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SOCIAL EPIDEMIOLOGY OF HIV IN KAZAKHSTAN: A MEASUREMENT CHALLENGE FOR 2007 FOURTH INTERNATIONAL CONFERENCE ON “ECOLOGY. RADIATION. HEALTH”, SEMEY STATE.

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Presentation on theme: "SOCIAL EPIDEMIOLOGY OF HIV IN KAZAKHSTAN: A MEASUREMENT CHALLENGE FOR 2007 FOURTH INTERNATIONAL CONFERENCE ON “ECOLOGY. RADIATION. HEALTH”, SEMEY STATE."— Presentation transcript:

1 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 MPH U.S. DEPT. OF STATE FULBRIGHT SCHOLAR IN GLOBAL HEALTH TO KAZAKHSTAN 28 SEPT. 2007

2 A TRULY GLOBAL PROBLEM REQUIRING GLOBAL COOPERATION, AWARENESS, AND ASSISTANCE A TRULY GLOBAL PROBLEM REQUIRING GLOBAL COOPERATION, AWARENESS, AND ASSISTANCE

3 The Silk Road of Drugs, Migration, HIV

4 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.  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.

5 SOCIAL EPIDEMIOLOGY MODELS BRIEFLY,  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 BRIEFLY,  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

6 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 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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9 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 sick concentration of sick around facilities; concentration of healthy around parks, or “younger” areas spacial variation in exposure to behavioral factors: drug/alcohol abuse, stress passive smoking, unsafe driving community 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

10 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  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

11 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 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

12  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  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

13 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) 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)

14 GLOBAL STAGING OF HIV ACROSS CENTRAL ASIAN REGIONS  WORLD BANK MODELS OF STAGING HIV EPIDEMIC 1-UNKNOWN 2-NASCENT Epidemic Stage 1: Dominant transmission - Sexual 3-CONCENTRATED Epidemic Stage 2: Concentrated Dominant transmission – IntraVenous Drug Use 4-GENERALIZED Epidemic Stage 3: Generalized Dominant transmission: >Sex+IVDU  H 0 : STAGE 5 - GENERATIONAL Epidemic Stage 4: Generational Dominant transmission: Adolescents & Children –parental-father-mother to child transmission – Young People lifestyle behaviors  WORLD BANK MODELS OF STAGING HIV EPIDEMIC 1-UNKNOWN 2-NASCENT Epidemic Stage 1: Dominant transmission - Sexual 3-CONCENTRATED Epidemic Stage 2: Concentrated Dominant transmission – IntraVenous Drug Use 4-GENERALIZED Epidemic Stage 3: Generalized Dominant transmission: >Sex+IVDU  H 0 : STAGE 5 - GENERATIONAL Epidemic Stage 4: Generational Dominant transmission: Adolescents & Children –parental-father-mother to child transmission – Young People lifestyle behaviors

15 STAGING OF HIV IN ECA REGION

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

17 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  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

18 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 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

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

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

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

22 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 YRS (WHO)  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 YRS (WHO)

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

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

25 HIV PREVALENCE IN SYPHILIS + (dark blue) & SYPHILIS – (lt. blue) PERSONS UNDER SURVEILLANCE, KAZAKHSTAN, 2006 (IDU, CSW, PRISONERS, STD+, PREGNANT WOMEN, from left to right)

26 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.

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

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

29 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)

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

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

32 Number IDU REPORTING CASUAL & CSW SEXUAL CONTACT BY OBLAST/ REGION, DURING PAST 6 MONTHS, KAZAKHSTAN, 2006 (total n=4553, National Average=47%)

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

34 NUMBER OF PERSONS SURVEYED FOR HIV, KAZAKHSTAN,

35 INCIDENCE OF HIV, KAZAKHSTAN,

36 CHANGES IN HIV EPIDEMIOLOGY DUE TO INCREASED SCREENING OF POPULATION FOR HIV OR CHANGES IN EPIDEMIOLOGICAL FACTORS, KAZAKHSTAN (orange=n cases based on changing factors; teal=n cases due to increased screening, 0 cases screened 2004 vs. 311 cases screened 2006)

37 ANNUAL REGISTRATION OF NEW HIV CASES, KAZAKHSTAN,

38  > N CASES HIV BETWEEN DUE TO > EPIDEMIC, NOT TO BETTER SCREENING OR TESTING  HIV AMONG IDU INCREASED FROM ,8% TO % 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  > N CASES HIV BETWEEN DUE TO > EPIDEMIC, NOT TO BETTER SCREENING OR TESTING  HIV AMONG IDU INCREASED FROM ,8% TO % 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

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

40 RESPONDENT DRIVEN SAMPLING (RDS) WAVE 2 CASES IDU #3 - #8

41 RESPONDENT DRIVEN SAMPLING (RDS) WAVE 3, IDU CASES # 9-16; WAVE 4, IDU CASES #17-30; WAVE 5, IDU CASES #31-45

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

43 NETWORK OF 400 IDU CASES RECRUITED IN YANGIUL, 2004

44 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

45 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  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

46 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  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

47 N DRUG USERSLIST 1 DRUG CLINIC REGISTRY LIST 2 POLICE REGISTRY GROUP A++ GROUP B__+ GROUP C+__ need to find GROUP X, not screened by list 1 or list 2 __

48 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

49 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  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

50 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  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

51 AVERAGE ESTIMATE m OF IDU COEFFICIENT m = ∑ k, p / 2 = IDU COEFFICIENT m = ∑ k, p / 2 = IDU

52 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 / YRS (200 OF EACH GENDER), TOTAL N = 7200  DESIGN OF SAMPLING FRAME OF OTHER RISK GROUPS WAS NOT EXPLICITY DESCRIBED IN THIS METHODOLOGICAL REPORT  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 / YRS (200 OF EACH GENDER), TOTAL N = 7200  DESIGN OF SAMPLING FRAME OF OTHER RISK GROUPS WAS NOT EXPLICITY DESCRIBED IN THIS METHODOLOGICAL REPORT

53 PREVALENCE DERIVED FROM OFFICIAL DATA & ESTIMATES  TOTAL N IDU OFFICIALLY REGISTERED IN KZ = 46, /100,000 POP, DIAGNOSED WITH DRUG ABUSE 2004 TOTAL IDU BY 4 METHODS  OFFICIAL CLINIC REGISTRYN = 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  TOTAL N IDU OFFICIALLY REGISTERED IN KZ = 46, /100,000 POP, DIAGNOSED WITH DRUG ABUSE 2004 TOTAL IDU BY 4 METHODS  OFFICIAL CLINIC REGISTRYN = 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

54 NARCOTICS USE AMONG KAZAKHSTAN YOUTH  2004 STUDY FOUND THAT YOUTH 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 = 823 EPISODIC USE REGISTRY N CHILDREN = 312; N ADOLESCENTS = METHODS STUDY TOTAL N = 13,158  2004 STUDY FOUND THAT YOUTH 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 = 823 EPISODIC USE REGISTRY N CHILDREN = 312; N ADOLESCENTS = METHODS STUDY TOTAL N = 13,158

55 HIV PREVENTION THROUGH Strategic information, including monitoring & evaluation, surveillance & management information systems

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


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