Perceived Discrimination and Health Outcomes Among Asian Indians in the United States

Background: Perceived interpersonal discrimination while seeking healthcare services is associated with poor physical and mental health. Yet, there is a paucity of research among Asian Americans or its subgroups. This study examined the correlates of reported interpersonal discrimination when seeking health care among a large sample of Asian Indians, the 3rd largest Asian American subgroup in the US, and identify predictors of adverse self-rated physical health, a well-accepted measure of overall health status. Methods: Cross-sectional survey. Participants comprised of 1824 Asian Indian adults in six states with higher concentration of Asian Indians. Results: Mean age and years lived in the US was 45.7 ± 12.8 and 16.6 ± 11.1 years respectively. The majority of the respondents was male, immigrants, college graduates, and had access to care. Perceived interpersonal discrimination when seeking health care was reported by a relatively small proportion of the population (7.2 %). However, Asian Indians who reported poor self-rated health were approximately twice as likely to perceived discrimination when seeking care as compared to those in good or excellent health status (OR 1.88; 95 % CI 1.12–3. 14). Poor self-rated health was associated with perceived health care discrimination after controlling for all of the respondent characteristics (OR 1.93; 95 % CI: 1.17–3.19). In addition, Asian Indians who lived for more than 10 years in the U.S. (OR 3.28; 95 % CI: 1.73–6.22) and had chronic illnesses (OR 1.39; 95 % CI: 1.17–1.64) (p \u3c 0.05) were more likely to perceive discrimination when seeking health care. However, older Asian Indians, over the age of 55 years, were less likely to perceive discrimination than those aged 18–34 years Indian American. Conclusion: Results offers initial support for the hypothesis that Asian Indians experience interpersonal discrimination when seeking health care services and that these experiences may be related to poor self-rated health status

Methodological issues in epidemiological studies of periodontitis - how can it be improved?

Background: This position paper was commissioned by the European Association of Dental Public Health, which has established six working groups to investigate the current status of six topics related to oral public health. One of these areas is epidemiology of periodontal diseases. Methods: Two theses "A systematic review of definitions of periodontitis and the methods that have been used to identify periodontitis" [1] and "Factors affecting community oral health care needs and provision" [2] formed the starting point for this position paper. Additional relevant and more recent publications were retrieved through a MEDLINE search. Results: The literature reveals a distinct lack of consensus and uniformity in the definition of periodontitis within epidemiological studies. There are also numerous differences in the methods used. The consequence is that data from studies using differing case definitions and differing survey methods are not easily interpretable or comparable. The limitations of the widely used Community Periodontal Index of Treatment Need (CPITN) and its more recent derivatives are widely recognized. Against this background, this position paper reviews the current evidence base, outlines existing problems and suggests how epidemiology of periodontal diseases may be improved. Conclusions: The remit of this working group was to review and discuss the existing evidence base of epidemiology of periodontal diseases and to identify future areas of work to further enhance it

Self-Reported Health Status in Primary Health Care: The Influence of Immigration and Other Associated Factors

OBJECTIVE: The aims of this study are to compare self-reported health status between Spanish-born and Latin American-born Spanish residents, adjusted by length of residence in the host country; and additionally, to analyse sociodemographic and psychosocial variables associated with a better health status. DESIGN: This is a cross-sectional population based study of Latin American-born (n = 691) and Spanish-born (n = 903) in 15 urban primary health care centres in Madrid (Spain), carried out between 2007 and 2009. The participants provided information, through an interview, about self-reported health status, socioeconomic characteristics, psychosocial factors and migration conditions. Descriptive and multiple logistic regression analyses were conducted. RESULTS: The Spanish-born participants reported a better health status than the Latin America-born participants (79.8% versus 69.3%, p<0.001). Different patterns of self-reported health status were observed depending on the length of residence in the host country. The proportion of immigrants with a better health status is greater in those who have been in Spain for less than five years compared to those who have stayed longer. Better health status is significantly associated with being men, under 34 years old, being Spanish-born, having a monthly incomes of over 1000 euros, and having considerable social support and low stress. CONCLUSIONS: Better self-reported health status is associated with being Spanish-born, men, under 34 years old, having an uppermiddle-socioeconomic status, adequate social support, and low stress. Additionally, length of residence in the host country is seen as a related factor in the self-reported health status of immigrants

Application of geographic information systems and simulation modelling to dental public health: Where next?

Public health research in dentistry has used geographic information systems since the 1960s. Since then, the methods used in the field have matured, moving beyond simple spatial associations to the use of complex spatial statistics and, on occasions, simulation modelling. Many analyses are often descriptive in nature; however, and the use of more advanced spatial simulation methods within dental public health remains rare, despite the potential they offer the field. This review introduces a new approach to geographical analysis of oral health outcomes in neighbourhoods and small area geographies through two novel simulation methods-spatial microsimulation and agent-based modelling. Spatial microsimulation is a population synthesis technique, used to combine survey data with Census population totals to create representative individual-level population datasets, allowing for the use of individual-level data previously unavailable at small spatial scales. Agent-based models are computer simulations capable of capturing interactions and feedback mechanisms, both of which are key to understanding health outcomes. Due to these dynamic and interactive processes, the method has an advantage over traditional statistical techniques such as regression analysis, which often isolate elements from each other when testing for statistical significance. This article discusses the current state of spatial analysis within the dental public health field, before reviewing each of the methods, their applications, as well as their advantages and limitations. Directions and topics for future research are also discussed, before addressing the potential to combine the two methods in order to further utilize their advantages. Overall, this review highlights the promise these methods offer, not just for making methodological advances, but also for adding to our ability to test and better understand theoretical concepts and pathways

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

<p>Abstract</p> <p>Background</p> <p>The World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults.</p> <p>Methods</p> <p>We conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance.</p> <p>Results</p> <p>Thirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%).</p> <p>Conclusions</p> <p>We found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.</p