Facteurs de risque de chutes chez les aînés vivant dans la communauté et ayant recours aux services de soutien à domicile : covariables dépendantes du temps et événements récurrents

Les chutes chez les personnes âgées représentent un problème majeur. Il n’est donc pas étonnant que l’identification des facteurs qui en accroissent le risque ait mobilisé autant d’attention. Les aînés plus fragiles ayant besoin de soutien pour vivre dans la communauté sont néanmoins demeurés le parent pauvre de la recherche, bien que, plus récemment, les autorités québécoises en aient fait une cible d’intervention prioritaire. Les études d’observation prospectives sont particulièrement indiquées pour étudier les facteurs de risque de chutes chez les personnes âgées. Leur identification optimale est cependant compliquée par le fait que l’exposition aux facteurs de risque peut varier au cours du suivi et qu’un même individu peut subir plus d’un événement. Il y a 20 ans, des chercheurs ont tenté de sensibiliser leurs homologues à cet égard, mais leurs efforts sont demeurés vains. On continue aujourd’hui à faire peu de cas de ces considérations, se concentrant sur la proportion des personnes ayant fait une chute ou sur le temps écoulé jusqu’à la première chute. On écarte du coup une quantité importante d’information pertinente. Dans cette thèse, nous examinons les méthodes en usage et nous proposons une extension du modèle de risques de Cox. Nous illustrons cette méthode par une étude des facteurs de risque susceptibles d’être associés à des chutes parmi un groupe de 959 personnes âgées ayant eu recours aux services publics de soutien à domicile. Nous comparons les résultats obtenus avec la méthode de Wei, Lin et Weissfeld à ceux obtenus avec d’autres méthodes, dont la régression logistique conventionnelle, la régression logistique groupée, la régression binomiale négative et la régression d’Andersen et Gill. L’investigation est caractérisée par des prises de mesures répétées des facteurs de risque au domicile des participants et par des relances téléphoniques mensuelles visant à documenter la survenue des chutes. Les facteurs d’exposition étudiés, qu’ils soient fixes ou variables dans le temps, comprennent les caractéristiques sociodémographiques, l’indice de masse corporelle, le risque nutritionnel, la consommation d’alcool, les dangers de l’environnement domiciliaire, la démarche et l’équilibre, et la consommation de médicaments. La quasi-totalité (99,6 %) des usagers présentaient au moins un facteur à haut risque. L’exposition à des risques multiples était répandue, avec une moyenne de 2,7 facteurs à haut risque distincts par participant. Les facteurs statistiquement associés au risque de chutes incluent le sexe masculin, les tranches d’âge inférieures, l’histoire de chutes antérieures, un bas score à l’échelle d’équilibre de Berg, un faible indice de masse corporelle, la consommation de médicaments de type benzodiazépine, le nombre de dangers présents au domicile et le fait de vivre dans une résidence privée pour personnes âgées. Nos résultats révèlent cependant que les méthodes courantes d’analyse des facteurs de risque de chutes – et, dans certains cas, de chutes nécessitant un recours médical – créent des biais appréciables. Les biais pour les mesures d’association considérées proviennent de la manière dont l’exposition et le résultat sont mesurés et définis de même que de la manière dont les méthodes statistiques d’analyse en tiennent compte. Une dernière partie, tout aussi innovante que distincte de par la nature des outils statistiques utilisés, complète l’ouvrage. Nous y identifions des profils d’aînés à risque de devenir des chuteurs récurrents, soit ceux chez qui au moins deux chutes sont survenues dans les six mois suivant leur évaluation initiale. Une analyse par arbre de régression et de classification couplée à une analyse de survie a révélé l’existence de cinq profils distinctifs, dont le risque relatif varie de 0,7 à 5,1. Vivre dans une résidence pour aînés, avoir des antécédents de chutes multiples ou des troubles de l’équilibre et consommer de l’alcool sont les principaux facteurs associés à une probabilité accrue de chuter précocement et de devenir un chuteur récurrent. Qu’il s’agisse d’activité de dépistage des facteurs de risque de chutes ou de la population ciblée, cette thèse s’inscrit dans une perspective de gain de connaissances sur un thème hautement d’actualité en santé publique. Nous encourageons les chercheurs intéressés par l’identification des facteurs de risque de chutes chez les personnes âgées à recourir à la méthode statistique de Wei, Lin et Weissfeld car elle tient compte des expositions variables dans le temps et des événements récurrents. Davantage de recherches seront par ailleurs nécessaires pour déterminer le choix du meilleur test de dépistage pour un facteur de risque donné chez cette clientèle.

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.

Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia

Background: It remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for cryptosporidiosis disease. This paper examined the impact of social economic and weather factors on cryptosporidiosis and explored the possibility of developing such a model using social economic and weather data in Queensland, Australia. ----- ----- Methods: Data on weather variables, notified cryptosporidiosis cases and social economic factors in Queensland were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Three-stage spatiotemporal classification and regression tree (CART) models were developed to examine the association between social economic and weather factors and monthly incidence of cryptosporidiosis in Queensland, Australia. The spatiotemporal CART model was used for predicting the outbreak of cryptosporidiosis in Queensland, Australia. ----- ----- Results: The results of the classification tree model (with incidence rates defined as binary presence/absence) showed that there was an 87% chance of an occurrence of cryptosporidiosis in a local government area (LGA) if the socio-economic index for the area (SEIFA) exceeded 1021, while the results of regression tree model (based on non-zero incidence rates) show when SEIFA was between 892 and 945, and temperature exceeded 32°C, the relative risk (RR) of cryptosporidiosis was 3.9 (mean morbidity: 390.6/100,000, standard deviation (SD): 310.5), compared to monthly average incidence of cryptosporidiosis. When SEIFA was less than 892 the RR of cryptosporidiosis was 4.3 (mean morbidity: 426.8/100,000, SD: 319.2). A prediction map for the cryptosporidiosis outbreak was made according to the outputs of spatiotemporal CART models. ----- ----- Conclusions: The results of this study suggest that spatiotemporal CART models based on social economic and weather variables can be used for predicting the outbreak of cryptosporidiosis in Queensland, Australia.

Signaling pathway genes for blood pressure, folate and cholesterol levels among hypertensives : an epistasis analysis

Irregular atrial pressure, defective folate and cholesterol metabolism contribute to the pathogenesis of hypertension. However, little is known about the combined roles of the methylenetetrahydrofolate reductase (MTHFR), apolipoprotein-E (ApoE) and angiotensin-converting enzyme (ACE) genes, which are involved in metabolism and homeostasis. The objective of this study is to investigate the association of the MTHFR 677 C>T and 1298A>C, ACE insertion–deletion (I/D) and ApoE genetic polymorphisms with hypertension and to further explore the epistasis interactions that are involved in these mechanisms. A total of 594 subjects, including 348 normotensive and 246 hypertensive ischemic stroke subjects were recruited. The MTHFR 677 C>T and 1298A>C, ACE I/D and ApoEpolymorphisms were genotyped and the epistasis interaction were analyzed. The MTHFR 677 C>T and ApoE polymorphisms demonstrated significant associations with susceptibility to hypertension in multiple logistic regression models, multifactor dimensionality reduction and a classification and regression tree. In addition, the logistic regression model demonstrated that significant interactions between the ApoE E3E3, E2E4, E2E2 and MTHFR 677 C>T polymorphisms existed. In conclusion, the results of this epistasis study indicated significant association between the ApoE and MTHFR polymorphisms and hypertension.

Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study

Abstract Background Smear negative pulmonary tuberculosis (SNPT) accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources. Methods The study enrolled 551 patients with clinical-radiological suspicion of SNPT, in Rio de Janeiro, Brazil. The original data was divided into two equivalent samples for generation and validation of the prediction models. Symptoms, physical signs and chest X-rays were used for constructing logistic regression and classification and regression tree models. From the logistic regression, we generated a clinical and radiological prediction score. The area under the receiver operator characteristic curve, sensitivity, and specificity were used to evaluate the model's performance in both generation and validation samples. Results It was possible to generate predictive models for SNPT with sensitivity ranging from 64% to 71% and specificity ranging from 58% to 76%. Conclusion The results suggest that those models might be useful as screening tools for estimating the risk of SNPT, optimizing the utilization of more expensive tests, and avoiding costs of unnecessary anti-tuberculosis treatment. Those models might be cost-effective tools in a health care network with hierarchical distribution of scarce resources.

Application and evaluation of classification trees for screening unwanted plants

Risk assessment systems for introduced species are being developed and applied globally, but methods for rigorously evaluating them are still in their infancy. We explore classification and regression tree models as an alternative to the current Australian Weed Risk Assessment system, and demonstrate how the performance of screening tests for unwanted alien species may be quantitatively compared using receiver operating characteristic (ROC) curve analysis. The optimal classification tree model for predicting weediness included just four out of a possible 44 attributes of introduced plants examined, namely: (i) intentional human dispersal of propagules; (ii) evidence of naturalization beyond native range; (iii) evidence of being a weed elsewhere; and (iv) a high level of domestication. Intentional human dispersal of propagules in combination with evidence of naturalization beyond a plants native range led to the strongest prediction of weediness. A high level of domestication in combination with no evidence of naturalization mitigated the likelihood of an introduced plant becoming a weed resulting from intentional human dispersal of propagules. Unlikely intentional human dispersal of propagules combined with no evidence of being a weed elsewhere led to the lowest predicted probability of weediness. The failure to include intrinsic plant attributes in the model suggests that either these attributes are not useful general predictors of weediness, or data and analysis were inadequate to elucidate the underlying relationship(s). This concurs with the historical pessimism that we will ever be able to accurately predict invasive plants. Given the apparent importance of propagule pressure (the number of individuals of an species released), future attempts at evaluating screening model performance for identifying unwanted plants need to account for propagule pressure when collating and/or analysing datasets. The classification tree had a cross-validated sensitivity of 93.6% and specificity of 36.7%. Based on the area under the ROC curve, the performance of the classification tree in correctly classifying plants as weeds or non-weeds was slightly inferior (Area under ROC curve = 0.83 +/- 0.021 (+/- SE)) to that of the current risk assessment system in use (Area under ROC curve = 0.89 +/- 0.018 (+/- SE)), although requires many fewer questions to be answered.

Spatial analysis of notified cryptosporidiosis infections in Brisbane, Australia

Purpose: This study explored the spatial distribution of notified cryptosporidiosis cases and identified major socioeconomic factors associated with the transmission of cryptosporidiosis in Brisbane, Australia. Methods: We obtained the computerized data sets on the notified cryptosporidiosis cases and their key socioeconomic factors by statistical local area (SLA) in Brisbane for the period of 1996 to 2004 from the Queensland Department of Health and Australian Bureau of Statistics, respectively. We used spatial empirical Bayes rates smoothing to estimate the spatial distribution of cryptosporidiosis cases. A spatial classification and regression tree (CART) model was developed to explore the relationship between socioeconomic factors and the incidence rates of cryptosporidiosis. Results: Spatial empirical Bayes analysis reveals that the cryptosporidiosis infections were primarily concentrated in the northwest and southeast of Brisbane. A spatial CART model shows that the relative risk for cryptosporidiosis transmission was 2.4 when the value of the social economic index for areas (SEIFA) was over 1028 and the proportion of residents with low educational attainment in an SLA exceeded 8.8%. Conclusions: There was remarkable variation in spatial distribution of cryptosporidiosis infections in Brisbane. Spatial pattern of cryptosporidiosis seems to be associated with SEIFA and the proportion of residents with low education attainment.

Difference in mosquito species (Diptera: Culicidae) and the transmission of Ross River Virus between coastline and inland areas in Brisbane, Australia

This study examined the distribution of major mosquito species and their roles in the transmission of Ross River virus (RRV) infection for coastline and inland areas in Brisbane, Australia (27°28′ S, 153°2′ E). We obtained data on the monthly counts of RRV cases in Brisbane between November 1998 and December 2001 by statistical local areas from the Queensland Department of Health and the monthly mosquito abundance from the Brisbane City Council. Correlation analysis was used to assess the pairwise relationships between mosquito density and the incidence of RRV disease. This study showed that the mosquito abundance of Aedes vigilax (Skuse), Culex annulirostris (Skuse), and Aedes vittiger (Skuse) were significantly associated with the monthly incidence of RRV in the coastline area, whereas Aedes vigilax, Culex annulirostris, and Aedes notoscriptus (Skuse) were significantly associated with the monthly incidence of RRV in the inland area. The results of the classification and regression tree (CART) analysis show that both occurrence and incidence of RRV were influenced by interactions between species in both coastal and inland regions. We found that there was an 89% chance for an occurrence of RRV if the abundance of Ae. vigifax was between 64 and 90 in the coastline region. There was an 80% chance for an occurrence of RRV if the density of Cx. annulirostris was between 53 and 74 in the inland area. The results of this study may have applications as a decision support tool in planning disease control of RRV and other mosquito-borne diseases.

Identification of area-level influences on regions of high cancer incidence in Queensland, Australia : a classification tree approach

Background: Strategies for cancer reduction and management are targeted at both individual and area levels. Area-level strategies require careful understanding of geographic differences in cancer incidence, in particular the association with factors such as socioeconomic status, ethnicity and accessibility. This study aimed to identify the complex interplay of area-level factors associated with high area-specific incidence of Australian priority cancers using a classification and regression tree (CART) approach. Methods: Area-specific smoothed standardised incidence ratios were estimated for priority-area cancers across 478 statistical local areas in Queensland, Australia (1998-2007, n=186,075). For those cancers with significant spatial variation, CART models were used to identify whether area-level accessibility, socioeconomic status and ethnicity were associated with high area-specific incidence. Results: The accessibility of a person’s residence had the most consistent association with the risk of cancer diagnosis across the specific cancers. Many cancers were likely to have high incidence in more urban areas, although male lung cancer and cervical cancer tended to have high incidence in more remote areas. The impact of socioeconomic status and ethnicity on these associations differed by type of cancer. Conclusions: These results highlight the complex interactions between accessibility, socioeconomic status and ethnicity in determining cancer incidence risk.

An analysis of assessment outcomes from eight years' operation of the Australian border weed risk assessment system

The majority of Australian weeds are exotic plant species that were intentionally introduced for a variety of horticultural and agricultural purposes. A border weed risk assessment system (WRA) was implemented in 1997 in order to reduce the high economic costs and massive environmental damage associated with introducing serious weeds. We review the behaviour of this system with regard to eight years of data collected from the assessment of species proposed for importation or held within genetic resource centres in Australia. From a taxonomic perspective, species from the Chenopodiaceae and Poaceae were most likely to be rejected and those from the Arecaceae and Flacourtiaceae were most likely to be accepted. Dendrogram analysis and classification and regression tree (TREE) models were also used to analyse the data. The latter revealed that a small subset of the 35 variables assessed was highly associated with the outcome of the original assessment. The TREE model examining all of the data contained just five variables: unintentional human dispersal, congeneric weed, weed elsewhere, tolerates or benefits from mutilation, cultivation or fire, and reproduction by vegetative propagation. It gave the same outcome as the full WRA model for 71% of species. Weed elsewhere was not the first splitting variable in this model, indicating that the WRA has a capacity for capturing species that have no history of weediness. A reduced TREE model (in which human-mediated variables had been removed) contained four variables: broad climate suitability, reproduction in less or than equal to 1 year, self-fertilisation, and tolerates and benefits from mutilation, cultivation or fire. It yielded the same outcome as the full WRA model for 65% of species. Data inconsistencies and the relative importance of questions are discussed, with some recommendations made for improving the use of the system.

Threshold detection: matching statistical methodology to ecological questions and conservation planning objectives

Two types of ecological thresholds are now being widely used to develop conservation targets: breakpoint-based thresholds represent tipping points where system properties change dramatically, whereas classification thresholds identify groups of data points with contrasting properties. Both breakpoint-based and classification thresholds are useful tools in evidence-based conservation. However, it is critical that the type of threshold to be estimated corresponds with the question of interest and that appropriate statistical procedures are used to determine its location. On the basis of their statistical properties, we recommend using piecewise regression methods to identify breakpoint-based thresholds and discriminant analysis or classification and regression trees to identify classification thresholds.

Sero-epidemiological evaluation of changes in Plasmodium falciparum and Plasmodium vivax transmission patterns over the rainy season in Cambodia

Background: In Cambodia, malaria transmission is low and most cases occur in forested areas. Seroepidemiological techniques can be used to identify both areas of ongoing transmission and high-risk groups to be targeted by control interventions. This study utilizes repeated cross-sectional data to assess the risk of being malaria sero-positive at two consecutive time points during the rainy season and investigates who is most likely to sero-convert over the transmission season. Methods: In 2005, two cross-sectional surveys, one in the middle and the other at the end of the malaria transmission season, were carried out in two ecologically distinct regions in Cambodia. Parasitological and serological data were collected in four districts. Antibodies to Plasmodium falciparum Glutamate Rich Protein (GLURP) and Plasmodium vivax Merozoite Surface Protein-119 (MSP-119) were detected using Enzyme Linked Immunosorbent Assay (ELISA). The force of infection was estimated using a simple catalytic model fitted using maximum likelihood methods. Risks for sero-converting during the rainy season were analysed using the Classification and Regression Tree (CART) method. Results: A total of 804 individuals participating in both surveys were analysed. The overall parasite prevalence was low (4.6% and 2.0% for P. falciparum and 7.9% and 6.0% for P. vivax in August and November respectively). P. falciparum force of infection was higher in the eastern region and increased between August and November, whilst P. vivax force of infection was higher in the western region and remained similar in both surveys. In the western region, malaria transmission changed very little across the season (for both species). CART analysis for P. falciparum in the east highlighted age, ethnicity, village of residence and forest work as important predictors for malaria exposure during the rainy season. Adults were more likely to increase their antibody responses to P. falciparum during the transmission season than children, whilst members of the Charay ethnic group demonstrated the largest increases. Discussion: In areas of low transmission intensity, such as in Cambodia, the analysis of longitudinal serological data enables a sensitive evaluation of transmission dynamics. Consecutive serological surveys allow an insight into spatio-temporal patterns of malaria transmission. The use of CART enabled multiple interactions to be accounted for simultaneously and permitted risk factors for exposure to be clearly identified.

MicroRNA Expression Array Identifies Novel Diagnostic Markers for Conventional and Oncocytic Follicular Thyroid Carcinomas

Objective:The most difficult thyroid tumors to be diagnosed by cytology and histology are conventional follicular carcinomas (cFTCs) and oncocytic follicular carcinomas (oFTCs). Several microRNAs (miRNAs) have been previously found to be consistently deregulated in papillary thyroid carcinomas; however, very limited information is available for cFTC and oFTC. The aim of this study was to explore miRNA deregulation and find candidate miRNA markers for follicular carcinomas that can be used diagnostically.Design:Thirty-eight follicular thyroid carcinomas (21 cFTCs, 17 oFTCs) and 10 normal thyroid tissue samples were studied for expression of 381 miRNAs using human microarray assays. Expression of deregulated miRNAs was confirmed by individual RT-PCR assays in all samples. In addition, 11 follicular adenomas, two hyperplastic nodules (HNs), and 19 fine-needle aspiration samples were studied for expression of novel miRNA markers detected in this study.Results:The unsupervised hierarchical clustering analysis demonstrated individual clusters for cFTC and oFTC, indicating the difference in miRNA expression between these tumor types. Both cFTCs and oFTCs showed an up-regulation of miR-182/-183/-221/-222/-125a-3p and a down-regulation of miR-542-5p/-574-3p/-455/-199a. Novel miRNA (miR-885-5p) was found to be strongly up-regulated (>40-fold) in oFTCs but not in cFTCs, follicular adenomas, and HNs. The classification and regression tree algorithm applied to fine-needle aspiration samples demonstrated that three dysregulated miRNAs (miR-885-5p/-221/-574-3p) allowed distinguishing follicular thyroid carcinomas from benign HNs with high accuracy.Conclusions:In this study we demonstrate that different histopathological types of follicular thyroid carcinomas have distinct miRNA expression profiles. MiR-885-5p is highly up-regulated in oncocytic follicular carcinomas and may serve as a diagnostic marker for these tumors. A small set of deregulated miRNAs allows for an accurate discrimination between follicular carcinomas and hyperplastic nodules and can be used diagnostically in fine-needle aspiration biopsies.

Pinus nigra (European black pine) as the dominant species of the last glacial pinewoods in south-western to central Iberia: a morphological study of modern and fossil pollen

Aim Our aim was to discriminate different species of Pinus via pollen analysis in order to assess the responses of particular pine species to orbital and millennial-scale climate changes, particularly during the last glacial period. Location Modern pollen grains were collected from current pine populations along transects from the Pyrenees to southern Iberia and the Balearic Islands. Fossil pine pollen was recovered from the south-western Iberian margin core MD95-2042. Methods We measured a set of morphological traits of modern pollen from the Iberian pine species Pinus nigra, P. sylvestris, P. halepensis, P. pinea and P. pinaster and of fossil pine pollen from selected samples of the last glacial period and the early to mid-Holocene. Classification and regression tree (CART) analysis was used to establish a model from the modern dataset that discriminates pollen from the different pine species and allows identification of fossil pine pollen at the species level. Results The CART model was effective in separating pollen of P. nigra and P. sylvestris from that of the Mediterranean pine group (P. halepensis, P. pinea and P. pinaster). The pollen of Pinus nigra diverged from that of P. sylvestris by having a more flattened corpus. Predictions using this model suggested that fossil pine pollen is mainly from P. nigra in all the samples analysed. Pinus sylvestris was more abundant in samples from Greenland stadials than Heinrich stadials, whereas Mediterranean pines increased in samples from Greenland interstadials and during the early to mid-Holocene. Main conclusions Morphological parameters can be successfully used to increase the taxonomic resolution of fossil pine pollen at the species level for the highland pines (P. nigra and P. sylvestris) and at the group of species level for the Mediterranean pines. Our study indicates that P. nigra was the dominant component of the last glacial south-western/central Iberian pinewoods, although the species composition of these woodlands varied in response to abrupt climate changes.