The health status of people who live in the rural, coal-producing counties of southwest Virginia remains problematic despite an apparent high primary care provider-to-population threshold. This descriptive exploratory study surveyed 922 households representing 2,188 people, with regard to the availability, need, and access to health care services. Findings indicated a population who had a greater morbidity for chronic illnesses such as heart disease and hypertension than the rest of the state, a large number of people without health and prescription coverage, and an overall perception of fair-to-poor health status. Findings also indicated a substantial proportion of the population who were in need of dental and visual care and general preventive services and those who were dealing with depression at home without outside intervention. One disturbing finding was the large number of people who shared prescriptive medications with family and friends. The Community As Partner Model might be used by community health nurses in this region to help structure interventions. Overall findings suggest a need for interventions aimed at screening for depression, managing prescriptive medications, and identification of low-cost and free preventive, dental, and visual care services.

No description available.

Increases in cases of diarrheal disease, respiratory infections and pregnancy complications have been reported in the literature following floods caused by heavy rainfall. Analyzing the association between health records of outcomes related to flooding demarcated by satellite observations will be helpful to evaluate the use of satellite observed products in the mitigation of health risks for future flood events. The Centers for Disease Control and Prevention Social Vulnerability Index (CDC SVI), a relative index assigned to census tracts, measures sociodemographic factors that may affect the ability of communities to prepare for, respond to, and recover from extreme weather events. This index, which quantifies social vulnerability is expected to have a positive relationship with health outcomes associated with flooding. This study uses an inundation map created using observations from active remote sensing satellites to classify census tracts that were flooded after the historic rainfall caused by Hurricane Harvey in Texas in 2017. The duration or period of the inundation was determined using United States Geological Survey (USGS) stream gauge data. A controlled before and after study design was used, and the relative risk (RR) of 11 cause-specific emergency department (ED) visits among the flooded census tracts compared to non-flooded tracts during and after the flood period was modelled using modified Poisson regression while adjusting for a baseline period and the age, ethnicity, race and sex of the patient. Further modification of this relationship by social vulnerability, as measured by CDC SVI quartiles, was examined. The results of this study show that flooding was associated with an increase in ED visits related to carbon monoxide poisoning, insect bites, dehydration, hypothermia, intestinal infectious diseases, and pregnancy complications during the flood period. The average rate of ED visits related to pregnancy complications and insect bites were greater among the flooded tracts compared to the non-flooded tracts in the month following the inundation. Modification of this association by CDC SVI was observed in some cases, such that ED visits were higher or lower in census tracts within higher vulnerable quartiles compared to the least vulnerable quartile. Evaluating the usefulness of earth observations and the CDC SVI in estimating the health risk associated with floods due to Hurricane Harvey has provided understanding the use of these products for future flooding events in identifying specific communities with increased health risks during and following flooding events. / Master of Science / Studies have shown that flooding following heavy rains might increase cases of flood-related health outcomes such as diarrhea, respiratory infections and pregnancy complications among the people in flooded communities. With advancements in satellite technology and image processing, areas that are flooded can be mapped using images captured by satellites within a few days after the flooding. Such maps can then be used to identify communities that might experience greater health risks due to the flooding. This study evaluated the use of such an inundation map created after Hurricane Harvey's floods of 2017 to determine the health risk among the flooded communities with respect to the non-flooded communities. We found that the census tracts that were identified as flooded using the inundation map experienced a greater number of ED visits related to carbon monoxide poisoning, insect bite, dehydration, hypothermia, intestinal infectious diseases, and pregnancy complications compared to the non-flooded census tracts during the period of 19 days after the landfall of Hurricane Harvey. Also, the month following this period, ED visits related to pregnancy complications and insect bites were still greater among the flooded tracts compared to the non-flooded tracts. As the socio-economic status, housing and transportation quality vary among different communities, the association between flooding and the health of the people in different communities may also differ. The Centers for Disease Control and Prevention Social Vulnerability Index (CDC SVI) is a relative vulnerability value assigned to each census tract based on the social characteristics of the population that influence the ability to prevent human suffering and financial loss in a disaster. We analyzed how census tracts grouped as very high, high and moderate vulnerability based on the SVI differ from least vulnerable census tracts in terms of this association. The results show that the association differs between different vulnerability groups for total ED visits and ED visits related to insect bites, intestinal infectious diseases and dehydration. We conclude that the satellite-based products along with the CDC SVI might be useful in identifying communities that might need support to overcome health risks following flooding.

Objective: To determine whether use of cannabis in adolescence increases the risk for psychotic outcomes by affecting the incidence and persistence of subclinical expression of psychosis in the general population (that is, expression of psychosis below the level required for a clinical diagnosis). Design: Analysis of data from a prospective population based cohort study in Germany (early developmental stages of psychopathology study). Setting: Population based cohort study in Germany. Participants: 1923 individuals from the general population, aged 14-24 at baseline. Main outcome measure: Incidence and persistence of subthreshold psychotic symptoms after use of cannabis in adolescence. Cannabis use and psychotic symptoms were assessed at three time points (baseline, T2 (3.5 years), T3 (8.4 years)) over a 10 year follow-up period with the Munich version of the composite international diagnostic interview (M-CIDI). Results: In individuals who had no reported lifetime psychotic symptoms and no reported lifetime cannabis use at baseline, incident cannabis use over the period from baseline to T2 increased the risk of later incident psychotic symptoms over the period from T2 to T3 (adjusted odds ratio 1.9, 95% confidence interval 1.1 to 3.1; P=0.021). Furthermore, continued use of cannabis increased the risk of persistent psychotic symptoms over the period from T2 to T3 (2.2, 1.2 to 4.2; P=0.016). The incidence rate of psychotic symptoms over the period from baseline to T2 was 31% (152) in exposed individuals versus 20% (284) in non-exposed individuals; over the period from T2 to T3 these rates were 14% (108) and 8% (49), respectively. Conclusion: Cannabis use is a risk factor for the development of incident psychotic symptoms. Continued cannabis use might increase the risk for psychotic disorder by impacting on the persistence of symptoms.

Made available in DSpace on 2016-03-22T17:27:39Z (GMT). No. of bitstreams: 1 LUCIANO SOUZA.pdf: 168635 bytes, checksum: 94c46027ad00ab09128c27fe9b95c3a6 (MD5) Previous issue date: 2006-10-31 / The aim of this study was to identify factors associated to high depressive sintomatology in adolescents with ages between 11 and 15 years in Pelotas - south of Brazil. In this cross-sectional population-based investigation, 79 of the 448 sections of Pelotas were randomly selected. All of the residences of the selected sections were visited seeking to the youths who answered to a self-report and secret questionnaire that contained a scale for depressive symptoms - Children's Depression Inventory (CDI). In total, 1265 adolescents were located for the research team and 1145 answered to the questionnaire. The prevalence found for high depressive sintomatology was 2,1%. After logistic regression adjusted to a hierarchical model, a worse socioeconomic condition of environment, history of academic disapproval, absence of religious practice, to have taken drunkenness in the last month as well as the indicative of conduct disorder, increased the adolescents chances to present high depressive sintomatology. Therefore, the needs of a review on public politics that approach the themes associated to the depressive sintomatology are evidence / O objetivo deste estudo foi identificar os fatores associados à elevada sintomatologia depressiva em adolescentes com idades entre 11 e 15 anos em Pelotas sul do Brasil. Nesta investigação transversal de base populacional, 79 dos 448 setores censitários de Pelotas foram selecionados aleatoriamente. Todas as residências dos setores selecionados foram visitadas visando à captação de jovens que respondessem a um questionário auto-aplicado e sigiloso que continha uma escala para aferição da sintomatologia depressiva Children's Depression Inventory (CDI). No total, 1265 adolescentes foram localizados pela equipe de pesquisa sendo que 1145 responderam ao questionário. A prevalência de elevada sintomatologia depressiva encontrada foi de 2,1%. Após a regressão logística ajustada a um modelo hierárquico, uma pior condição sócio-econômica da ambiente, história de reprovação acadêmica, ausência de prática religiosa, ter tomado porre no último mês assim como o indicativo de transtorno de conduta, aumentaram as chances dos adolescentes em estudo apresentarem elevada sintomatologia depressiva. Portanto, se evidenciam as necessidades de reformulações de políticas públicas que abordem os temas associados à sintomatologia depressiva.

Esta Tese apresenta um conjunto de técnicas propostas com o objetivo de aprimorar processos de Agrupamento de Dados (AD). O principal objetivo é fornecer à comunidade científica um ferramental para uma análise completa de estruturas implícitas em conjuntos de dados, desde a descoberta dessas estruturas, permitindo o emprego de conhecimento prévio sobre os dados, até a análise de seu significado no contexto em que eles estão inseridos. São dois os pontos principais desse ferramental. O primeiro se trata do algoritmo para AD fuzzy semi-supervisionado SSL+P e sua evolução SSL+P*, capazes de levar em consideração o conhecimento prévio disponível sobre os dados em duas formas: rótulos e níveis de proximidade de pares de exemplos, aqui denominados Dicas de Conhecimento Prévio (DCPs). Esses algoritmos também permitem que a métrica de distância seja ajustada aos dados e às DCPs. O algoritmo SSL+P* também busca estimar o número ideal de clusters para uma determinada base de dados, levando em conta as DCPs disponíveis. Os algoritmos SSL+P e SSL+P* envolvem a minimização de uma função objetivo por meio de um algoritmo de Otimização Baseado em População (OBP). Esta Tese também fornece ferramentas que podem ser utilizadas diretamente neste ponto: as duas versões modificadas do algoritmo Particle Swarm Optimization (PSO), DPSO-1 e DPSO-2 e 4 formas de inicialização de uma população inicial de soluções. O segundo ponto principal do ferramental proposto nesta Tese diz respeito à análise de clusters resultantes de um processo de AD aplicado a uma base de dados de um domínio específico. É proposta uma abordagem baseada em Mineração de Textos (MT) para a busca em informações textuais, disponibilizadas digitalmente e relacionadas com as entidades representadas nos dados. Em seguida, é fornecido ao pesquisador um conjunto de palavras associadas a cada cluster, que podem sugerir informações que ajudem a identificar as relações compartilhadas por exemplos atribuídos ao mesmo cluster / This Thesis presents a whole set of techniques designed to improve the data clustering proccess. The main goal is to provide to the scientific community a tool set for a complete analyses of the implicit structures in datasets, from the identification of these structures, allowing the use of previous knowledge about the data, to the analysis of its meaning in their context. There are two main points involved in that tool set. The first one is the semi-supervised clustering algorithm SSL+P and its upgraded version SSL+P*, which are able of take into account the available knowlegdge about de data in two forms: class labels and pairwise proximity levels, both refered here as hints. These algorithms are also capable of adapting the distance metric to the data and the available hints. The SSL+P* algorithm searches the ideal number of clusters for a dataset, considering the available hints. Both SSL+P and SSL+P* techniques involve the minimization of an objective function by a Population-based Optimization algorithm (PBO). This Thesis also provides tools that can be directly employed in this area: the two modified versions of the Particle Swarm Optimization algorithm (PSO), DPSO-1 and DPSO-2, and 4 diferent methods for initializing a population of solutions. The second main point of the tool set proposed by this Thesis regards the analysis of clusters resulting from a clustering process applied to a domain specific dataset. A Text Mining based approach is proposed to search for textual information related to the entities represented by the data, available in digital repositories. Next, a set of words associated with each cluster is presented to the researcher, which can suggest information that can support the identification of relations shared by objects assigned to the same cluster

Aggregate models incorporate the variation between individual parameters of individualbased models to construct a population-based model. This thesis focuses on the comparison of two different methods for creating these population-based models. The first method, the individual parameter distribution technique (IPD) focuses on the similarities and variation of parameters in an individual-based model as calculated using individual data sets [4]. The second method we consider is the nonlinear mixed effect method (NLME), which is primarily used in modeling repeated measurement data. In the NLME approach, both the fixed effects and random effects of the parameter values are estimated in the model by assuming a normal distribution for the parameter values across individuals[2]. Both methods were implemented on a one-compartment pharmacokinetic concentration model. Using the variation in parameters estimated using the two different approaches, a population model was generated and then compared to the dynamics seen in the individual data sets. We compare three features of the concentration data to the simulated population models. The values for all three features were captured by both methods; however, the biggest difference observed is 2 that there is a longer tail in the distribution for the population model developed using NLME than observed in the dynamics in the original data.

Doctor of Philosophy / Over the past decade, population-based cancer registry data have been used increasingly worldwide to evaluate and improve the quality of cancer care. The utility of the conclusions from such studies relies heavily on the data quality and the methods used to analyse the data. Interpretation of comparative survival from such data, examining either temporal trends or geographical differences, is generally not easy. The observed differences could be due to methodological and statistical approaches or to real effects. For example, geographical differences in cancer survival could be due to a number of real factors, including access to primary health care, the availability of diagnostic and treatment facilities and the treatment actually given, or to artefact, such as lead-time bias, stage migration, sampling error or measurement error. Likewise, a temporal increase in survival could be the result of earlier diagnosis and improved treatment of cancer; it could also be due to artefact after the introduction of screening programs (adding lead time), changes in the definition of cancer, stage migration or several of these factors, producing both real and artefactual trends. In this thesis, I report methods that I modified and applied, some technical issues in the use of such data, and an analysis of data from the State of New South Wales (NSW), Australia, illustrating their use in evaluating and potentially improving the quality of cancer care, showing how data quality might affect the conclusions of such analyses. This thesis describes studies of comparative survival based on population-based cancer registry data, with three published papers and one accepted manuscript (subject to minor revision). In the first paper, I describe a modified method for estimating spatial variation in cancer survival using empirical Bayes methods (which was published in Cancer Causes and Control 2004). I demonstrate in this paper that the empirical Bayes method is preferable to standard approaches and show how it can be used to identify cancer types where a focus on reducing area differentials in survival might lead to important gains in survival. In the second paper (published in the European Journal of Cancer 2005), I apply this method to a more complete analysis of spatial variation in survival from colorectal cancer in NSW and show that estimates of spatial variation in colorectal cancer can help to identify subgroups of patients for whom better application of treatment guidelines could improve outcome. I also show how estimates of the numbers of lives that could be extended might assist in setting priorities for treatment improvement. In the third paper, I examine time trends in survival from 28 cancers in NSW between 1980 and 1996 (published in the International Journal of Cancer 2006) and conclude that for many cancers, falls in excess deaths in NSW from 1980 to 1996 are unlikely to be attributable to earlier diagnosis or stage migration; thus, advances in cancer treatment have probably contributed to them. In the accepted manuscript, I described an extension of the work reported in the second paper, investigating the accuracy of staging information recorded in the registry database and assessing the impact of error in its measurement on estimates of spatial variation in survival from colorectal cancer. The results indicate that misclassified registry stage can have an important impact on estimates of spatial variation in stage-specific survival from colorectal cancer. Thus, if cancer registry data are to be used effectively in evaluating and improving cancer care, the quality of stage data might have to be improved. Taken together, the four papers show that creative, informed use of population-based cancer registry data, with appropriate statistical methods and acknowledgement of the limitations of the data, can be a valuable tool for evaluating and possibly improving cancer care. Use of these findings to stimulate evaluation of the quality of cancer care should enhance the value of the investment in cancer registries. They should also stimulate improvement in the quality of cancer registry data, particularly that on stage at diagnosis. The methods developed in this thesis may also be used to improve estimation of geographical variation in other count-based health measures when the available data are sparse.

Background: Depressive and anxiety disorders are common health problems, affecting women at least twice as often as men. Although some studies have been made on pregnant women or, especially, in the postpartum period, most of these studies have been performed on small samples, mainly specific risk groups such as teenage mothers, women of low socioeconomic status and certain ethnic groups. Also, there is a lack of studies on antenatal and postpartum depression and/or anxiety using diagnostic criteria adhering to the Diagnostic and Statistical Manual of Mental disorders, fourth edition (DSM-IV). Aims and methods: The aims were to estimate the point prevalence of mood, anxiety and eating disorders, based on DSM-IV criteria, in an unselected population during the second trimester of pregnancy, and to assess the obstetric and neonatal outcome, as well as the health care consumption during pregnancy, delivery and the early postpartum period among women with a psychiatric disorder, compared to healthy subjects. Finally, we aimed to investigate depression and anxiety, and associated maternal characteristics and events through pregnancy and the postpartum period in the same group of women. The Primary Care Evaluation of Mental Disorders (PRIME-MD) was used for assessment of psychiatric disorders during the second trimester of pregnancy and three to six months after delivery. From October 2nd, 2000, to October 1st, 2001 all women attending the second trimester routine ultrasound-screening at two different hospitals in northern Sweden (at Umeå University Hospital and at Sunderby Central Hospital) were approached for participation in the study. After delivery, data were extracted from the medical records of the mothers and their offspring to evaluate obstetric and neonatal outcome. Three to six months after delivery, the women who had an antenatal depression and/or anxiety were contacted for an assessment using the PRIME-MD. The same procedure was made in a control group, consisting of 500 women, randomly selected among those who did not have any psychiatric diagnosis according to the PRIME-MD investigation during the second trimester of pregnancy. Results and conclusions: Of the 1555 women in the study population, 220 (14.1%) had one or more PRIME-MD diagnoses. Living single, low socioeconomic status, smoking, multiparity and a body mass index of 30 or more were significantly associated with a psychiatric diagnosis in the second trimester of pregnancy. Women with antenatal depression and/or anxiety more often suffered from nausea and vomiting during pregnancy were more often on sick leave, and they visited their obstetrician more often than healthy subjects, specifically because of fear of childbirth and premature contractions. Also, they were more commonly delivered by elective caesarean section, had an increased use of epidural analgesia and reported a longer self-experienced duration of labor. Severe complications of pregnancy, delivery, and the early postpartum period were not affected by antenatal depression and/or anxiety. There was no significant difference in neonatal outcome depending on antenatal depressive or anxiety disorder. Fewer cases of depressive and/or anxiety disorders were prevalent postpartum, but there was a significant shift from a majority of sub-threshold diagnoses during pregnancy to full DSM-IV diagnoses during the postpartum period. Previous psychiatric disorder and living singly were significantly associated with both a new-onset and a postpartum continuation/recurrence of depression and/or anxiety. Postpartum continuation/recurrence of a psychiatric disorder was additionally associated with smoking, obesity, and adverse obstetric events.

Background: It is unclear whether generalized anxiety disorder (GAD) has a specific relationship to pain syndromes, going beyond the established association of pain with anxiety syndromes in general. Methods: Mental disorders were assessed in a community sample (N = 4181; 18–65 years) using the DSM-IV/M-CIDI. Several threshold definitions were used to define GAD and medically unexplained pain. Results: The association between pain and GAD (odds ratio, OR = 5.8 pain symptoms; OR = 16.0 pain disorder) is stronger than the association between pain and other anxiety disorders (OR = 2.4 pain symptoms; OR = 4.0 pain disorder). This association extends to subthreshold level definitions of GAD with some indication for a non-linear dose–response relationship. The GAD-pain link cannot sufficiently be explained by demographic factors, comorbid mental or physical disorders. Conclusions: The association of pain and generalized anxiety is not artifactual. Compared to other anxiety syndromes, it appears to be stronger and more specific suggesting the need to explore clinical and public health implications.