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CHAPTER 8CARDIOVASCULAR DISEASE PREVENTION POLICY IN HUMAN IMMUNODEFICIENCY VIRUS: RECOMMENDATIONS FROM A MODELING STUDY

Mikaela Smit*Rosan A van Zoest*Brooke E NicholsIlonca VaartjesColette SmitMarc van der ValkArd van SighemFerdinand W WitTimothy B HallettPeter Reiss

On behalf of the Netherlands AIDS Therapy Evaluation inThe Netherlands (ATHENA) Observational HIV Cohort

Clinical Infectious Diseases 2018; 66: 743-750.doi: 10.1093/cid/cix858.

* both authors contributed equally


| Chapter 8170

ABSTRACT

Background

Cardiovascular disease (CVD) is expected to contribute a large non-communicable

disease burden among human immunodeficiency virus (HIV)-infected people. We

quantify the impact of prevention interventions on annual CVD burden and costs

among HIV-infected people in the Netherlands.

Methods

We constructed an individual-based model of CVD in HIV-infected people using

national ATHENA (AIDS Therapy Evaluation in the Netherlands) cohort data on

8,791 patients on combination antiretroviral therapy (cART). The model follows

patients as they age, develop CVD (by incorporating a CVD risk equation), and start

cardiovascular medication. Four prevention interventions were evaluated: (1) increasing

the rate of earlier HIV diagnosis and treatment; (2) avoiding use of cART with increased

CVD risk; (3) smoking cessation; and (4) intensified monitoring and drug treatment of

hypertension and dyslipidemia, quantifying annual number of averted CVDs and costs.

Results

The model predicts that annual CVD incidence and costs will increase by 55% and 36%

between 2015 and 2030. Traditional prevention interventions (ie, smoking cessation

and intensified monitoring and treatment of hypertension and dyslipidemia) will avert

the largest number of annual CVD cases (13.1% and 20.0%) compared with HIV-

related interventions—that is, earlier HIV diagnosis and treatment and avoiding cART

with increased CVD risk (0.8% and 3.7%, respectively)—as well as reduce cumulative

CVD-related costs. Targeting high-risk patients could avert the majority of events and

costs.

Conclusions

Traditional CVD prevention interventions can maximize cardiovascular health and

defray future costs, particularly if targeting high-risk patients. Quantifying additional

public health benefits, beyond CVD, is likely to provide further evidence for policy

development.


Cardiovascular Prevention Policy in HIV | 171

8

BACKGROUND

As life expectancy of people living with human immunodeficiency virus (PLHIV)

on effective combination antiretroviral therapy (cART) has improved (1), human

immunodeficiency virus (HIV) care focus is shifting from treatment and prevention of

opportunistic infections to that of noncommunicable diseases (NCDs). Cardiovascular

disease (CVD) is predicted to contribute one of the greatest NCD burdens among

PLHIV on cART (2). CVD prevalence and lifetime CVD risk appear to be higher in

PLHIV than in HIV-uninfected controls (3–5).

Although cardiovascular pathophysiology in the HIV context is not yet fully

understood, likely mechanisms include complex interactions between traditional (eg,

smoking), and HIV-related (eg, greater HIV-related inflammation linked to cART

initiation at low CD4 counts) risk factors (6,7). Late HIV diagnosis and thus low

CD4 count at cART initiation remains a problem in many European countries (8,9),

despite new HIV guidelines recommending immediate cART initiation (10). The

prospective observational Data Collection on Adverse Effects of Anti-HIV Drugs

(D:A:D) Study carried out one of the most detailed analysis of modifiable CVD risk

factors in >49,000 PLHIV from 11 cohorts across 212 clinics in Europe (including

the Netherlands), Argentina, Australia, and the United States, combining them into

a risk model predicting 5-year CVD risk (11). These factors include smoking, total

and high-density lipoprotein (HDL) cholesterol, systolic blood pressure, current CD4

count, current abacavir use, and cumulative exposure to protease inhibitors (PIs) and

nucleoside reverse transcriptase inhibitors, suggesting these could be key targets for

CVD prevention interventions in HIV care.

Prevention interventions will need to be firmly integrated into HIV care, and supported

by evidence-based studies to effectively mitigate the emerging CVD burden, mortality

risk, and impact on quality of life of PLHIV. Yet no studies have systematically

compared the impact of different CVD prevention interventions on CVD burden in

PLHIV, who are at CVD risk due to both traditional and HIV-related factors. We

adapt an existing model of aging PLHIV on cART (2) to explore the impact of CVD

prevention interventions and evaluate which one would maximize cardiovascular

health. Interventions include HIV-related interventions (earlier HIV diagnosis and

treatment and avoiding use of cART regimens with increased CVD risk) and traditional

interventions (smoking cessation and intensified monitoring and drug treatment of

hypertension and dyslipidemia).


| Chapter 8172

METHODS

Model

We adapted an existing individual-based model of aging PLHIV in the Netherlands to

generate detailed predictions of CVD (2). Figure 8.1 shows the basic model structure,

with technical details in Supplement 8.1. The model follows PLHIV on cART from

2010 to 2030, and probabilistically simulates clinical events (hypertension, dyslipidemia,

diabetes, CVD, and death) and CVD treatment initiation. CVD includes myocardial

infarction, stroke, coronary artery angioplasty, coronary bypass, carotid endarterectomy,

and CVD-related death.

The model is populated with all 8,791 PLHIV on cART in care and registered with

the Dutch national ATHENA (AIDS Therapy Evaluation in the Netherlands) cohort

in 2010 aged ≥18 years, infected with HIV type 1 (HIV-1) only, cART naïve at entry,

who initiated cART from 1996 (baseline patients, Figure 8.1A) by directly importing

anonymized patient records into the model. Multiple imputation by chained equation

was used to deal with missing data (MICE package, R Foundation for Statistical

Computing, version 3.2.2), creating 20 imputed datasets. Values were assumed to be

randomly missing, and extensive checks were carried out (Supplement 8.1).

The majority of ATHENA patients were virologically suppressed Dutch men who have

sex with men, and median age was 44 years (Table 8.1). In 2010, 44.9% of moderate-

to high-risk (HR, ie, predicted 5-year CVD risk ≥5%) patients with hypertension

used antihypertensive medication, of whom 7.9% reached blood pressure levels of

<120/80 mmHg. In HR patients with dyslipidemia, 32.5% used lipid-lowering

medication, of whom 61.1% reached a total/HDL cholesterol ratio of <5 (Table 8.1).

All clinical definitions were based on European guidelines (10,12) and consultation

with cardiovascular specialists; hypertension was defined as systolic blood pressure ≥140

mmHg, diastolic blood pressure ≥90 mmHg, and/or use of antihypertensive medication;

dyslipidemia was defined as total cholesterol >6.5 mmol/L (>250 mg/dL), total/HDL

cholesterol ratio >5, and/or use of lipid-lowering medication, and CVD was based on

clinical patient records.

Each month, new simulated patients joined the modeled cohort representing cART

initiators (Figure 8.1A). New patients’ profiles were randomly selected from baseline

patients, accounting for predicted changes in age up to 2030, CD4 cell count at cART

initiation up to 2015, and changes in treatment guidelines in 2014 (use of integrase

inhibitors for first-line cART) (2,13,14).



8

2010 2030

+ 1 month

Model check Interventions

2010-2015 2017-2030

ATHENA

cART

Icons made by Freepik from www.flaticon.com

D:A:D risk equation (Friis-Møller et al 2016)

DemographicAge

GenderClinicalDiabetes

Family historySmoking (current/former)

Total cholesterolHDL cholesterol

Systolic blood pressureHIV-related

Current CD4 countAbacavir use

Cumulative PI exposureCumulative NRTI exposure

DEMOGRAPHICS

CLINICAL FACTORS

HIV-RELATED FACTORS

CARDIOVASCULAR MEDICATION

A

B C

FIGURE 8.1. MODEL DESIGN. A, Illustrates the basic model structure. The model starts on 1 January 2010 with all human immuno-

deficiency virus (HIV)–infected individuals on combination antiretroviral therapy (cART) in care and

registered with the ATHENA cohort aged ≥18 years old, infected with HIV-1 only, cART naive prior

to entering the cohort, who had initiated cART after 1 January 1996 (depicted in red). Each month, the

model simulated calendar events (eg, aging), new events in a probabilistic manner (eg, deaths and new

cardiovascular events), and new patients are added as they start cART (depicted in blue). When the

model stops on 31 December 2030, the modeled patient cohort contains all patients from 2010 who are

still alive as well as new patients who have entered care between 1 January 2010 and 31 December 2030

and who are still alive. The period from 2010 to 2015 is used to carry out out-of-sample model validation,

and interventions targeting cardiovascular disease are simulated between 2017 and 2030. B, Shows the

risk factors included in the D:A:D risk equation. C, Illustrates the interactions simulated in the model

between demographic (red), HIV-related (green), cardiovascular treatment (yellow), and clinical factors

(blue), for example, how the risk of death is determined by age, sex, and clinical factors. The dashed lines

show interactions between individual factors and solid lines between a category and a specific risk factor.

Abbreviations: ART, antiretroviral therapy; ATHENA, AIDS Therapy Evaluation in The Netherlands;

cART, combination antiretroviral therapy; CVD, cardiovascular disease; D:A:D, Data Collection on Ad-

verse Effects of Anti-HIV Drugs; HDL, high-density lipoprotein; HIV, human immunodeficiency virus;

NRTI, nucleoside reverse transcriptase inhibitor; PI, protease inhibitor.


| Chapter 8174

TABLE 8.1. COHORT CHARACTERISTICS OF HUMAN IMMUNODEFICIENCY VIRUS–INFECTED INDIVIDUALS ON COMBINATION ANTIRETROVIRAL THERAPY, ALIVE AND ENROLLED IN THE ATHENA COHORT ON 1 JANUARY 2010

Characteristic

ATHENA

Cohort Populationa

(n = 8,791)

Demographic characteristics

Age, years, median (IQR) 43.8 (37.4-50.6)

Male sex 6,851 (77.9%)

HIV transmission risk categoryb

Men who have sex with men 4,619 (52.5%)

Heterosexual 3,183 (36.2%)

Injecting drug user 137 (1.6%)

Other or unknown 851 (9.7%)

Region of originb

Netherlands 4,901 (55.8%)

Sub-Saharan Africa 1,583 (18.0%)

Europe 676 (7.7%)

Other 1,630 (18.5%)

Smoking status b

Never smoker 1,302 (23.2%)

Ex-smoker 3,217 (57.4%)

Current smoker 1,090 (19.4%)

Diabetes prevalenceb 373 (4.2%)

Cardiovascular treatment in patients with moderate to high CVD risk

(predicted 5-y CVD risk ≥5%)

(n=2,895)

Hypertensionc 1,469 (50.7%)

Receiving antihypertensive medication 660 (44.9%)

Blood pressure < 120/80 mmHg among those receiving antihypertensive medication 52 (7.9%)

Dyslipidemiad 1,521 (52.5%)

Receiving lipid-lowering medication 494 (32.5%)

Total/HDL cholesterol ratio ≤5 among those receiving lipid-lowering medication 302 (61.1%)

HIV-related characterstics (n=8,791)

Time on combination antiretroviral therapy, years, median (IQR) 4.1 (1.6-7.5)

HIV RNA <200 copies/mL among cART-treated patients in the year prior to enrollmentb 7,477 (86.7%)

Prior diagnosis of AIDS 2,394 (27.2%)

Nadir CD4 cell countb, cells/μL, median (IQR) 180 (74-270)

Current CD4 cell count, cells/μL, median (IQR) 500 (362-660)

Data are presented as No. (%) unless otherwise indicated.Abbreviations: ATHENA, AIDS Therapy Evaluation in The Netherlands; cART, combination antiretroviral therapy; CVD, cardiovascular disease; HDL, high-density lipoprotein; HIV, human immunodeficiency virus; IQR, interquartile range.aThe model is populated with all 8,791 HIV-infected individuals on cART in care and registered with the ATHENA cohort aged ≥18 years old, infected with HIV-1 only, cART naive prior to entering the cohort, who had initiated cART after 1 January 1996.bData for some individuals were missing on 1 January 2010 and hence not included in this table.cHypertension was defined as systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg, and/or use of antihypertensive medication.dDyslipidemia was defined as total cholesterol >6.5 mmol/L (250 mg/dL), total/HDL cholesterol ratio >5, and/or use of lipid-lowering medication.



8

CVD was simulated using a CVD risk equation, developed in PLHIV by the D:A:D

study (11). The equation accounts for demographic factors, traditional risk factors, and

HIV-related risk factors to calculate 5-year CVD risk (Figure 8.1B). Constant risk

was assumed within the 5-year timeframe to convert to monthly risks. CVD type was

determined using the reported CVD distribution (11).

The model accounts for a large number of interactions between demographic (Figure

8.1C), HIV-related (Figure 8.1C), therapy-related (Figure 8.1C), and clinical factors

(Figure 8.1C) – for example, the impact of age, sex, and clinical factors on mortality, or

sex, age, use of lipid-lowering drugs, and cART regimen on cholesterol. For example,

the model includes sex-specific monthly increments in blood pressure and cholesterol

per 10-year age group based on the Dutch Doetinchem Cohort Study data (15),

cholesterol changes triggered by cART switches based on an extensive literature review,

and blood pressure and cholesterol changes triggered by treatment based on ATHENA

data (Supplement 8.1).

Model performance was evaluated via out-of-sample checks against 2010-2015

ATHENA data (Supplement 8.1). CVD burden was projected for 2010-2030,

assuming demographic, epidemic, and clinical trends remain constant while accounting

for predicted increasing CD4 count trends at cART initiation, and current levels in

treatment of hypertension and dyslipidemia (baseline scenario). The impact of CVD

prevention interventions was evaluated for 2017-2030, by comparing with baseline.

Model results were based on the average of 100 simulations, consisting of running each

of the 20 imputed dataset 5 times.

The model was used to evaluate the following interventions:

HIV-related interventions:

1. Increasing the rate of earlier HIV diagnosis and treatment (ie, initiating cART at

CD4 counts ≥500 cells/μL)

2. Avoiding use of cART regimens with increased CVD risk (as defined by the D:A:D

risk equation, ie, switching off/not initiating abacavir [ABC] and PIs)

Traditional CVD interventions:

1. Increasing smoking cessation

2. Intensified monitoring and treatment of hypertension and dyslipidemia with

antihypertensive and lipid-lowering drugs (to ensure patients achieve target blood

pressure [<120/80 mmHg] and cholesterol levels [total/HDL cholesterol ratio of

≤5]).


| Chapter 8176

In addition, we evaluated the effect of implementing all interventions jointly. For each

intervention, we simulated the impact of reaching (1) all patients in care or HR patients

only (except reducing “late” presenters, which was not evaluated for HR patients

separately); or (2) 100% or 50% of patients successfully via the interventions.

For example, the use of cART regimens with no known increased CVD risk at

50% successful implementation switched 50% of patients off ABC and PIs, while

the remainder continued on their cART regimens, including ABC and PIs. For

the intensified monitoring and treatment of hypertension and dyslipidemia at 50%

successful implementation, all patients with hypertension and dyslipidemia were

prescribed treatment, but only 50% reached target blood pressure and cholesterol levels,

while the remainder reached blood pressure and cholesterol levels currently observed in

ATHENA among patients on antihypertensive and lipid-lowering medication for ≥6

months (ie, some patients do not reach target levels and some do).

Economic evaluation

An economic evaluation quantified the annual costs of CVD care in PLHIV between

2015 and 2030. In addition, annual CVD-related cost reductions were calculated for

each intervention in 2017-2030. Economic evaluations accounted for lifetime CVD-

related costs incurred; these comprised costs related to hospitalization, diagnostics,

surgical procedures, and outpatient visits. Costs relating to intervention costs of

medication were not included in the analysis. Cost sources included Dutch databases of

healthcare, and literature, and costs were discounted at 3% per year (Supplement 8.1).

RESULTS

Predicted CVD burden and effect of prevention interventions

Annual CVD incidence is predicted to increase from 6.0 to 9.3 events per 1,000 person

years between 2015 and 2030. Prevalence of ever having had CVD will increase from

2.7% to 11.4%, and the proportion of HR patients from 14.1% to 32.4%, between 2015

and 2030 (Figure 8.2).

All interventions tested are expected to reduce CVD burden by 2030. Traditional CVD

interventions are predicted to have greater impact, and targeting HR patients would

avert a majority of CVD cases (Figure 8.3). Earlier HIV diagnosis and treatment will

have the smallest effect on CVD burden, preventing on average 0.1%-0.8% of CVD

cases annually, assuming 50%-100% successful implementation. Avoiding the use of

cART regimens with increased CVD risk could avert an average of 1.7%-3.7% of CVD



8

cases annually in all patients and 0.3%-1.5% of CVD cases in HR patients, assuming

50-100% successful implementation.

FIGURE 8.2. PREDICTED CARDIOVASCULAR DISEASE (CVD) RISK BETWEEN 2010 AND 2030. The proportion of human immunodeficiency virus–infected individuals on combination antiretroviral

therapy in low (<5%, green), moderate (5%–10%, yellow), and high (≥10%, red) predicted 5-year CVD

risk groups between 2010 and 2030 as simulated by the model using the D:A:D risk equation.

Smoking cessation could prevent an average of 6.0%-13.1% of CVD cases annually

in all and 5.1%-12.6% of CVD cases in HR patients, assuming 50%-100% successful

implementation. Intensified monitoring and treatment of hypertension and dyslipidemia

could have the greatest impact on future CVD burden, averting an annual average of

17.0%-20.0% of CVD cases in all and 11.9%-15.9% of CVD cases in HR patients,

assuming 50%-100% successful implementation.

Implementing all interventions simultaneously could avert an average of 20.2%-30.5%

of CVD cases annually in all, and 13.1%-20.4% of CVD cases in HR patients, assuming

50%-100% successful implementation. Targeting HR patients only is likely to reduce

the majority of CVD cases, particularly in smoking cessation, intensified monitoring

and treatment of hypertension and dyslipidemia, and a joint intervention program

(averting 96%, 80%, and 67%, respectively, compared with targeting all patients when

assuming 100% successful implementation).

Annual CVD-related care costs for PLHIV are estimated to increase by 36%, from

€471,000 in 2015 to €642,000 in 2030, in absence of additional CVD prevention


| Chapter 8178

FIGURE 8.3. AVERAGE ANNUAL PERCENTAGE OF AVERTED CARDIOVASCULAR DISEASE (CVD) CASES. Averages per intervention are calculated based on annual reductions in CVD cases as simulated by the

model between 2017 and 2030 in all patients (A) and interventions limited to those at moderate to

high CVD risk only (B). Interventions include (1) reducing the number of “late” presenters (ie, initiate

combination antiretroviral therapy [cART] at CD4 counts >500 cells/μL); (2) use of cART with no

known increase in CVD risk; (3) smoking cessation; (4) intensified monitoring and drug treatment

of hypertension and dyslipidemia; (5) joint intervention, in which all interventions are implemented

simultaneously. Reduction of late presenters was only evaluated in all patients, not in high-risk patients

separately. The dark-colored area represents percentage averted assuming 50% successful implementation

and the light-colored area for 100% successful implementation. Abbreviations: cART, combination

antiretroviral therapy; CVD, cardiovascular disease; HIV, human immunodeficiency virus.

0

5

10

15

20

25

30

35

Earlier HIVdiagnosis and

treatment

Avoiding cARTwith

increased CVDrisk

Smokingcessa on

Monitoring/treatment ofhypertension

anddyslipidaemia

Jointinterven on

Ave

rage

ann

ual p

erce

ntag

e re

duc

on in

CVD

ca

ses

A

0

5

10

15

20

25

Avoiding cARTwith increased

CVD risk

Smokingcessa on

Monitoring/treatment ofhypertension

anddyslipidaemia

Jointinterven on

Aver

age

annu

al p

erce

ntag

e re

duc

on in

CVD

ca

ses

B



8

interventions. This is despite HIV costs being predicted to decrease by 13% in the same

period. Cumulative CVD-related costs in 2017-2030 could decrease by €63,000 (0.8%)

with earlier HIV diagnosis and treatment, €308,000 (3.7%) by avoiding the use of

cART regimens with increased CVD risk, €1,093,000 (13.1%) with smoking cessation,

€1,670,000 (20.0%) with intensified monitoring and treatment of hypertension and

dyslipidemia, and €2,539,000 (30.5%) with a joint intervention, assuming all patients

are targeted 100% successfully. The reduction in CVD-related costs is largely driven

by HR patients. Cumulative CVD-related costs, for example, could be reduced by

€1,330,000 for intensified monitoring and treatment of hypertension and dyslipidemia,

or 80% of the costs reduction achievable by targeting all PLHIV.

DISCUSSION

The growing burden of CVD among PLHIV in Europe will necessitate evidence-based

prevention interventions to mitigate the impact on mortality, quality of life, clinical care,

and healthcare budgets. The interventions considered in this study could jointly avert

30.5% of CVD events. Based on the output of this work, we recommend that CVD

prevention policy in HIV care focus on traditional CVD prevention interventions,

particularly intensifying the monitoring and successful treatment of hypertension and

dyslipidemia. Such an intervention could most likely maximize cardiovascular health and

reduce CVD-related costs among the set of CVD prevention interventions evaluated

in this study. Targeting HR patients could avert the majority of CVD events and costs,

averting 80% of CVD events and costs between 2017 and 2030, while targeting only

14.1%-32.4% (2015-2030) of PLHIV. Greater impact could be achieved if rolling out

a program implementing all interventions simultaneously and targeting all PLHIV,

though likely at much higher expected programmatic costs.

Our findings are in line with preliminary work in PLHIV showing that dyslipidemia,

hypertension, and smoking had much larger population attributable fractions for

myocardial infarctions than HIV-related factors (16). Intensification of monitoring

and treatment of hypertension and dyslipidemia should be feasible, as measurement

and management of blood pressure and cholesterol are standard in HIV care in the

Netherlands. Current European and Dutch HIV guidelines recommend annual

CVD risk assessment and treatment of hypertension (in patients with high CVD

risk) and dyslipidemia (in patients with moderate to high CVD risk) (10,17). CVD

risk assessment, utilizing for example the D:A:D equation, provides a simple and

inexpensive tool to identify patients at elevated CVD risk. Yet, in 2010, 55%-68% of

Dutch PLHIV at moderate to high CVD risk remain untreated and the majority did


| Chapter 8180

not reach optimal blood pressure or cholesterol levels (Table 8.1). Suboptimal CVD risk

management has been described in other settings (18,19). The proposed intervention

would therefore focus on strengthening implementation of current HIV guidelines,

utilizing existing frameworks, and addressing gaps including undiagnosed and/or

undertreated hypertension and dyslipidemia. Further research is needed to identify

innovative strategies to support patients and healthcare providers in addressing these

gaps, for which mobile phone- or web-based interventions might be promising tools.

Smoking cessation is also predicted to substantially impact CVD burden. However,

our assumption that 50%-100% of individuals will successfully cease to smoke is bold

considering previous smoking cessation studies (20–23). In sensitivity analyses of

more realistic estimates (ie, 10% smoking cessation) an average of 1.3% of CVD cases

annually were averted in all PLHIV (Supplement 8.1). The low success rates of smoking

cessation despite tailored interventions and the modest effect of a more realistic smoking

cessation scenario on CVD burden means smoking cessation might not be the most

effective and feasible CVD prevention intervention, despite its large theoretical impact.

Studies suggest that apart from their lipid-lowering properties, statins may also act as

inflammation modulators (24). In PLHIV, in whom immune activation/dysfunction

can persist despite viral suppression, studies have shown that statins can reduce certain

markers of inflammation and immune activation (25) as well as subclinical CVD (26).

The Randomized Trial to Prevent Vascular Events in HIV (REPRIEVE) trial (27) may

quantify the effectiveness of statins in preventing CVD in PLHIV, and inform further

modeling studies in the field.

The model captures key factors involved in CVD pathophysiology. Extensive

consultations with HIV and CVD physicians ensure the model incorporated key

interactions between factors influencing CVD, ensuring the model captured the natural

progression and aggregation of risk factors. The model created a direct representation

of patients on cART in the Netherlands by directly importing patient records from the

large national non-selective cohort, with parameter values based on large cohort studies

and in-depth literature reviews. Extensive out-of-sample model checks and sensitivity

analyses ensured that the model output was robust.

Despite these strengths, the study has a number of limitations. Some ATHENA data

(eg, family history, smoking status) are collected at entry and not systematically time-

updated. These and other variables (eg, cholesterol levels) can also be incomplete. We

used multiple imputation to handle missing data, assuming variables were missing

randomly; any heterogeneity could introduce bias of the results. Other interventions,

including interventions targeting lifestyle and diet, were not evaluated. These risk



8

factors are historically poorly collected in HIV cohorts and, although their importance

is not disputed, consensus on their relative impact in the context of HIV is yet to be

reached. As information about their relative impact becomes available, the model can

be expanded to evaluate lifestyle interventions. In the absence of robust data in PLHIV,

the model assumes that changes in blood pressure and cholesterol with age are the same

in PLHIV and the general population. Although uncertainty around the superiority of

various CVD risk equations remains (28–31), the D:A:D equation is likely among the

superior tools for CVD risk prediction in PLHIV in Europe. It is the only one specifically

developed in PLHIV, taking into account HIV-related factors. Yet the D:A:D equation

does not distinguish between well and poorly controlled diabetes, nor between CVD

risk of individual PIs (cumulative exposure to all PIs predicted CVD risk better than

individual PIs, despite preliminary work describing that not all contemporarily used

PIs are independently associated with an increased CVD risk (32)). We were therefore

unable to evaluate interventions centred around diabetes, or individual PIs. In addition,

the D:A:D equation does not account for the long-term ‘fading’ effect of stopping PIs

or smoking on CVD risk, although we tested this in sensitivity analyses (Supplement

8.1), with results indicating that the model results remain robust. Additionally, we

have presented the amount of CVD-related costs that could be averted if a program

is 100% successful. It is important to note that these figures do not take into account

any intervention-related costs, as these costs are too uncertain to estimate reliably for

all interventions, as well as have impact beyond CVD. It is likely that many of these

interventions will actually incur costs once programmatic costs are taken into account.

In the model dyslipidemia was defined using total/HDL cholesterol ratio due to poorly

collected low-density lipoprotein cholesterol data. While this might not fully represent

management and recommendations in clinical care, we believe it to be a reasonable

proxy. While 100% successful implementation scenarios served to illustrate the

maximum intervention impact, even 50% successful implementation scenarios could be

overly optimistic – for example, for smoking cessation, with well-documented difficulty

of long-term smoking cessation in PLHIV (20–23).

While the focus of this study is on CVD prevention, it is likely that all interventions

investigated in this study could have wider public health benefits. For example, smoking

cessation could reduce the incidence of other diseases including lung cancer (33),

other malignancies (34), chronic obstructive pulmonary disease (35), and peripheral

atherosclerotic disease (36). Furthermore, reducing “late” presenters could prevent

AIDS, HIV transmission, and NCDs other than CVD (7). Systematically comparing

these interventions with a wide public health perspective, including a cost-effectiveness

analysis, could provide further evidence regarding policy development.


| Chapter 8182

We expect the results to be generalizable to other settings with a long history of access

to cART and a concentrated and mature HIV epidemic. It is not clear how the results

would translate to epidemics with larger proportions of female patients. In settings such

as sub-Saharan Africa, which is also seeing PLHIV aging (37), there is an urgent need

for country-specific estimates of future CVD and NCD burdens to identify effective

intervention and best use of resources, particularly in light of overstretched healthcare

systems.

The CVD burden among PLHIV on cART in Europe is increasing. Intensified

monitoring plus successful treatment of hypertension and dyslipidemia in PLHIV is

expected to be the most feasible intervention accompanied by the largest cardiovascular

health benefit and could safeguard the quality of life of HIV-infected people.

ACKNOWLEDGEMENTS

The authors acknowledge A. Blokstra, H.S.J. Picavet, and W.M.M. Verschuren for

providing access to Doetinchem Cohort Study data, and Luuk Gras for his advice in

analysing changes in CD4 count with time on cART.

FUNDING

The work was supported by the Stichting HIV Monitoring, which is funded by The

Netherlands Ministry of Health, Welfare and Sport through the Center for Infectious

Disease Control of the National Institute for Public Health and Environment. The

funder had no role in the analysis or the decision to publish.

PRESENTED IN PART

This study was presented in part at the 20th International Workshop on HIV and

Hepatitis Observational Databases, Budapest, Hungary, April 2016, and the Conference

on Retroviruses and Opportunistic Infections, Seattle, Washington, United States, 13-

16 February 2017.



8

AUTHORS’ CONTRIBUTIONS

MS formulated the research question, constructed the original model and made all

modifications to the adapted model, analyzed data for model parameterization,

generated the model output, interpreted the results, and co-authored the first draft of

the manuscript. RAvZ contributed to the research question and method development,

carried out data analyses for model modification and parameterization, carried out all

imputations, advised on the scientific and medical aspects of the model adaptation

and interpretation of the results, and co-authored the first draft of the manuscript.

BEN carried out the economic evaluations. CS prepared the ATHENA cohort data

for model population and parameterization. IV advised on the model imputation and

with MvdV, FWW, and PR advised on the medical aspects relating to the methods and

results interpretation. TBH and AvS provided technical help with the model design. All

authors contributed to the redrafting of the manuscript and approved the final draft.


| Chapter 8184

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over Framingham and SCORE algorithms. Eur J Prev Cardiol. 2014 Jun;21(6):739–48.

29. Thompson-Paul AM, Lichtenstein KA, Armon C, Palella FJ, Skarbinski J, Chmiel JS, et al.

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This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Clinical

Infectious Diseases following peer review. The definitive publisher-authenticated version "Smit M*, van

Zoest RA*, Nichols BE, Vaartjes I, Smit C, van der Valk M, van Sighem A, Wit FW, Hallett TB, Reiss

P; Netherlands AIDS Therapy Evaluation in The Netherlands (ATHENA) Observational HIV Cohort.

Cardiovascular Disease Prevention Policy in Human Immunodeficiency Virus: Recommendations From

a Modeling Study. Clin Infect Dis 2018 Feb 10;66(5):743–750. doi: 10.1093/cid/cix858." is available

online at: https://dx.doi.org/10.1093/cid/cix858.



8

SUPPLEMENT 8.1: MODEL TECHNICAL DETAILS

This supplementary material provides technical details of the model design including

parameter values and results of model validation and sensitivity analyses. This model

is an expansion of a previous model looking at the ageing people living with HIV

(PLHIV) in The Netherlands (1), with this supplement explaining the major additions

and modification to the model structure. Additional details on the basic model

structure can be found in the supplement by Smit and colleagues (1). Briefly, the model

is an individual-based model of cardiovascular disease (CVD) in ageing PLHIV on

combination antiretroviral therapy (cART) in The Netherlands. Supplementary Figure

8.1 shows the basic model design. The model follows PLHIV on cART from 1st January

2010 until 31st December 2030 or death (Supplementary Figure 8.1A), and simulates

how patients age, experience clinical events (hypertension, dyslipidemia, diabetes and

CVD), and start cardiovascular medication.

CVD includes myocardial infarction, stroke, coronary artery angioplasty, coronary by-

pass, carotid endarterectomy, and deaths from other coronary heart disease and these are

simulated by incorporating the D:A:D risk equation, a CVD risk equation specifically

developed in PLHIV, into the model (Supplementary Figure 8.1B) (2). Every month

new patients are added to the model, joining the modelled patient cohort on cART.

The model ends on 31st December 2030 with all patients who were on cART on 1st

January 2010 and are still alive (Supplementary Figure 8.1A see patients in red), as well

as a cohort of new patients who started cART between 2010 and 2030 (Supplementary

Figure 8.1A see patients in blue). The model takes into account a large number of

interactions between demographic (Supplementary Figure 8.1C red), HIV-related

(Supplementary Figure 8.1C green), treatment-related (Supplementary Figure 8.1C

yellow) and clinical factors (Supplementary Figure 8.1C blue). For example, the model

accounts for the impact of age, sex and clinical factors on the risk of death, or how

cholesterol levels can vary with the use of lipid lowering drugs, as a patient ages or

switches cART regimen (see below for more details).

The period from 2010 to 2015 is used to carry out out-of-sample model validation

(see section below “Model checks”), and interventions targeting CVD are simulated

between 2017 and 2030. Interventions tested are HIV-specific interventions including

i) an outreach intervention targeting “late” presenters (i.e. initiating cART at CD4

counts ≥500 cells/mm3), and ii) the use of cART regimens with no known increased

CVD risk, and ‘traditional’ interventions including i) a smoking cessation program and

ii) the intensified monitoring and treatment of hypertension and dyslipidemia.


| Chapter 8188

SUPPLEMENTARY FIGURE 8.1 MODEL DESIGN. A. Illustrates the basic model structure. The model starts on 1st January 2010 with all PLHIV on cART in care

and registered with the ATHENA cohort aged ≥18 years old, infected with HIV-1 only, cART naïve prior to

entering the cohort, who had initiated cART after 1st January 1996 (depicted in red). Each month, the model

simulated calendar events (e.g. ageing), new events in a probabilistic manner (e.g. deaths and new CVD events),

and new patients are added as they start cART (depicted in blue). When the model stops on 31st December

2030, the modelled patient cohort contains all patients from 2010 who are still alive as well as new patients who

have entered care between 1st January 2010 and 31st December 2030. The period from 2010 to 2015 is used

to carry out out-of-sample model validation, and interventions targeting CVD are simulated between 2017

and 2030. B. Shows the risk factors included in the D:A:D risk equation (2). C. Interactions simulated in the

model between demographic (red), HIV-related (green), cardiovascular treatment (yellow) and clinical factors

(blue), for example how the risk of death is determined by age, sex and clinical factors. The dashed lines show

interactions between individual factors and solid lines between a category and a specific risk factor.

2010 2030

+ 1 month

Model check Interventions

2010-2015 2017-2030

ATHENA

cART

Icons made by Freepik from www.flaticon.com

D:A:D risk equation (Friis-Møller et al 2016)

DemographicAge

GenderClinicalDiabetes

Family historySmoking (current/former)

Total cholesterolHDL cholesterol

Systolic blood pressureHIV-related

Current CD4 countAbacavir use

Cumulative PI exposureCumulative NRTI exposure

DEMOGRAPHICS

CLINICAL FACTORS

HIV-RELATED FACTORS

CARDIOVASCULAR MEDICATION

A

B C



8

ATHENA data, definitions and baseline population

The ATHENA cohort is a national observational cohort which collects biological,

immunological and clinical data from all PLHIV in care in The Netherlands at entry

and at each follow-up visit. The cohort was established in 1998, and contains data

retrospectively from 1996. Its design has been described previously (3). ATHENA

data was used to populate the model on 1st January 2010 and to calculate parameters,

including HIV incidence, increases in mean age, changes in cART regimens, and age-

and-sex specific diabetes incidence. All patients from the ATHENA cohort aged ≥18

years old, infected with HIV-1 only, cART naïve prior to entering the cohort, and who

had initiated cART after 1st January 1996 were included.

Diabetes mellitus was defined as use of antidiabetic medication, a “diabetes mellitus”

diagnosis in clinical records, elevated fasting plasma glucose (≥7.0 mmol/L (126 mg/dL)

at ≥2 consecutive moments in time), or HbA1c ≥48 mmol/mol (6.5%). Hypertension

was defined as systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90

mmHg, and/or use of antihypertensive medication (4). Dyslipidaemia was defined as

total cholesterol >6.5 mmol/L (250 mg/dL), total/HDL cholesterol ratio >5, and/or use

of lipid lowering medication. CVDs were based on clinical patient records, and the vast

majority of CVDs were validated according to procedures developed by D:A:D (92% of

strokes, 93% of myocardial infarctions, and 84% of coronary/cardiovascular procedures).

Data up to and including 1st January 2010 were used for model parameterisation,

comprising 10,005 individuals, and patients from 1st January 2010 and up to 31st

December 2014 (validation population) were used for out-of-sample validation.

8,791 patients were still in care on 1st January 2010 and were used to populate the

model at the start by directly importing patient records, in order to capture the real-

life complexity and overlap of CVD risk factors. A number of people in 2010 had

missing data (Supplementary Table 8.1) regarding for example family history or

missing measurements for current CD4 counts, blood pressure or cholesterol. Prior to

importing the patient records directly into the model we used imputation to handle

these missing data. The imputation approach is described in detail below. The imputed

baseline population was imported directly into the model with all their characteristics

(e.g. sex, age, smoking status, blood pressure and cholesterol levels) via anonymised

patient records.

Imputation of missing data

Missing data in the baseline and validation population was handled by using multiple

imputation, assuming values were missing at random. R software (version 3.2.2; The R

Foundation for Statistical Computing) and the MICE package were used for multiple


| Chapter 8190

imputation, using a chained equations approach. Continuous variables, binary variables

and unordered categorical variables were imputed using predictive mean matching,

logistic regression, and polytomous logistic regression, respectively. All variables listed in

Supplementary Table 8.1 were included in the imputation procedure as either predictors

or imputed variables. Twenty imputed datasets were generated during the imputation,

using twenty iterations during the imputation procedure. Quality and consistency over

the twenty imputed datasets was assessed by comparing distributions and proportions

of imputed variables over the twenty imputed datasets. Graphs illustrating the imputed

mean and standard deviation with increasing iterations were inspected by eye for

stable distributions. Comparison of the observed and imputed values are described in

Supplementary Table 8.1.

New patients

In order to simulate the addition of new patients into the modelled patient cohort

between 2010 and 2030, we needed to:

a) make projections of the number of people starting cART each year, and

b) ‘recycle’ existing patients from the cohort on 1st January 2010 to use as ‘new’ patients

while accounting for future changes in cART regimens, mean age and CD4 counts.

To construct reasonable projections of future number of PLHIV starting cART, we

applied the same method as used in our previous model of the ageing PLHIV in The

Netherlands (see supplement of Smit et al 2015 (1)). In summary, a compartmental

model of HIV incidence, disease progression and cART initiation was constructed

to explore the different trajectories HIV incidence could take in the future. The

compartmental model simulated the HIV cascade, including HIV incidence, disease

progression and cART initiation, and assumed a minimum, medium and maximum

HIV incidence scenario. The manuscript presents the results for the medium scenario

with additional results listed at the bottom of this supplement (see section “Sensitivity

analyses”). The model projects the number of people starting cART each year for each

incidence scenario (Supplementary Table 8.2), and the model assumes that the number

of patients starting cART each month is constant over the year.

In order to model the entry of new patients we ‘recycled’ patients from the cohort of

patients in care on 1st January 2010. At the start of every year the model randomly

selects a sample of patients from the 2010 baseline cohort to match the number of

people projected to start cART that year (from Supplementary Table 8.2). The model

accounts for the fact that these new patients are expected to i) increase in mean age

up to 2030, ii) have increased in CD4 count at cART initiation up to 2015, and iii)

start different first-line cART regimens from 2015 onwards as reflected by updated

treatment guidelines.



8

SUPPLEMENTARY TABLE 8.1. LIST OF PATIENT VARIABLES INCLUDED IN THE MODEL, THE NUMBER OF MISSING VALUES FOR EACH VARIABLE OF INTEREST, THE OBSERVED AND IMPUTED VALUES FOR EACH VARIABLE OF INTERESTS, AND REASONS FOR MISSING VALUES. (SOURCE: ATHENA COHORT DATA AND IMPUTED DATASET).

Variables

Number (%) of

missing values

Observed values per

imputed variable

Imputed values per

imputed variable

Reasons for missing

values


Age 0 (0%) n/a n/a n/a

Sex 0 (0%) n/a n/a n/a

CVD 0 (0%) n/a n/a n/a

Pregnancy 0 (0%) n/a n/a n/a

Cardiovascular disease risk factors

Diabetes 0 (0%) n/a n/a n/a

Smoking status 3,182 (36.2%)ATHENA cohort data

collection is incomplete

regarding smoking

status and family

history of CVD

Never 1,302 (23.2%) 2,187 (24.9%)

Ever 3,217 (57.4%) 4,897 (55.7%)

Current 1,090 (19.4%) 1,707 (19.4%)

Family history of CVD 6,186 (70.4%) 255 (9.8%) 835 (9.5%)

Recent1 systolic blood pressure 3,087 (35.1%) 125 (115-136) 123 (115-135) Blood pressure/

cholesterol levels

were included only if

assessed between 1st

January 2009 and 1st

January 2010

Recent1 diastolic blood pressure 3,089 (35.1%) 80 (70-85) 80 (70-85)

Recent1 total cholesterol 2,400 (27.3%) 4.9 (4.2-5.6) 4.9 (4.2-5.6)

Recent1 HDL cholesterol 4,801 (54.6%) 1.2 (0.9-1.5) 1.2 (1.0-1.5)

HIV-related characteristics

Recent1 CD4 count 697 (7.9%) 500 (360-660) 500 (362-660) CD4 count levels

were included only if

assessed between 1st

January 2009 and 1st

January 2010

CD4 count at entry ATHENA

cohort

33 (0.4%) 280 (110-460) 280 (110-460)

Cumulative use of protease

inhibitors

1 (0.01%) 0.3 (0-2.4) 0.3 (0-2.4)

Cumulative use of nucleoside reverse

transcriptase inhibitors

1 (0.01%) 4.1 (1.6-7.5) 4.1 (1.6-7.5)

Current use of abacavir 1 (0.01%) 1,172 (13.3%) 1,172 (13.3%)

Current use of protease inhibitor 0 (0%) n/a n/a n/a

Current use of nucleoside reverse

transcriptase inhibitors

0 (0%) n/a n/a n/a

Current use of non-nucleoside

reverse transcriptase inhibitors

0 (0%) n/a n/a n/a

Current use of integrase inhibitors 0 (0%) n/a n/a n/a

Lipid lowering medication 38 (0.4%) 688 (7.9%) 697 (7.9%) The start/stop date

of medication was

missing for a number

of patients.

Antihypertensive medication 66 (0.8%) 981 (11.2%) 995 (11.3%)

Antidiabetic medication 13 (0.1%) 242 (2.8%) 251 (2.9%)

Data are presented as number (%) or median (interquartile range).1 Defined as <12 months prior to 1 January 2010.


| Chapter 8192

The parameters describing increases in mean age over time can be found in the

supplement of Smit et al 2015 (1). In summary, the mean age at cART initiation

has been increasing linearly over time since 1996, and the model assumes that this

increase will continue until 2030. When the model randomly selects a sample of ‘new’

patients from the baseline cohort, the selection of patients are matched to the estimated

predicted mean age between 2010 and 2030 (i.e. the mean age of the selected sample

of patients from baseline cohort matched the estimated predicted mean age for that

specific year). In this manner, the sample of ‘new’ patients age over time, with their

profiles (e.g. blood pressure and cholesterol measurements) matching this demographic

transition. In addition, upon entry into the model patients’ CD4 counts and cART

regimens are adjusted to match expected changes. The annual CD4 count increase

between 2010 and 2015 amongst patients starting cART was calculated based on recent

changes in CD4 counts at cART initiation in the total patient population reported by

SUPPLEMENTARY TABLE 8.2. PROJECTED NUMBER OF PLHIV STARTING CART AS PREDICTED BY THE DETERMINISTIC MODEL OF HIV-INFECTION USING THREE INCIDENCE SCENARIOS FOR THE EPIDEMIC; MINIMUM, MEDIUM AND MAXIMUM. (SOURCE: SMIT ET AL 2015 SUPPLEMENT (1)).

Number of new treatment initiations

Year Minimum scenario Medium scenario Maximum scenario

2010 1009 1009 1009

2011 897 897 897

2012 805 805 805

2013 730 730 734

2014 667 667 681

2015 612 612 642

2016 563 563 614

2017 518 518 594

2018 476 476 580

2019 436 441 570

2020 397 414 564

2021 359 394 559

2022 322 379 556

2023 285 369 554

2024 252 361 552

2025 225 356 551

2026 204 352 550

2027 189 349 550

2028 177 348 550

2029 169 346 549

2030 163 345 549



8

the ATHENA cohort between 2005 and 2013 (see Supplementary Figure 8.2) (5).

We assume the linear increase observed in this period will continue up to 2015, after

which CD4 counts at cART initiation are assumed to remain at 2015 levels. From

2015 onwards, the model puts new PLHIV on cART regimens reflecting changes in

European AIDS Society (EACS) guidelines, which increasingly recommended the use

of integrase inhibitors over other combinations (6). Parameters describing changes in

first-line cART regimens were taken from the ATHENA cohort (Supplementary Table

8.3).

SUPPLEMENTARY FIGURE 8.2. CHANGES OVER TIME IN MEDIAN CD4 COUNTS AT THE START OF CART IN THE TOTAL POPULATION, MEN WHO HAVE SEX WITH MEN (MSM), AND HETEROSEXUAL MEN AND WOMEN BETWEEN 2005 AND 2013. (SOURCE: SHM REPORT 2014 (5)).

SUPPLEMENTARY TABLE 8.3. PROPORTIONS AND PARAMETERS DESCRIBING CHANGES IN FIRST-LINE CART REGIMEN IN 2015 COMPARED TO 2010-2014 AS PER ATHENA COHORT DATA.

PIs NNRTIs IIs Other

A. Data

2010-2014 (average) 32.0% 61.6% 4.3% 2.1%

2015 19.5% 33.1% 46.3% 1.1%

B. Parameters

Proportion starting IIs instead 39.1% 46.3%

A. Proportion of people starting Protease inhibitors (PIs), Non-Nucleoside Reverse Transcriptase Inhibitors

(NNRTIs), or Integrase inhibitors (IIs) in 2010 to 2014 (on average between those four years) compared to

2015 as per ATHENA data. B. Parameters describing proportion of ‘new’ patients initiating IIs instead of PIs

and NNRTIs from 2015 at cART initiation to reflect first-line cART guideline changes. (Source: ATHENA

cohort data).


| Chapter 8194

Mean age, mortality, diabetes, blood pressure and cholesterol

Demographic projections of sex-specific increases in mean age at start of cART, sex-

and age-specific diabetes incidence, and age-, sex- and cause-specific mortality rates

have been described previously (see supplement of Smit et al (1)). The modified model

further accounts for the impact of age on total cholesterol, HDL cholesterol, and

systolic and diastolic blood pressure. Literature suggests that the risk of hypertension

and dyslipidaemia changes with age and that these changes may differ by sex (7,8).

To account for this in our model we integrated age-and-sex-specific annual changes

in blood pressure and lipid levels based on data from the Doetinchem Cohort Study.

The Doetinchem Cohort Study is an ongoing long-lasting general population-based

health study in the Netherlands city of Doetinchem among men and women aged

20-59 years, which was initiated in 1987 (9). The study collects information on

demographics, lifestyle, biological factors, and comorbidities, including CVD. Data up

to and including January 2013 were used, encompassing 6,390 individuals. We included

patients not on antihypertensive or lipid lowering medication to estimate annual

changes in blood pressure and cholesterol, respectively. For every 10-year age group we

included all measurements of individuals within this age group, and any measurements

that occurred 5 years prior to the lower age limit or after the upper age limit. Individuals

with ≥2 measurements available were used to obtain the annual slope per individual,

from which we calculated the mean slope per age group, stratified by sex. When no

statistically significant difference in slope was found between males and females, the

combined estimate was used. The parameters describing these age-related changes are

listed in Supplementary Table 8.4 below.

SUPPLEMENTARY TABLE 8.4. ANNUAL CHANGES IN BLOOD PRESSURE AND LIPID LEVELS PER 10-YEAR AGE GROUP AND SEX AMONG INDIVIDUALS NOT USING BLOOD PRESSURE OR LIPID LOWERING THERAPY. (SOURCE: DOETINCHEM COHORT STUDY (9)).

Age Group

(years)

Systolic blood

pressure (mmHg)

Diastolic blood

pressure (mmHg)

Total cholesterol

(mmol/L)

HDL cholesterol

(mmol/L)

Men Women Men Women Men Women Men Women

20 to 30 -0.03031 -0.03031 0.3971 0.3971 0.0474 -0.00293 0.0103 0.0173

30 to 40 0.151 0.584 0.4791 0.4791 0.0283 0.0158 0.00811 0.0173

40 to 50 0.682 1.036 0.444 0.530 0.0168 0.0503 0.00813 0.0151

50 to 60 1.084 1.119 0.3381 0.3381 0.00254 0.0522 0.009491 0.009491

60 to 70 1.289 1.140 0.2101 0.2101 -0.00784 0.0177 0.00645 0.000787

Over 70 1.0131 1.0131 0.1541 0.1541 -0.01861 -0.01861 -0.001761 -0.001761

1 Males and females were combined to calculate the annual change in blood pressure/lipid levels when no

significant sex-difference in slope was observed.



8

SUPPLEMENTARY TABLE 8.5. VARIABLES INCLUDED IN FULL AND REDUCED D:A:D RISK EQUATION USED TO PREDICT THE 5-YEAR CVD RISK. (SOURCE: FRIIS -MØLLER ET AL 2016 (2))

Variables Full D:A:D risk

equation1

Reduced D:A:D risk

equation2

Variable type


Age X X continuous

Gender X X binary

Clinical characteristics

Diabetes X X binary

Family history of CVD X X binary

Current smoker X X binary

Past smoker X X binary

Total cholesterol X X continuous

HDL cholesterol X X continuous

Systolic blood pressure X X continuous

HIV-related characteristics

Current CD4 count X X continuous

Abacavir use (current) X binary

Cumulative PI exposure X continuous

Cumulative NRTI exposure X continuous

1 The full D:A:D risk equation was used for individuals with cumulative NRTI exposure up to 9 years, and

PI exposure up to 5.5 years. 2The reduced D:A:D risk equation was used for individuals who were exposed to

NRTIs for more than 9 years and/or to PIs for more than 5.5 years.

D:A:D risk equation

As suggested by Friis-Møller et al (2) the full risk equation was used for individuals

with cumulative nucleoside reverse transcriptase inhibitors (NRTIs) exposure up to

9 years and protease inhibitors (PIs) exposure up to 5.5 years, and the reduced risk

equation for those exposed to NRTIs or PIs for a longer time period (Supplementary

Table 8.5). The 5-year CVD risk was calculated for every patient in the model, and

converted to a monthly risk assuming constant risk within the 5-year timeframe. The

occurrence of CVD was based on the calculated CVD risk, and assigned to the patient

in a probabilistic manner. Once a patient was predicted to have CVD, the specific CVD

event was determined using the parameters described in Supplementary Table 8.6 (e.g.

a patient who developed CVD had 41% chance to have had a non-fatal myocardial

infarction).


| Chapter 8196

SUPPLEMENTARY TABLE 8.6. DISTRIBUTION OF VARIOUS TYPES OF CVD WITHIN D:A:D COHORT STUDY. (SOURCE: FRIIS -MØLLER ET AL 2016 (2)).

Type of CVD Number of events (%)

Myocardial infarction (non-fatal) 413 (41%)

Myocardial infarction (fatal) 80 (8%)

Stroke (non-fatal) 255 (25%)

Stroke (fatal) 40 (4%)

Coronary artery angioplasty 129 (13%)

Coronary bypass 36 (4%)

Carotid endarterectomy 13 (1%)

Death from other coronary heart disease 44 (4%)

TOTAL 1,010

Co-medication

The model simulates treatment with lipid lowering medication and antihypertensive

medication. The model assumes that prescription behaviour remains stable at 2010

levels, with data suggesting that the probability of being prescribed antihypertensive

and lipid lowering medication depending on the type of CVD and diabetes status of the

patient. Parameters describing prescription behaviour are listed in Supplementary Table

8.7 and Supplementary Table 8.8 and treatment is being modelled probabilistically.

SUPPLEMENTARY TABLE 8.7. PARAMETERS DEFINING USE OF LIPID LOWERING MEDICATION BASED ON ATHENA DATA.

Proportion starting lipid

lowering drugs

CVD only 0.75

High cholesterol only 0.13

High cholesterol and diabetes (no CVD) 0.29

High cholesterol and CVD (irrespective of the presence of diabetes) 0.76

Very high cholesterol only 0.17

Very high cholesterol and diabetes (no CVD) 0.33

Very high cholesterol and CVD (irrespective of the presence of diabetes) 0.75

CVD includes myocardial infarction, stroke, coronary artery angioplasty, coronary bypass and carotid

endarterectomy; high cholesterol is defined as a total/HDL cholesterol ratio above 5 and very high cholesterol

as a total/HDL cholesterol ratio above 8. (Source: ATHENA cohort data).

Once patients are started on antihypertensive or lipid lowering medication, they are



8

assumed to remain on treatment for life and their blood pressure and cholesterol is

adjusted. The new blood pressure and cholesterol levels are set to the levels observed

amongst patients in care in 2010 in ATHENA who have been on medication for longer

than 6 months on 1st January 2010, with some patients returning to normal target levels,

and some patients remaining at elevated level, reflecting levels observed in the cohort

(Supplementary Table 8.9). We assumed that blood pressure and cholesterol levels

remained stable once patients started on antihypertensive or lipid lowering medication.

We tested normal distribution, Weibull and gamma distributions for best fit describing

the distribution of systolic, diastolic blood pressure, HDL and total cholesterol on co-

medication amongst patients on co-medication in ATHENA, with parameters listed in

Supplementary Table 8.9.

SUPPLEMENTARY TABLE 8.9. PARAMETERS DESCRIBING TOTAL CHOLESTEROL, HDL, SYSTOLIC BLOOD PRESSURE AND DIASTOLIC BLOOD PRESSURE DISTRIBUTION AMONGST PLHIV ON LIPID LOWERING OR ANTIHYPERTENSIVE MEDICATION. (SOURCE: ATHENA COHORT DATA).

Parameters Function type

Total cholesterol(mmol/L)1 Mean = 5.17; SD = 1.31 Normal distribution

HDL (mmol/L)1 Alpha= 8.74; Beta = 0.137 Gamma distribution

Systolic (mmHg)2 Mean = 135.77; SD = 19.11 Normal distribution

Diastolic (mmHg) 2 Mean = 83.39; SD = 12.03 Normal distribution

SD=standard deviation1 Lipid levels distribution based on individuals using lipid lowering medication for ≥6 months on 1st January

2010.2 Blood pressure distribution based on individuals using antihypertensive drugs for ≥6 months on 1st January

SUPPLEMENTARY TABLE 8.8. PARAMETERS DEFINING USE OF ANTIHYPERTENSIVE MEDICATION BASED ON ATHENA DATA.

Proportion starting

antihypertensives

CVD only 0.58

High blood pressure only 0.25

High blood pressure and diabetes (no CVD) 0.56

High blood pressure and CVD (irrespective of the presence of diabetes) 0.80

CVD includes myocardial infarction, stroke, coronary artery angioplasty, coronary bypass and carotid

endarterectomy; high blood pressure as systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90

mm Hg. (Source: ATHENA cohort data).


| Chapter 8198

2010.

CD4 increases on cART

The model simulates increases in CD4 counts with time on cART, accounting for factors

such as age and time since cART initiation. Previous work by Gras et al showed that the

increase is steepest soon after cART initiation and levelling off as duration on cART

is prolonged, with recovery differing in patients under and over 50 years old (10). We

used data from the ATHENA cohort to calculate increases in CD4 count by age and

time on cART. The ATHENA data showed that CD4 recovery rate differed between

patients aged over and under 50 years old (except in those starting cART at CD4 count

above 500 cells/mm3) and by CD4 count at cART initiation. Recovery rates also differ

according to duration on cART, with steepest recovery observed in the first 6 months,

followed by 6 months to two years, levelling off after two years. It was assumed that

no further changes would occur after 10 years. Parameters describing monthly CD4

recovery are listed in Supplementary Table 8.10.

Impact of switching cART on cholesterol

We reviewed the literature to assign realistic estimates in cholesterol alterations as a

result of switching antiretroviral drugs. We included randomized clinical trials that

described the effect of switching i) from a protease inhibitor (PI) to non-nucleoside

reverse-transcriptase inhibitor (NNRTI), ii) from PI to integrase inhibitor (II), and

iii) from NNRTI to II. We included studies conducted in patients aged 18 years and

over on cART, in high-income settings, and with undetectable viral load. Exclusion

criteria were mono- or dual-therapy prior to cART initiation. A list of included studies

SUPPLEMENTARY TABLE 8.10. MONTHLY CD4 COUNT IN CELLS/MM3 RECOVERY BY AGE AND TIME ON CART. (SOURCE: ATHENA COHORT DATA)

CD4 count at cART initiation (cells/mm3)

Time on cART <200 200-350 350-500 ≥500

0 to 6 months

Under 50 years old 14.80 16.50 18.50 19.80

Over 50 years old 12.70 15.80 16.00 19.80

6 to 24 months

Under 50 years old 7.30 5.67 6.11 5.94

Over 50 years old 5.39 5.33 5.44 5.94

2 to 10 years

Under 50 years old (-3.40*t +51.88)/12 (-3.11*t +43.32)/12 (0.74*t +18.2)/12 (0.12*t +14.02)/12

Over 50 years old (-4.71*t +48.14)/12 (-2.69*t +41.24)/12 (-7.33*t +66.16)/12 (0.12*t +14.02)/12

t=time since start of cART (in years)



8

is shown in Supplementary Table 8.11.The model included the impact of switching

from a PI to an NNRTI, a PI to an II, or from an NNRTI to an II on total cholesterol,

assumed to be reduced by 0.6 mmol/L, 0.8 mmol/L, or 0.2 mmol/L, respectively, with

no effect on HDL cholesterol.

List of incorporated interactions

The model takes into account a number of interaction between demographic, clinical,

HIV-related and co-medication factors (see Supplementary Figure 8.1C), including:

• Mortality: the impact of gender, age and co-morbidity on the risk of mortality (see

supplement of Smit et al 2015 for more details (1))

• CD4 count: the impact of cART and age on CD4 (see section “CD4 increases on

cART”). The model takes into account CD4 increases after cART initiation from

2010 to 2015 (see section “New patients”)

• Blood pressure: the impact of age (see section “Mean age, mortality, diabetes,

blood pressure and cholesterol”) and the use of antihypertensives (see section “Co-

medication”) on blood pressure levels

• Cholesterol: the impact of age (see section “Mean age, mortality, diabetes, blood

pressure and cholesterol”), the use of lipid lowering drugs (see section “Co-

medication”) and of cART regimen changes (see section “Impact of switching cART

on cholesterol”) on cholesterol levels

• Diabetes: the impact of age on diabetes (see supplement Smit et al 2015 (1))

• Co-medication: impact of developing a new clinical event (diabetes, hypertension or

dyslipidaemia and serious CVD event) on the probability of starting co-medication

(see supplement of Smit et al 2015 (1) for anti-diabetics and section “Co-medication”

for antihypertensive and lipid lowering drugs)

• cART: the impact of changes in EACS guidelines on first-line regimens (see section

“New patients”)

Economic evaluation

An economic evaluation of the mathematical modelling output was carried out.

Supplementary Table 8.12 shows costs (€) in The Netherlands in 2016. These were used

to calculate total costs for the baseline scenario and per intervention.

Model checks

A number of checks were carried out to ensure that the model adequately captures clinical

care in The Netherlands and could be used to reliably predict the future age-structure,

CD4 count trends, and burden of non-communicable disease in The Netherlands. These

checks include doing out-of-sample checks comparing 2010 to 2015 model output

with 2010 to 2015 observational ATHENA cohort data. The results of these model

validation checks show that the model consistently generates output of the right order

of magnitude, leading to the conclusion that the model provides projections of the right


| Chapter 8200

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8

SUPPLEMENTARY TABLE 8.12. COSTS.

Lifetime disease cost components Cost (2016, €) Source

Myocardial Infarction1 €4,037 Open data van de Nederlandse Zorgautoriteit (19,20)

Stroke1 €2,498 Open data van de Nederlandse Zorgautoriteit (19,21)

Coronary artery angioplasty2 €17,062 Open data van de Nederlandse Zorgautoriteit (19,22)

Coronary by-pass2 €14,521 Open data van de Nederlandse Zorgautoriteit (19,22)

Carotid endarterectomy2 €7,670 Open data van de Nederlandse Zorgautoriteit (19)

Yearly HIV-related costs3

Antiretrovirals

ABC+3TC+PI €11,939 Zorginstituut Nederland Medicijn Kosten (23)

ABC+3TC+II €9,817 Zorginstituut Nederland Medicijn Kosten (23)

ABC+3TC+NNRTI €7,232 Zorginstituut Nederland Medicijn Kosten (23)

TDF/ZDV+FTC+PI €11,058 Zorginstituut Nederland Medicijn Kosten (23)

TDF/ZDV+FTC+II €11,861 Zorginstituut Nederland Medicijn Kosten (23)

TDF/ZDV+FTC+NNRTI €9,790 Zorginstituut Nederland Medicijn Kosten (23)

Patient monitoring4 €523 Nichols et al 2015 (24)

Abbreviations: 3TC, lamivudine, ABC, abacavir; FTC, emtricitabine; II, integrase inhibitor, NNRTI, non-

nucleoside reverse-transcriptase inhibitors; PI, protease inhibitor; TDF, tenofovir; ZDV, zidovudine. 1 Cost weighted by deaths by MI/Stroke2Including long-term recovery3Weighted by the percentage of patients on each potential regimen combination4Twice yearly visits, viral load monitoring, basic laboratory monitoring

direction and magnitude.

Population in care

Results of the model output for number of patients in care at the 31st December of

each validation year (2010-2014) were compared to out-of-sample observational

ATHENA cohort data, to check if the deterministic model of HIV incidence was

reliable in predicting the number of new patients starting cART and the number of

PLHIV on cART in care. The comparison between the number of new patients starting

cART annually in the model and the ATHENA data are presented in Supplementary

Figure 8.3A and the total number of PLHIV on cART annually in Supplementary

Figure 8.3B. The results show that the model is able to accurately recreate the number

of patients on cART in care annually up to 2015, despite a slight underestimation in the

number of patients starting in cART.

In addition, we compared sex-specific mean age of PLHIV in care in 2010 to 2014

between the model and ATHENA data, to check whether projected age-trends were

adequately capturing trends ongoing in the observation cohort (Supplementary Figure

8.4). The validation results show that the model is performing adequately in projecting

short-term trends in mean age amongst PLHIV in care in The Netherlands.


| Chapter 8202

Mortality

The full details of the validation checks for mortality can be found in the supplement

by Smit et al 2015 (1). Mortality was validated in three ways; i) by comparing the

standardized mortality ratio between the PLHIV and the general population, ii) by

comparing age-specific death rates amongst PLHIV and the general Dutch population,

and iii) by comparing annual percentage of deaths amongst PLHIV on cART between

the model and the observational ATHENA cohort data (Supplementary Figure 8.5).

Model results show that mortality patterns were accurately reconstructed.

Smoking status

In order to test our assumption that smoking status at entry will not change substantially

over time, we compared smoking status amongst new patients between observational

ATHENA cohort data and model output in 2010 to 2014, stratified by sex. The results

are presented in Supplementary Figure 8.6 and show that smoking status in both new

female and male patients starting cART are comparable between the model and the

SUPPLEMENTARY FIGURE 8.3. COMPARISON OF POPULATION IN CARE STRATIFIED BY SEX BETWEEN THE MODEL OUTPUT AND OBSERVATIONAL ATHENA COHORT DATA. A. Number of PLHIV newly starting cART and B. Total number of PLHIV on cART.

SUPPLEMENTARY FIGURE 8.4. MEAN AGE ACCORDING TO OBSERVATIONAL DATA FROM ATHENA COHORT AND MODEL OUTPUT FOR A. MEN AND B. WOMEN.

4041424344454647484950

2010 2011 2012 2013 2014 2015

Age

Year

Mean Age - Men

95% CIs Mean age - Data Mean age - Model

4041424344454647484950

2010 2011 2012 2013 2014 2015

Age

Year

Mean Age - Women

95% CIs Mean age - Data Mean age - Model

A B

0

500

1.000

1.500

Num

ber o

f peo

ple

Year

Number of New Entries

Men - Data Men - Model Women - Data Women - Model

A

0

5.000

10.000

15.000

Num

ber o

f peo

ple

Year

Total Popula on on ART

Men - Data Men - Model Women - Data Women - Model

B



8

SUPPLEMENTARY FIGURE 8.5. ANNUAL PERCENTAGE OF DEATHS AMONGST PLHIV ON CART ACCORDING TO THE OBSERVATIONAL ATHENA COHORT DATA AND THE MODEL, STRATIFIED BY SEX.

012345

2010 2011 2012 2013 2014

Perc

enta

ge

Year

Percentage Deaths

Men -Data Men - Model Women - Data Women - Model

SUPPLEMENTARY FIGURE 8.6 . SMOKING STATUS AMONG PLHIV WHO ENTERED CARE AFTER 1 JANUARY 2010 ACCORDING TO OBSERVED DATA FROM THE ATHENA COHORT AND MODEL OUTPUT FOR A. MEN AND B. WOMEN.

0

10

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60

70

80

90

100

Perc

enta

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Smoking Status Amongst New Entries - Men

Never Smoker - Data Never Smoker - ModelEver Smoker - Data Ever Smoker - ModelCurrent Smoker - Data Current Smoker - Model

0

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Perc

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Smoking Status Amongst New Entries - Women

Never Smoker - Data Never Smoker - ModelEver Smoker - Data Ever Smoker - ModelCurrent Smoker - Data Current Smoker - Model

Year

A B

data.

Diabetes, blood pressure and cholesterol

The model simulates the development of diabetes based on age and sex. Changes in

blood pressure and cholesterol levels are simulated based on age and sex, use of CVD

co-medication, as well the impact of switches in cART regimens on cholesterol. In order

to check that the model is able to recreate new diagnosis of diabetes and sex-and-age-

specific blood pressure and cholesterol profiles as seen in the observational ATHENA

cohort data we compared model output with data in 2010 to 2014. The results for

diabetes (Supplementary Figure 8.7) show that the model is able to recreate trends in

new diagnosis of diabetes between 2010 and 2014. Age-and sex-specific cholesterol

(Supplementary Figure 8.8) and blood pressure (Supplementary Figure 8.9) profiles


| Chapter 8204

0

1

2

3

4

5

2010 2011 2012 2013 2014

Perc

enta

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New Diabetes Events - Percentage

Data Model

SUPPLEMENTARY FIGURE 8.8. CHOLESTEROL PROFILES ACCORDING TO OBSERVATIONAL ATHENA COHORT DATA AND MODEL OUTPUT FOR A. MEN UNDER 50 YEARS OLD, B. MEN OVER 50 YEARS OLD, C. WOMEN UNDER 50 YEARS OLD AND D. WOMEN OVER 50 YEARS OLD.

0102030405060708090

100

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enta

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Year

Cholesterol - Men Under 50 Year Old

TC - Data TC - ModelTC/HDL 5-8 - Data TC/HDL 5-8 - ModelTC - Data TC - Model

0102030405060708090

100

Perc

enta

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Year

Cholesterol - Men Over 50 Year Old


0102030405060708090

100

Perc

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Cholesterol - Women Under 50 Year Old


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Cholesterol - Women Over 50 Year Old


A B

C D

SUPPLEMENTARY FIGURE 8.7. PERCENTAGE OF NEWLY DIAGNOSED DIABETES CASES ACCORDING TO OBSERVATIONAL ATHENA COHORT DATA AND THE MODEL.



8

also show robust model predictions.

CVD incidence

The model simulated CVD incidence (including myocardial infarction, stroke, coronary

artery angioplasty, coronary by-pass, carotid endarterectomy, and deaths from other

coronary heart disease) by using the D:A:D risk equation. In order to check the

robustness of incorporating this equation into the model the percentage of incident

CVDs among individuals without a prior history of CVD was compared between

out-of-sample ATHENA data and the model output in 2010 to 2014. CVDs in the

ATHENA data base were based on clinical patient records and the vast majority of

CVDs were validated according to procedures developed by the D:A:D Study. The

results are presented in Supplementary Figure 8.10 and show that the model predicted

0

50

100

Perc

enta

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Year

Blood Pressure - Men Under 50 Year Old

Normal - Data Normal - ModelPrehypertension - Data Prehypertension - ModelStage 1 - Data Stage 1 - ModelStage 2 - Data Stage 2 - Model

0

50

100Pe

rcen

tage

Year

Blood Pressure - Men Over 50 Year Old


A B

A B

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50

100

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Year

Blood Pressure - Women Over 50 Year Old


0

50

100

Perc

enta

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Blood Pressure - Women Under 50 Year Old


SUPPLEMENTARY FIGURE 8.9. BLOOD PRESSURE PROFILES ACCORDING TO OBSERVATIONAL ATHENA COHORT DATA AND MODEL OUTPUT FOR A. MEN UNDER 50 YEARS OLD, B. MEN OVER 50 YEARS OLD, C. WOMEN UNDER 50 YEARS OLD AND D. WOMEN OVER 50 YEARS OLD. Normal blood pressure: <120/80 mmHg; Prehypertension: 120/80 to 139/89 mmHg; Stage 1 hyperten-

sion: 140/90 to 159/99 mmHg; Stage 2 hypertension: 160/100 mmHg and above.


| Chapter 8206

the incidence of CVD robustly.

Co-medication

We further compared the percentage of people using co-medication (including

antidiabetics, antihypertensives and lipid lowering drugs) between the model and

observational ATHENA cohort data from 2010 and 2014. The results are presented in

Supplementary Figure 8.11A, B and C for anti-diabetics, antihypertensives and lipid

lowering drugs, respectively and show robustness of model predictions.

CD4 count

Finally, we compared the mean CD4 counts between 2010 and 2014 amongst new

entries and in the total population according to observational ATHENA cohort data

and model output. The results amongst new entries and the total population are shown

in Supplementary Figure 8.12A and B, respectively, and demonstrate that the model

was able to recreate CD4 count trends observed in the ATHENA data in the short-

term.

Additional results and sensitivity analyses

Sensitivity analyses

The below section shows additional results for sensitivity analyses we carried out to test

model output robustness and specificity. We tested alternative HIV incidence scenarios

(minimum and maximum), as well as the impact of fading-out the impact of cumulative

time on PIs once patients switch off PIs as well as the long-term impact of smoking

cessation. In addition, we evaluated the effect of reaching 10% of smokers successfully,

0,00

0,50

1,00

1,50

2,00

2010 2011 2012 2013 2014

Perc

enta

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Year

Serious CVD Events

Data Model

SUPPLEMENTARY FIGURE 8.10. COMPARISON OF PERCENTAGE OF NEW CVD ACCORDING TO OBSERVATIONAL ATHENA COHORT AND MODEL OUTPUT BETWEEN 2010 AND 2014.



8

SUPPLEMENTARY FIGURE 8.11. COMPARISON OF USE OF CO -MEDICATION ACCORDING TO OBSERVATIONAL ATHENA COHORT DATA AND MODEL OUTPUT BETWEEN 2010 AND 2014 FOR A. ANTI-DIABETICS, B. ANTIHYPERTENSIVE MEDICATION, AND C. LIPID LOWERING DRUGS.

A B

0

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2010 2011 2012 2013 2014

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An -diabe cs

Data Model

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2010 2011 2012 2013 2014

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An -hypertensives

Data Model

C

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2010 2011 2012 2013 2014

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Lipid lowering drugs

Data Model

SUPPLEMENTARY FIGURE 8.12. COMPARISON OF MEAN CD4 COUNTS ACCORDING TO OBSERVATIONAL ATHENA COHORT DATA AND MODEL FROM 2010 TO 2014 AMONGST A. NEW ENTRIES AND B. TOTAL PATIENT POPULATION.

A B

200250300350400450500550600650700750

2010 2011 2012 2013 2014 2015

CD4

coun

t

Year

Mean CD4 Count - New Entries

95% CIs Mean CD4 count - Data Mean CD4 count - Model

200250300350400450500550600650700750

2010 2011 2012 2013 2014 2015

CD4

coun

t

Year

Mean CD4 Count - Total Popula on

95% CIs Mean CD4 count - Data Mean CD4 count - Model

since low rates of smoking cessation have been reported despite tailored interventions

(25–28).

1. Minimum and maximum HIV incidence scenario

In this scenario we assume that HIV incidence decreases more rapidly or less rapidly over


| Chapter 8208

time (see section “Entry of new patients”). The results are presented in Supplementary

Figure 8.13 and show that the overall trends of CVD burden and risk do not change

according to the HIV incidence scenario. The results further show that if HIV incidence

continues to remain high, CVD burden will be greater, probably driven by the larger

patient population. However, CVD risk in the patient population will be lower if HIV

incidence remains high, probably due to the addition of new patients of younger age

keeping the risk at the population level lower.

SUPPLEMENTARY FIGURE 8.13. THE PROPORTION OF PATIENTS IN LOW (<5%, GREEN), MODERATE (5-10%, YELLOW), AND HIGH (≥10%, RED) PREDICTED 5-YEAR CVD RISK GROUPS BETWEEN 2010 AND 2030 AS SIMULATED BY THE MODEL USING THE D:A:D RISK EQUATION ASSUMING A. MINIMUM HIV INCIDENCE AND B. MAXIMUM HIV INCIDENCE.

2. Decrease in cumulative time on PI effect

We tested a scenario where the effect of cumulative time on PI ‘fades-out’ over time

once patients switch off PIs, by assuming that five years after patients were switched off

PIs the effect of cumulative time on PIs is zero. In the D:A:D risk equation, any time on

PIs contributes toward CVD risk with no reduction in its effect even after long periods

when patients no longer use PIs. We compared the CVD burden at baseline assuming

no fading effect and fading after five years, as any fading impact of cumulative time

on PIs after switching would impact baseline and all interventions tested. The results

showed that the cumulative CVD burden between 2010 and 2030 would be 0.5% lower

if the effect of cumulative PIs was to fade out completely five years after switching off

PIs.

3. Decrease in ever smoking status effect

We tested a scenario where patients who had stopped smoking five or more years ago no

longer have a CVD risk attributed to being ‘ex-smoker’ as per the risk equation. In the

A B10%

5-10%

<5%

10%

5-10%

<5%



8

D:A:D risk equation the same risk is assumed for ex-smokers, irrespective of duration

since smoking cessation. Literature suggests that the effect of being a smoker fades over

time after cessation (29). We compared the CVD burden at baseline assuming no fading

effect and fading after five years, as any fading impact of smoking after cessation would

impact baseline and all interventions tested. The results showed that the cumulative

CVD burden between 2010 and 2030 would be 7.4% lower if the effect of being an ex-

smoker was to fade out completely five years after smoking cessation.

4. Smoking cessation intervention - reaching 10% of patients successfully

We tested a more feasible smoking cessation scenario of 10% (25–28) (as opposed to

the scenarios of 50-100% successful implementation described in the manuscript).

Smoking cessation could prevent an average of 1.3% of CVD cases annually in all and

HR patients if implemented 10% successfully.


| Chapter 8210

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