Abstract
Abstract
Introduction
There is a high level of older people neglect in Nigeria, especially in the rural setting, and they did not receive much attention in terms of their overall health and well-being. Government social interventions are usually geared towards the children, adolescents, pregnant women and lactating mothers. Evaluating the level of functional decline and social support among these groups and how it affects their overall well-being will enable policy formulations geared towards holistic care for them. This study aimed to determine the level of functional dependence in some basic activities of daily living (ADLs: mobility, dressing, grasp and bathing) and social support in older people to enhance evidence-based advocacy to all stakeholders in older people care.
Methods
This was a hospital-based cross-sectional study of 160 (75 males and 85 females) older people aged 65–98 years selected through systematic random sampling. The χ2 test, t-test and logistic regression were used for analysis.
Results
The response rate was 100%. The mean age of male respondents was 76.31±8.34 years and that of the female respondents was 76.87±7.47 years. A statistically significant association was found between age >75 years, absence of a spouse, low education level and functional dependence in all ADLs studied. Although age independently predicted dependence in all studied ADLs, except dressing and grasp, marital status predicted dependence in dressing and bathing, and availability of care also predicted dependence in mobility.
Conclusion
Age is an independent risk factor for functional dependence in mobility and bathing, and marital status independently predicted dependence in dressing and bathing. Not receiving care also independently predicted dependence in mobility. Thus, improvements in the biopsychosocial, biomedical and economic well-being of older people will ameliorate the impact of poor care on functional status and ADLs.
Keywords: Aging, Health, Caregivers, Family
Strengths and limitations of this study.
This study supports the practice of routine evaluation of the nutritional and functional status of older people by family physicians in our day-to-day running of the geriatric clinic.
This study further supports the need for a regular patient-centred assessment of the psychosocial and biomedical problems of older people based on individual peculiarities.
This is a hospital-based cross-sectional study; hence, its findings may not adequately represent all older people. Therefore, results of a population-based study of a larger cohort of this group will be more generalisable.
The presence of co-morbidity was not assessed; hence, its impact on the overall functional status was not ascertained.
Recall and non-response bias in data collection might have been possible.
Introduction
Older people are functionally vulnerable due to physiological changes, in addition to a lack of financial support and poor access to healthy food and healthcare.1 The ageing process is affected by various factors. These include genetic make-up, food supply, social events, political factors, exposure to diseases, climate and natural disasters, as well as other environmental factors such as smoking, alcohol consumption, stress, diet, exercise and medications.2 3 The various changes arising from these factors increase the vulnerability of individuals to life-threatening diseases, organ dysfunction and possible death.2 Furthermore, policy issues regarding the older population (pensions, social security, economic policy and protection of the rights of older people) are a major concern.4
The incidence and severity of disability are said to be a function of socio-economic status, age and sex.2 In recent decades, the rate of disability has declined in older adults in developed countries due to goal-oriented policies.5 This is not the case in developing countries like Nigeria, which has no clear policy direction for the older population. There is a paradigm shift from hospital-based care to non-institutionalised care models that address chronic care cost-effectively.2 Chronic diseases are prevalent among older people. This is even doubled among people of African descent than among Americans.6 The difference in long-term health and the burden associated with chronic diseases is dependent on healthy behaviours.6 Problems among older people in Africa are rapidly increasing, as societies are locked up in diverse conflicts, economic challenges, natural and man-made disasters, diseases and deterioration in family relationships.7 There is a negative affinity towards older people, as various programmes are fashioned out only for other population groups.7 A Nigerian older person is disadvantaged in terms of support; however, this growing population makes it imperative to demand social support, since the majority of them live in rural areas. This population mainly includes agricultural workers and subsistence farmers, who do not benefit from government grants, and retirees are hardly paid their pensions and gratuities.8 Moreover, it is customary that older people are taken care of by their relatives. This age-long tradition has been hampered by the economic crisis in the country.7 Rural–urban migration of the young people in search of better economic realities has left so many of this population unattended. Those who could have provided traditional support have been limited by high mortality rates arising from this migration.7
In a study conducted in Ondo State, Nigeria, the majority of older males reported participating in sanitation and vigilante activities to make a living, whereas many of their female counterparts participate in market trading and fetching of sacred water to bathe for longevity. Only a minority of the older population reported patronising the hospital when sick and lacking accommodation; however, many others made use of local herbs.7 No social security or policy system has ever been put in place to cater for older people in Nigeria. Even the primary healthcare system has no special provision for enhancing the healthcare of older people.9 Owing to poverty and poor infrastructural developments, older people in Nigeria face lower life expectancies and live the greater part of their lives in poor health.9 This poor health condition is usually aggravated by old age, being separated after marriage, visits by only male children and not receiving financial support, among others. The stance of the government towards the overall health of older people is insufficient, whereas the family structure, which provided support for them in the past, has collapsed.10 A descriptive study in Nigeria reported a mortality rate of 22.4% in this population. This was attributed to stroke and infections, which were worsened by poor social services for older people.11 Changes in the structure and function of the respiratory tract, reduced immunity with ageing, poverty, illiteracy and poor sanitation are said to be major contributors to this mortality.11 Non-inclusion of older people in most healthcare programmes has not helped.12 This study sought to provide an insight as to whether functional status in older people is associated with socio-economic status and the level of social support in Nigeria, aimed at drumming up social support on their behalf.
Subjects and methods
Study design
This was a cross-sectional analytical study. The participants were included over a 3-month period (June 2018 to September 2018).
Study population
The study population comprised older patients who presented at the geriatric clinic of the hospital within the study period. Older patients were regarded as men and women aged ≥65 years.13
Selection criteria
Contacts with the patients were made at presentation in the hospital. These patients were selected based on inclusion and exclusion criteria. Patients aged ≥65 years who gave their consent to participate in the study were included. Conversely, patients with severe or terminal illness were excluded because their functional status might have been influenced by the illness and not by their patient characteristics.
Sample size determination
The sample size (N) was determined using Fisher’s formula14:
N = Z2pq/d2, where N is the minimum sample size, Z is the standard normal deviate at the 95% confidence level (which is 1.96), P is the prevalence of functional decline in older people from previous studies, which is 17%,5 q is regarded as 1 p (the proportion of the population who does not have functional decline, 0.83) and d is the degree of precision (the level of accuracy desired) and is usually set at 0.05. From the above figures, the sample size is 216.82.
Since the study population (from the clinic record) was 606 (not up to 10 000 and above), the sample size was corrected using the formula: Nf=n/[1+(n/N)],15 where Nf is the corrected sample size, n is the population size (606), N is the sample size (216.83).
Applying the formula, Nf=159.89 (approximately 160). Hence, the sample size was 160.
Sampling techniques
The study participants were selected by systematic random sampling and included over a 3-month period using a sampling frame of 4. That is, for every four patients, one was selected on presentation to the clinic. The number of the first older patient included in the sample was randomly chosen by picking 1 out of every 4 by balloting among them. Assuming number 3 was picked, every other fourth patient was included starting with number 3 until 160 older patients were selected (i.e, 3, 7, 11 ….). However, during the selection, any patient of a selected number who did not meet the inclusion criteria was dropped, and the next number picked. Such a selection did not affect the next random selection.
Study instruments
Data were collected by face-to-face interviewer-administered structured and pre-tested questionnaires. Pre-testing was conducted at the general outpatient clinic of a similar health facility; thereafter, the questionnaire was adjusted where necessary.
The questionnaires were prepared in the English language but were interpreted in the Igbo language for those who could not understand English. To ensure validity, the questionnaires were transcribed from the English language to the Igbo language by an Igbo language lecturer of a college. This translation was back-translated to the English language by an English-language lecturer of the same school and was compared with the previous questionnaire. Both translations conveyed the same meaning. The questionnaire consisted of two parts (see online supplemental figure 1). The first part documented the socio-demographic characteristics such as age, education level, income, presence of a caregiver, social support, distance from supply centres, availability of transportation services, religion and occupation.
The second part assessed the functional status of the patients (basic and instrumental activities of daily living (ADLs)) using a modified Katz and Lawton’s index (14 items, minimum score of 0, maximum score of 4, lower score inferred better function).16 17 Based on self-reporting, an older person who needed help in one or more items was regarded as functionally dependent. Research assistants were selected and trained by the principal investigator. The training included discussions on the objectives of the study, consent form, contents of the questionnaires, data collection techniques and issues of participant confidentiality. The questionnaires were regularly reviewed by the principal investigator for completeness and necessary feedback.
Data analysis
All questionnaires were checked visually and were exported to SPSS version 20.0 (IBM SPSS Inc. Chicago, IL, USA) for analysis. Mean and SD were estimated for numerical variables, whereas number and percentages were estimated for categorical variables. Comparison between numerical variables was conducted using Student’s t-test, whereas the χ2 test was used to compare categorical variables. Logistic regression analysis was employed in finding the independent association between socio-demographic characteristics and functional status. The strength of the association between the independent and dependent variables was assessed using the OR at a 95% CI. The associations were considered statistically significant at the p<0.05 level.
Results
Socio-demographic characteristics and functional support (mobility)
Functional dependence on mobility was statistically and significantly associated with age (p<0.001), marital status (p=0.008), education level (p=0.012) and religion (p=0.016). Receiving care was significantly associated with dependence in mobility (p=0.036) (table 1).
Table 1. Association between socio-demographic variables and functional support (mobility).
| Variable | Functional support (mobility) | Total | χ2 | P value | |
|---|---|---|---|---|---|
| Independent (n=25) | Dependent (n=135) | ||||
| Age group (years) | |||||
| 65–75 | 22 (30.6%) | 50 (69.4%) | 72 (100%) | 22.135 | <0.001 |
| >75 | 3 (3.4%) | 85 (96.6%) | 88 (100%) | ||
| Sex | |||||
| Male | 16 (21.3%) | 59 (78.7%) | 75 (100%) | 3.489 | 0.062 |
| Female | 9 (10.6%) | 76 (89.4%) | 85 (100%) | ||
| Marital status | |||||
| With spouse | 20 (22.5%) | 69 (77.5%) | 89 (100%) | 7.132 | 0.008 |
| Without spouse | 5 (7.1%) | 66 (92.9%) | 71 (100%) | ||
| Occupation | |||||
| Employed | 19 (20.2%) | 75 (79.8%) | 94 (100%) | 3.640 | 0.056 |
| Unemployed | 6 (9.1%) | 60 (90.9%) | 66 (100%) | ||
| Education level | |||||
| Below secondary | 13 (11.2%) | 103 (88.8%) | 116 (100%) | 6.243 | 0.012 |
| Secondary and above | 12 (27.3%) | 32 (72.7%) | 44 (100%) | ||
| Income level | |||||
| ≤N10,000 | 22 (15.6%) | 119 (84.4%) | 141 (100%) | 0.000 | 0.983 |
| >N10,000 | 3 (15.8%) | 16 (84.2%) | 19 (100%) | ||
| Religion | |||||
| Christianity | 25 (18.4%) | 111 (81.6%) | 136 (100%) | 6.214* | 0.016 |
| Others | 0 (0.0%) | 24 (100.0%) | 24 (100%) | ||
| Receiving care | |||||
| Yes | 7 (9.7%) | 65 (90.3%) | 72 (100%) | 3.460 | 0.036 |
| No | 18 (20.5%) | 70 (79.5%) | 88 (100%) | ||
| Receiving social support | |||||
| Yes | 4 (12.1%) | 29 (87.9%) | 33 (100%) | 0.387 | 0.534 |
| No | 21 (16.5%) | 106 (83.5%) | 127 (100%) | ||
| Distance to food supply | |||||
| <1 hour | 14 (18.7%) | 61 (81.3%) | 75 (100%) | 0.991 | 0.320 |
| ≥1 hour | 11 (12.9%) | 74 (87.1%) | 85 (100%) | ||
Fisher’s exact test was used.
Logistic regression on socio-demographic variables and functional support (mobility)
Table 2 details the logistic regression analysis of socio-demographic factors influencing functional mobility, revealing that age is the most robust predictor of dependence. In the unadjusted analysis, being over 75 years old, not having a spouse, having lower education levels and not receiving care were significantly associated with higher odds of mobility dependence (p<0.05). However, after adjusting for potential confounders in the multivariate model, marital status and education level lost their statistical significance (p>0.05). Consequently, age and receiving care remained significant independent predictors, with individuals aged >75 years being nearly nine times more likely to be dependent than those aged 65–75 (AOR=8.84; 95% CI 2.21 to 35.30). The probability of dependence in functional mobility was lower by about 76% in respondents who did not receive care compared with those who received care (OR=0.241; 95% CI 0.074 to 0.788).
Table 2. Logistic regression on socio-demographic variables and functional status (mobility).
| Variable | Unadjusted OR | 95% CI for the UOR | P value | Adjusted OR | 95% CI for the AOR | P value |
|---|---|---|---|---|---|---|
| Age group (years) | ||||||
| 65–75 (ref.) | 1 | 1 | ||||
| >75 | 12.47 | 3.55 to 43.77 | <0.001 | 8.84 | 2.21 to 35.30 | 0.002 |
| Marital status | ||||||
| With spouse (ref.) | 1 | 1 | ||||
| Without spouse | 3.83 | 1.36 to 10.79 | 0.014 | 1.52 | 0.44 to 5.26 | 0.507 |
| Education level | ||||||
| Below secondary (ref.) | 1 | 1 | ||||
| Secondary and above | 0.34 | 0.14 to 0.81 | 0.024 | 0.21 | 0.63 to 6.42 | 0.241 |
| Receiving care | ||||||
| No (ref.) | 1 | 1 | ||||
| Yes | 0.38 | 0.28 to 0.86 | 0.026 | 0.241 | 0.074 to 0.788 | 0.019 |
AOR, adjusted OR; ref., reference; UOR, unadjusted OR.
Association between socio-demographic variables and functional support (dressing)
Functional dependence in dressing among respondents was statistically and significantly associated with age (p<0.001), sex (p=0.017), marital status (p<0.001), education level (p<0.001), income level (p<0.001) and religion (p=0.035) (table 3).
Table 3. Association between socio-demographic variables and functional support (dressing).
| Variable | Functional support (dressing) | Total | χ2 | P value | |
|---|---|---|---|---|---|
| Independent (n=32) | Dependent (n=128) | ||||
| Age group (years) | |||||
| 65–75 | 32 (44.4%) | 40 (55.6%) | 72 (100%) | 48.889* | <0.001 |
| >75 | 0 (0.0%) | 88 (100%) | 88 (100%) | ||
| Sex | |||||
| Male | 21 (28.0%) | 54 (72.0%) | 75 (100%) | 5.647 | 0.017 |
| Female | 11 (12.9%) | 74 (87.1%) | 85 (100%) | ||
| Marital status | |||||
| With spouse | 30 (30.7%) | 59 (66.3%) | 89 (100%) | 23.554 | <0.001 |
| Without spouse | 2 (2.8%) | 69 (97.2%) | 71 (100%) | ||
| Occupation | |||||
| Employed | 18 (19.1%) | 76 (80.9%) | 94 (100%) | 1.274 | 0.259 |
| Unemployed | 14 (21.2%) | 52 (78.8%) | 66 (100%) | ||
| Education level | |||||
| Below secondary | 12 (10.3%) | 104 (89.7%) | 116 (100%) | 24.577 | <0.001 |
| Secondary and above | 20 (52.6%) | 24 (54.5%) | 44 (100%) | ||
| Income level | |||||
| ≤N10,000 | 22 (15.6%) | 119 (84.4%) | 141 (100%) | 14.349 | <0.001 |
| >N10,000 | 10 (52.6%) | 9 (47.7%) | 19 (100%) | ||
| Religion | |||||
| Christianity | 31 (22.8%) | 105 (77.2%) | 136 (100%) | 4.424* | 0.035 |
| Others | 1 (4.2%) | 23 (95.8%) | 24 (100%) | ||
| Receiving care | |||||
| Yes | 16 (22.2%) | 56 (77.8%) | 72 (100%) | 0.404 | 0.525 |
| No | 16 (18.2%) | 72 (71.8%) | 88 (100%) | ||
| Receiving social support | |||||
| Yes | 9 (27.3%) | 24 (72.7%) | 33 (100%) | 1.374 | 0.241 |
| No | 23 (18.1%) | 104 (81.9%) | 127 (100%) | ||
| Distance to the food supply | |||||
| <1 hour | 19 (25.3%) | 56 (74.7%) | 75 (100%) | 2.510 | 0.113 |
| ≥1 hour | 13 (15.3%) | 72 (84.7%) | 85 (100%) | ||
Fisher’s exact test used.
Logistic regression on socio-demographic variables and functional status (dressing)
Table 4 presents the logistic regression analysis for functional status in dressing, identifying marital status as the sole significant independent predictor of dependence after adjustment. While the unadjusted analysis initially indicated that sex (p=0.029), education (p<0.001) and income (p<0.001) were significant determinants, these variables lost their statistical significance when controlled for in the multivariate model (p>0.05). However, the impact of marital status remained robust; participants without a spouse were found to be over 11 times more likely to be dependent in dressing than those with a spouse (AOR=11.42; 95% CI 2.50 to 52.10; p=0.002),
Table 4. Logistic regression on socio-demographic variables and functional status (dressing).
| Variable | Unadjusted OR | 95% CI for the UOR | P-value | Adjusted OR | 95% CI for the AOR | P value |
|---|---|---|---|---|---|---|
| Sex | ||||||
| Male (ref.) | 1 | 1 | ||||
| Female | 2.62 | 1.16 to 5.88 | 0.029 | 2.06 | 0.79 to 5.38 | 0.140 |
| Marital status | ||||||
| With spouse (ref.) | 1 | 1 | ||||
| Without spouse | 17.54 | 4.02 to 76.52 | <0.001 | 11.42 | 2.50 to 52.10 | 0.002 |
| Education level | ||||||
| Below secondary (ref.) | 1 | 1 | ||||
| Secondary and above | 0.14 | 0.06 to 0.32 | <0.001 | 2.59 | 0.88 to 7.63 | 0.085 |
| Income | ||||||
| ≤N10 000 (ref.) | 1 | 1 | ||||
| >N10 000 | 0.17 | 0.06 to 0.46 | <0.001 | 1.71 | 0.45 to 6.52 | 0.431 |
| Religion | ||||||
| Christianity (ref.) | 1 | 1 | ||||
| Others | 6.79 | 0.88 to 52.32 | 0.068 | 5.06 | 0.58 to 44.19 | 0.143 |
AOR, adjusted OR; ref, reference; UOR, unadjusted OR.
Association between socio-demographic variables and functional status (gasp)
Statistically significant associations existed between functional dependence in grasp and age (p=0.001), education level (p<0.001) and income level (p<0.001). While a statistically significant association existed between a longer distance to the source of food and functional dependence in grasp (p=0.002), no significant association existed between grasp and the availability of care or the source of social support (table 5).
Table 5. Association between socio-demographic variables and functional support (grasp).
| Variable | Functional support (Grasp) | Total | χ2 | P value | |
|---|---|---|---|---|---|
| Independent (n=8) | Dependent (n=152) | ||||
| Age group (years) | |||||
| 65–75 | 8 (11.1%) | 64 (88.9%) | 72 (100%) | 10.292* | 0.001 |
| >75 | 0 (0.0%) | 88 (100%) | 88 (100%) | ||
| Sex | |||||
| Male | 6 (8.0%) | 69 (92.0%) | 75 (100%) | 2.675* | 0.148 |
| Female | 2 (2.4%) | 83 (97.6%) | 85 (100%) | ||
| Marital status | |||||
| With spouse | 7 (7.9%) | 82 (82.1%) | 89 (100%) | 9.025* | 0.079 |
| Without spouse | 1 (1.4%) | 70 (98.6%) | 71 (100%) | ||
| Occupation | |||||
| Employed | 3 (3.2%) | 91 (96.8%) | 94 (100%) | 0.180* | 0.276 |
| Unemployed | 5 (7.6%) | 61 (92.4%) | 66 (100%) | ||
| Education level | |||||
| Below secondary | 1 (0.9%) | 115 (99.1%) | 116 (100%) | 15.205* | 0.001 |
| Secondary and above | 7 (15.9%) | 37 (84.1%) | 44 (100%) | ||
| Income level | |||||
| ≤N10 000 | 3 (2.1%) | 138 (97.9%) | 141 (100%) | 20.624* | 0.001 |
| >N10 000 | 5 (26.3%) | 14 (73.7%) | 19 (100%) | ||
| Religion | |||||
| Christianity | 8 (5.9%) | 128 (94.1%) | 136 (100%) | 1.488* | 0.607 |
| Others | 0 (0.0%) | 24 (100.0%) | 24 (100%) | ||
| Receiving care | |||||
| Yes | 5 (6.9%) | 67 (93.1%) | 72 (100%) | 1.042* | 0.469 |
| No | 3 (3.4%) | 85 (96.6%) | 88 (100%) | ||
| Receiving social support | |||||
| Yes | 1 (3.0%) | 32 (97.0%) | 33 (100%) | 0.340* | 1.000 |
| No | 7 (5.5%) | 120 (94.5%) | 127 (100%) | ||
| Distance to food supply | |||||
| <1 hour | 8 (10.7%) | 67 (89.3%) | 75 (100%) | 9.554* | 0.002 |
| ≥1 hour | 0 (0.0%) | 85 (100.0%) | 85 (100%) | ||
Fisher’s exact test was used.
Logistic regression on socio-demographic variables and functional status (grasp)
Table 6 outlines the logistic regression analysis for functional grasping status, demonstrating that while socio-economic factors appeared significant initially, they are not robust independent predictors. In the unadjusted analysis, both education and income were strongly associated with functional status (p<0.001). However, after adjusting for potential confounders, neither variable retained statistical significance.
Table 6. Logistic regression on socio-demographic variables and functional status (grasp).
| Variable | Unadjusted OR | 95% CI for the UOR | P value | Adjusted OR | 95% CI for the AOR | P value |
|---|---|---|---|---|---|---|
| Education level | ||||||
| Below secondary (ref.) | 1 | 1 | ||||
| Secondary and above | 0.05 | 0.01 to 0.39 | <0.001 | 6.95 | 0.57 to 84.86 | 0.129 |
| Income | ||||||
| ≤N10 000 (ref.) | 1 | 1 | ||||
| >N10 000 | 0.06 | 0.01 to 0.28 | <0.001 | 5.25 | 0.87 to 31.58 | 0.070 |
AOR, adjusted OR; ref, reference; UOR, unadjusted OR.
Association between socio-demographic characteristics and functional status (bathing)
Functional dependence in bathing was statistically and significantly associated with age (p<0.001), marital status (p<0.001), education level (p<0.001), income level (p<0.001) and religion (p=0.005) (table 7).
Table 7. Association between socio-demographic variables and functional support (bathing).
| Variable | Functional support (bathing) | Total | χ2 | P value | |
|---|---|---|---|---|---|
| Independent (n=44) | Dependent (n=116) | ||||
| Age group (years) | |||||
| 65–75 | 38 (52.8%) | 34 (47.2%) | 72 (100%) | 41.954 | <0.001 |
| >75 | 6 (6.8%) | 82 (93.2%) | 88 (100%) | ||
| Sex | |||||
| Male | 24 (32.0%) | 51 (68.0%) | 75 (100%) | 1.434 | 0.231 |
| Female | 20 (23.5%) | 65 (76.5%) | 85 (100%) | ||
| Marital status | |||||
| With spouse | 38 (42.7%) | 51 (53.7%) | 89 (100%) | 23.231 | <0.001 |
| Without spouse | 6 (8.5%) | 65 (91.5%) | 71 (100%) | ||
| Occupation | |||||
| Employed | 24 (25.5%) | 70 (74.5%) | 94 (100%) | 1.251 | 0.328 |
| Unemployed | 20 (30.3%) | 46 (69.7%) | 66 (100%) | ||
| Education level | |||||
| Below secondary | 20 (17.2%) | 96 (82.8%) | 116 (100%) | 22.266 | <0.001 |
| Secondary and above | 24 (54.5%) | 20 (45.5%) | 44 (100%) | ||
| Income level | |||||
| ≤N10 000 | 31 (22.0%) | 110 (78.0%) | 141 (100%) | 18.108 | <0.001 |
| >N10 000 | 13 (68.4%) | 6 (31.8%) | 19 (100%) | ||
| Religion | |||||
| Christianity | 43 (31.6%) | 93 (68.4%) | 136 (100%) | 7.710 | 0.005 |
| Others | 1 (4.2%) | 23 (95.8%) | 24 (100%) | ||
| Receiving care | |||||
| Yes | 21 (29.2%) | 51 (70.8%) | 72 (100%) | 0.182 | 0.669 |
| No | 23 (26.1%) | 65 (73.9%) | 88 (100%) | ||
| Receiving social support | |||||
| Yes | 9 (27.3%) | 24 (72.7%) | 33 (100%) | 0.001 | 0.974 |
| No | 35 (27.6%) | 92 (72.4%) | 127 (100%) | ||
| Distance to food supply | |||||
| <1 hour | 25 (33.3%) | 50 (66.7%) | 75 (100%) | 2.409 | 0.121 |
| ≥1 hour | 19 (22.4%) | 66 (77.6%) | 85 (100%) | ||
Logistic regression on socio-demographic variables and functional status (bathing)
Table 8 details the logistic regression analysis for functional independence in bathing, highlighting age and marital status as definitive independent predictors of dependence. While the unadjusted analysis initially implicated education, income and religion as significant factors (p<0.05), these variables lost statistical significance in the adjusted multivariate model (p>0.05), suggesting their effects were likely confounded by other variables. Consequently, the adjusted model reveals that individuals aged over 75 years are nearly seven times more likely to be dependent in bathing than their younger counterparts (AOR=6.90; 95% CI 2.26 to 21.05; p=0.001), and those without a spouse had three times higher odds of dependence than their married participants (AOR=3.04; 95% CI 1.02 to 9.11; p=0.047).
Table 8. Logistic regression on socio-demographic variables and functional status (bathing).
| Variable | Unadjusted OR | 95% CI for the UOR | P value | Adjusted OR | 95% CI for the AOR | P value |
|---|---|---|---|---|---|---|
| Age (years) | ||||||
| 65–75 (ref.) | 1 | 1 | ||||
| >75 | 15.27 | 5.91 to 39.47 | <0.001 | 6.90 | 2.26 to 21.05 | 0.001 |
| Marital status | ||||||
| With spouse (ref.) | 1 | 1 | ||||
| Without spouse | 8.07 | 3.17 to 20.58 | <0.001 | 3.04 | 1.02 to 9.11 | 0.047 |
| Education level | ||||||
| Below secondary (ref.) | 1 | 1 | ||||
| Secondary and above | 0.17 | 0.08 to 0.37 | <0.001 | 1.22 | 0.38 to 3.93 | 0.742 |
| Income | ||||||
| ≤N10 000 (ref.) | 1 | 1 | ||||
| >N10 000 | 0.13 | 0.05 to 0.37 | <0.001 | 2.03 | 0.50 to 8.17 | 0.321 |
| Religion | ||||||
| Christianity (ref.) | 1 | 1 | ||||
| Others | 10.63 | 1.39 to 81.34 | 0.011 | 4.13 | 0.44 to 38.71 | 0.214 |
AOR, adjusted OR; ref, reference; UOR, unadjusted OR.
Discussion
In this study, a response rate of 100% was recorded among 75 male and 85 female respondents aged 65–98 years (an average of 76.61±7.87 years).
Association between socio-demographic characteristics and functional support (mobility)
As shown in table 1, a statistically significant association was seen between functional mobility and age, marital status, education level and religion. Mobility function has been said to be closely associated with cardiovascular and muscular fitness, depending on the regularity of exercise among these individuals, and is usually worsened by falls among older people.18 19 In the multivariate logistic regression (table 2), only age and availability of care to respondents showed a statistically significant independent association with functional support in mobility on elimination of confounders. Participants aged >75 years have nearly nine times higher probability of being dependent in mobility than those aged 65–75 years. Participants not receiving care have 76% percent less probability of being dependent in mobility than those receiving care. The significant association between functional mobility and age as reported above is in agreement with the results of a cross-sectional study by Kagawa et al who demonstrated that increasing age was statistically and significantly associated with functional decline in mobility.20 Although Lestari et al observed a statistically significant association between age >80 years, absence of a partner, low education level and dependence in mobility, no significant association was found with employment status, unlike the present study. Ahmed et al were not different in their assertion.5 21 This slight variation could be due to the fact that the former made a cross-country comparative study that did not consider religion, whereas the latter mainly studied depressed older populations, unlike the present study.
Association between socio-demographic variables and functional support (dressing)
As shown in table 3, statistically significant associations existed between dressing and age, sex, marital status, education level, income level and religion. No significant associations existed between dressing and employment status, availability of care, social support and distance to the source of food supply.
As shown in table 4, marital status is the sole significant independent predictor of dependence after adjustment. Although the unadjusted analysis initially indicated that sex, education and income were significant determinants, these variables lost their statistical significance when controlled for in the multivariate model. However, the impact of marital status remained robust; participants without a spouse were found to be over 11 times more likely to be dependent in dressing than those with a spouse. The statistically significant independent associations between functional dependence in dressing and marital status in this study did not differ from the results of another cross-sectional study by Kagawa et al, who also demonstrated statistically significant associations between dependence in dressing and absence of a spouse among the older population.20 Ahmed et al also made similar assertions5 but studied depressed older population, unlike the present study that focused on the older population seeking care for various medical conditions. However, a cross-sectional study in southwest Nigeria did not find associations between dependence in dressing and marital status.22 Similarly, another study in India revealed that only age and sex were significantly associated with dependence in dressing.23 Although the Nigerian study used the modified Barthel index as a study instrument and attributed its finding on poverty and social neglect, the Indian study was conducted in an environment where the government is responsible for the psychosocial and socio-economic well-being of older people. The present study used the modified Katz and Lawton’s index and was conducted in an environment of high neglect for older people.
Association between socio-demographic variables and functional status (grasp)
In table 5, statistically significant associations were noted between functional status (grasp) and age, education level, income level and distance to the food supply. However, no statistically significant differences were found between functional status in grasp and sex, marital status, employment status, religion, availability of care and availability of social support.
Table 6 outlines the logistic regression analysis for functional grasp status, demonstrating that while socio-economic factors appeared significant initially, they were not robust independent predictors. In the unadjusted analysis, both education level and income were strongly associated with functional status in grasp (p<0.001). However, after adjusting for potential confounders, neither variable retained statistical significance. Notably, the adjusted OR displays extremely wide CIs (eg, 0.57 to 84.86 for education), which likely reflects statistical instability because of the very small sample size of independent participants (n=8) for this specific functional task.
The non-significant independent association between dependence in functional grasp and the variables, as demonstrated in this study, is different from the results of a study conducted in Nigeria by Ejechi et al, who asserted that increasing age, absence of a spouse, low education level, low income level and increased distance of travel were significantly associated with disability in physical function (including grasp). This was attributed to frailty and poor socio-economic well-being.24 In a Malaysian study, Diaz and colleagues et al showed that only increasing age and marital status were significantly associated with disability in functional grasp in older people and attributed this to fear of falling, frailty and cognitive deficit.25 However, the Malaysian study was an observational cross-sectional study among people with diabetes and did not include the income level and distance to the food supply, unlike the present hospital-based cross-sectional study among older healthcare seekers. Our finding demonstrates that grip strength in older people is not altered by these variables as studied, probably due to the absence of factors that alter grip strength.
Association between socio-demographic characteristics and functional status (bathing)
Table 7 reveals statistically significant associations between bathing and age, marital status, education level, income level and religion. Moreover, no statistically significant association was found between functional status in bathing and sex, employment status, availability of care, availability of social support and distance to source of food supply.
Table 8 details the logistic regression analysis for functional independence in bathing, highlighting age and marital status as definitive independent predictors of dependence. Although the unadjusted analysis initially implicated education, income and religion as significant factors, these variables lost statistical significance in the adjusted multivariate model. This suggested that their effects were likely confounded by other variables. Consequently, the adjusted model revealed that the probability of being functional dependent in bathing was about seven times higher in those aged >75 years than respondents aged 65–75 years. The odds were about three times higher in respondents without a spouse than in those with a spouse. The statistically significant associations between dependence in bathing and some socio-demographic characteristics (age and marital status) are in line with the results of a cross-country comparative study, which demonstrated that age >80 years and the absence of a partner were significantly associated with dependence in bathing.21 This finding also did not differ from the results of other cross-sectional studies.5 20 The presented significant findings might be due to frailty, cognitive impairment and lack of partner-based care. Our finding varied from the results of Ajayi et al in their study conducted in southwest Nigeria, which recorded no significant independent association between functional dependence in bathing and age and marital status.22 Bathing was said to be among the last set of activities to be lost in older people.22 However, Ajayi et al used the modified Barthel index in a different region of the country, unlike the present study.
Relevance of the study to family medicine
This study provides evidence to support the practice of routinely incorporating evaluation of nutritional and functional status of the older population by family physicians in daily geriatric clinic practice. It further exposes the need for regular training of all healthcare staff in the management of these individuals. An extensive assessment of the psychosocial and biomedical problems among older people is needed, not only to ensure proper policy formulation in providing them with essential healthcare, but also to develop appropriate support and nursing services aimed at obtaining relevant patient-centred information with respect to their individual peculiarities.
Recommendations for future research
The association between nutritional and functional status in the older population should be researched further.
The prevalence of co-morbidities and its association with functional decline in ADLs of older people in the study area is still obscure and needs further investigation.
Whether there is a molecular basis for the increased sex-specific vulnerability to functional decline in older people is an important research question that needs future response.
Conclusion
Age is an independent risk factor for functional dependence in mobility and bathing, and marital status independently predicted dependence in dressing and bathing. Not receiving care also independently predicted dependence in mobility. These findings portray the wide gap in disability management in older people between developed and developing countries and explicitly underscore the fact that an improvement in the biopsychosocial, biomedical and economic well-being of this population will ameliorate the impact of decline in functional status in ADLs.
Supplementary material
Footnotes
Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Prepublication history and additional supplemental material for this paper are available online. To view these files, please visit the journal online (https://doi.org/10.1136/bmjopen-2025-108641).
Provenance and peer review: Not commissioned; externally peer reviewed.
Patient consent for publication: Not applicable.
Ethics approval: This study involves human participants and was approved by the Research and Ethics Committee of the study institution on 1 June 2018, with reference number FETHA/REC/VOL.2/2018/060. This study complied with the Declaration of Helsinki version 2013 on research involving the use of human subjects. All selected participants were informed about the objectives and contributions of the study, and written informed consents were obtained before the questionnaires were administered. Participants were assured of confidentiality and informed that they had the right to decline participation in the study. Participants gave informed consent to participate in the study before taking part.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Data availability statement
Data are available upon reasonable request.
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