Individual- and state-level factors associated with functional limitation prevalence among Colombian elderly: a multilevel analysis

Factores individuales y departamentales asociados con la prevalencia de limitación funcional entre ancianos colombianos: un análisis multinivel

Fatores individuais e estaduais associados à prevalência de limitação funcional em idosos colombianos: uma análise multinível

Abstract:

This study aimed to identify the main regional factors associated with variations in the prevalence of functional limitation on the older adult in Colombia adjusted by individual characteristics. This multilevel study used cross-sectional data from 23,694 adults over 60 years of age in the SABE, Colombia nationwide survey. State-level factors (poverty, development, inequity, violence, health coverage, and access to improved water sources), as well as individual health related, socioeconomic and demographic characteristics, were analyzed. The overall prevalence of functional impairment for the basic activities of daily living (ADL) was 22%. The presence of comorbidities, low educational level, physical inactivity, no participation in social groups, mistreatment and being over 75 years old were associated with functional limitation. At the group level, the analysis showed significant differences in the functional limitation prevalence across states, particularly regarding the socioeconomic status measured according to the Human Development Index (median OR = 1.22; 95%CI: 1.13-1.30; p = 0.011). This study provides evidence on the impact of socioeconomic variation across states on FL prevalence in the Colombian elderly once adjusted for individual characteristics. The findings of this study, through a multilevel approach methodology, provide information to effectively address the conditions that affect the functionality in this population through the identification and prioritization of public health care in groups with economic and health vulnerability.

Keywords:
Activities of Daily Living; Aged; Socioeconomic Factors; Multilevel Analysis

Resumen:

Este estudio tuvo por objetivo identificar los principales factores regionales, asociados con variaciones en la prevalencia de la limitación funcional en adultos mayores en Colombia, ajustados por características individuales. Este estudio multinivel usó datos transversales de 23.694 adultos, con más de 60 años de edad, en el SABE, encuesta nacional colombiana. Los factores nacionales (pobreza, desarrollo, inequidad, violencia, cobertura sanitaria, y acceso a fuentes mejoradas de agua), así como en relación con su salud individual, al igual que se analizaron las características socioeconómicas y demográficas. La prevalencia general de discapacidad funcional para las actividades básicas de la vida diaria (ABVD) fue de un 22%. La presencia de comorbilidades, bajo nivel educacional, inactividad física, la no participación en grupos sociales, maltrato y tener más de 75 años de edad estuvo asociado con la limitación funcional. En el nivel del grupo, el análisis mostró significativas diferencias respecto a la prevalencia de limitación funcional, a través de los diferentes estados, particularmente en lo referente al estatus socioeconómico, medido según el Índice de Desarrollo Humano (OR mediano = 1,22; IC95%: 1,13-1,30; p = 0,011). Este estudio proporciona evidencia sobre el impacto de la variación socioeconómica a través de los estados sobre la prevalencia de limitación funcional en los ancianos colombianos, una vez ajustadas las características individuales. Los resultados de este estudio, mediante una metodología de aproximación multinivel, proporcionan información con el fin de orientar efectivamente sobre las condiciones que afectan la funcionalidad de este tipo de población, mediante la identificación y priorización de los cuidados en la salud pública con grupos vulnerables económicamente y desde la perspectiva de la salud.

Palabras-clave:
Actividades Cotidianas; Anciano; Factores Socioeconómicos; Análisis Multinivel

Resumo:

O estudo teve como objetivo identificar os principais fatores regionais associados a variações na prevalência de limitação funcional na população idosa colombiana, ajustada por fatores individuais. O estudo multinível usou dados transversais de 23.694 adultos com mais de 60 anos de idade do estudo SABE colombiano. Foram analisados fatores de nível estadual (índices de pobreza, desenvolvimento, inequidade, violência, cobertura de saúde e acesso a água potável) e fatores individuais (sociodemográficos e de saúde). A prevalência global de comprometimento funcional nas atividades de vida diária (AVD) foi de 22%. A presença de comorbidades, escolaridade baixa, sedentarismo, falta de participação em grupos sociais, maus tratos e idade acima de 75 anos estiveram associados à limitação funcional. Em nível de grupo, a análise mostrou diferenças significativas na prevalência de limitação funcional entre os estados, particularmente quanto à condição socioeconômica, medida pelo Índice de Desenvolvimento Humano (OR médio = 1,22; IC95%: 1,13-1,30; p = 0,011). O estudo oferece evidências do impacto da variação socioeconômica entre estados na prevalência de limitação funcional nos idosos colombianos depois de ajustar por fatores individuais. Através de uma metodologia multinível, os achados fornecem informações para tratar efetivamente as condições que afetam a funcionalidade dessa população idosa através da identificação e priorização dos cuidados de saúde em grupos com vulnerabilidade econômica e sanitária.

Palavras-chave:
Atividades Cotidianas; Idoso; Fatores Socioeconômicos; Análise Multinível

Introduction

Functional limitation, defined as decreased ability of an individual to independently perform activities of daily living (ADL) ¹, is strongly associated with increased prevalence of falls, depression and decreased quality of life in the elderly population ^2,3,4. Functional decline also impacts on economic and social factors as it is related to augmented mortality rates and health care costs ^5,6. In developed countries, the functional limitation prevalence varies from 9.6 to 12.3% ^2,5, while in developing countries it affects between 13 to 28% of the older adults ^7,8, being the latter the most affected, due to the demographic transition ^9,10, with growth rates of older adults almost three times higher than in developed regions ¹¹.

Demographic reports indicate that the functional limitation prevalence is larger with aging ^12,13,14 and sex (females) ^8,15,16, and associated with poor self-perceived health ¹⁷, non-white population ¹³, physical inactivity ^14,18, obesity ¹⁹ and presence of co-morbidities such as type 2 diabetes, stroke, depression, heart disease, hypertension and cognitive impairment ¹⁸. Moreover, individual socioeconomic status characteristics such as income ²⁰, educational level ^8,14,15, health care access ¹³ and occupation ^8,15 have impact on elderly functionality.

Besides these factors, the health status of older adults is also influenced by environmental characteristics that may act on individual and population health through different pathways such as economic opportunities and healthcare services ²¹. Contextual factors such as area-level wealth have been related to differences between regions in limitations for ADL. Populations living in economically developed provinces and in wealthier villages are less likely to report difficulties with ADLs ^22,23. In this regard, the resource availability of a region to fulfil its population needs is reported to be associated with their mental and physical health ^21,24. Previous studies indicate that regional socioeconomic deprivation and inequality have a negative impact on the facilities for physical activity and on the availability and accessibility to healthy food ^25,26; therefore, these factors are related to increased prevalence of chronic conditions such as diabetes and hypertension in older adults ^21,27; conditions whose association with poor performance in ADL has been previously reported ^14,18. Prior research also indicates that elderly individuals living in wealthier villages experience fewer depressive symptoms ²⁴. Furthermore, depression is associated with decreases in energy production to perform activities and consequently with alterations in functional capacity ¹⁴. However, in particular contexts, as China, it has been found higher per capita gross domestic product (GDP) at the community level is associated with a higher rate of ADL disability of older people ²⁸.

In addition, the region’s income inequality has also been related to ADL prevalence discrepancies between areas. Previous reports indicate that the odds of having ADL limitations for an individual in a state with the highest inequality is approximately 32% higher than those in the states with the lowest income inequality ²⁹. Neighborhood safety has also been related to ADL in elderly population. Longitudinal studies claim that older adults who reported to be functionally independent at baseline and considered their neighborhood to be unsafe were 21% more likely to experience functional decline after 10-year follow-up compared with those who perceived their neighborhood to be very safe ³⁰.

On the other hand, inequalities in the availability of health care services at the regional level have been associated with health disparities between provinces in China ³¹. Moreover, previous research indicates that older adults living in states with higher percentages of uninsured population and lower-than-average annual per capita health expenditure had lower odds of receiving quality preventative care ³². Furthermore, adequate access to health services is suggested to delay functional decline among aged population ¹³. Other contextual factors such as having a sewage system and a continuous supply of electricity were associated with reduced physical limitations ²².

Area-level resources have reported to be more influent in the maintenance of health for the elderly population than for their younger counterparts ³³. Nonetheless, comprehensive research for factors related to elderly ADL disability is still sparse in the Colombian population. Therefore, this study aimed to identify the regional factors related to the functional limitation prevalence across states in Colombian elderly after individual characteristics’ adjustment.

Methods

SABE study

This study is based on the sociodemographic and health related data obtained from the cross-sectional SABE (from initials, in Spanish, Salud, Bienestar y Envejecimiento - Health, Well-being and Aging) Colombian study. A total of 23,694 household surveys were undertaken in community-dwelling adults aged 60 and above living in urban and rural areas of 246 municipalities out of 1,122 and 32 states of the country in 2015 ³⁴. SABE Colombia study replicated SABE international survey methods and procedures ⁷. The Folstein Mini-Mental State Examination was used to assess the ability of the subjects to complete the study procedures, for those whose scores were less than ¹³, a proxy interview was developed. Sampling strategy implied a multistage, probabilistic and stratified selection scheme with national representativeness. Municipalities were selected as primary sampling units, blocks within them as the secondary ones, and housing and household units as the third and fourth stages, respectively. For the four large cities of the country (Bogota, Cali, Medellín and Barranquilla), a fixed sample size of 3,500 adults was taken, and the same selection scheme was maintained ³⁴. The University of Caldas and University of Valle Committees on the Ethics of Human Research approved the protocol.

Individual-level variables

Functional status was measured using the Barthel Index scale, which covers the self-report independence in performing ten basic ADLs including, bathing, dressing, grooming, toileting, feeding, continence, transferring, mobility and use of stairs ³⁵. Functional limitation was defined herein by the report of difficulty in performing at least one of these activities ⁸. Independent individual variables such as sex, age, educational level, self-reported comorbidities, lifestyle characteristics, mistreatment, income, participation in social groups, presence of barriers to health services and internal displacement due to armed conflict were included in the analyses.

Regional-level variables

Regional socioeconomic status were analyzed using the following state-level variables: the Unsatisfied Basic Needs index (UBN) in 2011 ³⁶, the Gini coefficient in 2014 ³⁷, the Human Development Index (HDI) in 2010 ³⁸, the participation percentage in the national GDP of 2014 ³⁹ and percentage of the population without access to improved water sources in 2005 ⁴⁰. States healthcare services were assessed by the percentage of health coverage in 2014 (Ministerio de Salud y Protección Social de Colombia. Estadísticas - afiliados cargados BDUA marzo 2014. https://www.minsalud.gov.co/estadisticas/Estadsticas/Forms/DispForm.aspx?ID=1046, accessed on 20/Apr/2017); and violence was analyzed using the homicide rate per 100,000 inhabitants in 2014 ⁴¹.

Statistical analysis

Study sample characteristics were assessed by using absolute and relative frequencies with 95% confidence intervals (95%CI) for qualitative variables, as well as measures of central tendency and dispersion were calculated for quantitative variables. Differences in baseline characteristics were compared using independent χ² test; variables with p-values below 20% were included in the adjusted models. A preliminary evaluation of the effect of individual-level variables was performed by 1-level stepwise logistic model, significant variables (p < 0.05) were included in the multilevel model. A first empty model (intercept only) was used to assess geographic variation, and the suitability of the 2-level approach was evaluated with the intraclass correlation coefficient. The associations and variance between individual variables and functional limitations were evaluated using odds ratios (ORs) and 95%CI in the fixed-effects part of the models. For the selection of the variables at state level, Wald tests were developed to evaluate their significance regarding the functional limitation prevalence. For the adjustment of regional-level variables, a two-level logit model ⁴² was used taking as random effects the state variables. To evaluate the variability in prevalence between regions, a median OR (MOR) was used to generate a reference value for comparison between two potential subjects in regions with opposite values of the regional aggregation variable under study. The MOR translates the area-level variance, due to area-level variables, to the odds ratio scale; therefore, MOR is a measure that allows comparison with the individual OR. In this research, this value shows the extent to which the individual probability of functional limitation is determined by the state-level variables ⁴³. All analyses were carried out using Stata version 14 software (StataCorp LP, Colege Station, USA).

Results

The overall functional impairment prevalence for ADL was 22% (95%CI: 21.73-22.27), ranging from 11.9% (95%CI: 11.75-12.05) in the west of Colombia (Chocó) to 28.9% (95%CI: 28.54-29.26) in the north (Atlantic). The mean (standard deviation - SD) age of the study sample was 70.82 (8.2) years; 70% (95%CI: 69.12-70.88) of the subjects were between 60 and 74 years old and 57.3% (95%CI: 56.50-58.10) were women. The main comorbidities in Colombian older adults were hypertension (53.7%; 95%CI: 53.02-54.38) and depression (46.2%; 95%CI: 45.55-46.85); 16.8% (95%CI: 16.57-17.03) reported suffering from some type of mistreatment by the members of their household during the three months before the application of the survey and 28.5% (95%CI: 28.14-28.86) reported having at least one access barrier to healthcare services, such as delay of appointment allocation and refusal to provide medications or medical procedures. About individual SES characteristics, most of the older adults (68.7%; 95%CI: 67.75-69.65) have an income of less than 7.83 dollars per day and 93.4% (95%CI: 92.21-94.59) have secondary education or lower (Table 1).

Table 1
Functional limitation and individual characteristics.

Variable	Total (N = 23,694)		With functional limitation (n = 5,229)		Without functional limitation (n = 18,465)		p-value
	n	%	n	%	n	%
Sex (1 = Female; 0 = Male)	13,582	57.3	3,547	67.8	10,035	54.3	< 0.001
Civil status (1 = Single, Widowed, Divorced; 0 = Other)	11,127	47.0	3,079	58.9	8,048	43.6	< 0.001
Health insurance regime (1 = Not contributive; 0 = Contributive)	14,672	62.0	3,393	65.0	11,279	61.1	< 0.001
Private supplemental health insurance (1 = No; 0 = Yes)	7,603	15.5	1,494	81.8	6,109	85.2	< 0.001
Culture (1 = Minority groups; 0 = Not minority groups)	4,759	26.7	662	24.0	4,097	27.2	0.001
Race (1 = Non-white; 0 = White)	12,925	71.3	1,950	68.9	10,975	71.7	0.003
Income (1 = Less than USD 7.83 per day)	13,468	68.7	3,154	76.9	10,314	66.5	< 0.001
Income (2 = USD 7.83 per day; 0 = More than USD 7.83 per day)	3,168	16.2	544	13.3	2,624	16.9
Education level (1 = Secondary or lower; 0 = Upper than secondary)	22,139	93.4	5,046	97.2	17,093	92.8	< 0.001
Physical inactivity (1 = Yes; 0 = No)	11,306	47.8	4,158	79.6	7,148	38.8	< 0.001
Victim of armed displacement (1 = Yes; 0 = No)	4,413	18.6	901	17.2	3,512	19.0	0.003
Regular perceived childhood economic situation (1 = Yes; 0 = Good perceived childhood economic situation)	8,000	42.3	1,244	41.7	6,756	42.4	0.001
Poor perceived childhood economic situation (2 = Yes; 0 = Good perceived childhood economic situation)	3,143	16.6	564	18.9	2,579	16.2
Regular self-perceived health status (1 = Yes; 0 = Good self-perceived health status)	8,265	34.9	1,582	52.8	6,683	41.8	< 0.001
Poor self-perceived health status (2 = Yes; 0 = Good self-perceived health status)	1,615	6.8	549	18.3	1,066	6.7
Diabetes (1 = Having; 0 = Not having)	3,893	16.5	1,141	21.9	2,752	15.0	< 0.001
Hypertension (1 = Having; 0 = Not having)	12,690	53.7	3,462	66.4	9,228	50.1	< 0.001
Cancer (1 = Having; 0 = Not having)	1,038	4.4	328	6.3	710	3.9	< 0.001
Respiratory disease (1 = Having; 0 = Not having)	2,423	10.2	894	17.2	1,529	8.3	< 0.001
Heart disease (1 = Having; 0 = Not having)	3,234	13.7	1,055	20.2	2,179	11.8	< 0.001
Cerebrovascular disease (1 = Having; 0 = Not having)	1,080	4.6	519	10.0	561	3.0	< 0.001
Joint disease (1 = Having; 0 = Not having)	6,158	26.0	2,015	38.7	4,143	22.5	< 0.001
Mental illness (1 = Having; 0 = Not having)	2,013	8.5	754	14.5	1,259	6.8	< 0.001
Cognitive impairment (1 = MMSE ≤ 12; 0 = MMSE > 12)	6,243	26.3	2,590	49.5	3,653	19.8	< 0.001
Depression symptoms (1 = GDS-VE ≥ 6; 0 = GDS-VE < 6)	10,953	46.2	3,371	78.1	7,582	50.9	< 0.001
Extremity amputation (1 = Having; 0 = Not having)	420	1.8	149	2.8	271	1.5	< 0.001
Joint pain (1 = Having; 0 = Not having)	13,048	55.1	2,791	53.4	10,257	55.6	0.005
Smoking (1 = Current or former smoker; 0 = Nonsmoker)	12,286	51.9	2,660	50.9	9,626	52.1	0.119
Poor quality of vision (1 = Yes; 0 = Good or regular quality of vision)	3,742	15.8	916	17.5	2,826	15.3	< 0.001
Hearing problems (1 = Having; 0 = Not having)	5,537	23.4	1,834	35.2	3,703	20.1	< 0.001
Falls (1 = Yes in the last year; 0 = Not in the last year)	7,310	30.9	2,287	43.7	5,023	27.2	< 0.001
Social groups participation (1 = No; 0 = Yes)	13,266	56.0	3,001	57.4	10,265	55.6	0.021
Access barriers to healthcare (1 = Ever experienced; 0 = Never experienced)	6,734	28.5	1,898	36.3	4,836	26.2	< 0.001
Mistreatment (1 = Yes in the last 3 months; 0 = Not in the last 3 months)	3,181	16.8	711	23.9	2,470	15.5	< 0.001
Group age 75-89 years old (0 = Group age < 75 years old)	6,699	28.3	2,565	49.1	4,134	22.4	< 0.001
Group age ≥ 90 years old (0 = Group age < 75 years old)	567	2.4	440	8.4	127	0.7
Rural residence (1 = Yes; 0 = No)	6,505	27.5	1,402	26.8	5,103	27.6	0.239
Low BMI ENT#091;˂ 22kg/m2ENT#093; (0 = 22kg/m2 ≤ BMI ≤ 27 kg/m2)	3,133	13.2	626	19.1	2,507	17.3	< 0.001
High BMI ENT#091;˃ 27 kg/m2ENT#093; (0 = 22kg/m2 ≤ BMI ≤ 27kg/m2)	7,557	42.6	1,485	45.4	6,072	41.9
Poor self-perceived childhood health status (1 = Yes; 0 = Good or regular self-perceived childhood health status)	1,968	10.4	394	9.1	1,574	10.7	0.002

• BMI: body mass index; MMSE: Mini-Mental State Examination; GDS-VE: Geriatric Depression Scale Spanish Version.
• Note: boldface indicates statistical significance (p < 0.05).

All crude comparisons among individual characteristics showed significant differences between subjects with and without functional limitation, except for smoking and urban/rural residence (Table 1). The prevalence of chronic diseases, overweight, mistreatment and physical inactivity were significantly higher among subjects withfunctional limitation, as well as the proportion of people with low education level (Table 1). The HDI (MOR = 1.186; 95%CI: 1.124-1.249; p = 0.009) and the percentage of participation in the national GDP (MOR = 1.017; 95%CI: 1.016-1.017; p < 0.001) were the only state-level variables that achieved statistical convergence and had a significant effect in the crude analysis without individual-level variable adjustments.

The adjusted analysis (Model II, Table 2) showed that secondary education level or lower, poor self-perceived health, no participation in social groups, age 75 or older, low subjective quality of vision, experience of falls during the last year, physical inactivity, mistreatment, and comorbidities such as depression, cognitive impairment, hypertension, diabetes and respiratory, cerebrovascular and mental diseases were factors associated with functional limitation.

Table 2
Significant effects at the individual- and state-level variables.

	Model I *		Model II **		Model III ***
General mean (SE)	-1.268 (0.0292)		-5.203 (0.175)		-5.076 (0.172)
			OR	95%CI	OR	95%CI
Individual-level
Education level			1.34	1.06-1.70	1.33	1.05-1.69
Regular self-perceived health status			1.52	1.35-1.70	1.52	1.36-1.71
Poor self-perceived health status			2.59	2.20-3.06	2.56	2.16-3.03
Social groups participation			1.20	1.08-1.32	1.19	1.07-1.31
Cognitive impairment			1.34	1.14-1.58	1.34	1.13-1.58
Group age 75 to 89 years old			2.33	2.10-2.60	2.35	2.10-2.61
Group age ≥ 90 years old			7.82	5.08-12.06	7.93	5.14-12.25
Depression			3.48	3.11-3.89	3.46	3.09-3.88
Mistreatment			1.36	1.20-1.54	1.37	1.21-1.54
Falls			1.48	1.34-1.65	1.47	1.33-1.64
Hypertension			1.35	1.21-1.50	1.34	1.20-1.49
Diabetes			1.30	1.15-1.47	1.27	1.18-1.44
Respiratory disease			1.44	1.24-1.67	1.45	1.25-1.68
Cerebrovascular disease			2.23	1.80-2.76	2.26	1.82-2.81
Joint disease			1.88	1.69-2.09	1.88	1.70-2.09
Mental illness			1.46	1.24-1.72	1.45	1.23-1.71
Poor quality of vision			1.36	1.21-1.53	1.37	1.22-1.54
Physical inactivity			3.37	3.03-3.75	3.31	2.98-3.69
State-level
Variance (SE)	0.099 (0.031)		0.112	(0.037)	0.066 (0.029)
ICC (%)	2.90		3.30		1.96
					MOR	95%CI
Random effects
HDI					1.22	1.13-1.30

• 95%CI: 95% confidence interval; HDI: Human Development Index; ICC: intraclass coeficient; MOR: median odds ratio; SE: standard error.
• * Model I: null model;
• ** Model II: individual-level variables only;
• *** Model III: include both individual-level and state-level variables.

The estimation of regional variance, by a null model analysis, showed inter-state variability regarding functional limitation prevalence was statistically significant (p < 0.001; intraclass correlation coefficient = 2.9%). In the multilevel models adjusted for individual-level variables, except for percentage of population without access to improved water sources, in which the convergence of the statistical model was not achieved, all state-level variables had significant effects regarding functional limitations prevalence (Table 3).

Table 3
State-level effects in functional limitations prevalence.

State-level variable	MOR	95%CI	p-value
Gini	1.004	1.003956-1.004024	< 0.001
UBN	1.005	1.004583-1,004674	< 0.001
HDI	1.218	1.134209-1.300876	0.011
Health coverage	1.000	1.000000-1.000000	< 0.001
Homicide rate	1.007	1.006972-1.007186	< 0.001
GDP	1.000	1.000000-1.000000	< 0.001

• 95%CI: 95% confidence interval; GDP: Gross Domestic Product; HDI: Human Development Index; MOR: median odds ratio; UBN: Unsatisfied Basic Needs index.

Nonetheless, the models with multiple state-level variables did not achieve statistical convergence, hence correlations between state-level variables were assessed. Strong correlations were identified between HDI and UBN, GDP, and percentage of the population without access to improved water sources. Medium strength correlations were identified between Gini and water access, percentage of population with health coverage and UBN, and GDP with homicide rate (Table 4). Due to these correlations and considering that the HDI is a measure that represents several dimensions of a region socioeconomic status, HDI was the state-level variable used to explain the plausible association between environmental socioeconomic characteristics and individual functional limitations (Model III, Table 2). The inclusion of regional variables had minimal effects on the ORs estimated for individual-level variables (Table 2).

Table 4
Correlations between state-level variables.

	Gini	UBN	HDI	Health coverage	Homicide rate	GDP	Not water access
Gini	1.00	0.27	-0.24	-0.11	-0.03	0.08	0.31
UBN		1.00	-0.86	0.31	-0.28	-0.62	0.87
HDI			1.00	-0.14	0.13	0.75	-0.75
Health coverage				1.00	-0.20	-0.10	-0.03
Homicide rate					1.00	0.04	-0.18
GDP						1.00	-0.55
Not water accessd *							1.00

• GDP: Gross Domestic Product; HDI: Human Development Index; UBN: Unsatisfied Basic Needs index.
• * Percentage of the population without access to improved water sources.

Discussion

This study verified that the functional limitations prevalence for ADLs in Colombian elderly varies regarding individual characteristics and factors that affect the subjects collectively, in particular the regional socioeconomic level; this situation reflects lags on the access, use or quality of primary prevention and health care services ^44,45,46. Similar findings have previously been reported for other chronic diseases, including mental illness ⁴⁷, type 2 diabetes ²¹, hypertension ²⁷ and coronary disease ⁴⁵.

As reported previously, low human development has been associated with lesser functional levels, in particular poor self-care prevalence has reported to be higher in low income countries ⁴⁸, and individuals living in highly developed regions reported better physical health than those living in developing regions ⁴⁹. Deprived regions are linked to high area-level crime rates, which has been correlated to physical inactivity and social isolation and, thus, functional decline ³⁰. Disadvantaged area-level socioeconomic status has also been related to reduced cohesiveness ⁵⁰. Previous research indicates that social cohesion promotes social participation and enables elderly population to actively participate in their community in actions that promote health, thereby it is related to fewer ADL disabilities ⁵¹. The human development index is also an indicator of the population literacy. Former literature report higher prevalence of self-care limitations in the lowest education level population compared with the highest ⁴⁸. For this work, HDI was considered as a proximal or indicator variable of the effect derived from any or several development or deprivation aspects in communities regarding functional limitations.

Low human development, as an indicator of low regional socioeconomic status ⁴⁶, has also previously been related to increased prevalence of health conditions that affect functional independence ²⁷. It is reported that disadvantaged regions show higher food insecurity prevalence ^21,25 and lower access to sport areas ²⁶, factors associated with chronic conditions like diabetes ²¹, whose consequent complications such as neuropathies, loss of limbs, cognitive impairment and microvascular dysfunction may affect motion capacity ⁵². Also, psychosocial stress and depression, caused by higher rates of violence and low levels of social support, may be factors linked with the mechanism that links individual functional capacity to perform tasks with regional economic deprivation ^21,50,53.

As in previous research, this study verified the association between functional limitations and individual variables such as age ^12,13,14, educational level ^12,14, participation in social groups ^12,18,54, physical activity ^14,18,55, and the presence of comorbidities ^12,13,14,18. However, the effect of overweight was discarded. In this regard, previous findings state the deficiency of body mass index (BMI) as an indicator of the probability of developing chronic diseases given its limitations to characterize body composition ^56,57. On the other hand, the relationship between low body weight and decreased muscle strength with individuals’ mobility affectations has been consistently reported ^12,14.

The self-reported perception of access barriers to healthcare did not have a significant association with function limitations in this study. Previous research has reported that elderly subjects without private supplemental health insurance (medication and diagnostic test coverage) are more likely to suffer chronic diseases related to function limitations than their counterpart with such coverage; however, among individuals with functional impairment, private supplemental insurance is not correlated to improving or weakening functional status ¹³.

Mistreatment was also associated with functional limitations in the current study. Previous research indicates that the presence of multiple types of abuse in the elderly population is associated with the manifestation of depressive symptoms and cognitive deficit ^58,59, health conditions whose negative impact on the physical and intellectual capacity of subjects to preserve independence has been widely reported ^12,14,17,18.

Our findings, however, have some limitations. Due to the cross-sectional nature of the data, derived inferences must be analyzed in the scenario of temporal transversality, in which it is impossible to define the temporality of causal inferences, and which could be bidirectional. Another limitation resides in the use of self-reports ⁶⁰, implying the use of perceptions, a situation that can distort the evaluation of the effect of the exposures of interest in a non-differential way; and in the lack of simultaneity of secondary sources, which does not guarantee the contemporaneity of the described effects ⁶¹. Moreover, further studies may include area-level factors such as social cohesion and social capital, which have been previously reported to play a role in the functional disability onset ⁵¹.

Nonetheless, this study provides evidence on the impact of regional socioeconomic variation on the functiona limitations prevalence for ADL in Colombian elderly. As the context is similar to other scenarios in the region, this study may constitute a benchmark to strengthen the understanding of the phenomena inherent to the aging process in the Latin American population. Also, this study provides information for the identification and priorization of public health care in groups with economic and health vulnerability ^21,46,62. Consistent with other studies, the evidence provided here reiterates the importance of caring for the elderly and the control of chronic diseases, particularly in a growing demographic transition framework ¹⁰. Therefore, the use of regional and individual information allows to effectively address the information research for the control and prevention of conditions that affect the functional status of older people ⁶³.

Acknowledgments

We thank Dr. Wim Grooten, Karolinska Institutet, for critically reviewing the manuscript and contributing to technical and language editing.

References

• ¹
  Lera L. Salud, bienestar y envejecimiento en Santiago, Chile: SABE 2000. Washington DC: Pan American Health Organization; 2005.
• ²
  Asakawa T, Koyano W, Ando T, Shibata H. Effects of functional decline on quality of life among the Japanese elderly. Int J Aging Hum Dev 2000; 50:319-28.
• ³
  Stenhagen M, Ekstrom H, Nordell E, Elmstahl S. Both deterioration and improvement in activities of daily living are related to falls: a 6-year follow-up of the general elderly population study Good Aging in Skane. Clin Interv Aging 2014; 9:1839-46.
• ⁴
  Weil J, Hutchinson SR, Traxler K. Exploring the relationships among performance-based functional ability, self-rated disability, perceived instrumental support, and depression: a structural equation model analysis. Res Aging 2014; 36:683-706.
• ⁵
  Fried TR, Bradley EH, Williams CS, Tinetti ME. Functional disability and health care expenditures for older persons. Arch Intern Med 2001; 161:2602-7.
• ⁶
  Millán-Calenti JC, Tubío J, Pita-Fernández S, González-Abraldes I, Lorenzo T, Fernández-Arruty T, et al. Prevalence of functional disability in activities of daily living (ADL), instrumental activities of daily living (IADL) and associated factors, as predictors of morbidity and mortality. Arch Gerontol Geriatr 2010; 50:306-10.
• ⁷
  Albala C, Lebrão ML, León Díaz EM, Ham-Chande R, Hennis AJ, Palloni A, et al. Encuesta Salud, Bienestar y Envejecimiento (SABE): metodología de la encuesta y perfil de la población estudiada. Rev Panam Salud Pública 2005; 17:307-22.
• ⁸
  Keddie AM, Peek MK, Markides KS. Variation in the associations of education, occupation, income, and assets with functional limitations in older Mexican Americans. Ann Epidemiol 2005; 15:579-89.
• ⁹
  Kinsella K, Phillips D. Global aging: the challenge of success. Popul Bull 2005; 60:1-44.
• ¹⁰
  Prince MJ, Wu F, Guo Y, Gutierrez Robledo LM, O'Donnell M, Sullivan RR, et al. The burden of disease in older people and implications for health policy and practice. Lancet 2015; 385:549-62.
• ¹¹
  Chatterji S, Byles J, Cutler D, Seeman T, Verdes E. Health, functioning, and disability in older adults: present status and future implications. Lancet 2015; 385:563-75.
• ¹²
  Cortés-Muñoz C, Cardona-Arango D, Segura-Cardona A, Garzón-Duque MO. Factores físicos y mentales asociados con la capacidad funcional del adulto mayor, Antioquia, Colombia, 2012. Rev Salud Pública (Bogotá) 2016; 18:167-78.
• ¹³
  Porell FW, Miltiades HB. Access to care and functional status change among aged Medicare beneficiaries. J Gerontol B Psychol Sci Soc Sci 2001; 56:S69-83.
• ¹⁴
  Alexandre TS, Corona LP, Nunes DP, Santos JL, Duarte YA, Lebrão ML. Gender differences in incidence and determinants of disability in activities of daily living among elderly individuals: SABE study. Arch Gerontol Geriatr 2012; 55:431-7.
• ¹⁵
  Guerra RO, Alvarado BE, Zunzunegui MV. Life course, gender and ethnic inequalities in functional disability in a Brazilian urban elderly population. Aging Clin Exp Res 2008; 20:53-61.
• ¹⁶
  Hosseinpoor AR, Bergen N, Kostanjsek N, Kowal P, Officer A, Chatterji S. Socio-demographic patterns of disability among older adult populations of low-income and middle-income countries: results from World Health Survey. Int J Public Health 2016; 61:337-45.
• ¹⁷
  Tas U, Verhagen AP, Bierma-Zeinstra SM, Hofman A, Odding E, Pols HA, et al. Incidence and risk factors of disability in the elderly: the Rotterdam Study. Prev Med 2007; 44:272-8.
• ¹⁸
  Rodrigues MA, Facchini LA, Thume E, Maia F. Gender and incidence of functional disability in the elderly: a systematic review. Cad Saúde Pública 2009; 25 Suppl 3:S464-76.
• ¹⁹
  Freitas RS, Fernandes MH, Coqueiro RS, Reis Júnior WM, Rocha SV, Brito TA. Capacidade funcional e fatores associados em idosos: estudo populacional. Acta Paul Enferm 2012; 25:933-9.
• ²⁰
  Alves LC, Leite IC, Machado CJ. Factors associated with functional disability of elderly in Brazil: a multilevel analysis. Rev Saúde Pública 2010; 44:468-78.
• ²¹
  Maier W, Holle R, Hunger M, Peters A, Meisinger C, Greiser KH, et al. The impact of regional deprivation and individual socio-economic status on the prevalence of type 2 diabetes in Germany. A pooled analysis of five population-based studies. Diabet Med 2013; 30:e78-86.
• ²²
  Yeatts DE, Pei X, Cready CM, Shen Y, Luo H, Tan J. Village characteristics and health of rural Chinese older adults: examining the CHARLS Pilot Study of a rich and poor province. Soc Sci Med 2013; 98:71-8.
• ²³
  Evandrou M, Falkingham J, Feng Z, Vlachantoni A. Individual and province inequalities in health among older people in China: evidence and policy implications. Health Place 2014;30:134-44.
• ²⁴
  Yeatts DE, Cready CM, Pei X, Shen Y, Luo H. Environment and subjective well-being of rural Chinese elderly: a multilevel analysis. J Gerontol B Psychol Sci Soc Sci 2014; 69:979-89.
• ²⁵
  Ng CD. Global analysis of overweight prevalence by level of human development. J Glob Health 2015; 5:020413.
• ²⁶
  Braubach M, Fairburn J. Social inequities in environmental risks associated with housing and residential location: a review of evidence. Eur J Public Health 2010; 20:36-42.
• ²⁷
  Melgarejo JD, Maestre GE, Thijs L, Asayama K, Boggia J, Casiglia E, et al. Prevalence, treatment, and control rates of conventional and ambulatory hypertension across 10 populations in 3 continents. Hypertension 2017; 70:50-8.
• ²⁸
  Zeng Y, Gu D, Purser J, Hoenig H, Christakis N. Associations of environmental factors with elderly health and mortality in China. Am J Public Health 2010; 100:298-305.
• ²⁹
  Fuller-Thomson E, Gadalla T. Income inequality and limitations in activities of daily living: a multilevel analysis of the 2003 American Community Survey. Public Health 2008; 122:221-8.
• ³⁰
  Sun VK, Cenzer IS, Kao H, Ahalt C, Williams BA. How safe is your neighborhood? Perceived neighborhood safety and functional decline in older adults. J Gen Intern Med 2012; 27:541-7.
• ³¹
  Fang P, Dong S, Xiao J, Liu C, Feng X, Wang Y. Regional inequality in health and its determinants: evidence from China. Health Policy 2010; 94:14-25.
• ³²
  Faul AC, Yankeelov PA, McCord LR. Inequitable access to health services for older adults with diabetes: potential solutions on a state level. J Aging Soc Policy 2015; 27:63-86.
• ³³
  Son KY, Park SM, Lee J, Kim CY. Difference in adherence to and influencing factors of a healthy lifestyle between middle-aged and elderly people in Korea: a multilevel analysis. Geriatr Gerontol Int 2015; 15:778-88.
• ³⁴
  Gomez F, Corchuelo J, Curcio CL, Calzada MT, Mendez F. SABE Colombia: Survey on Health, Well-Being, and Aging in Colombia-Study design and protocol. Curr Gerontol Geriatr Res 2016; 2016:7910205.
• ³⁵
  Sainsbury A, Seebass G, Bansal A, Young JB. Reliability of the Barthel Index when used with older people. Age Ageing 2005; 34:228-32.
• ³⁶
  Departamento Administrativo Nacional de Estadística. Necesidades básicas insatisfechas-NBI. http://www.dane.gov.co/index.php/estadisticas-por-tema/pobreza-y-condiciones-de-vida/necesidades-basicas-insatisfechas-nbi (accessed on 28/Apr/2017).
  » http://www.dane.gov.co/index.php/estadisticas-por-tema/pobreza-y-condiciones-de-vida/necesidades-basicas-insatisfechas-nbi
• ³⁷
  Departamento Administrativo Nacional de Estadística. Pobreza monetaria y multidimensional en Colombia. http://www.dane.gov.co/index.php/estadisticas-por-tema/pobreza-y-condiciones-de-vida/pobreza-y-desigualdad/pobreza-monetaria-y-multidimensional-en-colombia-2015#pobreza-monetaria-y-multidimensional-en-colombia-2015 (accessed on 17/Apr/2017).
  » http://www.dane.gov.co/index.php/estadisticas-por-tema/pobreza-y-condiciones-de-vida/pobreza-y-desigualdad/pobreza-monetaria-y-multidimensional-en-colombia-2015#pobreza-monetaria-y-multidimensional-en-colombia-2015
• ³⁸
  Machado A. Colombia rural razones para la esperanza. Informe Nacional de Desarrollo Humano 2011. Bogotá: Programa de la Naciones Unidas para el Desarrollo; 2011.
• ³⁹
  Departamento Administrativo Nacional de Estadística. Cuentas nacionales departamentales. http://www.dane.gov.co/index.php/estadisticas-por-tema/cuentas-nacionales/cuentas-nacionales-departamentales (accessed on 23/Apr/2017).
  » http://www.dane.gov.co/index.php/estadisticas-por-tema/cuentas-nacionales/cuentas-nacionales-departamentales
• ⁴⁰
  Departamento Administrativo Nacional de Estadística. Censo general, 2005. http://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y-poblacion/censo-general-2005-1 (accessed on 29/Apr/2017).
  » http://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y-poblacion/censo-general-2005-1
• ⁴¹
  Instituto Nacional de Medicina Legal y Ciencias Forenses. Forensis 2014: datos para la vida. http://www.medicinalegal.gov.co/documents/88730/1656998/Forensis+Interactivo+2014.24-JULpdf.pdf/9085ad79-d2a9-4c0d-a17b-f845ab96534b (accessed on 20/Apr/2017).
  » http://www.medicinalegal.gov.co/documents/88730/1656998/Forensis+Interactivo+2014.24-JULpdf.pdf/9085ad79-d2a9-4c0d-a17b-f845ab96534b
• ⁴²
  Kleinbaum D, Klein M. Statistics for biology and health logistic regression. 3rd Ed. Atlanta: Springer; 2010.
• ⁴³
  Merlo J, Chaix B, Ohlsson H, Beckman A, Johnell K, Hjerpe P, et al. A brief conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual phenomena. J Epidemiol Community Health 2006; 60:290-7.
• ⁴⁴
  Shah A. A replication of the relationship between elderly suicide rates and the human development index in a cross-national study. Int Psychogeriatr 2010; 22:727-32.
• ⁴⁵
  Zhu KF, Wang YM, Zhu JZ, Zhou QY, Wang NF. National prevalence of coronary heart disease and its relationship with human development index: a systematic review. Eur J Prev Cardiol 2016; 23:530-43.
• ⁴⁶
  Fidler MM, Soerjomataram I, Bray F. A global view on cancer incidence and national levels of the human development index. Int J Cancer 2016; 139:2436-46.
• ⁴⁷
  Coutinho LM, Matijasevich A, Scazufca M, Menezes PR. Prevalência de transtornos mentais comuns e contexto social: análise multinível do São Paulo Ageing & Health Study (SPAH). Cad Saúde Pública 2014; 30:1875-83.
• ⁴⁸
  Hosseinpoor AR, Stewart Williams JA, Itani L, Chatterji S. Socioeconomic inequality in domains of health: results from the World Health Surveys. BMC Public Health 2012; 12:198.
• ⁴⁹
  Skevington SM. Qualities of life, educational level and human development: an international investigation of health. Soc Psychiatry Psychiatr Epidemiol 2010; 45:999-1009.
• ⁵⁰
  Muramatsu N. County-level income inequality and depression among older Americans. Health Serv Res 2003; 38(6 Pt 2):1863-83.
• ⁵¹
  Aida J, Kondo K, Kawachi I, Subramanian SV, Ichida Y, Hirai H, et al. Does social capital affect the incidence of functional disability in older Japanese? A prospective population-based cohort study. J Epidemiol Community Health 2013; 67:42-7.
• ⁵²
  Dhamoon MS, Moon YP, Paik MC, Sacco RL, Elkind MS. Diabetes predicts long-term disability in an elderly urban cohort: the Northern Manhattan Study. Ann Epidemiol 2014; 24:362-8.
• ⁵³
  Wilkinson RG, Pickett KE. Income inequality and population health: a review and explanation of the evidence. Soc Sci Med 2006; 62:1768-84.
• ⁵⁴
  Virués-Ortega J, de Pedro-Cuesta J, del Barrio JL, Almazan-Isla J, Bergareche A, Bermejo-Pareja F, et al. Medical, environmental and personal factors of disability in the elderly in Spain: a screening survey based on the International Classification of Functioning. Gac Sanit 2011; 25 Suppl 2:29-38.
• ⁵⁵
  McEniry M. Early-life conditions and older adult health in low- and middle-income countries: a review. J Dev Orig Health Dis 2013; 4:10-29.
• ⁵⁶
  Liu P, Ma F, Lou H, Liu Y. The utility of fat mass index vs. body mass index and percentage of body fat in the screening of metabolic syndrome. BMC Public Health 2013;13:629.
• ⁵⁷
  Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev 2012; 13:275-86.
• ⁵⁸
  Chokkanathan S. Elder mistreatment and health status of rural older adults. J Interpers Violence 2015; 30:3267-82.
• ⁵⁹
  Ogioni L, Liperoti R, Landi F, Soldato M, Bernabei R, Onder G. Cross-sectional association between behavioral symptoms and potential elder abuse among subjects in home care in Italy: results from the Silvernet Study. Am J Geriatr Psychiatry 2007; 15:70-8.
• ⁶⁰
  Metzger MH, Goldberg M, Chastang JF, Leclerc A, Zins M. Factors associated with self-reporting of chronic health problems in the French GAZEL cohort. J Clin Epidemiol 2002; 55:48-59.
• ⁶¹
  Schlomer BJ, Copp HL. Secondary data analysis of large data sets in urology: successes and errors to avoid. J Urol 2014; 191:587-96.
• ⁶²
  Fraga S, Lindert J, Barros H, Torres-González F, Ioannidi-Kapolou E, Melchiorre MG, et al. Elder abuse and socioeconomic inequalities: a multilevel study in 7 European countries. Prev Med 2014; 61:42-7.
• ⁶³
  Rose G. Sick individuals and sick populations. Int J Epidemiol 2001; 30:427-32.

Publication Dates

• Publication in this collection
  20 Aug 2018
• Date of issue
  2018

History

• Received
  19 Sept 2017
• Reviewed
  23 Feb 2018
• Accepted
  12 Mar 2018