Applied Multivariable Modeling in Public Health: Use of CART and Logistic Regression to Help Diagnose HIV Infection
 

Applied Multivariable Modeling in Public Health: Use of CART and Logistic Regression to Help Diagnose HIV Infection

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    Applied Multivariable Modeling in Public Health: Use of CART and Logistic Regression to Help Diagnose HIV Infection Applied Multivariable Modeling in Public Health: Use of CART and Logistic Regression to Help Diagnose HIV Infection Presentation Transcript

    • Applied Multivariable Modeling in Public HealthUse of CART® and Logistic Regression to Help Diagnose HIV Infection Jason S. Haukoos, MD, MSc Associate Professor & Director of Research Department of Emergency Medicine Denver Health Medical Center University of Colorado School of Medicine Department of Epidemiology Colorado School of Public Health Denver, Colorado Salford Analytics and Data Mining Conference 2012 May 25, 2012
    • Supported by an Independent ScientistAward (K02 HS017526) from the Agency for Healthcare Research and Quality
    • Awareness in the United StatesNumber with HIV infection 1,200,000Number unaware of HIV infection 250,000Annual new infections 56,000
    • HIV Epidemic in the U.S.The numbers of people infected with HIV is growingfastest among Racial and ethnic minorities Social and economically disadvantagedThese are the same groups that are Under- or uninsured Do not have primary medical care Commonly seek medical care in emergency departments
    • www.DenverEDHIV.org
    • Earlier Diagnosis of HIV Infection Benefits both Patient and PublicBenefits for the Patient Timely linkage to care Reduced morbidity and mortality due to HAART Reduced at-risk behaviorBenefits for the Public Earlier treatment, decreased viral load, decreased transmission Reduced unscheduled medical care and length of inpatient hospitalizations
    • Strategies for Uncovering HIVFigure 1. Conceptual model for performing HIV testing in emergency departments. Nontargeted HIV No HIV Screening Testing (“Routine Screening”) Diagnostic Testing Traditional Targeted Enhanced Targeted (Signs or Symptoms) Screening Screening Selection of Selection of Individuals Populations Referral for outpatient Operational Considerations for All Testing Models HIV testing Opt-in versus Opt-out Consent Education versus Counseling Rapid versus Conventional Assay Point-of-Care Testing versus Laboratory-based Testing Result Notification, Reporting, and Linkage of Positives Native versus External Resources Rothman et al. Acad Emerg Med 2007;14:653-657.
    • Haukoos. Arch Intern Med 2012;172:20-22.
    • Objective To systematically evaluate a large number of characteristics to derive a clinically meaningful andvalid prediction tool to accurately categorize patients into risk groups for undiagnosed HIV infection
    • Derivation MethodsDenver Metro Health STD Clinic, Denver, CO10,000 annual visits & 0.5% HIV prevalenceConsecutive patients from 1/1/1996 – 12/31/2008
    • Candidate VariablesDemographics Hepatitis B Sexual Practices Age Hepatitis C Vaginal sex Sex Previous HIV Testing Given oral sex Race/ethnicity Ever tested Received oral sexSymptoms Last tested Given anal sex Urethral discharge Sexual History Received anal sex Anal discharge Time since last sex Sexual Contact Anal lesion Number of partners in prior month Male Genital rash Number of new partners prior month Female Genital Itch Number of partners in prior 4 months Sexual orientationPast STIs Number of new partners in prior 4 months Transgender Gonorrhea Number of lifetime partners Transexual Chlamydia Other Risks Contraception HSV Injection drug use (IDU) None Genital warts Sexual contact with prostitute Condom and percent use Scabies Acting as a prostitute Condom rupture/slip off Crabs Sexual contact with IDU Scabies Sexual contact HIV positive Trichomonas Sexual contact HCV positive Epidiymitis Blood transfusion and year Syphilis
    • LogisticRegression
    • Web Figure 1. 0.012 High 0.010 High Moderate 0.008 Probability of HIV Infection 0.006 Moderate 0.004 0.002 Low 0.000 0 20 40 60 80 100 Age, years
    • Variable β (95% CI)‡ Score§Age <22 or >60 years Ref - 0 22-25 or 55-60 years 0.43 (0.14 – 0.72) +4 26-32 or 47-54 years 1.01 (0.65 – 1.35) +10 33-46 years 1.09 (0.77 – 1.39) +11Sex Female Ref - 0 Male 2.67 (2.19 – 3.14) +27Race/Ethnicity Black 0.84 (0.64 – 1.04) +8 Hispanic 0.26 (0.03 – 0.49) +3 Other* 0† - 0 White Ref - 0Sexual Behaviors Sex with Male 2.10 (1.69 – 2.53) +21 Sex with Female in Past Year -0.60 (-0.18 – -1.00) -6 Sex with HIV-Infected Partner 0.55 (0.33 – 0.77) +6Type of Sexual Contact Vaginal Intercourse -0.57 (-0.16 – -0.99) -6 Insertive Anal Intercourse -0.26 (-0.02 – -0.51) -3 Receptive Anal Intercourse 0.85 (0.59 – 1.12) +9 Oral Intercourse -0.37 (-0.09 – -0.64) -4Other Behaviors Injection Drug Use 0.71 (0.43 – 1.01) +7 Prostitution in Past Year 0.70 (0.14 – 1.31) +7 Past HIV Test -0.48 (-0.29 – -0.68) -5Symptoms History of Syphilis 0.38 (0.00 – 0.83) +4 Rash 0.53 (0.21 – 0.88) +5 Genital Discharge 0.27 (0.10 – 0.45) +3*Represents American or Alaskan Native, Native Hawaiian, or non-Hawaiian
    • Area under the Slope (β) of the R2 of the linear receiver linear regression regression line operating line for the for the calibration characteristics calibration plot plotModel* curveModel 14 0.82 0.88 0.86Model 15 0.71 0.67 0.85Model 16 0.78 0.91 0.86Model 17 0.93 0.78 0.86Model 18 0.84 0.87 0.86Model 19 1.05 0.78 0.85Model 20 0.95 0.94 0.85Model 21 0.79 0.96 0.85Model 22† 1.02 0.91 0.85Model 23 0.91 0.69 0.85Model 24 1.42 0.82 0.85Model 25 1.41 0.90 0.85Calibration plots were graphed using the predicted and observed prevalence of HIV infection in 10 distinct groups categorized using deciles of the predicted prevalence.*See text for descriptions of variables included in each model.† Model 22 was chosen as the final model and includes: age, sex, race/ethnicity, sex with a male, vaginal intercourse, receptive anal intercourse, injection drug use, and past HIV test.
    • Denver HIV Risk Score Variable β (95% CI) Score Age <22 or >60 years ref - 0 22-25 or 55-60 years 0.4 (0.3 – 0.8) +4 26-32 or 47-54 years 1.0 (0.7 – 1.3) +10 33-46 years 1.2 (0.9 – 1.5) +12 Gender Female ref - 0 Male 2.1 (1.8 – 2.4) +21 Race/Ethnicity Black 0.9 (0.7 – 1.1) +9 Hispanic 0.3 (0.1 – 0.5) +3 Other* -0.1 (-0.3 – 0.1) 0 White ref - 0 Sexual Practices Sex with a male 2.2 (2.0 – 2.5) +22 Vaginal intercourse -1.0 (-0.8 – -1.2) -10 Receptive anal intercourse 0.8 (0.6 – 1.0) +8 Other Risks Injection drug use 0.9 (0.7 – 1.1) +9 Past HIV test -0.4 (-0.2 – -0.6) -4*Represents American or Alaskan Native, Native Hawaiian, or non- Hawaiian Pacific Islander. Haukoos et al. Am J Epidemiol 2012;175:838-846.
    • 7 Derivation 6 ValidationHIV Prevalence, % 5 4 3.59% 3 2 1.59% 1 0.99% 0 0.41% 0.31% <20 20 - 29 30 - 39 40 - 49 >50 _ HIV Risk Score
    • A) 18 Derivation 16 Validation 14 Observed HIV Prevalence, % 12 10 8 6 4 2 0 0 2 4 6 8 10 12 14 16 18 Predicted HIV Prevalence, %
    • B) Derivation 100 Derivation ValidationSensitivity, % 80 60 Validation 40 Derivation AUC = 0.85 20 (95% CI: 0.83-0.88) Validation AUC = 0.75 (95% CI: 0.70-0.78) 0 0 20 40 60 80 100 1-Specificity, %
    • Classification Tree
    • Denver HIV Decision Tree Yes 0/1,229Age <16 or >61 years (0%, 95% CI: 0% - 0.2%) Yes 170/83,444MSW or Female (0.2%, 95% CI: 0.2% - 0.2%) Yes 334/7,962MSM or MSB (4.2%, 95% CI: 3.8% - 4.7%)
    • Future DirectionsExternal validation of the Denver HIV Decision TreeComparative clinical and cost effectiveness of thedifferent HIV screening approaches
    • ConclusionsMultivariable analyses have become increasinglyimportant in clinical researchParametric regression techniques have historically beenthe root for analyzing multivariable problemsSpecific public health-related research questions lendthemselves to both traditional and non-traditionalmultivariable techniques
    • Applied Multivariable Modeling in Public HealthUse of CART® and Logistic Regression to Help Diagnose HIV Infection Jason S. Haukoos, MD, MSc Associate Professor & Director of Research Department of Emergency Medicine Denver Health Medical Center University of Colorado School of Medicine Department of Epidemiology Colorado School of Public Health Denver, Colorado Salford Analytics and Data Mining Conference 2012 May 25, 2012