HIV and AIDS:
A Changing Epidemic
Nathea Nicolay
AIDS Committee
May 2012
Agenda
•
•
•
Projecting future mortality in SA – a challenge
Access and take-up of ART
Mortality reduction in SA and drivers
–
–
•
Herbst study, 2011, Kzn
StatsSA, 2011, Causes of deaths
Survival on ART in Africa
•
Mills study, 2011, Uganda
• Reduction in HIV incidence(delayed mortality reduction)
•
•
•
Condom usage
Behaviour change
ART
"Investment in AIDS will be repaid a thousand-fold in
lives saved and communities held together.“
Dr. Peter Piot, Executive Director, UNAIDS
ASSA2008 AIDS and demographic model
New HIV infections versus AIDS deaths
700,000
Number of People
600,000
500,000
400,000
300,000
200,000
100,000
-
Year
AIDS Deaths
Total new infections (in the year starting 1 July)
Source: Actuarial Society of South Africa, March 2011. ASSA2008 AIDS and demographic
model, www.actuarialsociety.org.za
Page 4
Survival
Median terms from infection to death increase significantly over time
22
Aggregate median
20
18
16
14
12
10
8
2000
2005
2010
2015
2020
2025
Year
ASSA2003 Ages 25 - 34
ASSA2008 Ages 14 - 24
ASSA2008 Ages 25 - 34
ASSA2008 Ages 35 +
Source: Actuarial Society of South Africa, Nov 2011. Select Population modelling course from
ASSA2008 and ASSA2003 AIDS and demographic models, www.actuarialsociety.org.za
5
What were our mortality estimates a
decade ago?
Adult mortality (45q15) has dropped significantly in each model update
90%
80%
70%
60%
50%
40%
Interventions
30%
20%
10%
0%
1985
1990
1995
2000
ASSA2000
2005
ASSA2003
2010
2015
2020
2025
ASSA2008
Source: Actuarial Society of South Africa, Nov 2011. Select Population modelling course from ASSA2008,
ASSA2003 and ASSA2000 AIDS and demographic models, www.actuarialsociety.org.za
6
Female AIDS mortality by age – impact of
interventions over time
16
Interventions push AIDS mortality hump down and
towards older ages. Less marked for Males
14
12
Per mille rate
10
8
6
4
2
0
20
25
30
35
2005
40
2010
45
50
2015
55
60
65
70
2025
Source: Actuarial Society of South Africa, Nov 2011. Select Population modelling course from
7
ASSA2008, ASSA2003 and ASSA2000 AIDS and demographic models,
www.actuarialsociety.org.za
Example HIV Impact Assessment results:
Demographic Impact – HIV Prevalence: Industry 5 –
Heavy construction industry
Source: Actuarial Society study for IDTT: 2010
Example HIV Impact Assessment results:
Demographic Impact – AIDS Mortality: Industry 5 –
Heavy construction industry
Source: Actuarial Society study for IDTT: 2010
Drivers of reduction in AIDS mortality in SA
• Access and take-up of ART
• Survival on ART:
• Average CD4 count at initiation
• Age, Gender, Loss to follow-up
• Regional differences (developed vs developing countries)
• Age of treatment programme
• Reduction in HIV incidence(delayed mortality reduction)
• Condom usage
• Behaviour change
• ART
Access to Antiretroviral Treatment in South
Africa, 2004 – 2011
•
•
Johnson, LF, 2012. Access to Antiretroviral Treatment in South Africa, 2004 –
2011. Southern African Journal of HIV Medicine, Vol 13, No 1 (2012)
Dr Leigh Johnson - Centre for Infectious Disease Epidemiology and Research,
University of Cape Town
Objective:
• Estimate ART coverage in SA and assess whether NSP targets have
been met (aimed ART take-up = 80% of the number of newly eligible
individuals in each year, by 2011)
• ART data were collected from public and private providers of ART.
• Estimates of HIV incidence rates were obtained from independent
demographic projection models.
Access to Antiretroviral Treatment in South
Africa, 2004 – 2011
Results:
• Mid 2011, patients receiving ART in SA increased to 1.79 million
(95% CI 1.65 - 1.93 million).
• Adult ART coverage
•
•
•
Previous ART eligibility criterion of CD4 <200/μl: 79% (95% CI 70 - 85%)
Reduced to 52% (95% CI 46 - 57%) when assessed according to the new
South African ART eligibility criteria (CD4<350/μl)
The number of adults starting ART in 2010/11 was 1.56 times (95% CI
1.08 - 1.97) the number of adults who became ART-eligible in 2010/11,
well in excess of the 80% target. However, this ratio was substantially
higher in women (1.96, 95% CI 1.33 - 2.51) than in men (1.23, 95% CI
0.83 - 1.58) and children (1.13, 95% CI 0.74 - 1.48).
Access to Antiretroviral Treatment in South
Africa, 2004 – 2011
• Public health sector: 85%
• Disease management programmes in the private sector: 11%
• Community treatment programmes run by NGOs: 4%
• 61% were women aged 15 or older
• 31% were men
• 8% children under the age of 15
• KwaZulu-Natal and Gauteng largest numbers of patients: 56% of all patients
receiving ART.
Access to Antiretroviral Treatment in South
Africa, 2004 – 2011
The 95% CIs that have been estimated reflect:
• uncertainty regarding rates of CD4 decline,
• rates of mortality and rates of ART retention,
• accuracy of reported ART programme statistics.
• CIs do not reflect the uncertainty regarding the HIV incidence
rates that have been estimated from the ASSA2008 model, and
this may lead to some exaggeration of precision.
Cause-specific mortality trends in rural
KwaZulu-Natal, South Africa, 2000-2009
Herbst et al. 2011. Verbal autopsy-based cause-specific mortality trends in rural
KwaZulu-Natal, South Africa, 2000-2009. Population Health Metrics 2011, 9:47.
http://www.pophealthmetrics.com/content/9/1/47
Aim:
•
Cause-specific mortality data for public health policy and evaluation
•
Study aims to describe cause-specific mortality trends
•
Based on verbal autopsies conducted on all deaths in a rural population in
KwaZulu-Natal, South Africa, over a 10-year period (2000-2009)
Methods:
•
Pop mortality data - demographic surveillance system (12,000 households).
•
Cause of death determined by verbal autopsy - standard INDEPTH/WHO verbal
autopsy questionnaire, assigned by physician review and the Bayesian-based
InterVA program
Contribution of each cause to mortality by year
•
•
HIV-related causes: max 56% of deaths in
2002, declining to min of 39% of deaths in
2009.
Indeterminate causes: min of 14% of deaths
during 2000, rising to a max of 25% in 2009.
Contribution of each age to mortality by year
and age-group
•
Under 5: Cause-specific mortality fraction (CSMF)
•
due to HIV related causes declined from 51% (95%
CI: 44, 58) in 2000 to 12% (95% CI: 6, 17) in 2009.
15 – 49 yr: CSMF for HIV-related causes declined
from 74% (95% CI: 71, 78) in 2000 to 55% (95% CI:
52, 59) in 2009.
Cause-specific mortality trends in rural
KwaZulu-Natal, South Africa, 2000-2009
Results:
•
11,281 deaths over 784,274 person-years of observation of 125,658 individuals between
2000 and 2009
•
Cause-specific mortality fractions (CSMF) for the population as a whole were:
–
–
–
–
–
–
•
•
•
•
HIV related (including tuberculosis), 50%;
other communicable diseases, 6%;
non communicable lifestyle-related conditions, 15%;
other non communicable diseases, 2%;
maternal, perinatal, nutritional, and congenital causes, 1%;
injury, 8%; indeterminate causes, 18%.
CSMF of HIV-related causes declined from a high of 56% in 2002 to a low of 39% in 2009
Largest decline in 2004 following the intro of ARTs
All-cause age standardized mortality rate (SMR) declined over the same period from a high
of 174 (95% confidence interval [CI]:165, 183) deaths per 10,000 person-years observed
(PYO) in 2003 to a low of 116 (95% CI: 109, 123) in 2009.
Decline in the SMR is predominantly due to a decline in the HIV-related SMR, which
declined in the same period from 96 (95% CI: 89, 102) to 45 (95% CI: 40, 49) deaths per
10,000 PYO.
Mortality and causes of death in South Africa,
2009: Findings from death notification
Statistics South Africa, 2011. Mortality and causes of death in South Africa, 2009:
Findings from death notification. http://www.statssa.gov.za/
Aim:
• Support for specialist investigations and check on data quality
• Outline trends in mortality from 1997 to 2009 and differentials of mortality by
selected demographic, social and geographic characteristics for deaths that
occurred in 2009; and
• Stats on causes of death for deaths that occurred in 2009
Background:
• Stats SA, Home Affairs (DHA) and Health (DOH)
• Annually produces statistical releases and data sets
• Mortality and causes of death
• Information on deaths from the civil registration system in South Africa.
• The registration of deaths in South Africa is governed by the Births and Deaths
Registration Act, 1992 (Act No. 51 of 1992), as amended.
Mortality and causes of death in South Africa,
2009: Findings from death notification
Mortality and causes of death in South Africa,
2009: Findings from death notification
Mortality and causes of death in South Africa,
2009: Findings from death notification
Mortality and causes of death in South Africa,
2009: Findings from death notification
Summary – Causes of deaths
Main findings:
• SA mort decreases from 2007
• Deaths decreased by 1,5% between 2007 and 2008, by 3,8% between 2008 and
2009
• Female deaths declining at a higher rate than male deaths
• Majority of deaths occurred among the black African population group
• Highest number of deaths occurred in Kzn, followed by Gaut and EC(but largest
population sizes)
• Majority of deaths due to natural causes
• Main group of certain infectious and parasitic diseases, responsible for a quarter of all
deaths. The number of both natural and non-natural causes decreased between 2008
and 2009 (7,2% for non-natural causes and 3,4% for natural causes).
• BUT: Between 2007 and 2009, the number of deaths consistently decreased for
tuberculosis, influenza and pneumonia, chronic lower respiratory diseases and
certain disorders involving the immune mechanism and consistently increased for
other forms of heart disease, HIV disease and hypertensive diseases. Although the
overall number of deaths due to tuberculosis is decreasing, those due to multidrugresistant tuberculosis and extensively drug-resistant tuberculosis continue to increase
at a high rate.
Life expectancy on HAART in low-income
countries - Uganda
Uganda study: Life Expectancy of Persons Receiving Combination Antiretroviral
Therapy in Low-Income Countries: A Cohort Analysis From Uganda
Mills, et al, 2011. Life Expectancy of Persons Receiving Combination Antiretroviral
Therapy in Low-Income Countries: A Cohort Analysis From Uganda. Ann Intern
Med. 2011;155:209-216.
Aim: To estimate life expectancy of patients once they initiate ART in Uganda.
Method: Prospective cohort study of public sector HIV and AIDS diseasemanagement program in Uganda.
• 22 315 eligible patients initiated ART, 1943 considered to have died
• Mortality rates were calculated and abridged life tables were constructed and
stratified by sex and baseline CD4 cell count status to estimate life expectancies
for patients receiving ART. The average number of years remaining to be lived
by patients who received ART at varying age categories was estimated.
Life expectancy on HAART in low-income
countries - Uganda
Findings:
• e 20 for the overall cohort was 26.7 (CI, 25.0 to 28.4)
• e 35 was 27.9 (CI, 26.7 to 29.1)
• Life expectancy increased substantially with increasing baseline CD4
cell count. Similar trends are observed for older age groups
• ART results in favourable life expectancy compared with the national
average
• Life expectancies varied considerably according to sex, with women
having a greater life expectancy than men
• Higher baselineCD4 cell count status at treatment initiation strongly
indicates potential life expectancy
Impact of ART and condom usage on HIV
incidence in SA
Johnson, et al, January 18, 2012, The effect of changes in condom usage
and antiretroviral treatment coverage on human immunodeficiency virus
incidence in South Africa: a model-based analysis. J. R. Soc. Interface
doi:10.1098/rsif.2011.0826 Published online:
http://rsif.royalsocietypublishing.org
Method:
• Trends in HIV incidence in SA - assess the extent to which prevention
and treatment programmes have reduced HIV incidence
• 2 models:
–
–
•
STI (sexually transmitted infection)–HIV Interaction model
ASSA2003 AIDS and Demographic model, were adapted
Fitted to age-specific HIV prevalence data from antenatal clinic surveys
and household surveys, using a Bayesian approach.
Impact of ART and condom usage on HIV
incidence in SA
Findings:
• Both models suggest that HIV incidence in 15–49 year olds declined
significantly between the start of 2000 and the start of 2008:
– by 27 % (95% CI: 21–32%) in the STI–HIV model
– by 31 % (95% CI: 23–39%) in the ASSA2003 model, when expressed as a
percentage of incidence rates in 2000.
•
•
•
By 2008, the percentage reduction in incidence owing to increased
condom use was 37 % (95% CI: 34–41%) in the STI–HIV model and 23
% (95% CI: 14–34%) in the ASSA2003 model.
Both models also estimated a small reduction in incidence owing to
antiretroviral treatment by 2008.
Increased condom use therefore appears to be the most significant
factor explaining the recent South African HIV incidence decline.
Conclusion
•
•
•
•
ASSA AIDS and demographic models
• show a big reduction in AIDS related mortality
• and HIV incidence
• over time
• between older and later models
More and improved quality of data
Reduction in AIDS related mortality:
• Access to ART
• Take-up on ART
• Survival on ART:
• Average CD4 count at initiation
• Age, Gender, Loss to follow-up
• Regional differences(developed vs developing countries)
• Age of treatment programme
Reduction in HIV incidence(delayed mortality reduction)
• Condom usage
• Behaviour change
• ART
1. Are actuaries well enough informed to allow for life
expectancies in a post ART world?
2. What is the role of the profession when it comes to
addressing the needs of all stakeholders:
1. Insurers – Solvency II
2. Policyholders - TCF
3. Legislators – FSA, FSB, Council for Medical
Schemes?