Visit to general practitioners as a proxy for accessing chronic benefits by members of medical schemes, South Africa

Abstract

Background: Prescribed Minimum Benefits is a list of conditions that all medical schemes need to cover in full, and includes a select of chronic conditions. Chronic conditions affect people's lifestyles and require ongoing management over a period of years for long-term survival. Objectives: This study examined the association between prevalence of selected chronic diseases and health service use, in particular visits to general practitioners (GPs) by medical scheme members. Method: This was a retrospective study on medical schemes data. The median imputation method was employed to deal with missing and unreported chronic diseases prevalence. Multivariate logistic regression analysis was employed to assess effects of chronic disease prevalence, age stratum and scheme size on GP visits per annum. Results: The study showed that prevalence of asthma was significantly associated with more than three GP visits (OR = 1.081; 95% CI = 1.008-1.159), as was prevalence of type 2 diabetes (OR = 1.087; 95% CI = 1.027-1.152), whilst prevalence of hyperlipidaemia (OR = 0.92; 95% CI = 0.875-0.97) was more likely to be associated with less than three GP visits. Prevalence of hypertension was associated with more than three GP visits per year (OR = 1.132; 95% CI = 1.017-1.26). Conclusion: This study shows that scheme size, prevalence of chronic diseases such as asthma, type 2 diabetes, hyperlipidaemia and hypertension are related to GP visits. GPs and managed care programmes employed by schemes should give special attention to certain disease states with high prevalence rates in an effort to better manage them.

Full text

Original Research
doi:10.4102/phcfm.v4i1.419
hp://www.phcfm.org
Visit to general praconers as a proxy for accessing
chronic benets by members of medical schemes,
South Africa
Authors:
Mncedisi M. Willie1
Monwabisi Gantsho1
Aliaons:
1Council for Medical
Schemes, South Africa
Correspondence to:
Mncedisi Willie
Email:
m.willie@medicalschemes.
com
Postal address:
Private Bag X34, Haield,
Pretoria 0028, South Africa
Dates:
Received: 25 Jan. 2012
Accepted: 14 May 2012
Published: 08 Oct. 2012
How to cite this arcle:
Willie MM, Gantsho M. Visit
to general praconers as a
proxy for accessing chronic
benets by members of
medical schemes, South
Africa. Afr J Prm Health Care
Fam Med. 2012;4(1), Art.
#419, 9 pages. hp://dx.doi.
org/10.4102/phcfm.v4i1.419
Background: Prescribed Minimum Benets is a list of conditions that all medical schemes need
to cover in full, and includes a select of chronic conditions. Chronic conditions affect people’s
lifestyles and require ongoing management over a period of years for long-term survival.
Objectives: This study examined the association between prevalence of selected chronic
diseases and health service use, in particular visits to general practitioners (GPs) by medical
scheme members.
Method: This was a retrospective study on medical schemes data. The median imputation
method was employed to deal with missing and unreported chronic diseases prevalence.
Multivariate logistic regression analysis was employed to assess effects of chronic disease
prevalence, age stratum and scheme size on GP visits per annum.
Results: The study showed that prevalence of asthma was signicantly associated with more
than three GP visits (OR = 1.081; 95% CI = 1.008–1.159), as was prevalence of type 2 diabetes
(OR = 1.087; 95% CI = 1.027–1.152), whilst prevalence of hyperlipidaemia (OR = 0.92; 95%
CI = 0.875–0.97) was more likely to be associated with less than three GP visits. Prevalence
of hypertension was associated with more than three GP visits per year (OR = 1.132; 95%
CI = 1.017–1.26).
Conclusion: This study shows that scheme size, prevalence of chronic diseases such as asthma,
type 2 diabetes, hyperlipidaemia and hypertension are related to GP visits. GPs and managed
care programmes employed by schemes should give special attention to certain disease states
with high prevalence rates in an effort to better manage them.
© 2012. The Authors.
Licensee: AOSIS
OpenJournals. This work
is licensed under the
Creave Commons
Aribuon License.
Page 1 of 9
Les visites chez les médecins généralistes comme indicateur de l’accès à la prise en charge
des maladies chroniques des membres des régimes d’assurance maladie, Afrique du Sud
Contexte: Les prestations minimums prescrites sont une liste de maladies que tous les régimes
d’assurance maladie doivent totalement prendre en charge, notamment une sélection de
maladies chroniques. Les maladies chroniques affectent le mode de vie des personnes et
nécessitent un suivi permanent pendant des années pour une survie à long terme.
Objectifs: Cette étude examine l’association entre la prévalence des maladies chroniques
et l’utilisation des services de santé, en particulier les consultations chez des médecins
généralistes par les membres des régimes d’assurance maladie.
Méthodes: Il s’agissait d’une étude rétrospective sur les données des régimes d’assurance
maladie. La méthode d’imputation médiane a été utilisée pour traiter la prévalence des maladies
chroniques manquantes et non déclarées. L’analyse de régression logistique multivariée a été
utilisée pour évaluer les effets de la prévalence des maladies chroniques, les tranches d’âge et
le nombre de consultations chez des médecins généralistes par an.
Résultats: L’étude montre que la prévalence de l’asthme était signicativement associée à plus
de trois consultations chez le médecin généraliste (OR = 1.081; 95% CI = 1.008–1.159), de même
que la prévalence du diabète de type 2 (OR = 1.087; 95% CI = 1.027–1.152), alors que la prévalence
de l’hyperlipidémie (OR = 0.92; 95% CI = 0.875–0.97) était plus susceptible d’être associée à
moins de trois consultations chez le médecin généraliste. La prévalence de l’hypertension était
associée à plus de trois consultations chez le médecin généraliste par an (OR = 1.132; 95%
CI = 1.017–1.26).
Conclusion: Cette étude montre que la taille du régime d’assurance maladie et la prévalence de
maladies chroniques telles que l’asthme, le diabète de type 2, l’hyperlipidémie et l’hypertension
sont liées aux consultations chez le médecin généraliste. Les médecins généralistes et les
programmes de soins utilisés par les régimes d’assurance maladie devraient accorder une
attention particulière à certaines maladies présentant un taux de prévalence élevé an de
mieux les prendre en charge.
Introducon
General practitioners’ (GPs) services have been shown to be a signicant determinant of population
health, effective cost-containment and promotion of equity objectives.1 Unger et al.2 showed that

Original Research
doi:10.4102/phcfm.v4i1.419
hp://www.phcfm.org
GPs were the most common providers of chronic disease
primary care, with over 90% of respondents reporting that
they had visited a GP at least once in the past 12 months.
Utilisation data on GPs’ services by medical scheme members
report an average of three annual visits. Barnes, Jonsson and
Klim3 report that Canadian patients visit doctors more often
(4.9 annual GP visits) than Australian patients (2.3–3.6 visits),
whilst Harris4 contends that on average Australians visit a
GP ve times per year.
Key focus
Medical scheme members are entitled to certain benets that
the schemes have to cover in full. These are called Prescribed
Minimum Benets (PMBs), and the PMB Chronic Disease
List is a list of conditions which all medical schemes need
to cover on all the plans they offer to their members. This
cover includes funding for diagnosis, treatment and ongoing
care for the listed conditions.5 However, from a member’s
perspective there is still a lack of understanding of what
these benets actually entail. A recent survey by Old Mutual
Consulting Actuaries6 revealed that 85% of members do not
understand their PMB entitlements, or where to access PMBs.
However, a greater part of the problem is how these benets
are communicated to members.
With regard to PMBs, schemes develop protocols to manage
the use of benets. Such protocols would specify, for example,
types of tests, investigations and number of consultations.7
Non-adherence to some of the guidelines can have
unintended consequences for the member, such as denial of
benets that a member is entitled to. Some schemes require
members to register on disease management programmes
prior to entitlement to such benets. Consequences of not
registering on such programmes are outlined in the medical
schemes’ rules, which include cases where an unlimited
benet such as a PMB could be considered as a day-to-day
benet, thus unknowingly compromising member’s day-to-
day benets, which are limited.
Literature reviews reveal inconsistencies or variation in
how protocols or treatment guidelines for the PMB Chronic
Disease List are employed, in particular with regard to
number of consultations per annum, which is also a proxy
for a benet. In some guidelines patients who suffer from
asthma and use chronic medication are entitled to a treatment
plan that allows them two visits to a pulmonologist per year;
two visits to a GP or physician; and tests such as peak-ow
evaluations.8 For the purposes of this article we use annual
average visits to the GP as a proxy for access to benets,
and nd associations with select chronic diseases. This
seeks to advance knowledge on GP visits for monitoring
and managing of chronic conditions and also as a tool to
control costs.
Background
Quantifying the impact of chronic disease on healthcare use
can assist in estimating the return on investment of health
promotion and other policies designed to prevent chronic
diseases or better manage the costs associated with them.9
Medical schemes employ managed care programmes to
monitor utilisation and control costs; these programmes
include protocols and guidelines that also prescribe the
number of visits to a GP. In The Netherlands a GP is responsible
for the primary care of an average of 2350 patients.10
Literature reveals that Dutch GPs are the ‘gatekeepers’ of
the healthcare system and provide most routine medical care
and diagnostic evaluations for their patients. A patient can
visit a specialist only after a GP referral.11 Other studies have
also shown that delivering optimal health care for chronic
illnesses requires health systems to move from a reactive
approach to a proactive one.12
Trends
Long-term conditions are chronic illnesses that greatly affect
people’s lifestyles and require ongoing management over
years or decades.13 Chronic conditions such as diabetes,
heart disease and chronic obstructive pulmonary disease
affect over 17.5 million people in the United Kingdom (UK).14
Approximately 75% – 85% of healthcare expenditure in the
UK is related to chronic disease.15 Data show that 60% of
people aged over 65 years have a chronic disease, and this is
set to double in next 10 years. The literature further illustrate
that in the UK 80% of GP consultations and more than half
of hospital bed usage relates to a long-term condition.16,17,18
Raonale
In the absence of complete and accurate data, measuring
the effect of primary health service use and chronic disease
management programmes becomes difcult to assess.
Prevalence data are frequently collected through surveys
based upon self-reports of disease.18 Literature shows that
people tend to under-report the presence of chronic disease;
under-reporting of HIV and AIDS cases, for instance, is a
common problem in HIV epidemiology and often skews
epidemiological projections.19 Other epidemiological studies
have dealt with skewed or missing cases, as has the work
of Acuna and Rodriguez.20 It is known that missing data
can introduce bias into estimates derived from a statistical
model.21 Missing data and under-reporting of chronic
conditions are also key challenges in the medical scheme
environment, as reported in the Council for Medical Schemes
report.22
Another example is HIV reporting in the mining sector.
A mining company such as Implats provides treatment
programmes for its employees through its own medical
facilities and in-house medical scheme; however, employees
may choose to receive treatment through external medical
facilities which do not report statistics to the company, or
through government-provided systems. As a result, HIV
and AIDS prevalence levels and other statistics related to the
impact of the virus are not known with absolute certainty.23
McLeod24 further states that the chronic diseases list covers
the majority of people with chronic conditions, but warns
that this would underestimate the burden of chronic disease
in medical schemes.
Page 2 of 9

Original Research
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Enders25 reviewed some of the recent methodological
advances related to missing data, and provides an overview of
two ‘modern’ analytical options: direct maximum likelihood
estimation and multiple imputations. In the current article
we considered multiple imputations for dealing with
missing data.
Objecves
The objective of this study was to examine the association
between the prevalence of selected chronic diseases on health
service use, in particular visits to GPs. The current work
seeks to identify specic chronic diseases that may need
more attention and can be better managed sooner.
Contribuon to eld
This study investigated factors that are associated with
primary healthcare use, in particular GPs’ services. Some
of the factors included most prevalent chronic disease
associated with visits to a GP. The ndings of this study are
essential in illustrating the signicant role of primary care
in managing care for patients, and also identifying chronic
diseases that need more attention and monitoring and can be
better managed sooner. The study seeks further to enhance
understanding of some of the best practice literature in
developing and determining clinical guidelines associated
with treating and managing chronic diseases. The study uses
GP visits as a proxy for accessing chronic benets.
Ethical consideraons
The current study was not a clinical trial study, and therefore
did not directly involve treatment of patients. The data were
assessed and only reported at consolidated level for privacy
and condentiality.
Methods
Materials
The data used were sourced from the annual statutory
return submissions which schemes submit to the Ofce
of the Registrar. The data were captured on the annual
statutory returns portal, then exported onto Microsoft Excel
spreadsheets prior to the analysis phase.
Seng
Data analysed included open and restricted schemes that
were registered during the assessment period (data observed
in 2009). Inclusion criteria were schemes that submitted
complete data on the variables of interest.
Design
This was a retrospective cross-sectional study which included
109 medical schemes that were registered and operational
in 2009. A purposive sampling technique was used to select
schemes based on specic characteristics: registered schemes
for the period under review and completeness of data. The
study was representative in terms of the number of schemes,
beneciaries covered and number of benet options. A
sample of schemes represented 99.8% of the private-sector
beneciaries and 99.1% of registered benet options in 2009.
Procedure
The total number of visits by beneciaries of each scheme
in each year was extracted from the utilisation section of
the annual statutory return data submissions. This was then
weighted to account for the number of beneciaries in each
scheme. The average age of beneciaries was computed at
scheme level (Table 1). This was further organised into two
strata, namely schemes with average member age of more
than 35 years, and those with less than or equal to 35 years.
This cut-off was motivated by the ndings of a study by
Aung, Recehl and Odermatt,26 which showed that being
younger than 35 years was a main barrier to accessing
primary healthcare services. A study by Fuster, Voute, Hunn
and Smith27 revealed that 41% of all deaths in South Africa
were due to heart disease, and this occurred in people 35–
64 years of age. Furthermore, actuarial projections in South
Africa suggest that chronic diseases are expected to increase,
with HIVand AIDS ravaging those aged 18–35 years.28 The
report further highlights the alarming fact that South Africa is
already losing a signicant amount of people in the workforce
age group of 35–64 years because of cardiovascular disease.28
Other covariates considered for predicting average number
of visits to a GP included a select list of chronic diseases
The following 10 selected chronic conditions are those most
prevalent with the medical schemes:23,29
• Hypertention
• Hyperlipideamia
• Asthma
• Coronary artery disease
• HIV
• Hypothyroidism
• Epilepsy
• Diabetes mellitus type 1
• Diabetes mellitus type 2
• Cardiac failure.
Chronic disease permeates several aspects of health service
utilisation, and can be implicated in many diagnoses;
therefore, all services for all relevant ICD-10 diagnostic
codes were included. Prevalence of chronic disease was
dened by counting every beneciary who has any of the
selected chronic conditions; where beneciaries had multiple
conditions, each condition was counted separately.
TABLE 1: Covariates under invesgaon: demographic characteriscs.
Medical schemes Average number of visits to a GP per beneciary per annum
Scheme type
Open scheme Medical schemes that freely admit everyone
Restricted schemes Employer group schemes which only admit applicants
belonging to a specic employment sector
Scheme size
Large More than 30 000 beneciaries
Medium More than 6000 principal members but not more than
30 000 beneciaries
Small All schemes with less that 6000 principal members
Scheme age strata
> 35 vs. ≤ 35 Average age of beneciaries at scheme level was straed by
> 35 and ≤35 years

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Stascal analysis
Descriptive statistics were calculated to characterise the
distribution of chronic disease in the sample population.
The median imputation method was employed to deal
with missing and unreported cases. This is one of the most
frequently used methods, especially when the distribution of
values of a given feature is skewed.30 According to Durrant,31
the imputation method reduces non-response bias due to
missing values. The median imputation method consists
of replacing the missing data for a given feature (attribute)
with the median of all known values of that attribute in the
class where an instance is missing. The capping or ooring
approach was employed to deal with the outliers.32
Multivariate logistic regression analysis methods were
employed to assess the effects of the prevalence of chronic
illnesses on visits to a GP. Average annual GP visits were
used to enhance ability of the statistical models to estimate
the variance in utilisation attributable to chronic disease.33
The outcome variable was stratied into two groups to form
a dichotomous outcome: schemes with average annual visits
to a GP > 3 and those with visits ≤ 3.
Continuous measurement of variables such as prevalence
of selected conditions such as HIV, asthma, hypertension,
diabetes types 1 and 2, epilepsy, hyperlipidaemia, coronary
artery disease, hypothyroidism and cardiac failure were
included as covariates in the multivariate logistic regression
model. The average age of beneciaries in schemes, scheme
type, and scheme size were also considered as covariates
in the model. We conducted all the analysis using SAS
software, version 9.2 (SAS Institute Inc., Cary, NC). Statistical
signicance tests were conducted at α = 0.05 level (p < 0.05);
odds ratio (OR) and the 95% condence intervals (CIs) were
also reported.
Results
Parcipant characteriscs
The sample of schemes analysed represented 99.8% of the
private-sector beneciaries and 99.1% of registered benet
options for the 2009 data. The median number of visits to
a GP in 2009 was 3.2 (IQR = 2.4–3.7), and the median of
the average age of beneficiaries was 32.91 years (IQR
= 30.1–36.6) (Table 2). The median prevalence rate per 1000
beneciaries for hypertension was 109.5 (IQR = 82.8–159.0),
followed by hyperlipidaemia at 52.9 (IQR = 29.9–78.9). The
median prevalence rate for asthma was 27.9 (IQR = 20.3
–37.5), hypothyroidism 19.8 (IQR = 11.7–31.5) and cardiac
failure 4.2 (IQR = 1.4 –7.1) per 1000 beneficiaries. The
prevalence of beneciaries with type 2 diabetes mellitus
was 30.1 (IQR = 20.7–38.3) per 1000 beneficiaries. The
prevalence of select chronic diseases per 1000 beneciaries
for the medical schemes considered in the current study.
The average number of GP visits for restricted schemes was
slightly higher than in open schemes (3.3 compared to 2.9
visits [Table 2]).
The prevalence of chronic disease in open schemes was
slightly higher than in restricted schemes, except for HIV
cases (7.8 compared to 6.4/1000 beneciaries) and cardiac
failure (7.4 compared to 5.0/1000 beneciaries). The
difference in average expenditure on GP visits between
open and restricted schemes was not signicant, at R52.70
compared R67.60 per beneciary per month. Overall, total
benets paid to providers were higher in open schemes than
in restricted schemes.
Results revealed that the prevalence rate of cardiac failure
in the older proled schemes was nearly twice that in the
older group (Table 3). The prevalence of coronary artery
disease in the older proled schemes was nearly three times
that in the younger proled schemes. Prevalence rates for
hyperlipidaemia, hypertension, hypothyroidism and type 2
diabetes were twice as high for the older proled schemes as
for the younger proled schemes. Average expenditure on
GPs was not signicantly different between the younger and
the older proled schemes, at R58.70 compared to R49.52 per
beneciary per month.
TABLE 2: Prevalence of select chronic diseases per 1000 beneciaries by scheme type.
Variables Total (N = 109) Open (N = 33) Restricted (N = 76) Median IQR p-value
GP visits per annum 3 2.9 3.3 3.2 2.4−3.7 0.065
Asthma 26.7 28.9 23.5 27.9 20.3−37.5 0.09
Cardiac failure 6 5 7.4 4.2 1.4−7.1 0.332
Coronary artery disease 14.9 16.9 11.8 14.6 7.7−23.3 0.958
Type 1 diabetes 6.5 7.3 5.2 4.4 2.8−7.9 0.774
Type 2 diabetes 28.5 29.2 27.5 30.1 20.7−38.3 0.966
Epilepsy 7.1 7.8 6 7.2 5.2−9.7 0.63
HIV 7 6.4 7.8 6 0.2−12.9 0.719
Hyperlipidaemia 48.4 52.8 42 52.9 29.9−78.9 0.222
Hypertension 107.7 113.3 99.3 109.5 82.8−159.0 0.887
Hypothyroidism 18 19.1 16.2 19.8 11.7−31.5 0.34
Benets paid per beneciary per month (ZAR)
GPs 58.7 52.7 67.6 59.8 44.7−70.7 0.0002*
Total hospitals 293 315.9 258.9 313.2 251.5−392.2 < 0.0001*
Total benets 790.1 827.3 734.8 860.2 707.8−1071.4 < 0.0001*
IQR, interquarle ranges.
*, p < 0.05; 1 ZAR/$ = 8.8

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Hypertension was the most prevalent chronic disease, with
117.17 compared to 96.81 per 1000 beneciaries for the
stratum, and an average of 3+ visits compared to ≤3 visits
for the stratum (Figure 1). The second most prevalent was
hyperlipidaemia with 60.24 compared to 35.54 per 1000
beneciaries and 3+ visits compared to ≤3 visits. Asthma
and diabetes type 2 were the third and fourth most prevalent
chronic diseases in the data presented.
Missing and non-reported cases were identied and cross-
validated by a comparison analysis of the conditions,
looking at previous years’ data on the same schemes and
also triangulating with the Risk Equalisation Fund data
submissions. Risk-equalisation is a mechanism that was
proposed for achieving equity and efciency in regulated
private health insurance markets. (The Risk Equalisation
Fund has been operating in shadow mode since 2005, with
data being collected from schemes but no money changing
hands; it was scheduled to be implemented 2012–2013.) All
of the adjusted cases were denoted with the sufx ‘2’, and
these were compared to the reported data. The median in
each plot was denoted with the prex ‘M’ (Figure 2).
A measurable deviation between reported cases and adjusted
cases was noted. The most prevalent non-responses were
cases of HIV, cardiac failure and hypothyroidism. In the
rst model, denoted by ML1, we employed a rule of thumb
where all non-reported cases of less than ve were replaced
by the median. The second model, denoted ML2, is where
all reported cases less than the 50th percentile for chronic
prevalence were replaced by the median. In the third and last
model, denoted ML3, all reported cases smaller than the 10th
percentile were replaced by the median. Capping for all three
models was at the 90th percentile. Comparative statistics on
the results of the three tted models are discussed in the
next section.
Modelling prevalence of chronic diseases associated with
primary healthcare visits
All three criteria for assessing goodness of t suggested
that ML3 was a better t for modelling GP visits, and the
test statistics conrming this are Chi-square = 26.14, p = 0.0249
(Table 4). Results obtained from tting this model are
presented (Table 5). Regression results for ML3 revealed that
scheme size, asthma, type 2 diabetes, hyperlipidaemia and
hypertension were signicantly associated with GP visits.
TABLE 3: Age stratum of beneciaries in schemes.
Beneciaries <35 years
(N = 39)
≥ 35 years
(N = 70)
p-value
Average number of GP visits per
year
3.2 2.5 0.6
Prevalence of select chronic diseases per 1000 beneciaries
Asthma 25.7 31.1 0.0*
Cardiac failure 5.3 8.6 <0.0001*
Coronary artery disease 11.3 30.1 0.0*
Type 1 diabetes 6.3 7.4 0.1
Type 2 diabetes 25.2 42.8 <0.0001*
Epilepsy 6.4 9.7 <0.0001*
HIV 7.2 6.1 0.3
Hyperlipidaemia 38.4 91.3 <0.0001*
Hypertension 89.9 184 <0.0001*
Hypothyroidism 13.7 36.1 <0.0001*
Benets paid per beneciary per month (ZAR)
GPs 58.7 49.5 0.3
Total hospitals 293 397.3 <0.0001*
Total benets 790.1 995.8 <0.0001*
*, p < 0.05; 1 ZAR/$ = 8.8
FIGURE 1: Prevalence rates of selected chronic diseases by General Praconer visit stratum at scheme level.
140.00
120.00
100.00
80.00
60.00
40.00
20.00
0.00
Per 1000 beneciaries
Average number of visits
Asthma
Cardiac Failure
Coronary artery disease
Diabetes type 1
Diabetes type 2
Epilepsy
HIV
Hyperlipidaemia
Hypertension
Hypothyroidism
29.06
24.15
7.51 4.29
19.96
8.08 4.70
26.25
31.05
8.08 5.96 3.19
11.14
60.24
117.71
96.81
21.63
13.94
> 3 visits
< 3 visits
9.33
35.54

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a
f
h
j
FIGURE 2: Graphic representaon of the selected chronic diseases (median vs. reported vs. adjusted prevalence rates).
Medical schemes unique id Medical schemes unique id
Medical schemes unique id Medical schemes unique id
Medical schemes unique id Medical schemes unique id
Medical schemes unique id Medical schemes unique id
Medical schemes unique id Medical schemes unique id
Prevalence per 1000 beneciaries
Prevalence per 1000 beneciaries
Prevalence per 1000 beneciaries
Prevalence per 1000 beneciaries
Prevalence per 1000 beneciaries
Prevalence per 1000 beneciaries
Prevalence per 1000 beneciariesPrevalence per 1000 beneciaries
Prevalence per 1000 beneciariesPrevalence per 1000 beneciaries
45.0
40.0
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0
140.0
120.0
100.0
80.0
60.0
40.0
20.0
0.0
1
11
21
31
41
51
61
71
81
91
101
hiM(hi) hiv2
25.0
20.0
15.0
10.0
5.0
0.0
b
1
11
21
31
41
51
61
71
81
91
101
ep2M(ep) ep
t2d2M(t2d) t2d
100.0
90.0
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
1
11
21
31
41
51
61
71
81
91
101
40.0
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0
d
1
11
21
31
41
51
61
71
81
91
101
t1d2M(t1d) t1d
hyper 2M(hyper) hyper
180.0
160.0
140.0
120.0
100.0
80.0
60.0
40.0
20.0
0.0
1
11
21
31
41
51
61
71
81
91
101
350.0
300.0
250.0
200.0
150.0
100.0
50.0
0.0
hyper 2M(hyper) hyper
c
1
11
21
31
41
51
61
71
81
91
101
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
hypo 2M(hypo) hypo
cad 2M(cad) cad
90.0
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
1
11
21
31
41
51
61
71
81
91
101
1
11
21
31
41
51
61
71
81
91
101
cf 2M(cf) cf
1
11
21
31
41
51
61
71
81
91
101
30.0
25.0
20.0
15.0
10.0
5.0
0.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
1
11
21
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e
g
ia2M(a) a

Original Research
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Results indicate that average age of beneciaries at scheme
level, scheme type, and prevalence of cardiac failure,
coronary artery disease, type 1 diabetes, epilepsy, HIV and
hyperlipidaemia are not signicant in terms of average
number of GP visits (Table 5). They further illustrated that
those on small schemes were likely to have more than three
visits compared to those on medium schemes (OR = 0.16;
95% CI = 0.039–0.661); otherwise there were no signicant
differences between small and large schemes.
The data further showed that prevalence of asthma was
significantly associated with GP visits (OR = 1.081; 95%
CI = 1.008–1.159). Thus, asthma prevalence rates were likely
to be associated with more than three GP visits, similar to the
prevalence of type 2 diabetes, with OR = 1.087; 95% CI = 1.027–
1.152. The prevalence of hyperlipidaemia (OR = 0.92; 95%
CI = 0.875–1.644) was more likely to be associated with less
than three GP visits. Lastly, prevalence rates for hypertension
were likely to be associated with more than three GP visits
per year (OR = 1.132; 95% CI = 1.017–1.26).
Discussion
Chronic illnesses greatly impact on the patients’ way of
life and require ongoing monitoring and management.
Proactively managing such illnesses through educational
and continuous monitoring methods could certainly improve
the health status of the country. A survey by Seghieri et al.34
conrmed that informing patients about their care and how
to manage condition-related symptoms may lead to more
effective chronic disease management and improved health
status. Informing patients about their care should incorporate
effectively communicating to patients or beneciaries as to
their benet entitlement, particularly PMBs.
The Medical Schemes Act35 requires that limitation on disease
coverage be developed on the basis of evidence-based
medicine. For instance, some schemes specify in their rules
that patients who suffer from asthma and use chronic
medication are entitled to a treatment plan that allows them
two visits to a pulmonologist per year; two visits to a GP or
physician; and tests such as peak-ow evaluations. All these
data should be communicated to the member, as should the
implication of not registering on a scheme’s chronic disease
management programme as per scheme rules, and how this
could affect their day-to-day benets.
The average number of GP visits in the private medical
schemes data in 2009 was three.36 This is slightly lower than
the Canadian average (Canadian patients visit doctors more
often than Australian patients, making 4.9 GP visits annually
compared to 2.3–3.6 in Australia3); however, Harris4 states
that on average Australians visit a GP ve times per year.
According to an HLC Financial Services publication, one of
the biggest open schemes in South Africa covers four GP
consultations per year for each approved chronic disease.37
Our study showed that the prevalence of asthma was
signicantly associated with more than the average of three
annual visits to a GP (OR = 1.081; 95% CI = 1.008–1.159; p =
0.0291). These results are consistent with the data analysed
by Barnes38, where it was recommended that patients with
mild asthma required three to ve visits to their GP annually.
Their study further illustrated that individuals with moderate
asthma appeared to contribute more to the burden of asthma
care than those with severe asthma.
Our study also revealed a signicant association between
prevalence of type 2 diabetes and the average number of GP
visits per year (OR = 1.087; 95% CI = 1.027–1.152, p = 0.0041).
Thus, beneciaries with type 2 diabetes were likely to make
more than three visits to a GP. These results are consistent
with the literature; for instance, a study by Johnson, Rabi,
Edwards and Balko39 showed that adults with diabetes made
more than nine GP visits on average, whilst those with no
diabetes made just over ve. Another study by Bottomley
and the T2ARDIS Steering Committee40 showed that patients
with type 2 diabetes visited their GP on average ve times a
year, and the GP visited them at home once every two years.
Rutten, Van Eijk, De Nobel, Beek and Van der Helden41
studied the relationship between the number of clinic visits
for diabetes patients and changes in blood glucose control;
their study illustrated that at the frequency of two visits per
year, HbAl decreased in 31% of patients, with three or four
visits in 35%, and with ve or more in 79% of patients (p < 0.005).
Our study also revealed a signicant association between
hyperlipidaemia and primary healthcare use. This is
consistent with the literature; Eaton et al.42 state that family
physicians have potential to make a major impact on reducing
the burden of cardiovascular disease through the optimal
assessment and management of hyperlipidemia. Their study
also found that the frequency of primary care visits seemed
to be fairly uniform for both well-controlled (average 2.2
TABLE 4: Summary of ng predictors of primary health care use.
Criterion ML1 ML2 ML3
-2 Llog likelihood 106.366 111.401 99.39
AIC (smaller is beer) 136.366 141.401 129.39
SC (smaller is beer) 176.736 181.771 169.76
AIC, Akaike Informaon Criterion; SC, Schwarz Criterion are criterion for the measure of the
relave goodness of t of a stascal model; ML1, Mixed Linear Model 1; ML2,Mixed Linear
Model 2; ML3, Mixed Linear Model 3.
TABLE 5: Mulvariate logisc regression results for predicon.
Covariates OR 95% LCI 95% UCI p - value
Scheme type
(restricted vs. open)
2.758 0.759 10.02 0.1232
Age stratum (> 35 vs. <35 ) 2.656 0.563 12.54 0.2174
Scheme size (large vs. small) 0.333 0.093 1.19 0.0907*
Scheme size (medium vs. small) 0.161 0.039 0.661 0.0343**
Asthma 1.081 1.008 1.159 0.0291**
Cardiac failure 1.108 0.893 1.375 0.3522
Coronary artery disease 0.947 0.862 1.04 0.2537
Type 1 diabetes 0.941 0.849 1.044 0.2543
Type 2 diabetes 1.087 1.027 1.152 0.0041**
Epilepsy 1.199 0.875 1.644 0.2597
HIV 1.049 0.967 1.138 0.252
Hyperlipidaemia 0.92 0.87 0.973 0.0037**
Hypertension 1.132 1.017 1.26 0.0233**
Hypothyroidism 0.997 0.975 1.019 0.7605
* p < 0.1; ** p < 0.05, OR, odds rao; LCI, lower condence interval; UCI, upper condence
interval; ≤3 visits versus 3+ visits.

Original Research
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Page 8 of 9
visits per year) and uncontrolled hyperlipidaemic (4.2 visits
per year) patients. Lastly, our study revealed a signicant
association between hypertension and GP visits (OR = 1.132;
95% CI = 1.017–1.26).
Limitaons of the study
One of the limitations of the study is that risk factors
associated with chronic diseases were not explored. These
include tobacco use, obesity or diet, hypercholesterolaemia,
alcohol abuse, sedentary lifestyle and certain infectious
diseases.43 Another limitation is that we did not risk-adjust
the reported chronic prevalence for particular age groups,
genders and ethnic groups. Al-Windi44 has shown that a
higher proportion of females than males had one to ve
or more than ve GP consultations per year. According to
Polisson,45 demand for GP visits is most likely driven by
health status and, for women, childbirth.
It is also known that some chronic diseases are more prevalent
in certain age groups and genders; hypothyroidism, for
example, is more common in older persons, especially women,
principally due to the rising incidence and prevalence of
auto-immune thyroiditis.46 A study by Pillar, Levy, Holcberg
and Sheiner47 showed that treated hyperthyroidism was
not associated with adverse perinatal outcome; however,
hyperthyroidism was found to be an independent risk
factor for caesarean delivery. Hyperthyroidism is common,
affecting approximately 2% of women and 0.2% of men.48
This further emphasises the importance of risk factors and
risk adjustments to get a more holistic and better perspective
of the results.
Lastly, data was analyses were at scheme level; a wide-
ranging assessment of chronic diseases and primary
healthcare benets at benet option level could certainly
enhance the ndings of the current study. However, it was
illustrated during the Risk Equalisation Fund shadow period
that even though benet options differ in design, the CDL is
about the same in each option.24
Recommendaons
Recommendations arising from the current study are that
primary healthcare services have an essential role in the
private health sector, in particular in managing chronic
disease. The results obtained and this study adds value
to managed care interventions employed by schemes in
advocating more awareness, educating members and
continuous monitoring of chronic diseases. This proactive
approach is vital for avoiding hospitalisations.
Other factors were not taken into account in this study,
such as risk factors and risk adjustments; however, it is
recommended that patients with chronic conditions visit
their GP frequently to identify specic problems that need
more attention and can be better managed sooner. Some of
the select chronic diseases need more attention than others;
also, the severity of the condition impacts on number of visits
to a GP. All these considerations should be taken into account
when designing protocols and guidelines for provision of
benets. Furthermore, there is a need to review protocols
employed by the schemes for provision of PMBs, to ensure
that these are consistent with recent best practice and comply
with the Medical Schemes Act, in particular Regulation 15.
Schemes need to educate members on their benet
entitlement, in particular chronic benets, and also on
the consequences of not registering on chronic diseases
programmes. Protocols and guidelines used as clinical risk
measurement tools should be communicated to members;
these should also outline the minimum standards required to
control or manage the conditions. Such protocols and clinical
risk measures should not compromise the health status of
beneciaries for cost-effectiveness.
Conclusion
The current study employed MI to account for missing data
and outliers. This method allowed for a more complete set of
data, to enhance the results of the multiple regression analysis
model. Using these statistical methods to deal with the
shortcomings of the data from medical schemes, we showed
that scheme size, asthma, type 2 diabetes, hyperlipidaemia
and hypertension were related to the annual number of GP
visits. Some of the key chronic diseases considered in the
current study were found not to have a signicant link with
number of GP visits, an indication that estimating the effect
of chronic disease on health service use is complex.
These results illustrate the minimum number of visits
required to manage select chronic diseases. The ndings of
the current study further enhance the role of primary health
care and preventative measures employed by managed
care entities as an effective tool to effectively avoid costly
hospitalisation.
Acknowledgements
The authors are grateful to Council for Medical Schemes
staff members for discussions and valuable comments in
concluding this research work.
Compeng interests
The authors declare that there are no nancial or
personal relationships which may have inuenced them
inappropriately in writing this article.
Authors' contribuons
The authors were responsible for data analysis and drafted
the article. The authors proofread the nal manuscript.
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