Abstract
Background
Malnutrition remains a problem in older populations globally. Most older persons do not meet the required dietary intake with the majority consuming mainly carbohydrate-based foods and vegetables. The current study therefore aimed at assessing the dietary practices, nutritional status and associated factors among older persons in Gulu district.
Methods
This was a cross-sectional study among older persons aged 60 years and above in Gulu District. The study used a multi-stage sampling procedure. Nutritional status was measured using the Mini Nutrition Assessment – Short Form (MNA-SF). Analysis was done in STATA 16 using modified Poisson regression considering a 5% level of significance.
Results
The study enrolled 141 participants with a median age (IQR) of 71(64,79) years. Majority of the respondents, 96(68.1%) were female and 72(51.1%) reported to be widows or widowers. The prevalence of malnutrition was 53.9%. Millet was eaten by 62(45.9%) participants 1-4 times a week while maize and its products were eaten by 56(40.6%) 1-2 times monthly. We found that increasing age (aPR=1.02, p-value=0.021), having primary education (aPR=1.51, p-value=0.046), and staying <5km away from the health facility (aPR=1.60, p-value=0.003) were significantly associated with having malnutrition.
Conclusion
Majority of the participants were malnourished. Age, education, and distance from healthcare facility were the factors that influenced nutritional status. The prevalence of malnutrition prompts for a need for nutritional and food relief aid in this population.
Keywords: Dietary practices, Older persons, Nutritional status
Background
Malnutrition remains a problem among the older populations worldwide with a prevalence reported at 22.8% globally [1]. The World Health Organization (WHO) through its programme, “The Decade of Healthy Ageing 2020–2030” has prioritized healthy aging and the needs of older people [2]. However, undernutrition continues to stifle older persons. Across Africa, the prevalence ranges from as low as 2.2 to 77.3% in various settings [3]. Malnutrition in Uganda varies across regions with estimations varying between 6% and 52% on various scales of measurement [4, 5].
Dietary practice studies among older persons show that most older persons do not meet the required food intake [6] with majority consuming more of carbohydrate-based foods and vegetables [4, 6, 7]. The practices have been linked to financial constraints and health challenges [8, 9]. Location of residence [10], age, educational background, and underlying conditions [9, 11] have also been seen to influence the nutritional status of this population. Other factors identified include number of meals a day, drug prescriptions, and individual nutrition perspectives [4]. As a result of undernutrition and poor dietary practices, older persons may experience nutritional deficiencies consequently leading to various systemics dysfunctions including sarcopenia [12], geriatric syndrome [13] and sometimes death [14, 15].
In Uganda, minimal research has been done on nutrition in this population [4, 5] and the household dietary survey that was done in northern Uganda last year focused on the younger population [10]. The Northern region of Uganda has suffered instability arising from a 20-year (1984–2008) civil war. This instability precipitated prolonged food insecurity and inadequate access to healthcare services which had a profound impact on the affected communities. Although various food relief programs have been on-going in Northern Uganda [16, 17], the nutritional status of older persons remains dire [18]. Whereas previous nutrition research from the region has highlighted poor nutrition status, especially among children under five and pregnant women [10, 19, 20], there is paucity of data on the nutritional status of older persons in these communities. It is important to understand this population’s dietary practices, nutritional status and the associated factors so as to inform the design strategies to curb malnutrition among older persons. This study therefore aimed to assess the nutritional status of older persons and the associated factors in Gulu district in Northern Uganda.
Methods
Study setting
The study was carried out in Bungatira Sub County in Aswa County, Gulu District in Northern Uganda.
Study design and population
This was a cross-sectional study conducted among older persons aged 60 years and above.
Sample size estimation
The study sample size was established using the Cochran, (1963) formula of sample size calculation – [(z2pq)/e2] using a Zα corresponding to 95% level of confidence (z) of 1.96, prevalence of malnutrition among older persons (p) of 6.0% [4] (the study was chosen because it used the same tool to measure malnutrition as the current study), level of precision (e) of 0.05, q = (1-p) = 94% and a design effect of 1.5 to cater for clustering. The desired sample size, N for this study was 143 after correcting for 10% non-response.
Sampling and data collection procedure
The study used a multi-stage sampling procedure. There are two Counties (Aswa and Gulu municipality) that constitute Gulu district. Aswa was purposively selected because it is the rural area of the district. Aswa is made up of Palaro, Awach, Bungatira, Paicho, and Unyama sub-counties from which Bugantira was randomly selected by lottery. The sample size was divided equally among the parishes in Bugantira sub-county. These parishes include Agonga, Punena, Pabwo, Oitino, Laliya, Laroo, and Atiyabar parish. Parishes were selected due to the sparse distribution of households in villages in Bungatira Sub-County. The list of households was obtained from the Local Council II (LC II) chairpersons from which the households were randomly selected using simple random sampling using the table of random numbers. Stage three involved picking the first household and this was done by closing eyes and randomly pointing a pen on the list of households. To choose the second household, the fourth household after the first on the list was considered until the required sample size was reached in each parish. Households that had no person of 60 years and above or were not available at the time of data collection were replaced. For households that had more than one person ≥ 60 years, one was randomly selected using simple random sampling.
Study variables and tools
The outcome variable was malnutrition which was measured using the Mini Nutrition Assessment – Short Form (MNA-SF) [21]. The MNA-SF is the short version of the original Mini Nutrition Assessment [22]. It has six questions covering food intake, weight loss in last 3 months, mobility, history of psychological distress in last 3 months, neuropsychological problems, and Body Mass Index (BMI) or calf circumference. This study used BMI (BMI-MNA-SF). Participants’ weight and height were measured using a Seca weighing scale and a stadiometer respectively. The MNA-SF tool has an overall score of 14 categorized as “0–7” malnourished, “8–11” at risk of malnutrition and “12–14” normal nutritional status. For this study, the scale was dichotomized into ≤ 7 as malnourished and > 7 as not malnourished [23] to form the study outcome - nutritional status.
Data on the regularity of food consumption using the food frequency questionnaire (FFQ) was collected. The tool utilized the recommended twelve food groups [24] which include cereals, root and tubers, green leafy vegetables, fruits, meat, eggs, fish, milk, and its products and legumes, nuts, and seeds. The list of foods was modified to include only those suited for Northern Uganda. Participants were asked to report on how often they had consumed certain foods in the past one year. The responses adapted from previous studies [10] were captured on a 5-point Likert scale; 5 “daily”, 4 “5–6 times per week”, 3 “1–4 times per week”, 2 “1–2 times per month”, 1 “1–4 times a year” and 0 “Never”. The independent variables included demographics of age, sex, marital status, education level, household size, source of income, and income level. Other variables studied included distance from health facility, satisfaction with health care and availability of nutritional services at the facility.
Statistical analysis
Data was analyzed using STATA 16 (Stata Corp, College Station, TX, USA). Means and standard deviations (SD) were reported for normally distributed data while medians and interquartile ranges (IQR) were reported where data was skewed in addition to frequencies and percentages for categorical variables. To determine the prevalence of malnutrition, the number of participants who scored ≤ 7 on the MNA-SF tool was divided by the sample size. Adjustment for clustering was not done during analysis because we found the design effect to be one. Models were built using the modified Poisson regression model with prevalence ratios (PR) reported as the measure of association. The study considered a level of significance of 5% and confidence level (CI) of 95%. Variables were assessed for interaction using the chunk test and for confounding using the change in PR between the adjusted and the crude models. Variables with a p-value of 0.2 at bivariate analysis were considered for multivariable analysis. Variables that were significant (p-value < 0.05) at multivariable analysis were considered as factors associated with malnutrition.
Results
Participant characteristics
The study enrolled 141 older persons with a median age (IQR) of 71(64,79) years with a response rate of 98.6%. Majority of the respondents, 96(68.1%) were female and 72(51.1%) of all participants reported to be widows or widowers. Nearly three quarters, 104(73.8%) were involved in agriculture as a source of livelihood. Sixty-four, 64(45.4%) had no education (Table 1).
Table 1.
Socio-demographic characteristics of study participants (n = 141)
| Variable | Categories | Frequency (n) | Percentage (%) |
|---|---|---|---|
| Sex | Male | 45 | 31.9 |
| Female | 96 | 68.1 | |
| Median age (IQR) in years [71(64,79)] | <71 | 64 | 45.4 |
| ≥71 | 77 | 54.6 | |
| Marital status | Single/ Divorced/separated | 5 | 3.6 |
| Married | 64 | 45.5 | |
| Widow/widower | 72 | 51.1 | |
| Religion | Christian | 134 | 95 |
| Islam | 4 | 2.8 | |
| Traditionalist | 3 | 2.1 | |
| Occupation | Agriculture | 104 | 73.8 |
| Civil/public servant | 4 | 2.8 | |
| Casual labor | 15 | 10.6 | |
| Business | 10 | 7.1 | |
| Other | 8 | 5.7 | |
| Median number (IQR) of people in the household 6(4,9) | <6 | 49 | 34.8 |
| ≥6 | 92 | 65.3 | |
| Education level | None | 64 | 45.4 |
| Primary | 54 | 38.3 | |
| Secondary | 13 | 9.2 | |
| Tertiary | 10 | 7.1 | |
| Average monthly household income | < 120000 | 89 | 63.1 |
| 121000-234000 | 41 | 29.1 | |
| 235000-400000 | 8 | 5.7 | |
| 401000-750000 | 3 | 2.1 |
Prevalence of malnutrition using the mini nutrition assessment short form (MNA-SF)
The prevalence of malnutrition was 53.9% [95%Cl: 45.6–62.0] and more than half of the respondents, 87(61.7%) had a moderate decrease in food intake (Table 2).
Table 2.
Nutritional status of older persons using the MNA-SF (n = 141)
| Variable | Frequency (n) | Percentage (%) |
|---|---|---|
| Malnutrition | ||
| No (not malnourished) | 65 | 46.1 |
| Yes (malnourished) | 76 | 53.9 |
| Has food intake declined over the past months | ||
| Severe decrease in food intake | 21 | 14.9 |
| Moderate decrease in food intake | 87 | 61.7 |
| No decrease in food intake | 33 | 23.4 |
| Involuntary weight loss in the last 3 months | ||
| Weight loss greater than 3kg | 18 | 12.8 |
| Does not know | 101 | 71.6 |
| Weight loss between 1 and 3kg | 10 | 7.1 |
| No weight loss | 12 | 8.5 |
| Current mobility | ||
| Bed or chair bound | 3 | 2.1 |
| Able to get out of bed/chair, but does not go out | 4 | 2.8 |
| Goes out | 134 | 95.0 |
| Has suffered psychological stress or acute disease in the past 3 months | ||
| Yes | 33 | 23.7 |
| No | 106 | 76.3 |
| Neuropsychological problem | ||
| Severe dementia or depression | 18 | 12.8 |
| Mild dementia | 53 | 37.6 |
| No psychological problems | 70 | 49.7 |
| Body Mass Index (BMI) | ||
| BMI less than 19 | 57 | 40.4 |
| BMI 19 to less than 21 | 31 | 22.0 |
| BMI 21 to less than 23 | 28 | 19.9 |
| BMI 23 or greater | 25 | 17.7 |
Dietary practices of the study participants
The study identified that 47(33.6%) participants ate Sorghum and its products daily and 64(45.7%) ate it 1–4 times per week. Millet was eaten by 62(45.9%) participants 1–4 times a week while maize and its products were eaten by 56(40.6%) people 1–2 times a month. Regarding consumption of root tubers, cassava was consumed by 30(21.4%) people daily and 56(40.0%) ate it 1–4 times a week. White sweet potatoes were consumed by 64(46.0%) participants 1–4 times a week. ‘Boo’ (greens in peanut or sim sim sauce) was the commonest green leafy vegetable, eaten by 119(84.4%) participants while ‘Otigo’ (okra) was eaten by 117(83.0%) 1–4 times per week. In addition, 108(77.7%) participants consumed pigeon peas (Lapena) while 93(68.4%) consumed beans 1–4 times a week respectively. Vitamin A fruits, meats, and milk products were the least consumed (Table 3).
Table 3.
Dietary practices of older persons in Northern Uganda
| Type of food | Frequency of consumption, n (%) | |||||
|---|---|---|---|---|---|---|
| Daily | 5-6times/week | 1-4times/week | 1-2times/month | 1-4times/year | Never | |
| Cereals | ||||||
| Sorghum & its products (n = 140) | 47(33.6) | 11(7.9) | 64(45.7) | 13(9.3) | 5(3.6) | 0(0) |
| Millet & its products (n = 135) | 11(8.2) | 5 (3.7) | 62(45.9) | 26(19.3) | 28(20.7) | 3(2.2) |
| Maize & its products (n = 138) | 16(11.6) | 27(19.6) | 56(40.6) | 14(10.1) | 19(13.8) | 6(4.5) |
| Rice (n = 131) | 0(0) | 2(1.5) | 25(19.1) | 61(46.6) | 32(24.4) | 11(8.4) |
| Wheat & its products (n = 119) | 6(5.1) | 2(1.7) | 16(13.6) | 52 (44.1) | 16(13.6) | 26(22.0) |
| White roots & tubers | ||||||
| Cassava (n = 140) | 30(21.4) | 9 (6.4) | 56(40.0) | 38 (27.1) | 5(3.6) | 2(1.4) |
| White fleshed sweet potatoes (n = 139) | 0(0) | 6 (4.3) | 64(46.0) | 40 (28.8) | 24(17.3) | 5 (3.6) |
| Others (Irish potatoes & yams) (n = 100) | 0(0) | 0(0) | 1 (1.0) | 8 (8.0) | 34(34.0) | 34(34.0) |
| Dark green leafy vegetables | ||||||
| Boo | 2(1.4) | 0(0) | 119(84.4) | 11(7.8) | 8(5.7) | 1(0.7) |
| Otigo | 2(1.4) | 0(0) | 117(83.0) | 12(8.5) | 8(5.7) | 2(1.4) |
| Akeyo (n = 135) | 0(0) | 0(0) | 15(11.1) | 20(14.8) | 89(65.9) | 11(8.2) |
| Malakwang (n = 135) | 0(0) | 0(0) | 28(20.7) | 37(27.4) | 61(45.2) | 0(0) |
| Dodo (Amaranthus) (n = 134) | 0(0) | 0(0) | 22(16.4) | 33 (24.6) | 70(52.2) | 9(6.7) |
| Pumpkin leaves (n = 129) | 0(0) | 0(0) | 15(11.6) | 20(15.5) | 76(58.9) | 18(14.0) |
| Egg plants (n = 137) | 1(0.7) | 1(0.7) | 41(30.2) | 27(19.9) | 60(44.1) | 6 (4.4) |
| Vit.A rich vegetables & tubers (orange sweet potatoes, pumpkin, carrot) (n = 130) | 1(0.8) | 0(0) | 4(3.1) | 16(12.3) | 96(73.9) | 13(10.0) |
| Cabbage (n = 137) | 1(0.7) | 1(0.7) | 44(32.4) | 34(25.0) | 53(39.0) | 3(2.2) |
| Vitamin A rich fruits | ||||||
| Ripe mangoes | 2(1.4) | 2(1.4) | 3(2.1) | 1(0.7) | 132(93.6) | 1(0.7) |
| Ripe pawpaws (n = 131) | 3(2.3) | 1(0.8) | 36(27.5) | 49 (37.4) | 37(28.2) | 5(3.8) |
| Other fruits (passion fruits, Tangarine, Avocado, pine apple, oranges, jack fruit, cwaa, sweet bananas, watermelon, guavas) (n = 133) | 3(2.3) | 3(2.3) | 38(28.6) | 51(38.4) | 31 (23.3) | 7(5.3) |
| Fresh meats | ||||||
| Beef (n = 135) | 0(0) | 0(0) | 14 (10.4) | 60 (44.4) | 56 (41.5) | 5 (3.7) |
| Chicken (n = 127) | 0(0) | 0(0) | 9 (7.1) | 33(26.0) | 69 (54.3) | 16(12.6) |
| Goat (n = 108) | 0(0) | 0(0) | 7 (6.5) | 14 (13.0) | 40(37.0) | 47(43.5) |
| Others (duck, game, insects-white ants, termites, grasshoppers) (n = 128) | 0(0) | 0(0) | 0(0) | 4(3.1) | 111(86.7) | 13(10.2) |
| Eggs (n = 108) | 1 (0.9) | 0(0) | 14 (13.0) | 15 (14.0) | 26 (24.1) | 52(48.2) |
| Fish | ||||||
| Mukene (n = 134) | 8(6.0) | 16 (11.9) | 90(67.2) | 14 (10.5) | 3 (2.2) | 3 (2.2) |
| Tilapia (n = 131) | 0(0) | 0(0) | 14(10.7) | 20 (15.3) | 75 (57.3) | 22(16.8) |
| Milk & its products (n = 103) | 5 (4.9) | 1 (1.0) | 15(14.6) | 10 (9.7) | 43(41.8) | 29(28.2) |
| Legumes, nuts and seeds | ||||||
| Beans (fresh and dried) (n = 137) | 5 (3.7) | 21 (15.3) | 92(67.2) | 11 (8.0) | 2 (1.5) | 6 (4.4) |
| Pigeon peas (Lapena) (n = 139) | 5 (3.6) | 10 (7.2) | 108(77.7) | 11 (7.9) | 3 (2.2) | 2 (1.4) |
| Sesame/simsim (paste, seeds) (n = 136) | 8 (5.9) | 25(18.4) | 93 (68.4) | 6 (4.4) | 4 (2.9) | 0(0) |
| Ground nuts (paste, powder, sauce, seed) (n = 136) | 5 (3.7) | 25(18.4) | 81(59.6) | 14 (10.3) | 9(6.6) | 2(1.5) |
Factors associated with malnutrition among older persons
The study found that increasing age (aPR = 1.02, 95%Cl = 1.00–1.03, p-value = 0.021), having primary education compared to having no education (aPR = 1.51, 95%Cl = 1.01–2.26, p-value = 0.046), and staying < 5 km away from the health facility compared to staying more 5 km away (aPR = 1.60, 95%Cl = 1.18–2.18, p-value = 0.003) were significantly associated with having malnutrition (Table 4)
Table 4.
Bivariate and multivariable analysis of factors associated with malnutrition among the older persons in Northern Uganda (n=141)
| Characteristic | Malnourished n(%) | cPR | 95%Cl | p -value | aPR | 95%Cl | p -value |
|---|---|---|---|---|---|---|---|
| Sex | |||||||
| Female | 44(45.8) | 1 | |||||
| Male | 32(71.1) | 1.55 | 1.16 – 2.07 | 0.003 | – | – | – |
| Median age in years | 1.01 | 0.99 – 1.03 | 0.188 | 1.02 | 1.00 – 1.03 | 0.021 | |
| Marital status | |||||||
| Widow/widower | 31(43.1) | 1 | |||||
| Single/ Divorced/separated | 3(60.0) | 1.39 | 0.65 – 2.99 | 0.396 | |||
| Married | 42(65.6) | 1.52 | 1.11 – 2.10 | 0.010 | – | – | – |
| Religion | |||||||
| Christian | 73(54.5) | 1 | |||||
| Muslim | 2(50.0) | 0.918 | 0.34 – 2.48 | 0.677 | |||
| Traditionalist | 1(33.3) | 0.612 | 0.12 – 3.07 | 0.984 | |||
| Occupation | |||||||
| Agriculture | 61(58.7) | 1 | |||||
| Civil/public servant | 1(25.0) | 0.43 | 0.08 – 2.36 | 0.329 | |||
| Casual labor | 12(52.2) | 0.89 | 0.58 – 1.36 | 0.589 | |||
| Business | 2(20.0) | 0.34 | 0.10 – 1.19 | 0.093 | – | – | – |
| # of people in the household | |||||||
| ≥6 | 26(53.1) | 1 | |||||
| <6 | 50(54.4) | 0.98 | 0.71 – 1.35 | 0.885 | |||
| Education level | |||||||
| None | 23(35.9) | 1 | |||||
| Primary | 38(70.4) | 3.23 | 1.62 – 6.45 | 0.001 | 1.51 | 1.01 – 2.26 | 0.046 |
| Secondary | 8(61.5) | 2.69 | 0.99 – 7.26 | 0.052 | 1.37 | 0.79 – 2.36 | 0.265 |
| Tertiary | 7(70.0) | 1.45 | 0.19 – 11.27 | 0.720 | 1.24 | 0.80 – 1.93 | 0.326 |
| Av. monthly household income | |||||||
| < 120000 | 62(69.7) | 1 | |||||
| 121000-234000 | 11(26.8) | 0.39 | 0.23 – 0.65 | <0.001 | – | – | – |
| 235000-400000 | 2(25.0) | 0.36 | 0.12 – 1.21 | 0.098 | – | – | – |
| 401000-750000 | 1(33.3) | 0.48 | 0.10 – 2.40 | 0.370 | |||
| Availability of nutritional services at the health facility | |||||||
| No | 72(53.3) | 1 | |||||
| Yes | 4(66.7) | 1.38 | 0.60 – 3.17 | 0.451 | |||
| Distance from health facility | |||||||
| >5km | 31(73.8) | 1 | |||||
| <5km | 13(48.2) | 1.65 | 1.19 – 2.28 | 0.002 | 1.60 | 1.18 – 2.18 | 0.003 |
| 5km | 30(44.8) | 1.07 | 0.67 – 1.73 | 0.764 | 1.23 | 0.81 – 1.90 | 0.329 |
| Satisfaction with healthcare services | |||||||
| Yes | 62(57.4) | 1 | |||||
| No | 8(36.4) | 0.42 | 0.17 – 1.04 | 0.062 | – | – | – |
Discussion
The aim of this study was to assess dietary practices and nutritional status of older persons in the post-conflict context of Northern Uganda. The study found the prevalence of malnutrition at 53.9%, with more than half (61.7%) of respondents reporting a moderate decrease in food intake in past months. The biggest proportion of the older persons mainly ate sorghum, millet, and maize products, beans, and pigeon peas. Increasing age, having primary education compared to no education, staying within < 5 km from the health facility, and a dietary diversity score ≥ 60 were significantly associated with malnutrition.
This study found majority of the participants to be malnourished. The prevalence is similar to that reported from other African settings that reported prevalence of up to 54% [9, 25]. However, the current study reports a slightly higher prevalence than the 33.3% reported by a study from Uganda [5]. Additionally, some other studies in Ethiopia and India also reported a much lower prevalence of malnutrition; that is 17.5% and 17.9% respectively [26, 27]. The discrepancy may be due to the differences in tools used to assess for malnutrition. The current study used the short form of MNA tool whereas e the previous studies used BMI along with the longer version of the MNA tool. Nonetheless, the comparatively higher prevalence of malnutrition in the current study could be a reflection of the food insecurity in this region and the need for nutritional and food relief programs among older persons.
In the current study, participants mainly consumed starchy cereals, legumes and tubers. Animal products and fruits were the least consumed as previously reported from other household surveys in northern [10] and central [4] Uganda. This could be explained by the fact that most people are farmers [10] and agriculture was the commonly practiced occupation even in our study. But also, it suffices to mention that the most consumed foods – sorghum, millet, Boo and Otigo are the staple foods of Northern Uganda [28]. Hence, ease of access could be one of the reasons for the routine consumption of staples by older persons. But also, majority of the participants barely had income. Perhaps they could not afford to purchase the other recommended nutritious foods which they don’t grow. Nevertheless, the consumption of carbohydrate-rich meals with a plant-based protein and minimal or no micro-nutrient foods is a common trend in Uganda [29].
The study found that malnutrition was likely to be more prevalent among older respondents compared to the younger older persons. This may be explained by the decreasing taste for food and preferences over time among older persons, which eventually affects the intake of nutrients hence infringing on their nutritional status. The finding concurs with studies from other African settings that found malnutrition to be more common among older respondents [3, 25, 26]. Contrary to previous studies [4, 9, 25, 27], having some education background did not protect older persons against malnutrition. Education empowers people with knowledge including appropriate dietary practices which may enable them to make proper nutrition-related choices [10] hence minimizing the chances of being undernourished. However, the discrepancy in the current findings may be attributable to the effects of the war on cognitive functioning like the memory of these survivors [30, 31]. The diminished memory leaves minimal or no difference in nutrition comprehension between participants who ever had an education and those who didn’t. This highlights a need for health promotion programs for older persons in post-conflict settings. But also, although the study was powered enough to answer the study objectives, the small sample size could have hindered detection of some probable associations. We recommend future studies to consider bigger samples.
Furthermore, the current study found that people who were residing within 5 km radius from a health facility were more likely to be malnourished contrary to what was reported in Rwanda [32] where improved healthcare accessibility reduced the likelihood of malnutrition. However, the finding is also not biologically plausible as one would expect ease of access to healthcare services to improve health outcomes. The discrepancy could be because “distance from the facility” in the current study was measured through self-report with no objective validation of the participants’ reports. This may have introduced information bias and some responses may have been influenced by social desirability bias. On the other hand, the findings may reflect under-utilization of healthcare services and poor health-seeking behaviors in this population. Older persons’ programs may consider community and home-based care approaches and qualitative studies may also help expound on this behavior.
The study findings are not to be considered without any reservations. Although the study extensively explored the dietary habits and frequency of food in-take, we did not study meal timing which is key in proper nutrition [33]. We recommend further studies to consider the timing aspect and how it may influence nutrition in this population. Likewise, a qualitative study to explore other factors like psychosocial, cultural and environmental factors may be considered for further research to further explain malnutrition in older persons. Needless to mention, this was a cross-sectional study hence we could not establish causal associations. Lastly, the study was limited to the rural setting. Although this may hinder generalization of the study findings to urban settings, in it lies the uniqueness of this study concept as the conflicts in northern Uganda coupled with the limited opportunities in rural areas left rural settings more vulnerable to the aftermaths of the wars [34] compared to the urban areas making them more of interest to study.
Conclusion and recommendations
Majority of the older persons were malnourished. The findings also revealed that older persons had a decline in food intake. Increasing age, being educated and residing within 5 km from a health facility negatively influenced nutritional status. The prevalence of malnutrition prompts for a need for nutritional and food relief aid to improve food intake and nutrition in this population. There’s a need for age-appropriate nutrition education programs for older persons and the programs could be designed to meet the differences in levels of education of the target population. Programs should aim at bringing healthcare services nearer to the older persons through community and home-based approaches. Future studies may explore follow-up study designs to assess changes in nutrition and dietary practices over time and qualitative studies to explain the dietary behaviors among the older persons.
Acknowledgements
The authors would like to appreciate the local leadership of Gulu district whose support made the project a success. Sincere gratitude goes to the study participants and the research assistants who supported the data collection process.
Abbreviations
- BMI
Body Mass Index
- FFQ
Food Frequency Questionnaire
- LC
Local Chairperson
- MNA
SF–Mini Nutritional Assessment–Short Form
- MUAC
Mid–Upper Arm Circumference
- WHO
World Health Organisation
Authors’ contributions
DO conceived the concept for the study. DO, JN and FA worked on proposal development and study tools. DO coordinated the data collection process. RN conducted the data analysis. DO, JN, JK, RN, FA reviewed the draft manuscript. All authors reviewed and approved the final manuscript.
Funding
The study received no external funding. It was self-funded by the first author.
Data availability
The data upon which conclusions of this study were based is available upon request from the corresponding author.
Declarations
Ethics approval and consent to participate
The study was approved by the Clarke International University Research Ethics Committee (CIUREC) (CLARKE-2021-68). Only participants who provided written informed consent and had a witness to attest to the study processes were included in the study.
Consent for publication
All authors reviewed and approved the final draft of the manuscript for publication.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
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References
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Data Availability Statement
The data upon which conclusions of this study were based is available upon request from the corresponding author.