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www.thelancet.com/lancetgh Vol 11 September 2023
e1372
Articles
The met and unmet health needs for HIV, hypertension, and
diabetes in rural KwaZulu-Natal, South Africa: analysis of a
cross-sectional multimorbidity survey
Urisha Singh, Stephen Olivier, Diego Cuadros, Alison Castle, Yumna Moosa, Thando Zulu, Jonathan Alex Edwards, Hae-Young Kim, Resign Gunda,
Olivier Koole, Ashmika Surujdeen, Dickman Gareta, Day Munatsi, Tshwaraganang H Modise, Jaco Dreyer, Siyabonga Nxumalo, Theresa K Smit,
Greg Ordering-Jespersen, Innocentia B Mpofana, Khadija Khan, Zinzile E L Sikhosana, Sashen Moodley, Yen-Ju Shen, Thandeka Khoza,
Ngcebo Mhlongo, Sanah Bucibo, Kennedy Nyamande, Kathy J Baisley, Alison D Grant, Kobus Herbst, Janet Seeley, Deenan Pillay, Willem Hanekom,
Thumbi Ndung’u, Mark J Siedner, Frank Tanser, Emily B Wong, on behalf of the Vukuzazi team*
Summary
Background The convergence of infectious diseases and non-communicable diseases in South Africa is challenging to
health systems. In this analysis, we assessed the multimorbidity health needs of individuals and communities in
rural KwaZulu-Natal and established a framework to quantify met and unmet health needs for individuals living with
infectious and non-communicable diseases.
Methods We analysed data collected between May 25, 2018, and March 13, 2020, from participants of a large, community-
based, cross-sectional multimorbidity survey (Vukuzazi) that oered community-based HIV, hypertension, and diabetes
screening to all residents aged 15 years or older in a surveillance area in the uMkhanyakude district in KwaZulu-Natal,
South Africa. Data from the Vukuzazi survey were linked with data from demographic and health surveillance surveys
with a unique identifier common to both studies. Questionnaires were used to assess the diagnosed health conditions,
treatment history, general health, and sociodemographic characteristics of an individual. For each condition (ie, HIV,
hypertension, and diabetes), individuals were defined as having no health needs (absence of condition), met health needs
(condition that is well controlled), or one or more unmet health needs (including diagnosis, engagement in care, or
treatment optimisation). We analysed met and unmet health needs for individual and combined conditions and
investigated their geospatial distribution.
Findings Of 18 041 participants who completed the survey (12 229 [67·8%] were female and 5812 [32·2%] were male),
9898 (54·9%) had at least one of the three chronic diseases measured. 4942 (49·9%) of these 9898 individuals had at
least one unmet health need (1802 [18·2%] of 9898 needed treatment optimisation, 1282 [13·0%] needed engagement
in care, and 1858 [18·8%] needed a diagnosis). Unmet health needs varied by disease; 1617 (93·1%) of 1737 people
who screened positive for diabetes, 2681 (58·2%) of 4603 people who screened positive for hypertension, and
1321 (21·7%) of 6096 people who screened positive for HIV had unmet health needs. Geospatially, met health needs
for HIV were widely distributed and unmet health needs for all three conditions had specific sites of concentration;
all three conditions had an overlapping geographical pattern for the need for diagnosis.
Interpretation Although people living with HIV predominantly have a well controlled condition, there is a high burden
of unmet health needs for people living with hypertension and diabetes. In South Africa, adapting current, widely
available HIV care services to integrate non-communicable disease care is of high priority.
Funding Fogarty International Center and the National Institutes of Health, the Bill & Melinda Gates Foundation, the
South African Department of Science and Innovation, the South African Medical Research Council, the South African
Population Research Infrastructure Network, and the Wellcome Trust.
Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
Introduction
Infectious diseases, including HIV and tuberculosis,
have dominated the burden of disease in sub-Saharan
Africa for decades.1 However, similar to other low-
income and middle-income countries, regions within
sub-Saharan Africa are experiencing an epidemiological
transition in which the prevalence of chronic non-
communicable diseases is increasing.2 These non-
communicable diseases include diabetes,3 hypertension
and cardiovascular diseases,4 chronic respiratory
diseases,5 chronic renal diseases,6 mental and substance
use disorders,7 and cancers.8
Although the transition of disease burden has
predominantly included shifts from infectious diseases to
non-communicable diseases globally, studies in South
Africa and other regions across sub-Saharan Africa have
reported a convergence of infectious diseases and non-
communicable diseases.9–14 This convergence could be
Lancet Glob Health 2023;
11: e1372–82
See Comment page e1317
For the isiZulu translation of the
abstract see Online for
appendix 1
*Vukuzazi team members are
listed in appendix 2 (pp 5–7)
Africa Health Research
Institute, KwaZulu-Natal,
South Africa (U Singh PhD,
S Olivier MA, A Castle MD,
Y Moosa MMedSci, T Zulu MSc,
R Gunda PhD, O Koole PhD,
A Surujdeen BSc, D Gareta MSc,
D Munatsi MBa, T H Modise MSc,
J Dreyer NDipIT, S Nxumalo BSc,
T K Smit PhD,
G Ordering-Jespersen NDipIT,
I B Mpofana MMedSci,
K Khan PhD, Z E L Sikhosana MSc,
S Moodley BSc, Y-J Shen PhD,
T Khoza MBChB,
N Mhlongo MBChB,
S Bucibo PGDip, K J Baisley MSc,
Prof A D Grant PhD,
K Herbst MSc, Prof J Seeley PhD,
Prof D Pillay PhD,
Prof W Hanekom PhD,
Prof T Ndung’u PhD,
M J Siedner MD,
Prof F Tanser PhD,
E B Wong MD); Nelson R
Mandela School of Medicine
(U Singh, M J Siedner), School of
Nursing and Public Health
(R Gunda, Prof J Seeley,
Prof F Tanser) and School of
Clinical Medicine (M J Siedner),
College of Health Sciences, and
Centre for the AIDS Programme
of Research in South Africa
(Prof F Tanser), University of
KwaZulu-Natal, Durban, South
Africa; Digital Epidemiology
Laboratory, Digital Futures,
University of Cincinnati,
Cincinnati, OH, USA
(D Cuadros PhD); Division of
Infectious Diseases (A Castle,
M J Siedner) and Ragon
Institute (Prof T Ndung’u),
Massachusetts General
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Hospital, Boston, MA, USA;
Harvard Medical School,
Harvard University, Boston,
MA, USA (A Castle,
Prof T Ndung’u); International
Institute for Rural Health,
University of Lincoln, Lincoln,
UK (J A Edwards MSPH,
Prof F Tanser); Department of
Biostatistics and
Bioinformatics, Rollins School
of Public Health and
Department of Biomedical
Informatics, Emory University
School of Medicine, Emory
University, Atlanta, GA, USA
(J A Edwards MSPH);
Department of Population
Health, New York University
Grossman School of Medicine,
New York University, New York,
NY, USA (H-Y Kim PhD); London
School of Hygiene and Tropical
Medicine, London, UK (O Koole,
K J Baisley, Prof A D Grant,
Prof J Seeley); Department of
Pulmonology and Critical Care,
Inkosi Albert Luthuli Hospital,
Durban, South Africa
(Prof K Nyamande PhD); School
of Public Health, University of
Witwatersrand, Johannesburg,
South Africa (Prof A D Grant);
Department of Science and
Innovation, Medical Research
Council, South African
Population Research
Infrastructure, Durban, South
Africa (K Herbst); Division of
Infection and Immunity,
University College London,
London, UK (Prof D Pillay,
Prof W Hanekom,
Prof T Ndung’u); HIV
Pathogenesis Programme,
Doris Duke Medical Research
Institute, Durban, South Africa
(Prof T Ndung’u); School of Data
Science and Computational
Thinking, Stellenbosch
University, Stellenbosch, South
Africa (Prof F Tanser); Division
of Infectious Diseases,
University of Alabama at
Birmingham, Birmingham, AL,
USA (E B Wong)
Correspondence to:
Dr Emily B Wong, Africa Health
Research Institute, KwaZulu-
Natal 4001, South Africa
emily.wong@ahri.org
See Online for appendix 2
linked to the ageing HIV-positive population in these
regions, the associated increasing burden of non-
communicable diseases among these individuals, and the
hastening eect of HIV on non-communicable disease
acquisition.15 Managing the convergence of diseases is of
even greater concern since the beginning of the COVID-19
pandemic because poorly controlled multimorbidity has
been associated with an increased risk of severe outcomes
from COVID-19.16,17 Moreover, an increase in ageing
among people living with HIV as a result of the success of
antiretroviral therapy has seen a subsequent increase in
non-communicable diseases in this group, resulting in
recognition of the need for integrated infectious disease
programmes and non-communicable disease care and
prevention programmes to avoid a loss of health gains
made through antiretroviral therapy.10,14,18,19 The UN
Sustainable Development Goal number 3, which aims to
ensure healthy lives and promote wellbeing for people at
all ages, advocates for the integration of infectious disease
and non-communicable disease prevention and
treatment.20 However, the extent to which the health needs
of individuals with multiple conditions overlap within
individuals and communities, and thus the most ecient
and eective approach of designing a health-systems
response, is not well established.
In this analysis, we used results from the Vukuzazi
study, a multimorbidity survey conducted in rural South
Africa9 to assess the health needs of individuals and
communities in rural KwaZulu-Natal and describe a
needs scale that assesses health needs for infectious
diseases and non-communicable diseases.
Methods
Study design and participants
This analysis used data collected during the Vukuzazi
study, a large, community-based, cross-sectional multi-
morbidity survey conducted in the uMkhanyakude district
in rural KwaZulu-Natal, South Africa, that were collected
between May 25, 2018, and March 13, 2020. The methods
of this survey have been described previously9,21 and are
provided in appendix 2 (pp 8–11). Briefly, the study area
covered a 482 km² radius within the demographic and
health surveillance area of the Africa Health Research
Institute (AHRI) in KwaZulu-Natal. The area has high
Research in context
Evidence before this study
We searched PubMed from database inception to
March 15, 2022, using the search terms “non-communicable
diseases”, “met health needs”, “unmet health needs”,
“prevalence”, “HIV”, “diabetes”, “hypertension”, “Africa”,
“sub-Saharan Africa”, and “South Africa” for articles published
in English. This search revealed that, in the global context, there
is an increasing burden of non-communicable diseases and the
burden of communicable diseases continues to be high in Africa
relative to other regions. This convergence of infectious and
non-communicable diseases in low-resource settings has led to
multiple calls for the integration of health systems that are
currently independent to address multimorbidity. Health-
systems data and reports on global burden of diseases show the
extent of the problem. However, patient-level data that define
the detailed health needs of individuals for both communicable
and non-communicable diseases are scarce. Furthermore, the
varied nature of non-communicable diseases and the widely
varying health needs of people with these conditions have
resulted in multiple approaches to defining individual and
community-level health needs. Thus, the complexity of
multimorbidity is a barrier to the development of unified
approaches to define gaps in the health system and the design
of interventions to address these gaps.
Added value of this study
This analysis introduces a simple framework for the definition
of health needs that is applicable to infectious and
non-communicable diseases. Use of this framework to analyse
data from a large, population-based, multimorbidity study
provides a comprehensive understanding of the met and
unmet health needs of individuals living with HIV, diabetes, or
hypertension in an HIV hyperendemic setting in a largely rural
region of South Africa from 2018 to 2020. This analytical
approach allows for the consideration of these health states
individually and in combination. Furthermore, this approach
allows for analysis at both the individual level and the
community level. Applied to one community in rural South
Africa, it shows that the health needs of people with HIV are
generally well met, whereas the health needs of people with
non-communicable diseases are poorly met by existing health
systems. The analysis also shows that, within the community,
areas of disease-specific and disease-non-specific health needs
can be identified.
Implications of all the available evidence
Taken together with global findings that show the increasing
burden and health needs of people living with non-
communicable diseases, the framework introduced by this
analysis will help countries to assess their health programmes,
identify priority areas for intervention, and consider integrated
approaches to communicable and non-communicable disease
management. Within South Africa, the findings of this analysis
suggest that the need for improved diagnosis, care, and disease
control for people with diabetes and hypertension can be
addressed by adapting the health systems that successfully
meet the health needs of people living with HIV. The increasing
non-communicable disease burden in low-income and middle-
income countries, alongside ongoing communicable disease
epidemics, indicates the need for improved integrative health
care and calls for creative and affordable approaches to disease
diagnosis and management.
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HIV prevalence and antiretroviral therapy has been
available through public health clinics since 2004. All
current 36 097 residents aged 15 years or older within the
survey area were considered eligible for the survey and
were invited to mobile health camps to complete a health
survey, multimorbidity screening, and collection of
samples for biobanking (appendix pp 10–11). All
participants who provided written informed consent and
were enrolled in the survey were included in this analysis
(appendix p 12). Female sex, older age, being unemployed,
having lower socioeconomic and educational status, and
being a resident of a rural area were characteristics that
were over-represented among participants compared with
eligible non-participants (appendix p 13).21
All participants of Vukuzazi were also members of
an ongoing demographic and health surveillance
programme, which has been described elsewhere.21
Briefly, the programme has conducted annual household,
demographic, and health surveys every year since 2017
and includes a clinic surveillance system (ClinicLink)
that provides clinic attendance data for the 11 primary
health facilities in the surveillance area.22 For this
analysis, data from the Vukuzazi survey and the
demographic and health surveillance surveys were linked
with a unique identifier that was common to both
studies.
The Vukuzazi study was approved by the University of
KwaZulu-Natal Biomedical Research Ethics Committee
and the institutional review board of Mass General
Brigham (Boston, MA, USA). Written consent for all
study procedures and linkage to health and demographic
surveillance information was obtained from all
participants at mobile health camps.
Procedures
Questionnaires were used to assess the diagnosed health
conditions and treatment history of an individual for each
disease (ie, HIV, diabetes, and hypertension) at the mobile
health camps (appendix pp 10–11, 17–28). Anthropometric
measures and blood pressure were collected according to
the WHO STEPwise approach to Surveillance (STEPS)
protocol. Blood samples were collected for assessment of
glycated haemoglobin (HbA1c) and HIV immunoassay
testing. Positive HIV immunoassay tests were followed by
a reflex HIV-1 RNA viral load assessment. Typical or
expected results were reported by telephone call or text
message, whereas participants with unexpected results
received an at-home visit for further assessment,
communication of results, and referral into the health
system for care.21
Data from the most recent annual surveillance before
Vukuzazi enrolment for each participant (ie, 2017–19
depending on date of enrolment) were used for this
analysis. Self-reported data, including socioeconomic
status, perceived overall health, residence status, and
geolocation, are collected regularly as part of the general
health and sociodemographic questionnaire (adminis-
tered by the Population Intervention Programme). The
number of clinic visits individuals made in the past
12 months before Vukuzazi enrolment was obtained
through linkage with the ClinicLink system.22 Participants
included in the study were geolocated to their homes with
a geographic information system.23 The Vukuzazi study
used self-report to collect sex data (options were male
or female).
We incorporated concepts that had previously been
used to define unmet health needs and the treatment
care cascade24,25 and defined five health states on the basis
of parallel diagnostic criteria for each of the three chronic
diseases included in this analysis. The five health states
were free of the condition, diagnosed and optimally
treated, diagnosed and suboptimally treated, diagnosed
but not engaged in care, and undiagnosed but had a
positive screening test in Vukuzazi (table 1).
We developed a novel framework to relate these five
health states to their respective health-system needs. The
health-system needs of each health state were captured by
a needs score in which the lowest score (0) represented an
absence of disease and thus no immediate needs from the
health system and the highest score (4) represented
individuals who had the highest health needs and required
diagnosis, engagement in care (defined as visiting a
health-care facility for treatment of a disease), treatment
optimisation (defined as receiving treatment that results
HIV Diabetes Hypertension
Free of the condition Immunoassay negative No previous diagnosis of diabetes and
HbA1c ≤6·5%
No previous diagnosis of hypertension and
blood pressure <140/<90 mm Hg
Diagnosed, engaged in care, and optimally
treated
Known diagnosis of HIV, on treatment, and HIV
viral load <40 copies per mL
Known diagnosis of diabetes, on treatment,
and HbA1c ≤6·5%
Known diagnosis of hypertension, on treatment,
and blood pressure ≤140/≤90 mm Hg
Diagnosed, engaged in care, and
suboptimally treated
Known diagnosis of HIV, on treatment, and HIV
viral load >40 copies per mL
Known diagnosis of diabetes, on treatment,
and HbA1c >6·5%
Known diagnosis of hypertension, on treatment,
and blood pressure ≥140/≥90 mm Hg
Diagnosed but not engaged in care Known diagnosis of HIV, not on treatment, and
HIV viral load >40 copies per mL
Known diagnosis of diabetes, not on
treatment, and HbA1c >6·5%
Known diagnosis of hypertension, not on
treatment, and blood pressure ≥140/≥90 mm Hg
Undiagnosed but had a positive screening
test in the Vukuzazi study
No previous diagnosis of HIV, immunoassay
positive, and HIV viral load >40 copies per mL
No previous diagnosis of diabetes and
HbA1c ≥6·5%
No previous diagnosis of hypertension and
blood pressure ≥140/≥90 mm Hg
HBA1c=glycated haemoglobin.
Table 1: Health state definitions for HIV, diabetes, and hypertension
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in reaching optimal therapeutic targets for a disease), and
provision of chronic medication (figure 1). Participants
were assigned needs scores based on their health state
and associated health needs. Needs scores for individuals
with a disease were then separated into two needs groups:
met health needs (needs score 1) and unmet health needs
(needs score 2–4). Participants who were diagnosed,
engaged in care, and optimally treated had an associated
health need for chronic medication, were assigned a needs
score of 1, and were included in the met needs group.
Participants who were diagnosed, engaged in care, and
suboptimally treated had an additional health need of
treatment optimisation, were assigned a needs score of 2,
and were included in the unmet health needs group.
Participants who were diagnosed but not engaged in care
had an additional health need of engagement in care, were
assigned a needs score of 3, and were included in the
unmet health needs group. Participants who were
undiagnosed and had a positive screening test in the
Vukuzazi study had all health needs, including the need
for diagnosis, were assigned a needs score of 4, and were
included in the unmet health needs group. Need scores
were calculated for individual diseases and for all three
diseases combined. In the combined analysis, individuals
with more than one disease were assigned the needs
score representing their highest need.
Geospatial analysis
Having previously observed little data on the overlapping
prevalence of HIV, diabetes, and hypertension within
KwaZulu-Natal,9 we sought to assess the geospatial
distribution of health needs for these three conditions in
the demographic surveillance area. Data visualisation
analysis of the distribution of health needs for each
condition (ie, HIV, diabetes, and hypertension) and for all
three diseases combined were generated with continuous
surface maps of the prevalence distribution of each need.
Spatial interpolations were generated with a standard
Gaussian kernel interpolation method (with a search
radius of 3 km), which has been used and validated in this
population for mapping multiple HIV outcomes in the
study area.25 Maps were created with ArcGIS Pro
version 3.1.
Statistical analysis
We calculated the proportion of participants with each
need score by disease and for all diseases combined.
We then compared the descriptive features of
individuals within each combined need score using
Pearson’s χ² test or the Kruskal-Wallis rank-sum test.
Due to the descriptive nature of the research and the
small proportion of missingness we used complete case
analysis to describe the data. Statistical analyses were
done in R version 4.2.1.
Role of the funding source
The funders of the analysis had no role in study design,
collection of data, data analysis, interpretation of data, or
writing or editing of the manuscript.
Results
Of the 18 041 individuals who enrolled in the Vukuzazi
study, 9898 (54·9%) had at least one of the three health
conditions measured (figure 2A). 12 229 (67·8%) of
18 041 participants were female and 5812 (32·2%) were
male. Of the individuals with health conditions,
6096 (61·7%) had HIV, 4063 (46·6%) had hypertension,
and 1737 (17·6%) had diabetes (figure 2B). The total
number of participants with no health needs identified
was 8143 (45·1%) of 18 041.
Distribution of sex, age, BMI, perceived general health
state, number of clinic visits in the past year, distance to
nearest clinic, residence location, socioeconomic status,
drinking status, and household size diered between
people with no health needs and people with dierent
health needs scores (table 2). Health needs varied
between age categories. For example, participants aged
25–44 years represented the largest proportion of people
with well controlled chronic disease (needs score 1) and
undiagnosed chronic disease (needs score 4), whereas
Health state
Free of the condition
Diagnosed, engaged in care, and optimally treated
Diagnosed, engaged in care, and suboptimally treated
Diagnosed but not engaged in care
Undiagnosed but had a positive screening test in the
Vukuzazi study
Healthy
Met needs
Unmet needs
0
1
2
3
4
Chronic
medication
Treatment
optimisation
Engagement
in care
Diagnosis
Needs scoreHealth needs Needs group
Figure 1: Framework for understanding the relationship between health states, health needs, needs scores, and needs groups
For ArcGIS Pro see
http://www.esri.com
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participants aged 45–64 years represented the largest
proportion of people with suboptimally controlled
chronic disease (needs score 2) and chronic disease that
was diagnosed but not treated (needs score 3).
9932 (55·7%) of 17 842 participants were overweight
or obese (ie, BMI >25 kg/m²; table 2). These individuals
were under-represented among those without health
needs (3495 [43·2%] of 8081 participants) and
over-represented among those with health needs:
2988 (60·9%) of 4909 participants had needs score 1,
1283 (73·0%) of 1758 had needs score 2, 959 (76·3%) of
1257 had needs score 3, and 1207 (65·7%) of 1837 had
needs score 4.
Despite having unmet health needs, the majority
of participants with undiagnosed and uncontrolled
diseases (needs score 4) perceived their health to be good
or very good overall (table 2). Similarly, 1207 (71·6%) of
1685 participants who required optimisation of treatment
(needs score 2) and 917 (75·8%) of 1210 participants who
required engagement in care (needs score 3), and thus
were collectively deemed to have a suboptimally
controlled condition, reported their perceived health
status as good or very good.
Many individuals with unmet health needs had visited
a clinic in the year before engaging in the Vukuzazi study
(table 2). Overall, 785 (42·2%) of 1858 participants who
were undiagnosed with an uncontrolled condition (need
score 4), 725 (56·6%) of 1282 participants who required
engagement in care (need score 3), and 1302 (72·3%) of
1802 participants who required optimisation of treatment
(need score 2) visited a clinic in the past year. 2287 (46·3%)
of 4942 participants with unmet health needs had two
visits or more in the previous year.
Individuals who lived in rural areas were over-
represented among people who had diagnosed chronic
disease but were not engaged in care (needs score 3).
People with the furthest distance to the nearest clinic
were similarly over-represented among this group.
Figure 2: Distribution of health needs in the Vukuzazi cohort for participants with HIV, diabetes, or hypertension
(A) Total number of participants with no health needs identified and with health needs identified. (B) Disease distribution among individuals with health needs
identified. (C) Distribution of met and unmet health needs for individual chronic health states. (D) Distribution of met and unmet health needs for all three
conditions combined (ie, HIV, diabetes, and hypertension). Green represents needs score 1 (ie, diagnosed with a well controlled condition), yellow represents needs
score 2 (ie, diagnosed with a suboptimally controlled condition), light pink represents needs score 3 (ie, diagnosed but not engaged in care), and pink represents
needs score 4 (ie, undiagnosed with an uncontrolled condition). Black error bars indicate 95% CIs.
No known health needs Known health needs
8143 (45·1%)
9898 (54·9%)
0
2000
4000
6000
8000
10000
12000
Number of participants
AB
HIV Diabetes Hypertension
61·7%
46·6%
17·6%
0
25
50
75
100
Participants (%)
HIV Hypertension Diabetes
0
25
50
75
100
Participants (%)
CD
Three diseases combined
0
25
50
75
100
Participants (%)
78·3%
Unmet health
needs (21·7%)
Unmet health
needs (58·2%)
Unmet health
needs (93·1%)
32·2%
27·2%
33·7%
15·4%
18·5%
24·3%
41·8%
10·2%
8·9%
2·5%
6·9%
50·1%
Unmet health
needs (49·9%)
18·2%
12·9%
18·8%
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Overall No health need
(needs score 0)
Diagnosed with a
well controlled
condition
(needs score 1)
Diagnosed with a
suboptimally
controlled condition
(needs score 2)
Diagnosed but not
engaged in care
(needs score 3)
Undiagnosed with
an uncontrolled
condition
(needs score 4)
p value*
Sex ·· ·· ·· ·· ·· ·· <0·0001
Male 5812/18 041 (32·2%) 3507/8143 (43·1%) 973/4956 (19·6%) 391/1802 (21·7%) 345/1282 (26·9%) 596/1858 (32·1%) ··
Female 12 229/18 041 (67·8%) 4636/8143 (56·9%) 3983/4956 (80·4%) 1411/1802 (78·3%) 937/1282 (73·1%) 1262/1858 (67·9%) ··
Age, years ·· ·· ·· ·· ·· ·· <0·0001
15–24 4962/18 041 (27·5%) 4152/8143 (51·0%) 375/4956 (7·6%) 82/1802 (4·6%) 59/1282 (4·6%) 294/1858 (15·8%) ··
25–44 6008/18 041 (33·3%) 2336/8143 (28·7%) 2328/4956 (47·0%) 367/1802 (20·4%) 284/1282 (22·2%) 693/1858 (37·3%) ··
45–64 4595/18 041 (25·5%) 1104/8143 (13·6%) 1626/4956 (32·8%) 751/1802 (41·7%) 550/1282 (42·9%) 564/1858 (30·4%) ··
65 or older 2476/18 041 (13·7%) 551/8143 (6·8%) 627/4956 (12·7%) 602/1802 (33·4%) 389/1282 (30·3%) 307/1858 (16·5%) ··
BMI ·· ·· ·· ·· ·· ·· <0·0001
Typical (18·5–24 kg/m²) 7053/17 842 (39·5%) 4058/8081 (50·2%) 1726/4909 (35·2%) 428/1758 (24·3%) 265/1257 (21·1%) 576/1837 (31·4%) ··
Underweight (<18·5 kg/m²) 857/17 842 (4·8%) 528/8081 (6·5%) 195/4909 (4·0%) 47/1758 (2·7%) 33/1257 (2·6%) 54/1837 (2·9%) ··
Overweight (25–30 kg/m²) 4048/17 842 (22·7%) 1660/8081 (20·5%) 1236/4909 (25·2%) 448/1758 (25·5%) 297/1257 (23·6%) 407/1837 (22·2%) ··
Obese (>30 kg/m²) 5884/17 842 (33·0%) 1835/8081 (22·7%) 1752/4909 (35·7%) 835/1758 (47·5%) 662/1257 (52·7%) 800/1837 (43·5%) ··
Perceived general health
(assessed via the PIP survey)
·· ·· ·· ·· ·· ·· <0·0001
Poor to fair 2192/15 912 (13·8%) 494/6780 (7·3%) 712/4578 (15·6%) 478/1685 (28·4%) 293/1210 (24·2%) 215/1659 (13·0%) ··
Good 8758/15 912 (55·0%) 3591/6780 (53·0%) 2694/4578 (58·8%) 908/1685 (53·9%) 657/1210 (54·3%) 908/1659 (54·7%) ··
Very good 4962/15 912 (31·2%) 2695/6780 (39·7%) 1172/4578 (25·6%) 299/1685 (17·7%) 260/1210 (21·5%) 536/1659 (32·3%) ··
Any clinic visits in the past year 9561/18 041 (53·0%) 2925/8143 (35·9%) 3824/4956 (77·2%) 1302/1802 (72·3%) 725/1282 (56·6%) 785/1858 (42·2%) <0·0001
Number of clinic visits in the
past year
·· ·· ·· ·· ·· ·· <0·0001
1 2068/9561 (21·6%) 1242/2925 (42·5%) 301/3824 (7·9%) 151/1302 (11·6%) 152/725 (21·0%) 222/785 (28·3%) ··
2–4 3084/9561 (32·3%) 1105/2925 (37·8%) 1153/3824 (30·2%) 324/1302 (24·9%) 230/725 (31·7%) 272/785 (34·6%) ··
5 or more 4409/9561 (46·1%) 578/2925 (19·8%) 2370/3824 (62·0%) 827/1302 (63·5%) 343/725 (47·3%) 291/785 (37·1%) ··
Distance to nearest clinic, km 2·63 (1·52–4·07) 2·75 (1·62–4·22) 2·46 (1·47–3·85) 2·53 (1·42–4·01) 3·29 (2·08–4·45) 2·27 (1·34–3·61) <0·0001
Smoking status ·· ·· ·· ·· ·· ·· <0·0001
Never 16 573/18 024 (91·7%) 7383/8126 (90·9%) 4622/4956 (93·3%) 1692/1802 (93·9%) 1168/1282 (91·1%) 1708/1858 (91·9%) ··
Ex-smoker 150/18 024 (0·8%) 58/8126 (0·7%) 46/4956 (0·9%) 19/1802 (1·1%) 16/1282 (1·2%) 11/1858 (0·6%) ··
Current smoker 1301/18 024 (7·2%) 685/8126 (8·4%) 288/4956 (5·8%) 91/1802 (5·0%) 98/1282 (7·6%) 139/1858 (7·5%) ··
Drinking status ·· ·· ·· ·· ·· ·· <0·0001
Never 15 752/18 024 (87·4%) 7009/8126 (86·3%) 4409/4956 (89·0%) 1627/1802 (90·3%) 1125/1282 (87·8%) 1582/1858 (85·1%) ··
No drinking in the past
12 months
306/18 024 (1·7%) 154/8126 (1·9%) 73/4956 (1·5%) 28/1802 (1·6%) 23/1282 (1·8%) 28/1858 (1·5%) ··
Drinking in the past
12 months
1966/18 024 (10·9%) 963/8126 (11·9%) 474/4956 (9·6%) 147/1802 (8·2%) 134/1282 (10·5%) 248/1858 (13·3%) ··
Household size ·· ·· ·· ·· ·· ·· <0·0001
Small to medium household
(1–5 members)
12 662/18 041 (70·2%) 5355/8143 (65·8%) 3668/4956 (74·0%) 1360/1802 (75·5%) 920/1282 (71·8%) 1359/1858 (73·1%) ··
Large household
(>5 members)
5379/18 041 (29·8%) 2788/8143 (34·2%) 1288/4956 (26·0%) 442/1802 (24·5%) 362/1282 (28·2%) 499/1858 (26·9%) ··
Residence location ·· ·· ·· ·· ·· ·· <0·0001
Rural 11 436/17 985 (63·6%) 5430/8119 (66·9%) 2951/4940 (59·7%) 1049/1795 (58·4%) 1104/1280 (86·3%) 902/1851 (48·7%) ··
Periurban 5599/17 985 (31·1%) 2342/8119 (28·8%) 1672/4940 (33·8%) 644/1795 (35·9%) 160/1280 (12·5%) 781/1851 (42·2%) ··
Urban 950/17 985 (5·3%) 347/8119 (4·3%) 317/4940 (6·4%) 102/1795 (5·7%) 16/1280 (1·3%) 168/1851 (9·1%) ··
Socioeconomic status ·· ·· ·· ·· ·· ·· <0·0001
Low 6457/17 468 (37·0%) 2920/7909 (36·9%) 1868/4768 (39·2%) 626/1744 (35·9%) 465/1259 (36·9%) 578/1788 (32·3%) ··
Middle 6043/17 468 (34·6%) 2762/7909 (34·9%) 1652/4768 (34·6%) 573/1744 (32·9%) 442/1259 (35·1%) 614/1788 (34·3%) ··
High 4968/17 468 (28·4%) 2227/7909 (28·2%) 1248/4768 (26·2%) 545/1744 (31·3%) 352/1259 (28·0%) 596/1788 (33·3%) ··
Data are n/N (%) or median (IQR). PIP=Population Intervention Programme. *All p values are Pearson’s χ², except Distance to nearest clinic, km, which is Kruskal-Wallis rank-sum test.
Table 2: Demographic and socioeconomic data disaggregated by health needs
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Of the 9898 participants found to have a chronic health
condition, the patterns of met and unmet health needs
diered by individual disease. While 4775 (78·3%) of
6096 participants who were HIV-positive had their
health needs met (ie, were diagnosed and on chronic
medication for optimal disease control), only 120 (6·9%)
of 1737 partici pants with diabetes and 1922 (41·8%) of
4603 participants with hypertension had their health
needs fully met (figure 2C). Unmet health needs for
individuals with HIV were predominantly driven by the
need for treatment optimisation (need score 2; 543 [8.9%]
of 6096) and diagnosis (need score 4; 624 [10·2%]), with
only 154 (2·5%) of participants with HIV requiring
engagement in care (need score 3).
By contrast, for hypertension and diabetes, all three
unmet needs, including engagement in care, contributed
substantially to the high levels of unmet health needs.
1617 (93·1%) of 1737 people who screened positive for
diabetes, 2681 (58·2%) of 4603 people who screened
positive for hypertension, and 1321 (21·7%) of 6096 people
who screened positive for HIV had unmet health needs
(figure 2C). The need for diagnosis (need score 4) was
greater for individuals with diabetes (472 [27·2%] of 1737)
and hypertension (852 [18·5%] of 4603) than HIV
(624 [10·2%] of 6096; appendix pp 14–15). Although
1145 (65·9%) of 1737 participants with diabetes and
1829 (39·7%) of 4603 participants with hypertension were
aware of their diagnosis, they either received suboptimal
treatment (need score 2) or were not initially engaged
in care (need score 3; figure 2C). 1145 (65·9%) of
1737 participants who knew they had diabetes and
1829 (39·7%) of 4603 participants who knew they had
hypertension required either engagement in care
(560 [32·2%] with diabetes and 710 [15·4%] with
Need score 1
Diagnosed with a well
controlled condition
Need score 2
Diagnosed with a
suboptimally controlled
condition
Need score 3
Diagnosed but not
engaged in care
Need score 4
Undiagnosed with an
uncontrolled condition
HIV
<21·2%
21·2–24·0%
24·1–26·3%
>26·3%
<0·8%
0·8–1·7%
1·8–2·5%
>2·5%
<0·8%
0·8–2·2%
2·3–3·9%
>3·9%
<1·9%
1·9–2·6%
2·7–3·6%
>3·6%
Hypertension
<10·5%
10·5–12·6%
12·7–15·8%
>15·8%
<4·7%
4·7–7·9%
8·0–13·1%
>13·1%
<0·8%
0·8–2·5%
2·6–4·2%
>4·2%
<0·9%
0·9–2·6%
2·7–5·8%
>5·8%
Diabetes
<0·5%
0·5–0·7%
0·8–1·1%
>1·1%
<0·5%
0·5–0·8%
0·9–1·3%
>1·3%
<0·3%
0·3–1·4%
1·5–2·2%
>2·2%
<0·8%
0·8–1·9%
2–3·6%
>3·6%
Combined
<23·1%
23·1–27·8%
27·9–31·7%
>31·7%
<9·9%
9·9–12·6%
12·7–15·5%
>15·5%
<8·6%
8·6–12·4%
12·5–16·0%
>16%
<4·7%
4·7–7·9%
8–13·5%
>13·5%
Figure 3: Geospatial distribution of health needs for HIV, hypertension, and diabetes individually and for all three chronic conditions combined
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hypertension) or optimisation of treatment (585 [33·7%]
with diabetes and 1119 [24·3%] with hypertension).
When we assessed the health needs of the population
for all three disease conditions combined, we found that
of the 9898 (54·9%) of 18 041 participants who had at least
one of the three health conditions, 4956 (50·1%) had their
health needs met and 4942 (49·9%) had at least one unmet
health need (figure 2D). Among those 4942 participants
with unmet health needs, 1802 (36·5%) were diagnosed
and on treatment that required optimisation (need
score 2), 1282 (25·9%) were diagnosed but not engaged in
care (need score 3), and 1858 (37·6%) were undiagnosed
and were therefore in need of further diagnostic testing,
engagement in care, optimisation of treatment, provision
of chronic medication, and routine monitoring (need
score 4; figure 2D; appendix p 14).
Health needs also varied by age and multimorbidity.
541 (11·5%) of 4684 participants with HIV only had a
need for diagnosis, compared with 303 (29·9%) of
1015 of participants with HIV and comorbid hypertension
and 86 (48·0%) of 179 participants with HIV and
comorbid diabetes (appendix p 16). Overall, 462 (33·1%)
of 1397 participants with HIV and a comorbid non-
communicable disease required a diagnosis. Younger
participants had the greatest need for diagnosis;
34 (66·7%) of 51 participants aged 15–29 years with
comorbid HIV and hypertension needed a diagnosis
compared with 178 (41·1%) of 433 participants aged
30–49 years and 91 (17·1%) of 531 participants aged
50 years or older with the same combination of
conditions.
In our geospatial analysis, needs score 1 was widely
distributed throughout the demographic surveillance
area (figure 3), indicating that the need for chronic
medication is present across the entire area for all three
conditions. By contrast, needs scores 2 and 3 were
specifically concentrated in more rural areas of the
demographic surveillance area for all three conditions.
Specifically, the need for optimisation of treatment for
hypertension and the need for engagement in care
for hypertension and diabetes were concentrated in the
northern part of the surveillance area; the need for
optimisation of treatment for diabetes was highest in the
south-eastern part of the surveillance area. Needs scores
2 and 3 had low density in the southern-eastern part of
the demographic surveillance area, the most densely
populated region, whereas needs score 4 overlapped for
all three conditions within this region, indicating a
possible target area for diagnostic interventions (figure 3).
Discussion
Using data from a large, community-based, cross-
sectional multimorbidity survey in rural KwaZulu-Natal,
South Africa, we assessed the complex health needs
of individuals and communities and proposed and
implemented a health-needs framework to conceptualise
the met and unmet health needs of communities that are
aected by the overlapping infectious disease and non-
communicable disease epidemics in the country. The
framework allows for establishment of similar health
needs across chronic disease and promotes comparison
between individuals with dierent health needs via
sociodemographic and other health determinants. In our
cohort in rural South Africa, we found that approximately
half of people living with chronic disease in this
community have unmet health needs. Use of this
health-needs framework also allows for geographical
visualisations that show colocalisation of individuals
with undiagnosed infectious diseases and non-
communicable diseases. Geospatial data visualisation by
health needs also shows that analysing populations by
their health needs provides useful disaggregation that
is obscured when people with a specific condition are
analysed in a group without regard to their other health
needs. Our framework shows that analysing chronic
disease separately and implementing public health
approaches independently misses the opportunity for
integration of communicable and non-communicable
disease chronic care. Consideration should be given to
health systems that are designed to address multiple
health conditions and serve people with multiple chronic
diseases.
More than half of the individuals who engaged
in community-based health screening had at least
one health need for the diagnosis or management of
HIV, diabetes, or hypertension, but the met or unmet
status of these needs diered between HIV and non-
communicable diseases. 78·3% of participants with HIV,
who were widely distributed throughout the geospatial
area, had a well controlled condition and were on
antiretroviral therapy. This finding shows the successful
public health response to HIV in its ability to diagnose,
optimally treat, and monitor people with a chronic
infection across a large rural area. However, it also
highlights the contrast between HIV and non-
communicable disease responses; 93·1% of people who
screened positive for diabetes and 58·2% of people who
screened positive for hypertension have unmet health
needs in this same community.
The lack of non-communicable disease control is
similar to results reported in other studies in the
region.26–28 For example, the South African National
Health and Nutrition Examination Survey (SANHANES),
which considered the prevalence of unmet health needs
in South Africa, estimated that 91·5% of people with
hypertension and 80·6% of people with diabetes had an
unmet health need.26,27 Although the SANHANES study
showed that older individuals and those with obesity
were more likely to have undiagnosed or poorly controlled
diabetes, our analysis shows that younger participants
(aged 15–29 years) were more likely to require diagnosis
of comorbid HIV and hypertension (66·7%) than older
participants (aged 30–49 years [41·1%] or aged >50 years
[17·1%]; appendix p 16). With obesity representing
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an emerging problem across all age groups in South
Africa and our analysis reporting associations between
increased BMI and unmet health needs, the need for
optimal diagnosis and treatment of non-communicable
diseases in people who are overweight or obese is
highlighted. Smaller studies in the province of
Mpumalanga also reported high prevalence of uncon-
trolled hypertension (54·2–56·8%).28 These studies
assessed the health needs of people with hypertension
and diabetes, but our analysis has provided a framework
for the assessment of these health needs simultaneously
with HIV.
This analysis revealed a discrepancy between the ability
of the South African health system to respond to the
health needs of people with communicable diseases and
the health needs of people with non-communicable
diseases; 11·5% of participants with HIV only required a
diagnosis whereas 33·1% of participants with HIV and
a comorbid non-communicable disease required a
diagnosis. Our results highlight the substantial need for
improved non-communicable disease care in rural South
Africa. With health systems currently reaching a wide
target population for HIV care, creative adaptation of
existing health pro grammes and frameworks could be
successful in treating multiple chronic diseases
concurrently.
Unmet health needs also varied by disease and
geospatial location in the community. For HIV, most
participants with unmet health needs required a
diagnosis (10·2%) or optimisation of treatment (8·9%).
Few participants required engagement in care, despite a
known diagnosis. These data indicate that individuals
who have been diagnosed with HIV have mostly been
engaged in care and are receiving optimal antiretroviral
therapy. Conversely, for non-communicable diseases,
people who knew they had diabetes or hypertension
required either engagement in care or optimisation of
treatment. These dierences could partly reflect
diculties in accessing care as individuals requiring
engagement in care tended to live furthest from a clinic
and were more likely to live in a rural setting compared
with those with other need scores. By contrast, the need
for treatment optimisation (need score 2) was higher in
older people and people with higher BMI. Individuals
with this health need were predominantly aged 45 years
or older and were typically overweight or obese.
The association between increased BMI and suboptimal
treatment of hypertension, diabetes, or other chronic
diseases has been reported in other studies in which
links between obesity, treatment-resistant hypertension,
and altered pharmacological activity of drugs have been
reported, with use of multiple agents suggested.29
Collectively, these data support the implementation of
decentralised, patient-centred treatment programmes
that consider patient variables such as barriers to health-
care access, BMI, and age when providing treatment for
non-communicable diseases.
The need for diagnosis (need score 4) was greater for
individuals with diabetes and hypertension than HIV.
The high prevalence of undiagnosed diabetes (45·4%)
and hypertension (48·7%) in South Africa has also been
reported in the SANHANES study.26,27 When disag-
gregated by age, participants in each age group needed
a diagnosis for HIV and comorbid diabetes or
hypertension, indicating a universal need for integration
of HIV and non-communicable disease care. Individuals
with a need for diagnosis for all three conditions were
concentrated in the southern part of the surveillance
area, the most densely populated region in this analysis.
Collectively, these data show a need to improve access to
testing for non-communicable diseases. They also show
an opportunity for targeted integrated interventions for
non-communicable diseases and HIV in the demographic
surveillance area, with more research required to
establish whether these results are applicable to the
country or region. Health-care facilities might have
missed opportunities to address the health needs of
people with a diagnosis requiring treatment optimisation
(needs score 2) or engagement in care (need score 3), or
even people who require a diagnosis (need score 4).
The majority of these participants had visited a clinic in
the area two or more times in the year before engaging
in the Vukuzazi study, but still had unmet health needs
at the time of the survey, which shows the need for
improved, integrated primary health care.
Our analysis has several limitations. First, only three
chronic disease conditions were considered. However,
the proposed framework oers flexibility and can be
extended to other conditions. Second, Vukuzazi only
enrolled half of the eligible population, which might bias
our description of health needs and their associations.
The direction of bias is hard to anticipate based on the
known demo graphic dierences between the sampled
and unsampled population because the health status of
the unenrolled population is unknown.9 The Vukuzazi
study enrolled more female participants than male
participants and more older people than younger people,
both of which could lead to overestimation of diabetes,
hypertension, and their health needs. The under-
representation of male participants highlights that they
have fewer interactions with both community-based and
routine health services, and that their health needs are
poorly understood and require particular attention in the
future. We acknowledge that people who screened
positive for diabetes and hypertension required
confirmatory testing before confirmation of diagnosis,
and that this testing could rule out a disease requiring
immediate treatment; thus, we might have overestimated
the burden of undiagnosed disease.30 Finally, we
acknowledge that ascribing the status of having no health
needs to people who screen negative for disease is an
oversimplification as it neglects the need for interventions
targeting disease prevention, which might be crucial for
optimal community health.
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We have introduced a needs framework that allows
for the analysis of health needs for multiple diseases
concurrently despite their individualised prevention,
treatment, and diagnostic parameters. This novel
framework provides a way to conceptualise and measure
individual and community health needs for people living
in communities with high rates of infectious and non-
communicable diseases. Applying this framework shows
that approximately half of the people living with HIV,
diabetes, or hypertension in a South African community
have unmet health needs and that the unmet needs are
particularly high in people living with non-communicable
diseases. Furthermore, the granularity of this framework
identifies unanticipated geospatial patterns of health-need
distribution that could inform strategies for improving
rural health, such as scheduled visits by mobile clinics for
health checks, medication distribution, or chronic disease
management. New approaches to addressing these unmet
health needs are urgently required and we suggest that
applying a health-needs framework could provide novel
insights and guide the design of integrated, decentralised,
and patient-centred programmes for the management of
infectious diseases and non-communicable diseases.
The findings of this analysis suggest that in South
Africa, health systems that have successfully met the
needs of people living with HIV should be used to
address the unmet needs of people living with
hypertension and diabetes. With the burden of non-
communicable diseases increasing globally, especially in
low-income and middle-income countries where they
occur alongside epidemics of communicable diseases,
there is an urgent need for integrating primary health
care and developing creative and aordable approaches
to multidisease diagnosis and management.
Contributors
US, EBW, MJS, and FT conceptualised and designed the analysis.
US, SO, EBW, and DC analysed the data. US, EBW, and SO accessed and
verified the underlying data. US, EBW, SO, MJS, and FT wrote the
Article. All authors reviewed and edited the manuscript, had access to all
data reported in this Article, and had final responsibility for the decision
to submit for publication.
Declaration of interests
We declare no competing interests.
Data sharing
Data and related documents for the Vukuzazi study and for this analysis,
including the study protocol, informed consent forms, de-identified
participant data, and a data dictionary defining each field, can be
accessed via the Africa Health Research Institute Data Repository
(RDMServiceDesk@ahri.org) after publication upon approval of the
proposed analyses by the Vukuzazi Scientific Steering Committee and
completion of a data access agreement.
Acknowledgments
This analysis was supported by the Africa Health Research Institute
(AHRI) and received funding from the Fogarty International Center and
the US National Institutes of Health (NIH; R21TW011687, D43TW010543,
and K24HL166024), the Bill & Melinda Gates Foundation, the South
African Department of Science and Innovation, the South African
Medical Research Council, and the South African Population Research
Infrastructure Network (SAPRIN). This research was partly funded by
the Wellcome Trust (201433/Z/16/A). The views expressed in this Article
are those of the authors and not those of the Fogarty international Center,
NIH, Gates Foundation, or Wellcome Trust. We sincerely thank the
residents of the AHRI demographic surveillance area and all those who
participated in the Vukuzazi study. We are grateful to the AHRI
Community Advisory Board for ongoing oversight of the Vukuzazi study.
We thank the local and provincial Department of Health for their
partnership and support of this analysis.
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