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Number of People in the United States Experiencing Ambulatory and Independent Living Difficulties Carlos Siordia
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Department of Epidemiology, Graduate School of Public Health , University of Pittsburgh , Pittsburgh , Pennsylvania , USA Accepted author version posted online: 09 May 2014.Published online: 08 Aug 2014.
Click for updates To cite this article: Carlos Siordia (2014) Number of People in the United States Experiencing Ambulatory and Independent Living Difficulties, Journal of Social Work in Disability & Rehabilitation, 13:3, 261-277, DOI: 10.1080/1536710X.2014.912187 To link to this article: http://dx.doi.org/10.1080/1536710X.2014.912187
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Journal of Social Work in Disability & Rehabilitation, 13:261–277, 2014 Copyright # Taylor & Francis Group, LLC ISSN: 1536-710X print=1536-7118 online DOI: 10.1080/1536710X.2014.912187
Number of People in the United States Experiencing Ambulatory and Independent Living Difficulties
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CARLOS SIORDIA Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
Estimating the characteristics of the ‘‘disabled’’ population is necessary for some governments and of interest to health researchers concerned with producing disability prevalence rates. Because generating easy-to-understand estimates of disability in the population is important, this article provides U.S. population estimates for two disability-related measures by using the 2009 to 2011 American Community Survey Public Use Microdata Sample file. The number of people who have ‘‘independent living’’ and ‘‘ambulatory’’ difficulties is calculated from a sample of 9,204,437 (representing >309 million people). The percentage for ‘‘disabled’’ is found to vary by racial and ethnic category, sex, age, citizenship status, educational attainment, and state-level regions divided by weather. KEYWORDS ambulatory difficulties, disability, elderly, ethnic minorities, gender equity, geographic information systems (GIS), independent living, people with disabilities
Differently abled (commonly referred to as ‘‘disabled’’; Wendell, 1989) U.S. residents are an underserved community whose physiological conditions present unique social and physical struggles—arising from the mismatch between their personal and social expectations and available resources in the environment. Some ‘‘able-bodied’’ people, by virtue of being ablebodied, lack an understanding of how small physical obstacles (e.g., lack Address correspondence to Carlos Siordia, National Research Science Award Postdoctoral Fellow, Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, 130 North Bellefield Ave., Pittsburgh, PA 15213, USA. E-mail: [email protected] 261
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of pedestrian ramps at street crossways) can present a noticeable challenge for the differently abled (Clarke & George, 2005). Even though it could be that definitions of disability vary by time and geography (Baynton, 2013), in a classical disablement process framework, having difficulties with walking up stairs or going shopping could be said to represent preclinical physiological limitations that could lead to the formation of clinical disability (Verbrugge & Jette, 1994). To be sure, the sociologically produced drive for physical sameness creates interest in understanding the characteristics of the differently abled population (Munyi, 2012). A view is taken throughout this article that disability is a problem that can only be situated in the interaction between disabled individuals and their social environment. From this perspective, addressing challenges faced by disabled individuals requires a psychosocial and physical adjustment on the part of society (Karger & Rose, 2013). This article is motivated by the perspective that resources for coping with physical limitations are nonrandomly (i.e., unevenly) distributed through a stratifying social system. Developing estimates on the prevalence of disability is intended to help federal, state, county, and local governments assess the impact of policies aimed at reducing discrimination against the differently abled and improving their participation in community activities (Brault, 2009). Some evidence exists that the unjust stigmatization of the differently abled (Bourke & Waite, 2013) can lead some of them to have lower levels of social participation (McPhedran, 2012). This point matters because arguments have been made that exposures over the life course can influence health outcomes in adulthood through a cumulative process (Blane, 1999). This approach views cumulative exposures to adverse social circumstances (e.g., poverty, discrimination) as being important risk factors for disease (Shoham et al., 2008). From this perspective, it can be argued that adverse health outcomes linked with the disabled status can be aggravated by the presence of other known risk factors. If you note that socioeconomic and racial or ethnic disparities in health are well documented (Arauz Boudreau, Kurowski, Gonzalez, Dimond, & Oreskovic, 2013), then you might be able to see why, for example, the effects on health (e.g., formation of excess adipose tissue) from not being able to walk could be compounded on by a person’s financial status (i.e., being ‘‘poor’’) or place of residence (e.g., living in a dangerous neighborhood where healthy food options are scarce). The specific aim of this article is to highlight how population estimates of disability prevalence vary in their precision in unique ways (Siordia, 2013b). Because data from the American Community Survey (ACS) can be argued to be the gold standard for producing reliable measures of disability in the general U.S. population, it has been deemed critical to the disability community. For example, local governments (e.g., states) use ACS data to decide how to distribute funds to local agencies for food, health care, and
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legal services for individuals with disabilities. If the best available data have the potential to under- or overestimate the prevalence of disability as a function of demographic characteristics, then there is a possibility that ‘‘multidisadvantaged’’ (e.g., disabled racial and ethnic minorities with moderate levels of educational attainment) groups might not be receiving the appropriate level of resources. Previous work has shown how data could be challenged if ‘‘selection bias’’ (Kleinbaum, Morgenstern, & Kupper, 1981), the selection of study participants by a third unmeasured variable believed to be associated with exposure and outcome, is suspect (Strandhagen et al., 2010). Please note that although ACS might have some limitations commonly found in population-based survey studies, an argument is not being made here that ACS data contain selection bias. In 2011 and at the national level, the ACS had a coverage rate of 98.6% and a ‘‘response rate’’ of 97.6%—numbers that could be argued to indicate there is a low probability of selection bias. Instead, the main point of this discussion is to advise researchers interested in estimating disability prevalence in the U.S. population to consider how precision of the estimates varies from group to group. Between-group comparisons is a particularly important point when you consider that research on ‘‘health disparities’’ continues to grow (e.g., Pollack et al., 2013). A related issue has been raised by others on how temporal comparisons (i.e., time dependent) of socioeconomic disparities between cross-sectional surveys might be affected when presumed variations on the meaning of a socioeconomic status are unaccounted for in statistical modeling (Chen, Beckfield, Waterman, & Krieger, 2013). Estimating the number of individuals within the U.S. population who might be facing uncommon social and physical challenges is important for assisting public health efforts toward developing assistance measures. U.S. government policies provide some protection for individuals with disability (Karger & Rose, 2013) and as a result, there is a mandate for U.S. federal agencies collecting information on the U.S. population to develop a count of individuals with disability. In particular, Title II of the Americans with Disabilities Act (ADA, 1990) requires government agencies to make services available to people with disabilities. In this study, two measures of disability are used to present a profile of the U.S. population. These could be said to be related to the popularly conceptualized and measured activities of daily living (Katz, 1983). Details of the survey questions used in the analysis are delineated later. The discussion here only focuses on the labels being used. Reported difficulties with independent living (e.g., ability to go shopping) are labeled as outdoor physical mobility (OutPM) and ambulatory (e.g., ability to walk) difficulties are referred to as indoor physical mobility (InPM). Both OutPM and InPM are treated as crude measures of potential capacity to function in the hypothetical tense (Glass, 1998). Because the items being used in the ACS
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to assess disability status are based on either self-report or proxy report (explained later), they are referred to as potential capacity—in contrast, enacted mobility refers to actual performance (Siordia, 2013a). OutPM and InPM subjectively assess whether a person perceives an ability to perform a task. As a consequence, the labels being used here only signal ‘‘potential for outdoor physical mobility’’ with the OutPM item and ‘‘potential for indoor physical mobility’’ with the InPM item. The report makes use of survey data derived from a U.S. population sample. A population sample is a subset of individuals from a complete population (i.e., the population universe). The need to generalize findings from a sample to the complete population from which they are drawn necessitates the use of random selection. Because simple random selection is so infrequently available, the use of complex sampling techniques produces the need to use intricate weighting methodologies to derive population estimates from samples (Siordia & Lee, 2013). Because the sample is much smaller than the population or universe, each study subject represents a certain number of his or her peers. The use of ‘‘weights’’ amplifies sample numbers to derive estimates of the population. I now pause to clarify the sometimes not so obvious meaning of a key term: estimates. Producing an estimate of disability means math is being used with intelligently gathered information to produce an informed guess of the number of people with x-type of disability (Siordia, 2013b). Inherent in this approach is the assumption that the informed guess (i.e., estimate) has a certain degree of uncertainty—a range of numbers where the ‘‘true’’ value is expected to exist. An estimate should not be seen as an inferior product with artificial significance. Instead, the reader should note that the ACS is the most reliable source of information for estimating disability prevalence in the U.S. population and that the estimates being presented here are among the most scientifically sophisticated measures available to nongovernment-affiliated researchers. The United States has more than 310 million people spread over more than 3.8 million square miles. Trying to gather data on this population to describe their characteristics is incredibly challenging and critical for the advancement of democratic governance. A discussion on the variability of precision in disability estimates should not distract the reader from the fact that the ACS is one of the most sophisticated products created by the U.S. Census Bureau. It is only because the ACS is so highly regarded, transparent to the public, and scientifically sound that a discussion on ‘‘estimate precision’’ is even possible—some small data sets purporting to estimate the prevalence of disability in the United States would not stand the scrutiny being presented here and as applied to ACS microdata. This article introduces the reader to the idea that U.S. population estimates of the disabled population vary in quality and as a function of person characteristics and geographical location. Although technical in nature, the
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article is meant to primarily provide detailed disability estimates by commonly used demographical characteristics. The specific aim of this article is to provide U.S. population estimates for OutPM and InPM. This aim is complemented by a discussion of various easy-to-understand ‘‘measures on uncertainty’’ in the estimations of OutPM and InPM for the U.S. population during the period from 2009 to 2011.
METHODS
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Data Disability in the U.S. population is measured by taking a ‘‘randomly selected sample’’ from the population. As alluded to earlier, a sample is a subgroup derived from the population and randomly selected refers to a very important principle in inferential statistics. Randomly selected means that a person was selected from a population of individuals where every person had an equal chance for selection. This is important because subjects in the sample are used to infer the characteristics of the population they were drawn from— inferences are made by using ‘‘population weights,’’ which assume a particular selection process. Because the ACS is not a simple random sample, population weights are variables said to account for the design of the study, the sampling error in the study—that is, variation in the probability of being randomly selected at different points of the complex survey design (Siordia, 2014). This study used data from the ACS. In particular, the Public Use Microdata Sample (PUMS) 3-year file for 2009 to 2011 is used. Because ACS is collected yearly, the 3-year file being used here represents data collected over 36 months. The microdata are used internally by the U.S. Census Bureau to produce estimates for geographies (e.g., counties) with as few as 20,000 people. Data from the PUMS files only allow geo-referencing to the Public Use Microdata Area (PUMA) geography—in 2013, the ‘‘5-year’’ ACS file (data from 60 months) will provide ‘‘disability’’ data all the way down to the block-group geography. Please note that data issues arising from the misapplication of disclosure-avoidance procedures (Alexander, Davern, & Stevenson, 2010) are not present in the microdata being used in this study. This is the first publication showing disability estimates for the population aged 65 and over where the PUMS data files are not believed to be contaminated by flawed disclosure-avoidance procedures. The estimates in this article’s tables use a total of 9,204,437 actual survey participants. The use of population weights are then applied to the 9,204,437 individuals—the actual number of people participating in the survey and referred to hereafter as the unweighted sample. When ‘‘weighted up’’ (using the PWGTP variable in the microdata), the unweighted sample is said to represent the U.S.
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population, which for this time period is about 309,231,245 people (estimated total population of the United States during the 2009–2011 period).
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‘‘Disability’’ Variables Although there is information on six ‘‘disabilities’’ (label used by the U.S. Census Bureau), this project only focuses on ‘‘independent living difficulty’’ and ‘‘ambulatory difficulty.’’ Readers should note that only one person per household participated in the survey and is technically referred to as the ‘‘reference person.’’ The reference person answered all questions for individuals in the household. Thus, if the reference person is responding to disability items for herself or himself, then disability can be said to be self-reported. However, if the reference person is responding about the disability of others in the household, then disability can be said to be proxy reported. To assess independent living difficulty, survey respondents were asked about themselves and others in the household: ‘‘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?’’ (U.S. Census, 2011). Because of the way the questions are framed and the fact that individuals are only allowed to respond in a forced choice format (i.e., either yes or no), it may be argued that survey responses are approximate measures for outdoor physical mobility potential (i.e., OutPM). To assess ambulatory difficulty, survey respondents were asked about themselves and others in the household: ‘‘Does this person have serious difficulty walking or climbing stairs?’’ (U.S. Census Bureau, 2011). This item could also be argued to be an approximate measure for indoor physical mobility potential (i.e., InPM). Elegant work has been undertaken to show that responding to these questions is primarily challenged by the need to determine whether or not the severity of a particular functional limitation warranted a positive response (Miller & DeMaio, 2006). For example, with regard to the InPM, cognitive research showed that individuals considered physical limitation factors (e.g., pain, fatigue) when evaluating their ability to walk or climb stairs, but considered other domains (e.g., emotional status) when responding about others in the household. Because the InPM question does not include the word usually, it might fail to prompt the respondent to think of long-term physical conditions. Because respondents are not asked about the use of assistive devices, some might see the use of a device as granting mobility and might thus fail to report any difficulty (Miller & DeMaio, 2006). With regard to OutPM, responses to the question could vary because individuals might interpret ‘‘difficulty doing errands alone’’ differently. Some might interpret the question as probing for the person’s access to transportation resources, whereas others might interpret it as pertaining to mobility and mind capacity (Miller & DeMaio, 2006). In short, care should be given when
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comparing disability prevalence between studies using different forms of questions.
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Demographic Variables InPM and OutPM estimates are presented by various demographic factors. Because the U.S. Census Bureau is forthcoming with how they collect information on the U.S. population, documentation detailing the various labels under these racial and ethnic categories is readily available online. I made use of six race and ethnic categories: non-Latino-White (NLW), nonLatino-Black (NLB), non-Latino-other (NLO), Mexican-Latino (ML), and non-Mexican-Latino (NML). The U.S. Census Bureau conceptualizes race and ethnicity as a social construct—not a genetically determined and biologically defined phenotype. Estimates are also provided for the following: males and females; three age groups (age 49, age 50–64, and age > 65); three citizenship status categories (U.S. born, naturalized, and noncitizen); and educational attainment (associate’s degree). To complement these popular measures, estimates by ‘‘warmer South’’ and ‘‘colder North’’ states were created. The ‘‘average annual extreme minimum temperature from 1976–2005’’ data from the U.S. Department of Agriculture (USDA) is used to qualitatively identify the states where about 50% of their geographical area experienced temperatures 10 F (USDA, 2012). This arbitrary and visual approach renders the following as warmer South states: Alabama, Arizona, California, Florida, Georgia, Hawaii, Louisiana, Mississippi, New Mexico, Puerto Rico (not technically a state), South Carolina, and Texas. All the others make up the colder North states.
Statistical Approach Because the U.S. Census Bureau is so transparent about their methods for estimating population characteristics, I was able to make use of 80 person weights (PWGTP1–PWGTP80 variables) provided in the PUMS files to estimate various measures of uncertainty surrounding the estimate. Replicate weights are used to calculate what is referred to as direct standard errors—measures of imprecision for the 80 possible estimates. I developed an algorithm, in SAS 9.3, using the 80 person weight by following instructions outlined in U.S. Census Bureau publications (U.S. Census Bureau, 2009a, 2009b). The formula for calculating the replicate weights standard errors is as follows: rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 4 X80 ðXr X Þ2 SE ð X Þ ¼ r¼1 80 After the replicate estimates X1 through X80 are computed, the standard error (SE) of X is estimated using the sum squared differences between each
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replicate estimate of Xr and the full sample estimate X (U.S. Census Bureau, 2009b). A single-person weight (i.e., PWGTP) to compute ‘‘weighed’’ versus ‘‘unweighted’’ estimates is used. What is hoped to be a more intuitive representation of variability of precision is the estimate presented by computing the Person Inflation Ratio (PIR), which is the average number of people being represented in weighted population by the unweighted counts. It is computed using the following formula: [weighted count weighted total population] (Siordia & Lee, 2013). An increase in PIR indicates that each individual within that group represents more of their counterparts in the population. When PIR increases, it indicates that the characteristics of fewer individuals might affect the estimation of disability prevalence. ACS PUMS files allow public data users to calculate the margin of error (MOE) for each of the population estimates. To highlight how any estimates have a certain degree of uncertainty, I present MOEs for the estimates being produced. MOE is present when, for example, large variation between samples occurs—leading survey-based estimates to deviate from accurately approximating the true population value. These deviations from the true count can be roughly measured by computing the standard error (SE) of the estimate (Siordia & Lee, 2013). The MOE of the estimate with 90% confidence is calculated as follows: [MOE ¼ 1.645 SE]. MOEs are then used to provide readers with the ‘‘upper’’ and ‘‘lower’’ 90% confidence limits around the disability estimates—the range in which the ‘‘true’’ number could lie with a 10% chance for making an error. Confidence limits are computed with the following equations: Lower Confidence Limit [LCL ¼ (estimate – MOE)]; and Upper Confidence Limit [UCL ¼ (estimate þ MOE)]. A more easy-tounderstand measure called range of uncertainty (RU; Siordia & Lee, 2013) is also provided. RU compares the level of uncertainty in the disability estimate by using the following formula: f[(SE 3) X] 100g, where X is the disability estimate. By using this approach, readers can more straightforwardly interpret level of uncertainty as follows: As RU numbers increase, the level of imprecision in the estimate increases. There is a very important point that has not been mentioned until now: To provide complete data banks, the U.S. Census Bureau undertakes many widely accepted statistical procedures for insuring coherent responses and the reduction of missing items. Although the issue of ‘‘allocation’’ is explained in greater detail elsewhere (Siordia & Young, 2013), I briefly mention that both probability (using statistical techniques) and nonprobability (using explicit logic rules) based computer algorithms are used to assign responses to individuals missing a response to a disability survey item or providing what is deemed an illogical response to a disability survey item. Please note that the U.S. Census Bureau first attempts to have the survey filled via mail correspondence; if that fails, they move to computer-assisted
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telephone interviewing, and if that fails they then move to computer-assisted personal interviewing (Brault, 2009). In an attempt to reduce individual item nonresponse, the U.S. Census Bureau also conducts failed-edit follow-up and telephone questionnaire assistance operations (Brault, 2009). The effort made by the U.S. Census Bureau to produce accurate information is admirable. Readers should not be dissuaded from thinking allocation procedures produce unreliable estimates of disability with ACS data. Instead, they should note how even this arguably gold standard data source has limitations that should be kept in mind when producing prevalence rates. The large scale and high quality of data found in ACS makes it tolerable to publicly discuss esoteric nuances of imprecision in the data. In contrast, it is possible that some widely used studies on disability could not stand such public scrutiny without losing face validity. Because the U.S. Census Bureau is confident about their products, they provide public data users with allocation ‘‘flag’’ variables (variables that can help identify when an allocation is present for a particular response) in PUMS files indicating if a value was observed or allocated (Siordia, 2013b). These procedures are undertaken under the direction of the Federal Committee on Statistical Methodology (FCSM), which has informed researchers that survey accuracy encompasses both sampling error and a broad spectrum of non-sampling-related errors (e.g., item nonresponse; Office of Information and Regulatory Affairs, Statistical Policy Office, 2001). The weighted number of allocations and percent allocated are calculated with the following equation: [(weighted allocated count total weighted population) 100]. The goal of introducing this last measure is to inform the reader that even though the largest available sample with quasi-preclinical disability items for the United States is being used in this study, the estimates also have other forms of error that might bias estimates away from true value. This form of bias on the estimates might be statistically unquantifiable (Siordia & Young, 2013). Despite these serious issues, the disability ‘‘population estimates’’ provided here might be among the most reliable—notwithstanding the ambiguity of the survey question. Many other studies, using problematic sample selection designs that severely limit their generalizability, make use of a few thousand people from a small U.S. geographical region to provide what they argue is acceptable ‘‘population estimates of X-disability.’’ This study uses more than 9 million people and provides transparent estimates by framing them through a discourse that includes their SEs, MOEs, RUs, and allocations. Please note throughout the discussion of the findings that only qualitative comparisons between estimates are made. I do not conduct quantitative testing to determine if the differences between groups (e.g., NLWs vs. MLs) are statistically significant. I deliberately avoid conducting tests to determine ‘‘statistically significant differences’’ to reiterate that disability population estimates have a range of uncertainty.
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FINDINGS I first review the general distribution of the analytic sample presented in Table 1. Both weighted counts (using person weights) and unweighted counts (using actual number of observations) are presented in Table 1. Instead of reviewing all the numbers, a descriptive discussion on the PIR shown in Table 1 is used. As explained earlier, PIR represents the average number of individuals each of our study participants represents and is calculated by dividing the weighted count by the unweighted number. For example, the PIR for MLs is computed as follows: [32,862,818 810,696] ¼ 40.54 41. This means that on average, each actual ML in the microdata represents about 41 other MLs after population weights are used to generalize the sample characteristics to the whole of the U.S. population. From the racial and ethnic groups, a qualitative comparison shows MLs and NMLs have the highest PIR at 41, followed by NLB (40), NLO (35), and NLW (31). MLs and NMLs on average represent 47 other MLs and NMLs, respectively. From the demographic variables, we see that males, those at or below the age of 49, southern states, those with less than one year of college, and non-U.S. citizens have the highest PIR values. InPM estimates are presented in Table 2. The number of individuals having difficulties with independent living, by racial and ethnic groups, is TABLE 1 Weighted and Unweighted Estimates from the Analytic Sample
Race or Ethnicity Non-Latino White Non-Latino Black Non-Latino other Mexican-Latino Non-Mexican Latino Demographics Female Male Age 49 Age 50–64 Age 65 U.S.-born Naturalized Noncitizen 1 year of college Associate’s degree Region by weather States in ‘‘Warmer South’’ States in ‘‘Colder North’’ a
Weighteda
Unweightedb
PIRc
196,951,654 37,801,756 23,803,937 32,862,818 17,811,080
6,326,562 948,585 686,797 810,696 431,797
31 40 35 41 41
157,199,566 152,031,679 209,708,347 59,026,386 40,496,512 269,337,364 17,601,889 22,291,992 219,810,714 77,559,393
4,725,640 4,478,797 5,753,859 1,955,685 1,494,893 8,174,101 502,953 527,383 6,483,206 2,409,301
33 34 36 30 27 33 35 42 34 32
117,899,464 191,331,781
3,456,228 5,748,209
34 33
Counts using the PWGTP (American Community Survey [ACS] person-weight) variable. Counts not using the PWGTP (ACS person-weight) variable. c Person Inflation Ratio (PIR) ¼ (weighted count weighted total population). b
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5.24 5.67 3.49 2.52 3.66 5.70 3.82 1.81 5.90 18.50 4.89 5.89 2.50 5.73 2.80 4.67 4.84
10,320,245 2,144,344 829,597 829,096 652,141 8,965,244 5,810,179 3,800,629 3,483,927 7,490,867 13,181,926 1,037,180 556,317 12,603,537 2,171,886 5,510,591 9,264,832
%D
b
15,955 18,504
18,080 15,861 14,567 11,896 14,841 26,116 13,853 5,814 22,745 9,842
26,237 10,183 12,202 7,460 6,137
SE
c
26,426 30,439
29,742 26,091 23,963 19,569 24,413 42,961 22,788 9,564 37,416 16,190
43,160 16,752 20,072 12,272 10,095
MOE
d
5,484,165 9,234,393
8,935,502 5,784,088 3,776,666 3,464,358 7,466,454 13,138,965 1,014,392 546,753 12,566,121 2,155,696
10,277,085 2,127,592 809,525 816,824 642,046
LCLe
UCLf
5,537,017 9,295,271
8,994,986 5,836,270 3,824,592 3,503,496 7,515,280 13,224,887 1,059,968 565,881 12,640,953 2,188,076
10,363,405 2,161,096 849,669 841,368 662,236
Independent Living
0.87 0.60
0.61 0.82 1.15 1.02 0.59 0.59 4.01 3.14 0.54 1.36
0.76 1.42 4.41 2.70 2.82
RUg(%)
b
Weighted number of people reporting difficulty with independent living (i.e., ‘‘disabled’’). Percentage disabled (%D) ¼ [(weighted disabled count weighted total population) 100] (Note: Total population available in Table 1). c Standard error (SE). d Margin of error (MOE). e Lower Confidence Limit (LCL): Low limit of 90% confidence interval ¼ [Disable – MOE]. f Upper Confidence Limit (UCL): High limit of 90% confidence interval ¼ [Disable þ MOE]. g Range of uncertainty (RU) ¼ f[(SE 3) disabled] 100g. h Allocated: Number of responses to ‘‘independent living’’ survey item that are assigned or changed. i Percentage allocated (%A) ¼ [(weighted allocated count weighted total population) 100] (Note: Total population available in Table 1).
a
Race or ethnicity Non-Latino White Non-Latino Black Non-Latino other Mexican-Latino Non-Mexican Latino Demographics Female Male Age < 49 Age 50–64 Age 65 U.S.-born Naturalized Noncitizen 1 year of college Associate’s degree Region by weather States in ‘‘Warmer South’’ States in ‘‘Colder North’’
Disabled
a
3,221,485 4,611,647
3,931,006 3,902,126 4,546,873 1,816,562 1,469,697 6,546,526 674,055 612,551 6,146,747 1,686,385
4,590,608 1,322,643 685,740 716,010 518,131
Allocatedh
TABLE 2 Weighted Estimates, Their Margins of Error and Allocation Rates for Difficulty with ‘‘Independent Living’’ Survey Item
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2.73 2.41
2.50 2.57 2.17 3.08 3.63 2.43 3.83 2.75 2.80 2.17
2.33 3.50 2.88 2.18 2.91
%Ai
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as follows: NLWs ¼ 5.24%, NLBs ¼ 5.67%, NLOs ¼ 3.49%, MLs ¼ 2.52%, and NMLs ¼ 3.66%. As is evident from these population-relative percentages, NLBs have the highest concentration of independent living difficulties and MLs have the least. In absolute terms, NLWs have the largest number (weighted n ¼ 10,320,245) of individuals experiencing independent living difficulties. Please note that uncertainty in the InPM estimates increases in the following order: NLWs, NLBs, MLs, NMLs, and NLOs. In other words, the highest level of imprecision is located in NLOs. Survey item allocations increase in the following order: MLs, NLWs, NLOs, NLMs, and then NLBs. This means the largest number of allocations occurs in the NLB group. Females have more InPM difficulties than males—their estimate has a smaller RU and they have lower rates of allocation than males. RU is lowest among those aged 65 and up (18.50% have an InPM difficulty)—although their age group has the largest number of allocation rates compared to those at or below age 64. In terms of citizenship status, InPM estimates for the naturalized group show they have the highest level of InPM difficulties (5.89%), the largest level of uncertainty (RU ¼ 4.01%), and the largest percentage of allocation rates (3.83%). Those with one year or less of college education have the most InPM difficulties (5.73%), lowest RU (0.54%), and largest allocation rate (2.80%), compared to people with an associate’s degree or above. Colder North states have greater levels (4.84%) of people experiencing InPM difficulties—although estimates for the warmer South states have higher uncertainty and allocations associated with independent living difficulty measures. OutPM estimates are shown in Table 3. The estimates show that ambulatory difficulty, by racial and ethnic groups, is as follows: NLWs ¼ 7.52%, NLBs ¼ 8.35%, NLOs ¼ 4.42%, MLs ¼ 3.73%, and NMLs ¼ 5.05%. As with InPM, NLBs have the highest concentration of ambulatory difficulties and MLs have the least. In absolute terms, NLWs have the largest number (weighted n ¼ 14,808,770) of individuals with ambulatory difficulties by virtue of their population size in the United States. The uncertainty in the OutPM estimates increases as follows: NLWs, NLBs, MLs, NLOs, and NMLs. The highest level of imprecision is located in NMLs. Survey item allocations increase as follows: NLWs, MLs, NMLs, NLOs, and NLBs. This means the largest number of allocations is concentrated in NLBs. When compared to men, women have more (7.99%) difficulties with OutPM than men, although their estimates have a larger RU and slightly lower rate of allocation than men. More than one fourth (25.78%) of individuals aged 65 and over have difficulties with OutPM—their RUs are lower than those aged 64 and below, but their age group has the largest number of allocation rates. Noncitizens, with 3.31% having OutPM difficulties, have the largest level of uncertainty (RU ¼ 2.99%), and naturalized individuals have the largest percentage of allocation rates (4.04%). On OutPM, those with one year or less of college education have the highest rate at 8.04%, lowest RU (0.45%), and largest rate of allocation at 3.42%, when
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7.52 8.35 4.42 3.73 5.05 7.99 5.64 2.05 10.86 25.78 7.08 7.63 3.31 8.04 4.48 6.78 6.88
14,808,770 3,157,191 1,052,731 1,225,915 898,840 12,566,126 8,577,321 4,291,901 6,412,965 10,438,851 19,061,922 1,342,743 738,782 17,671,037 3,472,410 7,988,976 13,154,471
%D
b
31,613 20,799
34,073 17,259 14,931 23,299 23,435 33,954 10,691 7,363 26,742 23,269
27,955 13,817 7,884 7,463 9,294
SE
c
52,003 32,214
56,051 28,391 24,562 38,327 38,550 55,854 17,587 12,113 43,990 38,278
45,986 22,730 12,969 12,277 15,289
MOE
d
7,936,973 13,122,257
12,510,075 8,548,930 4,267,339 6,374,638 10,400,301 19,006,068 1,325,156 726,669 17,627,047 3,434,132
14,762,784 3,134,461 1,039,762 1,213,638 883,551
LCLe
Ambulatory
8,040,979 13,186,685
12,622,177 8,605,712 4,316,463 6,451,292 10,477,401 19,117,776 1,360,330 750,895 17,715,027 3,510,688
14,854,756 3,179,921 1,065,700 1,238,192 914,129
UCLf
1.19 0.47
0.81 0.60 1.04 1.09 0.67 0.53 2.39 2.99 0.45 2.01
0.57 1.31 2.25 1.83 3.10
RUg(%)
b
Weighted number of people reporting ambulatory difficulties (i.e., ‘‘disabled’’). Percentage disabled (%D) ¼ [(weighted disabled count weighted total population) 100] (Note: Total population available in Table 1). c Standard error (SE). d Margin of error (MOE). e Lower Confidence Limit (LCL): Low limit of 90% confidence interval ¼ [Disable – MOE]. f Upper Confidence Limit (UCL): High limit of 90% confidence interval ¼ [Disable þ MOE]. g Range of uncertainty (RU) ¼ f[(SE 3) disabled] 100g. h Allocated: Number of responses to ‘‘ambulatory’’ survey item that are assigned or changed. i Percentage allocated (%A) ¼ [(weighted allocated count weighted total population) 100] (Note: Total population available in Table 1).
a
Race or ethnicity Non-Latino White Non-Latino Black Non-Latino other Mexican-Latino Non-Mexican Latino Demographics Female Male Age < 49 Age 50–64 Age 65 U.S.-born Naturalized Noncitizen 1 year of college Associate’s degree Region by weather States in ‘‘Warmer South’’ States in ‘‘Colder North’’
Disabled
a
TABLE 3 Weighted Estimates, Their Margins of Error and Allocation Rates for Difficulty with ‘‘Ambulatory’’ Survey Item
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3,782,733 5,397,595
4,581,660 4,598,668 59,724 1,780,750 1,427,138 7,809,003 710,682 660,643 7,520,697 1,659,631
5,269,698 1,538,750 838,770 919,594 613,516
Allocatedh
3.21 2.82
2.91 3.02 0.03 3.02 3.52 2.90 4.04 2.96 3.42 2.14
2.68 4.07 3.52 2.80 3.44
%Ai
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compared to those with an associate’s degree or more. Warmer South states, with a 6.78% OutPM rate of difficulty in the population, have greater levels of uncertainty and allocations associated with ambulatory difficulty measures.
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CONCLUSIONS There are some limitations within this study. For those interested in ‘‘health disparities’’ (Horner-Johnson, Dobbertin, Lee, & Andresen, 2013; Reichard, Stolzle, & Fox, 2011), the between-group comparisons provided in the discussion are only qualitative—I do not claim any of the observed differences are statistically significant. Please note that some do compare estimates by observing whether their confidence intervals overlap to determine if statistically significant differences exist. Statistical significance might not always be relevant in the policy world (Ward, Greenhill, & Bakke, 2010). It could be that an unwarranted amount of attention is paid to finding statistically significant relationships, and too little attention is given to finding practical means of improving the structural systems affecting the disabled. Because stakeholders might want to see high-quality estimates, or acceptable estimates with complexity that is reduced to digestible interpretations, the tables in this report might provide such a product: where transparency and easy-to-follow measures highlight proportions of people with difficulties with InPM and OutPM in the U.S. population. Because some might be interested in whether the differences are statistically different, future studies should explore this important topic. Future work should also be undertaken to explore these estimates by geographies smaller than the state (e.g., PUMAs). In general and throughout the qualitative comparisons of InPM and OutPM rates, OutPM is more prevalent than InPM. It is also found that NLBs, women, naturalized, colder Northern states, and individuals aged 65 and above have the highest rates of difficulties with both InPM and OutPM. In both OutPM and InPM, MLs have the lowest concentration of difficulties compared to all the other groups. The range of uncertainty for InPM estimates is highest among NLOs, men, those aged 49 and below, naturalized, and warmer South states. The range of uncertainty for OutPM estimates is highest among NMLs, women, those aged 50 to 64, noncitizens, and those in warmer South states. For both OutPM and InPM, allocation rates are highest among NLBs, men, those aged 65 and over, the naturalized, and for those in warmer South states. Notwithstanding the limitations of the article, it offers health scientists and policymakers a previously unavailable source of information on the estimates of disability in the U.S. population. It is my hope that this article helps with continuing efforts to move society toward a place where inequality, by any compositional or physical characteristic, is mitigated.
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FUNDING Funding is supported by the National Institutes of Health (Grant T32 AG000181).
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REFERENCES Alexander, J. T., Davern, M., & Stevenson, B. (2010). Inaccurate age and sex data in the Census PUMS files: Evidence and implications. Public Opinion Quarterly, 74, 551–569. Americans with Disabilities Act of 1990, 42 U.S.C.A. x 12101 et seq. (West 1993). Arauz Boudreau, A. D., Kurowski, D. S., Gonzalez, W. I., Dimond, M. A., & Oreskovic, N. M. (2013). Latino families, primary care, and childhood obesity: A randomized controlled trial. American Journal of Preventive Medicine, 44, S247–S257. Baynton, D. C. (2013). Disability and the justification of inequality in American history. The Disability Studies Reader, 17, 33–57. Blane, D. (1999). The life course, the social gradient, and health. In M. Marmot & R. Wilkinson (Eds.), Social determinants of health (pp. 64–80). Oxford, UK: Oxford University Press. Bourke, L., & Waite, C. (2013). ‘‘It’s not like I have a disability or anything!’’ Perceptions of disability and impairment among young, rural people. Disability Studies Quarterly, 33(3). Retrieved from http://dsq-sds.org/article/view/3261/3265 Brault, M. W. (2009). Review of changes to the measurement of disability in the 2008 American Community Survey. Washington, DC: U.S. Census Bureau. Chen, J. T., Beckfield, J., Waterman, P. D., & Krieger, N. (2013). Can changes in the distributions of and associations between education and income bias temporal comparisons of health disparities? An exploration with causal graphs and simulations. American Journal of Epidemiology, 177, 870–881. Clarke, P., & George, L. K. (2005). The role of the built environment in the disablement process. American Journal of Public Health, 95, 1933–1939. Glass, T. A. (1998). Conjugating the ‘‘tenses’’ of function: Discordance among hypothetical, experimental, and enacted function in older adults. Gerontologist, 38, 101–112. Horner-Johnson, W., Dobbertin, K., Lee, J. C., & Andresen, E. M. (2013). Disparities in chronic conditions and health status by type of disability. Disability and Health Journal, 6, 280–286. Retrieved from http://dx.doi.org/10.1016/ j.dhj0.2013.04.006 Karger, H., & Rose, S. R. (2013). Revisiting the Americans with Disabilities Act after two decades. Journal of Social Work in Disability and Rehabilitation, 9(2–3), 73–86. Katz, S. (1983). Assessing self-maintenance: Activities of daily living, mobility, and instrumental activities of daily living. Journal of the American Geriatrics Society, 31, 721–727. Kleinbaum, D. G., Morgenstern, H., & Kupper, L. L. (1981). Selection bias in epidemiologic studies. American Journal of Epidemiology, 113, 452–463.
Downloaded by [Michigan State University] at 11:32 07 April 2015
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C. Siordia
McPhedran, S. (2012). Disability and community life: Does regional living enhance social participation? Journal of Disability Policy Studies, 22, 40–54. Miller, K., & DeMaio, T. J. (2006). Report of cognitive research on proposed American Community Survey disability questions (Survey series: Survey methodology #2006–6). Washington, DC: National Center for Health Statistics, Statistical Research Division. Munyi, C. W. (2012). Past and present perceptions towards disability: A historical perspective. Disability Studies Quarterly, 32(2). Retrieved from http://dsq-sds. org/article/view/3197 Office of Information and Regulatory Affairs, Statistical Policy Office. (2001). Measuring and reporting sources of error in surveys (Statistical Policy Working Paper 31). Washington, DC: Office of Management and Budget. Pollack, C. E., Cubbin, C., Sania, A., Hayward, M., Vallone, D., Flaherty, B., & Bravemen, P. A. (2013). Do wealth disparities contribute to health disparities within racial=ethnic groups? Journal of Epidemiology and Community Health, 67, 439–445. Reichard, A., Stolzle, H., & Fox, M. H. (2011). Health disparities among adults with physical disability and cognitive limitations compared to individuals with no disability in the United States. Disability and Health Journal, 4, 59–67. Shoham, D. A., Vupputuri, S., Kaufman, J. S., Kshirsagar, A. V., Diez Roux, A. V., Coresh, J., & Heiss, G. (2008). Kidney disease and the cumulative burden of life course socioeconomic conditions: The Atherosclerosis Risk in Communities (ARIC) Study. Social Science & Medicine, 67, 1311–1320. Siordia, C. (2013a). Evaluating response mechanisms in a life-space mobility instrument with a ‘‘stem and leaf’’ format. Journal of Frailty and Aging. Retrieved from http://www.jfrailtyaging.com/all-issues.html?article=106 Siordia, C. (2013b). Methodological note: Allocations with health insurance items in the American Community Survey. Journal of Frailty and Aging, 6, 149–153. doi:10.1016=j.dhj0.2012.11.007 Siordia, C. (2014). Detecting ‘‘real’’ population changes with American Community Survey data: The implicit assumption of treating between-year differences as ‘‘trends.’’ Journal of Sociological Research, 4, 494–509. Siordia, C., & Lee, V. D. (2013). Precision of disability estimates for Southeast Asians in American Community Survey 2008–2010 microdata. Central Asian Journal of Global Health, 1(2), 1–9. doi:10.5195=cajgh.2012.2166–7403 Siordia, C., & Young, R. L. (2013). Methodological note: Allocation of disability items in the American Community Survey. Disability and Health Journal. doi:10.1016=j.dhj0.2012.11.007 Strandhagen, E., Berg, C., Lissner, L., Nunez, L., Rosengren, A., Toren, K., & Thelle, D. S. (2010). Selection bias in a population survey with registry linkage: Potential effect on socioeconomic gradient in cardiovascular risk. European Journal of Epidemiology, 25, 163–172. U.S. Census Bureau. (2009a). A compass for understanding and using American Community Survey data: What PUMS data users need to know. Washington, DC: U.S. Government Printing Office. U.S. Census Bureau. (2009b). Design and methodology, American Community Survey. Washington, DC: U.S. Government Printing Office.
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277
Downloaded by [Michigan State University] at 11:32 07 April 2015
U.S. Census Bureau. (2011). Retrieved from http://www.census.gov/acs/www/ Downloads/data_documentation/SubjectDefinitions/2011_ACSSubjectDefinitions. pdf U.S. Department of Agriculture. (2012). Temperature zone map. Washington, DC: Agricultural Research Service, U.S. Department of Agriculture. Retrieved from http://planthardiness.ars.usda.gov/PHZMWeb/Images/All_states_halfzones_ poster_rgb_300dpi.pdf Verbrugge, L. M., & Jette, A. M. (1994). The disablement process. Social Science and Medicine, 38(1), 1–14. Ward, M., Greenhill, B. D., & Bakke, K. M. (2010). The perils of policy and p-value: Predicting civil conflicts. Journal of Peace Research, 47, 363–375. Wendell, S. (1989). Towards a feminist theory of disability. Hypatia, 4, 104–124.
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Number of People in the United States Experiencing Ambulatory and Independent Living Difficulties Carlos Siordia
a
a
Department of Epidemiology, Graduate School of Public Health , University of Pittsburgh , Pittsburgh , Pennsylvania , USA Accepted author version posted online: 09 May 2014.Published online: 08 Aug 2014.
Click for updates To cite this article: Carlos Siordia (2014) Number of People in the United States Experiencing Ambulatory and Independent Living Difficulties, Journal of Social Work in Disability & Rehabilitation, 13:3, 261-277, DOI: 10.1080/1536710X.2014.912187 To link to this article: http://dx.doi.org/10.1080/1536710X.2014.912187
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Journal of Social Work in Disability & Rehabilitation, 13:261–277, 2014 Copyright # Taylor & Francis Group, LLC ISSN: 1536-710X print=1536-7118 online DOI: 10.1080/1536710X.2014.912187
Number of People in the United States Experiencing Ambulatory and Independent Living Difficulties
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CARLOS SIORDIA Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
Estimating the characteristics of the ‘‘disabled’’ population is necessary for some governments and of interest to health researchers concerned with producing disability prevalence rates. Because generating easy-to-understand estimates of disability in the population is important, this article provides U.S. population estimates for two disability-related measures by using the 2009 to 2011 American Community Survey Public Use Microdata Sample file. The number of people who have ‘‘independent living’’ and ‘‘ambulatory’’ difficulties is calculated from a sample of 9,204,437 (representing >309 million people). The percentage for ‘‘disabled’’ is found to vary by racial and ethnic category, sex, age, citizenship status, educational attainment, and state-level regions divided by weather. KEYWORDS ambulatory difficulties, disability, elderly, ethnic minorities, gender equity, geographic information systems (GIS), independent living, people with disabilities
Differently abled (commonly referred to as ‘‘disabled’’; Wendell, 1989) U.S. residents are an underserved community whose physiological conditions present unique social and physical struggles—arising from the mismatch between their personal and social expectations and available resources in the environment. Some ‘‘able-bodied’’ people, by virtue of being ablebodied, lack an understanding of how small physical obstacles (e.g., lack Address correspondence to Carlos Siordia, National Research Science Award Postdoctoral Fellow, Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, 130 North Bellefield Ave., Pittsburgh, PA 15213, USA. E-mail: [email protected] 261
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of pedestrian ramps at street crossways) can present a noticeable challenge for the differently abled (Clarke & George, 2005). Even though it could be that definitions of disability vary by time and geography (Baynton, 2013), in a classical disablement process framework, having difficulties with walking up stairs or going shopping could be said to represent preclinical physiological limitations that could lead to the formation of clinical disability (Verbrugge & Jette, 1994). To be sure, the sociologically produced drive for physical sameness creates interest in understanding the characteristics of the differently abled population (Munyi, 2012). A view is taken throughout this article that disability is a problem that can only be situated in the interaction between disabled individuals and their social environment. From this perspective, addressing challenges faced by disabled individuals requires a psychosocial and physical adjustment on the part of society (Karger & Rose, 2013). This article is motivated by the perspective that resources for coping with physical limitations are nonrandomly (i.e., unevenly) distributed through a stratifying social system. Developing estimates on the prevalence of disability is intended to help federal, state, county, and local governments assess the impact of policies aimed at reducing discrimination against the differently abled and improving their participation in community activities (Brault, 2009). Some evidence exists that the unjust stigmatization of the differently abled (Bourke & Waite, 2013) can lead some of them to have lower levels of social participation (McPhedran, 2012). This point matters because arguments have been made that exposures over the life course can influence health outcomes in adulthood through a cumulative process (Blane, 1999). This approach views cumulative exposures to adverse social circumstances (e.g., poverty, discrimination) as being important risk factors for disease (Shoham et al., 2008). From this perspective, it can be argued that adverse health outcomes linked with the disabled status can be aggravated by the presence of other known risk factors. If you note that socioeconomic and racial or ethnic disparities in health are well documented (Arauz Boudreau, Kurowski, Gonzalez, Dimond, & Oreskovic, 2013), then you might be able to see why, for example, the effects on health (e.g., formation of excess adipose tissue) from not being able to walk could be compounded on by a person’s financial status (i.e., being ‘‘poor’’) or place of residence (e.g., living in a dangerous neighborhood where healthy food options are scarce). The specific aim of this article is to highlight how population estimates of disability prevalence vary in their precision in unique ways (Siordia, 2013b). Because data from the American Community Survey (ACS) can be argued to be the gold standard for producing reliable measures of disability in the general U.S. population, it has been deemed critical to the disability community. For example, local governments (e.g., states) use ACS data to decide how to distribute funds to local agencies for food, health care, and
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legal services for individuals with disabilities. If the best available data have the potential to under- or overestimate the prevalence of disability as a function of demographic characteristics, then there is a possibility that ‘‘multidisadvantaged’’ (e.g., disabled racial and ethnic minorities with moderate levels of educational attainment) groups might not be receiving the appropriate level of resources. Previous work has shown how data could be challenged if ‘‘selection bias’’ (Kleinbaum, Morgenstern, & Kupper, 1981), the selection of study participants by a third unmeasured variable believed to be associated with exposure and outcome, is suspect (Strandhagen et al., 2010). Please note that although ACS might have some limitations commonly found in population-based survey studies, an argument is not being made here that ACS data contain selection bias. In 2011 and at the national level, the ACS had a coverage rate of 98.6% and a ‘‘response rate’’ of 97.6%—numbers that could be argued to indicate there is a low probability of selection bias. Instead, the main point of this discussion is to advise researchers interested in estimating disability prevalence in the U.S. population to consider how precision of the estimates varies from group to group. Between-group comparisons is a particularly important point when you consider that research on ‘‘health disparities’’ continues to grow (e.g., Pollack et al., 2013). A related issue has been raised by others on how temporal comparisons (i.e., time dependent) of socioeconomic disparities between cross-sectional surveys might be affected when presumed variations on the meaning of a socioeconomic status are unaccounted for in statistical modeling (Chen, Beckfield, Waterman, & Krieger, 2013). Estimating the number of individuals within the U.S. population who might be facing uncommon social and physical challenges is important for assisting public health efforts toward developing assistance measures. U.S. government policies provide some protection for individuals with disability (Karger & Rose, 2013) and as a result, there is a mandate for U.S. federal agencies collecting information on the U.S. population to develop a count of individuals with disability. In particular, Title II of the Americans with Disabilities Act (ADA, 1990) requires government agencies to make services available to people with disabilities. In this study, two measures of disability are used to present a profile of the U.S. population. These could be said to be related to the popularly conceptualized and measured activities of daily living (Katz, 1983). Details of the survey questions used in the analysis are delineated later. The discussion here only focuses on the labels being used. Reported difficulties with independent living (e.g., ability to go shopping) are labeled as outdoor physical mobility (OutPM) and ambulatory (e.g., ability to walk) difficulties are referred to as indoor physical mobility (InPM). Both OutPM and InPM are treated as crude measures of potential capacity to function in the hypothetical tense (Glass, 1998). Because the items being used in the ACS
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to assess disability status are based on either self-report or proxy report (explained later), they are referred to as potential capacity—in contrast, enacted mobility refers to actual performance (Siordia, 2013a). OutPM and InPM subjectively assess whether a person perceives an ability to perform a task. As a consequence, the labels being used here only signal ‘‘potential for outdoor physical mobility’’ with the OutPM item and ‘‘potential for indoor physical mobility’’ with the InPM item. The report makes use of survey data derived from a U.S. population sample. A population sample is a subset of individuals from a complete population (i.e., the population universe). The need to generalize findings from a sample to the complete population from which they are drawn necessitates the use of random selection. Because simple random selection is so infrequently available, the use of complex sampling techniques produces the need to use intricate weighting methodologies to derive population estimates from samples (Siordia & Lee, 2013). Because the sample is much smaller than the population or universe, each study subject represents a certain number of his or her peers. The use of ‘‘weights’’ amplifies sample numbers to derive estimates of the population. I now pause to clarify the sometimes not so obvious meaning of a key term: estimates. Producing an estimate of disability means math is being used with intelligently gathered information to produce an informed guess of the number of people with x-type of disability (Siordia, 2013b). Inherent in this approach is the assumption that the informed guess (i.e., estimate) has a certain degree of uncertainty—a range of numbers where the ‘‘true’’ value is expected to exist. An estimate should not be seen as an inferior product with artificial significance. Instead, the reader should note that the ACS is the most reliable source of information for estimating disability prevalence in the U.S. population and that the estimates being presented here are among the most scientifically sophisticated measures available to nongovernment-affiliated researchers. The United States has more than 310 million people spread over more than 3.8 million square miles. Trying to gather data on this population to describe their characteristics is incredibly challenging and critical for the advancement of democratic governance. A discussion on the variability of precision in disability estimates should not distract the reader from the fact that the ACS is one of the most sophisticated products created by the U.S. Census Bureau. It is only because the ACS is so highly regarded, transparent to the public, and scientifically sound that a discussion on ‘‘estimate precision’’ is even possible—some small data sets purporting to estimate the prevalence of disability in the United States would not stand the scrutiny being presented here and as applied to ACS microdata. This article introduces the reader to the idea that U.S. population estimates of the disabled population vary in quality and as a function of person characteristics and geographical location. Although technical in nature, the
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article is meant to primarily provide detailed disability estimates by commonly used demographical characteristics. The specific aim of this article is to provide U.S. population estimates for OutPM and InPM. This aim is complemented by a discussion of various easy-to-understand ‘‘measures on uncertainty’’ in the estimations of OutPM and InPM for the U.S. population during the period from 2009 to 2011.
METHODS
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Data Disability in the U.S. population is measured by taking a ‘‘randomly selected sample’’ from the population. As alluded to earlier, a sample is a subgroup derived from the population and randomly selected refers to a very important principle in inferential statistics. Randomly selected means that a person was selected from a population of individuals where every person had an equal chance for selection. This is important because subjects in the sample are used to infer the characteristics of the population they were drawn from— inferences are made by using ‘‘population weights,’’ which assume a particular selection process. Because the ACS is not a simple random sample, population weights are variables said to account for the design of the study, the sampling error in the study—that is, variation in the probability of being randomly selected at different points of the complex survey design (Siordia, 2014). This study used data from the ACS. In particular, the Public Use Microdata Sample (PUMS) 3-year file for 2009 to 2011 is used. Because ACS is collected yearly, the 3-year file being used here represents data collected over 36 months. The microdata are used internally by the U.S. Census Bureau to produce estimates for geographies (e.g., counties) with as few as 20,000 people. Data from the PUMS files only allow geo-referencing to the Public Use Microdata Area (PUMA) geography—in 2013, the ‘‘5-year’’ ACS file (data from 60 months) will provide ‘‘disability’’ data all the way down to the block-group geography. Please note that data issues arising from the misapplication of disclosure-avoidance procedures (Alexander, Davern, & Stevenson, 2010) are not present in the microdata being used in this study. This is the first publication showing disability estimates for the population aged 65 and over where the PUMS data files are not believed to be contaminated by flawed disclosure-avoidance procedures. The estimates in this article’s tables use a total of 9,204,437 actual survey participants. The use of population weights are then applied to the 9,204,437 individuals—the actual number of people participating in the survey and referred to hereafter as the unweighted sample. When ‘‘weighted up’’ (using the PWGTP variable in the microdata), the unweighted sample is said to represent the U.S.
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population, which for this time period is about 309,231,245 people (estimated total population of the United States during the 2009–2011 period).
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‘‘Disability’’ Variables Although there is information on six ‘‘disabilities’’ (label used by the U.S. Census Bureau), this project only focuses on ‘‘independent living difficulty’’ and ‘‘ambulatory difficulty.’’ Readers should note that only one person per household participated in the survey and is technically referred to as the ‘‘reference person.’’ The reference person answered all questions for individuals in the household. Thus, if the reference person is responding to disability items for herself or himself, then disability can be said to be self-reported. However, if the reference person is responding about the disability of others in the household, then disability can be said to be proxy reported. To assess independent living difficulty, survey respondents were asked about themselves and others in the household: ‘‘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?’’ (U.S. Census, 2011). Because of the way the questions are framed and the fact that individuals are only allowed to respond in a forced choice format (i.e., either yes or no), it may be argued that survey responses are approximate measures for outdoor physical mobility potential (i.e., OutPM). To assess ambulatory difficulty, survey respondents were asked about themselves and others in the household: ‘‘Does this person have serious difficulty walking or climbing stairs?’’ (U.S. Census Bureau, 2011). This item could also be argued to be an approximate measure for indoor physical mobility potential (i.e., InPM). Elegant work has been undertaken to show that responding to these questions is primarily challenged by the need to determine whether or not the severity of a particular functional limitation warranted a positive response (Miller & DeMaio, 2006). For example, with regard to the InPM, cognitive research showed that individuals considered physical limitation factors (e.g., pain, fatigue) when evaluating their ability to walk or climb stairs, but considered other domains (e.g., emotional status) when responding about others in the household. Because the InPM question does not include the word usually, it might fail to prompt the respondent to think of long-term physical conditions. Because respondents are not asked about the use of assistive devices, some might see the use of a device as granting mobility and might thus fail to report any difficulty (Miller & DeMaio, 2006). With regard to OutPM, responses to the question could vary because individuals might interpret ‘‘difficulty doing errands alone’’ differently. Some might interpret the question as probing for the person’s access to transportation resources, whereas others might interpret it as pertaining to mobility and mind capacity (Miller & DeMaio, 2006). In short, care should be given when
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comparing disability prevalence between studies using different forms of questions.
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Demographic Variables InPM and OutPM estimates are presented by various demographic factors. Because the U.S. Census Bureau is forthcoming with how they collect information on the U.S. population, documentation detailing the various labels under these racial and ethnic categories is readily available online. I made use of six race and ethnic categories: non-Latino-White (NLW), nonLatino-Black (NLB), non-Latino-other (NLO), Mexican-Latino (ML), and non-Mexican-Latino (NML). The U.S. Census Bureau conceptualizes race and ethnicity as a social construct—not a genetically determined and biologically defined phenotype. Estimates are also provided for the following: males and females; three age groups (age 49, age 50–64, and age > 65); three citizenship status categories (U.S. born, naturalized, and noncitizen); and educational attainment (associate’s degree). To complement these popular measures, estimates by ‘‘warmer South’’ and ‘‘colder North’’ states were created. The ‘‘average annual extreme minimum temperature from 1976–2005’’ data from the U.S. Department of Agriculture (USDA) is used to qualitatively identify the states where about 50% of their geographical area experienced temperatures 10 F (USDA, 2012). This arbitrary and visual approach renders the following as warmer South states: Alabama, Arizona, California, Florida, Georgia, Hawaii, Louisiana, Mississippi, New Mexico, Puerto Rico (not technically a state), South Carolina, and Texas. All the others make up the colder North states.
Statistical Approach Because the U.S. Census Bureau is so transparent about their methods for estimating population characteristics, I was able to make use of 80 person weights (PWGTP1–PWGTP80 variables) provided in the PUMS files to estimate various measures of uncertainty surrounding the estimate. Replicate weights are used to calculate what is referred to as direct standard errors—measures of imprecision for the 80 possible estimates. I developed an algorithm, in SAS 9.3, using the 80 person weight by following instructions outlined in U.S. Census Bureau publications (U.S. Census Bureau, 2009a, 2009b). The formula for calculating the replicate weights standard errors is as follows: rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 4 X80 ðXr X Þ2 SE ð X Þ ¼ r¼1 80 After the replicate estimates X1 through X80 are computed, the standard error (SE) of X is estimated using the sum squared differences between each
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replicate estimate of Xr and the full sample estimate X (U.S. Census Bureau, 2009b). A single-person weight (i.e., PWGTP) to compute ‘‘weighed’’ versus ‘‘unweighted’’ estimates is used. What is hoped to be a more intuitive representation of variability of precision is the estimate presented by computing the Person Inflation Ratio (PIR), which is the average number of people being represented in weighted population by the unweighted counts. It is computed using the following formula: [weighted count weighted total population] (Siordia & Lee, 2013). An increase in PIR indicates that each individual within that group represents more of their counterparts in the population. When PIR increases, it indicates that the characteristics of fewer individuals might affect the estimation of disability prevalence. ACS PUMS files allow public data users to calculate the margin of error (MOE) for each of the population estimates. To highlight how any estimates have a certain degree of uncertainty, I present MOEs for the estimates being produced. MOE is present when, for example, large variation between samples occurs—leading survey-based estimates to deviate from accurately approximating the true population value. These deviations from the true count can be roughly measured by computing the standard error (SE) of the estimate (Siordia & Lee, 2013). The MOE of the estimate with 90% confidence is calculated as follows: [MOE ¼ 1.645 SE]. MOEs are then used to provide readers with the ‘‘upper’’ and ‘‘lower’’ 90% confidence limits around the disability estimates—the range in which the ‘‘true’’ number could lie with a 10% chance for making an error. Confidence limits are computed with the following equations: Lower Confidence Limit [LCL ¼ (estimate – MOE)]; and Upper Confidence Limit [UCL ¼ (estimate þ MOE)]. A more easy-tounderstand measure called range of uncertainty (RU; Siordia & Lee, 2013) is also provided. RU compares the level of uncertainty in the disability estimate by using the following formula: f[(SE 3) X] 100g, where X is the disability estimate. By using this approach, readers can more straightforwardly interpret level of uncertainty as follows: As RU numbers increase, the level of imprecision in the estimate increases. There is a very important point that has not been mentioned until now: To provide complete data banks, the U.S. Census Bureau undertakes many widely accepted statistical procedures for insuring coherent responses and the reduction of missing items. Although the issue of ‘‘allocation’’ is explained in greater detail elsewhere (Siordia & Young, 2013), I briefly mention that both probability (using statistical techniques) and nonprobability (using explicit logic rules) based computer algorithms are used to assign responses to individuals missing a response to a disability survey item or providing what is deemed an illogical response to a disability survey item. Please note that the U.S. Census Bureau first attempts to have the survey filled via mail correspondence; if that fails, they move to computer-assisted
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telephone interviewing, and if that fails they then move to computer-assisted personal interviewing (Brault, 2009). In an attempt to reduce individual item nonresponse, the U.S. Census Bureau also conducts failed-edit follow-up and telephone questionnaire assistance operations (Brault, 2009). The effort made by the U.S. Census Bureau to produce accurate information is admirable. Readers should not be dissuaded from thinking allocation procedures produce unreliable estimates of disability with ACS data. Instead, they should note how even this arguably gold standard data source has limitations that should be kept in mind when producing prevalence rates. The large scale and high quality of data found in ACS makes it tolerable to publicly discuss esoteric nuances of imprecision in the data. In contrast, it is possible that some widely used studies on disability could not stand such public scrutiny without losing face validity. Because the U.S. Census Bureau is confident about their products, they provide public data users with allocation ‘‘flag’’ variables (variables that can help identify when an allocation is present for a particular response) in PUMS files indicating if a value was observed or allocated (Siordia, 2013b). These procedures are undertaken under the direction of the Federal Committee on Statistical Methodology (FCSM), which has informed researchers that survey accuracy encompasses both sampling error and a broad spectrum of non-sampling-related errors (e.g., item nonresponse; Office of Information and Regulatory Affairs, Statistical Policy Office, 2001). The weighted number of allocations and percent allocated are calculated with the following equation: [(weighted allocated count total weighted population) 100]. The goal of introducing this last measure is to inform the reader that even though the largest available sample with quasi-preclinical disability items for the United States is being used in this study, the estimates also have other forms of error that might bias estimates away from true value. This form of bias on the estimates might be statistically unquantifiable (Siordia & Young, 2013). Despite these serious issues, the disability ‘‘population estimates’’ provided here might be among the most reliable—notwithstanding the ambiguity of the survey question. Many other studies, using problematic sample selection designs that severely limit their generalizability, make use of a few thousand people from a small U.S. geographical region to provide what they argue is acceptable ‘‘population estimates of X-disability.’’ This study uses more than 9 million people and provides transparent estimates by framing them through a discourse that includes their SEs, MOEs, RUs, and allocations. Please note throughout the discussion of the findings that only qualitative comparisons between estimates are made. I do not conduct quantitative testing to determine if the differences between groups (e.g., NLWs vs. MLs) are statistically significant. I deliberately avoid conducting tests to determine ‘‘statistically significant differences’’ to reiterate that disability population estimates have a range of uncertainty.
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FINDINGS I first review the general distribution of the analytic sample presented in Table 1. Both weighted counts (using person weights) and unweighted counts (using actual number of observations) are presented in Table 1. Instead of reviewing all the numbers, a descriptive discussion on the PIR shown in Table 1 is used. As explained earlier, PIR represents the average number of individuals each of our study participants represents and is calculated by dividing the weighted count by the unweighted number. For example, the PIR for MLs is computed as follows: [32,862,818 810,696] ¼ 40.54 41. This means that on average, each actual ML in the microdata represents about 41 other MLs after population weights are used to generalize the sample characteristics to the whole of the U.S. population. From the racial and ethnic groups, a qualitative comparison shows MLs and NMLs have the highest PIR at 41, followed by NLB (40), NLO (35), and NLW (31). MLs and NMLs on average represent 47 other MLs and NMLs, respectively. From the demographic variables, we see that males, those at or below the age of 49, southern states, those with less than one year of college, and non-U.S. citizens have the highest PIR values. InPM estimates are presented in Table 2. The number of individuals having difficulties with independent living, by racial and ethnic groups, is TABLE 1 Weighted and Unweighted Estimates from the Analytic Sample
Race or Ethnicity Non-Latino White Non-Latino Black Non-Latino other Mexican-Latino Non-Mexican Latino Demographics Female Male Age 49 Age 50–64 Age 65 U.S.-born Naturalized Noncitizen 1 year of college Associate’s degree Region by weather States in ‘‘Warmer South’’ States in ‘‘Colder North’’ a
Weighteda
Unweightedb
PIRc
196,951,654 37,801,756 23,803,937 32,862,818 17,811,080
6,326,562 948,585 686,797 810,696 431,797
31 40 35 41 41
157,199,566 152,031,679 209,708,347 59,026,386 40,496,512 269,337,364 17,601,889 22,291,992 219,810,714 77,559,393
4,725,640 4,478,797 5,753,859 1,955,685 1,494,893 8,174,101 502,953 527,383 6,483,206 2,409,301
33 34 36 30 27 33 35 42 34 32
117,899,464 191,331,781
3,456,228 5,748,209
34 33
Counts using the PWGTP (American Community Survey [ACS] person-weight) variable. Counts not using the PWGTP (ACS person-weight) variable. c Person Inflation Ratio (PIR) ¼ (weighted count weighted total population). b
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5.24 5.67 3.49 2.52 3.66 5.70 3.82 1.81 5.90 18.50 4.89 5.89 2.50 5.73 2.80 4.67 4.84
10,320,245 2,144,344 829,597 829,096 652,141 8,965,244 5,810,179 3,800,629 3,483,927 7,490,867 13,181,926 1,037,180 556,317 12,603,537 2,171,886 5,510,591 9,264,832
%D
b
15,955 18,504
18,080 15,861 14,567 11,896 14,841 26,116 13,853 5,814 22,745 9,842
26,237 10,183 12,202 7,460 6,137
SE
c
26,426 30,439
29,742 26,091 23,963 19,569 24,413 42,961 22,788 9,564 37,416 16,190
43,160 16,752 20,072 12,272 10,095
MOE
d
5,484,165 9,234,393
8,935,502 5,784,088 3,776,666 3,464,358 7,466,454 13,138,965 1,014,392 546,753 12,566,121 2,155,696
10,277,085 2,127,592 809,525 816,824 642,046
LCLe
UCLf
5,537,017 9,295,271
8,994,986 5,836,270 3,824,592 3,503,496 7,515,280 13,224,887 1,059,968 565,881 12,640,953 2,188,076
10,363,405 2,161,096 849,669 841,368 662,236
Independent Living
0.87 0.60
0.61 0.82 1.15 1.02 0.59 0.59 4.01 3.14 0.54 1.36
0.76 1.42 4.41 2.70 2.82
RUg(%)
b
Weighted number of people reporting difficulty with independent living (i.e., ‘‘disabled’’). Percentage disabled (%D) ¼ [(weighted disabled count weighted total population) 100] (Note: Total population available in Table 1). c Standard error (SE). d Margin of error (MOE). e Lower Confidence Limit (LCL): Low limit of 90% confidence interval ¼ [Disable – MOE]. f Upper Confidence Limit (UCL): High limit of 90% confidence interval ¼ [Disable þ MOE]. g Range of uncertainty (RU) ¼ f[(SE 3) disabled] 100g. h Allocated: Number of responses to ‘‘independent living’’ survey item that are assigned or changed. i Percentage allocated (%A) ¼ [(weighted allocated count weighted total population) 100] (Note: Total population available in Table 1).
a
Race or ethnicity Non-Latino White Non-Latino Black Non-Latino other Mexican-Latino Non-Mexican Latino Demographics Female Male Age < 49 Age 50–64 Age 65 U.S.-born Naturalized Noncitizen 1 year of college Associate’s degree Region by weather States in ‘‘Warmer South’’ States in ‘‘Colder North’’
Disabled
a
3,221,485 4,611,647
3,931,006 3,902,126 4,546,873 1,816,562 1,469,697 6,546,526 674,055 612,551 6,146,747 1,686,385
4,590,608 1,322,643 685,740 716,010 518,131
Allocatedh
TABLE 2 Weighted Estimates, Their Margins of Error and Allocation Rates for Difficulty with ‘‘Independent Living’’ Survey Item
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2.73 2.41
2.50 2.57 2.17 3.08 3.63 2.43 3.83 2.75 2.80 2.17
2.33 3.50 2.88 2.18 2.91
%Ai
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as follows: NLWs ¼ 5.24%, NLBs ¼ 5.67%, NLOs ¼ 3.49%, MLs ¼ 2.52%, and NMLs ¼ 3.66%. As is evident from these population-relative percentages, NLBs have the highest concentration of independent living difficulties and MLs have the least. In absolute terms, NLWs have the largest number (weighted n ¼ 10,320,245) of individuals experiencing independent living difficulties. Please note that uncertainty in the InPM estimates increases in the following order: NLWs, NLBs, MLs, NMLs, and NLOs. In other words, the highest level of imprecision is located in NLOs. Survey item allocations increase in the following order: MLs, NLWs, NLOs, NLMs, and then NLBs. This means the largest number of allocations occurs in the NLB group. Females have more InPM difficulties than males—their estimate has a smaller RU and they have lower rates of allocation than males. RU is lowest among those aged 65 and up (18.50% have an InPM difficulty)—although their age group has the largest number of allocation rates compared to those at or below age 64. In terms of citizenship status, InPM estimates for the naturalized group show they have the highest level of InPM difficulties (5.89%), the largest level of uncertainty (RU ¼ 4.01%), and the largest percentage of allocation rates (3.83%). Those with one year or less of college education have the most InPM difficulties (5.73%), lowest RU (0.54%), and largest allocation rate (2.80%), compared to people with an associate’s degree or above. Colder North states have greater levels (4.84%) of people experiencing InPM difficulties—although estimates for the warmer South states have higher uncertainty and allocations associated with independent living difficulty measures. OutPM estimates are shown in Table 3. The estimates show that ambulatory difficulty, by racial and ethnic groups, is as follows: NLWs ¼ 7.52%, NLBs ¼ 8.35%, NLOs ¼ 4.42%, MLs ¼ 3.73%, and NMLs ¼ 5.05%. As with InPM, NLBs have the highest concentration of ambulatory difficulties and MLs have the least. In absolute terms, NLWs have the largest number (weighted n ¼ 14,808,770) of individuals with ambulatory difficulties by virtue of their population size in the United States. The uncertainty in the OutPM estimates increases as follows: NLWs, NLBs, MLs, NLOs, and NMLs. The highest level of imprecision is located in NMLs. Survey item allocations increase as follows: NLWs, MLs, NMLs, NLOs, and NLBs. This means the largest number of allocations is concentrated in NLBs. When compared to men, women have more (7.99%) difficulties with OutPM than men, although their estimates have a larger RU and slightly lower rate of allocation than men. More than one fourth (25.78%) of individuals aged 65 and over have difficulties with OutPM—their RUs are lower than those aged 64 and below, but their age group has the largest number of allocation rates. Noncitizens, with 3.31% having OutPM difficulties, have the largest level of uncertainty (RU ¼ 2.99%), and naturalized individuals have the largest percentage of allocation rates (4.04%). On OutPM, those with one year or less of college education have the highest rate at 8.04%, lowest RU (0.45%), and largest rate of allocation at 3.42%, when
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7.52 8.35 4.42 3.73 5.05 7.99 5.64 2.05 10.86 25.78 7.08 7.63 3.31 8.04 4.48 6.78 6.88
14,808,770 3,157,191 1,052,731 1,225,915 898,840 12,566,126 8,577,321 4,291,901 6,412,965 10,438,851 19,061,922 1,342,743 738,782 17,671,037 3,472,410 7,988,976 13,154,471
%D
b
31,613 20,799
34,073 17,259 14,931 23,299 23,435 33,954 10,691 7,363 26,742 23,269
27,955 13,817 7,884 7,463 9,294
SE
c
52,003 32,214
56,051 28,391 24,562 38,327 38,550 55,854 17,587 12,113 43,990 38,278
45,986 22,730 12,969 12,277 15,289
MOE
d
7,936,973 13,122,257
12,510,075 8,548,930 4,267,339 6,374,638 10,400,301 19,006,068 1,325,156 726,669 17,627,047 3,434,132
14,762,784 3,134,461 1,039,762 1,213,638 883,551
LCLe
Ambulatory
8,040,979 13,186,685
12,622,177 8,605,712 4,316,463 6,451,292 10,477,401 19,117,776 1,360,330 750,895 17,715,027 3,510,688
14,854,756 3,179,921 1,065,700 1,238,192 914,129
UCLf
1.19 0.47
0.81 0.60 1.04 1.09 0.67 0.53 2.39 2.99 0.45 2.01
0.57 1.31 2.25 1.83 3.10
RUg(%)
b
Weighted number of people reporting ambulatory difficulties (i.e., ‘‘disabled’’). Percentage disabled (%D) ¼ [(weighted disabled count weighted total population) 100] (Note: Total population available in Table 1). c Standard error (SE). d Margin of error (MOE). e Lower Confidence Limit (LCL): Low limit of 90% confidence interval ¼ [Disable – MOE]. f Upper Confidence Limit (UCL): High limit of 90% confidence interval ¼ [Disable þ MOE]. g Range of uncertainty (RU) ¼ f[(SE 3) disabled] 100g. h Allocated: Number of responses to ‘‘ambulatory’’ survey item that are assigned or changed. i Percentage allocated (%A) ¼ [(weighted allocated count weighted total population) 100] (Note: Total population available in Table 1).
a
Race or ethnicity Non-Latino White Non-Latino Black Non-Latino other Mexican-Latino Non-Mexican Latino Demographics Female Male Age < 49 Age 50–64 Age 65 U.S.-born Naturalized Noncitizen 1 year of college Associate’s degree Region by weather States in ‘‘Warmer South’’ States in ‘‘Colder North’’
Disabled
a
TABLE 3 Weighted Estimates, Their Margins of Error and Allocation Rates for Difficulty with ‘‘Ambulatory’’ Survey Item
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3,782,733 5,397,595
4,581,660 4,598,668 59,724 1,780,750 1,427,138 7,809,003 710,682 660,643 7,520,697 1,659,631
5,269,698 1,538,750 838,770 919,594 613,516
Allocatedh
3.21 2.82
2.91 3.02 0.03 3.02 3.52 2.90 4.04 2.96 3.42 2.14
2.68 4.07 3.52 2.80 3.44
%Ai
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compared to those with an associate’s degree or more. Warmer South states, with a 6.78% OutPM rate of difficulty in the population, have greater levels of uncertainty and allocations associated with ambulatory difficulty measures.
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CONCLUSIONS There are some limitations within this study. For those interested in ‘‘health disparities’’ (Horner-Johnson, Dobbertin, Lee, & Andresen, 2013; Reichard, Stolzle, & Fox, 2011), the between-group comparisons provided in the discussion are only qualitative—I do not claim any of the observed differences are statistically significant. Please note that some do compare estimates by observing whether their confidence intervals overlap to determine if statistically significant differences exist. Statistical significance might not always be relevant in the policy world (Ward, Greenhill, & Bakke, 2010). It could be that an unwarranted amount of attention is paid to finding statistically significant relationships, and too little attention is given to finding practical means of improving the structural systems affecting the disabled. Because stakeholders might want to see high-quality estimates, or acceptable estimates with complexity that is reduced to digestible interpretations, the tables in this report might provide such a product: where transparency and easy-to-follow measures highlight proportions of people with difficulties with InPM and OutPM in the U.S. population. Because some might be interested in whether the differences are statistically different, future studies should explore this important topic. Future work should also be undertaken to explore these estimates by geographies smaller than the state (e.g., PUMAs). In general and throughout the qualitative comparisons of InPM and OutPM rates, OutPM is more prevalent than InPM. It is also found that NLBs, women, naturalized, colder Northern states, and individuals aged 65 and above have the highest rates of difficulties with both InPM and OutPM. In both OutPM and InPM, MLs have the lowest concentration of difficulties compared to all the other groups. The range of uncertainty for InPM estimates is highest among NLOs, men, those aged 49 and below, naturalized, and warmer South states. The range of uncertainty for OutPM estimates is highest among NMLs, women, those aged 50 to 64, noncitizens, and those in warmer South states. For both OutPM and InPM, allocation rates are highest among NLBs, men, those aged 65 and over, the naturalized, and for those in warmer South states. Notwithstanding the limitations of the article, it offers health scientists and policymakers a previously unavailable source of information on the estimates of disability in the U.S. population. It is my hope that this article helps with continuing efforts to move society toward a place where inequality, by any compositional or physical characteristic, is mitigated.
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FUNDING Funding is supported by the National Institutes of Health (Grant T32 AG000181).
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REFERENCES Alexander, J. T., Davern, M., & Stevenson, B. (2010). Inaccurate age and sex data in the Census PUMS files: Evidence and implications. Public Opinion Quarterly, 74, 551–569. Americans with Disabilities Act of 1990, 42 U.S.C.A. x 12101 et seq. (West 1993). Arauz Boudreau, A. D., Kurowski, D. S., Gonzalez, W. I., Dimond, M. A., & Oreskovic, N. M. (2013). Latino families, primary care, and childhood obesity: A randomized controlled trial. American Journal of Preventive Medicine, 44, S247–S257. Baynton, D. C. (2013). Disability and the justification of inequality in American history. The Disability Studies Reader, 17, 33–57. Blane, D. (1999). The life course, the social gradient, and health. In M. Marmot & R. Wilkinson (Eds.), Social determinants of health (pp. 64–80). Oxford, UK: Oxford University Press. Bourke, L., & Waite, C. (2013). ‘‘It’s not like I have a disability or anything!’’ Perceptions of disability and impairment among young, rural people. Disability Studies Quarterly, 33(3). Retrieved from http://dsq-sds.org/article/view/3261/3265 Brault, M. W. (2009). Review of changes to the measurement of disability in the 2008 American Community Survey. Washington, DC: U.S. Census Bureau. Chen, J. T., Beckfield, J., Waterman, P. D., & Krieger, N. (2013). Can changes in the distributions of and associations between education and income bias temporal comparisons of health disparities? An exploration with causal graphs and simulations. American Journal of Epidemiology, 177, 870–881. Clarke, P., & George, L. K. (2005). The role of the built environment in the disablement process. American Journal of Public Health, 95, 1933–1939. Glass, T. A. (1998). Conjugating the ‘‘tenses’’ of function: Discordance among hypothetical, experimental, and enacted function in older adults. Gerontologist, 38, 101–112. Horner-Johnson, W., Dobbertin, K., Lee, J. C., & Andresen, E. M. (2013). Disparities in chronic conditions and health status by type of disability. Disability and Health Journal, 6, 280–286. Retrieved from http://dx.doi.org/10.1016/ j.dhj0.2013.04.006 Karger, H., & Rose, S. R. (2013). Revisiting the Americans with Disabilities Act after two decades. Journal of Social Work in Disability and Rehabilitation, 9(2–3), 73–86. Katz, S. (1983). Assessing self-maintenance: Activities of daily living, mobility, and instrumental activities of daily living. Journal of the American Geriatrics Society, 31, 721–727. Kleinbaum, D. G., Morgenstern, H., & Kupper, L. L. (1981). Selection bias in epidemiologic studies. American Journal of Epidemiology, 113, 452–463.
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276
C. Siordia
McPhedran, S. (2012). Disability and community life: Does regional living enhance social participation? Journal of Disability Policy Studies, 22, 40–54. Miller, K., & DeMaio, T. J. (2006). Report of cognitive research on proposed American Community Survey disability questions (Survey series: Survey methodology #2006–6). Washington, DC: National Center for Health Statistics, Statistical Research Division. Munyi, C. W. (2012). Past and present perceptions towards disability: A historical perspective. Disability Studies Quarterly, 32(2). Retrieved from http://dsq-sds. org/article/view/3197 Office of Information and Regulatory Affairs, Statistical Policy Office. (2001). Measuring and reporting sources of error in surveys (Statistical Policy Working Paper 31). Washington, DC: Office of Management and Budget. Pollack, C. E., Cubbin, C., Sania, A., Hayward, M., Vallone, D., Flaherty, B., & Bravemen, P. A. (2013). Do wealth disparities contribute to health disparities within racial=ethnic groups? Journal of Epidemiology and Community Health, 67, 439–445. Reichard, A., Stolzle, H., & Fox, M. H. (2011). Health disparities among adults with physical disability and cognitive limitations compared to individuals with no disability in the United States. Disability and Health Journal, 4, 59–67. Shoham, D. A., Vupputuri, S., Kaufman, J. S., Kshirsagar, A. V., Diez Roux, A. V., Coresh, J., & Heiss, G. (2008). Kidney disease and the cumulative burden of life course socioeconomic conditions: The Atherosclerosis Risk in Communities (ARIC) Study. Social Science & Medicine, 67, 1311–1320. Siordia, C. (2013a). Evaluating response mechanisms in a life-space mobility instrument with a ‘‘stem and leaf’’ format. Journal of Frailty and Aging. Retrieved from http://www.jfrailtyaging.com/all-issues.html?article=106 Siordia, C. (2013b). Methodological note: Allocations with health insurance items in the American Community Survey. Journal of Frailty and Aging, 6, 149–153. doi:10.1016=j.dhj0.2012.11.007 Siordia, C. (2014). Detecting ‘‘real’’ population changes with American Community Survey data: The implicit assumption of treating between-year differences as ‘‘trends.’’ Journal of Sociological Research, 4, 494–509. Siordia, C., & Lee, V. D. (2013). Precision of disability estimates for Southeast Asians in American Community Survey 2008–2010 microdata. Central Asian Journal of Global Health, 1(2), 1–9. doi:10.5195=cajgh.2012.2166–7403 Siordia, C., & Young, R. L. (2013). Methodological note: Allocation of disability items in the American Community Survey. Disability and Health Journal. doi:10.1016=j.dhj0.2012.11.007 Strandhagen, E., Berg, C., Lissner, L., Nunez, L., Rosengren, A., Toren, K., & Thelle, D. S. (2010). Selection bias in a population survey with registry linkage: Potential effect on socioeconomic gradient in cardiovascular risk. European Journal of Epidemiology, 25, 163–172. U.S. Census Bureau. (2009a). A compass for understanding and using American Community Survey data: What PUMS data users need to know. Washington, DC: U.S. Government Printing Office. U.S. Census Bureau. (2009b). Design and methodology, American Community Survey. Washington, DC: U.S. Government Printing Office.
U.S. Estimates of Difficulty with Ambulatory and Independent Living
277
Downloaded by [Michigan State University] at 11:32 07 April 2015
U.S. Census Bureau. (2011). Retrieved from http://www.census.gov/acs/www/ Downloads/data_documentation/SubjectDefinitions/2011_ACSSubjectDefinitions. pdf U.S. Department of Agriculture. (2012). Temperature zone map. Washington, DC: Agricultural Research Service, U.S. Department of Agriculture. Retrieved from http://planthardiness.ars.usda.gov/PHZMWeb/Images/All_states_halfzones_ poster_rgb_300dpi.pdf Verbrugge, L. M., & Jette, A. M. (1994). The disablement process. Social Science and Medicine, 38(1), 1–14. Ward, M., Greenhill, B. D., & Bakke, K. M. (2010). The perils of policy and p-value: Predicting civil conflicts. Journal of Peace Research, 47, 363–375. Wendell, S. (1989). Towards a feminist theory of disability. Hypatia, 4, 104–124.
