Developing Countries and AID between 1994-1997

Under 5 mortality rate (U5MR) in increasing or constant in countries with very high adult HIV (-2.6%-4.6%)

U5MR small decreasing trend in countries with moderate high rates of adult HIV infection

U5MR has largest decrease in countries with lowest rates of adult HIV infection

In Zimbabwe 61% of U5MR is from HIV, in high HIV countries 13%-61% of U5MR is from HIV

Also consider that children living iwth HIV divert resources from other children.

After 12 years there has been a big change in the % of HIV by income

Initally there was no difference in HIV status/risk by income, but with time it has changed so that education has a gradiated effect for women but not men.

This is at a time of interventions the idea is that the information "takes" more among the higher income/educated.

HIV in South Africa changed from 1% in 1990 to 29% in 2005.

1. unemployment and social inequity -> vulnerable groups especially poor women -> poor women trading sex for money or resources

2. Lower marriage rates and more 1 person households, men can't afford to marry and there are less resources not as a couple (no economy of scale?)

3. Increase in female migration, circles between rural areas and informal settlements in urban areas. 

In settlement the HIV% is twice the nat'l mean.

Argues that people's social networks perception determines how they see their risk of HIV

1. Social learning from others

2. Joint evaluation, not just getting new information, but judging it as you go.  Take the information and reshape/reintepret it in ways that make sense to your situation

3. Social influence if your friends hate condoms it will influence you.

Findings

1. Married people feel more at risk

2. if you have 1 or more friends who feel at high risk it increases your fear

3. If many friends feel there is a low risk it reduces your fear

4. If you have many friends who feel they are at high or low risk (versus medium risk?) the more likely you are to talk to your spouse.

Although risk of exposure to HIV in unprotected sex is .1%-1%, it is cumulative w/exposure and causes 70-80% HIV cases globablly

In Malawi the wealthier you are the more likely you are the more likely to to have HIV

Women are more likely than men at every income level

why? more girlfriends, partners with HIV

Money-> live in the city -> more exposure to HIV.

AID reshapes the population structure. Increases mortality and decreases mortality

Lower LE from AIDS some countries have LE <40years

in some countries there is a huge difference in LE w/ and w/o AID.

The UN and US differ in predictions (differrent input: census, refugee data, vital statistics, clinics).

Right now high feraility in most countries with high AIDS mortality

Little data on the number and causes and deaths in Africa.

UN 2012

LE @ birth was rising in Africa until 1985-90, then, with AID, LE dropped sharply.

Some recent and predicted turn around, but it still hasn't caught up to the past LE.  Now people are dying in 35-50 range (peak of working lives)

Note - not from study - Uganda is doing the best and stabilizing HIV rate and LE, Kenya is 2nd best.

Children with HIV positive moms are much more likely to die as infants or 1, than those with HIV negative moms.

Those with living momo or moms who died a long time ago have much better survival than kids with terminally ill or recently dead mom.

“Is poverty or wealth driving HIV transmission?” AIDS, Supplement 7, S5-S16.

Literature review of recent findings on the relationship between wealth and HIV infection in sub-Saharan Africa

- Some researchers theorize that in the early stages of the HIV epidemic, the wealthy were more likely to contract the disease due to higher rates of partner change stemming from greater personal autonomy and spatial mobility

- In later stages of the epidemic it has been argued that the poor are more likely to contract the disease due to increased sexual risk taking and decreased immune function (less resistant to the disease in the event of an unprotected sexual encounter)

Some key patterns from recent literature

- At the national level, income inequality is associated with HIV prevalence, but poverty rates are not

- Poor women and wealthy men are particularly likely to engage in transactional sex and to have more sexual partners

- In fact, gender inequality at the population level may lead to riskier sexual behaviors

Two key problems with cross-sectional studies

- Cannot determine whether poverty causes HIV infection or vice versa

- Unable to control for the fact that individuals from richer households may survive longer with HIV, and are thus more likely to be in the population

- Education has a strong negative relationship with HIV infection

- This association changed over time; at the early stage of the epidemic there was little association between education and HIV, but later in the epidemic education was significantly negatively correlated with HIV (similar to association between wealth and infectious diseases over time as discussed by Preston)

- Individuals with more education tend not to engage in risky behaviors, which reduces their chance of HIV infection

- The urban poor appear to have higher rates of HIV than the rural poor

- Authors speculate that this may be due to reduced privacy in urban slums, which allows children to view sexual activity and thus become sexually active at a much earlier age

- In sum, income and gender inequality are particularly predictive of HIV at the national level

- Educational attainment predictive at the individual level

“The uneven tides of the health transition.” Social Science and Medicine, 55, 313-322.

- Uses the Global Burden of disease data to compare mortality patterns of the 20% of the world population living in the poorest countries, provinces, and states and the 20% of the world’s population living in the richest countries

- Find that poorest populations experience higher mortality in each of the three main groups of mortality, but that the excess mortality of the poorest populations is mostly due to their higher incidence of communicable diseases (77% of excess deaths)

- These diseases only account for 34.2% of deaths in the world but still dominate mortality among the poorest 20% of the world’s population (58.6% of deaths)

- Although developing countries have, to a certain extent, undergone an epidemiological transition, poorest populations still suffer from Group I diseases (in a sense they have been left behind by the epidemiological transition)

- This is partially due to the young age structure of poorer populations, but finding persists even with age standardization

- Most likely this trend has only gotten worse with the increase of AIDS

In most African Countries, mothers will even limited eduation have lower infant mortality

Mom's eduation as a proxy for household SES

shows variation in HIV burden.

  “Measuring the global burden of disease and epidemiological transitions: 2002-2030. Annals of Tropical Medicine and Parasitology, 100(5), 481-499.

- The aim of the Global Burden of Disease Study was to assess global patterns of disease burden and recommend interventions

- This articles utilizes a measure called the Disability Adjusted Life Year (DALY)

- Composed of years of life lost due to premature death and years of life lived with disability

Diseases classified into 3 groups

   - Group I: Communicable diseases

   - Group II: Non-communicable diseases

   - Group III: Injuries

- Find that globally ½ of deaths among 15-59 year olds in 2002 due to Group II and 1/3 due to Group I

- If HIV is removed, only 1/5 of death due to Group I

- Group I deaths predominate in low and middle income countries (esp. in Africa)

- Ten leading causes of disease differ in low/middle income countries versus high income countries

- 3 main causes of death globally are cardiac diseases, stroke, and respiratory diseases

- In high income countries, depression, heart disease, and cardiovascular disease are the three main causes of loss of productive life years

- In low/middle income countries, perinatal conditions, respiratory infections, and AIDS are the three main causes of loss of productive life years

- These diseases rank much higher in terms of years of life lost than the leading causes in high income countries

- People in developing countries not only have lower life expectancies, but they also live a larger proportion of their lives in poor health

Small number of risk factors account for a large percentage of mortality and disease burden

These include poor nutrition, unsafe sex, smoking, and alcohol use

- Policies and programs that target these risk factors could reduce multiple causes of poor health

- Over the next 30 years, authors predict decrease in overall Group I diseases, except for HIV/AIDS

Also predict increase number of deaths caused by Group II and III diseases

“The timing and pace of health transitions around the world.” Population and Development Review, 31(4), 741-764.

Aim of article is to describe regional and global life expectancy gains across time and space

3 divergent trends in life expectancy since the early 1980s

1. Most countries, even those with already high levels of life expectancy, continued to add years at a fairly robust pace

2. A second group of countries that were previously part of the Soviet Union saw a stagnation or slight decline in life expectancy, particularly among males

3. A third group of countries, primarily in central and southern Africa where HIV/AIDS in rampant, saw a dramatic decrease in life expectancy by as much as 19 years

- It is often difficult to determine when a health transition begins

- Population composition can greatly affect death rates

- For example, in GB and FR in the mid-1800s, death rates at the national level appeared to be stagnant, but in reality death rates were decreasing while greater numbers of people were migrating to urban areas (where death rates were higher)

- Countries that began the health transition prior to 1850 experienced slower gains in life expectancy than countries that began the transition more recently

- It is difficult to generalize common causes of life expectancy gain because countries have experienced gains under very diverse circumstances

- Gains have been made under differing stages of economic development, historical conditions, and levels of literacy and education, among other things

- Riley suggests that rather than studying mortality transitions in one country during one period in time, a more comparative approach is necessary to test specific explanations that may be relevant for reducing mortality in countries where life expectancy is still low

What explains the rural-urban gap in infant mortality in Africa, household or community characteristics?

In rural 2/3 of increased mortality is due to observed and unobserved hh characteristics

(i.e. safe wather, electricity, quality of housing, finished floors, mom's age, mom's education, birth interval, contraception, birth order)

community characteristics explain 1/4 of gap (2/3 in unobserved hetereogenity and 1/3 observed)

In rural areas IMR is 14%

in Urban 9.6%

“Demographic and socioeconomic impact of AIDS: Taking stock of the empirical evidence.” AIDS, Supplement 2, S1-S7.

Article based on meeting on the Demographic and Socioeconomic Impact of AIDS in Durban, South Africa, March 26-28, 2003

- Most dramatic impact of AIDS on adult mortality 

          - After infection, average person lives 9 years

- Child mortality

            - Increased child mortality among infected mothers

- Family structure

            - Increased orphans and dissolution of infected households

Many reasons make it difficult to assess the socioeconomic impact of AIDS

- Lack of empirical data, too early to tell, lack of data on effects of process of illness

- It is possible that survivors could fare better on the labor market because of decreased labor supply

We have more data on the microeconomic impact of AIDS at the hh level than on the macroeconomic impact at the national level

- Death and sickness affect hh income and assets

- Infected individuals much more likely to live in poverty