Prognosis, literally translated from its Greek roots, means "fore-knowing." It is one of the three classic responsibilities of a physician, the others being diagnosis and therapy. Although the breadth and detail of scientific evidence to support medical practice has expanded significantly in recent decades, there is a case to be made that evidence about prognosis has lagged behind evidence for physicians' other work. Clinical questions in perinatal epidemiology demonstrate several important issues related to the conduct of prognostic research. Using examples from the study of prognosis for infants born with congenital diaphragmatic hernia, a condition for which estimates of survival vary widely, we illustrate the importance of selecting and specifying appropriate prognostic categories and contexts (e.g., time and place) to promote the appropriate interpretation of prognosis. In a study of extremely preterm birth--another condition with wide variation in available survival statistics--we show why decisions made by patients and physicians need to be accounted for in prognostic research. By revealing several potential pitfalls of prognostic research, each of the projects described in this thesis also illuminates important opportunities for the better conduct and interpretation of such work. Making predictions about the future and providing this information to patients may always be difficult work for physicians. But better scientific evidence and interpretation of that evidence can at least make predictions more accurate. The aim of this thesis is to advance our knowledge about how to achieve such improvements.

Nonsyndromic orofacial clefts (OFCs) are major birth defects that include cleft lip with or without cleft palate (CL/P) and cleft palate (CP). The etiology of OFCs is thought to be multifactorial, and several gene variants and environmental exposures have been reported. Previous reports are equivocal for most environmental exposures studied, including those that examined parental exposure to pesticides or metals. The present set of three studies used data from the National Birth Defects Prevention Study (NBDPS) to examine associations between parental occupational pesticide exposures, selected maternal occupational metal exposures, and maternal multisource arsenic exposure and OFCs in offspring. NBDPS data for parental occupational exposures to insecticides, herbicides, and fungicides, alone or in combination, during the critical period of lip and palate development were compared between OFC cases and controls. Any (yes, no) and cumulative (no exposure, NBDPS data for any and cumulative maternal occupational exposures to cadmium, cobalt, nickel, and lead during the critical period of lip and palate development were compared between OFC cases and controls. Small sample sizes precluded analyses of cobalt, nickel, and combinations of metal exposure. After adjustment for relevant covariables, no significant, positive associations were observed for maternal exposure to cadmium or lead and all OFC cases combined or OFC subtypes, with most estimates near unity. Iowa NBDPS data and public water and well water testing data were used to compare maternal occupational and environmental exposures to arsenic between OFC cases and controls. Expert-rater review of maternal job histories was used to assign ratings for occupational exposure to any arsenic and inorganic arsenic only. Drinking water data for public water supplies or private wells were linked to maternal residential histories and combined with their reports of water consumption during pregnancy to estimate arsenic ingestion through drinking water. Reported concentrations of arsenic and inorganic arsenic in food were combined with maternal responses to a food frequency questionnaire to estimate arsenic consumption through diet. Positive, non-significant associations were observed for maternal occupational exposure to any arsenic or inorganic arsenic and all OFC cases combined. Also, significant, positive associations with any arsenic and inorganic arsenic and CP were observed. Associations for ingestion of arsenic through drinking water or dietary arsenic and OFCs were largely near unity. Findings suggest possible relations between paternal occupational pesticide exposure and maternal occupational arsenic exposure and OFCs. Additional epidemiologic research using methods to reduce possible sources of bias is needed to further elucidate the role of these exposures in the etiology of OFCs.

The purpose of this study was to more fully understand the reasons underlying poor nutritional status among adults in the United States (US) and to provide research findings that can be used to develop programs and policies to help improve nutritional status in the US. The National Health and Nutrition Examination Survey (NHANES) dataset and the correlational quantitative study design were used to explore the associations between food security, household smoking, and demographics and nutritional status. The social ecologic theory, specifically the social ecology of health as it relates to interventions, was used as the study's theoretical framework. The results of the regression analyses conducted found statistical significance with respect to the effect of food security on nutritional status. In addition, significant moderation of the relationship by the demographic variable race/ethnicity was found using additional regression models, which incorporated interaction effects. Additionally, correlational analysis was conducted between independent and dependent variables in order to determine whether multicollinearity was present, and strong multicollinearity was found with food security but not with living in a smoking house. Public health professionals should focus on these findings when creating new programs and policies. Doing so may help to improve the nutritional status of the U.S. population.

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Bladder antimuscarinic medications (BAMs) are commonly used among the aged population to manage urinary incontinence (UI), which is a frequent health problem among nursing home (NH) residents. However, a number of BAMs produce anticholinergic effects in the central nervous system (CNS), and may thereby cause delirium and lead to antipsychotic medication (APM) use. This study examined the associations of BAMs and delirium incidence, and the associations of BAMs and APM initiations in NHs in Iowa. The main data sources were Medicare data, Minimum Data Set (MDS), and the Online Survey Certification and Reporting (OSCAR) or Survey Provided Enhanced Reporting (CASPER) from 2011 to 2014. Propensity score (PS) matching was used to balance baseline covariates between groups. Proportional hazard models with stratification on matched sets were used to analyze time to the study outcomes with 90-day, 183-day, 365-day, and the entire follow up. The study found out that compared with those without BAM use, residents with new BAM use had a non-significant higher risk for delirium incidence during the entire follow up, with a hazard ratio (HR) of 1.12, and 95% confidence interval (95% CI) of 0.92-1.36. New BAM users also had a non-significant higher risk for APM initiation (HR: 1.07, 95% CI: 0.78-1.49). Compared to residents who had new use of other BAMs, those who had new use of bladder-selective BAMs (darienacin and solifenacin) or quaternary-amine BAM (trospium) had a lower risk (HR: 0.83, 95% CI: 0.49-1.38) for delirium incidence and APM initiation (HR: 0.85, 95% CI: 0.36-2.03). However, none of the differences was statistically significant. APM prescribing rate at the facility level was not random in Iowa geographically. There were spatial variations in the associations between BAM prescribing rate and APM prescribing rate across the state.

Objective: Recurrent methicillin-resistant Staphylococcus aureus (MRSA) infections are a significant problem in the healthcare system. Our objective was to create a clinical prediction rule to identify Veterans at high-risk of recurrent MRSA infections. Methods: A retrospective cohort study of Veterans with MRSA bacteremia was performed using patient data from 2003 to 2011. Recurrent MRSA infection was defined as a positive blood culture between two days and 180 days after discharge from the index hospitalization. Severity of illness was measured at the time of admission using a modified APACHE score. Patients were randomly split into a development or validation cohort. Using the development cohort, variables significant in predicting recurrence on univariate analysis were input into a logistic regression model. The final model, c-statistics, and receiver operating characteristic curves were compared in each cohort. Results: Of 9,279 patients in the combined cohort, 1,127 (12.1%) had a recurrent MRSA infection within 180 days of the index infection. Using the development cohort, the risk factors identified and included in the logistic regression model were severity of illness, duration of bacteremia, distance to care, lack of MRSA-directed antibiotic therapy, renal failure, coagulopathy, cancer, and cardiac arrhythmia. The model had average discrimination (c-statistic, 0.657), with 68.9% sensitivity and 54.0% specificity. The validation cohort also had average discrimination (c-statistic, 0.625), with 66.8% sensitivity and 52.6% specificity. Conclusions: Our results identify important risk factors for MRSA recurrence and may help to guide clinicians in targeting high-risk patients for treatment and aggressive follow-up.

Despite the tremendous efforts made in recent years towards improving overall health status of adolescents, road traffic crashes remain a global problem worldwide among teen drivers. It is well established that the first few months of independent driving are the most dangerous. Indeed, crash risk among adolescent drivers is particularly high during the early months of independent driving, after which it starts to rapidly decrease over a period of over a period of years. Hypotheses for this decline have focused on Some researchers have hypothesized accumulation of driving experience, maturation, and increasing self-regulation. However, the mechanisms by which they interact to decrease teen crash risk in few months are not well understood. Additionally, safety researchers are engaged in a longstanding quest to fundamentally improve teen driving. To that end, increasing number of studies have been striving for solutions. Understanding the processes underlying patterns in teen crash risk and catalyze effective teen driving interventions can benefit from techniques for modelling complexity. The goal of this project was to develop a model that provides initial insights into the mechanisms underlying adolescent risky driving patterns over time. The purpose of the modeling is to investigate how much faster the early improvement of teen risky driving could be with interventions. This study utilized naturalistic driving data derived from a clinical trial study. A sample of newly-licensed teen drivers and at least one of their parents was recruited from high schools in Iowa and randomly assigned to one out of three groups: control group, feedback group, and feedback plus parent communication group. Each participant's vehicle was equipped with an event triggered video recording system to gather data on near-crashes and crashes as well as their proxies denoted risky driving events. The video recording system was installed in the vehicles of the control group only for data collection purposes. For the feedback intervention group, teen drivers received an immediate feedback via blinking of LED lights on the in-vehicle video system when a driving error occurred. In addition, each teen and their parent in this feedback group received a weekly report card that summarized the types of driving errors made by the teen and provided video clips of those errors. The feedback plus parent communication group was exposed to the feedback intervention described above plus communication strategies for discussing safe driving with teens. The video recording system was also used to collect data on mileage, driver behaviors (eg. traffic violations, cell phone use), and traffic conditions (eg. snow, rain). The first aim of this study thoroughly investigated heterogeneity in driving outcomes within the population of teen drivers. Results showed two distinct risky driving trajectories, including one inverted U-shaped pattern (initial increase in risky driving followed by a steady decrease) and one relatively constant pattern over time. Risk-taking behavior trajectories were found to follow the same patterns as risky driving. The study also identified two groups of teens with respect to amount of driving: one group has a linear increase in the amount of driving and the second group has an upward U-shaped pattern. Teens classified in the high risk-taking behavior group are more likely to be in the high risky driving group whereas the teens classified in the low risk-taking behavior are more likely to be in the low risky driving group. Results showed that males are more likely to be in high risky driving and high risk-taking behavior groups compared to females. The second aim of this project was to develop a dynamic model of teen risky driving and use this framework as a guide to leverage an understanding of the dynamic process underlying patterns in teen risky driving over time. The analysis suggests that the natural risky driving behavior (absent intervention) is slow improvement followed by faster improvement, and finally a plateau: that is, S-shaped decline in errors. The results showed that a model that includes cumulative miles driven and recent risky driving events as stock variables and their feedback is capable of explaining the dynamics of teen risky driving over time. The analysis suggests the existence of a reinforcing loop and two negative feedbacks. The reinforcing loop arises from a decline in recent events leading to a faster increase in driving; this leads to a faster accumulation of driving and thus a greater decrease in driving error rate; the decrease in driving error rate leads to a further decline in recent events via a slow replenishing of the stock “recent events”. The first negative feedback is from recent events to amount of driving. By this feedback mechanism, more recent events (or memories of events) lead to less driving, and thus slow accumulation of driving experience (cumulative miles driven). The second feedback in the model is from recent events to event rate. A greater number of recent events (or memories of events) leads to a decrease in event rate perhaps via corrective actions taken by the teen driver. Thus, more recent events (or memories of events) lead to a decrease in event rate, but slow accumulation of experience via less driving. The results highlight that variations of individualized trends in driving event rate and monthly driving are more likely due to significant variations in the stock “cumulative miles driven” and the stock “recent events”. Variations in these stocks are influenced by initial event rates and driving need. The methodological approach provides an explanation for the peak in crash rates during the latter months post-licensure rather than the first month, which was not fully understood. The third, and final, aim of this teen driving dynamic model project sought to simulate driver feedback intervention and conduct its cost effectiveness analysis. To examine the impact of driver feedback intervention and its tradeoffs, a previous version of the model was extended to create a model that allows the simulation of the intervention and the comparison between its expected costs and benefits. The analysis suggested that the simulated intervention data are comparable to data from actual feedback intervention group. The simulation results indicate significant differences in the period over which the intervention is needed. While the intervention is economically beneficial for some drivers, it is worthless for others. The model also suggests the need of combining several interventions for some drivers for a faster improvement in risky driving. This research offered initial insights for understanding risky driving patterns, risk-taking behavior, and amount of driving among adolescent drivers and can be helpful when designing teen driving interventions, as the different trajectories may represent unique strata of crash risk level. The dynamic model developed can be used to design and evaluate teen driving interventions in order to identify key leverage points to guide policy and direct the optimum combination of prevention strategies.

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