Due 11/20/01 at 1 pm sectionEpidemiologic Methods (Epi 203)Problem Set: “Confounding and Interaction I” Due 11/20/01 at 1 pm section 1. Recently, there have been several notable hypotheses generated regarding the following exposures and outcomes/conditions. For each, describe which variables you would consider as potential confounding factors (either causing positive or negative confounding) when evaluating/planning a study regarding the putative association. For each potential confounding factor, state whether this would cause positive or negative confounding.a) Cell phone use and motor vehicle accidentsb) Cell phone use and brain tumorsc) Chlamydia pneumoniae infection and coronary heart diseased) Use of cloth diapers and earlier “potty” training2. Consider the following abstract:Racial Disparity in Pregnancy-related Mortality Associated with Livebirth:Can Established Risk Factors Explain It? Am J Epidemiol 2000;152:413–19.The authors conducted a nested case-control study to determine whether the fourfold increased risk of pregnancy-related mortality for US Black women compared with White women can be explained by racial differences in sociodemographic and reproductive factors. Cases were derived from a national surveillance database of pregnancy-related deaths and were restricted to White women (n = 840) and Black women (n = 448) whose pregnancies resulted in a livebirth and who died of a pregnancy-related cause between 1979 and 1986. Controls were derived from national natality data and were randomly selected White women and Black women who delivered live infants and did not die from a pregnancy-related cause (n = 5,437). Simultaneous adjustment for risk factors by using logistic regression did notexplain the racial gap in pregnancy-related mortality. The largest racial disparity occurred among women with the lowest risk of pregnancy-related death: those of low to moderate parity who delivered normal-birth-weight babies (adjusted odds ratio = 3.53, 95% confidence interval: 2.9, 4.4). In contrast, no racial disparity was found among women with the highest risk of pregnancy-related death: high-parity women who delivered low-birth-weight babies. These findings indicate that reproductive health care professionals need to develop strategies to reduce pregnancy-related deaths among both high- and low-risk Black women. The authors adjusted for the four main known risk factors for pregnancy-related mortalityusing the following measurements:-Maternal age at delivery: <20, 20-24, 25-29, 30-34, 35-39, >40 years-Education: <12th grade, thru 12th grade; >12th grade -Parity: livebirth order was defined as the number of children born alive, including the index pregnancy. This variable was obtained from the birth certificate and served as proxy for parity, which was not available from the birth certificate.-Adequacy of prenatal care: inadequate, intermediate, adequate, adequate plus, no care as determined by the Kotelchuck index, which accounts for gestational age at delivery, number and timing of prenatal care visits, sex of the infant, and birth weight.All information was derived from a combination of birth certificates, fetal death certificates, and maternal death certificates.Despite adjustment for all four of these factors, there still remained a racial gap in pregnancy – related mortality. One formal possible explanation for the findings is that genetic differences are responsible for the racial gap. Before accepting this conclusion, (and assuming the findings are not the result of chance, selection bias, or measurement error of the women’s race or mortality status), can you provide any alternative explanations for the findings?3. Consider the following abstract:Risk factors for cervical intraepithelial neoplasia in southwestern American Indian women. Am J Epidemiol 2000 152:716-26The authors assessed risk factors for cervical intraepithelial neoplasia (CIN) among southwestern American Indian women using case-control methods. Cases were New Mexico American Indian women with biopsy-proven grade I (n = 190), grade II (n = 70), or grade III (n = 42) cervical lesions diagnosed between November 1994 and October 1997. Controls were American Indian women from the same Indian Health Service clinics with normal cervical epithelium (n = 326). All subjects underwent interviews and laboratory evaluations. Interviews focused on history of sexually transmitteddiseases, sexual behavior, and cigarette smoking. Laboratory assays included polymerase chain reaction-based tests for cervical human papillomavirus infection, tests for gonorrhea and chlamydia, wet mounts, and serologic assays for antibodies to Treponema pallidum, herpes simplex virus, and hepatitis B and C viruses. In multiple logistic regression analysis, the strongest risk factors for CIN II/III among American Indian women were human papillomavirus type 16 infection (adjusted odds ratio (OR) = 7.6; 95% confidence interval (CI): 2.4, 23.2), any human papillomavirus infection (OR = 5.8; 95% CI: 3.3, 10.0), low income (OR = 3.3; 95% CI: 1.7, 6.2), and history of any sexually transmitted disease (OR = 2.0; 95% CI: 1.1, 3.5). Unlike previous research, this study found no strong associations between CIN and sexual activity or cigarette smoking.The authors felt it was notable that unlike previous research, this study found no strong association between CIN and sexual activity. Assuming this was not because of chance, selection bias, or measurement bias, can you explain why no association was