SOCIAL EPIDEMIOLOGY OF HIV IN KAZAKHSTAN: A MEASUREMENT CHALLENGE FOR 2007

FOURTH INTERNATIONAL CONFERENCE ON “ECOLOGY. RADIATION. HEALTH”, SEMEY STATE MEDICAL ACADEMY,

MINISTRY OF HEALTH THE REPUBLIC OF KAZAKHSTAN

IRINA CAMPBELL, PhD MPHU.S. DEPT. OF STATE FULBRIGHT SCHOLAR IN

GLOBAL HEALTH TO KAZAKHSTAN 28 SEPT. 2007

A TRULY GLOBAL PROBLEM

REQUIRING GLOBAL COOPERATION,

AWARENESS, AND ASSISTANCE

The Silk Road of Drugs, Migration, HIV

EVIDENCE-BASED HIV PREVENTION GREATER ACCURACY & PRECISION IN DESCRIBING ROUTES OF

TRANSMISSION OF HIV AMONG MOST-AT-RISK GROUPS RATIONALIZES PREVENTION PROGRAMS

THIS PRESENTATION WILL TOUCH ONLY ON A BASIC ISSUE IN ESTIMATING HIV PREVALENCE IN KAZAKHSTAN

ACCURACY OF ESTIMATES IMPACTS ON DESIGN AND TARGETING OF EFFECTIVE PROGRAMS

IN 1994, CDC, USA CENTERS FOR DISEASE CONTROL & PREVENTION, BEGAN RECOMMENDING THAT HIV PREVENTION PLANNING GROUPS APPLY THE PRINCIPLES OF EPIDEMIOLOGY, EVALUATION & BEHAVIORAL SCIENCE THEORIES TO DESIGN PREVENTION PROGRAMS IN ORDER TO GET GRANT FUNDING

SCIENTIFIC METHODOLOGIES WHICH ARE MOST RELEVANT TO DEFINING & SOLVING THE HIV EPIDEMIC ARE -

EPIDEMIOLOGY & SOCIAL RESEARCH METHODS, BASIC BEHAVIORAL SCIENCE & CHANGE THEORY, EVIDENCE-BASED INTERVENTIONS & EVALUATION

METHODS.

SOCIAL EPIDEMIOLOGY MODELSBRIEFLY,

EPIDEMIOLOGY IS THE STUDY OF POPULATION HEALTH

THE OCCURRENCE, DISTRIBUTION, NATURAL HISTORY, SOCIAL ETIOLOGY & CAUSAL PATHWAYS OF DISEASE IN A POPULATION WITH

MICRO + MACRO MODELS

BIOMEDICINE IS THE STUDY OF INDIVIDUAL HEALTH IN THE CLINICAL

CONTEXT WITH MICRO MODELS

SOCIAL EPIDEMIOLOGY ENCOMPASSES A MULTIDISCIPLINARY, INTERDISCIPLINARY PARADIGM WHICH OVERLAPS ENVIRONMENTAL EPIDEMIOLOGY, ECOLOGY, SMALL AREA ANALYSIS, CHRONIC DISEASE EPIDEMIOLOGY, GEOGRAPHY, &

SOCIOLOGICAL CONCEPTS, SUCH AS SOCIAL NETWORKING, SOCIAL COHESION, SOCIAL CAPITAL, & SOCIAL SUPPORT, TO ESTIMATE & PREDICT DISEASE PREVALENCE

SOCIAL EPIDEMIOLOGY MODELS

ESTIMATE INCIDENCE, NEW INFECTIONS OF HIV

ESTIMATES PREVALENCE, TOTAL INFECTIONS OF HIV (WHAT)

ESTIMATE DISTRIBUTIONS ACROSS PLACES (WHERE) AND GROUPS (WHO) - ECOLOGICAL FACTORS

ESTIMATE DISTRIBUTION OF STRUCTURAL (MACRO) & BEHAVIORAL (MICRO) RISK FACTORS DETERMINING INCIDENCE & PREVALENCE RATES (WHY) (see FIGURE 1)

HEALTHY LIFESTYLES MOVEMENT IN PREVENTIVE MEDICINE & PUBLIC HEALTH IS A RESULT OF THE SCIENTIFIC WORK OF SOCIAL EPIDEMIOLOGISTS

FIGURE 1: MACRO & MICRO PROPOSITIONS OF GEOGRAPHIC

VARIATION IN HEALTH Macro proposition:

geographic variation due to Contextual/social causation hypothesis:

Micro proposition:

geographic variation due to Compositional/individual selection hypothesis:

spacial variation in exposure to environmental/structural factors:

poverty; pollution, traffic, housing; quality, crime, recreational resources, sanitation, access to material or social resources

spacial variation in direct selection: at-risk people moving/staying in area: poor people living in rundown areas;downward SES drift/mobility of sickconcentration of sick around facilities;concentration of healthy around parks, or “younger” areas

spacial variation in exposure to behavioral factors: drug/alcohol abuse, stresspassive smoking, unsafe drivingcommunity group activities religious group membership

spacial variation in indirect selection:at-risk people with certain traits moving/staying in area – large, younger, low-income families blue collar manual workers older persons w/ low educational level

ATOMISTIC & ECOLOGICAL FALLACIES VS. MULTILEVEL MODELS

ATOMISTIC FALLACY – ATTRIBUTING TRAITS OF AN INDIVIDUAL TO A POPULATION

(HI SES PERSONS LIVING IN SEMEY HAVE HIGHER THAN AVERAGE LIFE EXPECTANCY & LOW CANCER RATE DOES NOT MEAN SEMEY IS A WEALTHY HEALTHY CITY - MICRO TO MACRO GENERALIZATION)

ECOLOGICAL FALLACY – ATTRIBUTING TRAITS OF A GROUP/ POPULATION TO INDIVIDUALS

(HI SES AREA DOES NOT MEAN PERSONS WITHIN AREA ARE WEALTHY - MACRO TO MICRO GENERALIZATION)

MULTILEVEL MODELS – i.e., SEPARATE ATTRIBUTION OF FACTORS MEASURED AT SPECIFIC LEVELS, SUCH AS MACRO STRUCTURAL POPULATION AND MICRO INDIVIDUAL FACTORS, FOR INDIVIDUAL HEALTH STATUS OUTCOMES

MULTILEVEL MODEL, i.e., can explain simultaneous effect of both personal SES + place SES on health

Total variance of Yij = sum of between-group vars+ within-group var

Yij = 00 + p0Xpij + 0qZqj + pqZqjXpij + u1jXpij + u0j + eij

where:

p is the number of explanatory variables X at level L1 (individuals),

q is the number of explanatory variables Z at level L2 (urban areas), and

ij is individual level L1 observation i in level L2 (urban areas) j ;

combining terms produces the following general hierarchical linear

equation which separates the fixed and random elements: Yij=

[ 00 + p0Xpij + 0qZqj + pqZqjXpij ]+[ u1jXpij + u0j + eij ] Fixed part of equation - Random part of equation - invariate between macro areas residual variance between OLS variation at micro level areas after controlling micro

fixed variables

and where:

Zqj is the cross-level interaction = value of Y-X slope at level L1 (individuals) with Z at level L2 (urban areas);

eij is the between individuals, random residual, mutually independent, mean=0, homoscedastic, normally distributed, constant across macro units, random effect = unexplained variability of dependent variable at micro level;

u0j is a between macro unit random residual, mutually independent, mean=0, homoscedastic, normally distributed, random effect of intercept = unexplained (by micro level intercept) variability of dependent variable at macro level;

u1jXpij is the random interaction between macro unit and X; u1j is a between macro unit and micro unit random residual, independent from the individual level residuals but correlated to the macro level residuals, random effect of slopes = unexplained (by micro level slopes) variability of dependent variable at macro level.

The basic difference between the ordinary least squares regression model (OLS) and the hierarchical linear model is the complex random residual term, [ u1jXpij + u0j + eij ]. The contextual effects or unexplained variance of the outcome due to macro units as estimated by the random residuals, u0j and u1j , are assumed to be independent between macro units but correlated within macro units; independent of the micro level residuals; with population mean = 0, a multivariate normal distribution, and constant covariance

WHAT RELEVANCE DOES THE MULTILEVEL EPIDEMIOLOGY MODEL HAVE FOR HIV

EPIDEMIOLOGY?

INCLUDE STRUCTURAL FACTORS (i.e., SOCIAL NETWORKS, PLACE) AS PREDICTORS + INDIVIDUAL RISK

FACTORS (IDU, MSM, CSW)

GLOBAL STAGING OF HIV ACROSS CENTRAL ASIAN REGIONS

WORLD BANK MODELS OF STAGING HIV EPIDEMIC

1-UNKNOWN

2-NASCENT Epidemic Stage 1: 1987- Dominant transmission - Sexual

3-CONCENTRATED Epidemic Stage 2: 1991- Concentrated Dominant transmission – IntraVenous Drug Use

4-GENERALIZED Epidemic Stage 3: 2005 - Generalized Dominant transmission: >Sex+IVDU

H0: STAGE 5 - GENERATIONAL Epidemic Stage 4: 2006 -Generational Dominant transmission: Adolescents & Children

– parental-father-mother to child transmission

– Young People lifestyle behaviors

STAGING OF HIV IN ECA REGION

PREVALENCE OF HIV/ OBLAST, KAZAKHSTAN, 2006 national average = 11.4/ 100,000 persons

IDU & HIV in Kazakhstan MAJOR TRANSMISSION ROUTES

IDU MAJOR ROUTE OF TRANSMISSION OF HIV - MOST-AT-RISK AND MOST-HARD-TO-FIND GROUPS

THUS DETERMINING SIZE/ LOCATION/ DEMOGRAPHIC COMPOSITION OF IDU POPULATION FOCUSES PREVENTION INTERVENTIONS AT THE POINT OF GREATEST TRANSMISSION TO CONTAIN EPIDEMIC

NEED > ACCURATE METHODS TO ESTIMATE & LOCATE THIS MOST-AT-RISK GROUP

KAZAKHSTAN HIV EPIDEMIC TRANSITIONING FROM

3-CONCENTRATED Dominant transmission – IDU

AND

4-GENERALIZED Dominant transmission: >Sex + IDU

TO

GENERATIONAL Increasing transmission:

– parent-father-mother to child transmission

– Young People lifestyle behaviors

HIV PREVALENCE IN PREGNANT WOMEN, SCREENING RESULTS, KAZAKHSTAN, 2006 (% HIV among screened)

HIV PREVALENCE AMONG IDU, HEALTH SCREENING RESULTS, KAZAKHSTAN, 2006 (% HIV among screened)

HIV PREVALENCE AMONG PRISON POPULATION, HEALTH SCREENING RESULTS, KAZAKHSTAN, 2006 (% HIV among screened)

MOST-AT-RISK GROUPS FOR HIV ALSO MOST-HARD-TO-FIND, ESTIMATES VARY BY

METHOD

STD – SEXUALLY TRANSMITTED DISEASE CASES

IDU – INJECTION DRUG USERS

CSW – COMMERCIAL SEX WORKERS

MSM – MEN HAVING SEX WITH MEN

HOMELESS YOUTH – ORPHANS, RUNAWAYS, ABANDONED

YOUNG PEOPLE – POPULATION AGE 10-24 YRS (WHO)

HIV PREVALENCE /100,000 POP, KAZAKHSTAN, 1987 – 2006, KAZAKHSTAN REPUBLICAN CENTER FOR THE PREVENTION OF HIV

NUMBER OF PERSONS WITH HIV+ (lt. blue), AIDS+ (dark blue), AND DEATHS (red), KAZAKHSTAN, 2004 – 2006, REPUBLICAN CENTER FOR THE PREVENTION

OF AIDS

HIV PREVALENCE IN SYPHILIS + (dark blue) & SYPHILIS – (lt. blue) PERSONS UNDER SURVEILLANCE, KAZAKHSTAN, 2006 (IDU, CSW, PRISONERS, STD+,

PREGNANT WOMEN, from left to right)

ESTIMATES OF % IDU AMONG HEPATITIS C SURVEILLANCE GROUPS (CSW n=2105, PRISONERS n=4487, STD n=4836) BY HEPATITIS C PREVALENCE (blue), IDU AMONG

HEPATITIS C (orange), IDENTIFIED SELF AS IDU IN SURVEY (green), KAZAKHSTAN, 2006.

HIV PREVALENCE AMONG COMMERCIAL SEX WORKERS (CSW), KAZAKHSTAN, 2006 (% of CSW in Oblast/ Region, National Average = 2.5%)

PREVALENCE OF SYPHILIS BY OBLAST/ REGION, KAZAKHSTAN, 2006 (% of Syphilis in Oblast/ Region, National Average = 26%)

HIV PREVALENCE AMONG CSW WITH SYPHILIS + AND/ OR HEPATITIS C+, KAZAKHSTAN, 2006 (HPT C+/Syphilis+; HPT C+/Syphilis-; HPT C-/Syphilis+; HPT

C-/Syphilis-; from left to right)

SYPHILIS PREVALENCE AMONG IDU, KAZAKHSTAN, 2006 (n=4553, National Average=11%)

HIV PREVALENCE AMONG IDU, KAZAKHSTAN, 2006 (n=4553, National Average=3.4%)

Number IDU REPORTING CASUAL & CSW SEXUAL CONTACT BY OBLAST/ REGION, DURING PAST 6 MONTHS, KAZAKHSTAN, 2006 (total n=4553, National

Average=47%)

Number IDU IDENTIFIED WITH VOLUNTARY HIV TESTING BY OBLASST/ REGION, KAZAKHSTAN, 2006 (total n=4553, National Average=47%)

NUMBER OF PERSONS SURVEYED FOR HIV, KAZAKHSTAN, 2004-2006

INCIDENCE OF HIV, KAZAKHSTAN, 2004-2006

CHANGES IN HIV EPIDEMIOLOGY DUE TO INCREASED SCREENING OF POPULATION FOR HIV OR CHANGES IN EPIDEMIOLOGICAL FACTORS, KAZAKHSTAN 2004-2006 (orange=n

cases based on changing factors; teal=n cases due to increased screening, 0 cases screened 2004 vs. 311 cases screened 2006)

ANNUAL REGISTRATION OF NEW HIV CASES, KAZAKHSTAN, 1987-2006

> N CASES HIV BETWEEN 2004 - 2006 DUE TO > EPIDEMIC, NOT TO BETTER SCREENING OR TESTING

HIV AMONG IDU INCREASED FROM

2003 3,8% TO 2006 5.8%

UNEVEN DISTRIBUTION AMONG OBLASTS

MOST REPUBLIC OF KAZAHSTAN AIDS PREVENTION CENTER DATA DERIVED FROM CDC SPONSORED SNOWBALL SAMPLING VARIANT, RESPONDENT DEVELOPED SAMPLE (RDS)

SNOWBALL SAMPLING = NONRANDOM SELECTION, NONREPRESENTATIVE, SAMPLE OF CONVENIENCE

– NEED SAMPLING AMONG RISK GROUPS TO > EFFICIENCY BUT PROBLEMS WITH GENERALIZATION FROM NONREPRESENTATIVE SAMPLE, THEREFOR RDS SAMPLING

RESPONDENT DRIVEN SAMPLING (RDS), NULL WAVE, IDU CASE #1 & IDU CASE #2, EACH ASKED FOR 3 REFERRALS

RESPONDENT DRIVEN SAMPLING (RDS)

WAVE 2 CASES IDU #3 - #8

RESPONDENT DRIVEN SAMPLING (RDS) WAVE 3, IDU CASES # 9-16;

WAVE 4, IDU CASES #17-30; WAVE 5, IDU CASES #31-45

NETWORK OF RECRUITED IDU CASES FROM IDU CASE #1, YANGIUL, 2004

NETWORK OF 400 IDU CASES RECRUITED IN YANGIUL, 2004

COMPARATIVE METHODOLOGICAL ASSESSMENT OF DRUG USE IN KAZAKHSTAN

RESEARCH STUDY BY MINISTRY OF HEALTH, REPUBLIC OF KAZAKHSTAN APPLIED RESEARCH CENTER

FOR MEDICOSOCIAL PROBLEMS IN NARCOTICS, NATIONAL CENTER FOR THE PREVENTION OF HEALTHY

LIFESTYLE DEVELOPMENT (NCPHLD), REPUBLIC OF KAZAKHSTAN CENTER FOR PSYCHIATRY,

REPUBLIC OF KAZAKHSTAN CENTER FOR PREVENTION OF AIDS, 2004

METHODOLOGICAL INNOVATIONS TO LOCATE MOST-AT-RISK GROUPS

COLLABORATIVE STUDY WITH ASSISTANCE FROM MINISTRY OF INTERIOR, JUSTICE, POLICE DEPT., CDC, UNICEF, UNAIDS

RESEARCH FRAMEWORK – ALL OBLASTS OF KAZAKHSTAN

COMPARED TO EXISTING SOCIOLOGICAL STUDIES OF HIV PREVALENCE

INVESTIGATION LOCATED 201,045 DRUG USERS IN KAZAKHSTAN IN 2004

METHOD USED = UN EXPRESS-EVALUATION/ MONITORING FOR DRUG ABUSERS, ADAPTED TO KAZAKHSTAN BY RK CENTER FOR PREVENTION OF AIDS

– 4 PARTS TO METHOD – 1 – BASED ON EXISTING OFFICIAL STATISTICS 2 – METHOD OF MULTIPLICATION 3 – METHOD OF NOMINATION 4 – METHOD OF TRADITIONAL SOCIOLOGICAL INVESTIGATIONS IN

MEDICINE

METHOD 1 TO LOCATE MOST-AT-RISK GROUPS

STUDY FRAME = 14 OBLASTS + ASTANA CITY, ALMATY CITY, ARKALYK, BALKHASH, ZHEZKAZGAN, SEMIPALATINSK, TEMIRTAU, EKIBASTUZ

DATA COLLECTION INSTRUMENT = SURVEY QUESTIONNAIRE

SAMPLING FRAME =

LIST 1 - DRUG USERS REGISTERED IN NARCOLOGICAL CLINICS

LIST 2 - DRUG USERS REGISTERED BY POLICE

N DRUG USERS LIST 1

DRUG CLINIC REGISTRY

LIST 2

POLICE REGISTRY

GROUP A + +

GROUP B __ +

GROUP C + __

need to findGROUP X, not

screened by list 1 or list 2

__ __

METHOD 1 TO ESTIMATE MOST-AT-RISK FOR HIV

LIST 1 + LIST 2 +

GROUP a

LIST 1 -- LIST 2 +

GROUP b

LIST 1 + LIST 2 –

GROUP c

LIST 1 - LIST 2 –

GROUP x

ax = bc x = bc/a

X = UNKNOWN POTENTIAL HIV / IDU CASES

TOTAL IDU N(1) = a + b + c + x

METHOD 2 (p) TO ESTIMATE MOST-AT-RISK GROUPS

SURVEYS OF RISK GROUPS ESTIMATED % OF IDU LOCATED BY SURVEY WHO ARE REGISTERED – CLINICS

CALCULATE MULTIPLICATIVE FACTOR p OF IDU NOT REGISTERED IN CLINICS

MULTIPLY EXISTING OFFICIAL LIST 1 OF CLINIC REGISTRY BY p

TOTAL IDU N(2) = N p

METHOD 3 (k) – SOCIAL NETWORK THEORY TO ESTIMATE MOST-AT-RISK GROUPS

DURING SURVEY - RESPONDENTS ASKED TO LIST FRIENDS WHO ARE IDU

CALCULATE NOMINATIVE FACTOR k OF IDU NOT LISTED IN CLINIC REGISTRY

TOTAL IDU N(3) = N k

AVERAGE ESTIMATE m OF IDU

COEFFICIENT

m = ∑ k , p / 2 = IDU

METHOD 4 – TRADITIONAL SURVEY RESEARCH TO ESTIMATE MOST-AT-RISK GROUPS

2004 QUESTIONNAIRES, DESIGNED BY UNICEF/ WHO, FOCUSED ON KNOWN RISK GROUPS – IDU, CSW, MSM, YOUTH – TOTAL N SURVEYED = 15,863

YOUTH SAMPLING FRAME – PROBABILITY NONREPEATING SELECTION OF 10 SCHOOLS, AGED 11-14 / 15-17 YRS (200 OF EACH GENDER), TOTAL N = 7200

DESIGN OF SAMPLING FRAME OF OTHER RISK GROUPS WAS NOT EXPLICITY DESCRIBED IN THIS METHODOLOGICAL REPORT

PREVALENCE DERIVED FROM OFFICIAL DATA & ESTIMATES

1.01.2004 TOTAL N IDU OFFICIALLY REGISTERED IN KZ = 46,940

316/100,000 POP, DIAGNOSED WITH DRUG ABUSE

2004 TOTAL IDU BY 4 METHODS OFFICIAL CLINIC REGISTRY N = 46,340

METHOD 1 (N=a+b+c+x) N = 175,024

COEFFICIENT M N = 227,066

AVERAGE OF OFFICIAL CLINIC REGISTRY DATA + METHOD 1 + COEFFICIENT M N=201,045

NARCOTICS USE AMONG KAZAKHSTAN YOUTH

2004 STUDY FOUND THAT YOUTH 11-17 YRS OLD IN 9 LARGE KZ CITIES N=13,158 HAD USED NARCOTICS RECREATIONALLY AT LEAST ONCE (WHICH CAN QUICKLY CHANGE TO ADDICTION)

THIS AMOUNT IS MANY TIMES LARGER IN JUVENILE DETENTION HOMES & ORPHANAGES (10% - 24%) THAN IN THE GENERAL POP OF YOUTH (2.2% - 4.6%)

NARCOTICS ARE MAJOR CAUSE FOR INITIATION INTO ADOLESCENT SEXUAL ACTIVITY

OFFICIAL REGISTRY DATA FOR YOUTH ARE INACCURATE UNDERESTIMATES, AS FOLLOWS :

DRUG REGISTRY N CHILDREN = 53; N ADOLESCENTS = 823EPISODIC USE REGISTRY N CHILDREN = 312; N ADOLESCENTS = 13422004 METHODS STUDY TOTAL N = 13,158

HIV PREVENTION THROUGH

Strategic information, including monitoring & evaluation,

surveillance & management information systems

http://www.globalhivevaluation.org/toolbox.aspx

ADDITIONAL RESOURCES

Title: Monitoring & Evaluation Capacity Building for Program Improvement - Training PresentationsAgency: Centers for Disease Control and Prevention/Global AIDS Program (CDC/GAP)