Later-life challenges: Assessing socio-demographic risk factors in - - PDF document

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Later-life challenges: Assessing socio-demographic risk factors in Uganda

Abel Nzabona Abstract Demographic studies in Uganda reveal fairly rich data on population sub-groups such as infant and women in reproductive age but comparatively less is known about older persons especially the challenges they face. This paper discusses risk factors of challenges among persons aged 60 and above. A structured questionnaire was used to collect data on 605 persons selected from four rural districts and one urban area. A total of ten Focus Group Discussions and 12 key informant interviews were also conducted to collect qualitative data. Loneliness, inadequate nutrition, dilapidated housing, sight problems, hearing constraints and mobility difficulties were the key challenges investigated. Using scaling technique, these constraints were subsequently aggregated into a single wholesome indicator of challenges. Binary logistic regression indicates that widowed and divorced older persons were more likely to experience severe aggregate challenge than their married counterparts. Household conditions and housing materials predicted severe aggregate

• challenge. Type of household cooking and shelter material also determined severe aggregate
• challenge. In comparison with the central region of the country, older persons living in the

Northern rural region and Kampala urban area were more likely to experience severe aggregate

• challenge. Empowering widowed older persons, establishing a special old age fund and addressing

regional disparities are recommended.

Key Words: Aggregate challenge Severe challenge Socio-demographic Older persons

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Introduction Population ageing is gaining momentum in Africa, a region often described as having the youngest age structure in the world. For example whereas the number of people aged 60 and above on the continent in 1950 numbered approximately 12 million (ECA, 2007), this figure had increased five- fold to about 60 million people by 2007 (UNFPA & HAI, 2012). Older persons present both a celebration and a challenge (UNFPA & HAI, 2012). The celebration arises from older persons’ value, while the challenge stems from constraints faced in later-life (Nzabona, 2014). The absolute number of older persons has been rising in Uganda. For example, while the number recorded during the 1969 census was 559,000, this figure increased to 686,300 as per the 1991 national census (Ministry of Finance and Economic Planning [MFEP], 1995). The 2002 national census indicated that older persons numbered 1,101,000 (UBOS, 2005) while the 2006 Uganda National Household Survey showed that this figure was 1,196,400. The recent national household survey has shown that there were 1,304,500 elderly persons in the country (UBOS, 2012). This population is projected to reach 5,420,000 by 2050 (UNFPA & HAI, 2012). This trend, unfortunately, is associated with more and more people living alone in later life (UNDESA, 2007) and may be linked with other later life challenges. Older persons face daunting challenges although differentials in magnitude exist between developing and more developed countries. The challenges include loneliness, poverty, poor housing, inadequate nutrition and ailment (UNFPA & HAI, 2012). Decline in health is one of the several challenges that aging populations face. This includes hearing, sight and memory loss in addition to a host of other health problems. Income short fall is another challenge older persons have to contend with (Barrientos, Gorman & Heslop, 2003). Owing to exclusion from the workforce upon reaching retirement age, older persons need to rely on their pensions and social security where they exist. However even in situations where social security systems are institutionalised, there are some older persons who continue to experience social and economic hardships (HelpAge International, 2004). In countries with high per capita incomes, older persons can retire earlier and thus survive the rigours of work at older ages. However, in low per capita income countries, older persons remain economically active for longer period because of the limited coverage of pension programmes and the relatively small incomes they provide (MoGLSD, 2009). In Uganda, almost 3 in 10 older persons remain actively engaged in income-generating activities well beyond age 60 (Nzabona, Ntozi & Rutaremwa, 2013). This could continue to cause stress and strain on the lives of these individuals. Loneliness is another challenge that older persons have to grapple with (Nzabona, Ntozi & Rutaremwa, 2015; Victor & Scambler, 2009). Because men tend to have a shorter life expectancy than women, more older females than older males become widows as they age (UNFPA & HAI, 2012). While some children stay with their older parents, some live too far a distance away to provide the proper support during emergencies. Furthermore, social relationships might be difficult to maintain in old age because of health limitations, death of family member, friends or workmates (Green, Richardson, Lago & Schatten-Jones, 2001). The lack of transportation is likely to greatly compound the problem (Gilhooly et al., 2002).

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Older persons in many Ugandan communities live in semi-permanent, grass-thatch and mud and wattle houses which are dilapidated (MoGLSD, 2009). The conditions of these structures are said to put older persons and their dependants in grave danger, especially during rainy seasons (Nzabona, 2014). Occurrence of similar poor living conditions has been established in Tanzania (Spitzer, Rwegoshora & Mabeyo, 2009). A study conducted in the country found that most of the

• lder people did not live in decent housing environment. The study also established rural-urban

differences as was indicated by a large proportion of respondents living in mud grass-roofed houses in rural Kineng’ene in contrast with residents of relatively more urban Kingugi where 88 percent lived in houses having an iron roof. Many of those iron roofs were, however, reported to have been in dilapidated condition with big holes through which rain water could have entered (Spitzer, Rwegoshora, & Mabeyo, 2009). Nutritional deficiency is another challenge experienced in later-life. Studies have indicated that

• lder persons in Uganda are hard-hit by food insecurity, poor nutrition and mainly feed on

carbohydrates while most take one meal a day (MoGLSD, 2009). Inadequate food and poor diet pre-disposes older persons to malnutrition, ill health, emaciation and chronic energy deficiency. In a study of nutritional status and functional ability of the elderly in Central Uganda, it was found that large percentage of older persons were malnourished and this influenced their daily activities, especially mobility and feeding (Kikafunda & Lukwago, 2005). In Uganda, some studies on the plight of older persons have been conducted, but most of them have concentrated on the HIV/AIDS challenge (Ntozi & Nakayiwa, 1999; Scholten et al., 2011; Ssengonzi, 2007). Many of these studies have yielded rich data on the adverse effects of the HIV/AIDS pandemic, but information on broader later-life challenges remains scanty. Paucity of information is particularly rife with respect to the risk factors as many of the studies have not gone beyond the traditional demographic factors in explaining the challenges. This paper provides evidence for socio-demographic risk factors that predict challenges facing older persons in the country.

Data and methods

The paper uses primary data from a large cross sectional study on Determinants of Value and Challenges of Older Persons in Uganda (Nzabona, 2015). Loneliness, inadequate nutrition, housing quality, sight problems, hearing constraints and mobility difficulty were the six challenges

• studied. In the study, stratification was used to select four districts from four strata that comprise

the major national zones of the country namely; Central, Eastern, Northern and Western regions. Using simple random sampling, Mukono, Tororo, Lira and Kisoro districts respectively were selected from the four regions. In addition, Kampala City was purposively selected as the fifth regional stratum to represent the urban sector. From each of the four rural districts, one sub-county was randomly selected and one municipality was similarly randomly chosen from the Kampala urban region. The randomly selected sub counties were Nyakabande, Kisoko, Adekokwok and Goma from Kisoro, Tororo, Lira and Mukono districts respectively. Makindye Municipality was the municipality randomly selected from Kampala urban area. Probability sampling approach was adopted to ensure ultimate national representativeness of results. An interviewer-administered questionnaire was used to collect data.

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The Kish method of sample size determination (Kish, 1965) was used to select 605 males and females aged 60 and above. Working with parish local leaders, a listing of households having

• lder persons in the selected parishes was compiled. From this listing, the desired number of

households were selected at random according to Ranjit, (2005). Age was the inclusion / exclusion criterion and any older person aged 60 and above from the selected household was eligible for inclusion in the study. In the event that a person proved to be aged below 60, he/she would be dropped from the study. Age of 60 years was adopted since this benchmark is widely used in defining older persons (UNFPA & HAI, 2012) and because categorisation of older persons in Uganda similarly follows this chronological cut off (MoGLSD, 2009). Scaling Technique was used to aggregate the six indicators of challenges into a single variable of ‘aggregate challenge’ or totality of challenges. The created variable enabled measurement of older persons’ overall constraints on a scale ranging from 0-6. Aggregate challenge was subsequently re-coded and dichotomized into ‘mild aggregate challenge’ ranging from 0-3 and ‘severe aggregate challenge’ varying from 4-6. The recoded variable was then cross-tabulated with a number of independent variables to establish association in bivariate analysis. Owing to the dichotomous nature of aggregate challenge, (mild aggregate challenge or severe aggregate challenge), binary logistic regression model was used to predict severe aggregate challenge at multivariate data analysis level. This model is expressed as: logit [p(X)] = log  

) ( 1 ) ( X p X p 

= α + β1x1 + β2x2 + β3x3 + . . . + βxxk; where α is the intercept and β1, β2, β3, e.t.c., are the regression coefficients of x1, x2, x3

• respectively. The independent variables, x1…xk, were age, sex, residence, education, marital

status, child out-migration status, limb joint health status, radio set ownership, TV ownership, possession of mobile phone, ownership of any means of transport, land ownership, possession of domestic animals, social protection status, type of fuel for cooking, material of shelter floor, material of shelter roof and material of shelter walls.

Results

Response rate All older persons identified for the face to face interviews accepted to participate in the larger study (owing to good rapport established between community leaders and interviewers on the one hand and older persons on the other). The universal acceptance compares with a similarly high 98 percent household response rate observed in the 2006 Uganda Demographic and Health Survey (UBOS, 2006). Socio-demographic characteristics of respondents Table 1 displays distribution of respondents by socio-demographic characteristics. The table indicates that, as expected, the proportion of the older persons decreased with increasing age. Not surprising almost two thirds of the older persons found in the sampled households were females (65 %), leaving only 35 percent as males because of the higher female life expectancy relative to

• males. Four-fifth of the respondents were living in rural areas while the rest were staying in

Kampala metropolitan city, the area purposively selected as an urban environment.

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Table 1 further shows that half of the respondents had never attended school and thus did not have formal education. Just over one third (35 %) attained primary level of education, 10 percent had secondary level of education while the proportion of those with tertiary and higher level of education was only 5 percent. Interestingly, these distributions of the sample are close to what is happening in the national population: high proportion being in rural areas and low percentages being in school (UBOS, 2005). Although 44 percent of the respondents were married, the overall level of widowhood was high. Slightly over two-fifth (41 %) were widowed. This is expected given that these people are in age bracket that is well above the average life expectancy of the country (between 50–55 years) and hence many of these would have lost their spouses. Unexpectedly, among the older persons interviewed, close to 3 percent of them belonged to the never-married category, which is contrary to what was expected of this overwhelmingly rural sample. The largest proportion of respondents belonged to Catholic and Anglican religious affiliations (55 % and 34 % respectively). According to the table membership to other religions exists though in much smaller proportions. In comparison with living with a spouse (10%), a higher proportion (15 %) of older persons were living alone. Over one-fifth of the elderly were living with grandchildren (23%).

Table 1 Distribution of respondents by selected socio-demographic characteristics Characteristic Number Percent Characteristic Number Percent Age Marital status 60-69 264 43.6 Never married 18 3.0 70-79 208 34.4 Married 266 44.1 80-89 101 16.7 Cohabiting 3 0.5 90+ 32 5.3 Widowed 249 41.1 Sex Divorced 29 4.8 Male 211 34.9 Separated 40 6.6 Female 394 65.1 Religion Residence Catholic 333 55.0 Urban 120 19.8 Anglican 205 33.9 Rural 485 80.2 Muslim 25 4.1 Region Pentecostal 26 4.3 Western 120 19.8 Seventh Day Adventist 5 0.8 Central 125 20.7 Others 11 1.8 Eastern 114 18.8 Living arrangement Northern 126 20.8 Alone 92 15.2 Kampala 120 19.8 Spouse 62 10.2 Education level Spouse & kids 89 14.7 No education 301 49.8 Grandchildren 137 22.6 Primary 212 35.0 Other 225 37.2 Secondary 61 10.1 Total 605 100.0 Tertiary+ 31 5.1

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Prevalence of later-life challenges Results in Table 2 show a high level of later life loneliness as almost 7 in 10 of the elderly (69%) reported feeling lonely. Almost two-thirds (64%) of the elderly were not having three meals a day; which could be an indicator of under nutrition. Similarly, nearly two thirds of the elderly (64%) were living in poor shelters. Regarding ill health, it is shown that almost three quarters (74%) of

• lder persons reported having sight difficulties. Although the proportion of the elderly with hearing

difficulty (33%) was comparatively lower, that of the elderly having mobility difficulties was substantially high (73%).

Table 2 Prevalence of challenges of the older persons Challenge Percent Loneliness (n=576) Feel lonely 68.8 Do not feel lonely 31.3 Inadequate nutrition (n=596) Have all daily meals 36.1 Not having all daily meals 63.9 Housing quality (n=601) Shelter has structural problems 64.2 No structural problems on shelter 35.8 Sight difficulty (n=601) Have sight difficulty 73.7 No sight difficulty 26.3 Hearing difficulty (n=597) Have hearing difficulty 32.5 No hearing difficulty 67.5 Mobility difficulty (n=601) Have mobility difficulty 72.7 No mobility difficulty 27.3

Table 3 shows the distribution of respondents by score level on the scale of aggregate challenge. It is shown that only 2 percent scored 0 on this scale and this is the proportion that may be regarded as being ‘without challenges’ within the context of the indicators operationalised in this study. Results further show that 11 percent obtained the maximum score of 6. These are the persons who may be regarded as having ‘severe challenge’, in terms of the six indicators. The largest proportion scored 4 and 5 on the scale (23.5% and 23.8 % respectively).

Table 3 Distribution of respondents by score level on the scale of aggregate challenge Score level Frequency Percent 11 1.8 1 47 7.8 2 79 13.1 3 116 19.2 4 142 23.5 5 144 23.8 6 66 10.9 Total 605 100.0

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Table 4 shows percentages of respondents by aggregate challenge and by selected variables. It is indicated that the proportion of older persons having severe challenge increased with age. Whereas 52 percent of older persons aged 60-69 were experiencing severe challenge, this figure increased to 62 percent and 65 percent among elderly aged 70-79 and 80 and above respectively. The higher proportion experiencing severe challenge among the oldest old is perhaps expected since, overall, as people age, there is decline in physical strength and increase in frailty. The association between age and aggregate challenge was statistically significant (p=0.029). The proportion of older persons with severe challenge was higher among older females (65%) than

• lder males (46%). Past disproportionate access to opportunities could explain the observed gender
• disparity. The association between sex and aggregate challenge was statistically significant

(p=0.000). Aggregate challenge also varied by region of residence. The highest proportions of

• lder persons with severe challenge were for the elderly living in Northern and Eastern regions

(71% and 69% respectively). The association between aggregate challenge and region was statistically significant (p=0.000). Table 4 further shows that severe aggregate challenge was inversely associated with education. Whereas only 32 percent of the elderly with secondary and higher education experienced severe aggregate challenge, the corresponding proportions for those with primary and no education were 58% and 66% respectively. The lower incomes and opportunities associated with lower education could be at the centre of this disparity. The association between aggregate challenge and education was statistically significant (p=0.002). Results indicate that aggregate challenge also varied by marital status. Whereas only 47 percent of the married experienced severe challenge, the corresponding proportion among the widowed and divorced/separated was 70% and 57%

• respectively. The higher proportion in these two categories could be associated with higher

loneliness and declining resources. The association between marital status and severe challenge was statistically significant (p=0.000). Aggregate challenge was also associated with living arrangement. The highest proportion was among the elderly living alone and staying with grandchildren (74% and 70% respectively) while the lowest existed among those staying with their spouse and kids and spouse alone (38% and 50% respectively). The high prevalence among older persons living alone is perhaps expected since such persons are less likely to have adequate emotional support, companionship and household helpers than the elderly in other living arrangements. High prevalence of severe challenge among the elderly staying with grandchildren could be a result of socioeconomic pressure associated with care for orphans and other vulnerable children. Living arrangement had a statistically significant association with aggregate challenge (p=0.000). Sixty three percent of older persons who did not have out-migrated children were experiencing severe challenge while the corresponding proportion among the elderly with children living in

• ther parts of the country was only 53%. Successful out-migrated children could have remitted

part of their earnings back home which enabled their parents to engage in income-generating

• activities. The association between child out-migration and aggregate challenge was statistically

significant (p=0.014). Table 4 also shows that aggregate challenge was statistically associated with

• wnership of media facilities such as radio, television and mobile phone. The proportion of older

persons experiencing severe aggregate challenge was higher among older persons who did not own

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radio, television and mobile phone (67%, 64% and 66% respectively) than among those who

• wned these facilities (51%, 28% and 44% respectively).

Table 4 Percentages of respondents by aggregate challenge by selected variables

Variable Mild challenge (%) Severe challenge (%) Number Age 41.8 58.2 605 60-69 47.7 52.3 264 70-79 38.5 61.5 208 80+ 35.3 64.7 133 𝜓2 =7.0, p=0.029 Sex 41.8 58.2 605 Male 54.5 45.5 211 Female 35.0 65.0 394 𝜓2 =21.4, p=0.000 Region 41.8 58.6 605 Western 41.7 58.3 120 Central 63.2 36.8 125 Eastern 30.7 69.3 114 Northern 28.6 71.4 126 Kampala 44.2 55.8 120 𝜓2 =38.6, p=0.000 Education 41.8 58.2 605 No education 33.6 66.4 301 Primary 42.0 58.0 212 Secondary+ 68.5 31.5 92 𝜓2 =35.3, p=0.000 Marital status 41.8 58.2 605 Married 52.6 47.4 270 Widowed 29.7 70.3 249 Divorced/separated 43.0 57.0 86 𝜓2 =27.9, p=0.000 Living arrangement 41.8 58.2 605 Alone 26.1 73.9 92 Spouse 50.0 50.0 62 Spouse & kids 61.8 38.0 89 Grandchildren 29.9 70.1 137 Other 45.3 54.7 225 𝜓2 =34.8, p=0.000 Child outmigration status 42.0 58.0 603 Has out-migrated children 46.9 53.1 303 No out-migrated children 37.0 63.0 300 𝜓2 =6.0, p=0.014 Radio set ownership 41.5 58.5 597 Owns radio 48.9 51.1 325 No radio 32.7 67.3 272 𝜓2 =16.0, p=0.000 TV set ownership 41.7 58.3 597 Owns TV 72.2 27.8 90 No TV 36.5 63.5 507 𝜓2 =40.1, p=0.000 Mobile phone ownership 41.7 58.3 597 Owns mobile phone 56.3 43.7 197 No mobile phone 34.5 65.5 400 𝜓2 =25.9, p=0.000

Ownership of domestic animals 41.1 58.9 593 Owns animals 46.6 53.4 268 No animals 36.6 63.4 325

𝜓2 =6.1, p=0.014

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Variable Mild challenge (%) Severe challenge (%) Number Current income generation

41.8 58.2 605

Active

54.9 45.1 164

Not active

37.0 63.0 441

𝜓2 =15.8, p=0.000

Ownership of means of transport 41.9 58.1 597 Owns any means of transport 54.6 45.4 99 No any means of transport 39.4 60.6 498

𝜓2 =7.8, p=0.005

Social protection status 41.8 58.2 605 Receives pension 65.7 34.3 35 No pension 40.3 59.7 570

𝜓2 =8.7, p=0.003

Floor material of shelter 42.2 57.8 600 Cement 53.9 46.1 284 Rammed earth 29.6 70.4 274 Other floor material 45.2 54.8 42

𝜓2 =34.0, p=0.000

Roof material of shelter 41.7 58.3 602 Iron sheets 43.3 56.7 533 Other roof material 29.0 71.0 69

𝜓2 =5.2, p=0.023

Wall material of shelter 42.1 57.9 596 Mud and poles 24.3 75.7 189 Burnt bricks and cement 55.1 44.9 236 Unburnt bricks and mud 31.7 68.3 79 Other wall material 54.3 45.7 92

𝜓2 =50.0, p=0.000 Aggregate challenge was also associated with ownership of domestic livestock. While the proportion of the elderly having severe challenge among persons who owned domestic animals was 53 percent, the corresponding figure among those who did not possess livestock was 63

• percent. Monetary scarcity arising from absence of resources such as cows, goats and poultry could

have contributed to the higher proportion of severe challenge among persons without domestic

• animals. The association between ownership of domestic animals and aggregate challenge was

statistically significant (p=0.014). It is further showed in Table 4 that 45 percent of the elderly who were currently engaged in income-generating activities were experiencing severe challenge while the corresponding figure among those who were not involved in any income-generating enterprise was 63 percent. The higher proportion could be attributed to relatively lower capacity to afford costs of healthcare and meals among the non-working elderly. The association between engagement in income-generation and aggregate challenge was statistically significant (p=0.000). Sixty one percent of the elderly who did not own any means of transport were experiencing severe challenge while the corresponding figure among those who owned any such facilities was 45

• percent. Lack of any transport facility such as vehicle, motorcycle or bicycle could have hampered

transportation of the elderly and thus curtailed their involvement in social and economic activities. The association between ownership of means of transport and aggregate challenge was statistically significant (p=0.005). Almost three fifth (60%) of the elderly who were not receiving pension were experiencing severe difficulty in contrast to just only over one third (34%) for the elderly who

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were receiving the old age retirement benefits. The association between social protection status and aggregate challenge was statistically significant (p=0.003). Table 4 also indicates that aggregate challenge was associated with shelter conditions. Whereas the proportion of the elderly experiencing severe challenge was comparatively low among those staying in houses with cement floor (46%) and other types of floor material (55%), the corresponding figure among older persons living in houses with rammed earth floor was as high as 70 percent. Similarly, whereas the proportion of the elderly having severe challenge residing in houses with iron sheets was 57 percent, the corresponding figure among older persons staying in houses roofed using non-iron sheet materials (such as tarpaulin, thatch and polythene) was as high as 71 percent. It is also observed that whereas the proportion of older persons with severe challenge was relatively low among older persons staying in shelters whose walls were made of burnt bricks and cement (45%) and other types of materials (46%), the corresponding figures were much higher among the aged living in houses built of mud and poles (76%) and unburnt bricks and mud (68%). Aggregate challenge displayed a statistically significant association with shelter floor, roof and exterior wall materials (p=0.000, p=0.023 and p=0.000 respectively). Predictors of severe aggregate challenge Table 5 presents results from logistic regression analysis of the factors predicting severe aggregate

• challenge. It is shown that in comparison with married older persons, the widowed and

divorced/separated older persons were more likely to have severe challenge (OR=2.1; p=0.005 and OR=1.9; p=0.049 respectively). The elderly who did not own television set were more likely to experience severe aggregate challenge than their counterparts who possessed television set (OR=2.0; p=0.033). Interestingly, older persons who did not own land were less likely to experience severe aggregate challenge than their counterparts who owned land (OR=0.5; p=0.008).

Table 5 Factors predicting severe aggregate challenge (* = Reference Category)

Variable Coefficients Odds Ratio

• Std. Err.

p Age 60-69* 1.000 70-79 0.392 1.480 0.321 0.071 80+ 0.404 1.498 0.387 0.117 Sex Male* 1.000 Female 0.418 1.519 0.362 0.080 Education No education 0.300 1.350 0.462 0.380 Primary 0.560 1.751 0.549 0.074 Secondary+* Marital status Married* 1.000 Widowed 0.730 2.075 0.533 0.005 Divorced/separated 0.659 1.933 0.647 0.049 TV set ownership Owns TV* 1.000 No TV 0.679 1.972 0.627 0.033 Radio set ownership Owns radio* No Radio 0.033 1.034 0.230 0.881 Mobile phone ownership

11 Variable Coefficients Odds Ratio

• Std. Err.

p Owns mobile phone* 1.000 No phone

• 0.156

0.855 0.218 0.541 Ownership of any means of transport Owns any means of transport* 1.000 No Transport 0.215 1.239 0.351 0.449 Ownership of domestic animals Owns domestic animals* 1.000 No domestic animals 0.370 1.448 0.304 0.078 Land ownership Owns land* 1.000 No land

• 0.651

0.522 0.128 0.008 Child outmigration status Has out migrated children* 1.000 No out migrated children 0.046 1.047 0.208 0.818 Fuel for cooking Charcoal* 1.000 Firewood 0.198 1.219 0.350 0.490 Straw/grass/shrub 1.615 5.026 3.238 0.012 Main material of the floor Cement* 1.000 Rammed earth 0.094 1.098 0.343 0.765 Other floor material

• 0.148

0.862 0.330 0.699 Main material of the roof Iron sheets* 1.000 Other roof material

• 0.009

0.991 0.355 0.980 Main material of exterior walls Bricks and cement* 1.000 Mud and poles 0.875 2.398 0.804 0.009 Un burnt bricks & mud 0.413 1.511 0.529 0.238 Other wall materials

• 0.076

0.926 0.293 0.809 Region Central region* 1.000 Western region 0.328 1.388 0.557 0.414 Eastern region 0.551 1.735 0.704 0.175 Northern region 1.045 2.843 1.028 0.004 Kampala urban region 0.657 1.929 0.629 0.044

* =Reference category

Table 5 further shows that the type of fuel used for cooking also predicted severe aggregate

• challenge. In comparison with older persons who were using charcoal, the elderly who used

grass/straw/shrub were more likely to experience severe aggregate challenge (OR=5.0; p=0.012). The main material for exterior wall also predicted severe aggregate challenge. In comparison with

• lder persons who were residing in houses constructed of bricks and cement, older persons who

lived in houses built using mud and poles were more likely to experience severe aggregate challenge (OR=2.4; p=0.009). Lastly, the region in which the elderly resided also predicted severe aggregate challenge. In comparison with older persons residing in the Central region of the country, older persons who lived in Northern and Kampala regions were more likely to experience severe aggregate challenge (OR=2.8; p=0.004 and OR=1.9; 0.044 respectively).

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Discussion The higher likelihood of loneliness among the widowed and divorced/separated older persons was probably because these were less socially and economically empowered and thus less able to experience quality life than their married counterparts. Studies elsewhere have indicated that the death of a spouse almost always means loss of income for the survivor (Frolik, 1996). It is argued, for example, that the death of a spouse may translate into cessation of social security upon the recipient’s death. Moreover, the death of a spouse can be costly because of significant expenditure

• n illness prior to death. The cost can be particularly high if the deceased suffered from a chronic

illness whose long-term management necessitated using the couple’s assets to pay medical bills prior to death. As a result, the financial resources of the couple are depleted and survivors may find that they have substantially fewer assets for their support than would otherwise have been the case without bereavement. In Uganda, divorced/separated older females are likely to be resource- constrained considering that female share of formal employment is low and livelihood largely depends on household resources (MoGLSD, 2009). Absence of media facility such as television could be a proxy indicator of low socio-economic status of the elderly. Older persons without television set are likely to have been people at the lower scale of the socioeconomic ladder and thus less able to afford desired household necessities. Other studies have reported statistically significant associations between low socio-economic status and poor health status (Ahnquist, Wamala & Lindstrom, 2012; Smith & Goldman, 2007). A probable explanation for the lower likelihood of loneliness among older persons who did not own could be that such persons had access to other kinds of assets that had better influence on their

• verall livelihood. Ownership of land may also not necessarily have translated into direct monetary

value since some land in rural Uganda is used for subsistence rather than commercial purposes. Other studies have however indicated that ownership of assets such as home and car is associated with a lower risk of disability in comparison with non-possession of such assets (Ebrahim, Papacosta, Wannamethee, & Adamson, 2004). Relevant literature about the association between land and severe aggregate challenge in Uganda is scanty and this calls for further investigation. Results further show a higher likelihood of severe aggregate challenge among persons who used grass/straw/shrub for cooking than those who utilized charcoal. In low-income Uganda, charcoal is relatively costly and a sizeable proportion of rural older persons may not afford charcoal price; resorting to grass/straw/shrub which is a less costly and more available alternative energy source. Older persons who utilized grass/straw/shrub are therefore also likely to have been poorer than those who used charcoal and therefore comparatively less able to afford day-to-day basic needs. As HelpAge International (2010) has observed, the phenomenon of low income is widespread among older persons especially those in the developing countries. Irregular, labour-intensive and piecemeal work are cited as some of the reasons for low incomes accruing to older persons in these

• countries. Low and seasonal farm yields as well as ill health are the other conditions that can

further reduce the amount older workers earn. Several studies have reported high poverty levels among the elderly. For example in Uganda, households with an older person have a poverty incidence of almost 29 percent compared with 25 percent for all households in the country (Wylde, Sewanyana, Ogen, & Kiconco, 2012). The poverty rate among the elderly as a vulnerable group is higher than the national average.

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The main material for exterior wall of the shelters in which older persons resided also predicted severe aggregate challenge. This is probably because housing is not only a physical shelter but also plays a significant role in a person’s physical, mental and emotional health conditions. A study

• n housing conditions and quality of life of Malaysian urban poor also established statistically

significant association between housing conditions and overall quality of life (Zainal, Kaur, Ahmad & Khalili, 2012). Cross tabulation of overall house condition with physical health condition indicated that 9 percent of respondents who were not satisfied with their shelter condition sought hospital treatment as compared to only 1 percent of respondents who were satisfied with their housing condition. Other studies have similarly indicated that the quality of housing in which the elderly live can influence older persons’ health (Smith & Goldman, 2007). WHO (2007) also

• bserves that the cost of housing is an influential factor of older persons quality of life and in some

parts of the world, expensive shelter construction and maintenance makes it difficult for older people to live a dignified life. The higher likelihood of severe aggregate challenge among the elderly staying in the Northern region of Uganda could be attributed to the effect of insurgency that raged on in the region for close to two decades since 1988. The civil unrest associated with the Lord’s Resistance Army adversely affected the social and economic fabric of the region and negatively impacted on the

• verall quality of life of the inhabitants (Erb, 2008). As Charlton and Rose (2001) have also

indicated, war is one of the risk factors of poor nutritional status among the elderly in the African

• context. Poor nutrition in turn can contribute to overall severe challenge among older persons.

Although Kampala is expected to have better healthcare services than rural regions, results have indicated that older persons residing in this area were more likely to have severe aggregate challenge than those in Central region. Expensive urban lifestyle and higher level of loneliness in the Kampala metropolitan region could have contributed to overall high level of severe aggregate challenge in the area. Unplanned settlement manifesting into squalid slum housing conditions could also have exacerbated the severe aggregate challenge in this urban environment.

Conclusions

Marital status, household conditions, housing material and region of the country in which the elderly resided influenced later-life challenges. Being widowed or divorced increased the odds of experiencing severe aggregate challenge. Low household situational status (as measured by absence of television, using cheap fuel for cooking and living in mud and pole shelters) also predicted severe aggregate challenge. Lastly the odds of having severe aggregate challenge were increased by living in the Northern and Kampala regions of the country.

Implications

The higher likelihood of severe aggregate challenge among the widowed older persons calls for

• pportunities that mitigate their loneliness. These could be in form of programmes that encourage

participation in gainful work according to the individual needs and capacities. Government may consider establishing a Special Old Age Fund that can enable older persons improve their socio- economic status and thus manage their later-life challenges. Such fund would supplement the existing Social Assistance Grants for Empowerment whose coverage is limited. Region-specific interventions may be required to address regional disparities in loneliness.

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Limitations

A major limitation of this study is that just a handful of direct questions were asked on each of the six indicators of challenges. An approach of asking several questions on each indicator to measure the level of challenges could probably have produced richer results. This was not possible because, as mentioned in the methodology section, the source of data was a larger study that also investigated the value of older persons. This points to the need for greater in-depth studies on the subject of later-life challenges in the country. References Ahnquist, J., Wamala, S. P., & Lindstrom, M. (2012). Social determinants of health – A question

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