Chronic disease, risk factors and disability in adults aged 50 and above living with and without HIV: findings from the Wellbeing of Older People Study in Uganda


Chronic disease, risk factors and disability in adults aged 50 and above living with and without HIV: findings from the Wellbeing of Older People Study in Uganda

Joseph O. Mugisha1,2*, Enid J. Schatz2, Madeleine Randell3, Monica Kuteesa1, Paul Kowal4,5, Joel Negin3 and Janet Seeley1,6

1MRC/UVRI, Uganda Research Unit on AIDS, Uganda; 2Department of Health Sciences, University of Missouri Columbia, Missouri, USA; 3School of Public Health, University of Sydney, Australia; 4World Health Organization, Study on global AGEing and adult health, Geneva, Switzerland; 5Research Centre for Gender, Health and Ageing, University of Newcastle, Australia; 6London School of Hygiene and Tropical Medicine, London UK

Background: Data on the prevalence of chronic conditions, their risk factors, and their associations with disability in older people living with and without HIV are scarce in sub-Saharan Africa.

Objectives: In older people living with and without HIV in sub-Saharan Africa: 1) to describe the prevalence of chronic conditions and their risk factors and 2) to draw attention to associations between chronic

conditions and disability.

Methods: Cross-sectional individual-level survey data from people aged 50 years and over living with and without HIV were analyzed from three study sites in Uganda. Diagnoses of chronic conditions were made

through self-report, and disability was determined using the WHO Disability Assessment Schedule

(WHODAS). We used ordered logistic regression and calculated predicted probabilities to show differences

in the prevalence of multiple chronic conditions across HIV status, age groups, and locality. We used linear

regression to determine associations between chronic conditions and the WHODAS.

Results: In total, 471 participants were surveyed; about half the respondents were living with HIV. The prevalence of chronic obstructive pulmonary disease and eye problems (except for those aged 60�69 years) was higher in the HIV-positive participants and increased with age. The prevalence of diabetes and angina was

higher in HIV-negative participants. The odds of having one or more compared with no chronic conditions were

higher in women (OR 1.6, 95% CI 1.1�2.3) and in those aged 70 years and above (OR 2.1, 95% CI 1.2�3.6). Sleep problems (coefficient 14.2, 95% CI 7.3�21.0) and depression (coefficient 9.4, 95% CI 1.2�17.0) were strongly associated with higher disability scores.

Conclusion: Chronic conditions are common in older adults and affect their functioning. Many of these conditions are not currently addressed by health services in Uganda. There is a need to revise health care

policy and practice in Uganda to consider the health needs of older people, particularly as the numbers of

people living into older age with HIV and other chronic conditions are increasing.

Keywords: Africa; aging; aging disability; HIV/AIDS; older adults; non-communicable diseases; Uganda

Responsible Editor: Jennifer Stewart Williams, Umeå University, Sweden.

*Correspondence to: Joseph O. Mugisha, MRC/UVRI, Plot 51�59, Nakiwogo Road, Entebbe, Uganda, Email:

Received: 25 January 2016; Revised: 27 April 2016; Accepted: 27 April 2016; Published: 24 May 2016

Introduction Chronic diseases are illnesses or conditions that require

ongoing medical attention and affect a person’s daily life

(1). Chronic diseases include cancers, cardiovascular

diseases, chronic respiratory diseases, diabetes, hyperten-

sion, mental disorders, and stroke. Other chronic impair-

ments that commonly affect people include arthritis;

rheumatism; and dental, vision, stomach, and intestinal

problems (2). In African countries, improved access to

antiretroviral treatment (ART) is increasing survival for

those with the human immunodeficiency virus (HIV).

Consequently, HIV is now considered a chronic condition

in many settings (3).

With shifts in the global burden of disease, chronic

diseases represent a substantial proportion of illnesses

even in low- and middle-income countries (LMICs) (4).

Global Health Action �

Global Health Action 2016. # 2016 Joseph O. Mugisha et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License (, allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.


Citation: Glob Health Action 2016, 9: 31098 – (page number not for citation purpose)



Few studies, however, have used individual-level data

to elucidate the prevalence of chronic conditions, risk

factors, and disability associated with chronic diseases in

older people in LMICs, and such research is particularly

scarce in sub-Saharan Africa. Comprehensive studies on

chronic diseases in LMICs primarily have concentrated

on younger and middle-aged people (5�10) with relatively few focusing on older adults (2, 9, 11, 12).

In sub-Saharan Africa, the number and proportion

of older people is increasing and is projected to continue to

grow in coming decades (13, 14). This makes it parti-

cularly important to understand how chronic disease

impacts on older Africans’ lives. As African populations

age, the prevalence of individuals with chronic conditions

in these settings is likely to increase. In Uganda, for

example, the population of older people has continued

to grow rapidly (15). In addition, the number of older

people living with HIV in Uganda is also increasing (16) in

line with a global trend (17�19). A number of studies have been conducted in sub-

Saharan Africa on chronic conditions in adults (7�9, 20�25). However, few provide information on concurrent chronic conditions, including HIV (23), and fewer still

have simultaneously examined chronic diseases in older

people living with and without HIV (26). In Uganda, as

well, there are few data on health differences in chronic

conditions between older persons living with and without

HIV (27�29). Chronic diseases can affect people of all age groups, but

they are more common and more likely to have negative

consequences in older adults. A 2005 study of mortality

and the burden of disease predicted an increase in deaths

for all ages worldwide due to chronic diseases (excluding

HIV) from 35 million deaths in 2005 to 41 million deaths

in 2015 (30). Nearly 60% of the deaths in each year are

estimated to occur among those aged 70-plus. Research

from southern Africa shows that chronic diseases (not

including HIV) are more prevalent among those aged

50-plus compared to those aged 18�49 (12). Another study in South Africa showed that there were more chronic

conditions (excluding HIV) in later older age (65-plus)

than early older age (ages 50�65) (9). With the exception of HIV, many chronic diseases share

common risk factors. These include excessive alcohol use,

tobacco use, unhealthy diets, and physical inactivity (31).

Current health behaviors, as well as the accumulated

impact of a lifetime of harmful health behaviors, con-

tribute to the higher likelihood of contracting a chronic

condition in older age (32, 33). Because the majority

of these risk factors are related to individual health

behaviors, most are potentially amenable to behavioral

interventions (34).

Using a unique dataset from Uganda, this paper

describes the prevalence of chronic diseases, including

angina; arthritis; chronic obstructive pulmonary disease

(COPD); depression; diabetes mellitus; and hypertension,

stroke, and vision problems, in older people living

with and without HIV. We also describe the prevalence

of related risk factors and association between chronic

disease and disability, using the World Health Organiza-

tion Disability Assessment Schedule (WHODAS 2.0) to

measure disability (35). This paper adds to the limited

body of literature on the prevalence and risk factors of

chronic conditions and how these impact on disability

in older Africans living with and without HIV.

Methods Data for this analysis came from the second wave of the

longitudinal World Health Organization Study on global

AGEing and adult health (SAGE)-Wellbeing of Older

People Study (WOPS). The SAGE-WOPS HIV study in

Uganda was implemented in people aged 50 plus. To date,

two waves of data are available: the first wave (WOPS1)

conducted in 2009�2010 and the second wave (WOPS2) conducted in 2012�2013. Details of the initial WOPS recruitment are described elsewhere (26). Although data

from two waves of WOPS are available, only data from

WOPS2 are analyzed here because of inconsistencies in

available variables across the two waves. We therefore

present findings on a fuller set of more recent variables

rather than longitudinal data on a limited set of variables.

Interviews were conducted in three sites on the shores of

Lake Victoria � in the Kalungu and Masaka districts and another in the Wakiso District, near Entebbe. The study

setting, study population, and data collection are also

described elsewhere (26, 36). Briefly, the WOPS1 sample

consisted of 510 older people (61.2% female, mean age

65 and age range 50�96 years). These included 1) older persons who were living with HIV but not yet on ART;

2) older persons living with HIV and on ART for at least

1 year; 3) older persons who had a child living with HIV;

4) older persons who had a child who died of AIDS-related

illness; and 5) older persons who were not HIV-positive

themselves but had not lost a child due to HIV infection.

During WOPS2, we re-interviewed those respondents

who were still living in the area; 148 respondents were

lost to follow-up (these included 67 who had died, 25 who

emigrated from the study area, 17 who were found but

refused to participate, 9 who were too sick to participate,

4 who had travelled on the day of the interviews, 4 who

were too busy to participate in the interviews, and 22 who

could not be located). The follow-up rate was over 70%.

In WOPS2, we recruited an additional 100 older people

living with HIV attending the AIDS Support Organiza-

tion (TASO), a non-governmental organization (NGO) in

Masaka town, close to the Kalungu District site. All the

new recruits were randomly selected from older people

attending TASO. These additional recruits increased the

number of people living with HIV in the cohort. In order

to avoid misclassification of the study groups, all older

Joseph O. Mugisha et al.

2 (page number not for citation purpose)

Citation: Glob Health Action 2016, 9: 31098 –



people who were HIV negative in WOPS1 were retested

for HIV using the Uganda Ministry of Health algorithms

for rapid HIV testing (37). The sample in this study is

stratified by HIV status between all those who were living

with HIV either in WOPS1 or WOPS2, and those who

were HIV negative in WOPS1 and remained so at the time

of testing in WOPS2.

Data collection

Study participants were either interviewed from home or

from a central hub (a central location in their village),

where a house was rented for survey activities. The

interviews were conducted by trained interviewers using

a validated questionnaire. After conducting the interviews,

the interviewers measured weight, height, blood pressure,

grip strengths, walking speed, and conducted a visual acuity

test. The WOPS questionnaire and other data collection

instruments were adapted from the WHO SAGE (38). All

instruments were pretested and piloted prior to use (26).


The components of the study questionnaire analyzed in

this paper include:

1. Sociodemographic characteristics: age, sex, marital

status, occupation (work status), education level,

and household assets.

2. Risk factors: smoking, alcohol use, stressful events,

sleep disorders, and body mass index (BMI).

3. Self-reported chronic conditions: self-reported diag-

noses of chronic conditions (including angina, ar-

thritis, cataract/eye sight problems, COPD, depression,

diabetes mellitus, hypertension, and stroke).

4. Objective measurements: weight, height, visual acuity

(using the Snellen charts), and blood pressure, mea-

sured three times in a sitting position.

Information from the interviews and assessments was

used to describe health states that included diagnoses,

risk factors, and impairments as described below. Dis-

ability was assessed using the 12-item version of WHO-

DAS 2.0 questionnaire (35).



For all study participants, systolic and diastolic blood

pressures were measured three times with participants

in a sitting position using a Boso Medistar-S-wrist

blood pressure monitor. An average blood pressure

for the three readings was computed and used in the

analysis. Hypertension was defined according to the

World Health Organization (WHO) criteria (systolic

blood pressure ]140 mmHg and/or diastolic blood pres-

sure ]90 mmHg) (39).

For the conditions listed below, respondents were

asked a range of questions on diagnosis and symptoma-

tology for these chronic conditions, and their responses

determined the diagnosis used here.

Diabetes mellitus, COPD, and eyesight problems/

cataracts For this analysis, prevalence estimates were based on

the self-report of a doctor’s diagnosis. Participants were

asked the following questions: Have you ever been told by

a doctor or a health worker that you have [condition]?

If yes, were you started on treatment and are you still on


Stroke and angina

The prevalence for the conditions of stroke and angina

was determined through algorithms using symptom-

reporting (40, 41).


A diagnosis of depression was based on a diagnostic

algorithm, with participant responses scored using the

International Neuropsychiatric interview (MINI) criteria

(42�44). The criteria used for determining depression were based on previous work using the MINI in Uganda

(45, 46). The following screening questions for a major

depressive episode were asked. For the past 2 weeks, were

you depressed or down, most of the day, nearly every day?

In the past 2 weeks, were you much less interested in most

things or much less able to enjoy the things you used to

enjoy, most of the time? If participants answered yes to

these questions, they were asked a number of additional

questions to ascertain a major depressive episode.

Arthritis First, participants were asked if a health worker had ever

diagnosed or told them that they have arthritis. If the

answer was yes, they were asked about medication use or

any other treatment for arthritis in the last 2 weeks and

the last 12 months, and about symptoms, such as aching,

stiffness, or swelling around the joints that were not

related to injury and lasted for 1 month. Prevalence was

determined using a diagnostic algorithm (40).


During WOPS1, participants were selected in the five

categories described above. In order to avoid misclassifi-

cation during WOPS2, all participants seen in WOPS1

who were previously HIV negative were subjected to

repeat HIV testing. HIV testing was done using an algo-

rithm for HIV-1 testing using three HIV-1 rapid tests as

recommended by the Uganda Ministry of Health. The

algorithm for HIV rapid testing consisted of an initial

screening with the rapid test Determine HIV1/2. If the test

result was negative the participant was given a diagnosis

of HIV negative with no further rapid testing. If the test

result was positive, the sample was retested with the rapid

test HIV-1/2 Stat-Pak. If both tests gave a positive result

the participant was given a diagnosis of HIV positive with

Chronic conditions and disability in older people with and without HIV in Uganda

Citation: Glob Health Action 2016, 9: 31098 – 3 (page number not for citation purpose)



no further rapid testing. If the tests gave discordant results

(i.e. one positive and the other negative), the sample was

further evaluated with the rapid test Uni-Gold Recombi-

nant HIV-1/2. For those samples assessed by all three

tests, two positive test results were interpreted as a positive

diagnosis. If two of the three tests gave negative results,

then the participant was diagnosed as being negative for

HIV. The two resulting categories for our analysis below

are those who tested HIV positive and those who tested

HIV negative.

Risk factors

Risk factors included tobacco use (if participants were

using tobacco, they were asked about the duration of

use), the method of tobacco consumption (whether they

were smoking or using chew or snuff), and the quantity

of tobacco consumed on each of the previous 7 days.

Alcohol use was determined by asking whether partici-

pants had ever or were currently consuming alcohol, the

duration of use, and the types of alcohol consumed. BMI

was determined from weight and height measurements

taken at the time of the survey. BMI was calculated by

dividing weight in kilograms by height in meters squared.


Questions necessary to generate the 12-item version of

WHODAS 2.0 were asked in the interview (47�49). These questions gather information across six domains: cogni-

tion, mobility, self-care, getting along, life activities, and

participation, asking about difficulty in these domains

during the 30 days preceding the interview. The possible

responses for each question were on a five-point scale:

‘none’, ‘mild’, ‘moderate’, ‘severe’, and ‘extreme or cannot

do’. The WHODAS 2.0 algorithm was used to compute

an overall score [range 0�100] for each respondent, with a higher score indicative of greater level of disability (47).

Ethical issues

Ethical approval to conduct this study was obtained from

the Uganda Virus Research Institute Science and Ethics

Committee, the Uganda National Council for Science

and Technology, and WHO’s Ethical Review Committee.

All participants gave a written and thumb-printed con-

sent to participate in the study. For non-literate partici-

pants, an impartial third party witnessed the entire

consent process and counter-signed the consent document

on which the participant had placed their thumb-print.

Statistical methods and data analysis

All analyses were conducted in Stata 13 (Stata Corp,

College Park, Tx, USA). We did not use any imputation

methods for missing data. However, the majority of

variables had two or fewer missing cases, only three

variables had more than 10 missing cases: BMI (11), stroke

(12), and current employment status (17). All descriptive

statistics and sample sizes are presented as un-weighted

values, with a p value of B0.05 considered statistically

significant (all p values are two-sided). We did not apply

sampling weights. The study sample was selected ran-

domly from lists of older people in the study population.

Analyses for descriptive statistics and risk factors were

stratified by HIV status for each of the following

characteristics: sociodemographic variables (mean age,

gender, locality, employment status, marital status, and

highest level of education), all past and current use of

tobacco, all past and current alcohol use, mean BMI,

sleep problems, and antiretroviral (ART) use-conditional

on HIV status. Analyses for chronic conditions (angina,

arthritis, diabetes, COPD, depression, eye problems,

hypertension, and stroke) were stratified by HIV status

and age group; chi-square statistics highlight whether

there were significant differences (1) across chronic con-

ditions by HIV status and age group, and (2) significant

differences between risk factors and HIV status. Median

differences in age and BMI were calculated for the two

respondent groups due to the data not being normally

distributed. Wilcoxon rank-sum analyses were used

to compare median differences in age and BMI for

the two respondent groups. We conducted an ordered

logistic regression and calculated predicted probabilities

to show the differences in the number of chronic con-

ditions across HIV status, gender, age group, and locality.

We defined the number of chronic conditions using an

algorithm that grouped respondents into three categories

being zero chronic conditions; one condition; or two or

more conditions. However, HIV was not considered a

chronic condition for the purposes of these counts. We

tested the proportional odds assumption for ordered

logistic regression. This assumes that the coefficients that

describe the relationship between the lowest versus all

higher categories of the response variable are the same as

those that describe the relationship between the next

lowest category and all higher categories. For this, we

used the omodel command in Stata and achieved a non-

significant result, meaning that there was no difference in

coefficients between models. For each respondent group,

mean WHODAS 2.0 scores were determined for each

chronic condition. T-tests were run within each respon-

dent group to compare WHODAS scores for those with

or without a chronic condition diagnosis.

Linear regression analyses were used to determine

existing associations between sociodemographic factors,

chronic conditions, and risk factors to WHODAS scores.

Univariate analyses first determined significant main

effects as well as interaction terms between HIV status

and other factors before a multiple linear regression with

these variables was undertaken. Although HIV was not

significant in the univariate analysis, we left it in the final

model as an a priori confounder together with age and

gender. In the linear regression modeling, HIV negative

was used as the reference category. Thus, compared to

Joseph O. Mugisha et al.

4 (page number not for citation purpose)

Citation: Glob Health Action 2016, 9: 31098 –



those who were HIV negative, HIV-positive individuals

were expected to have higher WHODAS scores (meaning

more disability). For all the univariate and multivariate

analyses, a significance level of 0.05 was used. Model fit

was assessed by examining residuals from the model. For

this analysis, a robust regression analysis was used.


Sociodemographic characteristics of study


Sociodemographic characteristics of the study popula-

tion by HIV status are provided in Table 1. In total, the

median age for the 471 participants was 63 (50�101). The majority of the sample was female (62.6%), widowed, still

working, and had less than primary school education.

About half of the study participants (51.8%) were HIV

positive. The HIV-positive respondents tended to be

younger. Only about 10% of older persons living with

HIV were aged 70 or older, whereas over half of the HIV-

negative sample was in the older age groups. Locality

differences by HIV status are in part due to the sampling


Chronic conditions by HIV status Several differences in the percentage of individuals report-

ing chronic conditions, other than HIV, were evident

between the two respondent groups (Table 2). When

comparing by HIV status, the prevalence of COPD and

eye problems (except for those aged 60�69 years) were higher in the HIV-positive participants and prevalence of

diabetes and angina were higher in HIV-negative partici-

pants. When comparing across age groups within HIV

status, significant differences were present for eye pro-

blems and hypertension, which generally increased with

age, and multi-morbidity for which the prevalence was

higher in those with advanced age. The percentage of

people with COPD decreased with age for both groups,

with a higher starting point and a steeper decline in the

percentage for the HIV-positive group.

The odds of having at least one or one or more,

compared with no chronic conditions (other than HIV)

Table 1. Sociodemographic factors by HIV status

HIV�(N �244) HIV�(N �227)

Demographics N % N %


Male 97 39.8 79 34.8

Female 147 60.3 148 65.2


50�59 135 55.3 33 14.5

60�69 82 33.6 69 30.4

70�79 23 9.4 82 36.1

80 � 4 1.6 43 18.9


Wakiso 64 26.2 105 46.3

Kalungu 73 29.9 120 52.9

Masaka 107 43.9 2 0.9

Marital status

Never married 3 1.2 9 4.0

Cohabitating/married 77 31.6 70 30.8

Divorced/separated 57 23.4 49 21.6

Widowed 107 43.9 99 43.6

Current employment status (n �241) (n �226)

Still working 213 88.4 166 73.5

No longer working 28 11.6 60 26.6

Education level (n �242) (n �212)

No formal education 35 14.5 53 23.5

Less than primary 96 39.7 113 50.0

Completed primary 43 17.8 16 7.1

Incomplete secondary 40 16.5 16 7.1

Completed secondary 15 6.2 14 6.2

Higher education than secondary 3 1.2 6 2.7

College/university or more 10 4.1 8 3.5

Chronic conditions and disability in older people with and without HIV in Uganda

Citation: Glob Health Action 2016, 9: 31098 – 5 (page number not for citation purpose)



are shown in Table 3. The odds of having one or more than

one chronic condition were significantly higher in women

and the oldest age group. The predicted probabilities of

having one or more chronic conditions (other than HIV)

in Table 4 give similar findings. Predicted probabilities are

higher in women and in those aged 70 years and above.

Risk factors by HIV status

Several significant differences in the percentage of respon-

dents reporting or having risk factors for chronic conditions

(other than HIV) by HIV status were also evident (Table 5).

BMI was higher for HIV-negative respondents compared

to those who were HIV positive. This, however, may be a

result of HIV status rather than a risk factor for chronic

conditions. A higher proportion of HIV-negative respon-

dents said they currently use both tobacco and alcohol

compared to HIV-positive respondents. A higher proportion

of HIV-negative respondents also experienced mild sleep

problems as compared to HIV-positive respondents.

Linear regression of WHODAS scores

We found no interaction effects between HIV and other

factors before undertaking the multiple regression analy-

sis. Tables 5 and 6 show that there are several significant

differences in the proportion of chronic conditions (other

than HIV) and risk factors between respondents living

with and without HIV. These reached significance in the

Table 2. Percentage of chronic conditions by age and HIV status

50�59 (N �168) 60�69 (N �151) 70�(N �152)


(N �135) (%)


(N �33) (%)


(N �82) (%)


(N �69) (%)


(N �27) (%)


(N �125) (%) p Value by age

p Value by

HIV status


Yes 23.7 48.5 30.5 27.5 33.3 56.8 0.00 0.00


Yes 2.2 9.1 0.0 8.7 3.7 8.1 0.287* 0.001*


Yes 6.7 9.1 6.1 2.9 7.4 4.9 0.743* 0.316


Yes 0.9 0.0 1.4 5.2 0.0 4.8 0.225* 0.05*


Yes 10.4 3.0 7.3 1.5 3.7 1.6 0.026* 0.002*

Eye problems

Yes 4.4 3.0 4.9 7.3 18.5 16.1 0.001* 0.017


Yes 12.6 3.0 8.5 7.3 7.4 7.2 0.464 0.114


Yes 1.5 3.0 1.2 0.0 3.7 4.0 0.140* 0.533*

Number of conditions

None 52.6 42.4 51.2 55.1 55.6 28.0 0.00* 0.004*

One 44.4 57.6 47.6 44.9 33.3 67.2

More than one 3.0 0.0 1.2 0.0 11.1 4.8

*Fisher’s exact test used due to small cell size.

Note: HIV not treated as a chronic condition throughout all tables.

Bold values are statistically significant at pB0.05.

Table 3. Ordered multivariate logistic regression of one or

more chronic conditions a

Independent variable OR (95% CI) p

HIV status


Negative 1.4 (0.9�2.2) 0.149



Female 1.6 (1.1�2.3) 0.024

Age group


60�69 0.9 (0.5�1.4) 0.538

70 � 2.1 (1.2�3.6) 0.006



Kalungu 0.5 (0.4�0.8) 0.005

Masaka 1.0 (0.6�1.8) 0.982

aZero chronic conditions is the reference group. HIV status,

gender, age group, and locality are included in the final model.

*values in italic are statistically significant at pB0.05.

Joseph O. Mugisha et al.

6 (page number not for citation purpose)

Citation: Glob Health Action 2016, 9: 31098 –



univariate analyses (Table 5), however, when controlling

for all other variables, many of the associations between

these variables and the WHODAS score were no longer

significant. These included current tobacco use, HIV

infection, and arthritis diagnosis.

Table 6 shows the factors that were significantly

associated with WHODAS. A diagnosis of depression

was associated with a 9.4 point (95% CI 1.2�17.7) increase in the WHODAS score, meaning a significant

increase in disability compared to respondents who

were not diagnosed with depression. A 1-year increase

in the age of the respondent was significantly associated

with a 1.0 (95% CI 0.7�1.2) increase in WHODAS score. Gender was also a significant factor relating to

WHODAS scores with women having higher scores

(14.5; 95% CI 7.8�21.2). Several risk factors were also associated with disability. Having a sleep problem

of any type was significantly associated with higher

WHODAS scores, with the more severe the sleeping

problem, the higher the score. Respondents who had not

consumed any alcohol in the past 30 days had, on

average,a 4.7 point higher WHODAS score than current


Discussion This study examines HIV status and non-HIV chronic

conditions in Ugandans aged 50 years and over. The

prevalence of chronic conditions (other than HIV) was

affected by both age and HIV infection. When compar-

isons were made by age group, there were significant

differences in the prevalence of COPD, eye problems,

hypertension, and multi-morbidity which increased with

age. When comparing by HIV status, there were sig-

nificant differences, as seen for age. In addition, angina

and diabetes were more common in those who were HIV

negative. Reported multi-morbidity of chronic conditions

was higher among respondents living with HIV than

those not living with HIV, even after excluding HIV as a

chronic condition.

Within African settings, there have been only three

cohort studies (one in Uganda and two in South Africa)

that have included a sufficient sample of HIV-positive

individuals in order to assess the health and wellbeing of

older people by HIV status (26, 50, 51). There are few

studies from sub-Saharan Africa with which to compare

our study findings. However, the pattern of a higher per-

centage of people with chronic conditions in HIV-negative

older adults and in older age groups (70 years and more)

was also observed in the WOPS1 data in both Uganda

and in a comparable study from South Africa (50). In

data from both these countries, the lower prevalence of

hypertension in HIV-positive older adults was particularly

striking (26). Hypertension was objectively measured

through measurement of blood pressure. It is not very

clear as to why HIV-negative older people have a higher

prevalence of hypertension compared to their HIV-

positive counterparts. It is possible that if HIV-positive

Table 4. Predicted probabilities of one or more chronic


No chronic


One chronic


More than one



Male 0.52 0.46 0.01

Female 0.41 0.56 0.03

Age group

50�59 0.50 0.48 0.02

60�69 0.54 0.44 0.02

70 � 0.32 0.64 0.04


Wakiso* 0.39 0.58 0.03

Kalungu 0.54 0.44 0.02

Masaka 0.39 0.58 0.03

*values are statistically significant at pB0.05.

Table 5. Risk factors by HIV status


(N �244)


(N �227)

N % N % p

Ever used tobacco

Yes 75 30.7 77 33.9 0.460

No 169 69.3 150 66.1

Current user of tobacco (of ever users)

Yes 16 21.3 37 48.1 B0.001

No 59 78.7 40 51.9

Ever consumed alcohol

Yes 198 81.2 171 75.3 0.126

No 46 18.9 56 24.7

Currently consume alcohol (of ever users)

Yes 59 29.8 74 43.3 0.007

No 139 70.2 97 56.7

Sleep problems

None 141 57.8 101 44.5 0.005

Mild 16 6.6 36 15.9

Moderate 45 18.4 40 17.6

Severe 28 11.5 35 15.4

Extreme 14 5.7 15 6.6

On ART (n �212)

Yes 192 90.6

No 20 9.4



Median age 57 71 �11.5 B0.001

Median BMI 21.4 22.7 �4.0 0.001

Chronic conditions and disability in older people with and without HIV in Uganda

Citation: Glob Health Action 2016, 9: 31098 – 7 (page number not for citation purpose)



older people are accessing more regular and better care,

they may be more likely to have been told that they have

another chronic condition, compared with HIV-negative

older persons who may not be accessing health care as

regularly. A study conducted among older people ‘infected

or affected by HIV’ established that 90% of the HIV-

positive older people were accessing treatment (52). The

data available from WOPS2 on health care utilization

show that 50% of the older people who were HIV negative

had taken more than 1 year without visiting a health

center. In future WOPS surveys, it will be important to

complement self-reported diagnoses of chronic diseases/

impairments with objective measures to see whether

these differences persist. Furthermore, the age differences

between the two groups (HIV positive and HIV negative)

might also be driving the differences in chronic conditions.

Those who were HIV positive were younger compared to

those who were HIV negative, with only a small propor-

tion of the HIV-positive respondents (11%) aged 70 years

and over. However as Table 2 illustrates, while older age

is associated with the reporting of chronic conditions

generally, for some chronic conditions, HIV infection is

also an important factor.

Tobacco use and alcohol consumption did not differ

between HIV-positive and HIV-negative older people

(53). A study conducted in rural areas of three African

countries showed that alcohol consumption and tobacco

smoking were significantly higher in men and women aged

50 years and over than in those under age 50; however,

that study did not collect information on respondents’

HIV status (11). While health behaviors and individual

factors increase the risk of chronic conditions, it is also

important to note that a majority of older persons in low-

income countries are poor and have access to limited

health resources. For example, poor living conditions

are a major risk factor for chronic respiratory diseases

(54, 55). Further, in many LMICs, due to poverty and

mobility issues, older persons are unable to seek medical

attention for the early detection and treatment of these

chronic conditions even though they recognize symptoms

or understand that the condition is treatable (56). Further,

the quality and quantity of services related to chronic

conditions, particularly related to the needs of older

persons, are limited in the majority of LMICs (21).

Thus, it is important to highlight that the need for health

service and structural changes as well as the lack of

available services contribute significantly to the quality of

life for those living with chronic conditions (21).

When we looked at disability using WHODAS2.0

scores, sleep problems and depression were significantly

related to higher scores (higher reported disability). While

it is not clear from our data if these are causing disability

or if disability is causing these problems, sleep and mental

health are arguably among the most under-reported

illnesses in lower level health facilities, particularly for

older adults. This calls for a greater focus on mental

health, and investigations into why these issues exist

among older Ugandans. Some of the reasons that have

been previously cited for poor mental health among older

Africans include lack of social connection, family sup-

port, HIV stigma, and caregiving burden (57, 58). There is

also need to examine best practices to treat mental health

issues in older people at lower levels of the health care

systems and through community-based interventions.

Confirming findings from WOPS1 (26), women had

significantly higher disability scores than men. It is

unclear why older women have these higher scores since

there is evidence that adult women generally have better

health seeking behavior than men (59�61). However, there

Table 6. Multivariable linear regression of WHO disability


Independent variable Coefficient (95% CI) p

Arthritis diagnosis


Yes �1.2 ( �9.9 to 7.6) 0.795

Depression diagnosis


Yes 9.4 (1.2 to 17.7) 0.025

COPD diagnosis


Yes 6.6 ( �2.2 to 15.4) 0.139

BMI 0.3 ( �1.1 to 0.8) 0.152

Age 0.98 (0.7 to 1.2) B0.001

HIV status


Positive �6.3 ( �15.8 to 3.1) 0.187



Female 14.5 (7.8 to 21.2) B0.001

HIV status/hypertension diagnosis

HIV � /no diagnosis �1.6 ( �8.0 to 4.7) 0.605

HIV � /no diagnosis 3.4 ( �3.2 to 9.9) 0.314

HIV status/gender

HIV � /female �6.8 ( �15.5 to 1.8) 0.120

Current alcohol consumption


No 4.7 (0.2 to 9.3) 0.04

Sleep problems (last 30 days)


Mild 14.2 (7.3 to 21.0) B0.001

Moderate 16.9 (10.8 to 23.0) B0.001

Severe 20.3 (13.9 to 26.7) B0.001

Extreme/can’t do 21.7 (11.4 to 31.9) B0.001

Currently employed


No 6.8 ( �0.2 to 12.3) 0.06

*Reference category.

Joseph O. Mugisha et al.

8 (page number not for citation purpose)

Citation: Glob Health Action 2016, 9: 31098 –



is evidence that older African women report poorer self-

rated health and quality of life than men, both of which

are associated with disability (51, 62). This relationship

could be related to various aspects of home and social life

including older women’s care giving responsibilities and

the interrelationship between mental and physical health

(63�65). The underlying reasons for older women having significantly higher disability scores than older men need

further research. Respondents who reported not consum-

ing alcohol in the past 30 days reported higher WHODAS

score than current drinkers. Although the odds ratio for

those who were currently not consuming alcohol was

high, the confidence intervals were wide with the lower

limit of 0.2. One possible explanation may be that those

who already knew they had chronic conditions were

abstaining from alcohol and tobacco use because of their

chronic condition. Given the preponderance of evidence

of the role of alcohol, tobacco, and diet in chronic

conditions in high-income countries (66�68), it will be important to track these relationships over time.

Strengths and weaknesses

This study has potential strengths and weaknesses. There

are very few studies in Uganda and indeed sub-Saharan

Africa that examine the differences in chronic conditions

between older people living with and without HIV. This

study provides initial data on chronic conditions, includ-

ing the prevalence of the risk factors and the association

between chronic conditions and disability, in older people

living with and without HIV in Uganda.

One limitation of these data is that most of the

diagnoses made were by self-report. Though these may

not be as accurate as diagnoses made by clinicians,

diagnoses by self-report have been widely used in other

studies (2, 38, 40). It will be important to continue to

explore and validate self-reports of various health condi-

tions and behaviors against more objective measures

in these and other data from sub-Saharan Africa. In

addition, because of anticipated mortality and loss to

follow-up in the original sample of WOPS1, we added 100

respondents who were HIV positive in the WOPS2 sample.

These new respondents might be different in a number of

ways from the original WOPS1 sample, and from other

HIV-positive individuals living in Uganda, as they were

identified through an NGO that serves people living with

HIV. Last, there were age differences between the HIV-

positive and negative groups with the HIV positive being

younger than the HIV negative; however, to manage this

in the regression models, we controlled for age.

Conclusion In conclusion, this study has identified a number of

factors, like sleep problems and depression, and COPD

among HIV-positive individuals, which are associ-

ated with high disability scores among older Ugandans.

Unfortunately, in the majority of lower level health centers

in Uganda, which are the first levels of care for most of the

older people, such factors are under-reported, and there

are not adequate resources for services to address these

problems. As the population of Uganda ages, with and

without HIV, there is need to revise Ugandan health policy

to consider the health needs of older people. It is essential

to begin focusing on community and health service

interventions that positively impact both physical and

mental health in order to reduce disability and improve

overall quality of life among older Ugandans.

Authors’ contributions JOM, EJS and JS conceived the idea; JOM and JS

designed the study; MR and JOM analyzed the data. All

the authors contributed equally in writing and revising

the manuscript .


We would like to thank all older people who participated in this

study. We would also like to thank Professor Sally Findley from

the University of Columbia, New York, for her useful comments in

the preparation of this manuscript. We would also like to thank the

organizers of the Union of African Population Studies conference

2015 for allowing us to present this paper at this conference. Joseph

Mugisha Okello is funded through a post-doctoral fellowship from

University of Missouri.

Conflict of interest and funding

The authors have not received any funding or benefits from

industry or elsewhere to conduct this study.

Paper Context Previous work on this topic has focused on chronic condi-

tions in HIV-negative older people. This paper adds new

information on chronic conditions and their impact on

disability in HIV-positive and HIV-negative older people. We

recommend that health care workers should always look for

symptoms and signs of chronic disease in older people

irrespective of their HIV status.


1. Anderson G, Horvath J. The growing burden of chronic disease

in America. Public Health Rep 2004; 119: 263.

2. Acosta D, Rottbeck R, Rodrı́guez JG, González LM, Almánzar

MR, Minaya SN, et al. The prevalence and social patterning of

chronic diseases among older people in a population undergoing

health transition. A 10/66 Group cross-sectional population-

based survey in the Dominican Republic. BMC Public Health

2010; 10: 344.

3. Hirschhorn LR, Kaaya SF, Garrity PS, Chopyak E, Fawzi MC.

Cancer and the ‘other’ noncommunicable chronic diseases in

older people living with HIV/AIDS in resource-limited settings:

a challenge to success. AIDS 2012; 26: S65�75. 4. Alwan A. Global status report on noncommunicable diseases

2010. Geneva, Switzerland: World Health Organization; 2011.

Chronic conditions and disability in older people with and without HIV in Uganda

Citation: Glob Health Action 2016, 9: 31098 – 9 (page number not for citation purpose)



5. Deepa M, Pradeepa R, Rema M, Mohan A, Deepa R,

Shanthirani S, et al. The Chennai Urban Rural Epidemiology

Study (CURES)-study design and methodology (urban compo-

nent) (CURES-1). J Assoc Physicians India 2003; 51: 863�70. 6. Ng N, Van Minh H, Tesfaye F, Bonita R, Byass P, Stenlund H,

et al. Combining risk factors and demographic surveillance:

potentials of WHO STEPS and INDEPTH methodologies for

assessing epidemiological transition. Scand J Public Health

2006; 34: 199�208. 7. Westaway MS, Maluka CS. Impact of chronic diseases on the

health-related quality of life of South Africans. S Afr Med J

2008; 94: 937.

8. Thorogood M, Connor MD, Hundt GL, Tollman SM. Under-

standing and managing hypertension in an African sub-district:

a multidisciplinary approach1. Scand J Public Health 2007;

35(69 suppl): 52�9. 9. Westaway MS. The impact of chronic diseases on the health

and well-being of South Africans in early and later old age.

Arch Gerontol Geriatr 2010; 50: 213�21. 10. Kahn K, Garenne ML, Collinson MA, Tollman SM. Mortality

trends in a new South Africa: hard to make a fresh start1. Scand

J Public Health 2007; 35(69 suppl): 26�34. 11. Negin J, Cumming R, de Ramirez SS, Abimbola S, Sachs SE.

Risk factors for non-communicable diseases among older adults

in rural Africa. Trop Med Int Health 2011; 16: 640�6. 12. Negin J, Martiniuk A, Cumming RG, Naidoo N, Phaswana-

Mafuya N, Madurai L, et al. Prevalence of HIV and chronic

comorbidities among older adults. AIDS 2012; 26: S55�63. 13. United Nations Population Fund (UNFPA) and HelpAge

International. Ageing in the twenty-first century: A celebration

and a challenge, New York: United Nations Population Fund.

14. Velkoff VA, Kowal PR. Population aging in Sub-Saharan Africa:

demographic dimensions 2006. U.S.Department of Commerce,

Economics and Statistics Administration. Washington DC, US:

Census Bureau; 2007.

15. Pillay NK, Maharaj P. Population ageing in Africa. In: Maharaj P,

ed. Aging and health in Africa. Springer; 2013, pp. 11�51. 16. Ministry of Health, Uganda (2012). Uganda AIDS Indicator

Survey 2011. Uganda: Ministry of Health.

17. Shippy RA, Karpiak SE. The aging HIV/AIDS population:

fragile social networks. Aging Ment Health 2005; 9: 246�54. 18. High KP, Brennan-Ing M, Clifford DB, Cohen MH, Currier J,

Deeks SG, et al. HIV and aging: state of knowledge and areas of

critical need for research. A report to the NIH Office of AIDS

Research by the HIV and Aging Working Group. J Acquir

Immune Defic Syndr 2012; 60(Suppl 1): S1�18. 19. Negin J, Wariero J, Cumming RG, Mutuo P, Pronyk PM. High

rates of AIDS-related mortality among older adults in rural

Kenya. J Acquir Immune Defic Syndr 2010; 55: 239�44. 20. Setel PW. Non-communicable diseases, political economy, and

culture in Africa: anthropological applications in an emerging

pandemic. Ethn Dis 2003; 13(Suppl 2): S149�57. 21. Joshi R, Alim M, Kengne AP, Jan S, Maulik PK, Peiris D, et al.

Task shifting for non-communicable disease management in low

and middle income countries � A systematic review. PLoS One 2014; 9: e103754.

22. Lewando Hundt G, Stuttaford M, Ngoma B. The social

diagnostics of stroke-like symptoms: healers, doctors and

prophets in Agincourt, Limpopo Province, South Africa. J

Biosoc Sci 2004; 36: 433�43. 23. Diouf A, Cournil A, Ba-Fall K, Ngom-Guèye NF, Eymard-

Duvernay S, Ndiaye I, et al. Diabetes and hypertension among

patients receiving antiretroviral treatment since in Senegal:

prevalence and associated factors. ISRN AIDS 2012; 2012:


24. Amuyunzu-Nyamongo M. Need for a multi-factorial, multi-

sectorial and multi-disciplinary approach to NCD prevention

and control in Africa. Glob Health Promot 2010; 17(2 suppl):

31�2. 25. Giles WH, Pacque M, Greene BM, Taylor HR, Munoz B,

Cutler M, et al. Prevalence of hypertension in rural west Africa.

Am J Med Sci 1994; 308: 271�5. 26. Scholten F, Mugisha J, Seeley J, Kinyanda E, Nakubukwa S,

Kowal P, et al. Health and functional status among older people

with HIV/AIDS in Uganda. BMC Public Health 2011; 11: 886.

27. Wandera SO, Kwagala B, Ntozi J. Prevalence and risk factors

for self-reported non-communicable diseases among older

Ugandans: a cross-sectional study. Glob Health Action 2015;

8: 27923, doi:

28. Wandera SO, Ntozi J, Kwagala B. Prevalence and correlates of

disability among older Ugandans: evidence from the Uganda

National Household Survey. Glob Health Action 2014; 7:

25686, doi:

29. Wandera SO, Golaz V, Kwagala B, Ntozi J. Factors associated

with self-reported ill health among older Ugandans: a cross

sectional study. Arch Gerontol Geriatr 2015; 61: 231�9. 30. Strong K, Mathers C, Leeder S, Beaglehole R. Preventing

chronic diseases: how many lives can we save? Lancet 2005; 366:

1578�82. 31. Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S, Murray CJ.

Selected major risk factors and global and regional burden of

disease. Lancet 2002; 360: 1347�60. 32. Vita AJ, Terry RB, Hubert HB, Fries JF. Aging, health risks,

and cumulative disability. New Engl J Med 1998; 338: 1035�41. 33. LaCroix AZ, Guralnik JM, Berkman LF, Wallace RB,

Satterfield S. Maintaining mobility in late life. II. Smoking,

alcohol consumption, physical activity, and body mass index.

Am J Epidemiol 1993; 137: 858�69. 34. Magnusson R. Developing a global framework to address

non-communicable diseases. Diabetes Voice 2008; 53: 9�12. 35. Üstün TB, Chatterji S, Kostanjsek N, Rehm J, Kennedy C,

Epping-Jordan J, et al. Developing the world health organiza-

tion disability assessment schedule 2.0. Bull World Health

Organ 2010; 88: 815�23. 36. Mugisha J, Scholten F, Owilla S, Naidoo N, Seeley J, Chatterji

S, et al. Caregiving responsibilities and burden among older

people by HIV status and other determinants in Uganda. AIDS

Care 2013; 25: 1341�8. 37. Nakanjako D, Kamya M, Daniel K, Mayanja-Kizza H, Freers

J, Whalen C, et al. Acceptance of routine testing for HIV among

adult patients at the medical emergency unit at a national

referral hospital in Kampala, Uganda. AIDS Behav 2007; 11:

753�8. 38. Naidoo N, Abdullah S, Bawah A, Binka F, Chuc NT, Debpuur

C, et al. Ageing and adult health status in eight lower-income

countries: the INDEPTH WHO-SAGE collaboration. Glob

Health Action 2010; 11: 5302, doi:


39. World Health Organization (International Society of Hyperten-

sion Writing Group). 2003 World Health Organization (WHO)/

International Society of Hypertension (ISH) statement on

management of hypertension. J Hypertens 2003; 21: 1983�92. 40. Kowal P, Chatterji S, Naidoo N, Biritwum R, Fan W, Ridaura

RL, et al. Data resource profile: the World Health Organization

Study on global AGEing and adult health (SAGE). Int J

Epidemiol 2012; 41: 1639�49. 41. Moussavi S, Chatterji S, Verdes E, Tandon A, Patel V,

Ustun B. Depression, chronic diseases, and decrements in

health: results from the World Health Surveys. Lancet 2007;

370: 851�8.

Joseph O. Mugisha et al.

10 (page number not for citation purpose)

Citation: Glob Health Action 2016, 9: 31098 –



42. Folstein MF, Folstein SE, McHugh PR. ‘‘Mini-mental state’’:

a practical method for grading the cognitive state of patients for

the clinician. J Psychiatr Res 1975; 12: 189�98. 43. Tombaugh TN, McIntyre NJ. The mini-mental state examina-

tion: a comprehensive review. J Am Geriatr Soc 1992; 40:

922�35. 44. Crum RM, Anthony JC, Bassett SS, Folstein MF. Population-

based norms for the Mini-Mental State Examination by age

and educational level. JAMA 1993; 269: 2386�91. 45. Nakimuli-Mpungu E, Mojtabai R, Alexandre PK, Katabira E,

Musisi S, Nachega JB, et al. Cross-cultural adaptation and

validation of the self-reporting questionnaire among HIV� individuals in a rural ART program in southern Uganda. HIV

AIDS (Auckl) 2012; 4: 51�60. 46. Petrushkin H, Boardman J, Ovuga E. Psychiatric disorders in

HIV-positive individuals in urban Uganda. Psychiatrist 2005;

29: 455�8. 47. Üstün TB. Measuring health and disability: manual for WHO

Disability Assessment Schedule (WHODAS 2.0). Geneva,

Switzerland: World Health Organization; 2010.

48. Janca A, Kastrup M, Katschnig H, Lopez-Ibor J, Jr., Mezzich J,

Sartorius N. The World Health Organization Short Disability

Assessment Schedule (WHO DAS-S): a tool for the assessment

of difficulties in selected areas of functioning of patients with

mental disorders. Soc Psychiatry Psychiatr Epidemiol 1996; 31:

349�54. 49. Rehm J, Üstün TB, Saxena S, Nelson CB, Chatterji S, Ivis F,

et al. On the development and psychometric testing of the

WHO screening instrument to assess disablement in the general

population. Int J Meth Psychiatr Res 1999; 8: 110�22. 50. Nyirenda M, Chatterji S, Falkingham J, Mutevedzi P, Hosegood

V, Evandrou M, et al. An investigation of factors associated

with the health and well-being of HIV-infected or HIV-affected

older people in rural South Africa. BMC Public Health 2012;

12: 259.

51. Gomez-Olive FX, Thorogood M, Clark BD, Kahn K, Tollman

SM. Assessing health and well-being among older people in

rural South Africa. Glob Health Action 2010; 3(Suppl 2):

23�35. 52. Negin J, Nyirenda M, Seeley J, Mutevedzi P. Inequality in

health status among older adults in Africa: the surprising

impact of anti-retroviral treatment. J Cross Cult Gerontol

2013; 28: 491�3. 53. Asiki G, Baisley K, Kamali A, Kaleebu P, Seeley J, Newton R.

A prospective study of trends in consumption of cigarettes and

alcohol among adults in a rural Ugandan population cohort,

1994�2011. Trop Med Int Health 2015; 20: 527�36.

54. Anto J, Vermeire P, Vestbo J, Sunyer J. Epidemiology of chronic

obstructive pulmonary disease. Eur Respir J 2001; 17: 982�94. 55. Prescott E, Vestbo J. Socioeconomic status and chronic

obstructive pulmonary disease. Thorax 1999; 54: 737�41. 56. Mayosi BM, Flisher AJ, Lalloo UG, Sitas F, Tollman SM,

Bradshaw D. The burden of non-communicable diseases in

South Africa. Lancet 2009; 374: 934�47. 57. Wright S, Zalwango F, Seeley J, Mugisha J, Scholten F.

Despondency among HIV-positive older men and women in

Uganda. J Cross Cult Gerontol 2012; 27: 319�33. 58. Kuteesa MO, Seeley J, Cumming RG, Negin J. Older people

living with HIV in Uganda: understanding their experience and

needs. Afr J AIDS Res 2012; 11: 295�305. 59. Courtenay WH. Constructions of masculinity and their influ-

ence on men’s well-being: a theory of gender and health. Soc Sci

Med 2000; 50: 1385�401. 60. Shaikh BT, Hatcher J. Health seeking behaviour and health

service utilization in Pakistan: challenging the policy makers.

J Public Health 2005; 27: 49�54. 61. Lawson D. Determinants of health seeking behaviour in

Uganda: is it just income and user fees that are important?

Manchester: University of Manchester; 2004.

62. Schatz E, Gómez-Olivé X, Ralston M, Menken J, Tollman S.

The impact of pensions on health and wellbeing in rural

South Africa: does gender matter? Soc Sci Med 2012; 75:

1864�73. 63. Schatz E, Gilbert L. ‘‘My heart is very painful’’: physical,

mental and social wellbeing of older women at the times of HIV/

AIDS in rural South Africa. J Aging Stud 2012; 26: 16�25. 64. Schatz E, Gilbert L. My legs affect me a lot . . . . I can no longer

walk to the forest to fetch firewood’’: challenges related to

health and the performance of daily tasks for older women in a

high HIV context. Health Care Women Int 2014; 35: 771�88. 65. Schatz E, Seeley J. Gender, ageing and carework in East

and Southern Africa: a review. Glob Public Health 2015; 10:

1185�200, doi: 66. Abegunde DO, Mathers CD, Adam T, Ortegon M, Strong K.

The burden and costs of chronic diseases in low-income and

middle-income countries. Lancet 2007; 370: 1929�38. 67. Ebrahim S, Smeeth L. Non-communicable diseases in low and

middle-income countries: a priority or a distraction? Int J

Epidemiol 2005; 34: 961�6. 68. Maher D, Waswa L, Baisley K, Karabarinde A, Unwin N,

Grosskurth H. Distribution of hyperglycaemia and related

cardiovascular disease risk factors in low-income countries:

a cross-sectional population-based survey in rural Uganda. Int J

Epidemiol 2011; 40: 160�71.

Chronic conditions and disability in older people with and without HIV in Uganda

Citation: Glob Health Action 2016, 9: 31098 – 11 (page number not for citation purpose)



© 2016 Joseph O. Mugisha et al. This work is licensed under the Creative Commons Attribution License (the

“License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Scroll to Top