2009 WHIV Report: Insurability HIV (written 2008)

HIV Insurability Expanded

Report of the HIV Working Group

June 2009

HIV Insurability Expanded 2

2009 Published by the Verbond van Verzekeraars [Dutch Association of Insurers] No rights may be derived from this report. The SHM is not liable for any errors arising from the calculations in this report.

Report number: 2008/bl/10611/HBLOE


CONTENTS

Summary .. 5

Introduction . 8

Chapter 1 Mortality data . 9

Chapter 2 Guarantees and special conditions ........................... 16

Chapter 3 Medical risk assessment 18

Chapter 4 Press articles about Verzekerbaarheid hiv dichterbij in March 2005... 27

Chapter 5 HIV survey results .......... 29

Bibliography ... 31

Annexes A Example letter to an insurance candidate ... 33

B Example letter to a treating specialist .. 34

C HIV seropositive questionnaire... 35

D Model for mortality after a diagnosis of HIV, without cART (Untreated model) 37

E Model for mortality after start of cART (Treated model) 38

F CDC categories. 39

G Terminology... 40


Summary

Mortality data In the 2005 report entitled Verzekerbaarheid hiv dichterbij [Insurability of HIV a step closer] it was not possible to indicate the mortality probability of persons diagnosed with HIV but who did not yet need therapy (cART: Combined Anti-Retroviral Therapy, a combination of three or more anti-HIV drugs). In 2008, sufficient data became available on this group too. Consequently, the aforementioned report needs to be updated under the new title: Verzekerbaarheid hiv uitgebreid [HIV insurability expanded]. As in the previous report, the gathering of data and the modelling was primarily carried out by the Stichting HIV Monitoring (SHM) [HIV Monitoring Foundation].

Two models were used for the mortality probability of people with HIV: model A for those who were not yet following cART 24 weeks after being diagnosed, and model B, for those who had started cART. The data was gathered up until 2008. The models calculate the difference in mortality probability between people with HIV and the entire population, with correction for age and sex. For the mortality probability of the population, the references used were the survival tables Gehele Bevolking Mannen 2000-2005 [Total Male Population 2000-2005] and Gehele Bevolking Vrouwen 2000-2005 [Total Female Population 2000-2005] of the Actuarial Society of the Netherlands (AG). In the report Verzekerbaarheid hiv dichterbij the AG tables from 1995-2000 were used in which the mortality probability for most ages was slightly higher.

Information on 3,479 patients was used for model A (from a database of 13,849 persons). Patients diagnosed with HIV prior to 1996 or after 2006 were excluded as were those who had become infected through drug use. The concentration of CD4 cells was not used as a parameter in the model because the CD4 count appeared not to be significant among the population of 2,717 patients for whom the CD4 count was known. Also excluded were patients with CDC-B or CDC-C events (HIV-related complaints) for whom it was not clear when this event had occurred, i.e. within 24 weeks or thereafter.

Information on 5,951 patients was used for model B (from a database of 11,000 persons). The same persons were excluded as in model A.

In both models, patients were excluded for whom AIDS was diagnosed in the first 24 weeks.

Model A showed that two parameters have a significant impact on the death of persons with HIV: The occurrence or absence of a CDC-B event within 24 weeks of being diagnosed with HIV. The age of the person involved 24 weeks after the diagnosis of HIV.

All the persons in model B were in treatment and were following modern medicinal therapies (cART). For the higher mortality rate in respect of the population mortality rate as a consequence of HIV, a mathematical model was developed that linked up well with the observations within the group referred to.


The study showed that three variables have a significant, large impact on the level of the higher mortality rate as a consequence of HIV: the CD4 concentration 24 weeks after the start of cART, the start date of the therapy is before or after 1 January 1998 and the time elapsed since the start of cART. Sex and the concentration of HIV in the blood, for example, were not deciding factors for the higher HIV mortality rate and were therefore not included in the model. These conclusions are also supported in recent literature.

For patients who started cART after 1997, the mathematical expectation regarding the additional deaths due to HIV is 0.09% per year for a CD4 concentration of 1.2 million per millilitre. This percentage rises up to 0.4% per year for a CD4 count of 0.2. For a CD4 under 0.2 the mortality probability due to HIV rises sharply for lower and lower CD4 concentrations.

Mortality among people with HIV in relation to the population mortality reveals outcomes that fall with age. For a CD4 count of 1.2, for example, the ratio is 2.28 at age 25 and 1.04 at age 65. For 95 % certainty that the factor is not too low, these factors would have to be doubled very broadly speaking.

Guarantees and special conditions

Using the sufficient statistical material provided by the SHM, survival probability that could be used by individual insurers as the basis for the premiums1 for life insurance policies was calculated for the people with HIV who were using cART. However, not enough is known about the mortality probability in the long term. There is, however, the expectation that there will be an increased chance of further improvements. When guaranteeing a premium for an unrestricted number of years, it should be kept in mind that in this regard no risk statistics exist for a longer period of time. An insurer that desires to stick close to the statistics can, for example, agree that after a guarantee period an en-bloc clause applies and then base the required premium on the new statistical figures for the entire group. Either an increase or a reduction could result. An insurer that chooses to issue a guarantee for the entire duration of the insurance policy could consider applying certain surcharges. It is left up to the individual insurers to fill in the details. In terms of medical acceptance, several limiting conditions apply while restrictions could also be considered for higher start and termination ages.

Medical risk assessment

The study focused on people with HIV who had not used intravenous drugs and who, 24 weeks after diagnosis or after the start of therapy, had no illness for which the diagnosis of AIDS could be made. If they were being treated with anti-HIV medication the treatment had to have had at least a reasonable effect. The probability of death for these people is only slightly higher as long as no other risk factors are involved. The CDC category that applies for an untreated person (he/she is, however, being monitored by a physician) with HIV is the major risk factor. In addition, the age of the patient 24 weeks after diagnosis has a predictive value. For treated people, the CD4 count plays a large role.

1 The word premium is used in this report to mean the cost of cover.


The effect of cART (Combined Anti-Retroviral Therapy, a combination of three or more anti-HIV drugs) is largely determined using the CD4 count. The most important value is the value 24 weeks after start of the therapy. The CD4 count at that time determines to a large extent the prognosis and thereby the insurability. Given that the prognosis clearly worsens if the CD4 count is at any time lower than 0.2 million per millilitre, this level is observed as a minimum limit for both treated and untreated persons. The amount of viruses still found despite the therapy only has any predictive value for the survival probability if it has risen sharply to more than 100,000 viruses per millilitre. Finally, the year in which treatment commenced has an impact on the prognosis in terms of survival probability. Although the length of the study was continually extended, there are as yet no indications of a dramatic change in the prognosis in the future. However, it does apply that the risk of death reduces relatively (i.e. after correction for age and sex) the longer the patient follows cART.

Side effects of the therapy are found among many people with HIV including within the group studied. Consequently, the measurement figures already include possible effects on the prognosis. The same applies to possible resistance which can be increasingly better combated. It can be expected that the treatment of people with HIV will improve in the near future. As people up to age 16 and from age 56 are poorly represented in the studied group, the model presented here is only of limited value for calculating the survival probability of people in these age brackets. The nature of the required information makes it difficult to structure the medical acceptance in the same way as for other illnesses. All additional risk factors, complications and illnesses can be individually assessed by the medical advisor.

In summary, the following are of importance in the assessment of the candidate for insurance: 1. the CDC category in the first 24 weeks after the diagnosis; 2. the CDC category (CDC-A, CDC-B, CDC-C) on start of the insurance policy; 3. the CD4 count 24 weeks after the start of the anti-retroviral therapy; 4. the most recent CD4 count (potentially high risk when under 0.2 million cells per millilitre); 5. the recent number of viruses (potentially high risk when above 100,000 viruses per millilitre); 6. treatment with cART and the start date of this treatment (before or after 1 January 1998); 7. the number of years that cART has been given; 8. the age in complete years 24 weeks after the diagnosis; 9. the age on start of the insurance policy (the model is less reliable up to age 16 and from age

56); 10. data regarding following the therapy, side effects of the therapy, resistance of the virus,

complications and illnesses and risk factors that arise (not components of the model).

Of the above, points 1, 3, 6, 7 and 8 are part of one of the models so that this data is essential in calculating the medical risk. The remaining points involve the acute risk on start of the insurance policy or the reliability of the model used.


Introduction

The first version of the report on insuring people with HIV was published in March 2005 under the title Verzekerbaarheid hiv dichterbij [Insurability of HIV a step closer].2 The report was greeted with much interest as witnessed by the amount of attention in the press, both within the Netherlands and beyond our borders.

The title of the previous report was framed cautiously unlike that of the current report. In the interim, it has become clear that increasingly more insurance companies are offering life insurance policies to people with HIV who are receiving cART therapy (Combined Active Anti-Retroviral Therapy, a combination of three or more anti-HIV drugs). The actuarial data that has been gathered so far shows that the probability of death for persons with HIV who accurately follow the therapy and who have no other physical problems, does not deviate very strongly from the rest of the population. The period that is involved here is naturally quite short, which demands caution.

The previous report did not include the insurability of persons with HIV whose health is such that they did not yet need to follow cART, although they were under the supervision of a physician. No statements could be made about these people at that time because no actuarial data was available. Now, however, with the assistance of the HIV Monitoring Foundation, this data has become available, and as a result the HIV working group of the Association of Insurers can advise the offering of life insurance for this group too.

The HIV working group would like to point out that, despite the significant improvement in treatments with new forms of cART, HIV remains a serious illness. The long-term effects of the therapy are not yet known. For this reason, the report needs to be regularly amended in accordance with the latest medical insights and based on the latest statistics. The report does not contain any acceptance guidelines.

Thanks for this report go to the HIV Monitoring Foundation (Ard van Sighem and Frank de Wolf) and the Netherlands HIV Association (Ronald Brands and Robert Witlox) for their constructive cooperation.

The Hague, November 2008

HIV working Group: R.F.J.M. Kneepkens MD Dr R. Bieger, internist

R. Bruning, AAG W. de Boer, AAG H. Bons, AAG P. van Zijp, AAG Dr H. Nijenhuis, secretary

2 At that time, the HIV working group was known as the AIDS working group.


Chapter 1 Mortality data

1.1 Basic model

In the Netherlands, the HIV Monitoring Foundation (SHM) tracks people with HIV. Among other things, this monitoring delivers statistical data regarding the deaths of persons with HIV. From this, the hazard rate h, the mortality intensity for someone infected with HIV, is derived.

The Standardized Mortality Rate (SMR) is used to illustrate the mortality probability of someone infected with HIV in respect of non-HIV infected persons. This can be demonstrated with the following formula:

SMR= 1 e h

q x

In this formula: - SMR (standardized mortality ratio): is the 1-year mortality probability of a person infected with

HIV divided by the 1-year mortality probability of someone without HIV but of the same age and sex.

- h: the hazard rate, is the mortality intensity for a person infected with HIV. )q(e=h xl 1ln

l: is here dependent on the parameters of the model used and is specified later. - qx: the 1-year mortality probability of someone without HIV, but of the same age and sex

according to the survival tables Gehele Bevolking Mannen/Vrouwen 2000-2005 [Total Male/Female Population 2000-2005] of the Actuarial Society.

The determining of the hazard rate h from the statistical data is described for two different groups: - the group of persons infected with HIV who had not started therapy (cART) 24 weeks after

being diagnosed (cf. paragraph 1.2: mortality after a diagnosis of HIV). - the group of persons infected with HIV following a course of therapy (cART) (cf. paragraph

1.3: mortality after the start of cART).

1.2 Model for mortality after a diagnosis of HIV (untreated model)

The Untreated model The database contains 13,849 persons who were diagnosed before 2008 as only being infected with HIV-1. Ultimately, data on 3,479 patients was used for the model. This group was not following cART 24 weeks after being diagnosed. Those excluded were, among others, participants diagnosed before 1996 or after 2006 and patients infected through intravenous drug use. Of the group of 3,479 patients, 61 died during the period of observation.

In the model, the concentration of CD4 cells was not included as a parameter because in the population of 2,717 patients for whom the CD4 measurement was known, the CD4-measurement was not of significance. In other and earlier models the CD4 measurement was used as a very important parameter.


Two parameters still seem to have a significant influence on the mortality rate of those infected with HIV: the occurrence or not of a CDC-B event within 24 weeks of being diagnosed with HIV and the age of the patient 24 weeks after being diagnosed with HIV.

The variable l, with which the hazard rate h can be determined (cf. paragraph 1.1), is derived as follows:

xfB)I(CDCfB)I(CDCf=l 321 }1{ ++ In which

eventBCDCaisthereifeventBCDCnoisthereif

=B)I(CDC10

x = the age 24 weeks after being diagnosed

The values for f1, f2 en f3 that delivered a good fit with the observed survival are:

average value 95% CI f1 -9.4048 -10.9696 -7.8401 f2 -8.2814 -10.2897 -6.2732 f3 0.0794 0.0464 0.1123

The table shows, for example, that the value of the term f1 lies with 95% certainty between the values -10.9696 and -7.8401, with an average value of -9.4048.

The 95% confidence interval for l is defined as follows:

21.96 l=llow and 21.96 +l=lup

In which 2 is calculated using the covariance matrix:

no CDC-B event CDC-B event age No CDC-B event 0.63736 0.69997 -0.01276 CDC-B event 0.69997 1.04991 -0.01554 Age at 24 weeks -0.01276 -0.01554 0.0002832

Outcomes of the Untreated model Table 1 shows the results regarding HIV over-mortality according to age 24 weeks after diagnosis according to the Untreated model. In both tables a distinction is made according to whether or not a CDC-B event occurred. The absolute value of the HIV increase on the mortality ratio is shown.

Table 1 below shows that in the Untreated model, the increase of the mortality ratio rises with age. The increase is not dependent on sex. If a CDC-B event occurs, the increase is in any case much higher than if there is no CDC-B event.


Table 1: Estimated HIV increase (absolute) on the mortality ratio according to age 24 weeks after being diagnosed with HIV (Untreated model)

age no CDC-B event CDC-B event 25 0.00060 0.00184 30 0.00089 0.00274 35 0.00132 0.00406 40 0.00197 0.00604 45 0.00293 0.00897 50 0.00435 0.01328 55 0.00643 0.01966

The SMR is, however, dependent on sex, because the normal mortality ratios for men and women are different. Table 2 shows the SMR for men and women separately for a number of ages. In both tables a distinction is made between the occurrence or not of a CDC-B event. The average SMR value is shown as well as the limits (above and below) of the 95% confidence interval (95% CI).

Table 2: Patients of different ages 24 weeks after being diagnosed in the Untreated model

The SMR falls after age 30 (women) or 35 (men), despite the fact that the increase for HIV over-mortality rises in an absolute sense with age, as demonstrated in Table 1. This is because the rise in the standardized mortality ratio is steeper with age than the increase for HIV over-mortality. If no CDC-B event occurs, the 95% confidence interval is relatively tight. The upper limit (above 95% CI) is a maximum of about 1.5 times as high as the average value of the SMR. The upwards risk i.e. of more unfavourable outcomes is thus reasonably limited. If a CDC-B event occurs, the uncertainty, particularly upwards, is much higher as shown by the distance between the upper limit of the 95% confidence interval and the average value.

woman age

Normal mortality

SMR no CDC-B

under 95% CI

above 95% CI

SMR CDC-B

under 95% CI

above 95% CI

25 0.000286 3.10 1.92 5.78 7.45 2.73 24.95 30 0.000375 3.37 2.18 5.77 8.29 3.21 25.05 35 0.000595 3.23 2.23 5.02 7.84 3.30 21.25 40 0.000988 2.99 2.19 4.34 7.11 3.25 17.51 45 0.001795 2.63 2.00 3.66 5.99 2.98 13.56 50 0.002843 2.53 1.91 3.55 5.67 2.94 12.22 55 0.004103 2.57 1.87 3.82 5.80 3.02 12.29

man age

Normal mortality

SMR no CDC-B

under 95% CI

above 95% CI

SMR CDC-B

under 95% CI

above 95% CI

25 0.000573 2.04 1.46 3.38 4.21 1.86 12.93 30 0.000661 2.35 1.67 3.71 5.14 2.25 14.65 35 0.000850 2.56 1.86 3.81 5.78 2.61 15.16 40 0.001298 2.52 1.90 3.54 5.65 2.72 13.56 45 0.002213 2.32 1.81 3.15 5.05 2.60 11.18 50 0.003651 2.19 1.71 2.99 4.64 2.51 9.73 55 0.005957 2.08 1.60 2.94 4.30 2.39 8.76


In this Untreated model, whether or not cART is followed is not a factor. Furthermore, it is worth noting that the concentration of CD4 cells in the blood is not a parameter in the model although this is generally regarded as the major predictor of death for HIV (cf. the previous report of the AIDS working group). A too-large proportion of the group on which this model is based had no known CD4 measurement so that this factor could not be included. Estimates of the missing CD4 measurements via multiple imputation delivered no indications of a predictive value of the CD4 measurement.

1.3 Model for mortality after the start of cART (Treated model)

The Treated model The database consists of 11,000 potential candidates from the group that had started cART. Ultimately, the data of 5,951 patients was used for the model. Those excluded were, among others, participants diagnosed with HIV before 1996 or after 2006, patients with a CDC-B or CDC-C event after 24 weeks with an unknown date of diagnosis and patients infected through intravenous drug use. Of the group of 5,951 patients, 253 died during the period of observation.

Two parameters still seem to have a significant influence on the mortality rate of those infected with HIV: the concentration of CD4 cells 24 weeks after being diagnosed with HIV and the time that has elapsed in years since the start of cART.

The variable l, with which the hazard rate h can be determined (cf. paragraph 1.1), is derived as follows:

tfcARTstartIfCDf=l 321 }1998{)]01.0;4ln[max( +


is calculated using the following covariance matrix: Intercept Start cART <

1998 Log CD4 Time

Intercept 0.05572 -0.01155 0.01469 -0.005 Start cART < 1998 -0.01155 0.03939 -0.001496 -0.002178 Log CD4 0.01469 -0.001496 0.008062 0.000365 Time -0.005 -0.002178 0.000365 0.002205

Results of the Treated model Tables 3a through 3f give examples of the SMR for a patient who started cART in 2000. The length of time since the start of cART is indicated by the letter t. The HIV over-mortality is in an absolute sense independent of the age at the start of cART and independent of sex. The age does, however, determine the SMR in part, because the normal mortality ratio is dependent on age and sex. The outcomes shown are for a person aged 35 in 2001. The tables show the results for a woman and a man for CD4 measurements of 0.4, 0.6 and 0.8 respectively (this is 400, 600 or 800 x 106 cells per litre of blood).

Tables 3a through 3c results for a 35-year-old woman according to CD4 measurement; t indicates the number of years of cART

Table 3a: female, CD4 measurement 0.4

year

t age normal

mortality SMR

estimate under

95% CI above 95% CI

HIV extra mortality

under 95% CI

above 95% CI

2001 1 35 0.00064 7.58 5.76 10.07 0.00392 0.00284 0.00541 2002 2 36 0.00072 6.10 4.79 7.86 0.00333 0.00248 0.00449 2003 3 37 0.00081 4.95 3.93 6.33 0.00283 0.00210 0.00382 2004 4 38 0.00090 4.01 3.16 5.18 0.00241 0.00173 0.00335 2005 5 39 0.00100 3.32 2.59 4.39 0.00205 0.00140 0.00299 2006 6 40 0.00112 2.76 2.13 3.74 0.00174 0.00112 0.00271

Table 3b: female, CD4 measurement 0.6

year

t age normal

mortality SMR

estimate under

95% CI above 95% CI

HIV extra mortality

under 95% CI

above 95% CI

2001 1 35 0.00064 5.64 4.36 7.40 0.00276 0.00200 0.00381 2002 2 36 0.00072 4.59 3.67 5.84 0.00235 0.00175 0.00316 2003 3 37 0.00081 3.79 3.07 4.76 0.00200 0.00148 0.00270 2004 4 38 0.00090 3.12 2.53 3.95 0.00170 0.00122 0.00236 2005 5 39 0.00100 2.64 2.12 3.39 0.00144 0.00099 0.00211 2006 6 40 0.00112 2.24 1.80 2.93 0.00123 0.00079 0.00191

Table 3c: female, CD4 measurement 0.8

yeas

t age normal

mortality SMR

estimate under

95% CI above 95% CI

HIV extra mortality

under 95% CI

above 95% CI

2001 1 35 0.00064 4.62 3.62 6.00 0.00216 0.00156 0.00297 2002 2 36 0.00072 3.81 3.08 4.78 0.00183 0.00136 0.00247 2003 3 37 0.00081 3.17 2.61 3.93 0.00156 0.00115 0.00210 2004 4 38 0.00090 2.65 2.19 3.30 0.00132 0.00095 0.00184 2005 5 39 0.00100 2.28 1.87 2.87 0.00113 0.00077 0.00165 2006 6 40 0.00112 1.97 1.62 2.51 0.00096 0.00062 0.00149


Tables 3d through 3f: Results for a 35-year-old man according to CD4-measurment, t indicates the number of years of cART

Table 3d: male, CD4-measurement 0.4

year

t age normal

mortality SMR

estimate under

95% CI above 95% CI

HIV extra mortality

under 95% CI

above 95% CI

2001 1 35 0.00085 5.60 4.33 7.34 0.00392 0.00284 0.00541 2002 2 36 0.00091 4.65 3.72 5.92 0.00333 0.00248 0.00449 2003 3 37 0.00099 3.86 3.12 4.85 0.00283 0.00210 0.00382 2004 4 38 0.00107 3.26 2.62 4.13 0.00241 0.00173 0.00335 2005 5 39 0.00117 2.75 2.20 3.56 0.00205 0.00140 0.00299 2006 6 40 0.00130 2.34 1.86 3.08 0.00174 0.00112 0.00271

Table 3e: male, CD4-measurement 0.6

year

T age normal

mortality SMR

estimate under

95% CI above 95% CI

HIV extra mortality

under 95% CI

above 95% CI

2001 1 35 0.00085 4.24 3.35 5.47 0.00276 0.00200 0.00381 2002 2 36 0.00091 3.58 2.91 4.47 0.00235 0.00175 0.00316 2003 3 37 0.00099 3.01 2.49 3.72 0.00200 0.00148 0.00270 2004 4 38 0.00107 2.59 2.15 3.21 0.00170 0.00122 0.00236 2005 5 39 0.00117 2.24 1.85 2.80 0.00144 0.00099 0.00211 2006 6 40 0.00130 1.94 1.61 2.47 0.00123 0.00079 0.00191

Table 3f: male, CD4-measurement 0.8

year

t age normal

mortality SMR

estimate under

95% CI above 95% CI

HIV extra mortality

under 95% CI

above 95% CI

2001 1 35 0.00085 3.53 2.83 4.49 0.00216 0.00156 0.00297 2002 2 36 0.00091 3.01 2.49 3.71 0.00183 0.00136 0.00247 2003 3 37 0.00099 2.57 2.17 3.12 0.00156 0.00115 0.00210 2004 4 38 0.00107 2.24 1.89 2.72 0.00132 0.00095 0.00184 2005 5 39 0.00117 1.96 1.66 2.41 0.00113 0.00077 0.00165 2006 6 40 0.00130 1.74 1.47 2.15 0.00096 0.00062 0.00149

In all cases the relative over-mortality SMR falls because on the one hand, the normal mortality rises with age, and on the other, the extra mortality through HIV falls the longer cART lasts. The choice of age 35 was random. The column HIV extra mortality remains the same if a different age is selected and is also the same for both men and women. It is the duration of cART since treatment commenced that determines the HIV extra mortality factor. The SMR is, however, dependent on the choice of age because this depends on the normal mortality.

It is clear that the HIV increase and the SMR are higher in proportion to the concentration of CD4 cells in the blood being lower. In the Treated model, the CD4 concentration factor is a determining factor and this has been confirmed in many other studies.

The HIV increase on the mortality ratio (HIV extra mortality) drops quickly the longer cART lasts. This demonstrates the positive effect of cART on life expectancy. The 95% confidence interval also becomes tighter the longer treatment lasts.


Comparison of the Treatment model to the 2005 model The difference between this Treatment model and the model from the previous report of the AIDS working group (March 2005) is that the duration of therapy has now been included as an additional parameter. In the 2005 model, only the CD4 concentration was a significant parameter alongside the fact that cART was started before 1998 or in 1998 or later. In Table 4 a comparison is made for several CD4 concentrations between the 2005 model (whereby the mortality probability was compared to the 1995-2000 AG Survival Tables) and the current Treatment model (whereby the mortality probability is compared to the 2000-2005 AG Survival Tables). The comparison is only made for start of cART in or after 1998:

Table: 4 HIV over-mortality (absolute) on start of cART in or after 1998; Model 2005 compared to the 1995-2000 AG Survival Tables, Model 2008 compared to the 2000-2005 AG Survival Tables

CD4 = 0.4 CD4 = 0.8 Model 2005 0.00234 0.00126

Treated model 2008 t=1 0.00392 0.00216 t=2 0.00333 0.00183 t=3 0.00283 0.00156 t=4 0.00241 0.00133 t=5 0.00205 0.00113

Table 4 shows that after four years of cART, the over-mortality is more favourable (lower) than according to the 2005 model in respect of the survival tables used. Because the latest AG survival tables show lower mortality probability depending on age and sex the turning point for total mortality will already be reached in most cases within 3 years. Uncertainty regarding the success of the therapy and regarding adherence to the therapy still leads to higher over-mortality in the first years of therapy.


Chapter 2 Guarantees and special conditions

Conclusions and recommendations

Using the results of the exhaustive analysis of the SHM, the HIV working group set down the risks of insurances for a particular group of people with HIV in the Netherlands. Taking the limiting conditions into account, a risk profile can be calculated for a category of products, the most important features of which are: 1. a higher cost of cover than that of normal risks; 2. the possibility of a guarantee for a limited number of years; 3. the possibility of an additional surcharge for a guarantee for the entire duration of the

insurance or to limit any necessary increases to a particular maximum (thats up to the insurer).

It will be left up to the insurers to add further structure to the insurance. The points above are discussed in greater detail below.

2.1 Technological background

Premiums for life insurance policies are based by the individual insurer on the survival tables for the entire population, possibly with a correction because the insured population has a different mortality pattern. In addition, in order to avoid anti-selection, the individual health condition of an insured person is tested to assess whether or not this deviates too far from the average for his/her age and sex group. Account needs to be taken, definitely in the case of a candidate for insurance with HIV, of the current state of affairs of the science regarding mortality probability in general and the personal scaling-in of the candidate. The mortality probability of people with HIV could improve considerably in the years ahead because the technology surrounding therapies is developing significantly. In the longer term, however, negative effects could also occur that could completely negate the improvements. For example, the virus could develop a resistance that could have the effect of the medications losing efficacy after ten years. Due to this uncertainty, the working group believes it would not be prudent to anticipate expected but not yet realized improvements. As already stated in Chapter 1, the period in which people with HIV have been using cART is limited (approximately ten years) so that as yet nothing can be said about the possible long-term effects.

2.2 Possible forms of guarantee

Given that no data is as yet available on the long-term effects, giving a guarantee for the entire duration of a long-term insurance policy is not an obvious move. Insurers could nonetheless choose to guarantee their individually established premiums at a certain level for the entire duration of a policy or to carry out periodic reviews. Such revisions could be calculated annually or be limited to longer periods.

Revisions can be justified based on two reasons: 1. the mortality probability for the entire group becomes smaller or greater; 2. over the course of time, the insured receives a different mortality probability than the original

one that was estimated at the time the insurance was taken out.


The insurer can relatively easily implement changes that arise from the first reason by applying an en-bloc clause. It is clear that a candidate can demand that when premiums are revised, after several years only a reduction is possible. When, for example, a life insurance policy is linked to a personal mortgage, the customer will otherwise run the risk of premium increases that mortgage providers as a rule wish to avoid. To cover that risk, a premium guarantee can be given for which a separate risk premium can be calculated.

Two alternatives are possible to determine if a change at the individual level has arisen from personal circumstances: an extensive system of controls and monitoring; a simple check as a limiting condition to becoming eligible for an improvement. In this regard,

an additional surcharge could be considered.

Broad market positioning It is socially desirable that the offer is positioned broadly on the market. Companies that, for instance, from the viewpoint of risk management would prefer not to sell these products, could for the most part reinsure the policies. Within the framework of risk management it is clear that the risk profiles are only indicative for those persons undergoing permanent treatment. To underscore that HIV/AIDS is considered to be a chronic illness, it would be desirable for an amendment to the HIV code of conduct to be studied, in close cooperation with the Netherlands HIV Association (Kneepkens 2005).

Offering every type of life insurance Assuming that the risk profile is set down sufficiently accurately, all kinds of life insurances can be considered. In its calculation, due to the relatively high degree of uncertainty, the working group indicated the possibility of observing a confidence interval if so desired. However, this precaution is mainly of importance for pure risk insurances. Particularly in the longer term, insurances with a savings arrangement have a considerably lower risk.


Chapter 3 Medical risk assessment

Conclusions and recommendations

The study was focused on persons with HIV who had not used drugs intravenously and had no other illness for which AIDS was diagnosed. They were divided into two groups: those for whom anti-HIV medication had a reasonable effect and those who did not yet require anti-HIV medication. For the latter group, the mortality probability was only slightly higher as long as no other risk factors were present.

The effect of cART is mainly determined using CD4 measurements. The most important value is the value 24 weeks after therapy is commenced. The CD4 measurement at that time determines the prognosis to a large extent and thereby the insurability. Because the prognosis is clearly less favourable if the measurement is lower than 0.2 million per millilitre, this amount is taken as the minimum limit on start of the insurance. The amount of viruses that is found despite the therapy only has a predictive value if it rises significantly on start of the insurance to over 100,000 viruses per millilitre. Further, the year in which treatment is started has an influence, although this effect is less than was earlier observed. Halfway through the 1990s, treatments were less effective and they increased the resistance of the virus to further treatment. The impact of this is, however, not such that no insurance cover is possible. The risk of death is, however, higher which results in a higher insurance premium. In 2005, the length of time during which people were treated with cART played no role in the calculation of the risk of death. Today, however, the length of treatment appears to be an important factor: the risk of death falls the longer people are treated with cART.

For those who are not yet being treated with anti-HIV medication but are under the supervision of a physician, it applies that the most important predictive factor is the CDC classification. When conditions in category B are present, the risk is significantly higher than when they are not present (category A). The CD4 measurement no longer appears to have a prognostic value.

Although the period covered by the study was only a few years, there are no indications of a dramatic change in the prognosis.

Many people suffer side-effects with cART as confirmed in the group studied. The possible effects of these on the prognosis are therefore included in the measurement figures. The same applies to any possible resistance which can be increasingly better combated. It can be expected that the treatment will improve in the near future. This improvement explains the possible reduction in the significance of earlier treatments and the CD4 measurements.

Persons aged over 55 are poorly represented in the group studied, so that the model presented here only has limited value for calculating their mortality probability.

The nature of the necessary information makes it possible to structure medical acceptance in the same way as for other illnesses. A separate HIV policy is therefore unnecessary.


3.1 Introduction

This chapter first examines the data that is important for medical risk in the case of persons who are seropositive. Subsequently, the medical assessment procedure for insuring people with HIV is discussed.

3.2 Target group The target group is people with HIV for whom insurers can calculate statistically substantiated risks. Those who cannot be insured due to the height of the anticipated risk, automatically fall outside the target group. This applies to intravenous drug users and to people with an AIDS-related illness at the time of applying for the insurance. The study was only aimed at adults because children only constitute a small proportion of the insured population and expanding the research group to cover more phases of life would reduce the reliability of the study.

3.3 Medical assessment for life insurance

The costs of the cover of a life insurance policy that pays out on death are calculated using some product-specific data and the financial risk that the insurance company runs. This financial risk is directly influenced by the amount to be paid out on death and by the probability that the insured will die. The probability of death is dependent in part on age, sex and the medical insurance risk constituted by the presence of illnesses and risk factors. Before the insurance commences, the life insurer asks the medical advisor to make a statement about the medical insurance risk based on medical information. The procedure for gathering medical information (medical acceptance [Kneepkens 2003, 2007]) for seropositive persons is the same as for those with other medical complaints (Insurance Medical Examination Protocol, Medical Examinations Act).

First, every candidate is asked for a health guarantee. This may be a simple questionnaire, an exhaustive questionnaire or a standardized medical examination. The questionnaire used is dependent on the insured pay-out on death and the policy of the insurance company concerned. Using the medical data from the health guarantee, the medical advisor issues a statement about the risk. The final recommendation of the medical advisor (medical recommendation) can be a statement regarding the special conditions as well as the level of the risk. Special conditions involve the length of the cover of the agreement, the increased risk is the degree to which the probability of death is higher than would normally be expected for an insured person of that age and sex.

On the health statement, or during the examination, the candidate should report that his/her status is seropositive. On the request of the medical advisor, information (CDC classification, CD4 measurement, the number of viruses per millilitre, any complications, therapy, and side-effects of the therapy and a report of the medical history) can be requested from the treating physician and a supplementary examination may possibly take place. The candidate should give consent for the release of the medical data (cf. annexes A and B).


If a candidate gives his/her consent, he or she probably expects to take out a policy. The medical advisor would be sensible to point out to the candidate in advance that sometimes no insurance coverage is possible or may be possible only against special premiums and conditions.

3.4 Medical recommendation and assessment by the company

The medical advisor will draw up a recommendation about the medical insurance risk by indicating for supplementary illnesses and other factors to what degree the annual mortality probability is increased. This can be given as a permillage, a relative risk or by an over-mortality percentage (Kneepkens 2003, 2007).

The two models for people with HIV (one model for those following cART and one for those being seen by a physician but who do not receive cART) are used to predict the medical insurance risk for a longer period than that for which statistical information is available. In this way the risks in the future may come out higher, for example, as a consequence of the as yet unknown long-term effects of anti-viral therapy (Gras 2007, Gras 2003, Van Sighem 2003). On the other hand, there is the observed trend of an increasingly favourable prognosis and the falling significance of individual risk factors. There is no indication that this trend will not continue. By observing the actuarial calculations and confidence interval, more certainty can be gained about the risks/costs of cover in the first five insurance years. This additional certainty makes it less risky to use the model for longer durations as well.

Based on the medical data, the medical advisor can indicate the increased medical risk according to the method and in the form of over-mortality or relative risk as used by insurance companies. The explanation by the medical advisor about a deviating recommendation or a rejected application for insurance and the right not to know of the candidate (Insurance Medical Examination Protocol, Medical Examinations Act) are the same as for other insured persons. In practice, it will be shown that the medical insurance risk for those infected with HIV has become comparable to the risk of chronic complaints such as diabetes mellitus and heart and vascular diseases.

The medical data that is provided by the treating specialist is most likely known to the candidate. The chance is small that he/she will be confronted by new information that could represent a great increase in the psychological burden of the person involved as a result of medical asessment. The chances of breaching the right not to know are negligible. In the opinion of the HIV working group, the obligation to inform a candidate personally about the medical recommendation can be fulfilled without problems. Due to the serious consequences if the right not to know is unintentionally violated, it would nonetheless be sensible to devote attention to this at the beginning of the medical acceptance letter to the customer.


3.5 Relevant medical data after start of cART

In 2005, the AIDS working group published a model (hereinafter referred to as the Treatment model) for calculating the mortality risk for people with HIV who were being treated with cART. The Treatment model of 2005 has been recalculated for this publication but has remained unchanged in structure. In general, it can be said that compared to 2005, individual risk factors have become less important. The average outcome of the model has remained unchanged.

IVDUs (intravenous drug users) and Hepatitis C The determination of the target group had consequences for the Treatment model. The absence of intravenous drug users resulted in infection with hepatitis C occurring less often in the research group and not having to be included in the model. Hepatitis C is one of the major risk factors for death among people with HIV (Jaggy 2003). The diagnosis of hepatitis C has become less rare in recent years among men who have had sexual intercourse with other men.

The number of viruses before the start of cART The number of viruses per millilitre before the start of cART has no predictive value.

The number of viruses after the start of cART, year of start and duration of cART The number of viruses per millilitre 24 weeks after the start of cART was not taken into consideration in the Treatment model because this value was known among too few people. The number of viruses per millilitre had no significant prognostic value among the people with HIV who were receiving treatment. During the course of 1995, cART, an effective combination therapy, was started. The chance of resistance of the AIDS virus and reduced effectiveness of the treatment is smaller if the patient has never been treated before with anti-retroviral medication. The percentage of people with a number of viruses per millilitre of over 100,000 is considerably higher in the group of people who started the therapy in 1997 or earlier than in the group that started cART in 1998 or later and were therefore not treated under older regimes. Their prognosis is better. In practice, the start date of the anti-retroviral therapy is a suitable indication of the chance of reduced effectiveness of the anti-retroviral therapy and thus of mortality if the number of viruses per millilitre is not known (Gras 2007, Gras 2003). In relative comparison to the population after matching for age and sex the risk of death falls the longer a person follows cART. This could be a survival effect. Over the course of the years the treatment protocol has been changed several times so that caution should be exercised in drawing conclusions. Interesting to note in this regard is a comparison with the study of the ART Cohort Collaboration in 2008, whereby the life expectancy of treated HIV seropositive patients was calculated using survival tables. If the extra risk of death falls the longer cART is followed, an extrapolation of the results in the first years probably delivers a high over-estimate of the risk in the longer term and therefore an under-estimate of life expectancy. The PYLL (potential years of life lost) calculated in the study implies that every death before the 65th year of life should be considered premature. The PYLL is therefore not only based on deaths that are directly or indirectly connected to being HIV seropositive. This study of the ART Cohort Collaboration is therefore not usable for the medical assessment of life insurance policies.


The number of viruses on start of the insurance No investigation was conducted into the extent to which the number of viruses per millilitre at the time of applying for insurance has a predictive value. For people with HIV who are receiving treatment, it is plausible that the number of viruses per millilitre at that time has a predictive value if it amounts to at least 100,000 per millilitre. Probably, there was an interruption to the therapy or insufficient adherence to the therapy so that the insurance risk can come out higher. A high number of viruses per millilitre generally does not indicate resistance of the virus to the therapy being administered. When resistance occurs, there is usually some degree of suppression and the number of viruses per millilitre comes out lower.

CD4 measurement The CD4 measurement, the number of cells with CD4+ in millions per millilitre, 24 weeks after the start of anti-retroviral therapy has turned out to be a major predictor of mortality probability in the measurement period. The CD4 measurement is a measurement of the bodys immunity. A CD4 measurement under 0.2 gives a highly increased chance of complications such as complaints caused by AIDS and worsens the prognosis considerably (Egger 2002, Chene 2003). The result of anti-retroviral therapy also falls if the CD4 measurement is under 0.2 million cells per millilitre prior to the start of the therapy (Sterling 2003). In general, therefore, anti-retroviral therapy is started before this limit is reached. The percentage of people that thanks to the therapy reach a CD4 measurement of at least 0.35 becomes greater the higher the CD4 measurement is prior to the start of the therapy (Gras 2007, Gras 2003). After the start of anti-retroviral therapy, on average, the CD4 measurement rises by 0.35 to 0.4 cells per microlitre over a period of 5 years. Approximately one-third of this increase is realized in the first 24 weeks, one-third in the following 12 months and the final third in the last three and a half years (Viard 2004). In individual cases, the increase can be minimal or even twice as high. The increase is also dependent on the adherence to therapy and its continuity (Kaufmann 2003). The total increase in the CD4 measurement can to some degree be predicted based on the rise in the CD4 measurement after 24 weeks of therapy. This value offers a relatively long follow-up period in which mortality can be measured. If the most recent CD4 measurement is less than 0.2, the therapy is insufficiently effective and the insurance risk is higher. This lower limit does not constitute part of the model. The study of the ART Cohort Collaboration published in 2008, also shows a strong prognostic value for the CD4 count that was given on the start of the study (baseline measurement), without however accurately specifying at what time in the course of the infection or treatment this was.

CDC category3 People with AIDS have a very high risk of death. People with AIDS i.e. those who are classified in the clinical category C of the CDC classification 24 weeks after the start of cART were therefore excluded in advance from the research group. Having already experienced AIDS at an earlier stage had no significant predictive meaning. This is definitely not the case for people who have AIDS at the time insurance cover is requested. Due to the overlap between a higher CDC category (CDC-B or CDC-C) and a low CD4 measurement, having undergone an illness in category B of the CDC classification has no predictive significance after correction for the CD4 measurement.

3 NB: according to the criteria, over the course of time the CDC category may stay the same or increase but not

become lower.


Age and sex In the models of the HIV working group, the survival probability of people with HIV is set off against the survival probability of the population, taking into consideration age and sex. In this way, no predicative value needs to be allocated to either factor.

Additional risk factors Due to the exclusion of intravenous drug users and people with AIDS and thanks to the limiting values observed for the CD4 measurement and the number of viruses per millilitre, there were few participants in the research population with other illness, complications or risk factors that weighed heavily in the balance. The risk that these factors entail is therefore not part of the model. All additional risk factors, complications and illnesses should be assessed separately by the medical advisor, for example, tumours, diabetes mellitus, persistent airway disorders and supplementary infectious diseases.

Side-effects of cART The side-effects of anti-retroviral therapy are an exception to the rule for additional risk factors. Although today many side-effects are described in medical literature, the related risks are still partially unknown (DADSG 2003, HLCDSG 2003). A high amount of side-effects is not, however, a reason to stop anti-retroviral therapy, although they could have an influence on the precision with which the therapy is followed (compliance). One of the side-effects of the so-called protease inhibitors is dyslipidaemia (a defect in lipoprotein metabolism). In 2007, the DAD Study Group published a study which showed that the increase of some 16% in myocardiac infarctions (heart attacks) was only caused in part by an increase of lipoproteins in the blood. If a correction were to be made for lipoproteins, the increase would still be 10%.

Few serious side-effects were observed in the target group because they compel the therapy to be discontinued and thus increase the chance of a rise in the number of viruses per millilitre or of the CD4 measurement falling outside the limits. If a candidate suffers from serious side-effects that in themselves deliver a clear increase in the medical risk, then the medical advisor can assess them separately. Minor side-effects can be disregarded.

Continuity and compliance The continuity of the therapy influences both the extent of the rise in the CD4 measurement and the number of people for whom the CD4 measurement remains low. Discontinuity through scheduled interruptions or a lack of adherence to the therapy deliver a less favourable result on both points (Kaufmann 2003), even if the CD4 measurement is constantly monitored (SMART 2006). The mortality probability too increases. Even is the event of multi-drug resistance, the temporary interruption of the treatment delivers a less favourable prognosis (Lawrence 2003). However, fewer side-effects are reported for interruptions (Ananworanich 2006). The adherence to the therapy of the candidate has an influence on the effectiveness of the therapy and thus indirectly on the mortality probability. Because the effect of the therapy is the starting point for calculating the medical insurance risk, the adherence to the therapy can largely be disregarded. In insurance practice, information about adherence to the therapy cannot be acquired in a reliable manner and is often based on the subjective judgement of the observer. Information about the adherence to the therapy cannot therefore be used in determining the medical insurance risk.


Resistance The resistance of the AIDS virus to the anti-retroviral medications used is becoming rarer now that use is being made of multiple drugs at the same time. The medication cocktail does give more side-effects which can lead to the careless use of the medications. This increases the risk of resistance. The cumulative risk of resistance to cART amounts to over 9% over a period of ten years, but drops every year by about one-seventh (Phillips 2007). For about 60% of the people with a resistant virus, the virus is undetectable in one or more of the determinations. It is usual to modify anti-retroviral treatment if resistance is found or if there are significant side-effects. However, the possibilities for modifying the treatment are limited and the resistance is often to more than one medication at a time. Currently, however, new medications are under development with different points of reference. The prospects do not appear to make therapy of a longer duration and thus insurance cover, impossible in advance.

In summary, in the assessment of a candidate under treatment the following points are of importance: 1. the CD4 count 24 weeks after the start of the anti-retroviral therapy; 2. treatment with cART and the start date of this treatment (before or after 1 January 1998); 3. the number of years for which cART has been given; 4. the most recent CD4 count (potentially high risk when under 0.2 million cells per millilitre); 5. the most recent number of viruses (potentially high risk when above 100,000 viruses per

millilitre); 6. CDC category (CDC-A, CDC-B, CDC-C) on start of the insurance 7. the age on start of the insurance (the reliability of the model is lower up to age 16 and from

age 56); 8. data regarding adherence to the therapy, side effects, resistance of the virus, complications

and illnesses and risk factors that arise (not components of the model). Of the above, points 1, 2 and 3 are part of the model so that this data is essential in calculating the medical risk.

3.6 Relevant medical data prior to the start of cART

A separate model was made for people with HIV who are not yet being treated with cART: the Untreated model. These persons were not being treated immediately after the diagnosis. After 24 weeks, 40.7% of all patients who met the criteria of the HIV working group had started treatment with cART. Refer to the Treated model for this group. Over 59% (27% of all patients) were not yet receiving cART after 24 weeks. The risk for this group can be calculated using the Untreated model. In this model, most of the risk factors have little or no significance.

IVDUs (intravenous drug users) and Hepatitis C The determination of the target group had consequences for the Treatment model. The absence of intravenous drug users (IVDUs) resulted in infection with hepatitis C occurring less often in the research group and not having to be included in the model. Hepatitis C and other liver disorders together cause about one-tenth of all deaths (CASCADE 2006). The rising contribution to the mortality rate is caused by the significant reduction in other infectious diseases as a cause of death and in AIDS-related causes of death since the introduction of cART (CASCADE 2006).


The number of viruses per millilitre The number of viruses per millilitre has no significant prognostic value. A high number of viruses per millilitre goes with a swift drop in the CD4 measurement (Rodrguez 2006). Due to this, anti-HIV medication will be commenced which again has a favourable effect on the risk. The risk-increasing effect of the high number of viruses per millilitre is thus quickly countered.

CD4 measurement For people with HIV who are not receiving treatment, the CD4 measurement has no significant prognostic value if a correction is made for the clinical CDC category (A or B). A CD4 measurement under 0.2 gives a highly increased chance of complications such as complaints caused by AIDS and worsens the prognosis considerably (Egger 2002, Chene 2003). The result of the subsequent anti-retroviral therapy also falls if the CD4 measurement is under 0.2 million cells per millilitre prior to the start of the therapy (Sterling 2003). Therefore, anti-retroviral therapy is started if this limit has already been reached in the first six months following the diagnosis. If the most recent CD4 measurement is less than 0.2, cART is generally started and the insurance risk is higher. This lower limit is not part of the model.

CDC category4 People with AIDS have a very high risk of death. People with AIDS i.e. those who are classified in the clinical category C of the CDC classification 24 weeks after the start of cART were therefore excluded in advance from the research group. Having experienced illnesses in the first 24 weeks that fall under clinical category B of the CDC classification (CDC-B) also has predictive value. The CDC categories B and A (absence of B) are part of the Untreated model. The CD4 measurement has no predictive value if the CDC classification is part of the model. Naturally, the risk is seriously higher for people who have AIDS at the time of applying for insurance cover and higher to a lesser degree at the time of suffering from a CDC-B illness.

Age and sex In the models of the HIV working group, the survival probability of people with HIV is set of against the survival probability of the population, taking into consideration age and sex. In this way, no predicative value needs to be allocated to sex. Age has some predictive value from which it appears that the relative survival probability (relative to the expected survival probability) falls as the age increases. The model assumes the age as of 24 weeks after the diagnosis.

Additional risk factors Due to the exclusion of intravenous drug users, people with AIDS and thanks to the limiting values observed for the CD4 measurement and the number of viruses per millilitre, there were few participants in the research population with other illnesses, complications or risk factors that weighed heavily in the balance. The risk that these factors entail is therefore not part of the model. All additional risk factors, complications and illnesses should be assessed separately by the medical advisor, for example, tumours, diabetes mellitus, persistent airway disorders and supplementary infectious diseases.

4 NB: according to the criteria over the course of time the CDC category may stay the same or increase but not

become lower.


In summary, in the assessment of a candidate who is not under treatment the following points are of importance: 1. the CDC category in the first 24 weeks after the diagnosis; 2. the age in complete years 24 weeks after the diagnosis; 3. the most recent CD4 count (potentially high risk when under 0.2 million cells per millilitre); 4. the most recent number of viruses (potentially high risk when above 100,000 viruses per

millilitre); 5. the CDC category (CDC-A, CDC-B, CDC-C) on start of the insurance policy; 6. the age on start of the insurance policy (the model is less reliable up to age 16 and from age

56); 7. data regarding adherence to the therapy, side effects of the therapy, resistance of the virus,

complications and illnesses and risk factors that arise (not components of the model).

Of the above, points 1 and 2 are part of the model so that this data is essential in calculating the medical risk.

3.7 General comments on the models

The calculation models make use of values obtained at an early stage of the infection or treatment.

The calculation models were designed for life insurance policies. It applies to life insurances that, on start of the insurance, the risk of death during the duration of the insurance must be predicted for many years. The longer the observation duration from the time that a risk indicator is measured, the more accurately the prognostic value of the risk indicator can be determined for the life insurances. A calculation model that is based on the most recent information about the patient can only make a statement about a very short period. Only the most serious abnormalities appear to be significant (to have a prognostic value) in this situation.

A second condition for use in the insurance practice is the usability of the data included in the model. The data that is used in the models should be determined for all insurance candidates at a comparable time with no possible doubt about the availability, the prognostic value, the time of the determination or the reliability of the result. For example: the CD4 measurement 24 weeks after the diagnosis is usable in this sense. A CD4 measurement after three years is not usable because too many insurance candidates have been under treatment for too short a time.


Chapter 4 Press about Verzekerbaarheid hiv dichterbij in March 2005

The Dutch press as a whole

A great deal of press attention (newspapers, radio and television) was paid to the release on 9 March 2005 of the Verzekerbaarheid hiv dichterbij report. On 9 March, two TV programmes, Twee Vandaag (a current affairs programme) and NOS-journaal (a news programme) looked extensively at the insurability of persons with HIV. In both programmes, Eric Fischer (then General Manager of the Dutch Association of Insurers), Robert Witlox (Director of the Dutch HIV Association) and Eric Koller (infected with HIV) were interviewed. Furthermore, the news programmes of the TV stations RTL 4 and 5 also looked at the issue. In addition, the news bulletins of various radio channels also carried reports. And all of this took place on 9 March 2005. On 9 March 2005, the report was also picked up by the newspapers the NRC Handelsblad, Parool and Trouw, and the latter carried a long article on its front page and published a double interview with Fischer and Witlox. On 10 March other national papers followed as well as virtually the whole of the regional press. Trouw also published another interview with Eric Koller on that day.

Positive publicity

The reporting was very factual: only persons with HIV for whom the medication is effective, who have no other complications and have not used narcotics intravenously are technically insurable. However, the persons concerned have to take into consideration that the risk is two or three times higher. This makes the Netherlands the first country in which people with HIV can take out life insurance against reasonable premiums. The reports also noted that it was strange that persons with HIV who do not yet need medication are not eligible for a life insurance policy. The only reason for this is that there is no statistical data available on this group. This report fulfils that need.

Foreign press

The following foreign papers, among others, picked up the story: Gazet van Antwerpen, Het Belang van Limburg, Jerusalem Post and Sddeutsche Zeitung. Furthermore, special interest sites focusing on medical and insurance subjects and of course the homosexual community picked up the report, both at home and abroad. On 11 March 2005, the Sddeutsche Zeitung included an article on the report in its economy section with the headline Aids-Risiko ist versicherbar. The paper asked for comments from the Gesamtverband der Deutschen Versicherungswirtschaft (the German sister organization of the Association of Insurers) that excludes making a similar recommendation: the calculation of risks is a matter for individual companies. The companies interviewed (Allianz and Ergo-Gruppe) were reserved and the Ergo-Gruppe considers having HIV to be an Ausschlusskriterium.


Belgian Parliament

At the meeting of the Belgian Parliament on 18 January 2006, MP Hilde Vautmans of the liberal party VLD, put questions to the Minister of the Economy, Energy, Foreign Trade and Scientific Policy, Marc Verwilghen, about insurance policies for AIDS and cancer patients.

Hilde Vautmans explicitly referred to the report of the Association with the question: Will a report be drawn up, as it was in the Netherlands, which could show that AIDS and cancer patients have quite high life expectancies and therefore could take out insurance policies?

The Minister answered that he is a supporter of the insurability of persons with AIDS but that he cannot become involved in the policies of insurance companies. He did, however, say that he would have a study carried out into the insurability of, among others, persons with AIDS so that he can enter into an open discussion with the insurance sector.


Chapter 5: HIV survey results

To investigate how many insurance policies were requested and concluded in the period from 1 March 2005 up to and including 31 December 2006, the Centre for Insurance Statistics (CVS) drew up a short survey. To illustrate the development of the insurability of people with HIV, two periods were defined in the survey. The first period is from 1 March 2005 up to and including 31 December 2005 and the second period covers the whole of 2006. Ultimately, 22 insurers participated in this survey. Measured according to the premium income from individual life insurance policies, in 2005 these insurers represented 73% of the market.

Diagram 1 shows the results of the survey. A total of 110 applications for life insurance policies were submitted by people with HIV to the insurers in the panel from March 2005 through December 2006. Of the 22 insurers, 16 indicated having received applications from people with HIV. For 9 of these 16 insurers the applications actually resulted in the conclusion of a life insurance policy.

Diagram 1: Total number of applications from people with HIV in the two periods

Figure 1 shows that 34% of the applications was submitted in the first period and 66% in the second. It should be noted in this regard that a number of insurers were unable to split their data accurately into two separate periods so that they could only give an estimate of the distribution. Despite this, it can clearly be seen that the number of applications in the second period was almost double that of the first period.

Total number of applications: 110

First period: 37 (34%)

Second period: 73 (66%)

Closed: 8 (22%)

Not accepted by candidate: 7 (19%)

Rejected by insurer: 22 (59%)

Closed: 31 (42.5%)

Not accepted by candidate: 12 (16%)

Rejected by insurer: 26 (36%)

Still under consideration 4 (5.5%)


Furthermore, more insurance policies were concluded in the second period. In the first period, 22% of the applications was accepted. In 2006, this percentage almost doubled: 42.5% of the applications resulted in the conclusion of an insurance policy.

In the first period, 78% of the applications did not result in the conclusion of an insurance policy, 59% of which were rejected by the insurer and 19% was the result of the candidate not accepting the offer. In 2006 the picture was different: 52% of the applications did not result in the conclusion of an insurance policy, 36% through rejection by the insurer and 16% through the decision of the candidate himself/herself. Insurers are therefore less quick to reject applications from people with HIV.

Due to the structure of the survey and the limited research possibilities, for reasons of privacy it is not possible to investigate whether or not there are double counts in the results. It is possible that a single person with HIV applied to several insurers for an insurance policy and was rejected by one or more of them. The limitations could adversely affect the results. The number of persons rejected could be lower than the measured number of rejections.

The following conclusions can be drawn from the survey: The number of applications in 2006 was almost double that in 2005. The number of insurance policies concluded almost doubled from 22% in 2005 to 42.5% in

2006. In absolute numbers, at least 39 insurance policies were concluded by people with HIV over

the entire period. The number of rejections fell sharply from 59% in 2005 to 36% in 2006.

What is the relationship between the number of insured persons with HIV and persons without HIV who have taken out life insurance? In the Netherlands in 2006 there were 12,0595 persons registered with HIV. In relation to the 31 insurance policies taken out in 2006, 0.3% of persons with HIV has taken insurance. In 2006 the population of the Netherlands was about 16.3 million of whom a minimum of 650,0006 had concluded a life insurance policy, or 4% of the population. This percentage is over 13x higher than the percentage among HIV patients.

5

HIV Monitoring Foundation (SHM), Amsterdam. 6 Individual life insurances production statistics of the Centre for Insurance Statistics (CVS).


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Annexes

Annex A Example letter to an insurance candidate

In your health declaration you answered the question about HIV with Yes. Despite the favourable reports of recent years, it is still not always possible to offer people with HIV an insurance policy. If insurance cover is possible, then restrictions apply in respect of the duration of the insurance and a relatively high premium is requested. Insurability is being continuously investigated by the Association of Insurers in collaboration with the HIV Monitoring Foundation (SHM). Good insurance cover for more people with HIV is being realized more and more often. It is now possible for many people with favourable biomarkers and no other serious complaints or problems to take out an insurance policy.

Although the chances of obtaining insurance cover against an affordable premium are not equally high for everyone, I am prepared to request the necessary information from your treating specialist. This only makes sense if you are being checked regularly and all kinds of data is well documented (such as the number of viruses per millilitre, CD4 measurement and CDC category'). Legally and morally speaking, you have a right not to know. Information about the medical situation and our calculation of the prognosis is considered by some applicants to be a considerable psychological burden. If you would like to call on your right not to know or are in doubt about this, I would urgently advise you to first discuss this letter with your treating specialist or an experienced counsellor.

The questions I will be putting to your treating specialist are given on the enclosed authorization. Please return the signed authorization in the enclosed reply envelope. Once I have received your specialists answer, I will calculate the medical insurance risk within the general framework as set down by the Association of Insurers using the research carried out by the HIV Monitoring Foundation (SHM).

Apart from the right not to know, you also have the right to be the first to hear what the recommendation of the medical advisor is. I assume you would like to make use of this right. I will therefore inform you without any reservations about my findings and my intended recommendation to the insurance company. You may then request me not to apprise the insurer of my (intended) recommendation.

If, however, you do not want to receive any information at all about my findings and my recommendation to the insurance company, please inform me to this effect in writing. You can enclose this letter with the authorization you will be returning to me.


Annex B Example letter to a treating specialist

Re: Request for information

Dear Colleague,

Your patient , date of birth. has applied for life insurance with ...

My records show that the party involved is being seen and treated by you as a result of testing HIV seropositive. Since 2005, it has been possible for a large proportion of HIV seropositive patients who are being treated with cART, to take out a life insurance policy. This is certainly the case for people with favourable biomarkers who have no other serious complaints or problems.

Assessment is dependent on a good medical file. In practice it is not always easy to acquire the necessary information so that some customers are perhaps unnecessarily rejected.

In order to estimate the future risk as accurately as possible, I would ask you to complete the enclosed HIV seropositive questionnaire and return it to me together with a copy of an extensive letter to the general practitioner concerned.

I have enclosed authorization and a business reply envelope. Your statement of expenses, etc.

Yours faithfully,


Annex C: HIV seropositive questionnaire

To be completed by the treating specialist

Re: your patient Mr/Ms ., date of birth

1. Good specialist information, in anticipation of the start of cART; a copy of a comprehensive letter to the general practitioner will suffice. This should include, among other things, the date of the HIV positive diagnosis and whether there were other coexisting infections.

2. Diagnosis Date of diagnosis: On diagnosis: CD4 cells on the diagnosis ............... Number of viruses per millilitre on diagnosis: . CDC category (CDC-A, CDC-B, CDC-C) on diagnosis: ... On the basis of what conditions was this category determined? . About 24 weeks after diagnosis: CD4 cells 6 months after diagnosis: . The number of viruses per millilitre 6 months after diagnosis: . CDC category (CDC-A, CDC-B, CDC-C) 24 weeks after diagnosis, if increased: ... On the basis of what conditions was this category determined? .

3. cART therapy Date of start of the therapy: .. The medication consists of: ...... At the time of the start of cART: CD4 cells 6 months after the start of cART: .. Number of viruses per millilitre 6 months after the start of cART: .. CDC-category (CDC-A, CDC-B, CDC-C) on start, if increased: ....... On the basis of what conditions was this category determined? About 24 weeks after the start of cART: CD4 cells 24 weeks after the start of cART therapy: ...... The number of viruses per millilitre 24 weeks after the start of cART therapy: ...... CDC category (CDC-A, CDC-B, CDC-C) 24 weeks after the start, if increased: On the basis of what conditions was this category determined? Side-effects: ............ Complications: ........ Compliance: . Changes to the regime: ..... Reasons: ..

4. Current situation CD4 cells today [date] Number of viruses per millilitre today [date].: . Current CDC category (CDC-A, CDC-B, CDC-C), if increased: .. On the basis of what conditions was this category determined?


5. Drug user In the past yes/no Currently yes/no

6. Serology Hepatitis B positive/negative Hepatitis C positive/negative

7. Blood chemistry [date]: ................ .. .. .. .. ASAT .. .. .. .. .. ALAT .. .. .. .. .. blood sugar .. .. .. .. .. cholesterol .. .. .. .. .. triglycerides .. .. .. .. ..

8. Additional information Date: Place: Signature:


Annex D: Model for death after being diagnosed with HIV, without cART

(Untreated model)

The table below shows how patients were selected for the model. There were in total 13,849 patients who were only infected with HIV-1 and who had been diagnosed before 2008.

patients that meet the criterion

after this and the previous criteria

deaths

criterion N % N % N % all patients 13,849 100 13,849 100 1,228 100 in follow-up 24 weeks after diagnosisa

13,055 94.3 13,055 94.3 1,099 89.5

HIV diagnosis between 1998 and 2006

8,118 58.6 7,845 56.6 269 21.9

Age on being diagnosed at least 16 years

13,631 98.4 7,710 55.7 269 21.9

No transmission from mother to child

13,695 98.9 7,709 55.7 269 21.9

No AIDS for 24 weeks after the diagnosis

11,744 84.8 6,436 46.5 171 13.9

No CDC events with unclear datesb

13,740 99.2 6,402 46.2 169 13.8

Untreated at 24 weeks 8,948 64.6 3,794 27.4 84 6.8 No non-cART treatment 11,492 83.0 3,721 26.9 83 6.8 Not infected through intravenous drug use

13,196 95.3 3,637 26.3 72 5.9

No history of drug use 12,890 93.1 3,479 25.1 61 5.0 CD4 measurement at 24 weeksc 5,446 39.3 2,717 19.6 41 3.3

a This excludes patients who were already dead or had disappeared from the follow-up before 24 weeks after diagnosis;

b This excludes patients for whom it was known that they had had or were having a certain event, but for which the diagnosis date was not known;

c This CD4 measurement is the measurement closest to the 24 weeks within an interval of interval of 12 to 36 weeks with no start of cART.

Source: HIV Monitoring Foundation (SHM)


Annex E: Model for death after start of cART (Treated model)

The table below shows how patients were selected for the model. There were in total 11,000 HIV-1 infected patients who had started cART before 2008. The criterion that there was no AIDS event at 24 weeks after the start of cART is new. In the old model, there were so few people with an AIDS event at 24 weeks that it played no role. This is now different with a larger dataset.

patients that meet the criterion

after this and the previous criteria

deaths

criterion N % N % N % all patients 11,000 100 11,000 100 1,061 100 in follow-up 24 weeks after the start of cARTa

10,298 93.6 10,298 93.6 901 84.9

start of cART between 1995 and 2006

10,230 93.0 9,959 90.5 895 84.4

Age on start at least 16 years 10,840 98.5 9,802 89.1 895 84.4 No new AIDS event between start of cART and 24 weeks after start

10,519 95.6 9,381 85.3 791 74.6

No CDC events with unclear datesb

10,896 99.2 9,284 84.4 781 73.6

No non-cART treatment 8,108 73.7 7,161 65.1 360 33.9 Not infected through intravenous drug use

10,447 95.3 6,875 62.5 303 28.6

No history of drug use 10,218 92.9 6,644 60.4 282 26.6 CD4-measurement at 24 weeksc 9,281 84.4 5,951 54.1 253 23.8

a This excludes patients who were already dead or had disappeared from the follow-up before receiving cART for 24 weeks;

b This excludes patients for whom it was known that they had had or were having a certain event, but for which the diagnosis date was not known;

c This CD4 measurement is the measurement closest to the 24 weeks mark within an interval of interval of 12 to 36 weeks after start of cART.

Source: HIV Monitoring Foundation (SHM)


Annex F: CDC categories

AIDS surveillance case definitions

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2009 WHIV Report: Insurability HIV (written 2008)

Design