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Statistics In Public Health Practice

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Statistics in Public Health Practice: The Role of Mathematics in the Fight Against AIDS

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Statistics In Public Health Practice

  1. 1. Statistics in Public Health Practice: The Role of Mathematics in the Fight Against AIDS Felicia P. Hardnett Mathematical StatisticianCenters for Disease Control and Prevention (CDC)The findings and conclusions in this presentation are those of the author(s) and do not necessarily represent the views of the Centers for Disease Control and Prevention.
  2. 2. Presentation Objectives Define public health practice Describe the role of the public health statistician Discuss how HIV/AIDS data are collected and used
  3. 3. Presentation ObjectivesHighlight 4 different projects that illustrate • Statistical methods research • Interdisciplinary collaborations • Training and mentorship opportunities • Important international work
  4. 4. What is Public Health?
  5. 5. What is Public Health? Responsibility of the American government to protect the health and welfare of the American people. Public health science is an organized approach to understanding the health needs of a population rather than just individuals. Public health practice is a combination of science, ethics and politics.
  6. 6. How is public health information used?Public health information is used by: • Physicians to establish treatment guidelines • Congress in making public policy decisions • Health advocacy groups in guiding prevention strategies • Print and broadcast media to make the public aware of ways to protect themselves
  7. 7. What is the Role of the Public Health Statistician?• Assists with the proper collection and processing of public health information• Mathematically analyzes public health information.• Asks and answers scientific questions related to specific disease outcomes such as…
  8. 8. Scientific Questions What subgroups are more at risk for disease? Which health behaviors are most beneficial in preventing or treating this disease? Which health or demographic factors are more closely associated with disease severity? What is the projected public health impact of this disease in the future?
  9. 9. Public Health Data Surveillance Data • Diagnoses • Deaths • Other disease outcomes Special Studies • Behavioral Interventions • Vaccine/Microbicide Trials
  10. 10. HIV/AIDSSurveillance Activity
  11. 11. Reportable Diseases Diseases that are considered to be of great public health importance. It is the responsibility of the healthcare provider, not the patient, to report diagnoses to the state health department.
  12. 12. Reportable Diseases Every state has its own reporting guidelines and list of reportable diseases Many of these diseases, by law, must be reported to CDC.
  13. 13. Reportable Diseases Reporting guidelines are frequently revised depending on the public health impact of the disease. Strict confidentiality guidelines are in place to protect patient privacy.
  14. 14. CDC Surveillance Activity State Health DepartmentPhysician Centers for Disease Control and Prevention
  15. 15. Possible Causes of Racial Disparity Among Women Poverty Education Healthcare Access Differences in Risk-Taking Behavior
  16. 16. Possible Racial Disparity Among Women Poverty Education Healthcare Access Differences in Risk-Taking Behavior
  17. 17. Explaining Racial Disparities in HIV/AIDS Incidence Among Women in the U.S.- A Literature ReviewMethods:Online search of 3 databasesEligibility criteria • Report rates of high-risk sexual or drug use behavior stratified by both race and gender • Findings must be representative of U.S. women ages 15-44 • Findings must be generalizable to the entire United States
  18. 18. Explaining Racial Disparities in HIV/AIDS Incidence Among Women in the U.S.- A Literature ReviewHypotheses:(2)Black women are more likely to engage inhigh- risk sexual activity than women ofother races.(3)Black women are more likely to abusedrugs (especially intravenous drugs) thanwomen of other races.
  19. 19. Explaining Racial Disparities in HIV/AIDS Incidence Among Women in the U.S.- A Literature ReviewHypotheses:(2)Black women are more likely to contractother sexually-transmitted diseases therebyfacilitating HIV transmission.(3)Black men are less likely than men ofother races to disclose same-sex behaviorwhich may lead to increased HIV riskbehavior with Black women.
  20. 20. Explaining Racial Disparities in HIV/AIDS Incidence Among Women in the U.S.- A Literature ReviewResults: A review of multiple studies suggests that Black women were -just as likely or sometimes even more likely to report consistent condom use -no more likely to report multiple sexual partners -no more likely to abuse intravenous drugs
  21. 21. Explaining Racial Disparities in HIV/AIDS Incidence Among Women in the U.S.- A Literature ReviewResults: However, the results do suggest that Black women are -more likely to report having risky sexual partners -more likely to have undiagnosed/untreated STDs
  22. 22. Explaining Racial Disparities in HIV/AIDS Incidence Among Women in the U.S.- A Literature ReviewResults: Also, there is considerable evidence suggesting that Black men are less likely than men of other races to disclose their same-sex behavior to their female sex partners.
  23. 23. Explaining Racial Disparities in HIV/AIDS Incidence Among Women in the U.S.- A Literature ReviewConclusion: Although the literature suggests some racial differences with regard to two of the four hypotheses, the findings were insufficient in explaining the 20-fold difference in HIV incidence observed between Black and White women. Future investigations should continue to explore these and other social and behavioral factors.
  24. 24. Possible Causes of Racial Disparity Among Women Poverty Education Healthcare Access Differences in Risk-Taking Behavior
  25. 25. Population Attributable Risk (PAR) ProjectGoal:To quantify the impact of socialdeterminants of health on racialdisparities in HIV incidence amongU.S. women.
  26. 26. Social Determinants of HealthThe economic and social conditions thatinfluence the health of people andcommunities. These conditions are shapedby the amount of money, power, andresources that people have, all of which areinfluenced by policy choices.
  27. 27. What is PAR?Definitionthe proportion of new cases in a populationthat could have been prevented if a riskfactor were neutralized.
  28. 28. Application of PARFormula: PAR = P(exposed) x (RR-1) 1+ P(exposed) x (RR-1)P(exposed) is the proportion of peopleexposed to the defined health risk factor.RR stands for relative risk.
  29. 29. Application of PARFormula: PAR = P(exposed) x (RR-1) 1+ P(exposed) x (RR-1)Relative Risk (RR):The ratio of the probability of disease amongexposed vs. unexposed.
  30. 30. Application of PARFormula: PAR = P(exposed) x (RR-1) 1+ P(exposed) x (RR-1)P(exposed) is the proportion of peopleexposed to the risk factor.
  31. 31. Application of PARMethodsUsing recently published U.S. Censusand HIV/AIDS surveillance data as inputvalues, we estimated the PAR associatedwith being African-American.
  32. 32. Application of PARResults• Preliminary findings indicate that as much as 66% of new HIV/AIDS cases among women are attributable to being African-American.• This corresponds to as many as 6408 of the 9708 HIV/AIDS diagnoses among U.S. women in 2005.
  33. 33. Application of PARLimitationsRace itself isn’t a modifiable riskfactor.Completely neutralizing a risk factorisn’t possible.
  34. 34. Application of PARAdvantagePAR assigns an actual measurequantifying the potential impact ofsocial determinants of health as itrelates to HIV/AIDS.
  35. 35. HIV/AIDSSpecial Studies
  36. 36. Special Studies Experimental Studies • Subjects are enrolled and assigned to intervention/control groups • Intervention is administered • Data are collected on the frequency of disease outcomes for treated and control participants Observational Studies • Subjects are enrolled and followed for a period of time • Data collected on the frequency of disease outcomes
  37. 37. Special Studies Experimental Studies • Subjects are enrolled and assigned to intervention/control groups • Intervention is administered • Data are collected on the frequency of disease outcomes for treated and control participants Observational Studies • Subjects are enrolled and followed for a period of time • Data collected on the frequency of disease outcomes
  38. 38. HIV/AIDSExperimental Studies
  39. 39. Research Process
  40. 40. HIV/AIDS Experimental Studies Behavioral Interventions Vaccine Trials
  41. 41. HIV/AIDS Behavioral InterventionsDescription Structured training or educational programs which aim to lower an individual’s HIV risk by modifying sexual decision-making and/or drug use behavior.
  42. 42. HIV/AIDS Behavioral InterventionsDescription These programs target specific psychological constructs (i.e., mediators) based on established psychological theories of behavior change.
  43. 43. HIV/AIDS Behavioral InterventionsPurposeTo inform and/or empower people to reduce their HIV risk through behavioral modifications.
  44. 44. What is Mediation? A mediating process is the mechanism by which a behavioral intervention causes a change in behavior. A mediator explains all or part of an intervention’s effectiveness
  45. 45. Characteristics of Mediators“In general, a given variable may be said to function as a mediator to the extent that it accounts for the relationship between the predictor (intervention) and the outcome (behavior)…Mediators explain how external physical events take on internal psychological significance.”Baron, R. M., & Kenny, D. A. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 1986, 51, 1173-1182.
  46. 46. Common Mediators in Behavioral HIV Studies• Self-efficacy (e.g. for condom use)• HIV/AIDS Knowledge• Attitudes (related to protecting self, protecting partner, about condom use)• Intentions to use condoms• Outcome Expectancies (beliefs about the consequences of behavior)
  47. 47. Purpose of Mediation Analysis in HIV Intervention Studies Important for basic research on mechanisms of effect Mediation analyses help to identify how an effective intervention works and why an ineffective one does not work
  48. 48. How is Mediation Measured?
  49. 49. Baron and Kenny (1986) Mediator α βIntervention Outcome τ’
  50. 50. Baron and Kenny (1986) Mediator α βIntervention Outcome τ’
  51. 51. Baron and Kenny (1986) Mediator α βIntervention Outcome τ’
  52. 52. Baron and Kenny (1986) Mediator α βIntervention Outcome τ’
  53. 53. Causal Steps- Most CommonAccording to Baron and Kenny (1986), a variable functions as a mediator when it meets the following conditions: (a) variations in levels of the independent variable significantly account for variations in the presumed mediator (i.e., Path a), (b) variations in the mediator significantly account for variations in the dependent variable (i.e., Path b), and (c) when Paths a and b are controlled, a previously significant relationship between the independent and dependent variables is no longer significant, with the strongest demonstration of mediation occurring when Path c is zero.
  54. 54. Controversy: Does the Intervention Effect Have to be Significant? A primary assumption of the Causal Steps approach Ignores the potential for suppressive effects For long-term processes, power may be low to detect an intervention to behavior effect
  55. 55. Difference in Coefficients (τ- τ’) Y = β01 + τ X + ε1 Y = β02 + τ ’X + βZ + ε2Y= OutcomeX= InterventionZ= Potential Mediatorβ01, β02 = Interceptsτ = coefficient relating independent and dependent variables (unadjusted)τ’= coefficient relating independent and dependent variables adjusted for mediator effect.ε1, ε2 = Unexplained variability
  56. 56. Product of Coefficients (αβ) Mediator α βIntervention Outcome τ’ Indirect effect: αβ Direct Effect: τ’ Total Effect: αβ + τ’
  57. 57. Statistical Test of Mediation (I) MacKinnon et al (1995) αβ z = α σ +β σ 2 2 β 2 2 α• z’= modified z statistic• Empirical critical value for .05 significance is .97rather than 1.96
  58. 58. Statistical Test of Mediation (II)Asymmetric Confidence Limits α β δα = ;δβ = σα σβValues of δα and δβ are compared withcritical values in tables published byMeeker et al. UCL= αβ + Mupper* σ αβ LCL= αβ + Mlower * σ αβ
  59. 59. Example- SUMIT TrialSeropositive Urban Men’s Intervention Trial • HIV-positive men • Study Design: 2-arm randomized trial (“standard” intervention vs. “enhanced” intervention) • Outcome variables: any unprotected insertive anal sex with negative or unknown status partner (UIAI) • 10 potential mediators • Challenge: weak intervention effect
  60. 60. Original SUMIT Analysis Association between IV and DV was not significant Formal mediation analysis was abandoned in favor of assessing correlates of behavior changeO’Leary, A., Hoff, C. C., Purcell, D. W., Gomez, C.A., Parsons, J. T., Hardnett, F., Lyles, C. M. (2005). What happened in the SUMIT trial? Mediation and behavior change. AIDS, 19, S111-S121.
  61. 61. SUMIT Re-analysisBy applying less stringent criteria and constructing ACLs: • Uncovered previously unidentified mediating effects • Identified a marginally significant suppressive effect
  62. 62. HIVVaccine/Microbicide Trials
  63. 63. Purpose An opinion piece was recently published in AIDS that attempts to explain the waning efficacy of HIV prevention methods observed over time. DHAP researchers want to consider the possible impact on DHAP intervention trials.
  64. 64. Background Two recently published trials (1 vaccine trial and 1 microbicide trial) concluded that intervention effectiveness decreased over time. The investigators attributed this to: • Waning vaccine efficacy (vaccine trial) • Decreasing adherence (microbicide trial)
  65. 65. Basic Assertions In addition to these phenomena, the authors assert that “selection bias due to heterogeneity in infection risk” is another possible explanation. This explanation is rarely cited in the literature as a possible explanation for declining efficacy.
  66. 66. Selection Bias is a statistical bias in which there is an error in choosing the individuals or groups to take part in a scientific study. Can cause misleading results if treatment groups differ in terms of a factor associated with the outcome.
  67. 67. Selection BiasFor example, in an HIV randomized trial, if the majority of the persons in the treatment group were drug free and had taken a vow of celibacy and the persons in the placebo group were IDU commercial sex workers, any difference that you may see in HIV incidence between these two groups cannot be directly attributed to the intervention.
  68. 68. Illustration of a hypothetical disease process within a population Population at risk # persons who never develop disease TimeAs people become infected, the population at risk decreases over time and eventually plateausThe rate of decline depends on disease incidenceThe curve plateaus at the number of persons who will never develop the disease (low/no risk people)
  69. 69. Illustration of “Selection Bias” as presented in the paper Population at risk High risk Low/no risk Timee paper asserts: gh risk individuals will be infected early on and will be removed from the population at risk.his will leave lower risk individuals in the risk population resulting in lower disease incidence at later time points.
  70. 70. Graphical representation of disease incidence N0 Population at risk (N) n# persons who neverdevelop disease Time t0 t1 Incidence= Number who become infected (n) From t0 to t1 Number intially at risk (N0)
  71. 71. Graphical representation of declining disease incidence as presented in paper Population at risk (N) n Time t0 t1• Fewer cases diagnosed at a later time point because the high risk people are gone.• Incidence, therefore decreases.
  72. 72. Population at risk Intervention Scenario Treatment arm Placebo# persons who neverdevelop disease Time • If the intervention is effective, it will prolong the time before high-risk individuals in the treatment arm will become infected. • Incidence decline in the placebo group will be larger because those at high risk will be quickly removed from the population at risk.
  73. 73. Intervention Scenario Rate ratio= incidence (treatment arm) incidence(placebo) Population at risk Treatment arm Placebo# persons who neverdevelop disease TimeAs a result, the time-specific rate ratio will increase from a value of less than onto a value of one or greater.This process is termed “frailty”,“survivor bias”, “survivor cohort effect”, “crossing of hazards” or “depletion of susceptibles”.
  74. 74. Possible Impact on Rate MeasuresWeighted average of the time specific rate ratioThis value becomes increasingly attenuated asfollow-up time increases.This occurs even if risk factors were balancedbetween study arms at baseline and if effect ofintervention is constant over time.This may cause investigators to reject anefficacious intervention.This may also cause investigators to overlook arisk factor that is, in fact, harmful.
  75. 75. Frailty ProjectPurposeTo measure the potential impact of frailty in HIV vaccine/microbicide trials.
  76. 76. Frailty ProjectMethodWe are designing a series of study scenarios that • Incorporates both the study-related and population-related parameters of a randomized trial • Manipulates these parameters • Measures the impact on final point estimates.
  77. 77. Frailty ProjectMethod Components of these scenarios will include measures of disease incidence, disease risk, waning immune response/decreased adherence and intervention effectiveness.
  78. 78. Frailty ProjectUnderlying model assumptions Equal sample sizes in both treatment arms. Disease risk is balanced between both treatment arms at the beginning of the study. Non-differential loss to follow up.
  79. 79. Frailty ProjectUnderlying model assumptions The intervention is effective at reducing the probability of disease. Intervention efficacy is constant across all risk groups. Intervention waning/non-adherence is constant across all risk groups and time intervals.
  80. 80. Frailty ProjectThe data collection process is taking place now. The results will be published as a response to the original opinion piece.
  81. 81. HIV/AIDS inSubsaharan Africa
  82. 82. Southern Africa 7 out of 10 countries have adult HIV prevalence rate of 15% or more1 • Zambia (16.5%) • Namibia (21.3%) • South Africa (21.5%) • Zimbabwe (24.6%) • Lesotho (31.7%) • Botswana and Swaziland (over 35%)UNAIDS, Report of the Global AIDS Epidemic, 20041
  83. 83. Contributing Factors Sexual exploitation • Rape • Abuse • Sexual traffickingPoverty • Limited access to prevention efforts • Limited access to healthcare Inadequate public health infrastructure High levels of other STDs
  84. 84. HIV Epidemic in Subsaharan Africa 10% of the world’s population 70% of all people living with HIV More than 2 million new infections every year Some regions have HIV prevalence of more than 30%.
  85. 85. Focus on Women 57% of infected adults are women 75% of infected young people are women and girls
  86. 86. Kenya Medical Research Institute (KEMRI/CDC) Serves as a platform for service delivery and scientific study in Kenya. Researchers measure the impact, effectiveness and safety of interventions. Collaborations between U.S. and Kenyan researchers to enhance scientific and analytic capacity.
  87. 87. Kenya Incidence Cohort Study (KiCoS)Examines trends in seroconversion rates, healthcare access and HIV prevention activity in the Nyanza Province (SW Kenya) among women, adolescents and high risk populations.
  88. 88. Ongoing Work in Kenya Examining cultural barriers to participation in HIV prevention efforts Examining factors associated with healthcare seeking behavior among men who have sex with men (MSM) Examining factors associated consistent condom use. The list goes on…
  89. 89. ConclusionsMany opportunities for mathematical researchers in the field of public health practice• Interdisciplinary collaborations• Training/mentorship opportunities• Opportunities to pursue personal research interests• International work
  90. 90. Thank You!!

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