Health and Health-Care Utilization of the Older Population of Ireland: Comparing the Intellectual Disability Population and the General Population

Abstract

Background/Objectives:

Largely unresearched are the similarities and differences compared to the general population in the aging of people with an intellectual disability (ID). Data reported here compare the health and health-care utilization of the general aging population in Ireland with those who are aging with ID.

Design:

Data for comparisons were drawn from the 2010 The Irish Longitudinal Study on Ageing (TILDA) and the Intellectual Disability Supplement (IDS)-TILDA Wave 1 data sets.

Setting:

TILDA participants were community dwelling only while IDS-TILDA participants were drawn from community and institutional settings.

Participants:

TILDA consists of a sample of 8,178 individuals aged 50 years and older who were representative of the Irish population. The IDS-TILDA consists of a random sample of 753 persons aged 40 and older. Using age 50 as the initial criterion, 478 persons with ID were matched with TILDA participants on age, sex, and geographic location to create the sample for this comparison.

Measurements:

Both studies gathered self-reported data on physical and mental health, behavioral health, functional limitations, and health-care utilization.

Results:

Rates of chronic disease appeared higher overall for people with ID as compared to the general population. There were also age-related differences in the prevalence of diabetes and cancer and different rates of engagement between the two groups in relevant behavioral health activities such as smoking. There were higher utilization levels among IDS-TILDA participants for allied health and general practitioner visits.

Conclusion:

Different disease trajectories found among IDS-TILDA participants raise concerns. The longitudinal comparison of data for people with ID and for the general population offered a better opportunity for the unique experiences of people with ID to be included in data that inform health planning.

Keywords

longitudinal intellectual disability utilization health

One of the most significant recent developments in longitudinal studies of aging has been a growing focus on health disparity within populations (McCallion, 2012). This in turn helps with the discovery of contributors and predictors of persons living to advanced old age without ill-health or successful aging among diverse groups. The significance of increased longevity among adults with lifelong disabilities and/or with multiple chronic conditions can thus be explored (McCallion, 2009; Tappen & Ouslander, 2010). Still largely unresearched in longitudinal studies on aging and important to the unique experiences of people with ID are the similarities and differences compared to the general population in the aging of people with an intellectual disability (ID; McCarron et al., 2013) and concern that when considering the interaction of chronic conditions, preexisting intellectual impairment in the ID population implies triple complexity (McCarron et al., 2013). Historically, limited data have been available to support the comparisons of key determinations of health and well-being for older people with lifelong disability (i.e., those with ID) and those without disability.

Similar to the general population, there have been marked changes in the life expectancy of persons with ID in Ireland particularly increases in the numbers of those aged 55 years and over (F. Kelly & Kelly, 2011), but this prolonged living remains less than that of the general population (Lavin, McGuire, & Hogan, 2006). Possible explanations for mortality differences are poorly understood, and methodological concerns such as small unrepresentative samples, cross-sectional designs, lack of control population, and differences in measurement tools and scales have hampered our ability to draw meaningful comparisons regarding those factors that influence health and well-being of people with ID as they grow older (Haveman et al., 2010). Yet good quality information is needed to influence future planning, policies, services, and supports to increase the likelihood that allocations of resources will be sufficient to meet the changing needs of persons with ID who are aging. Some key areas for understanding the aging of persons with ID suggested by the international literature and considered here include physical and mental health status and use of health services (Bittles et al., 2002; van Schrojenstein Lantman-De Valk, Metsemakers, Haveman, & Crebolder, 2000).

Physical and Mental Health Status

From international studies, individuals with ID appear to have a greater variety of health-care needs compared to those others without ID of the same age and sex in the general population (U.S. Department of Health, 2002). A Dutch study compared 318 people with ID within a general practice with others and found that people with ID had 2.5 times the health problems of those without such lifelong disabilities (van Schrojenstein Lantman-De Valk et al., 2000). A higher prevalence (more than one third) of psychiatric disabilities has also been reported for older people with ID (Cooper, Smiley, Morrison, Williamson, & Allan, 2007). Bhaumik et al. highlighted higher psychiatric morbidity among older (compared with younger) adults with ID. The initiation of a longitudinal study of a nationally representative sample of adults with ID is warranted to help confirm these largely cross-sectional findings, so that similarities and differences with the general population become better established.

Health Service Utilization

It has been reported that people with ID are more likely (compared to the general population) to lead unhealthy lifestyles and to not access health promotion and health screening services, contributing to physical ailments in later life (Department of Health and Children, 2001; Evenhuis, Theunissen, Denkers, Verschuure, & Kemme, 2001; Iacono & Sutherland, 2006; Jones & Kerr, 1997; Kerr, Dunstan, & Thapar, 1996; World Health Organization, 2001). Health problems of persons with ID are not being recognized (Cooper, Melville, & Morrison, 2004) and the lack of specialist knowledge and training is contributing to this poor identification of health needs (Gilbert, Todd, & Jackson, 1998; Kerr et al., 2006; Singh, 1997). Different patterns of comorbidity and different influences on likelihood of health promotion practices influence both utilization and its effectiveness (McCallion, Burke, Swinburne, McGlinchey, & Carroll, 2013; McCarron et al., 2013). Davis, Proulx, and van Schrojenstein Lantman-de Valk (2014) highlighted the lower incidence of cancer overall among the ID population but higher prevalence of specific cancers such as gastrointestinal and esophageal.

Better understanding of the patterns of health-care use and nonuse among people with ID as compared to the general population will help improve understanding of these issues and yield clues to strategies for health improvement and greater longevity as people with ID age. It was with these issues in mind that an Intellectual Disability Supplement to The Irish Longitudinal Study on Ageing (IDS-TILDA; McCarron et al., 2011) was developed and a baseline comparison of subjects with ID and subjects in the larger sample was completed.

Method

Data for comparisons were drawn from the 2010 The Irish Longitudinal Study on Ageing (TILDA) and IDS-TILDA Wave 1 data sets.

Samples

TILDA established a stratified sample of 10,128 households (8,178 individuals aged 50 years and older) drawn from the Irish Geodirectory with 6,282 households interviewed, for a 62% response rate. The sample was stratified by socioeconomic group and geography to establish a sample representative of the population. Each participant completed an in-person interview and returned an independently completed questionnaire. A number of participants were also invited to complete a physical health assessment at a dedicated center or in their own home (Barrett et al., 2011).

For IDS-TILDA, given the greater concentration of individuals with ID in community and institutional settings compared to the general population and the poor representation of people with ID in the geodirectory, a random sample of adults with ID had to be recruited separate from TILDA.

The IDS-TILDA sample was drawn from the National Intellectual Disability Database (NIDD) that collates information on all people with an ID in the Republic of Ireland eligible for or receiving services (C. Kelly, 2012; F. Kelly & Kelly, 2011). At the time of sampling, there were 26,066 people registered on the NIDD. A representative sample of 1,800 people aged 40 years and over were identified from the database and invited to participate in the IDS-TILDA study. The chronological age of 40 years and over was selected (not age 50 as in TILDA) to denote an older individual with ID in NIDD as opposed to age 50 in TILDA because of the lower life expectancy for some individuals with ID (Heller & Sorensen, 2013; Leeder & Dominello, 2005). This decision was made to ensure that opportunities for insights into aging for those who may age prematurely is provided. Of the sample of 1,800 persons with ID aged 40 and over from the NIDD, 753 persons with an ID consented to participate in the study. This represents an overall response rate of 46% and 8.9% of the total population of persons aged 40 and over registered on the 2008 NIDD database. A comparison with the published demographics of the 2008 NIDD cohort confirmed that the IDS-TILDA random sample was also representative of the larger NIDD sample. For the data collection of IDS-TILDA, participants or someone they designated to assist completed an in-person interview and a returned questionnaire. This was usually the person’s key worker, other paid staff, or a family member with 37% of the interviews completed by a proxy respondent. In the cases of key workers or other paid staff, it was required that the person had worked with the individual for at least 6 months. In Wave 1 of the IDS-TILDA, there was no physical health assessment.

A special issue on TILDA published by The Journal of the American Geriatrics Society highlighted the value in linking longitudinal studies and in having shared questions among such studies. Despite differences in their approaches to the development of their initial samples, both TILDA and IDS-TILDA achieved representative Irish samples and they have the additional advantages that their baseline or Wave 1 data collections occurred in the same year, many survey questions were the same, training of data collectors was similar, and both relied upon computer-assisted interviewing and related protocols to maintain quality and consistency in data collection.

Matching Strategy

Given that the TILDA data set contains participants aged 50 and over, IDS-TILDA participants younger than 50 years of age were excluded from the analysis in this study, resulting in 478 persons with ID. They were then matched with TILDA participants on the variables age, sex, and geographic location. The geographic location was included as a matching variable. As TILDA data were drawn from the Irish Geodirectory, the opportunity was taken to ensure that the IDS-TILDA data matched in terms of their urban, suburban, and rural distribution. Geographic location was chosen because while IDS-TILDA was selected randomly from NIDD, the TILDA sample was generated from postal addresses and geographic location was a critical variable in ensuring representativeness. Traditionally, longitudinal data sets were also matched using educational levels (Savva et al., 2013), but low levels of completing first and second-level education among people with ID compared to TILDA respondents (73.2% vs. 1.5% in those aged 50–59 years and 76% vs. 2% in those aged 60–69 years) meant that education was not a useful matching variable. Age and sex are used as matching variables because of the extensive evidence of their contributions to differences in the health and social lives of older adults (Verdonschot, de Witte, Reichrath, Buntix, & Curfs, 2009). It should be noted though that the prevalence of ID tends to be higher among males than females (Maulik & Harbour, 2002).

Both data sets were first cleaned and variables recoded to ensure uniformity in data presentation. Extraneous variables (and for IDS-TILDA, cases) were deleted, and the two data sets merged. In order to match participants in different groups within an observational study, a propensity score approach was used. Propensity score matching is a method of generating a single score based on observed covariates in order to match participants in one group in an observational study with participants in a second group (in this case, matching participants with an ID to participants from the general population; Thoemmes, 2012). Propensity score matching was then completed in SPSS 20 using the R-plugin and the “psmatching” custom dialogue. Nearest neighbor matching without replacement was used based on a greedy matching algorithm with a caliper of .15 of the standard deviation of the logit of the propensity score (to reduce potential imbalances among matches). Given that several covariates were represented, a single propensity score was generated which can be viewed as the absolute difference between individuals in each group. A data set of 998 matched participants was then generated.

Measures

Both studies gathered self-reported data on individuals’ health as well as social and economic circumstances. The IDS-TILDA protocol largely corresponded to the TILDA protocol to support analysis of determinants of well-being and health for both groups. Measures addressed physical and mental health, behavioral health, functional limitation, and health-care utilization and are listed with values in Table 1.

Table 1.

Variables, Question Asked, and Response Options.

Variable	Question asked	Possible Responses
Physical health
Diagnosed hypertension	Has a doctor ever told you that you have high blood pressure or hypertension?	Yes	No
Diabetes	Has a doctor ever told you that you have diabetes or high blood sugar?	Yes	No
Heart attack	Has a doctor told you that you have had a heart attack (including myocardial infarction or coronary thrombosis)?	Yes	No
Cancer	Has a doctor ever told you that you have cancer or a malignant tumor (including leukemia or lymphoma but excluding minor skin cancers)?	Yes	No
Lung disease	Has a doctor ever told you that you have chronic lung disease such as chronic bronchitis or emphysema?	Yes	No
Diagnosed emotional nervous or psych condition	Has a doctor ever told you that you have an emotional, nervous, or psychiatric condition?	Yes	No
Poor self-rated health	Would you say your health is…?	Fair/poor	Good/very good/excellent
Poor self-rated eyesight	Is your eyesight (using glasses or contact lenses if you use them)…?	Fair/poor	Good/very good/excellent
Poor self-rated hearing	Is your hearing (with or without a hearing aid)…?	Fair/poor	Good/very good/excellent
≥Two chronic conditions	Self-report presence of range of chronic conditions recoded as two or more chronic conditions	Yes	No
Behavioral health
Frequent alcohol consumption	During the last 6 months, how often have you drunk any alcoholic beverages, like beer, cider, wine, spirits, or cocktails?	Drinking alcohol 3 or 4 times or more often in a week	Drinking alcohol less than 3 or 4 times in a week
Current smoker	Do you smoke at this present time?	Yes	No
Ever smoked	Have you ever smoked cigarettes, cigars, cigarillos, or a pipe daily for a period of at least 1 year?	Yes	No
Overweight/obese	In general, would you consider yourself to be overweight, underweight, or about the right weight?	Overweight\obese	Normal\underweight
Low exercise level	How often do you take part in vigorous, moderate, or mildly energetic levels of physical activity? Recoded into low and moderate/high levels	Low	Moderate\high
Functional limitations
≥2 ADLs	Barthel index of ADLs used and recoded as presence of two or more ADLs	Yes	No
≥2 IADLs	Barthel index of activities of instrumental daily living used and recoded as presence of two or more ADLs	Yes	No
Limited mobility	Presence of functional limitations in walking, running, sitting, climbing stairs, stooping, kneeling, reaching/extending arms; pulling/pushing; picking up small coin; lifting/carrying 10 pounds	Yes	No
Disability limits activities	Do these conditions limit your activities in any way?	Yes	No
Health-care utilization
≥1 Emergency department visits	In the last year, about how often did you visit a hospital emergency department (sometimes called A&E or Accident and Emergency) as a patient? recoded as more than one visit	Yes	No
≥2 GP visits	In the last year, about how often did you visit your GP or did your GP visit you? recoded as two or more visits or less than two visits	Yes	No
≥1 Nights in general hospital	In the last year, how many nights did you spend in a general hospital? recoded as more than one night	Yes	No
≥1 Outpatient visits	In the last your, about how many visits did you make to a hospital outpatient department? recoded as more than one visit	Yes	No
Visits from public health nurse	In the last year, did you receive…	Yes	No
Dental services	In the last year, did you receive…	Yes	No
Dietitian services	In the last year, did you receive…	Yes	No
Optician services	In the last year, did you receive…	Yes	No
Chiropody services	In the last year, did you receive…	Yes	No
Physiotherapy services	In the last year, did you receive…	Yes	No
Hearing services	In the last year, did you receive…	Yes	No
Occupational therapy	In the last year, did you receive…	Yes	No

Note. ADLs = activities of daily living; IADLs = instrumental activities of daily living.

Physical and mental health variables included a doctor’s previous diagnosis of hypertension (yes/no); a doctor’s previous diagnosis of diabetes (yes/no); a doctor’s previous diagnosis of heart attack (yes/no); a doctor’s previous diagnosis of emotional, nervous, or psychiatric conditions; self-rated health measured on a Likert-type scale (very good to poor); self-rated eyesight measured on a Likert-type scale (very good to poor) and registered legally blind; self-rated hearing measured on a Likert-type scale (very good to poor) and registered legally deaf; and co-occurrence of two or more previously diagnosed chronic conditions (glaucoma, cataracts, or any other eye disease; angina, heart attack, or congestive heart failure; hypertension; endocrine disease; joint disease; chronic lung disease; gastrointestinal disease; mental health condition; stroke; cancer; neurological disease; and liver damage).

Behavioral health variables included drinking alcohol “3 or 4 times” or more often in a week, current and past smoking, and body mass index and level of exercise.

Functional limitation variables included difficulty with two or more activities of daily living (ADLs; dressing, walking, bathing, or showering, cleaning teeth/taking care of dentures, eating, getting in or out of bed, and using the toilet), difficulty with two or more instrumental activities of daily living (IADLs; preparing a hot meal, shopping for groceries, making telephone calls, managing money, and doing household chores), and mobility difficulties (walking 100 yards, getting up from a chair after sitting for long periods, climbing one flight of stairs without resting, stooping, kneeling, or crouching).

Health-care utilization variables included self-reported emergency department visits, General Practitioner (GP) visits, nights in a general hospital, and outpatient visits over the previous 12 months. Also included was participant use of any of the following services in the previous 12 months: dental, dietitian, optician, chiropody, physiotherapy, hearing, and occupational therapy.

Analysis

Thirty-one health-related variables were included in a descriptive comparison in which prevalence of physical and mental health, behavioral health, functional limitation, and health-care utilization was graphed by age category and by TILDA and IDS-TILDA. Crosstabs were used to establish the relevant percentages, and χ² tests for independence were used to test for significant associations between IDS-TILA/TILDA on the relevant health-related variable. The variables are shown in Table 1. Analyses, recognizing that there are varying levels of missing data on some items, report on valid percentages for descriptive statistics and pairwise deletion addressed missing data issues in χ² analyses.

Results

As expected, given the matching process, there were no significant differences found between the IDS-TILDA and TILDA samples. There was equal geographic dispersal throughout the Republic of Ireland, and just over 50% of individuals in both groups were aged 50–59 years, 30% were aged 60–69, and the remainder were aged 70+ years (see Table 2). Both TILDA and IDS-TILDA participants had a mean age of 60 years and a median age of 58 years. Approximately, both groups were 40% male and 60% female.

Table 2.

Demographic Profile.

Variable	TILDA			IDS-TILDA
	n = 478	%	95% CI	n = 478	%	95% CI
Gender
Male	288	60.3	[55.7, 64.5]	275	57.5	[53.0, 61.8]
Female	190	39.7	[35.4, 44.2]	203	42.5	[38.1, 46.9]
Age-groups
50–59	259	54.2	[49.7, 58.5]	266	55.6	[51.1, 60.0]
60–69	151	31.6	[27.5, 35.8]	151	31.6	[27.5, 35.8]
70+	68	14.2	[11.3, 17.6]	61	12.8	[10.0, 16.0]
Region
Border	84	17.6	[14.4, 21.2]	48	10	[7.6, 13.0]
Midland	61	12.8	[10.0, 16.0]	28	5.9	[4.0, 8.3]
West	43	9	[6.7, 11.9]	67	14	[11.1, 17.4]
Dublin	28	5.9	[4.0, 8.3]	144	30.1	[26.1, 34.3]
Mid-East	34	7.1	[5.1, 9.8]	36	7.5	[5.4, 10.2]
Mid-West	104	21.8	[18.2, 25.6]	45	9.4	[7.0, 12.3]
South East	55	11.5	[8.9, 14.6]	50	10.5	[8.0, 13.5]
South West	69	14.4	[11.5, 17.8]	60	12.6	[9.8, 15.8]

Note. ADLs = activities of daily living; IADLs = instrumental activities of daily living; IDS = Intellectual Disability Supplement; TILDA = The Irish Longitudinal Study on Ageing.

A number of significant differences were found in the reported presence of chronic conditions with higher rates for people in the IDS-TILDA sample of reported doctor’s diagnosis of diabetes (11.1% vs. 6.5%), emotional nervous or psychological conditions (53.4% vs. 8.2%), and poor self-rated eyesight (19.7% vs. 6.9%); see Table 3. However, there were higher rates overall of diagnosed hypertension among the TILDA sample (31.8% compared to 20.6%; see Table 1). Participants in IDS-TILDA were also more likely to report two or more chronic conditions. Differences in self-rated health were not significant.

Table 3.

Percentages for Physical Health, Behavioral Health, Mobility, Social Participation, and Health Service Utilization by Age Category IDS-TILDA Participants Compared With Matched TILDA Study Participants.

Variable	IDS-TILDA n (%)	95% CI	N	TILDA n (%)	95% CI	p Value	N
Physical health
Diagnosed hypertension	96 (20.6)	[16.9, 24.3]	465	152 (31.8)	[27.6, 36.0]	<.001	478
Diabetes	52 (11.1)	[8.25, 14.0]	468	31 (6.5)	[4.3, 8.7]	.012	478
Heart attack	7 (1.5)	[0.4, 2.6]	474	15 (3.1)	[1.6, 4.7]	.088	478
Cancer	27 (5.6)	[3.6, 7.7]	469	29 (6.1)	[3.9, 8.2]	.84	478
Lung disease	16 (3.4)	[1.7, 5.0]	472	16 (3.3)	[1.7, 5.0]	.971	478
Diagnosed emotional nervous or psych condition	244 (53.4)	[48.8, 58.0]	457	39 (8.2)	[5.7, 10.6]	<.001	478
Poor self-rated health	74 (15.5)	[12.3, 18.8]	476	94 (19.7)	[16.1, 23.2]	.095	478
Poor self-rated eyesight	93 (19.7)	[16.1, 23.3]	472	33 (6.9)	[4.6, 9.2]	<.001	478
Poor self-rated hearing	64 (13.4)	[10.3, 16.5]	477	55 (11.5)	[8.6, 14.4]	.371	478
≥ 2 chronic conditions	177 (46.3)	[41.3, 51.4]	382	169 (35.4)	[31.1, 39.7]	.001	478
Behavioral health
Frequent alcohol consumption	12 (2.5)	[1.1, 3.9]	450	101 (22.4)	[18.6, 26.3]	<.001	478
Current smoker	48 (10.0)	[7.3, 12.7]	478	87 (18.2)	[14.7, 21.7]	<.001	478
Ever smoked	91 (19.0)	[15.5, 22.6]	478	260 (54.4)	[49.9, 58.9]	<.001	478
Overweight/obese	222 (59.5)	[54.5, 64.5]	373	283 (78.4)	[74.1, 82.7]	<.001	361
Low exercise level	360 (75.5)	[71.6, 79.3]	477	126 (26.7)	[22.7, 30.7]	<.001	472
Functional limitations
≥2 ADLs	252 (52.7)	[48.2, 57.2]	478	15 (3.1)	[1.6, 4.7]	<.001	478
≥2 IADLs	428 (89.5)	[86.8, 92.3]	478	19 (4.0)	[2.2, 5.7]	<.001	478
Limited mobility	269 (56.3)	[51.8, 60.7]	478	149 (31.2)	[27.0, 35.3]	<.001	478
Disability limits activities	170 (35.6)	[31.3, 39.9]	478	92 (19.2)	[15.7, 22.8]	<.001	478
Health-care utilization
≥1 Emergency department visits	92 (20.4)	[16.6, 24.1]	452	75 (15.7)	[12.4, 19.0]	.064	478
≥2 GP visits	410 (93.0)	[90.6, 95.4]	441	322 (67.4)	[63.1, 71.6]	<.001	478
≥1 Nights in general hospital	51 (11.6)	[8.6, 14.6]	441	64 (13.4)	[10.3, 16.4]	.404	478
≥1 Outpatient visits	224 (52.0)	[47.2, 56.7]	478	200 (41.8)	[37.4, 46.3]	.002	478
Visits from public health nurse	56 (11.7)	[8.8, 14.6]	478	21 (4.4)	[2.5, 6.2]	<.001	478
Dental services	283 (59.2)	[54.8, 63.6]	478	55 (11.5)	[8.6, 14.4]	<.001	478
Dietitian services	128 (26.8)	[22.8, 30.8]	478	8 (1.7)	[0.5, 2.8]	<.001	478
Optician services	224 (46.9)	[42.4, 51.3]	478	52 (10.9)	[8.1, 13.7]	<.001	478
Chiropody services	326 (68.2)	[64.0, 72.4]	478	21 (4.4)	[2.6, 6.2]	<.001	478
Physiotherapy services	129 (27.0)	[23.0, 31.0]	478	20 (4.2)	[2.4, 6.0]	<.001	478
Hearing services	60 (12.6)	[9.6, 15.5]	478	9 (1.9)	[0.7, 3.1]	<.001	478
Occupational therapy	110 (23.0)	[19.2, 26.8]	478	3 (0.6)	[−0.1, 1.3]	<.001	478

Note. ADLs = activities of daily living; IADLs = instrumental activities of daily living; IDS = Intellectual Disability Supplement; TILDA = The Irish Longitudinal Study on Ageing.

TILDA participants were significantly more likely to more frequently consume alcohol (22.4% vs. 2.5%), to currently smoke (18.2% vs. 10%), to have ever smoked (54.4% vs. 19%), and to be overweight or obese (78.4% vs. 59.5%), but IDS-TILDA participants were significantly more likely to have low levels of exercise (75.5% vs. 26.7%). Limited mobility (56.3% vs. 31.2%) and disabilities limiting activities were significantly more prevalent (35.6% vs. 19.2%) in IDS-TILDA participants as was having two or more ADL (52.7% vs. 3.1%) and IADL limitations (89.5% vs. 4%).

There were also differences in the levels of health-care utilization, with IDS-TILDA participants reporting significantly higher usage of GP (93% vs. 67.4%), public health nurse visits (11.7% vs. 4.4%), and outpatient services (52% vs. 41.8%). There were also significant differences in the use of dental, dietitian, optician, chiropody, physiotherapy, hearing, and occupational therapy services. Significant differences in the use of chiropody (68.2% vs. 4.4%), physiotherapy (27% vs. 4.2%), occupational therapy (23% vs. 0.6%), and dental services (59.2% vs. 11.5%) were particularly dramatic. When these overall trends are reexamined by age-group (50–59, 60–69, and 70+ years) and graphed, other differences emerge.

Physical and Mental Health

For chronic conditions (see Figure 1), trends and prevalence rates by age-group for hypertension, heart attack, and lung disease appeared very similar for both IDS-TILDA and TILDA participants, with rates for hypertension and heart attack a little higher for TILDA participants in the oldest age-group (for the 70+ age-group: 54.4% vs. 37.7% for hypertension and 11.8% vs. 5.0% for heart attack). Also, for vision problems and having two or more chronic conditions, there were consistent trends across age-groups with prevalence rates to be greatest for IDS-TILDA participants. For diabetes, somewhat higher prevalence rates of disease (9.5% vs. 3.9% for the 50–59 age-group) among IDS-TILDA participants were greater for the 60–69 age-group; however, by age 70 (8.2% vs. 10.3%), the trend was toward lower prevalence as compared to TILDA participants.

Figure 1.

Chronic conditions.

Conversely, the rates for cancer were higher for TILDA participants (6.2% vs. 3.4%), but this was reversed for the 60–69 and over 70 age-groups with higher rates for the IDS-TILDA group (10% vs. 4.4% for participants age 70+). The most significant difference was in rates of doctor-diagnosed emotional, nervous, and psychiatric conditions, with TILDA participants reporting rates of 10.4% at age 50–59 years and a rate of 2.9% by age 70 and older. For IDS-TILDA participants, rates were 49.4% at age 50–59, 58.9% at age 60–69, and 56.7% at age 70 and older. Finally, the rates for poor self-rated health reports were higher with age for IDS-TILDA participants and poor hearing was higher with age for TILDA participants.

Behavioral Health

Levels of frequent alcohol consumption were consistently lower for IDS-TILDA participants (e.g., 1.6% for the 70+ years group in IDS-TILDA compared to 26% for the same age TILDA group), but the picture for smoking was more complex; see Figure 2. Current smoking and having ever smoked were much lower among IDS-TILDA participants aged 50–59 years, but prevalence rates were higher for both current and ever smoked and exceeded TILDA rates for the 60–69 and 70+ years groups (21% vs. approximately 9% for the 70+ years group).

Figure 2.

Behavioral health.

Rates of overweight/obesity appeared to be lower among older IDS-TILDA participants (from 62% for persons aged 50–59 years to 47.9% for persons aged 70+) whereas rates were higher with age for TILDA participants (from 77.7% for persons aged 50–59 years to 84.8% for persons aged 70+). However, 70–83% of IDS-TILDA participants (rising with age) had low levels of exercise compared to 26–33% of TILDA participants (also rising with age).

Need for Assistance

As may be seen in Figure 3, there were higher needs for assistance reported for IDS-TILDA participants than for TILDA participants. The number with more than two ADL or IADL needs of TILDA participants was in the 1–5% range regardless of age, but 47% of IDS-TILDA participants had two or more ADL needs in the 50–59 age-group and this rose to 70.5% by age 70; and 86.5% had IADL needs in the 50–59 age-group and this rose to 95.1% by age 70. Mobility limitation increased from 23.9% of TILDA participants in the 50–59 age-group to 39.7% in the 60–69 age-group and held steady at this level for older participants. For IDS-TILDA participants, 51.5% had mobility limitations in the 50–59 age-group. This rose to 57% for the 60–69 group but continued to rise to 75.4% for the 70+ group. The combination of ADL, IADL, and mobility needs in the TILDA group resulted in 17.8% of the 50–59 age-group reporting that disability limited their activities, and this rose to 23.5% for the 70+ age-group. For IDS-TILDA participants (who have lifelong disability), 32.7% of the 50–59 age-group reported that disability limited their activities, and this rose to 39.3% for the 70+ age-group.

Figure 3.

Need for assistance.

Health-Care Utilization

Use of emergency departments was lowest for TILDA participants in the age-group of 60–69 years and highest for those over age 70+ (16.6% for age 50–59, 13.2% for age 60–69, and 17.6% for age 70+). For IDS-TILDA participants, utilization steadily increased with age (18.3% for age 50–59, 22.1% for age 60–69, and 24.6% for age 70+). As seen in Figure 4, two or more GP visits were true for 71.4% of TILDA participants aged 50–59 years and this gradually declined to 58.8% by age 70 and older. For IDS-TILDA participants, rates climbed from 88.9% of participants at age 50–59 years to 98.6% for age 60–69 and 96.6% for age 70 and older. Rates of hospitalization in the previous 12 months were 13.9% of TILDA participants spending one or more night in a hospital for those aged 50–59 dropping to 10.6% for those aged 60–69 and then rising to 17.6% for those aged 70 and older. For IDS-TILDA participants, rates rose from 9.5% (50 to 59 year-olds) to 10.7% (60 to 69 year-olds) and then to 22.4% for those aged 70 and older.

Figure 4.

Health-care utilization.

In terms of use of other health services, as seen in Figure 4, rates were consistently higher for IDS-TILDA participants with highest percentage usage of dental, optician, physiotherapy, and outpatient services being among those 60–69 years old and higher rates of dietitian, hearing, and public health nurse services for the 70+ age-group. By age 70 years, only use of outpatient services was approaching similar rates (44.1% and 55.4%, respectively) for TILDA and IDS-TILDA participants.

Discussion

The comparison between the two data sets on prevalence of chronic conditions reported here offers interesting data to be considered and confirmed in incidence plotting in future waves of data collection. There were similar trends between the two groups for increased hypertension, heart attack, lung disease, and hearing concerns. There are of course concerns to be addressed as to whether there are the same rates and activities in identification of health conditions for the two populations. Also, TILDA reports that there were significant increases in rates of hypertension in the subsample where it was measured as compared to the same subjects’ self-report (Barrett et al., 2011). This is yet to be investigated for people with ID but plans by IDS-TILDA to systematically and independently measure blood pressure in all participants in Wave 2 data collections will hopefully provide a better picture of hypertension prevalence in people with ID in Ireland.

Diabetes and cancer provided a different picture. In the case of diabetes, higher rates were initially observed in IDS-TILDA participants with rates becoming lower in older age. This was different from the TILDA findings of increasing prevalence. Also, cancer prevalence was lower among the younger age cohort of IDS-TILDA participants when compared with TILDA participants. In the 70 years and older age cohort, however, cancer prevalence rates among IDS-TILDA participants were twice as high compared to TILDA participants. This may be indicative of underdiagnosis of cancers among people with ID in younger age cohorts or of different types of cancers or lower mortality rates when cancer is present. Further investigation is needed and additional data being gathered in IDS-TILDA Wave 2 and subsequent waves may help offer additional insights. Such differences raise questions about rates of mortality when cancer and/or diabetes are present for people with ID as a possible explanation for declining prevalence with age. It will also be of interest to investigate the typical types of cancers for people with ID and the general population in Ireland, age of onset, and the extent to which other chronic conditions or lifestyle issues predispose groups to cancers. This will be an important consideration for future waves of data collection and both TILDA and IDS-TILDA are now following up to gather additional information on deaths. Finally, although lung disease rates were similar, the finding that rates of smoking (although lower overall) were highest for the oldest adults with ID also deserves further investigation including possible links to chronic diseases such as cancers, diabetes, and hypertension.

Rates of chronic disease appeared higher overall for people with ID as compared to the general population (e.g., higher numbers have two or more chronic conditions), but it is of note that the rates of physical health chronic conditions found in IDS-TILDA were lower than prior reports of 2.5 times the rate of disease for people with ID (van Schrojenstein Lantman-De Valk et al., 2000). However, rates of diagnosed emotional, nervous, and psychiatric conditions were similar to international findings (Bhaumik et al., 2001).

Two possible explanations of better physical health than reported elsewhere for people with ID are behavioral health practices and health-care utilization. The lower levels overall of smoking, alcohol consumption, and overweight/obesity found among people with ID as compared to the general population in Ireland may help explain some of the differences in actual versus expected prevalence. However, the lower rates of exercise remain of concern in the behavioral health area.

Daily life was clearly more compromised for people with ID as highlighted by needs for assistance with ADLs, IADLs, and mobility concerns, and there was acknowledged concern that disabilities whether lifelong or late onset interfered more with daily activities for people with ID. It was noteworthy that there was higher use of physiotherapy and occupational therapy by people with ID, but it is not clear the extent to which this type of health-care utilization or indeed other health-care utilization is contributing to reducing the impact of functional limitations that increase requirements for assistance with activities. This is another area where longitudinal follow-up may provide helpful answers.

Again a key advantage of this study is that the two data sets have been demonstrated to be representative of the total populations over 50 being compared increasing the value of the findings. It is possible that the current cohorts look different from upcoming cohorts of both the general population and the population of people with ID. It will be important, as IDS-TILDA and TILDA proceed through subsequent waves, including any refreshing of the samples, to also compare across waves to identify the impact of cohort effects.

Clearly, there are challenges in the interpretation of the data posed by the TILDA sample being exclusively community based and the IDS-TILDA sample representing a mix of individuals in institutions, group homes, family, and independent living situations. However, the mix of living situations described for people with ID is representative of their lives and the percentage of the general population in nursing homes is small. Comparisons across all health and health-care utilization sectors are still valid, as they highlight the differences at a population level for aging people with ID as compared to the general aging population.

Study Limitations

There were several potentially important differences between the two studies which might have impacted the results and interpretations of this study. Because of the communication challenges experienced by some IDS-TILDA participants, there was greater reliance on proxy respondents, but training of interviewers and commitment to inclusion meant that the majority of respondents either responded for themselves or were present and contributed even though a proxy answered some questions. It did mean, however, that there were varying levels of missing data for questions, and some items were only answered by those who could speak for themselves.

Another concern was that TILDA excluded people living in nursing homes/residential placements while given the population of interest, people with ID, the IDS-TILDA sample included people in institutions and group homes as well as family and independent living situations. This difference does introduce the possibility that a greater percentage of the healthiest older adults in Ireland are more likely to be found among TILDA rather than in IDS-TILDA participants. Certainly, examination of the IDS-TILDA data has found that institutional placement was more likely associated with severe and profound ID and with higher rates of chronic conditions (McCarron et al., 2011). Nevertheless, out-of-home placement is a greater reality for people with ID than the general population, and with many, particularly in group home settings, utilizing the same community-based health services, it is important that this group be included in comparisons with the general older population. A final limitation is the reliance on self-reported rather than measured data for chronic conditions, behavioral health, and health-care utilization. TILDA has gathered some physical measures, and a range of physical measures were gathered during IDS-TILDA Wave 2. This objective data will be compared with the self-reported data so a more definitive consideration of the impact of self-report on data quality will be possible. However, the ability to compare responses across the two questionnaires (self-completed and direct interview) found high reliability in responses. It is more likely that completion of physical measures will identify unknown rather than denied chronic conditions.

More generally in terms of issues for people with ID, future waves of IDS-TILDA will continue to explore how rates of response on key issues may be raised by testing questions where concordance between self and proxy is increased. As a study concerned with the lives of people with ID, there are also concerns that level of ID was not and cannot be independently ascertained. However, in future waves, there will be continued efforts to gather the most robust information on functioning.

Limitations of cross-sectional studies in general include difficulties in making causal inference. Cross-sectional studies provide only a snapshot, meaning the situation may provide differing results if another time frame had been chosen. Another limitation is the occurrence of prevalence–incidence bias (also called Neyman bias; Delgado-Rodriguez & Llorca, 2004), especially in the case of longer lasting diseases, any risk factor that results in death will be underrepresented among those with the disease. However, cross-sectional data are particularly useful starting point when there is an opportunity to compare with a second cross-sectional data set that is representative of the population.

Conclusion

A great value in longitudinal studies of aging is that they help track prevalence of risk factors for and contributors to disease occurrence and progress and to quality of life. They also offer evidence to support decisions about the allocation and utilization of resources to maintain people in the communities of their choice, improve health care, and achieve key societal goals (McCallion, 2009; Tappen & Ouslander, 2010). As noted here, the incidence and prevalence of risk factors and contributors and the experience of wellness, ill-health, and challenges to independence are sometimes different for people with ID. The longitudinal gathering and consideration of the same types of data for people with ID and for the general population offers a better opportunity that the unique experiences of people with ID will be considered and included in the data that inform such planning.

Footnotes

Acknowledgments

The authors thank the IDS-TILDA team and IDS-TILDA participants. Particular thanks to the funders: the Health Research Board and the Department of Health in Ireland.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Health Research Board of Ireland and Department of Health in Ireland.

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