SOCIAL EPIDEMIOLOGY OF HIV IN KAZAKHSTAN: A MEASUREMENT CHALLENGE FOR 2007 FOURTH INTERNATIONAL...
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Transcript of SOCIAL EPIDEMIOLOGY OF HIV IN KAZAKHSTAN: A MEASUREMENT CHALLENGE FOR 2007 FOURTH INTERNATIONAL...
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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
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A TRULY GLOBAL PROBLEM
REQUIRING GLOBAL COOPERATION,
AWARENESS, AND ASSISTANCE
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The Silk Road of Drugs, Migration, HIV
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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.
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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
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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
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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
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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
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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
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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
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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)
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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
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STAGING OF HIV IN ECA REGION
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PREVALENCE OF HIV/ OBLAST, KAZAKHSTAN, 2006 national average = 11.4/ 100,000 persons
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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
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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
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HIV PREVALENCE IN PREGNANT WOMEN, SCREENING RESULTS, KAZAKHSTAN, 2006 (% HIV among screened)
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HIV PREVALENCE AMONG IDU, HEALTH SCREENING RESULTS, KAZAKHSTAN, 2006 (% HIV among screened)
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HIV PREVALENCE AMONG PRISON POPULATION, HEALTH SCREENING RESULTS, KAZAKHSTAN, 2006 (% HIV among screened)
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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)
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HIV PREVALENCE /100,000 POP, KAZAKHSTAN, 1987 – 2006, KAZAKHSTAN REPUBLICAN CENTER FOR THE PREVENTION OF HIV
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NUMBER OF PERSONS WITH HIV+ (lt. blue), AIDS+ (dark blue), AND DEATHS (red), KAZAKHSTAN, 2004 – 2006, REPUBLICAN CENTER FOR THE PREVENTION
OF AIDS
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HIV PREVALENCE IN SYPHILIS + (dark blue) & SYPHILIS – (lt. blue) PERSONS UNDER SURVEILLANCE, KAZAKHSTAN, 2006 (IDU, CSW, PRISONERS, STD+,
PREGNANT WOMEN, from left to right)
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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.
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HIV PREVALENCE AMONG COMMERCIAL SEX WORKERS (CSW), KAZAKHSTAN, 2006 (% of CSW in Oblast/ Region, National Average = 2.5%)
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PREVALENCE OF SYPHILIS BY OBLAST/ REGION, KAZAKHSTAN, 2006 (% of Syphilis in Oblast/ Region, National Average = 26%)
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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)
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SYPHILIS PREVALENCE AMONG IDU, KAZAKHSTAN, 2006 (n=4553, National Average=11%)
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HIV PREVALENCE AMONG IDU, KAZAKHSTAN, 2006 (n=4553, National Average=3.4%)
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Number IDU REPORTING CASUAL & CSW SEXUAL CONTACT BY OBLAST/ REGION, DURING PAST 6 MONTHS, KAZAKHSTAN, 2006 (total n=4553, National
Average=47%)
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Number IDU IDENTIFIED WITH VOLUNTARY HIV TESTING BY OBLASST/ REGION, KAZAKHSTAN, 2006 (total n=4553, National Average=47%)
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NUMBER OF PERSONS SURVEYED FOR HIV, KAZAKHSTAN, 2004-2006
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INCIDENCE OF HIV, KAZAKHSTAN, 2004-2006
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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)
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ANNUAL REGISTRATION OF NEW HIV CASES, KAZAKHSTAN, 1987-2006
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> 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
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RESPONDENT DRIVEN SAMPLING (RDS), NULL WAVE, IDU CASE #1 & IDU CASE #2, EACH ASKED FOR 3 REFERRALS
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RESPONDENT DRIVEN SAMPLING (RDS)
WAVE 2 CASES IDU #3 - #8
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RESPONDENT DRIVEN SAMPLING (RDS) WAVE 3, IDU CASES # 9-16;
WAVE 4, IDU CASES #17-30; WAVE 5, IDU CASES #31-45
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NETWORK OF RECRUITED IDU CASES FROM IDU CASE #1, YANGIUL, 2004
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NETWORK OF 400 IDU CASES RECRUITED IN YANGIUL, 2004
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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
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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
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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
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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
__ __
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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
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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
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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
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AVERAGE ESTIMATE m OF IDU
COEFFICIENT
m = ∑ k , p / 2 = IDU
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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
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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
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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
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HIV PREVENTION THROUGH
Strategic information, including monitoring & evaluation,
surveillance & management information systems
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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)