KAIS 2007 1 - Kenya National AIDS & STI Control Programme ...

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Comparison of HIV Prevalence in the chapter 32003 KDHS and 2007 KAIS3.1 KEY FINDINGS• The national HIV prevalence estimate in 2007 was 7.4% among adults aged 15‐49years, compared with 6.7% in 2003. This difference was not statistically significant.• HIV prevalence significantly increased among men living in rural populationsbetween 2003 and 2007.• HIV prevalence tended to increase in five of the eight provinces; these increases weremarginally significant or significant.• Significant changes in HIV prevalence by wealth index and education level wereobserved between 2003 and 2007. Individuals of lower socioeconomic status hadsignificantly higher prevalence in 2007 than in 2003.3.2 INTRODUCTIONPrevious demographic and health surveys were conducted in Kenya in 1989, 1993, 1998 and 2003. The2003 KDHS was the first to include HIV testing in a nationally representative sample of women aged15‐49 years and men aged 15‐54 years. The 2007 KAIS included HIV testing for women and men aged15‐64 years. Thus, to understand changes in HIV prevalence between 2003 and 2007, we comparedHIV prevalence between women and men in the age groups covered in both surveys. Most of thecomparisons presented in this chapter are among women and men aged 15‐49 years.Appendix B.3 provides sample sizes and 95% confidence intervals for estimates presented in thischapter. The z‐test statistic was used to compare the two weighted estimates from the 2003 KDHSand the 2007 KAIS and to determine if differences were statistically significant. Methods used forcalculating the z‐test statistic are described in Appendix A. Throughout the chapter, the termsignificant indicates a p‐value less than 0.05; marginally significant indicates a p‐value between0.05 and 0.10, inclusive; and not significant indicates a p‐value greater than 0.10.KAIS 2007 52
DATA IN CONTEXTUNDERSTANDING STATISTICAL SIGNIFICANCEWhenever a population is sampled for a survey, there is some degree of uncertaintyassociated with the results obtained; results from samples of human populations are alwaysestimates. Standard errors represent the degree of uncertainty around an estimate, includingeach estimate in 2003 KDHS and 2007 KAIS. The formula for calculating standard errors isdescrubed in Appendix C. Because of the uncertainty of survey estimates, statistical testsusing standard errors can provide a range of potential values of the true estimate; this rangeis referred to as the confidence interval (CI). A 95% CI, for example, means that if a surveywas repeated 100 times in the same population, the CI would be expected to contain the trueestimate 95 times out of 100. The 95% CIs presented in this chapter are shown as lines at thetop and bottom of each bar in the figures.When comparing an estimate between two surveys in the same population, there are formalstatistical methods to test the probability that the differences seen between the two surveysare real and not due to chance. When comparing estimates from 2003 KDHS to estimatesfrom the 2007 KAIS, we used the z-test, which computes the probability that the differencewas due to chance alone. We used a conservatively low probability, less than 5%, todetermine whether these differences were likely to be real and not due to chance. If theprobability that chance caused the differences was less than 5%, we said the results werestatistically significant. If the probability was between 5% and 10%, we said the results weremarginally significant. Visually, one way to approximate whether point estimates from the2003 KDHS and the 2007 KAIS are different is to visually assess whether the 95% CIs forthe two estimates overlap; that is, the CI of one survey overlaps with the CI of the other. If so,the estimates are likely to not be different, and conversely, if they do not overlap, thedifference is most likely significant.KAIS 2007 53
Page 1 and 2: KAIS 2007 1
Page 4 and 5: P.O. Box: 9361 Code: 00202 Nairobi,
Page 6: KEMRIKNASPKNBSKshLLITNMCHmlμLMOMSM
Page 10 and 11: KEY FINDINGS: HIV PREVENTION• Amo
Page 12 and 13: Among HIV‐uninfected women, 71.2%
Page 14 and 15: Men 5.4 26.3 1.9Total 7.1 35.1 1.8O
Page 17 and 18: Kenya is also committed to the “T
Page 19 and 20: Table 1.4 Distribution of sampled c
Page 22 and 23: 1.7 SURVEY IMPLEMENTATIONTrainingIn
Page 24 and 25: Data processing included a number o
Page 26 and 27: Characteristic 2003 KDHS 2007 KAISQ
Page 28 and 29: Figure 1.11 Sampled clusters and el
Page 30 and 31: Table 1.11c Survey response rates b
Page 32 and 33: Population estimates reported in th
Page 34 and 35: 2.4 HIV PREVALENCE AMONG YOUTHFigur
Page 36 and 37: Figure 2.6b HIV prevalence among ru
Page 38 and 39: 2.7 HIV PREVALENCE BY PROVINCEFigur
Page 40 and 41: Figure 2.7c Estimated number of HIV
Page 42 and 43: 2.9 HIV PREVALENCE BY EDUCATION LEV
Page 44 and 45: Figure 2.10b HIV prevalence among r
Page 46 and 47: 2.11 HIV PREVALENCE BY TIME AWAY FR
Page 48 and 49: 2.13 MALE CIRCUMCISION AND ASSOCIAT
Page 50 and 51: Figure 2.13c HIV prevalence among c
Page 54 and 55: 3.3 SEXFigure 3.3a HIV prevalence a
Page 56 and 57: Figure 3.4b. HIV prevalence among m
Page 58 and 59: 3.7 PROVINCEFigure 3.7a HIV prevale
Page 60 and 61: 2003 KDHS 1 2007 KAIS 2Marital stat
Page 62 and 63: 3.10 WEALTH INDEXFigure 3.10a HIV p
Page 64 and 65: DATA IN CONTEXT: APPROACHES TO HIV
Page 68 and 69: Figure 4.3c Ever been tested for HI
Page 70 and 71: Figure 4.3e Ever been tested for HI
Page 72 and 73: Figure 4.3g Women aged 15-49 years
Page 74 and 75: Figure 4.4a Time since last HIV tes
Page 76 and 77: Figure 4.5a Reasons for not testing
Page 78 and 79: Increasing access to HIV testing wi
Page 80 and 81: 4.7. Gaps and unmet needs• Two‐
Page 84 and 85: Figure 5.3b Self-reported HIV statu
Page 86 and 87: This section examines participants
Page 88 and 89: Figure 5.5a Knowledge of HIV status
Page 90 and 91: Figure 5.5c Percent of partnerships
Page 92 and 93: uninfected. A couple was considered
Page 94 and 95: Figure 5.6c Women and men aged 15-6
Page 96 and 97: Sexual Partners, Sexual DebutCircum
Page 98 and 99: Figure 6.3b. HIV prevalence among w
Page 100 and 101: Figure 6.3d. HIV prevalence among w
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Figure 6.3f. Men aged 15-64 years r
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In this section, “consistent cond
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Figure 6.4c. Marital or cohabiting
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Figure 6.5b. Young women and men ag
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Figure 6.5d. Young women and men ag
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Figure 6.6a. Male circumcision amon
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Figure 6.6d. HIV prevalence among m
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Knowledge, Attitudes and Beliefs ch
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Figure 7.3b. Most common source of
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Figure 7.3c Women and men aged 15-6
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Figure 7.3e Overall scores for 12 q
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Figure 7.3g Women and men aged 15-6
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Figure 7.3i Correct responses to se
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• If a female teacher has the AID
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7.5. Perceived risk of HIV infectio
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Figure 7.5c Reasons given for havin
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7.6. Attitudes toward women’s rol
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who had never been tested for HIV o
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8.2 INTRODUCTIONIn the absence of i
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8.3 ANTENATAL CLINIC ATTENDANCE, 20
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8.4 KNOWLEDGE OF MOTHER-TO-CHILD TR
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The analysis in this section consid
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Figure 8.5b Women aged 15-49 years
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• Incorrect communication or unde
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8.8 CURRENTLY PREGNANT WOMEN: HIV T
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Among currently pregnant women, 7.1
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Among pregnant, HIV‐uninfected wo
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Figure 8.11b Desire for a child in
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8.12 CONTRACEPTIVE USEThe next figu
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Figure 8.12c Contraceptive use* amo
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Blood and Injection Safety chapter
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D ATA IN C ONTEXT: N ATIONAL B LOOD
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Figure 9.3a Source of blood donatio
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Figure 9.3b Source of blood donatio
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Figure 9.3d Source of blood donatio
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9.4 Blood transfusionsAlthough the
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10080Injec tionsPillsNo preferenceW
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HIV prevalence among adults who rep
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9.6 Gaps and unmet needs• HIV pre
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The Ministry of Medical Services re
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10.3 COTRIMOXAZOLE PROPHYLAXIS FOR
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Coverage also varied significantly
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10.4 ARV ELIGIBILITY, COVERAGE AND
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ARV coverage was estimated by takin
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Figure 10.4b ARV coverage among HIV
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DATA IN CONTEXTEstimates of HIV-inf
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10.5 GAPS AND UNMET NEEDS• The es
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debilitating illness leading to hos
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Among HIV‐infected adults (83.6%
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Figure 11.3d HIV-infected adults ag
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Figure 11.4b HIV-infected adults ag
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Figure 11.4d HIV-infected adults ag
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DATA IN CONTEXTHIV Basic Care Packa
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Figure 11.5b HIV-infected adults ag
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Figure 11.6b Percent of HIV-infecte
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11.8 GAPS AND UNMET NEEDS• Counse
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12.3 HSV-2 prevalenceHSV‐2 preval
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807071.0WomenMen64.3HSV-2 Prevalenc
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Figure 12.3d HSV-2 prevalence among
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Figure 12.4b HSV-2 prevalence among
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Figure 12.5a HIV prevalence by HSV-
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Figure 12.6b Condom use during last
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12.7 Gaps and unmet needs• Increa
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also at substantially elevated risk
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Figure 13.3b Prevalence of syphilis
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Figure 13.3d Prevalence of syphilis
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Figure 13.3f Prevalence of syphilis
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Figure 13.4b Prevalence of syphilis
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13.6 GAPS AND UNMET NEEDS• Patien
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Household Characteristics andImpact
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Figure 14.3a Sex of head of househo
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Figure 14.3d Household population,
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14.5 PREVALENCE OF HIV-AFFECTED HOU
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Figure 14.5c HIV-affected household
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social and economic conditions of h
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households used chemical disinfecta
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14.7 HOUSEHOLD OWNERSHIP OF MOSQUIT
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Figure 14.7b HIV-affected and HIV-u
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Table 14.7b Source of bednets among
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Overall, 52.3% percent of children
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14.9 CARE AND SUPPORT FOR ORPHANS A
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For consistency with other populati
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Returning Test Results15.1 KEY FIND
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Figure 15.3b. Participants aged 15-
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Figure 15.3d. Participants aged 15-
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Figure 15.3f. Participants aged 15-
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15.4 RETURNING TO RECEIVE TEST RESU
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Figure 15.5 Women aged 15-64 years
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Methods of the 2007 KAISThis Append
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ClustersHouseholdsProvince Rural Ur
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and treatment services and other he
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The KEMRI Ethical Review Committee
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Red top (5 ml)Whole blood collected
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algorithm using two HIV EIAs (Viron
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Figure A5.a. Structure and flow of
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Recruitment and training of results
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observe confidentiality of particip
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Documentation. Results counsellors
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Normalisation of weightsNormalised
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This report presents the results of
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