title: Cervical cancer precursors and hormonal contraceptive use in HIV-positive women: application of a causal model and semi-parametric estimation methods.
abstract: OBJECTIVE
  To demonstrate the application of causal inference methods to observational data in the obstetrics and gynecology field, particularly causal modeling and semi-parametric estimation.
  
  
  BACKGROUND
  Human immunodeficiency virus (HIV)-positive women are at increased risk for cervical cancer and its treatable precursors. Determining whether potential risk factors such as hormonal contraception are true causes is critical for informing public health strategies as longevity increases among HIV-positive women in developing countries.
  
  
  METHODS
  We developed a causal model of the factors related to combined oral contraceptive (COC) use and cervical intraepithelial neoplasia 2 or greater (CIN2+) and modified the model to fit the observed data, drawn from women in a cervical cancer screening program at HIV clinics in Kenya. Assumptions required for substantiation of a causal relationship were assessed. We estimated the population-level association using semi-parametric methods: g-computation, inverse probability of treatment weighting, and targeted maximum likelihood estimation.
  
  
  RESULTS
  We identified 2 plausible causal paths from COC use to CIN2+: via HPV infection and via increased disease progression. Study data enabled estimation of the latter only with strong assumptions of no unmeasured confounding. Of 2,519 women under 50 screened per protocol, 219 (8.7%) were diagnosed with CIN2+. Marginal modeling suggested a 2.9% (95% confidence interval 0.1%, 6.9%) increase in prevalence of CIN2+ if all women under 50 were exposed to COC; the significance of this association was sensitive to method of estimation and exposure misclassification.
  
  
  CONCLUSIONS
  Use of causal modeling enabled clear representation of the causal relationship of interest and the assumptions required to estimate that relationship from the observed data. Semi-parametric estimation methods provided flexibility and reduced reliance on correct model form. Although selected results suggest an increased prevalence of CIN2+ associated with COC, evidence is insufficient to conclude causality. Priority areas for future studies to better satisfy causal criteria are identified.
authors: Division of Epidemiology, University of California, Berkeley, California, United States of America.
authors: Hannah H
authors: HH
authors: Leslie
authors: Division of Epidemiology, University of California, Berkeley, California, United States of America.
authors: Deborah A
authors: DA
authors: Karasek
authors: Joint Medical Program, University of California, Berkeley, and University of California San Francisco, San Francisco, California, United States of America.
authors: Laura F
authors: LF
authors: Harris
authors: Pulmonary Medicine, University of California San Francisco, San Francisco, California, United States of America.
authors: Emily
authors: E
authors: Chang
authors: Center for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya.
authors: Naila
authors: N
authors: Abdulrahim
authors: FACES, Family AIDS Care and Education Services, Kisumu, Kenya.
authors: May
authors: M
authors: Maloba
authors: Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, San Francisco, California, United States of America.
authors: Megan J
authors: MJ
authors: Huchko
mesh_terms: D003278:Contraceptives, Oral, Hormonal / Q000009:adverse effects*; D005260:Female; D006679:HIV Seropositivity / Q000150:complications*; D006801:Humans; D015994:Incidence; D007630:Kenya / Q000453:epidemiology; D015233:Models, Statistical; D002583:Uterine Cervical Neoplasms / Q000453:epidemiology / Q000209:etiology*
chemical_list: D003278:Contraceptives, Oral, Hormonal