030 with Asian Indians becoming the largest subgroup. Another important point to consider when formulating MSAs and metropolitan divisions pertains to which boundaries to include. While the Chicago-Naperville division was straightforward because it is a relatively large and well-defined area and one which encompassed a large portion of the older Asian Indian population in Illinois, the areas in northern California required a better understanding of current population dynamics. For example, the Oakland-Hayward MSA was selected for two main reasons: (1) the number of Asian Indians in this area was relatively large and (2) Asian Indians are not gravitating towards traditional gateway cities, such as New York or San Francisco, due to shifting preferences since 2000 (Li, Skop, & Yu, 2007, pp. 222–236; Pitkin & Myers, 2015). The MSA selection for northern California was also supported by drawing upon (a) previous studies which identified areas with a high concentration of Asian Indians, (b) the study commissioned by the Office of Economic Development at San Jose, (c) demographic profiles from Zip Atlas, and (d) analysis of the 2012–2016 ACS (Berry et al., 2002; Pitkin & Myers, 2015; Zip Atlas, 2019). According to recent population profiles from Zip Atlas (© 2019 ZipAtlas.Com), Fremont ranks #3, Union City ranks #7, and Hayward ranks #39 among all major cities in California in terms of the Asian Indian population (Zip Atlas, 2019). Another MSA which has become a prime residential choice for Asian Indians is San Jose-Sunnyvale-Santa Clara. It has experienced tremendous growth during the past decade and state demographers expect this trend will continue until 2040 (Simonson, 2015). Projections from the Population Dynamics Research Group in the Sol Price School of Public Policy at the University of Southern California show Asian Americans represented less than 8% of the racial composition in Santa Clara County in 1980 but this increased to 33% in 2010. It is expected to reach 40% by 2030. Additionally, Asian Indians will replace Chinese as the largest subgroup. This increase can be attributed to the area having stable communities, easy work commutes, and a good public infrastructure. Additionally, this MSA has a very high number of residents employed in software development and programming relative to other California MSAs. It also boasts a vibrant professional, scientific, and technical services industry, attributes which are attractive to Asian Indians given their education in engineering-oriented fields (DataUSA, 2018). According to Zip Atlas, Sunnyvale ranks #5, Santa Clara #8, and San Jose #31 for the Asian Indian population in California. 2. Further explanation regarding the SNP framework When modelling binary outcomes, researchers typically employ a logit or probit framework. However, these models assume the error terms follow a specified distribution, which is a strong assumption (i.e., standardized logistic for logit and Gaussian for probit). If violated, the estimators will be inconsistent (Cosslett, 1983; Klein & Spady, 1993). As initially proposed by Gallant and Nychka (1987) and then extended by De Luca (2008), a semi-nonparametric or SNP framework can be employed to model the unknown distribution of the error term. Instead of following the logistic or Gaussian distribution, the error term can be approximated with Hermite polynomial expansions. SNP was employed to model disability (Model #4) using an order of three, four, and five (i.e., Hermite expansions). An order equal to two produces the same estimates as probit. Specification tests showed only order ¼ 4 fulfilled the likelihood ratio (LR) test where SNP was preferred over probit (χ 2 ¼ 41.15, p < .01) with the variance approxi­ mating close to the preferred value of three and the skewness approximating close to the preferred value of zero. Additionally, the SNP coefficients for the orders were significant (V1 ¼ 0.51, p < .01; V2 ¼ 0.05, p ¼ .74; V3 ¼ 0.20, p < .01; V4 ¼ 0.04, p < .10).

Fig. 1. Panel C. Overview of spatial analysis area denoted by red circle. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

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Fig. 2. Panel C. Overview of spatial analysis area denoted by red circle. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

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