Early detection and intervention for infants at risk of autism spectrum disorders

Autism is a developmental disorder that is characterized by abnonnal social interactions and communications as well as repetitive and restricted activities and interests. There is evidence of a genetic component, as 5% of younger siblings are diagnosed if their older sibling has been diagnosed. Autism is generally not diagnosed until age 3 at the earliest, yet it has been shown that early intervention for children with autism can greatly increase their functioning. Because of this, it is important that symptoms of autism are identified as early as possible so that diagnosis can occur as soon as possible to allow these children the earliest intervention. This thesis was divided into two parts. The first looked at the psychometrics of two proposed measures, the Parent Observation Checklist (POC), administered monthly, and the Infant Behavior Summary Evaluation (mSE), administered bimonthly, to see if they can be used with the infant population to identify autistic symptoms in infants who are at high risk for autism or related problems because they have an older sibling with autism. Study 1 reported acceptable psychometric properties of both the POC and IBSE in terms of test-retest reliability, internal consistency, construct validity and predictive validity. These results provide preliminary evidence that parent report measures can help to detect early symptoms of ASD in infants. The POC was shown to differentiate infants who were diagnosed from a matched group that was not diagnosed by 3 years of age. The second part of this thesis involved a telephone interview of parents who reported developmental and/or behavior problems in their high-risk infants that may be early signs of Autism Spectrum Disorder (ASD). During the interview, a service questionnaire was administered to see what interventions (including strategies recommended by the researchers) their at risk infants and affected older siblings were receiving, how satisfied the parents were with them and how effective they felt the interventions were. 3 Study 2 also yielded promising results. Parents utilized a variety of services for at risk infants and children with ASD. The interventions included empirically validated early intervention (e.g., ABA) to non-empirically validated treatments (e.g., diet therapy). The large number of nonempirically validated treatments parents used was surprising, yet parents reported being involved and satisfied, and thought that the services were effective. Parents' perceptions of their stress levels went down slightly and feelings of competence rose when they accessed services for their infants. Overall, the results of this thesis provide new evidence that parent-report methods hold promise as early detection instruments for ASD in at-risk infants. More research is needed to further validate these instruments as well as to understand the variables related to the parents' choice of early intervention for their at risk and affected children.

"Where is the value in education for me?": experiences and perspectives of Ontario secondary students at risk of non-completion of their Ontario Secondary School Diploma /

This study examined students considered at risk of non-completion of their Ontario Secondary School Diploma and aimed to offer insight into the questions, "What factors currently lead to school disconnect" and "How can these factors be addressed?" Eight students currently enrolled in an alternative learning environment participated in the study. Each was asked to take part in two, digitally recorded interviews that were subsequently transcribed by the researcher. The data were then coded and analysed according to specific themes: obstacles, empowerment, goals, views about success, opinions of school, and power of the teacher. From these themes, three broad focus areas emerged that were used to keep the data analysis focused: worldview, school effects, and self-image. Variances between the data collected and ideas presented in the current literature were highlighted as a reminder that when dealing with a human population, we cannot rely on textbook definitions and theory alone.

Predicting the Risk of Lung Cancer in Never-smokers

Despite being considered a disease of smokers, approximately 10-15% of lung cancer cases occur in never-smokers. Lung cancer risk prediction models have demonstrated excellent ability to discriminate cases from non-cases, and have been shown to be more efficient at selecting individuals for future screening than current criteria. Existing models have primarily been developed in populations of smokers, thus there was a need to develop an accurate model in never-smokers. This study focused on developing and validating a model using never-smokers from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Cox regression analysis, with six-year follow-up, was used for model building. Predictors included: age, body mass index, education level, personal history of cancer, family history of lung cancer, previous chest X-ray, and secondhand smoke exposure. This model achieved fair discrimination (optimism corrected c-statistic = 0.6645) and good calibration. This represents an improvement on existing neversmoker models, but is not suitable for individual-level risk prediction.