Impact de l'épilepsie partielle sur l'acquisition des processes de lecture chez l'enfant d'âge scolaire

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

Multimorbidity and quality of life in primary care: a systematic review

BACKGROUND: Many patients with several concurrent medical conditions (multimorbidity) are seen in the primary care setting. A thorough understanding of outcomes associated with multimorbidity would benefit primary care workers of all disciplines. The purpose of this systematic review was to clarify the relationship between the presence of multimorbidity and the quality of life (QOL) or health-related quality of life (HRQOL) of patients seen, or likely to be seen, in the primary care setting. METHODS: Medline and Embase electronic databases were screened using the following search terms for the reference period 1990 to 2003: multimorbidity, comorbidity, chronic disease, and their spelling variations, along with quality of life and health-related quality of life. Only descriptive studies relevant to primary care were selected. RESULTS: Of 753 articles screened, 108 were critically assessed for compliance with study inclusion and exclusion criteria. Thirty of these studies were ultimately selected for this review, including 7 in which the relationship between multimorbidity or comorbidity and QOL or HRQOL was the main outcome measure. Major limitations of these studies include the lack of a uniform definition for multimorbidity or comorbidity and the absence of assessment of disease severity. The use of self-reported diagnoses may also be a weakness. The frequent exclusion of psychiatric diagnoses and presence of potential confounding variables are other limitations. Nonetheless, we did find an inverse relationship between the number of medical conditions and QOL related to physical domains. For social and psychological dimensions of QOL, some studies reveal a similar inverse relationship in patients with 4 or more diagnoses. CONCLUSIONS: Our findings confirm the existence of an inverse relationship between multimorbidity or comorbidy and QOL. However, additional studies are needed to clarify this relationship, including the various dimensions of QOL affected. Those studies must employ a clear definition of multimorbidity or comorbidity and valid ways to measure these concepts in a primary care setting. Pursuit of this research will help to better understand the impact of chronic diseases on patients

Rural-urban disparities in the management and health issues of chronic diseases in Quebec (Canada) in the early 2000s

Abstract: Introduction: The ‘Commission on the Future of Health Care in Canada’ recognized that people living in rural and remote areas of Canada are at a disadvantage in health status, access to care and health professionals, and it considers the fight against these problems as a national priority. Although some attention has been paid to the prevalence of chronic diseases, very few studies have studied specifically the management and health issues in populations with chronic diseases in relation to rurality. The objective of this study was to describe systematic gaps across rural and urban populations in incidence, mortality, morbidity, material and human resources utilization, and drug management for three important chronic diseases: atherosclerosis, osteoporosis and diabetes. Methods: Three retrospective population-based cohort studies were used. Three study populations were selected: an atherosclerotic population including patients newly hospitalized for a myocardial infarction (MI), an osteoporotic population including the at risk population who have suffered from a fragility fracture (FF) and, finally, a diabetic population that includes only incident cases of diabetes patients. For each of the three chronic diseases, variables were selected and classified in six categories: incidence, mortality, morbidity, material resources utilization, physician consultation and drug treatment. The Statistical Area Classification (SAC) was used as the rurality definition and contains six categories including two urban areas − Census Metropolitan Areas (CMA), or metropolitan areas, and Census Agglomeration (CA), or small towns − and four rural areas: Strong, Moderate, Weak and No Metropolitan influenced zones (MIZ), depending on the proportion of the workforce that commutes to urban areas. Each disease-related variable was described using age- and sex-adjusted rates. For comparing rates between rurality classes, the adjusted relative risks were calculated using the CMA as the reference group. The χ2 was used to test for the equality of risks. Results: A common pattern was identified from this study: for all three studied diseases, the material resources utilization rates and the specialist (other than internist) consultation rates were almost always statistically lower in small towns and rural areas when compared with metropolitan areas. Mortality rates and drug utilization rates were very similar among regions, except for hormone replacement therapy in women where utilization rates were higher in small towns and rural areas compared with metropolitan areas. Among observations that were not common to all three chronic diseases, the first is that MI incidence was greater in small towns and in Weak MIZ compared with metropolitan areas, fragility fractures seem to be marginally more frequent in small towns but less frequent in rural areas compared with metropolitan areas, while an increased incidence rate of diabetes is observed in remote region and a smaller risk in moderate MIZ compared with metropolitan areas. For both atherosclerosis and diabetes, morbidity rates were always statistically higher in small towns and in rural areas. This was not the case for patients with osteoporotic fractures where similar morbidity rates across regions were observed, except in strong MI which show the lowest morbidity rate. Conclusions: There was substantially lower utilization of specialized services in non-metropolitan areas for all three diseases (myocardial infarction, osteoporosis, and diabetes). However, this did not translate into consistent differences in mortality and morbidity outcomes. This suggests that the impact of differential care utilization is specific to each disease, with indications that some important services may be under-utilized in rural areas, while others may be over-utilized in urban areas without improvement in outcomes

Prevalence of Multimorbidity Among Adults Seen in Family Practice

PURPOSE There are few valid data that describe the extent of multimorbidity in primary care patients. The purpose of this study was to estimate its prevalence in family practice patients by counting the number of chronic medical conditions and using a measure that considers the severity of these conditions, the Cumulative Illness Rating Scale (CIRS). METHODS The study was carried out in the Saguenay region (Québec, Canada) in 2003. The participation of adult patients from 21 family physicians was solicited during consecutive consultation periods. A research nurse reviewed medical records and extracted the data regarding chronic illnesses. For each chronic condition, a severity rating was determined in accordance with the CIRS scoring guidelines. RESULTS The sample consisted of 320 men and 660 women. Overall, 9 of 10 patients had more than 1 chronic condition. The prevalence of having 2 or more medical conditions in the 18- to 44-year, 45- to 64-year, and 65-year and older age-groups was, respectively, 68%, 95%, and 99% among women and 72%, 89%, and 97% among men. The mean number of conditions and mean CIRS score also increased significantly with age. CONCLUSIONS Whether measured by simply counting the number of conditions or using the CIRS, the prevalence of multimorbidity is quite high and increases significantly with age in both men and women. Patients with multimorbidity seen in family practice represent the rule rather than the exception

Machine learning to improve frequent emergency department use prediction: a retrospective cohort study

Abstract Frequent emergency department use is associated with many adverse events, such as increased risk for hospitalization and mortality. Frequent users have complex needs and associated factors are commonly evaluated using logistic regression. However, other machine learning models, especially those exploiting the potential of large databases, have been less explored. This study aims at comparing the performance of logistic regression to four machine learning models for predicting frequent emergency department use in an adult population with chronic diseases, in the province of Quebec (Canada). This is a retrospective population-based study using medical and administrative databases from the Régie de l’assurance maladie du Québec. Two definitions were used for frequent emergency department use (outcome to predict): having at least three and five visits during a year period. Independent variables included sociodemographic characteristics, healthcare service use, and chronic diseases. We compared the performance of logistic regression with gradient boosting machine, naïve Bayes, neural networks, and random forests (binary and continuous outcome) using Area under the ROC curve, sensibility, specificity, positive predictive value, and negative predictive value. Out of 451,775 ED users, 43,151 (9.5%) and 13,676 (3.0%) were frequent users with at least three and five visits per year, respectively. Random forests with a binary outcome had the lowest performances (ROC curve: 53.8 [95% confidence interval 53.5–54.0] and 51.4 [95% confidence interval 51.1–51.8] for frequent users 3 and 5, respectively) while the other models had superior and overall similar performance. The most important variable in prediction was the number of emergency department visits in the previous year. No model outperformed the others. Innovations in algorithms may slightly refine current predictions, but access to other variables may be more helpful in the case of frequent emergency department use prediction

CARDIOPATHIES CONGÉNITALES : PHYSIOPATHOLOGIE, FACTEURS DE RISQUE ET PROFILS NEUROPSYCHOLOGIQUES

Les cardiopathies congénitales (CC) sont les malformations les plus fréquentes chez le nouveau-né. La littérature des dernières décennies a mis en évidence des retards développementaux fréquents dans cette population, notamment sur le plan de la motricité, du fonctionnement intellectuel global, du langage, de la mémoire, de l’attention et des fonctions exécutives. Le présent article propose une revue de littérature concernant la physiopathologie de ces retards et les facteurs susceptibles d’en augmenter les risques. Les profils neuropsychologiques associés à la présence d’une CC, ainsi que leur impact sur les acquisitions scolaires, l’adaptation comportementale et la qualité de vie seront présentés.Congenital heart diseases (CHD) are the most common congenital anomalies in newborns. The literature of the last decades demonstrates that developmental delays are frequent in this population, especially in the areas of gross and fine motor skills, global intellectual functioning, language, memory, attention and executive abilities. This article offers a literature review covering the pathophysiology of these delays and their risk factors. Neuropsychological profiles associated with CHD and their impact on school achievement, behavioral adaptation and quality of life will be presented

Rural-urban disparities in the management and health issues of chronic diseases in Quebec (Canada) in the early 2000s

Abstract: Introduction: The ‘Commission on the Future of Health Care in Canada’ recognized that people living in rural and remote areas of Canada are at a disadvantage in health status, access to care and health professionals, and it considers the fight against these problems as a national priority. Although some attention has been paid to the prevalence of chronic diseases, very few studies have studied specifically the management and health issues in populations with chronic diseases in relation to rurality. The objective of this study was to describe systematic gaps across rural and urban populations in incidence, mortality, morbidity, material and human resources utilization, and drug management for three important chronic diseases: atherosclerosis, osteoporosis and diabetes. Methods: Three retrospective population-based cohort studies were used. Three study populations were selected: an atherosclerotic population including patients newly hospitalized for a myocardial infarction (MI), an osteoporotic population including the at risk population who have suffered from a fragility fracture (FF) and, finally, a diabetic population that includes only incident cases of diabetes patients. For each of the three chronic diseases, variables were selected and classified in six categories: incidence, mortality, morbidity, material resources utilization, physician consultation and drug treatment. The Statistical Area Classification (SAC) was used as the rurality definition and contains six categories including two urban areas − Census Metropolitan Areas (CMA), or metropolitan areas, and Census Agglomeration (CA), or small towns − and four rural areas: Strong, Moderate, Weak and No Metropolitan influenced zones (MIZ), depending on the proportion of the workforce that commutes to urban areas. Each disease-related variable was described using age- and sex-adjusted rates. For comparing rates between rurality classes, the adjusted relative risks were calculated using the CMA as the reference group. The χ2 was used to test for the equality of risks. Results: A common pattern was identified from this study: for all three studied diseases, the material resources utilization rates and the specialist (other than internist) consultation rates were almost always statistically lower in small towns and rural areas when compared with metropolitan areas. Mortality rates and drug utilization rates were very similar among regions, except for hormone replacement therapy in women where utilization rates were higher in small towns and rural areas compared with metropolitan areas. Among observations that were not common to all three chronic diseases, the first is that MI incidence was greater in small towns and in Weak MIZ compared with metropolitan areas, fragility fractures seem to be marginally more frequent in small towns but less frequent in rural areas compared with metropolitan areas, while an increased incidence rate of diabetes is observed in remote region and a smaller risk in moderate MIZ compared with metropolitan areas. For both atherosclerosis and diabetes, morbidity rates were always statistically higher in small towns and in rural areas. This was not the case for patients with osteoporotic fractures where similar morbidity rates across regions were observed, except in strong MI which show the lowest morbidity rate. Conclusions: There was substantially lower utilization of specialized services in non-metropolitan areas for all three diseases (myocardial infarction, osteoporosis, and diabetes). However, this did not translate into consistent differences in mortality and morbidity outcomes. This suggests that the impact of differential care utilization is specific to each disease, with indications that some important services may be under-utilized in rural areas, while others may be over-utilized in urban areas without improvement in outcomes