Cross-Country Differences in the Additive Effects of Socioeconomics, Health Behaviors and Medical Comorbidities on Disability among Older Adults with Heart Disease.

BACKGROUND: Patients with heart disease experience limited activities of daily living (ADL). This is a cross-country comparison of the additive effects of Socioeconomics, health behaviors, and the number of medical comorbidities on disability among patients with heart disease.
METHODS: The current study used a cross-sectional design. Data came from the Research on Early Life and Aging Trends and Effects (RELATE). The current analysis utilized data on elderly individuals (age ≥60 y) from 13 countries. The outcome was any ADL limitation (i.e. bathing, dressing, using toilet, transferring, lifting heavy things, shopping, and eating meals). Socioeconomics (i.e. age, gender, education, and income), health behaviors (i.e. exercise, smoking, and drinking), and number of chronic medical conditions (i.e. hypertension, respiratory, arthritis, stroke, and diabetes) were entered into country-specific logistic regressions, considering at least one limitation in ADL as the main outcome.
RESULTS: Number of comorbid medical conditions and age were positively associated with disability in 85% of the countries. Physical activity and drinking were linked to disability in 54%and 31% of countries, respectively. Higher education and income were associated with lower disability in 31% and 23% of the countries, respectively. Female gender was associated with higher disability only in 15% of the countries. Smoking was not associated with disability, while the effects of socioeconomics, drinking, exercise, and medical comorbidities were controlled.
CONCLUSION: Determinants of disability depend on the country; accordingly, locally designed health promotion interventions may be superior to the universal interventions for patients with heart disease. Medical comorbidities, however, should be universally diagnosed and treated.

Introduction

Functional limitation of patients with heart disease has been documented consistently by cross-sectional and longitudinal studies.[1, 2] In a recent study conducted in 15 countries, with no exception, heart disease was associated with poor subjective health, above and beyond the effect of socioeconomics.[3] The study, however, showed cross-country differences in the interactions between socioeconomic factors and heart disease in shaping well-being of populations. Heart disease had a larger effect on subjective health of the elderly in the U.S. and China, women in the U.S., South Africa, and India, low-income people in China and Costa Rica, and individuals with low education in Uruguay and Ghana.[3]

Most of the research on the determinants of disability and the well-being of patients with heart disease has focused on either psychological, clinical, or behavioral characteristics.[4] Thus, less is known about the additive effects of social, behavioral, and comorbid conditions.

Symptoms associated with heart disease may result in withdrawal from social activities.[5] Multiple aspects of the daily life of patients with heart disease may be influenced by the condition.[4] Heart disease may interfere with relationship, eating, and sexual activity of patients.[4] Heart disease may be accompanied with a wide range of symptoms (e.g. dyspnea, tiredness, and fatigue) leading to functional limitation.[6, 7] Patients with heart disease experience limitations in activities of daily living (ADL).[7] Impaired functional capacity and disturbing symptoms reduce health-related quality of life of patients with heart disease.[5, 8] Additional research on the effect of the social determinants of the well-being of patients with heart disease is needed.

Socioeconomic factors influence the well-being and function of individuals.[9-12] Old age is associated with limitation in function and impaired well-being, physical, and mental health.[13, 14] Gender also influences perceived health, with women tending to report higher levels of disability and morbidity.[15, 16] Low socioeconomic status impairs health and well-being.[17, 18] Education and income, the most commonly accepted proxies of socioeconomic position,[19] are associated with subjective health, chronic disease, and mortality.[20-24] Individuals with high education and income commonly report better quality of life and function.[14]

Health behaviors also influence the well-being and disability of individuals.[10] Physical activity, drinking, and smoking influence well-being and disability.[25] Physical activity and exercise reduce the likelihood of health-related disability, especially during old age, and improve health-related quality of life.[26-30] Total time spent physically active is positively related to quality of life.[31-36] Drinking, smoking, and physically inactive life style carry individual risks to ADL, especially later in life.[37-40]

Chronic medical conditions associated with heart disease are also major causes of morbidity and mortality. Most studies have documented lower health and well-being, functional status, and health-related quality of life in the presence of chronic medical conditions.[41-51]

Although we already know that patients with heart disease experience and report functional limitations, the contribution of various determinants on disability may differ across countries.[3, 10–12] Unfortunately, our information is very limited about cross-country differences in the additive effects of determinants of disability associated with heart disease.

In response to the gap of knowledge on cross-country variations in the determinants of disability among patients with heart disease, we compared[13] countries for the additive effects of social, behavioral, and medical determinants and disability among older adults with heart disease. This analysis included countries from America, Asia, and Africa.[52, 53]

Methods

This study had a cross-sectional design. We used publicly available data of the Research on Early Life and Aging Trends and Effects (RELATE), a collection of multiple surveys from different countries across the world.[53] The RELATE data are composed of the following surveys: 1) Wisconsin Longitudinal Study; 2) China Health and Nutrition Study; 3) Chinese Longitudinal Healthy Longevity Survey (CLHLS); 4) Costa Rican Study of Longevity and Healthy Aging; 5) Puerto Rican Elderly: Health Conditions; 6) Study of Aging Survey on Health and Well Being of Elders; and 7) WHO Study on Global Ageing and Adult Health.[52, 53] Most but not all studies comprising RELATE have enrolled community-based samples. The sample size distribution of each country in the publicly available data is presented in Table 1.

Table 1.

Sample size distribution of the participating countries in the RELATE data

Country-Survey	Unweighted Frequency	Percentage
Costa Rica-CRELES	2827	3.2
Puerto Rico-PREHCO	4291	4.9
Barbados-SABE	1508	1.7
Brazil-SABE	2143	2.4
Chile-SABE	1301	1.5
Cuba-SABE	1905	2.2
Mexico-SABE	1247	1.4
Mexico-WHO/SAGE	4142	4.7
Uruguay-SABE	1450	1.6
India-WHO/SAGE	7150	8.1
Ghana-WHO/SAGE	4724	5.4
South Africa-WHO/SAGE	3830	4.3
Russia-WHO/SAGE	4511	5.1
China-WHO/SAGE	13368	15.1
China-CHNS	6452	7.3
China-CLHLS	16064	18.2

The original RELATE study enrolled more countries than were entered into the current analysis. This manuscript is limited to data from China, Costa Rica, Puerto Rico, Mexico, Barbados, Brazil, Chile, Cuba, Uruguay, India, Ghana, South Africa, and Russia

RELATE, Research on Early Life and Aging Trends and Effects (RELATE); CRELES, Costa Rican Longevity and Healthy Aging Study; PREHCO, Puerto Rican Elderly: Health Conditions; SABE, Survey on Health, Well-Being, and Aging in Latin American and the Caribbean; WHO, World Health Organization; SAGE, Study on Global Ageing and Adult Health; CHNS, China Health and Nutrition Survey; CLHLS, Chinese Longitudinal Healthy Longevity Survey

The participating countries represent a diverse range of national income levels and were selected from multiple continents.[52, 53] Ghana represents low-income countries; China and India represent lower middle-income countries; Cuba, Uruguay, Chile, Costa Rica, Brazil, Mexico, and Russia represent upper middle-income countries; and Puerto Rico and Barbados represent high-income countries.

Although the original RELATE study included a few other countries as well, countries participating in the current analysis were limited to those with available data on our variables of interest and included China, Costa Rica, Puerto Rico, Mexico, Barbados, Brazil, Chile, Cuba, Uruguay, India, Ghana, South Africa, and Russia.

The presence of self-reported physician diagnosis of heart disease and age over 65 years were considered as eligibility criteria. Self-reported data on physician-diagnosis of chronic medical conditions such as heart disease is valid and closely associated with the physician-diagnosis of heart disease and medical record data.[54]

Measures

The socioeconomic data included age (continuous variable), gender (dichotomous variable), education (continuous variable), and income (continuous variable). Income in this study was per capita annual household income calculated as purchase power parity dollars (PPP$).[55-57]

To provide the PPP$ or international dollar, costs (or incomes) in local currency units were converted to international dollars using PPP exchange rates. An international dollar is a hypothetical currency that is used as a means of translating and comparing costs from one country to the other using a common reference point, the US dollar. The PPP$ exchange rates are provided by the World Health Organization. A PPP$ exchange rate can be defined as the number of units of a country's currency required to buy the same amounts of goods and services in the domestic market as the U.S. dollar would buy in the United States.[54-56]

The number of comorbid medical conditions was calculated based on the presence of self-reported physician diagnosis of diabetes, respiratory conditions, stroke, hypertension, and arthritis. Self-reported data on chronic medical conditions are believed to be in agreement with physician diagnosis of conditions (kappa: 0.74–0.92).[54]

We approached physical disability from an operational point of view, focusing on limitations in ADL. Thus, our measure of ADL focused on very specific functions. The ADL items included in this study comprised bathing, getting dressed, going to the toilet, transferring, lifting heavy objects, shopping, and eating meals. These items have frequently been used to assess ADL in the community sample.[58-60]

Data were collected anonymously. All the studies have received approval by the institutional review boards. Informed consent was also provided by all the participants of all the surveys.

For the statistical analyses, the statistical software SPSS version 20.0 for Windows (SPSS Inc., Chicago, IL) was used. As weights were not applicable to surveys from China (CHNS), we did not apply sampling weights. Socioeconomic factors (age, gender, education, and income), health behaviors (exercise, smoking, drinking), and number of chronic medical conditions (hypertension, respiratory, arthritis, stroke, and diabetes) were entered into country-specific hierarchical logistic regressions. In the first step (Model I), we tested the main effects of socioeconomic factors. In the next step (Model II), we also entered health behaviors. In the third step (Model III), we also included the number of chronic medical conditions. Odds ratios (ORs) and 95% confidence intervals (95% CI) were reported. P less than 0.05 was considered statistically significant.

Results

The socioeconomic factors of the participants in each country have been reported elsewhere.[3, 10–12, 52]

In Model I, high age was predictive of ADL limitation in all the countries other than Uruguay, Ghana, and South Africa. Female gender was not associated with ADL limitation in most countries, with the exception of Mexico. In South Africa, the association between gender and ADL limitation was marginally significant. High income was linked to lower odds of ADL limitation only in Costa Rica and Puerto Rico. In Chile, the association between income and limitation in ADL was marginally significant. Higher education was associated with lower ADL limitation in Mexico, India, and Russia. In Chile, the association between education and ADL limitation was marginally significant (Table 2).

Table 2.

Cross-country differences in associations between socioeconomic factors and disability among patients with heart disease

	95% CI for Odds Ratio
	Odds Ratio	Lower	Upper	P value
China
Age	1.096	1.086	1.105	< 0.001
Female gender	1.133	0.908	1.414	0.270
Education	0.954	0.836	1.088	0.481
Income	1.001	0.967	1.036	0.957
Costa Rica
Age	1.089	1.060	1.118	< 0.001
Female gender	1.226	0.760	1.978	0.403
Education	0.815	0.544	1.222	0.322
Income	0.881	0.788	0.984	0.025
Puerto Rico
Age	1.023	1.000	1.046	0.050
Female gender	0.971	0.677	1.392	0.872
Education	0.971	0.778	1.214	0.798
Income	0.934	0.890	0.980	0.006
Mexico
Age	1.055	1.030	1.081	< 0.001
Female gender	1.755	1.153	2.672	0.009
Education	0.729	0.555	0.957	0.023
Income	1.028	0.973	1.086	0.323
Barbados
Age	1.116	1.064	1.172	< 0.001
Female gender	1.126	0.475	2.668	0.788
Education	0.688	0.371	1.276	0.235
Income	0.905	0.783	1.046	0.178
Brazil
Age	1.056	1.024	1.089	< 0.001
Female Gender	1.050	0.634	1.737	0.850
Education	0.843	0.599	1.186	0.326
Income	0.963	0.916	1.013	0.141
Chile
Age	1.061	1.014	1.110	0.011
Female gender	1.678	0.733	3.839	0.220
Education	0.719	0.497	1.040	0.080
Income	0.809	0.638	1.027	0.082
Cuba
Age	1.070	1.035	1.106	< 0.001
Female gender	1.159	0.544	2.470	0.702
Education	0.849	0.578	1.248	0.406
Income	0.953	0.858	1.058	0.367
Uruguay
Age	0.995	0.934	1.060	0.878
Female gender	2.165	0.802	5.846	0.127
Education	0.824	0.492	1.381	0.462
Income	0.999	0.914	1.092	0.979
India
Age	1.045	1.021	1.069	< 0.001
Female gender	1.393	0.871	2.228	0.167
Education	0.769	0.602	0.981	0.035
Income	0.930	0.846	1.021	0.127
Ghana
Age	1.023	0.980	1.067	0.301
Female gender	1.150	0.486	2.718	0.750
Education	0.742	0.502	1.095	0.133
Income	0.981	0.911	1.056	0.603
South Africa
Age	1.009	0.981	1.037	0.545
Female gender	1.698	0.947	3.045	0.076
Education	1.002	0.840	1.195	0.986
Income	0.997	0.970	1.025	0.838

As Table 3 depicts, in Model II, only in 4 countries (i.e. China, Puerto Rico, Brazil, and Cuba) was exercise associated with lower ADL limitation. In 3 countries (i.e. India, Costa Rica, and Mexico), the association between exercise and ADL limitation was marginally significant. With a few exceptions (i.e. China, Brazil, Chile, and Uruguay), most countries did not show an association between drinking and ADL limitation. Smoking was not associated with ADL limitation among individuals with heart disease, above and beyond the socioeconomic factors, exercise, and drinking.

Table 3.

Cross-country differences in associations between socioeconomic factors and health behaviors among patients with heart disease

	95% CI for Odds Ratio
	Odds Ratio	Lower	Upper	P value
China
Age	1.102	1.091	1.112	< 0.001
Female gender	1.047	0.810	1.353	0.726
Education	1.015	0.886	1.163	0.831
Income	0.996	0.961	1.031	0.805
Smoking	1.088	0.839	1.412	0.525
Drinking	0.736	0.581	0.933	0.011
Exercise	0.711	0.574	0.881	0.002
Costa Rica
Age	1.085	1.056	1.114	< 0.001
Female gender	0.929	0.484	1.784	0.825
Education	0.874	0.578	1.321	0.523
Income	0.888	0.797	0.988	0.030
Smoking	1.281	0.721	2.275	0.398
Drinking	0.628	0.339	1.163	0.139
Exercise	0.515	0.250	1.059	0.071
Puerto Rico
Age	1.014	0.991	1.037	0.245
Female gender	0.949	0.635	1.419	0.800
Education	1.038	0.828	1.302	0.746
Income	0.945	0.901	0.992	0.021
Smoking	1.324	0.905	1.938	0.148
Drinking	0.760	0.422	1.370	0.361
Exercise	0.482	0.317	0.733	0.001
Mexico
Age	1.053	1.028	1.079	< 0.001
Female gender	1.800	1.095	2.958	0.020
Education	0.714	0.541	0.941	0.017
Income	1.031	0.973	1.093	0.299
Smoking	1.077	0.683	1.697	0.750
Drinking	1.145	0.744	1.763	0.538
Exercise	0.605	0.335	1.093	0.096
Barbados
Age	1.110	1.055	1.169	< 0.001
Female gender	1.205	0.434	3.346	0.720
Education	0.636	0.317	1.276	0.202
Income	0.906	0.784	1.048	0.186
Smoking	1.769	0.619	5.058	0.287
Drinking	0.585	0.171	2.005	0.394
Exercise	0.757	0.279	2.058	0.586
Ghana
Age	1.023	0.980	1.068	0.301
Female gender	1.269	0.490	3.286	0.623
Education	0.717	0.483	1.063	0.097
Income	0.982	0.918	1.050	0.589
Smoking	1.418	0.482	4.170	0.526
Drinking	1.274	0.531	3.059	0.587
Exercise	0.601	0.251	1.436	0.252
South Africa
Age	1.009	0.980	1.038	0.559
Female gender	1.718	0.934	3.163	0.082
Education	1.018	0.849	1.220	0.849
Income	0.998	0.970	1.025	0.860
Smoking	0.710	0.331	1.523	0.379
Drinking	1.635	0.698	3.829	0.257
Exercise	1.714	0.671	4.379	0.260
Brazil
Age	1.053	1.020	1.087	0.001
Female gender	1.027	0.576	1.833	0.927
Education	0.847	0.587	1.222	0.376
Income	0.979	0.933	1.027	0.385
Smoking	1.408	0.814	2.436	0.221
Drinking	0.347	0.159	0.757	0.008
Exercise	0.389	0.166	0.908	0.029
Chile
Age	1.062	1.012	1.114	0.014
Female gender	1.267	0.521	3.079	0.601
Education	0.707	0.482	1.038	0.076
Income	0.804	0.634	1.020	0.072
Smoking	1.328	0.617	2.856	0.468
Drinking	0.276	0.113	0.678	0.005
Exercise	0.946	0.358	2.499	0.911
Cuba
Age	1.071	1.035	1.109	< 0.001
Female gender	1.197	0.510	2.810	0.680
Education	0.872	0.589	1.291	0.494
Income	0.945	0.851	1.050	0.294
Smoking	1.540	0.835	2.839	0.167
Drinking	0.593	0.200	1.755	0.345
Exercise	0.367	0.154	0.873	0.023
Uruguay
Age	0.996	0.932	1.065	0.913
Female gender	0.973	0.311	3.044	0.962
Education	0.850	0.490	1.474	0.563
Income	1.021	0.927	1.123	0.677
Smoking	0.623	0.247	1.570	0.315
Drinking	0.222	0.066	0.749	0.015
Exercise	0.455	0.086	2.395	0.353
India
Age	1.037	1.012	1.063	0.003
Female gender	1.205	0.682	2.129	0.521
Education	0.744	0.579	0.957	0.021
Income	0.932	0.850	1.021	0.130
Smoking	1.156	0.711	1.880	0.560
Drinking	0.734	0.377	1.430	0.364
Exercise	0.607	0.341	1.081	0.090
Russia
Age	1.067	1.026	1.110	0.001
Female gender	0.734	0.289	1.864	0.515
Education	0.601	0.369	0.980	0.041
Income	1.015	0.892	1.156	0.819
Smoking	0.574	0.216	1.527	0.266
Drinking	0.932	0.448	1.940	0.851
Exercise	0.543	0.218	1.347	0.188

As Table 4 demonstrates, in Model III, number of medical comorbidities was positively associated with odds of ADL limitation in 10 countries. The number of medical comorbidities was marginally associated with ADL limitation in one country (Barbados).

Table 4.

Cross-country differences in associations between socioeconomic factors, health behaviors, and medical conditions among patients with heart disease

	95% CI for Odds Ratio
	Odds Ratio	Lower	Upper	P value
China
Age	1.108	1.097	1.119	0.000
Female gender	1.041	0.802	1.350	0.765
Education	1.032	0.897	1.187	0.659
Income	1.000	0.965	1.035	0.987
Smoking	1.089	0.837	1.419	0.525
Drinking	0.749	0.589	0.953	0.018
Exercise	0.736	0.591	0.915	0.006
Medical comorbidities	0.957	0.902	1.014	0.137
Costa Rica
Age	1.077	1.047	1.107	0.000
Female gender	0.811	0.411	1.599	0.545
Education	0.855	0.559	1.308	0.471
Income	0.873	0.775	0.984	0.026
Smoking	1.044	0.571	1.911	0.888
Drinking	0.743	0.387	1.426	0.372
Exercise	0.599	0.287	1.250	0.172
Medical comorbidities	1.620	1.226	2.142	0.001
Puerto Rico
Age	1.006	0.982	1.030	0.620
Female gender	0.858	0.567	1.300	0.470
Education	1.027	0.816	1.293	0.820
Income	0.943	0.898	0.991	0.021
Smoking	1.291	0.873	1.910	0.201
Drinking	0.842	0.464	1.530	0.573
Exercise	0.502	0.328	0.768	0.001
Medical comorbidities	1.576	1.285	1.932	0.000
Mexico
Age	1.044	1.018	1.070	0.001
Female gender	1.475	0.879	2.476	0.141
Education	0.680	0.512	0.904	0.008
Income	1.040	0.980	1.104	0.194
Smoking	0.942	0.587	1.510	0.803
Drinking	1.009	0.644	1.581	0.968
Exercise	0.642	0.348	1.185	0.156
Medical comorbidities	2.070	1.611	2.661	0.000
Barbados
Age	1.108	1.052	1.167	0.000
Female gender	1.123	0.397	3.181	0.826
Education	0.602	0.298	1.214	0.156
Income	0.914	0.792	1.054	0.216
Smoking	1.647	0.560	4.841	0.364
Drinking	0.592	0.171	2.046	0.407
Exercise	0.841	0.305	2.316	0.738
Medical comorbidities	1.488	0.933	2.372	0.095
Brazil
Age	1.040	1.006	1.075	0.020
Female gender	0.911	0.498	1.667	0.763
Education	0.815	0.559	1.187	0.286
Income	0.975	0.926	1.026	0.331
Smoking	1.413	0.801	2.493	0.232
Drinking	0.369	0.167	0.819	0.014
Exercise	0.425	0.180	1.008	0.052
Medical comorbidities	1.808	1.357	2.410	0.000
Russia
Age	1.055	1.014	1.099	0.009
Female gender	0.565	0.208	1.533	0.262
Education	0.586	0.355	0.966	0.036
Income	1.012	0.882	1.160	0.869
Smoking	0.456	0.160	1.298	0.141
Drinking	0.915	0.432	1.935	0.816
Exercise	0.538	0.209	1.387	0.199
Medical comorbidities	1.686	1.245	2.284	0.001
Chile
Age	1.063	1.010	1.118	0.018
Female gender	1.087	0.424	2.789	0.862
Education	0.668	0.452	0.989	0.044
Income	0.794	0.603	1.046	0.102
Smoking	1.091	0.481	2.477	0.834
Drinking	0.354	0.138	0.908	0.031
Exercise	0.970	0.346	2.717	0.953
Medical comorbidities	2.171	1.400	3.366	0.001
Cuba
Age	1.067	1.030	1.104	0.000
Female gender	1.060	0.443	2.538	0.896
Education	0.881	0.590	1.315	0.534
Income	0.941	0.850	1.041	0.236
Smoking	1.425	0.764	2.657	0.265
Drinking	0.618	0.206	1.854	0.391
Exercise	0.387	0.162	0.927	0.033
Medical comorbidities	1.509	1.101	2.068	0.011
Uruguay
Age	0.990	0.921	1.064	0.792
Female gender	0.664	0.193	2.287	0.517
Education	1.030	0.561	1.893	0.923
Income	1.012	0.907	1.130	0.826
Smoking	0.474	0.169	1.327	0.155
Drinking	0.193	0.054	0.697	0.012
Exercise	0.465	0.070	3.089	0.428
Medical comorbidities	3.823	2.082	7.020	0.000
India
Age	1.035	1.010	1.061	0.006
Female gender	1.132	0.634	2.022	0.674
Education	0.740	0.574	0.953	0.020
Income	0.936	0.858	1.022	0.141
Smoking	1.113	0.680	1.821	0.671
Drinking	0.708	0.360	1.391	0.316
Exercise	0.623	0.347	1.116	0.112
Medical comorbidities	1.434	1.128	1.825	0.003
Ghana
Age	1.023	0.979	1.068	0.307
Female gender	1.306	0.501	3.409	0.585
Education	0.736	0.494	1.098	0.133
Income	0.982	0.922	1.047	0.582
Smoking	1.398	0.473	4.135	0.545
Drinking	1.390	0.568	3.404	0.471
Exercise	0.581	0.241	1.401	0.227
Medical comorbidities	1.297	0.799	2.106	0.294
South Africa
Age	1.009	0.980	1.039	0.541
Female gender	1.580	0.848	2.944	0.150
Education	1.017	0.845	1.223	0.860
Income	0.998	0.972	1.026	0.902
Smoking	0.649	0.300	1.404	0.272
Drinking	1.666	0.707	3.928	0.243
Exercise	1.738	0.670	4.508	0.256
Medical comorbidities	1.433	1.081	1.899	0.012

Discussion

This study revealed major cross-country differences in the additive effects of socioeconomic, behavioral, and medical characteristics on disability among patients with heart disease. The number of medical comorbidities and age were predictive of disability in most countries, while gender and income were linked to disability in very few countries. Exercise and drinking were linked to disability in 7 and 4 countries, respectively. Surprisingly, smoking was not associated with disability in any of the countries, while socioeconomic factors and other health behaviors (i.e. exercise and drinking) were constant. To summarize, the number of comorbid medical conditions, age, physical activity, drinking, education, income, and gender were associated with disability in 85%, 85%, 54%, 31%, 31%, 23%, and 15% of the countries.

There are very few previous studies to compare our findings with.[3, 10–12] Based on a recent study that compared 15 countries, age in the U.S. and China; gender in the U.S., South Africa, and India; income in China and Costa Rica; and education in Uruguay and Ghana modified the effect of heart disease on subjective health. In Puerto Rico, Argentina, Barbados, Brazil, Chile, Cuba, and Russia, the effect of heart disease on subjective health was above and beyond the influence of socioeconomic factors.[3]

The findings of a recent in press study revealed that countries largely vary in the contributors of ADL limitation in the general population. The study particularly found considerable cross-country differences for the relationship between age and ADL. The contribution of age and gender in explaining the variance of ADL was very high in China and Cuba, respectively. More variation was seen in the effect of education than income as a factor contributing to the ADL across countries. Health behaviors such as exercise and also chronic conditions (in general) consistently explained a significant portion of the variance of ADL across all the 8 countries included in that study.

Based on our study, age was linked to disability among individuals with heart disease in 10 of the 13 countries. Age is known to be positively associated with ADL limitation.[61, 62]

In almost all countries, number of medical comorbidities was associated with disability among individuals with heart disease. Chronic conditions such as heart disease and diabetes limit abilities to perform ADL.[14, 15, 63] Individuals with diabetes are more likely to experience restrictions in ADL, along with reduced mobility and role functioning.[64, 65] A recent study documented a significant correlation between the comorbidity score and all the measures of well-being among patients with ischemic heart disease. The comorbidity score was correlated with physical and mental quality of life, psychological distress, sleep quality, and dyadic adjustment. Authors emphasized that primary health care physicians, family physicians, and cardiologists have a major role in identifying and treating comorbid somatic conditions among patients with ischemic heart disease.[12]

According to a cross-country study, in all countries and with no exception, heart disease was associated with higher odds of poor subjective health, above and beyond the effect of age, gender, education, and income.[3] This is in line with previous studies suggesting the role of heart disease on well-being, quality of life, and disability.[4-8] In a study, well-being was mostly affected by heart conditions, followed by asthma/chronic bronchitis, joint complaints, back problems, and diabetes.[66] Another study suggested that heart diseases, musculoskeletal diseases, lung diseases, neurological disorders, diabetes, and cancer may have more influence on disability at the population level, compared to other conditions.[67] Another study showed that patients with heart disease, as well as patients with hearing impairment, neurological disease, and vision impairment, report the highest levels of distress.[68] A study also showed that after controlling the effect of age, sex, educational level, comorbidities, disability and pain, coronary artery disease and chronic hemodialysis were linked to the highest levels of depression.[69]

According to a cross-country study, heart disease was the only factor consistently associated with poor perceived health among individuals with diabetes.[10]

Only in two countries, female gender was associated with higher disability among elderly with heart disease. Women report lower levels of quality of life, whereas men have lower mortality.[70, 71] In general, women report higher rates of chronic diseases[16] and mental health-related conditions.[16, 72]

The current study also documented cross-country differences in the association between education and income and ADL limitation among elderly with heart disease. Literature suggests that the education level maybe related to health and ADL.[73-76]

The results of this study may have implications for cardiologists in different countries. Based on the current study, clinicians in different countries may need to consider different socioeconomic and behavioral factors to estimate or reduce disability (ADL limitation) among patients with heart disease. Based on our findings, locally designed health promotions may be superior to universal programs for patients with heart disease. In almost all countries, however, disability may be reduced if comorbid medical conditions are properly diagnosed and treated. That is, attention to comorbid conditions may be considered as a common component of disability prevention for patients with heart disease.

Similar to other studies, the current study is limited in several ways. Due to the cross-sectional design, causative inferences are implausible. Cross-country differences in the validity of ADL are not known. Health behaviors such as smoking, drinking, and exercise were measured using single items. Only a few comorbid medical conditions were included, and the type of conditions was not entered into the model.

Conclusion

To conclude, there are major cross-country differences in the determinants of disability among patients with heart disease. The findings advocate designing and implementing country-specific programs to reduce disability among patients with heart disease.