PROBLEM C: Trade-offs in the fight against HIV/AIDS p1 k/ _3 Q4 n3 n1 }) I$ y# {
As the HIV/AIDS pandemic enters its 25th year, both the number of infections and number of deaths due to the disease continue to rise. Despite an enormous amount of effort, our global society remains uncertain on how to most effectively allocate resources to fight this epidemic. You are a team of analysts advising the United Nations (UN) on how to manage the available resources for addressing HIV/AIDS. Your job is to model several scenarios of interest and to use your models to recommend the allocation of financial resources. The narrative below provides some background information, and outlines specific tasks. 1 c3 {9 k/ A8 f Task #1: For each of the continents (Africa, Asia, Europe, North America, Australia, and South America), choose the country you believe to be most critical in terms of HIV/AIDS. Build a model to approximate the expected rate of change in the number of HIV/AIDS infections for these countries from 2006 to 2050, in the absence of any additional interventions. Fully explain your model and the assumptions that underlie your model. In addition, explain how you selected the countries to model. Use as a list of countries for inclusion in your analysis the countries included in the attached spreadsheet, which include all member states of the World Health Organization (WHO) as of 2003. . z$ [1 o# K m SData: “list_WHO_member_states.xls” , g, n! I* j1 h/ v) g" p" y
Reliable data on HIV prevalence rates by county are generally difficult to obtain. The attached spreadsheet includes several worksheets of data which you may use in your analysis. * x; l: O* m* Q$ p9 N' n% J) z5 b Data: “hiv_aids_data.xls” 7 J( f( u; I" {9 {: H! B% l2 j+ h" m7 [0 s& Z
a. “Global HIV- AIDS cases, 1999”: These data come from UNAIDS (the Joint United Nations Programme on HIV/AIDS) and report the estimated number of HIV positive 0 to 49 year olds by country at the end of 1999. " U5 `' d# W3 ]5 g$ s5 K
' V; R' x& e$ w S6 p3 n, @) jb. “HIV- AIDS in Africa over time”: These data come from the US government and give some piecemeal time series data on measured HIV prevalence rates among women of childbearing age, in urban areas, over time for some African countries. & m9 W# L( ]( C3 ~5 g. b5 V: L$ o: [# D
c. “HIV- AIDS subtypes”: These data come from UNAIDS and give the geographic distribution of HIV-1 subtypes by country. 5 [! `3 U# W* K
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Also attached, for your use, are some basic population and demographic data. 6 ?% D R9 P4 C6 K+ NData: & s, i' Z. v0 g) Z , G) ^- J; C3 a$ a(1) “fertility_data.xls”: These data come from the UN and give age-specific fertility rates by major area, region, and country, 1995-2050 (births per thousand women) 8 t' _) c2 T) m! n! q' y& x 9 L! c2 }4 \$ @5 P2 H; ra. Estimates for 1995-2005 8 U0 u: H \$ X6 `
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b. Projections (under the assumption of medium fertility levels) for 2005-2050 [4 H0 W; U) G* @
( n. P6 V+ q9 }! F$ f/ c! N(2) “population_data.xls”: These data come from the UN and give total population (both sexes combined) by major area, region, and country, annually for 1950-2050 (thousands). 6 W+ h, z0 R, h- k2 N; t6 Z5 H* j
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a. Estimates for 1950-2005 2 z& ^9 V6 D8 g/ T6 l6 r9 l
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b. Projections (under the assumption of medium fertility levels) for 2006-2050 - o$ ^9 |+ s( d1 f
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(3) “age_data.xls”: These data come from the UN and give population (for both sexes, and by gender) by five-year age groups, major area, region, and country, 1950-2050 (thousands). 6 ?3 J! Y0 U2 k9 m- ^. [ 8 K( @% }6 T5 S% l+ h7 y6 _a. Estimates, 1950-2005 0 ~2 t# C; d; ^/ t# N. }7 E9 E7 |" L+ _- e4 [3 ?/ o# r
b. Projections (under the assumption of medium fertility levels) for 2010-2050 - f2 U" y }& }7 z' k ) H: J6 u8 l+ z6 q(4) “birth_rate_data.xls”: These data come from the UN and give crude birth rates by major area, region, and country, 1950-2050 (births per thousand population). : `% J8 |( t7 M& Q. a) ^7 W$ M
, f1 e. W7 H: v% f* M0 a5 ca. Estimates, 1950-2005 7 W3 o8 ^3 n: N7 }5 t
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b. Projections (under the assumption of medium fertility levels) for 2005-2050 " p# b% L8 X4 l u+ H! |' a4 J
% [+ M" a1 _& B$ ~4 V G(5) “life_expectancy_0_data.xls”: These data come from the UN and give life expectancy at birth (by sex and both sexes combined) by major area, region, and country, 1950-2050 (years). 0 h+ r2 X- Y* [8 }; t/ g$ w
& L/ g/ s( R+ l) w% s" T/ [b. Projections (under the assumption of medium fertility levels) for 2005-2050 There are a number of interventions that HIV/AIDS funding could be directed towards -- including prevention interventions (voluntary counseling and testing, condom social marketing, school-based AIDS education, medicines to prevent mother-to-child transmission, etc.) and care interventions (treating other untreated sexually transmitted diseases, treating opportunistic infections, etc.). You should focus on only two potential interventions: provision of antiretroviral (ARV) drug therapies, and provision of a hypothetical HIV/AIDS preventative vaccine. 9 W' @2 U! a& _/ D& d" e+ @ V$ E- t, Z' c Task #2: First, estimate the level of financial resources from foreign aid donors that you realistically expect to be available to address HIV/AIDS, by year, from 2006 to 2050, for the countries you selected in Task #1. Then use the model you developed in Task #1 and these estimates of financial resources to estimate the expected rate of change in the number of HIV/AIDS infections for your selected countries from 2006 to 2050 under realistic assumptions for the following three scenarios: # A7 I8 F- k$ p( i {- H5 y. O
# h1 [! H- Z# A5 Y$ {7 T(1) Antiretroviral (ARV) drug therapy ' {0 B: Y! V0 a8 M: U3 Y+ c1 J0 o: \( j$ o( W
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(2) A preventative HIV/AIDS vaccine * r- [" I7 Q8 E
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# C% i; \3 P5 v3 g7 [- R" V(3) Both ARV provision and a preventative HIV/AIDS vaccine . M8 V U5 p. z# E- A$ U
Assume in these scenarios that there is no risk of emergence of drug-resistant strains of HIV (you ! g! F% z+ b/ z3 F# I5 {
will examine this issue in Task #3). 7 ?) ~0 y! c6 m5 u) a/ R" I6 a @Be sure to carefully describe the assumptions that underlie your model. 3 e# d& b9 }, V7 s3 c0 TYou can choose whether these scenarios should be implemented for all of the countries you selected 0 E4 L) D! H- n! ` Sin Task #1, or for certain subsets of countries based on income cut-offs, disease burden, etc. 6 d$ ]. E& @3 ~+ J
Available for use if you wish is a spreadsheet of country-level income data. 7 K$ z( A4 }, |0 O n
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6 m: {7 L5 I! P( H/ D0 A0 KData: “income_data.xls”: These data are from the World Bank (2002) and give per-capita gross national product (GNP) data as well as broad income classifications that you are free to use in your analysis if you wish. ARV drug therapies can have tremendous benefits in terms of prolonging the lives of individuals infected with HIV/AIDS. ARVs are keeping a high proportion of HIV/AIDS-infected individuals in rich countries alive, and policy makers and international institutions are facing tremendous political pressure to increase access to ARVs for individuals in poor countries. Health budgets in low-income countries are very limited, and it seems unlikely that poor countries will be able to successfully expand these programs to the majority of their populations using their own resources. Appendix 1 presents country-specific data from UNAIDS on current access to ARVs for a number of countries. The efficacy of ARVs depends in large part on adherence to the treatment regimen and to proper monitoring. The most favorable conditions for ARVs are structured programs with extensive counseling and physician care, as well as regular testing to monitor for disease progression and the onset of opportunistic infections. Non-adherence or inadequate treatment carries with it two very serious consequences. First, the treatment may not be effective for the individual undergoing treatment. Second, partial or inadequate treatments are thought to directly lead to the emergence of drug-resistant strains of HIV. The price of the drugs initially used to treat patients has come down to several hundred dollars a year per patient, but delivering them and providing the necessary accompanying medical care and further treatment is the key administrative and financial challenge. It is estimated that purchasing and delivering antiretrovirals using the clinically-recommended approach (DOTS, or directly observed short course treatments) which is intended to minimize the emergence of drug-resistant strains would cost less than $1,100 per person per year. (Adams, Gregor et al. [2001]. “Consensus Statement on Antiretroviral Treatment for AIDS in Poor Countries,” http://www.hsph.harvard.edu/bioe ... us_aids_therapy.pdf ) For a preventative HIV vaccine, make assumptions you feel are reasonable about the following (in addition to other factors you may choose to include in your model): [7 G) u7 L. e# m
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(1) The year in which an HIV/AIDS preventative vaccine might be available ! }4 e+ w' u. N9 ^% V# V8 Q0 K9 D" L! C1 }# T" R
(2) How quickly vaccination rates might reach the following steady-state levels of vaccination: + U# Y4 Z% P8 o) T9 |: r& T
; T. c# b5 A* L7 b+ _a. If you wish to immunize new cohorts (infants), assume the steady-state level for new cohorts of the country-by-country immunization rates for the third dose of the diphtheria-pertussis-tetanus vaccine (DTP3), as reported by the WHO (2002) Data: “vaccination_rate_data.xls” 8 \; F4 Y9 R& }( j0 Y# i1 Q* o " v8 @4 F# b- M6 R b. If you wish to immunize adults (any group over age 5), assume the steady-state level for older cohorts is the second dose of the tetanus toxoid (TT2) rate, as reported by the WHO (2002) Data: “vaccination_rate_data.xls” : {& _* F, g2 a5 ^7 s8 L; l$ [' L. d1 `& s9 S: _8 T1 W
(3) The efficacy and duration of protection of the vaccine 5 M2 X' I8 t9 [" _+ f& g; J
* s" P; p* r6 b& h; R- o(4) Whether there would be epidemiological externalities from vaccination $ R# X8 w9 J) j: K9 q3 }: K& I4 ?/ U5 X% H/ X9 ^2 B/ i
(5) Assume the vaccine is a three-dose vaccine, and can be added to the standard package of vaccines delivered under the WHO’s Expanded Programme on Immunization (EPI) at an incremental cost of addition of $0.75 9 _5 K# u1 g/ z0 P+ h4 s# H & h! p* }( `. f* ]4 x2 ETask #3: Re-formulate the three models developed in Task #2, taking into consideration the following assumptions about the development of ARV-resistant disease strains. Current estimates suggest that patients falling below 90-95 percent adherence to ARV treatment are at a “substantial risk” of producing drug resistant strains. Use as an assumption for your analysis that a person receiving ARV treatment with adherence below 90 percent has a 5 percent chance of producing a strain of HIV/AIDS which is resistant to standard first-line drug treatments. Second- and third-line ARV drug therapies are available, but assume for your analysis that these drugs are prohibitively expensive to implement in countries outside of Europe, Japan, and the United States. : T. i9 k5 f. |' h) X; STask #4: Write a white paper to the United Nations providing your team’s recommendations on the following: 1 G* m. V" k7 e1 j5 r; U8 r0 w2 M* P
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(1) Your recommendations for allocation of the resources available for HIV/AIDS among ARV provision and a preventative HIV vaccine - }+ j0 z' e' t3 P5 M2 O' O! T) U4 e
' o' m) L% c: x4 S(2) Your argument for how to weigh the importance of HIV/AIDS as an international concern relative to other foreign policy priorities & O" ~9 `3 i: W. z W9 n
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(3) Your recommendations for how to coordinate donor involvement for HIV/AIDS For (1): assume that between now and 2010 the available financial resources could be allocated so as the speed the development of a preventative HIV vaccine – through directly-financing vaccine research and development (R&D), or through other mechanisms. Any gains from such spending would move the date of development you assumed in Task #2 to some earlier date. / T' A7 C' }7 { 3 d3 ^. d- t5 D4 w- RAppendix 1. . Y* x4 Y X& V/ o- B% A
Percentage of adults with advanced HIV infection receiving antiretroviral treatment Country Notes: * := Countries with low prevalence/concentrated epidemics; AAI := Accelerated Access Initiative; UNGASS CR 2003 := program monitoring data from UNGASS (United Nations General Assembly Special Session) country reports 2003; WHO 2002 := 2002 program monitoring data through WHO (World Health Organization) country offices; MoH := Ministry of Health; NACP := National AIDS Control Programme; NCHADS := National Centre for HIV/AIDS, Dermatology and STIs (sexually transmitted infections). 8 }" T8 d# i) P) g8 w3 jC 题: 抗击艾滋病的协调& y' l' i, {6 z+ b" h
HIV/AIDS(人体免疫缺损病毒/艾滋病)的大范围流行已经进入第25 年，由于这种疾病导致的感染人数和死亡人数一直在不断上升. 尽管已经付出了巨大的努力，但是我们的国际社会对怎样最有效地分配资源来抗击这种流行病仍然心中无数. ( f3 `! ~3 Y$ a( Q3 q, S7 C
你们是联合国的一个专家组, 就怎样管理可利用的资源来抗击HIV/AID S 向联合国提出建议. 你们的工作就是要对令人关注的几种方案进行建模, 并用你们的模型就资金分配提出建议. 下面的说明提供了某些背景资料, 也概述了特定的任务. " f e8 [5 \9 F0 \, i+ n
任务1：在每个大洲(非洲、亚洲、欧洲、北美洲、澳洲和南美洲)选择一个你们认为在HIV/AIDS 方面是最严重的国家. 建立模型来粗略估计这些国家在没有任何其他的干预时从2006 年到2050 年HIV/AIDS 感染人数的变化率. 确切地解释你们的模型以及作为你们的模型的基础的那些假设. 另外, 解释一下你们是如何选择要对之进行建模的国家的. 6 ?% g8 T' T! k# w" [; M8 N( s! _作为你们的分析中要包括的一组国家, 请利用附件中的电子数据表中的国家, 该表包括直到2003 年的世界卫生组织(WHO)所有成员的名单. ' ~ }: e9 m3 E# y% ^* u. u数据:“list_WHO_member_states.xls”$ q; u- i4 W$ V! @
有关由国家给出的HIV 流行率的可靠数据一般难以得到. 所附的电子数据表包括你们也许可以用于你们的分析中的几个工作数据表. 1 [ v8 Q& a V* C2 ~. h数据:“hiv_aids_data.xls” 0 A2 t( f' G) y& b7 K5 J( H% F3 j# q1 l, y' g4 I$ M6 O/ `7 u
8 b; J! o, ?( S( S h a. “Global HIV-AIDS cases, 1999”: 这些数据来自 UNAIDS( 关于HIV/AIDS 的联合国联合课题组), 它们报告了1999 年底由国家提供的0 到49 岁HIV 检验为阳性的估计人数. ' J6 J4 G$ ~; {% b+ z! p; C$ L& t) X% ^4 m b. “HIV-AIDS in Africa over time”: 这些数据来自美国政府, 它们给出了分开的时间段上有关某些非洲国家城市地区育龄妇女中随时间推移的HIV 流行率的数据. % o0 |- D# c$ v5 H7 D- |
4 Z5 a2 x+ g2 u& D; }8 _ c. “HIV-AIDS subtypes”: 这些数据来自 UNAIDS, 它们给出了按国家的HIV-1 子类型的地区分布. 附件中还有可以为你所用的某些基本人口数和人口统计学数据. D5 P5 b& E( m. ~+ p6 i
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评论 8 @. }0 W. @) {, W & d. o: | s5 [2 {. A( a. _% l2006 C J HIV The Math..pdf(143.43 KB, 下载次数: 317)
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