Estimates of alternative scenarios of
scaling-up of ART treatment in an agentbased micro-simulation model
Bruno Ventelou (1), Yves Arrighi (1), Erik Lamontagne (2), Robert Greener (2), Jean‐Paul Moatti (1) (1)INSERM/IRD/University of the Mediterranean Research Unit SE4S (Marseille, France) (2)UNAIDS (Geneva, Switzerland) Very preliminary version – first draft
(For example, results on Tanzania are not given for 2033 but for 2028 only)
This paper is a common project between the Inserm 912 IRD SE4S research unit and UNAIDS.
1
ABSTRACT Aim
This paper focuses on the long term impact of Antiretroviral Treatment (ART) policies on the populations of Swaziland, Tanzania and Cameroon.
Methods A Micro‐simulation model based on individual‐level data was defined and calibrated using the 2003/04 Tanzanian HIV/AIDS Indicator Survey, the Swaziland Demographic and Health
Survey 2006-07 (SDHS) and the Cameroon Demographic and Health Survey 2004 (EDSC
III). Three ART Coverage scenarios were designed and compared for various economic and epidemiological indicators from 2008 to 2028: 1) No Treatment, 2) observed ART coverage in 2008 with a freeze of ART provision in the future due to the current world financial crisis, 3) 100 % ART‐Requirement Coverage . Results The results can be summarized in terms of the three following dimensions: the dynamic of the epidemic, the macroeconomic impact and the distributive effects (GINI indexes measuring income inequalities). The higher the ART coverage, the more both HIV prevalence and requirement for ART are important. On the other hand, HIV incidence rate and deaths caused by HIV are reduced. For Tanzania, in the Universal Access scenario (scenario 3), 900,000 lives are saved by 2028 compared to No Access (scenario 1 or benchmark), although in Aid Freeze scenario (i.e. scenario 2, reflecting the impact of the current world financial crisis), “only” 100,000 lives are saved by 2028 compared to scenario 1. For Swaziland (respectively Cameroon), these two latter figures are respectively 190,000 –up to 2036‐ (resp. 470,000) (scenario 3) against 25,000 (50,000) lives saved (scenario 2). With regard to the macroeconomic impact, scaling‐up ART has a positive impact on workers’ productivity, leading to an economical surplus measured in extra‐GDP points. Although these policies are costly (costs increase as ART Coverage level increases), they are nonetheless shown to be cost‐effective. Among the two scenarios with ART Coverage, the cost‐benefit ratio at the 2030 horizon exceeds 5 for Swaziland: that is to say that for every 1 billion dollars invested in ART, the regulator can expect 5 billion dollars in GDP gain. In Tanzania, since GDP per capita is lower, we did not obtain an equally optimistic GDP gain/cost ratio (about 0.7). Cameroon illustrates an intermediate example: with a GDP per capita between the two other countries, so is the self financing ration associated to the Universal Access Scenario (ratio is greater than 2). GINI measures of economic inequalities always decrease with ART access, but private copayments limit this movement. Conclusion The analyses presented here for Tanzania, Swaziland and Cameroon will be conducted for other African countries documented using DHS. Our results suggest that the Aid Freeze Scenario could be attractive in the short term when the simple cost‐benefit analysis comparison rule is used. However, the Universal Access scenario leads to the ‘saving’ of far more human lives and dominates in the long term. Accordingly, incorporating the ‘value’ of human life in any future analysis may result in a large improvement on the current analysis.
2
Introduction In its report to the G8 Gleneagles Summit of July 2005, the Commission for Africa, a group of seventeen prominent experts in the field of economics, advocated a doubling of the then current Sub‐Saharan Africa targeted Official Development Assistance (ODA) for the 2006‐10 periodi. In the more realistic estimates, an implementation of the Commission’s recommendation would have implied 10 billion US $ of extra annual aid for health and HIV/AIDS. Today, after five years of operation, an overall value of only 7 billion US $ has been collected by the Global Fund against AIDS, Tuberculosis & Malaria (GFATM) and redistributed to several countries in need around the world. Hence the goal has not been reached, and great uncertainty remains not only about the amount of international aid that will be targeted to scaling‐up access to HIV treatment in the coming yearsii, but also in terms of the future prospects of resources allocated to the GFATM. Specifically, the current world financial crisis is suspected of resulting in adverse consequences on the generosity of donors, and could even reverse the growing trend of fundingiii.
Consequently a “forecasting tool” which enables the evaluation not only of different scenarios of treatment access scaling‐up programs, but possibly also of various hypotheses of reduced access, could prove to be useful: on the one hand in order to try to estimate the human impact of various scenarios of public policies (treatment and survival rates) and on the other hand, to calculate the economic consequences. In this paper we propose a forecasting method using an agent‐based model, constructed using data from the DHS surveys available for different developing countries. We construct an epidemio‐economic model in which the agents evolve, from the present to a future period, following health state transitions: HIV‐, HIV+, HIV NT and death. The values of the transition matrixes, i.e. the probability of passing from one state to another, especially for HIV‐ /HIV+‐ seroconversion, are attributed using information already available for each country from the DHS, UNAIDS, WHO. The values are then adjusted, according to the policy scenario under investigation: no access to antiretrovirals, 100% access to those requiring treatment (Universal Accessiv) and various intermediate scenarios, some of these latter 3
reflecting a slight or zero increase in ART access programs for HIV+ populations requiring treatment. By construction (sampling and data collection), each agent of the database may be seen as “representative” of a portion of the general population of the country. At the end of the artificial ageing process, we can therefore calculate the overall future of the nation for a given prediction horizon, 30 years for example. Accordingly we can propose evaluations focusing not only on the “lives saved” and the sizes of cohorts under treatment, but also on the costs and economic benefits (including measurements of income inequalities) of various possible public policy options. 4
Materials and Methods General Principle To simulate the future state of health of populations, we choose to use an “agent‐based" approach. We use an artificial ageing model based on real economic agents, selected from a database (the Demographic and Health Survey “DHS”). The ageing process in an individual may be formalized in the following manner: Let be the law defining Ei,t the state of an individual i on a date t. We obtain the ageing of this . The relation M is said to be Markovian individual by the relation: when the state on the date t is assumed to summarize, on its own, all the information necessary to predict the future of the individualv, i.e. when In this case, when is a state vector (in our model may have 4 states: at a time t, an individual is either: Hiv‐, or HIV+, or HIV+NT (requiring treatment) or Deceased), we have: M is a transition matrix, composed probabilities P, where Pk,j is the probability of transition from state k to state j. In the subsequent time period, i.e. t+1 (one period lasting 5 years), the individual’s health is determined by the probabilities of his transition line (differenciated by sex and age). Interaction between agents: seroconversion Agents are connected each other by their risk of seroconversion. With the probability of agent i to experience a transition from state 0 (HIV negative), to state 1 (HIV positive), we state that: As for all infectious diseases (epidemics), the individual risk of infection is endogenously determined by health status of the other agents in the population. Nevertheless, another variable 5
is included in function f to take into account individual or collective behaviors which may counter the spontaneous dynamics of the epidemic (safe sexual behavior, prevention campaigns, public health policies). In the following, the variable will also depend on hypotheses about access to treatment. Granich et al. 2009 documented that high antiretroviral coverage in the population has a preventive effect on the spread of HIV infections. Databases The simulations performed in this paper are based on two databases, the Tanzania HIV/AIDS
Indicator Survey 2003-04 (THIS)vi,vii the Cameroon Demographic and Health Survey (EDSC III)viii
and the Swaziland Demographic and Health Survey 2006-07 (SDHS)ix. These datasets are part of the
worldwide MEASURE Demographic and Health Surveys (DHS) program, funded by the United
States Agency for International Development (USAID). THIS, SDHS and EDSC are the first
nationwide surveys to provide HIV/AIDS prevalence estimates: in addition to the data collected
through interviews, respondents were asked to provide a blood sample for subsequent HIV testing.
One limitation of the survey is that the blood test results provided do not reflect the severity of the
disease (no CD4 count).
THIS (and respectively SDHS-data in brackets; EDSC in bold italic) comprised interviews from
around 6,500 households (4,800; 5,300) among which a total of 13,400 (10,000; 10,900) adults aged
15-49 were identified. The overall coverage of HIV testing among eligible women and men aged
between 15 and 49 reached 80.5% ; 82.7% and 91.0% respectively. After combining these tests with
the survey databases, the final sample for the simulations comprised 10,747 observations, weighted to
represent the total Mainland Tanzanian population, a total of 8,187 men and women aged 15-49 were
included for Swaziland, while 9,751 individuals will represent Cameroon.
Health Status:
People who become infected with HIV do not need antiretroviral treatment immediately. There is an
asymptomatic period during which the body’s immune system controls the HIV infection. After some
period of time the rapid replication of the virus overwhelms the immune system and the patient then
requires antiretroviral treatment (ART)x. In order to take into account the severity of the disease we
decided to split the HIV positive subpopulation into two health statuses: those HIV positive requiring
treatment (HIV+TN) and the proportion of those HIV positive asymptomatic (HIV+) not requiring
treatment, as estimated by WHO/UNAIDS1 (xi). There is some evidence that this latter proportion is
higher among the oldest HIV positive individuals. This “ratio” was therefore heterogenized across 51
The World Health Organization (WHO) recommends ART for HIV infected people with a CD4 cell count <200 cells/µl, for those in clinical stage III with a CD4 cell count < 350 cells/µl, and for those with a diagnosis of WHO stage IV disease. 6
year time-intervals for age and gender (see appendix for mathematical formulas). HIV positive
individuals from DHS surveys were then randomly incorporated into one of the two groups (i.e.
HIV+TN or HIV+) according to the previously defined ratio.
Aging Process: Discrete Time Markov Chain
First, an individual’s future health status is forecasted using a microsimulation model as follows. At a time t, an individual is either HIV‐negative, asymptomatic HIV‐positive, HIV‐positive in need of ART or deceased; at the next period in time i.e. t+1 (one period lasting 5 years), the same person’s health is determined by a transition rate matrix according to his last health status: . The transition rate matrixes contain probabilities of transition from one given health status to another. We consider a matrix by age group (5 year age brackets) and by gender. The following transitions are thus computed for each age group and each gender: the probability of going from HIV‐ to HIV‐, from HIV‐ to HIV+, from HIV‐ to death, from HIV+ to HIV+, from HIV+ to TN, from HIV+ to death, from TN to TN and from TN to death; the HIV+ (and TN statuses) being kinds of absorbing states as you can’t go from HIV+ to HIV‐ (or from TN to HIV+). HIVHIV+
TN
D
HIVP00
0
0
0
HIV+
P01
P11
0
0
7
TN
P02
P12
P22
0
D
P03
P13
P23
1
In a first step, we identify the transition matrix in the benchmark case, as if there is no-access to any ARV treatment. Appendix 1 shows how we precisely identify each value Pk,j in the matrixes. A crucial point is that, thanks to an explicit modeling of individual ageing (and sero‐conversion), we can include the consequences of different scenarios of ART scaling‐up: the Markovian process changes. Comparing Scenarios
Basically three ART access scenarios will be compared in this paper. The first two, “No Access” and
“Universal Access” can be considered as hypothetical boundary scenarios. “No Access” provides a
picture of what the world could look like if ARVs did not exist or were not distributed in Tanzania and
Swaziland, while the “Universal Access” scenario –i.e. all those who need ART have access to itxii, xiiishows what could happen if the scaling-up of ART programs were already achieved today.
Between these two boundaries, an alternative scenario representing current and future ART responses
has to be evaluated using the micro-simulation model. The model can take in consideration an infinity
of TC levels. In this paper dedicated to the financial crisis adverse effects on international aid, we
selected a scenario in which the number of PLWHIV receiving ARVs remains constant during all the
observation period (i.e.: a “freezing” in the absolute number of ARV treatments delivered to requiring
people)xiv, xv.
In the “Universal Access” scenario, the risk of infection (P01 ) decreases: ART not only reduce patient
mortality, but are also thought to have a preventive effect in terms of their contagiousness when a
large proportion of the infected population is treated (Granich et al., 2009)xvi.
Antiretroviral Treatment Policy Timing
The timing of the implementation of the policy to fight HIV/AIDS differs across the various scenarios:
we suppose an immediate start of the policy in the initial stage for the “No Access” and “Universal
Access” scenarios: 0% or 100% of the TN individuals have their need for treatment fulfilled. In the
“Aid Freeze” scenario, TN individuals from the initial period receive ARVs according to the observed
level of TC (at the time of the DHS in that country) and remain on ART until they die –or become
censored- in subsequent periods. HIV infected individuals newly requiring ART during subsequent
periods (i.e. 2011, 2016…etc.) will be proposed ART according to the remaining stock of ARVs,
which are in turn decided by the modeler according to different international aid hypotheses (i.e.
generosity of donors etc.).
Accounting for Demographic Changes For every 5-year period, individuals older than 49 are removed from the simulation sample while
individuals aged 15-19 are newly included. In order to take demographic evolution into account, the
8
15-19 years population growth rate over 5 years
is estimated2 and allows us to include a new
“demographic-adjusted size” cohort at every period such that:
; where t is the period of cohort inclusion.
Outcomes: participation, productivity and GDP gains In all the three surveys, respondents were asked if they were currently working. A binary Logit model was used to compute age and gender specific employment ratesxvii. These probabilities were then integrated with HIV status work‐absenteeism rates to obtain productivity rates (see table below, from Kyereh and Koffman, 2008xviii). Status No. Days Absent 1 ‐ Absent. Rate HIV ‐ 0 100% HIV+ 1 100% TN & T=1 1 95% TN & T=0 5 75% Wages were finally calculated by multiplying these rates by the average wage3 an individual could receive (i.e. the average income per working adultxix). After simple calculations on micro‐data, we obtained aggregated measures (GDP). Then the “Universal Access“ and “Aid Freeze” scenarios were contrasted to that of “No Access” by comparing the incremental cost per year of life gained with the incremental GDP gains per year of life gained. Direct costs of treatment were derived from Médecins sans Frontières estimates xx,xxi. Accompanying costs (Laboratory Costs, Service Delivery Costs) were taken from UNAIDS data. We stipulated that patients receive first‐line ART for 5 years (562$ direct + accompanying). After this 5 years period, they receive second‐line ART (862$).
2
has been estimated using UN Population Division projections for the 5 years following the survey; but does
not vary over time. 3
The average wage is defined by the ratio between the overall GDP ‐measured by the World Bank when the survey took place‐ and the number of adults in the country –UN Pop estimates‐ times the DHS observed employment rate. 9
Results
What will be the future of (a selection of) individuals according to our modeling?
Insert Table 1 here
Table 1 presents the trajectories for the state of health between 2003 and 2028 of those individuals included in the TDHS 2003‐04 Survey, according to the three scenarios analyzed in this paper. The columns 2003,.., 2028 show these states of health: the green circles correspond to the HIV‐ state, the yellow to HIV+, the red to HIV+NT and the black to deceased individuals. The ART columns provide information on individuals requiring treatment and whether they have access or not to ARV. Individual 116 is a man aged between 15 and 19 years in 2003; in 2012 he contracts HIV according to the model in the scenarios “ No Access “ and “ Aid Freeze “. He progresses to the point of requiring treatment in 2018, a period during which he is not treated. Finally, he dies in 2023.
If the “Universal Access” program had been put into place, he would never have contracted HIV and would have died in 2028 from other causes. Individual 3518 already required treatment in 2003, but only receives treatment in the “Universal Access” scenario, which enables her to survive in this state for another two periods. She subsequently dies (due to the inefficiency of treatment, non‐adherence or an external cause). The subsequent individual (N° 6074) contracts HIV between 2003 and 2008, then requires treatment in 2018, but she only obtains it in scenario 3 “Universal access”. Individual 10,438 is over 45 years old in 2003: consequently he exits from the observation window in the following period (beyond 54 years, we can no longer establish the transition probability). The final three individuals are introduced into the process of simulation as follows: in 2013 (individual 13975), in 2018 (n°16179) and finally in 2023 (n°18093). The “new born” in 2013 is HIV+ and receives treatment in Scenario 3 in 2023 when he requires it. The two other individuals are introduced already requiring ART treatment and receive it in both scenarios.
Swaziland
Insert Table 2
Benchmark case (no access)
On the basis of 1,600,500 individuals, 349,600 of whom living with HIV, the prevalence of the epidemic diminishes by 4.0 points between 2006 and 2036. However, this reduction is explained by wide scale death among those seropositive and requiring treatment. The number in this latter 10
category continues to increase slightly throughout the whole of this duration (Figure1): 438,000 individuals die during the course of the simulation, with 79.9% of these deaths being attributed to HIV. Moreover, knowing that the initial GDP was 4,424 US$, we notice an increase of 11% of the GDP per capita from 2006 to 2036.
Effect of access to treatment
Treating individuals with ART produces a mechanical effect on the prevalence of the disease : those requiring treatment survive, especially in the “Universal Access” scenario, which results in a higher number of HIV carriers (survivors) being observed than in the Benchmark scenario, irrespective of the time period considered. However, in the Universal Access Scenario, this differential of PLWHIV is attenuated by the slowing down of the speed of the spread of the epidemic (i.e. due to the preventative effect of the treatment). The global cost of treatment programs is also considerably affected by ART policies: to the cost associated with people who recently started treatment is added the cost for those people previously treated and who survived. This latter cost –including care costs‐ increases by 50% when the patient must be provided with a second‐line regimen. The costs associated with the “AID Freeze” scenario therefore increase by 31% over 30 years, even though the number of treated individuals remains by definition constant (approximately 20,000 beneficiaries between 2006 and 2036). The costs increase most sharply for the Universal Access scenario: they are multiplied by 4 in 30 years, the number of patient increases by 2.9 times, reaching nearly 650 million dollars for the single period of 2031‐2036.
ART treatment programs increase the number of lives saved: the “Universal Access” scenario brings the total number of lives saved for the period 2006‐2036 to 192,500.
The Aid Freeze scenario enables a country to obtain higher returns per “saved” individual over the period 2006‐2036 than does the Universal Access scenario (Figure 4). Effectively in the universal treatment scenario, the allocation of ART does not depend on the individual’s age. Conversely, in the case of the financial crisis scenario (“aid freeze”), treated individuals are on average young (the freeze on finance results in few older patients being treated for those who contract the disease after 2011). During the ageing process, these young individuals’ salaries grow and so does the GDP. Do ART programs have a positive cost‐benefit? With regard to the ratio between receipts and expenses, that exceeds 200% (Figure 4) at the first simulation period, we can conclude that these programs can be tolerable and self‐financed –if the fiscal revenue of the government can capture the economic surplus. The “Art freeze” scenario can be described as a short term political choice: the cost benefit of the program increases quickly from 2006 ‐220%‐ to 2006 ‐500%- (with a capacity to auto‐finance greater than the « Universal Access » scenario) but then it growth rate diminishes and 11
becomes negative during the final observation periods, where the auto financing rate is lower than that of the Universal Access scheme (510% vs. 570%).
If we adopt now a distributive perspective, the GINI indexes4 generally decrease when ART coverage rises: economic inequalities generated by production losses of HIV+ people tend to disappear in case of general access to ART (figure at left). An interesting point is to compare two financing options: public financing versus private payments for treatment access (figure at right). We obtain that a substantial part of the gains in GINI index (1.5 points) could be annihilated if people would have to pay for 100% of the treatment costs. 4
GINI gives a standard measurement of income inequalities in the country. 12
Tanzania
Insert Table 3 Results are similar to those of Swaziland: 8.6 times more lives are saved in the Universal Access scenario than in the Aid Freeze one. The cost‐benefit ratio also increases with time, with the Universal Access Scenario dominating over the long term (see table 2 and Figure 4). However, since the GDP per capita is lower, the GDP Gains/ART costs ratio is lower (Figure 3 and 4): around 70% for both scenarios; financing ART programs cannot be only financed by expected GDP gains and overseas financial help is needed. This could appear as a limitation for the ART programs. Note however that in this simplistic comparative “cost‐benefit analysis”, we do not take the value of human life nor the quality of added surviving years into account. Incorporating this step in any future analysis may provide a large improvement on the current analysis.
GNI indexes for Tanzania… To be included latter. Cameroon
Insert Table 4
Cameroon results point out the same conclusions and thus reinforce the accuracy of the previous results: Universal Access dominates Aid Freeze in the long run in our cost‐benefit approach. Cumulated GDP gains in 2034 are 2.4 times larger than ART program costs in Scenario 3 against 2.2 in Scenario 2, while it saves 467,000 lives instead of 51,000. However this long term dominance takes around 20 years to be setup, seemingly to the two other countries.
13
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Discussion
Using data from three DHS surveys (Demographic and Health Surveys), this representative agentbased model enables us to evaluate the demographic, epidemiological and macroeconomic effects of
scaling-up scenarios with respect to access to antiretroviral drugs in Sub-Saharan African countries. It
may act as a future tool to help decision-making processes when selecting various scaling-up options,
according to the defined priorities of political authorities as follows: program costs, number of people
covered, number of people saved and expected benefits in terms of gross domestic product (GDP). By
comparing various scenarios with the ambitious one of Universal Access, it provides an evaluation of
the effect of restricting international aid, something of great importance considering the recent world
financial crisis of 2008-2009.
The results show that taking the macroeconomic aspect into account in the impact scenarios of
programs is very important in order to adequately evaluate the cost-benefit of the various options in
public health policies. The growth rate of an economy and future national wealth are endogenous to
the choices made (i.e. the people saved and the number of contaminations avoided have a productive
value which adds to the social value of human lives). The extra economic value created, provides
funding for treatment access programs. In purely financial terms, Universal Access may be considered
as an investment in productive human capital. Results regarding GINI indexes add that distributive
justice considerations also support scaling-up, even in the case of private copayments. Reciprocally,
scenarios dealing with freezing programs (e.g. reduced international aid because of the world financial
crisis) despite (initially) yielding higher tax returns generate smaller benefits in the long term and have
recessive effects on the world economy.
Of course this tool is only a first step which needs to be improved upon. Amongst other limitations, for
example, the agent behaviors proposed here are quite mechanical. We do not consider for example
questions of adherence or therapeutic failure in an in-depth manner: a “delivered” treatment, for us, is
one which operates with a certain percentage of exogenous efficacy (without consideration for age,
illness duration or treatment duration) and the probabilities of survival of cohorts of agents benefitting
from ART (HIV+ ND) are estimated approximately with the aid of results found in the literature.
However, we do not know how correct these results are for the populations from Tanzania, Cameroon
or Swaziland (we will shortly propose a variant of the model in which these probabilities of survival
may be lessened or increased according to both patient adherence to treatment and efficiency of the
treatment utilized). A second limitation arises from the epidemiological data injected during the
construction of the transition matrices, especially data concerning agents’ infection risk. As no data
were available providing precise probabilities of future infections (i.e. in terms of age-group and
gender), we consider a parameter
included at the micro-level for capturing complex interactions
15
between cohorts of infected agents. We estimated
by calibration and we assume it is constant in
time. It is obvious that “events” may modify the calibration: large conversion of populations to nonrisky sexual practices, a civil war or unexpected migratory fluxes, may all modify the dynamic of
contaminations and substantially weaken the hypothesis of the constancy of the parameter
over the
long term.
Another weakness of the model is that it only distinguishes workers according to their level of
participation in production. The gains linked to the populations under ART programs are not
distinguished from each other in terms of level of contribution to the GDP (the value of wealth created
by a worker being highly dependent on the activity sector), nor in terms of contribution level in
international exchanges. It could have been interesting to distinguish, for example, an export sector in
the economy, and from this, calculate the international gains created by providing access to ART. Our
approach, which deals with a representative individual (i.e. the average individual), is “real “ at the
aggregated level, but does not enable us to go into the finer detail of production for the various sectors.
The general message underscored by the results is that rapid ART scaling-up strategies, which are
more costly in the short term, “dominate” other strategies in the long term. When the preventative
effect of treatment begins to bear fruit, extensive access to treatment not only starts to manifest itself
in more lives being saved and a continuity in the productivity of HIV+ people, but also in less
seroconversion within cohorts of people who were HIV- at the outset. The resulting epidemiological
effects together with the macroeconomic gains are therefore greater.
16
Tables and Figures
Table 1: Micro‐simulation results for selected individuals 17
Table 2: Swaziland : Initial Stage Description and Micro‐simulation results in 2036 for 3 ART Coverage Scenarios Cross‐Sectional Indicators 2006 2036
Initial Scenario1 Scenario2 Scenario3 Characteristics « No Access » « Aid Freeze » “Universal Coverage” 42.37% 0%
17.30 100 ART ART Coverage (in percents)
Coverage Individuals under ART (in thousands)
19.8
None
19.7 180.5
Individuals needing ART (in thousands)
46.7
101.5
113.9 180.5 HIV HIV Prevalence (in percents)
25.88 21.84
22.43 23.00
Prevalence PLWHIV (in thousands)
150.6 349.6
361.2 394.1
Number of Adults aged 15‐49 (in thousands)
582.1
1,600.5
1,610.4 1,713.6
GDP per Capita (in USD)
4,419 4,906
4,919 5,080
Overall GDP (in billion USD)
2.60
7.85
7.92
8.71
Deaths from 2031 onwards (in thousands)
111.1
108.2 65,003
Deaths from 2031 onwards due to HIV (in percents)
79.92
79.13
60.97
Cum. Deaths from 2006 onwards (in thousands)
437.8
412.9
245.3
GDP Deaths Retrospective Indicators Swaziland
Cum. Lives saved from 2006 onwards (in thousands)
Reference 24.8
192.5
ART Costs Total ART Cost from 2006 onwards (in million USD)
0 497
2,881 GDP Gains GDP gap from 2006 onwards (in million USD)
0
2,520
16,460
Reference 508% 571%
GDP gains / ART Costs from 2006 onwards
18
Table 3: Tanzania: Initial Stage Description and Micro‐simulation results in 2028 for 3 ART Coverage Scenarios Cross‐Sectional Indicators Indicators 2008 2028 Initial Scenario1 Scenario2 Scenario3 Characteristics « No Access » « Aid Freeze » ART ART Coverage 31.41% 0%
26% “Universal Coverage” 100% Coverage Individuals under ART 121,650 0
127,374 882,290 Individuals needing ART 387,331 428,662 496,618 882,290 6.31% 5.34% 5.44% 5.99% HIV HIV Prevalence Prevalence PLWHIV 1,188,891 1,727,550 1,764,980 2,288,065 Total Population 18,832,759 32,348,990 32,427,204 32,638,329
GDP per Capita $698 $702 $703 $706 GDP Overall GDP Deaths Retrospective Tanzania
$13,153,702,891 $22,721,475,110 $22,771,980,858
$23,229,849,963 Deaths from 2023 onwards 599,526 572,736 356,654 % Deaths from 2023 onwards due to HIV 54.37% 58.10% 23.30% 2,467,619 2,361,957 1,564,642 Cumulated Deaths from 2003 onwards Cumulated Lives saved from 2003 onwards
Reference 105,662 902,977 ART Costs Total ART Cost from 2003 onwards 0 $2,239,969,555 $10,275,945,946 GDP Gains GDP gap from 2003 onwards 0
$1,526,572,035 $7,910,283,490 Reference 68% 77% GDP gains / ART Costs 19
Table 4: Cameroon: Initial Stage Description and Micro‐simulation results in 2034 for 3 ART Coverage Scenarios Cross‐Sectional Indicators 2009 Initial Scenario1 Scenario2 Scenario3 Characteristics « No Access » « Aid Freeze » ART Coverage (in percents)
25.76 0
14.78
“Universal Coverage” 100% Coverage Individuals under ART (in thousands)
35.5 0
36.6 421.0
Individuals needing ART (in thousands)
137.7 229
248.4
421.0
HIV HIV Prevalence (in percents)
5.14 4.63 4.78 5.08% Prevalence PLWHIV (in thousands)
472
855
885 952
Number of Adults aged 15‐49 (in thousands)
9,172
18,479
18,498
18,750
GDP per Capita (in USD)
2,062 2,159
2,160
2,172
Overall GDP (in billion USD)
18.91
39.89
39.95
40.73
409
401
309
45.56
43.15 23.05
1,816
1,765
1,349
Reference 51
467 0 868
6,541
0
1,900
15,600
Reference 216%
239% GDP Deaths from 2031 onwards (in thousands)
Deaths from 2031 onwards due to HIV (in Indicators 2034
ART Deaths Retrospective Cameroon
percents)
Cum. Deaths from 2006 onwards (in thousands)
Cum. Lives saved from 2006 onwards (in thousands)
ART Costs Total ART Cost from 2006 onwards (in million USD)
GDP Gains GDP gap from 2006 onwards (in million USD)
GDP gains / ART Costs from 2006 onwards 20
Figure 1 - Swaziland: Number of PLWHIV needing ART & Number of
whom being under ART in the 3 ART Coverage Scenarios
Figure 2 - Swaziland: Cumulative Number of Deaths from 2006 onwards;
Comparison of Three ART Coverage Scenarios
21
22
Figure 4: Swaziland: Cost-Benefit Analysis;
Ratio between the Cumulated GDP gap (compared to Scenario 1)
and ART Programme Cumulated Cost
Figure 3: Swaziland: HIV Prevalence
Comparative Analysis of Three ART Coverage Scenarios
23
Figure 5: Tanzania: Number of PLWHIV needing ART & Number of
whom being under ART in the 3 ART Coverage Scenarios
Figure 6 - Tanzania: Cumulative Number of Deaths from 2003 onwards;
Comparison of Three ART Coverage Scenarios
24
Figure 8: Tanzania: Cost-Benefit Analysis; Ratio between the Cumulated
GDP gap (compared to Scenario 1) and ART Programme Cumulated Cost
Figure 7: Tanzania: HIV Prevalence
Comparative Analysis of Three ART Coverage Scenarios
25
Figure 10 - Cameroon: Cumulative Number of Deaths from 2003 onwards;
Comparison of Three ART Coverage Scenarios
Figure 9: Cameroon: Number of PLWHIV needing ART
& Number of whom being under ART in the 3 ART Coverage Scenarios
26
Figure 12: Cameroon: Cost-Benefit Analysis; Ratio between the
Cumulated GDP gap (compared to Scenario 1) and ART Program
Cumulated Cost
Figure 11: Cameroon: HIV Prevalence
Comparative Analysis of Three ART Coverage Scenarios
27
Model Parameters
Demographic 15-24 Population
15-49 Population
Sample
Economics
Estimated
Coefficients
Swaziland
Tanzania
14.18%
13.56%
Growth Rate
582
16,220
In Thousands
71
1,521
Weight
Logistic Regression for Employment Rate
1.538
1.339
20-24
2.378
2.284
25-29
2.649
2.514
30-34
2.836
2.702
35-39
2.696
2.934
40-44
2.691
2.677
45-49
-0.7567
-0.369
Women
-1.580
0.196
Constant
HIV -
100%
100%
1.299 2.025 2.618 2.929 3.094 2.977 ‐0.596 ‐0.663 100%
TNN
100%
100%
100%
TN & T=1
95%
95%
95%
TN & T=0
75%
75%
75%
In billion
USD
Annual Avg. Wage
Per Worker
Age-Sex Specific
15-19 M
Smoothed
20-24 M
5 years Death
25-29 M
Rates
30-34 M
35-39 M
40-44 M
45-49 M
15-19 W
20-24 W
25-29 W
30-34 W
35-39 W
40-44 W
45-49 W
Prop. of HIV+ in Need of
Treatment
ART Coverage
T0
T0-T1
T1
α1(HIV history)
v (HIV+ Mortality Gap)
u (TN Mortality Gap)
γ( Diffusion Parameter)
2.648
11.351
15.775
$10,381
0.88% 2.00% 5.19% 12.03% 19.07% 20.67% 17.74% 1.04% 3.52% 10.65% 16.92% 16.53% 14.05% 11.18% 31.05%
$870
0.85%
1.53%
2.52%
4.28%
5.07%
6.39%
6.46%
1.23%
2.18%
3.53%
4.38%
4.84%
5.49%
4.83%
30.00%
$3,223
1.44%
2.20%
3.08%
4.10%
5.11%
6.01%
6.62%
1.49%
2.62%
3.36%
3.81%
4.09%
4.39%
4.99%
29.91% 42.37%
Unknown
Unknown
0.1 0.005
0.20
0.518
0.71%
16%
31%
0.1
0.005
0.20
0.415
8.75% 18% 26% 0.1
0.005
0.2
0.45
0.2
1.30
0.2
1.397
0.2
1.37
Absenteeism Rate
GDP at M.P
Epidemiology
Cameroon
11.71%
7,708
790
α3 (Prevent Parameter)
α4 (Transition Booster)
28
29
Appendix 1 Mortality Rates & Cause of Death: Another advantage of the DHS datasets is that they provide an approach to estimate adult mortality: the DHS surveys included a sibling history questionnaire5 provided to women, wherein a series of
questions were asked about all of the respondent’s biological brothers and sisters and their survival
statuses. These data enable the overall adult mortality (by age and sex) to be directly estimated. For every age class and gender , the probability of dying in the subsequent five years can thus be obtained. We broke down this probability of mortality according to the three causes of mortality in our population: Reviews conducted by Zwahlen and Egger (2006)xxii on time from ART eligibility to death by AIDS
indicate a median time of about 3 years for those without treatment; UNAIDS/WHO assumes that
adults with advanced HIV infection who fit eligibility criteria for treatment die of AIDS in about 2
years if not treated (UNAIDS, 2006xxiii; UNAIDS 2007xxiv). The literature about the long term survival
on ART remains scarce. Stover et al. (2008) undertook a literature review which showed a median 24month survival rate of 84%xxv. Etard (2006)xxvi and Leger (2009)xxvii indicate a survival rate of 75% at
5 years after diagnosis. With no real data going back far enough in time, we assume that mortality rates are substantially reduced with ART, but remain greater than those for HIV negative individuals with the same age and sex characteristics: We also assume that a HIV+ individual who does not require treatment is “slightly” more likely to die
in the following five years than someone in the general HIV negative population, as he may have had
.
more risky behaviors. We thus note
can then be expressed:
5
This module was not included in the THIS 2003‐04. Instead we used information provided in the Tanzania Demographic Health Survey (TDHS) 2004‐2005. A 6 year “look‐back” was adopted for estimating age‐specific mortality rates in the EDSC III, this period being 7 years in the 2004‐05 TDHS. 2006 WHO Life Tables were used for Swaziland 30
Note: Although P03 is determined in T0 according to the average Treatment Coverage level
known before the interviews took place (see note 2), it does not vary over time (i.e. when TCT
varies)6.
HIV infection: HIV negative individuals have a certain probability of becoming seropositive in any subsequent 5 year
period. This probability is closely linked with age and gender specific HIV incidence, this latter being
the key indicator used to assess the course of an epidemicxxviii. However, even in a retrospective view,
it is difficult to obtain this value for HIV due to its long asymptomatic period. The gold standard to
measure HIV incidence is still the prospective cohort study where individuals are tested for HIV at
relatively short intervals. However such data are scarce in many developing countries (including
Tanzania, Swaziland and Cameroon) and data obtained at date t (say 2010) cannot be extrapolated
forward (say 2015, 2020, … etc. ) without discussion. For our purpose, using an agent-based model,
we then propose to build contamination rates using the following assumption:
Seroconversion is endogenous in the microsimulation model, reflecting behavioral interactions between agents. The probability linearly depends on the proportion of HIV+ people which already exists in the cohort of the next age class. The factor can capture (and vary with) several considerations: sexual partnership patterns, use of condom, etc. Technically, is calibrated in such
a way as to reproduce, at a macro-epidemiological level, the aggregated prevalence and incidence
forecasts available for each country from UNAIDS. It is obvious that this measure is partly arbitrary
(at the very least due to the arbitrariness of the UNAIDS forecasts), especially when we consider that
is constant over the long run.
We also assume that high coverage has a preventive effect on the spread of HIV: in the Universal Access Scenario, the probability of becoming HIV positive will be lowered by a factor (Granish
2009).
6
ART was not available in Tanzania before 2003. The last available data for Swaziland is from 2003, when less than 10% of the population required treatment. We considered that most people dying from AIDS between 2000 and 2007 did not receive ART. 31
From infection to requiring treatment: The time from new infection to requiring treatment varies according to the individual. The Alpha
Network Analyzing Longitudinal Population-based HIV/AIDS data on Africa in November 2007
reviewed results from 10 cohort studies (Todd et al, 2007)xxix. These indicate that the median time
from infection to death by AIDS in the absence of treatment is about 11 years in most countries.
Subtracting the median time from requiring treatment to death from this value produces an estimate of
the median time from infection to requiring treatment of about 8 years. The speed of progression
varies according to age and sex, with older people progressing more quickly and women tending to
become infected at younger ages than men. UNAIDS suggest that the median time from infection to
requiring treatment is 7.5 years for men and 8.5 years for women. We propose to estimate the
progression from infection to requiring treatment using this equation:
The percentage of people needing treatment within a cohort of HIV+ individuals evolves in such a way that this percentage is the same as that which already exists in the cohort of the next higher age group (i.e. 5 ‐years later)7. References
i
Stern and al. (2005). Report for the G8 Commission, Report, www.commissionforafrica.org.
WHO (2009). Towards Universal Access: Scaling up priority HIV/AIDS interventions in the health
sector.
iii
UNAIDS (2009). The Global Economic Crisis and HIV Prevention and Treatment Programme:
Vulnerabilities and Impact. Report.
iv
WHO (2009). Global Treatment Working Group. Treatment White Paper. 27th November Draft.
v
Bercu & Chafaï (2007). Stochastic Models and Simulation. Dunod.
vi
Tanzania National Bureau of Statistics, ORC Macro (2005). Tanzania Demographic and Health
Survey 2004-2005. Report.
vii
Swaziland Central Statistical Office, ORC Macro (2008). Swaziland Demographic and Health
Survey 2006-07. Report. ii
viii
Institut National de la Statistique, Cameroon ; ORC Macro (2005). Enquête Démographique et de Santé
Cameroon 2004. Report.
ix
Swaziland Central Statistical Office, ORC Macro (2008). Swaziland Demographic and Health
Survey 2006-07. Report. x
WHO HIV/AIDS Programme (2006). Antiretroviral therapy for HIV Infection in Adults and
Adolescents: Recommendations for a Public Health Approach.
7
This formula assumes that HIV positive individuals were newly infected, even in the first period (in the original set of individuals). As individuals from the datasets may have contracted HIV for a longer time, we multiplied this probability by a factor in the first period of simulation. 32
xi
WHO, UNAIDS, UNICEF. Epidemiological Fact Sheet on HIV and AIDS – Swaziland, 2008
Update; Tanzania 2004 Update, Tanzania 2008 Update.
xii
WHO (2002). Scaling Up Antiretroviral Therapy in Resource Limited Settings. Guidelines for a
Public Health Approach.
xiii
WHO (2009). Towards Universal Access: Scaling up priority HIV/AIDS interventions in the health
sector
xiv
UNAIDS (2009). The Global Economic Crisis and HIV Prevention and Treatment Programme:
Vulnerabilities and Impact. Report.
xv
World Bank (2009). Averting a Human Crisis during the Global Downturn. Policy options from the
World’s Bank Development Network. Report.
xvi
Granich et al (2009). Universal voluntary HIV testing with immediate antiretroviral therapy as a
strategy for elimination of HIV transmission: a mathematical model. The Lancet 373: 48-57.
xvii
Wooldridge (2002). Econometric Analysis of Cross Section and Panel Data. MIT Press.
xviii
Kyereh & Hoffman (2008). The Impact of HIV/AIDS on Skills availability in South African Coal
Mines. Dublin Conference on Children affected by HIV/AIDS.
xix
UN Population Division (2008). World Population Prospects. The 2008 Revision.
xx
Goldie et al. (2006). Cost-effectiveness of HIV-Treatment in Resource-Poor Settings – The Case of
Côte d’Ivoire. The New England Journal of Medicine 355:1141-53.
xxi
UNITAID (20009). New Prices Reductions for key drugs.
http://www.unitaid.eu/fr/20090417198/ACTUALITES/UNITAID-and-the-Clinton-HIV/AIDSInitiative-Announce-New-Price-Reductions-for-key-drugs.html
xxii
Zwahlen, Egger (2006). Progression and mortality of untreated HIV-positive individuals living in
resource-limited settings: Update of Literature review and evidence synthesis. UNAIDS.
xxiii
UNAIDS Reference Group (2006). Improving parameter estimation, projection methods,
uncertainty estimation, and epidemic classification. Report.
xxiv
UNAIDS Reference Group (2007). Methods for estimation of ART’s impact on deaths
averted/delayed. Report.
xxv
Stover et al. (2008). The Spectrum projection package: improvements in estimating mortality, ART
needs, PMTCT impact and uncertainty bounds. Sexually Transmissible Infections 84 (Suppl I): i24i30.
xxvi
Etard et al. (2006). Mortality and causes of deaths in adults receiving highly active antiretroviral
therapy in Senegal: a 7-year cohort study. AIDS 20: 1181-9.
xxvii
Leger et al. (2009): 5-year survival of patients with AIDS receiving antiretroviral therapy in Haiti.
New England Journal of Medicine 361(8):828-9.
xxviii
UNAIDS (2008). Improving the EPP and Spectrum estimation tools for the 2008-9 round of
national estimates. Report.
xxix
Todd et al. Time from HIV sero-conversion to death prior to ART: a collaborative analysis of eight
studies in six developing countries. AIDS 21 (suppl 6): S55-63.
33