INTE 398 Lecture Notes - Lecture 3: Hans Rosling, Epidemiology, Malaria
5 May 2018
School
Department
Course
Professor
Ffar 291/Inte 398 – Intro HIV Class
Lesson 3
3.1 Intro Lecture
-how experts or med officials identify and track health threats around the world
-how they collect data and make figures & how they use those patterns to understand
HIV/AIDS around the world
Vocab
-Endemic - steady presence in a particular region
-ex Malaria endemic to some parts of Africa, bc particular life forms that breed in
particular climates)
-Epidemic - increase in presence
*Pandemic - presence in multiple locations as a transmissible infection or syndrome; NOT
something that comes and goes; it's an ongoing threat (ex. HIV)
-Epidemiology - increase in precedence of some sort of med prob or threat that's transmissible
- like a virus
-figuring out those patterns and figuring out reasons and ways to eliminate those pattern
3.2 Hans Rosling on HIV: New Facts and Stunning Data Visuals Video
-Rosling - data epidemiologist
-AIDS discovered in 1981 and virus in 1983
-in 1983, US had very low % infected but due to big pop, size of bubble so quite many ppl
-Uganda had almost 5% but due to small pop quite a big bubble
-MOST infected country in the world
-fast rise in Uganda and Zimbabwe
-first heavily infected country in Asia = Thailand - 1-2%
-then Uganda turned back whereas Zimbabwe skyrocketed and some years later SA got rise of
HIV
-2-3 years ago reached steady state (took 25 yrs)
-DOES NOT mean things are getting better; just means that they are not getting worse
-steady state - 1% of the adult pop is infected so 30-40 mil ppl
-Botswana - started low, skyrocketed and peaked in 2003 and then went down
-good econ and gvt so can manage to treat ppl
-if teated, do’t die i AIDS so %s o't oe do ppl a suie i -20 yrs so
some prob w this metric
-poorer countries in Africa - rates fall faster bc ppl still die
-not all ppl treated
-focus now back on PREVENTION - only by stopping transmission that we'll be able
to deal w it (waiting for vaccine)
-higher HIV rate in the world in African countries and yet same rate in US
-terrible simplification that things go on in 1 way in Africa
-seems that more money, more HIV but that is simplistic
-ex Tanzania - highest income places = more HIV
find more resources at oneclass.com
find more resources at oneclass.com
-see higher rates in younger women than younger men
-in the southern parts of Africa, there's 4% of the world pop and 50% of HIV-infected ppl
-if completely healthy and have heterosexual sex, risk of infection is 1 in 1000
*it could be concurrency
-the fact of 2+ events happening or existing at the same time
-having more than 1 partner in a month is more dangerous for HIV
3.3 Epidemiology Lecture
-epidemiology NOT as self-evident as it might appear
-lot of diff factors involved and they don't always relate well to each other and don't always
make logical sense
-sometimes preconceptions change or impact how we understand those figures
-Africa is a lot of diff places, not just in terms of lang or culture, lots of diff economies and diff
situations
-can see very graphically and clearly that what is happening in Madagascar is NOT
happening in South Africa
-or what is happening in US is NOT happening in Peru
-even if geographically talking about similar parts of the world, there are LOT of diff
factors involved when talking about how HIV infection functions from place to place
-very hard not to just compare (how things done in each society like dating practices,
etc and to say how HIV works)
Vocab
-Incidence - increase in new infections (usually measured over a year)
-expressed often as a % and can understand it over a period of time
-talks about new infections in a year - gives idea of current rate of infections in a year -
how many people are getting HIV
-Prevalence - presence of existing cases (usually presented in a specific region or pop)
-as a total amount - how many people have HIV
-in North America, as total number of existing cases of HIV+ ppl
-among Latinos in the US, among drug users, etc
*definition of AIDS MUST be consistent to be able to compare it bc if changes, means that
whole bunch of new cases
-if looking at numbers before vs after definition change, see huge increase
-but actually its just how cases are tabulated
-ppl are living longer w the virus
-if ppl diagnosed late in the exposure of the virus, won't live long time bc not getting treated
-ppl passing away at faster rate so prevalence rate remains quite consistent
-but as ppl living longer and longer, even not that many new infections, the total number of ppl
living w HIV gonna grow
-so prevalence gonna sig grow bc ppl live longer and longer w HIV
-they provide info but don't give the total pic
Regional HIV/AIDS Stats 2015
-notice that sometimes, fairly broad margin provided for these figures
find more resources at oneclass.com
find more resources at oneclass.com
-ex 2 mil ppl living w HIV in America and Caribbeans - 600 000 ppl gap +/- - HUGE GAP
-how consistently info is kept, maybe not maintaining records properly, started keeping
records late, didn't compile all info in time, might rely on estimates
-so gives GENERAL SNAPSHOT
-if changes in definition not integrated consistently
-or if trying to change rates to be able to get funding, etc
-these numbers reflect infections that we KNOW about
-sometimes estimates include the assumption of how many ppl are living w HIV but haven't
been tested
-based on pop data that we have, we can EXTRAPOLATE a larger number presuming that
lage ue liig HIV ut hae’t ee tested et o those esults ae't aailale
-can assume that this data reps time even before that
-by time data is categorized and verified, etc - there is some gap and time
-so new cases identified, new gaps, new time that not identified here
-soeties ppl test eg ut ae i fat posi ut hae’t seooeted ido peiod
hasn't ended yet
-so they transmitted the neg result
*this snapshot is NOT perf
-it’s eall the est ues that e hae
-always gonna be incomplete and imperfect
-bc changes in definition, missing data so inconsistent, human error, factors that
are/aren't in control
*hard to keep track of cases when no adequate info before
-always somewhat arbitrary
-he lookig at ues o HIV, it’s ipefet ad ioplete BUT the est
snapshot we can do at that time
3.4 The Epidemiology of HIV in Canada Reading
-an estimated 71 300 Canadians were living w HIV at the end of 2011
-HIV prevalence started to rise again in late 1990s bc new infections and fewer deaths
-1/4 of ppl living w HIV in Canada = unaware that they have HIV
-almost 25k ppl living w HIV have died since the beginning of the epidemic
-the epidemic is concentrated in specific pops (prevalence)
-number of new infections remained stable in past several yrs but its not insig (incidence)
3.5 HIV in Canada Lecture
-always have to keep the international/local balance
Canadian Context - HIV and AIDS in Canada: Surveillance Report to Dec 31 2013
*data collection sometimes flawed and imperfect
-looking at the proportions of HIV cases amongst adults - who is at risk and who is infected in
Can contexts
*delays in compilations BUT gives good overview
-the VAST majority (almost 1 on 2 ppl infected) is a gay man - man who has sex with another
man
-other breakdowns that we can look at
find more resources at oneclass.com
find more resources at oneclass.com
Document Summary
How experts or med officials identify and track health threats around the world. How they collect data and make figures & how they use those patterns to understand. Endemic - steady presence in a particular region. Ex malaria endemic to some parts of africa, bc particular life forms that breed in particular climates) *pandemic - presence in multiple locations as a transmissible infection or syndrome; not something that comes and goes; it"s an ongoing threat (ex. Epidemiology - increase in precedence of some sort of med prob or threat that"s transmissible. Figuring out those patterns and figuring out reasons and ways to eliminate those pattern. 3. 2 hans rosling on hiv: new facts and stunning data visuals video. Aids discovered in 1981 and virus in 1983. In 1983, us had very low % infected but due to big pop, size of bubble so quite many ppl. Uganda had almost 5% but due to small pop quite a big bubble.
