The Effect of Ignoring Statistical Interactions in Regression Analyses Conducted in Epidemiologic Studies: An Example with Survival Analysis Using Cox Proportional Hazards Regression Model

Objective: To demonstrate the adverse impact of ignoring statistical interactions in regression models used in epidemiologic studies. Study design and setting: Based on different scenarios that involved known values for coefficient of the interaction term in Cox regression models we generated 1000 samples of size 600 each. The simulated samples and a real life data set from the Cameron County Hispanic Cohort were used to evaluate the effect of ignoring statistical interactions in these models. Results: Compared to correctly specified Cox regression models with interaction terms, misspecified models without interaction terms resulted in up to 8.95 fold bias in estimated regression coefficients. Whereas when data were generated from a perfect additive Cox proportional hazards regression model the inclusion of the interaction between the two covariates resulted in only 2% estimated bias in main effect regression coefficients estimates, but did not alter the main findings of no significant interactions. Conclusions: When the effects are synergic, the failure to account for an interaction effect could lead to bias and misinterpretation of the results, and in some instances to incorrect policy decisions. Best practices in regression analysis must include identification of interactions, including for analysis of data from epidemiologic studies

Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies

The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis

Clinical diagnosis of Plasmodium falciparum among children with history of fever, Sindh, Pakistan

Objective: To identify clinical predictors for malaria and develop a clinical algorithm to more accurately identify malaria from non-malaria cases.Methods: Four hundred thirty eight children aged 6-120 months attending the rural health center between August 15 and October 5, 1997, in Jhangara town of district Dadu, Sindh were recruited. A standard questionnaire was used to record symptoms and duration of child\u27s illness. Each child was physically examined, had their axillary temperature measured, and blood samples were collected from which Giemsa stained thick and thin blood films were prepared and examined for presence of Plasmodium parasites. The sensitivity and specificity of several candidate algorithms for parasitemia were evaluated using various combinations of identified predictors.Results: Twenty-six of 438 children (6%) were slide positive for malaria. An algorithm comprised of fever 3 days duration and (absence of cough or having rigors) had 100% sensitivity and 63% specificity for detecting P. falciparum.CONCLUSION: In this low malaria prevalence region, restricting the diagnosis of malaria to persons who had \u3e3 days of fever and absence of cough or rigors, remained highly sensitive but was more specific than current practice. If validated prospectively, this algorithm could reduce misdiagnosis and mis-treatment

Frequency and determinants of vaginal infection in postpartum period: a cross sectional survey from low socioeconomic settlements, Karachi, Pakistan

Objective: To determine the frequency and factors associated with perceived vaginal infections among married women in their postpartum period. Methods: A cross-sectional study was conducted from July 2000 to October 2000 in five squatter settlements of Karachi, Pakistan. These squatter settlements were selected on the basis of an existing surveillance system run by female community health workers for maternal and child healthcare which identified women who had delivered 42-56 days prior to the date of interview. Vaginal infection was considered present when a mother perceived foul smelling vaginal discharge during the postpartum period. Mothers were interviewed to gain insight into socioeconomic and demographic variables, materials used to staunch lochia, duration of labour, personal and perineal hygiene and past obstetric history. Results: A total of 525 women were interviewed. The estimated prevalence of perceived vaginal infection was 5.1%. Factors associated with perceived vaginal infections included, delivery conducted by a non-medical personnel (AOR 3.5, CI 1.3-9.5) and use of unhygienic cloth or cotton for staunch of lochia (AOR 2.7, CI 1.1-6.2). Conclusion: Among women who reported perceived vaginal infection, a higher proportion were delivered by non-medical personnel, and used unhygienic material (cloth or cotton) for staunch of lochia as compared to women who did not perceive vaginal infection. We recommend deliveries to be conducted by trained personnel and provision of health education for persons who conduct delivery and women to use hygienic material for staunch of lochia during post partum period (JPMA 56:99;2006)

Undiagnosed Diabetes and Pre-Diabetes in Health Disparities

Globally half of all diabetes mellitus is undiagnosed. We sought to determine the extent and characteristics of undiagnosed type 2 diabetes mellitus and pre-diabetes in Mexican Americans residing in the United States. This disadvantaged population with 50% lifetime risk of diabetes is a microcosm of the current pandemic. We accessed baseline data between 2004 and 2014 from 2,838 adults recruited to our Cameron County Hispanic Cohort (CCHC); a two-stage randomly selected \u27Framingham-like\u27 cohort of Mexican Americans on the US Mexico border with severe health disparities. We examined prevalence, risk factors and metabolic health in diagnosed and undiagnosed diabetes and pre-diabetes. Two thirds of this Mexican American population has diabetes or pre-diabetes. Diabetes prevalence was 28.0%, nearly half undiagnosed, and pre-diabetes 31.6%. Mean BMI among those with diabetes was 33.5 kg/m2 compared with 29.0 kg/m2 for those without diabetes. Significant risk factors were low income and educational levels. Most with diabetes had increased waist/hip ratio. Lack of insurance and access to health services played a decisive role in failure to have diabetes diagnosed. Participants with undiagnosed diabetes and pre-diabetes had similar measures of poor metabolic health similar but generally not as severe as those with diagnosed diabetes. More than 50% of a minority Mexican American population in South Texas has diabetes or pre-diabetes and is metabolically unhealthy. Only a third of diabetes cases were diagnosed. Sustained efforts are imperative to identify, diagnose and treat individuals in underserved communities

Association of Total and Differential White Blood Cell Counts to Development of Type 2 Diabetes in Mexican Americans in Cameron County Hispanic Cohort

Objective: To evaluate the relationship between total and differential White Blood Cell (WBC) counts with time to transition to type 2 diabetes in Mexican Americans using prospective data from the Cameron County Hispanic Cohort (CCHC). Results: Multivariable Cox proportional hazards regression models revealed that obese Mexican-American cohort participants whose total WBC or granulocyte count increased over time had 1.39 and 1.35 times higher risk respectively of transition to type 2 diabetes when compared to overweight participants. The granulocyte or total WBC count in participants with BMI≥35 were significant risk factors for transition to type 2 diabetes. Conclusions: Increased total WBC and WBC differential counts, particularly lymphocytes and granulocytes, are associated with risk of transition to type 2 diabetes in obese Mexican Americans, after adjusting for other potential confounders. Screening and monitoring the WBC counts, including lymphocytes and granulocytes can help with monitoring potential transition to type 2 diabetes

Concentrations of lead, mercury, arsenic, cadmium, manganese, and aluminum in the blood of Pakistani children with and without autism spectrum disorder and their associated factors

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with early onset in utero or childhood. Environmental exposure to six metals (Pb, Hg, As, Cd, Mn, Al) is believed to be associated with ASD directly or interactively with genes. Objective: To assess the association of ASD among Pakistani children with the six metals and genotype frequencies of three GST genes (GSTP1, GSTM1, GSTT1).Methods: We enrolled 30 ASD cases, age 2-12 years old, and 30 age- and sex-matched typically developing (TD) controls in Karachi, Pakistan. We assessed associations of ASD status with various factors using Conditional Logistic Regression models. We also used General Linear Models to assess possible interaction of blood Mn and Pb concentrations with the three GST genes in relation to ASD status.Results: The unadjusted difference between ASD and TD groups in terms of geometric mean blood Pb concentrations was marginally significant (p = 0.05), but for Al concentrations, the adjusted difference was marginally significant (p = 0.06).Conclusions: This is the first study reporting six blood metal concentrations of Pakistani children with ASD. Estimates provided for possible interactions of GST genes with Mn and Pb in relation to ASD status are valuable for designing future similar studies

Liver and other Gastrointestinal Cancers are frequent in Mexican Americans

Background Disease patterns in Mexican-American health-disparity populations differ from larger United States populations. Aims Determine frequency of gastrointestinal cancers in Mexican Americans. Methods We analyzed self-reported data from the Cameron County Hispanic Cohort where we find high rates of risk factors for cancer: obesity (48.5%), diabetes (30.7%). Participants provided cancer histories about themselves and first and second degree relatives. Logistic regression models assessed risk factors. Frequencies of cancer sites were ranked and validated using concurrent age local cancer registry data. Results Among 9,249 individuals (participants and their relatives) there were 1,184 individuals with reports of cancer. Among cohort participants under 70 years of age the most significant risk factor for all-cause cancers was diabetes (OR 3.57, 95% CI 1.32, 9.62). Participants with metabolic syndrome were significantly more likely to report cancer in relatives (1.73 (95%CI 1.26, 2.37). Among cancers in fathers, liver cancer was ranked third, stomach fourth, colorectal sixth and pancreas tenth. In mothers, stomach was third, liver fourth, colorectal seventh and pancreas eleventh. The unusual prominence of these cancers in Mexican Americans, including liver cancer, was supported by age-adjusted incidence in local registry data. Conclusions Gastrointestinal system cancers, particularly liver cancer, in a Mexican American health disparity cohort and their relatives rank higher than in other ethnicities and are associated with high rates of diabetes and metabolic syndrome. Effective prevention of diabetes and low-tech, high-quality screening strategies for gastrointestinal cancers are needed in health disparity communities

Statistical competencies for medical research learners: What is fundamental?

IntroductionIt is increasingly essential for medical researchers to be literate in statistics, but the requisite degree of literacy is not the same for every statistical competency in translational research. Statistical competency can range from 'fundamental' (necessary for all) to 'specialized' (necessary for only some). In this study, we determine the degree to which each competency is fundamental or specialized.MethodsWe surveyed members of 4 professional organizations, targeting doctorally trained biostatisticians and epidemiologists who taught statistics to medical research learners in the past 5 years. Respondents rated 24 educational competencies on a 5-point Likert scale anchored by 'fundamental' and 'specialized.'ResultsThere were 112 responses. Nineteen of 24 competencies were fundamental. The competencies considered most fundamental were assessing sources of bias and variation (95%), recognizing one's own limits with regard to statistics (93%), identifying the strengths, and limitations of study designs (93%). The least endorsed items were meta-analysis (34%) and stopping rules (18%).ConclusionWe have identified the statistical competencies needed by all medical researchers. These competencies should be considered when designing statistical curricula for medical researchers and should inform which topics are taught in graduate programs and evidence-based medicine courses where learners need to read and understand the medical research literature