OBJECTIVES: During health guideline development, panel members often have implicit, different definitions of health outcomes that can lead to variability in evidence synthesis and recommendations. McMaster GRADE Centre researchers developed a standardized description of health outcomes using the health marker state format. We aimed to determine which aspects of the development, content, and use of marker states were valuable to guideline developers. STUDY DESIGN & SETTING: We conducted a case study of marker state development with the European Commission Initiative on Breast Cancer (ECIBC) Guidelines Development Group (GDG). Eighteen GDG members provided written and interview feedback on the process. Using the health marker states, 2 health utility rating surveys were conducted near the beginning and end of development respectively. RESULTS: We developed 24 marker states for outcomes related to breast cancer screening and diagnosis. Feedback from GDG members revealed that marker states could be useful for developing recommendations and improving transparency of guideline methods. Comparison of the two health utility surveys showed a decrease in standard deviation in the second survey across 21 (88%) of the outcomes. CONCLUSIONS: Health marker states are a promising method, satisfying the pre-requisite of being feasible, acceptable, and with some initial result on reduction of variance of health utility scores. / Thesis / Master of Public Health (MPH) / OBJECTIVES: During health guideline development, panel members often have implicit, different definitions of health outcomes that can lead to variability in evidence synthesis and recommendations. McMaster GRADE Centre researchers developed a standardized description of health outcomes using the health marker state format. We aimed to determine which aspects of the development, content, and use of marker states were valuable to guideline developers. STUDY DESIGN & SETTING: We conducted a case study of marker state development with the European Commission Initiative on Breast Cancer (ECIBC) Guidelines Development Group (GDG). Eighteen GDG members provided written and interview feedback on the process. Using the health marker states, 2 health utility rating surveys were conducted near the beginning and end of development respectively. RESULTS: We developed 24 marker states for outcomes related to breast cancer screening and diagnosis. Feedback from GDG members revealed that marker states could be useful for developing recommendations and improving transparency of guideline methods. Comparison of the two health utility surveys showed a decrease in standard deviation in the second survey across 21 (88%) of the outcomes. CONCLUSIONS: Health marker states are a promising method, satisfying the pre-requisite of being feasible, acceptable, and with some initial result on reduction of variance of health utility scores.

The dissertation deals with the links between health care systems and health outcomes in the so-called 'transition' countries. The main questions to be addressed are: ''Do health care systems and their transitions influence health outcomes in the transition area and i f they do - how?" The combination o f qualitative techniques and econometric methods allowed for a creation o f the structural classifications o f the health care systems in transition and produced important findings. Firstly, health care transitions, and especially their structural component, are found to be significant in determining health status in the CEE and CIS countries. Secondly, however, the socio-economic determinants o f health were established to also play a major role in determining health inequalities in the transition area. Powered by TCPDF (www.tcpdf.org)

Many health geography studies, including the Ottawa Neighbourhood Study (ONS), have faced significant challenges uncovering local variation in patterns of community health in rural areas. This is due to the fact that sparsely populated rural areas make it difficult to define neighbourhoods that are representative of the social and resource utilization patterns of the individuals therein. Moreover, rural areas yield small samples from population-based regional health surveys and this leads to insufficient sample sizes for reliable estimation of health determinants and outcomes. In response to this issue this thesis combines geographical and statistical techniques which allow for the simulation of health variables within small areas and populations within rural Ottawa. This methodological approach combines the techniques of dasymetric mapping and statistical micro-simulation in an innovative way, which will allow health geography researchers to explore health determinants and health outcomes at small spatial scales in rural areas. Dasymetric mapping is used to generate a statistical population surface over Ottawa and then estimate socio-economic (SES) variables within small neighbourhood units within rural Ottawa. The estimated SES variables are then used as correlate variables to simulate health determinant and health outcome variables form the Canadian Community Health Survey (CCHS) using statistical micro-simulation. Through this methodology, simulations of specific health determinants and outcome can be investigated at small spatial scales within rural areas. Dasymetric mapping provided neighbourhood-level population estimates that were used to re-weight as set of SES variables that were correlates with those in the Canadian Community Health Survey (CCHS). These neighbourhood-level correlates allowed microsimulation and consequent spatial exploration of prevalence for smoking, binge drinking, obesity, self-rated mental health, and the presence of two or more chronic conditions. The methodology outlined in this paper, provides and innovative way of exploring health determinants and health outcomes in neighbourhoods for which population and health statistics are not traditionally collected at levels that would allow traditional statistical analyses of prevalence.

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Many health geography studies, including the Ottawa Neighbourhood Study (ONS), have faced significant challenges uncovering local variation in patterns of community health in rural areas. This is due to the fact that sparsely populated rural areas make it difficult to define neighbourhoods that are representative of the social and resource utilization patterns of the individuals therein. Moreover, rural areas yield small samples from population-based regional health surveys and this leads to insufficient sample sizes for reliable estimation of health determinants and outcomes. In response to this issue this thesis combines geographical and statistical techniques which allow for the simulation of health variables within small areas and populations within rural Ottawa. This methodological approach combines the techniques of dasymetric mapping and statistical micro-simulation in an innovative way, which will allow health geography researchers to explore health determinants and health outcomes at small spatial scales in rural areas. Dasymetric mapping is used to generate a statistical population surface over Ottawa and then estimate socio-economic (SES) variables within small neighbourhood units within rural Ottawa. The estimated SES variables are then used as correlate variables to simulate health determinant and health outcome variables form the Canadian Community Health Survey (CCHS) using statistical micro-simulation. Through this methodology, simulations of specific health determinants and outcome can be investigated at small spatial scales within rural areas. Dasymetric mapping provided neighbourhood-level population estimates that were used to re-weight as set of SES variables that were correlates with those in the Canadian Community Health Survey (CCHS). These neighbourhood-level correlates allowed microsimulation and consequent spatial exploration of prevalence for smoking, binge drinking, obesity, self-rated mental health, and the presence of two or more chronic conditions. The methodology outlined in this paper, provides and innovative way of exploring health determinants and health outcomes in neighbourhoods for which population and health statistics are not traditionally collected at levels that would allow traditional statistical analyses of prevalence.

A large number of studies have shown that both temperature and air pollution (eg, particulate matter and ozone) are associated with health outcomes. So far, it has received limited attention whether air pollution and temperature interact to affect health outcomes. A few studies have examined interactive effects between temperature and air pollution, but produced conflicting results. This thesis aimed to examine whether air pollution (including ozone and particulate matter) and temperature interacted to affect health outcomes in Brisbane, Australia and 95 large US communities. In order to examine the consistency across different cities and different countries, we used two datasets to examine interactive effects of temperature and air pollution. One dataset was collected in Brisbane City, Australia, during 1996-2000. The dataset included air pollution (PM10, ozone and nitrogen dioxide), weather conditions (minimum temperature, maximum temperature, relative humidity and rainfall) and different health outcomes. Another dataset was collected from the 95 large US communities, which included air pollution (ozone was used in the thesis), weather conditions (maximum temperature and dew point temperature) and mortality (all non-external cause mortality and cardiorespiratory mortality). Firstly, we used three parallel time-series models to examine whether maximum temperature modified PM10 effects on cardiovascular hospital admissions (CHA), respiratory hospital admissions (RHA), cardiovascular emergency visits (CEV), respiratory emergency visits (REV), cardiovascular mortality (CM) and non-external cause mortality (NECM), at lags of 0-2 days in Brisbane. We used a Poisson generalized additive model (GAM) to fit a bivariate model to explore joint response surfaces of both maximum temperature and particulate matter less than 10 μm in diameter (PM10) on individual health outcomes at each lag. Results show that temperature and PM10 interacted to affect different health outcomes at various lags. Then, we separately fitted non-stratification and stratification GAM models to quantify the interactive effects. In the non-stratification model, we examined the interactive effects by including a pointwise product for both temperature and the pollutant. In the stratification model, we categorized temperature into two levels using different cut-offs and then included an interactive term for both pollutant and temperature. Results show that maximum temperature significantly and positively modified the associations of PM10 with RHA, CEV, REV, CM and NECM at various lags, but not for CHA. Then, we used the above Poisson regression models to examine whether PM10 modified the associations of minimum temperature with CHA, RHA, CEV, REV, CM and NECM at lags of 0-2 days. In this part, we categorized PM10 into two levels using the mean as cut-off to fit the stratification model. The results show that PM10 significantly modified the effects of temperature on CHA, RHA, CM and NECM at various lags. The enhanced adverse temperature effects were found at higher levels of PM10, but there was no clear evidence for synergistic effects on CEV and REV at various lags. Three parallel models produced similar results, which strengthened the validity of these findings. Thirdly, we examined whether there were the interactive effects between maximum temperature and ozone on NECM in individual communities between April and October, 1987-2000, using the data of 60 eastern US communities from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS). We divided these communities into two regions (northeast and southeast) according to the NMMAPS study. We first used the bivariate model to examine the joint effects between temperature and ozone on NECM in each community, and then fit a stratification model in each community by categorizing temperature into three levels. After that, we used Bayesian meta-analysis to estimate overall effects across regions and temperature levels from the stratification model. The bivariate model shows that temperature obviously modified ozone effects in most of the northeast communities, but the trend was not obviously in the southeast region. Bayesian meta-analysis shows that in the northeast region, a 10-ppb increment in ozone was associated with 2.2% (95% posterior interval [PI]: 1.2%, 3.1 %), 3.1% (95% PI: 2.2%, 3.8 %) and 6.2 % (95% PI: 4.8%, 7.6 %) increase in mortality for low, moderate and high temperature levels, respectively, while in the southeast region, a 10-ppb increment in ozone was associated with 1.1% (95% PI: -1.1%, 3.2 %), 1.5% (95% PI: 0.2%, 2.8%) and 1.3% (95% PI: -0.3%, 3.0 %) increase in mortality. In addition, we examined whether temperature modified ozone effects on cardiovascular mortality in 95 large US communities between May and October, 1987-2000 using the same models as the above. We divided the communities into 7 regions according to the NMMAPS study (Northeast, Industrial Midwest, Upper Midwest, Northwest, Southeast, Southwest and Southern California). The bivariate model shows that temperature modified ozone effects in most of the communities in the northern regions (Northeast, Industrial Midwest, Upper Midwest, Northwest), but such modification was not obvious in the southern regions (Southeast, Southwest and Southern California). Bayesian meta-analysis shows that temperature significantly modified ozone effects in the Northeast, Industrial Midwest and Northwest regions, but not significant in Upper Midwest, Southeast, Southwest and Southern California. Nationally, temperature marginally positively modified ozone effects on cardiovascular mortality. A 10-ppb increment in ozone was associated with 0.4% (95% posterior interval [PI]: -0.2, 0.9 %), 0.3% (95% PI: -0.3%, 1.0%) and 1.6% (95% PI: 4.8%, 7.6%) increase in mortality for low, moderate and high temperature levels, respectively. The difference of overall effects between high and low temperature levels was 1.3% (95% PI: - 0.4%, 2.9%) in the 95 communities. Finally, we examined whether ozone modified the association between maximum temperature and cardiovascular mortality in 60 large eastern US communities during the warmer days, 1987-2000. The communities were divided into the northeast and southeast regions. We restricted the analyses to the warmer days when temperature was equal to or higher than the median in each community throughout the study period. We fitted a bivariate model to explore the joint effects between temperature and ozone on cardiovascular mortality in individual communities and results show that in general, ozone positively modified the association between temperature and mortality in the northeast region, but such modification was not obvious in the southeast region. Because temperature effects on mortality might partly intermediate by ozone, we divided the dataset into four equal subsets using quartiles as cut-offs. Then, we fitted a parametric model to examine the associations between temperature and mortality across different levels of ozone using the subsets. Results show that the higher the ozone concentrations, the stronger the temperature-mortality associations in the northeast region. However, such a trend was not obvious in the southeast region. Overall, this study found strong evidence that temperature and air pollution interacted to affect health outcomes. PM10 and temperature interacted to affect different health outcomes at various lags in Brisbane, Australia. Temperature and ozone also interacted to affect NECM and CM in US communities and such modification varied considerably across different regions. The symmetric modification between temperature and air pollution was observed in the study. This implies that it is considerably important to evaluate the interactive effect while estimating temperature or air pollution effects and further investigate reasons behind the regional variability.

Thesis (M.S.)--University of Florida, 2004. / Typescript. Title from title page of source document. Document formatted into pages; contains 33 pages. Includes Vita. Includes bibliographical references.

Many health geography studies, including the Ottawa Neighbourhood Study (ONS), have faced significant challenges uncovering local variation in patterns of community health in rural areas. This is due to the fact that sparsely populated rural areas make it difficult to define neighbourhoods that are representative of the social and resource utilization patterns of the individuals therein. Moreover, rural areas yield small samples from population-based regional health surveys and this leads to insufficient sample sizes for reliable estimation of health determinants and outcomes. In response to this issue this thesis combines geographical and statistical techniques which allow for the simulation of health variables within small areas and populations within rural Ottawa. This methodological approach combines the techniques of dasymetric mapping and statistical micro-simulation in an innovative way, which will allow health geography researchers to explore health determinants and health outcomes at small spatial scales in rural areas. Dasymetric mapping is used to generate a statistical population surface over Ottawa and then estimate socio-economic (SES) variables within small neighbourhood units within rural Ottawa. The estimated SES variables are then used as correlate variables to simulate health determinant and health outcome variables form the Canadian Community Health Survey (CCHS) using statistical micro-simulation. Through this methodology, simulations of specific health determinants and outcome can be investigated at small spatial scales within rural areas. Dasymetric mapping provided neighbourhood-level population estimates that were used to re-weight as set of SES variables that were correlates with those in the Canadian Community Health Survey (CCHS). These neighbourhood-level correlates allowed microsimulation and consequent spatial exploration of prevalence for smoking, binge drinking, obesity, self-rated mental health, and the presence of two or more chronic conditions. The methodology outlined in this paper, provides and innovative way of exploring health determinants and health outcomes in neighbourhoods for which population and health statistics are not traditionally collected at levels that would allow traditional statistical analyses of prevalence.

Research elucidating high rates of burnout, depression, and suicide among US clinicians has caused national concern for the sustainability of our healthcare workforce and the quality of patient care. In response, US healthcare organizations are using measures of clinician burnout in new contexts beyond their traditional use in research. Outcome measures of clinician burnout are being used to evaluate health system performance, identify demographic disparities, and educate individual clinicians regarding their own outcomes. Yet, critical gaps in the literature exist regarding the measurement properties of burnout assessments in these contexts and the relationship between clinician distress and healthcare quality. This dissertation contains three studies on measuring clinician distress and its relationship with healthcare quality. Studies 1 and 2 aim to advance what is known regarding the interpretability, reliability, and validity of a commonly used clinician burnout assessment, the Maslach Burnout Inventory-Human Services Survey for Medical Personnel (MBI-HSS). In Study 1, we aimed to improve the interpretation of the MBI-HSS by using item response theory to describe the burnout symptoms and precision associated with MBI-HSS scores in US physicians. We produced response profiles that allow health policy makers and healthcare leaders to relate actionable, qualitative meaning regarding individuals’ and groups’ burnout symptom burden to the MBI-HSS’s quantitative subscale scores. In Study 2, we examined whether demographic disparities in US physician burnout are explained by differences in the MBI-HSS’s functioning across physician age, gender, and specialty groups. Our findings revealed that differences in the MBI-HSS’s functioning across age, gender, and specialty groups did not account for observed disparities, supporting the use of the MBI-HSS as a valid tool for identifying demographic disparities in physician burnout. In Study 3, we examined the association of clinician depression, anxiety, and burnout with the inappropriate use of antibiotic prescriptions for acute respiratory tract infections (RTIs) in a retrospective cohort study of outpatient visits at Boston Medical Center. We found a significant positive association between clinician depression, anxiety, and burnout and inappropriate prescribing for acute RTIs, which depended on the visit location and diagnosis group. Our findings suggest that clinician depression, anxiety, and burnout may play an important role in the quality of routine outpatient care. As federal agencies and healthcare organizations seek to address clinician distress on local and national levels, our findings offer important implications for future assessment and intervention. / 2023-01-19T00:00:00Z

Poverty it is said to have harsh outcomes on one's antisocial or even delinquent behavior. Other factors as well lead an individual to antisocial behavior those are the environment that they are living, and their genes that have passed from one generation to the other one. Parents, poverty, environment, genes and social determinants affect one's involvement in a crime and also affect the health in distinct ways. The focus of this essay is on the African American population within the American society and the health disparities that exist in this situation. We try to understand and answer if these health disparities were created after their birth, or pre-existed and were carried through the generations. One of the main issues is the examination of how much is the prison or incarceration in charge for these health disparities, or how much did these disparities previously exist, out of other reasons. These issues are put on a life-course perspective, and there is shown how much these morbidities affect one's life from the beginning till incarceration. A certain policy implication is made to eliminate those disparities by improving maternal, child and family health status.