Annual Health Survey Research Articles

Comparison of anxiety and depression rates among LGBTQ college students before and during the COVID-19 pandemic

Objective To compare rates of anxiety and depression among LGBTQ college students before and during the COVID-19 pandemic and examine pandemic-related stressors and protective factors. Participants Two cohorts of LGBTQ college students, aged 18–25, sampled before (N = 3,484) and during (N = 1,647) the pandemic. Methods The Healthy Minds Study (HMS) is an annual mental health survey administered to college students in the United States, which included additional items related to the COVID-19 pandemic. Results Mean anxiety symptoms were significantly lower mid-pandemic compared to pre-pandemic, but no differences in mean depressive symptoms were detected. Results varied by sexual and gender minority status. Financial stress, pandemic-related concerns, and witnessing discrimination were risk factors while academic persistence, positive mental health, and formal support were protective factors. Conclusions Mental health and instrumental support from institutions will be critical for college students during the pandemic. Affirming and empowering spaces for LGBTQ college students may leverage protective factors.

Abstract 10308: Rural-Urban Differences in Health Characteristics and Health Behaviors in 14,955 US Stroke Survivors

Introduction: Rural residence is associated with increased CVD risk, including incident stroke. Less is known about the association of rurality on health characteristics and health behaviors among stroke survivors. The purpose of this study was to determine the presence and magnitude of rural/urban differences in health characteristics and health behaviors among stroke survivors. Methods: Data from 14,599 stroke survivors were available from the 2017 & 2019 Behavioral Risk Factor Surveillance System, an annual health survey of non-institutionalized US adults. Inclusion criteria included history of self-reported stroke and age ≥65 years. Respondents with missing data regarding rural vs. urban dwelling status were excluded from analysis. Respondents provided self-reported data on sociodemographics (sex, race, marital status, education, and income), health characteristics, and health behaviors (see table for characteristics and behaviors). Logistic regression was used to compute odds ratios and 95% CI’s to examine the association among rural/urban status and health characteristics and health behaviors. Results: Prevalence of health characteristics and health behaviors, and computed adjusted odds ratios, are presented in the table. Although the prevalence of disability, poor general health, and insufficient physical activity were higher among rural-dwelling stroke survivors, odds ratios adjusted for sociodemographic and health characteristics demonstrated no association among rurality, health characteristics, and health behaviors. Conclusions: Although no differences in health characteristics and behaviors were found between rural and urban-dwelling stroke survivors, prevalences of many CVD risk factors were high, which increase risk for recurrent stroke, secondary conditions, and chronic disease. Interventions for improving risk factors and health behaviors in this health disparate population are needed.

Feature selection for unbiased imputation of missing values: A case study in healthcare.

Datasets in healthcare are plagued with incomplete information. Imputation is a common method to deal with missing data where the basic idea is to substitute some reasonable guess for each missing value and then continue with the analysis as if there were no missing data. However unbiased predictions based on imputed datasets can only be guaranteed when the missing mechanism is completely independent of the observed or missing data. Often, this promise is broken in healthcare dataset acquisition due to unintentional errors or response bias of the interviewees. We highlight this issue by studying extensively on an annual health survey dataset on infant mortality prediction and provide a systematic testing for such assumption. We identify such biased features using an empirical approach and show the impact of wrongful inclusion of these features on the predictive performance.Clinical relevance- We show that blind analysis along with plug and play imputation of healthcare data is a potential pitfall that clinicians and researchers want to avoid in finding important markers of disease.

Elements of the care environment influence coronary artery bypass surgery readmission

BackgroundCoronary artery bypass grafting 30-day unplanned readmission is a focus for the CMS Hospital Readmissions Reduction Program. Awareness of the critical elements of the care delivery environment, including hospital infrastructure and patient clinical profiles that predispose toward readmission, is essential to proactively decrease readmissions. MethodsThe Healthcare Cost and Utilization Project-State Inpatient Database, American Hospital Association Annual Health Survey Database, and Healthcare Information Management Systems Society data sets were merged to create a single data set of patient- and hospital-level data from 8 states. Isolated coronary artery bypass grafting procedures were queried for all-cause 30-day readmission, and backwards stepwise logistic regression was performed. Readmission rate was then used to categorize hospitals into quartiles, and analysis focused on the hospitals with the lowest (Q1) and highest (Q4) readmission rates. Univariate analysis was performed comparing Q1 and Q4 hospitals. ResultsA total of 150,215 patients underwent isolated coronary artery bypass grafting with 23,244 (15.5%) readmitted patients among 903 hospitals. Model area under the curve was 0.709 (95% confidence interval, 0.702–0.716), with the top 3 readmission determinants related to discharge disposition. Compared to Q1, Q4 patients more often were female, were >70 years of age, and had Medicare as a primary payor (P < .001). Low readmission rate hospitals were characterized by higher costs; not-for-profit status; having Joint Commission accreditation; and higher total admissions, operative volume, hospital/ICU beds, full-time physicians, nurses, and ancillary personnel (P < .001). ConclusionReadmission after coronary artery bypass grafting is strongly influenced by discharge disposition. However, hospital factors such as scale, personnel, and ownership structure are significant contributors to readmission. Focus beyond patient factors to include the entire continuum of care is required to enhance outcomes, of which readmission is one surrogate measure.

Asthma Surveillance - United States, 2006-2018.

ProblemAsthma is a chronic disease of the airways that requires ongoing medical management. Socioeconomic and demographic factors as well as health care use might influence health patterns in urban and rural areas. Persons living in rural areas tend to have less access to health care and health resources and worse health outcomes. Characterizing asthma indicators (i.e., prevalence of current asthma, asthma attacks, emergency department and urgent care center [ED/UCC] visits, and asthma-associated deaths) and determining how asthma exacerbations and health care use vary across the United States by geographic area, including differences between urban and rural areas, and by sociodemographic factors can help identify subpopulations at risk for asthma-related complications.Reporting Period2006–2018.Description of SystemThe National Health Interview Survey (NHIS) is an annual cross-sectional household health survey among the civilian noninstitutionalized population in the United States. NHIS data were used to produce estimates for current asthma and among them, asthma attacks and ED/UCC visits. National Vital Statistics System (NVSS) data were used to estimate asthma deaths. Estimates of current asthma, asthma attacks, ED/UCC visits, and asthma mortality rates are described by demographic characteristics, poverty level (except for deaths), and geographic area for 2016–2018. Trends in asthma indicators by metropolitan statistical area (MSA) category for 2006–2018 were determined. Current asthma and asthma attack prevalence are provided by MSA category and state for 2016–2018. Detailed urban-rural classifications (six levels) were determined by merging 2013 National Center for Health Statistics (NCHS) urban-rural classification data with 2016–2018 NHIS data by county and state variables. All subregional estimates were accessed through the NCHS Research Data Center.ResultsCurrent asthma was higher among boys aged <18 years, women aged ≥18 years, non-Hispanic Black (Black) persons, non-Hispanic multiple-race (multiple-race) persons, and Puerto Rican persons. Asthma attacks were more prevalent among children, females, and multiple-race persons. ED/UCC visits were more prevalent among children, women aged ≥18 years, and all racial and ethnic groups (i.e., Black, non-Hispanic Asian, multiple race, and Hispanic, including Puerto Rican, Mexican, and other Hispanic) except American Indian and Alaska Native persons compared with non-Hispanic White (White) persons. Asthma deaths were higher among adults, females, and Black persons. All pertinent asthma outcomes were also more prevalent among persons with low family incomes. Current asthma prevalence was higher in the Northeast than in the South and the West, particularly in small MSA areas. The prevalence was also higher in small and medium metropolitan areas than in large central metropolitan areas. The prevalence of asthma attacks differed by MSA category in four states. The prevalence of ED/UCC visits was higher in the South than the Northeast and the Midwest and was also higher in large central metropolitan areas than in micropolitan and noncore areas. The asthma mortality rate was highest in non-MSAs, specifically noncore areas. The asthma mortality rate was also higher in the Northeast, Midwest, and West than in the South. Within large MSAs, asthma deaths were higher in the Northeast and Midwest than the South and West.InterpretationDespite some improvements in asthma outcomes over time, the findings from this report indicate that disparities in asthma indicators persist by demographic characteristics, poverty level, and geographic location.Public Health ActionDisparities in asthma outcomes and health care use in rural and urban populations identified from NHIS and NVSS can aid public health programs in directing resources and interventions to improve asthma outcomes. These data also can be used to develop strategic goals and achieve CDC’s Controlling Childhood Asthma and Reducing Emergencies (CCARE) initiative to reduce childhood asthma hospitalizations and ED visits and prevent 500,000 asthma-related hospitalizations and ED visits by 2024.

AN ANALYSIS OF INTER-DISTRICT VARIATION IN HEALTH STATUS OF ODISHA WITH A SPECIAL REFERENCE TO HEALTHCARE

The objective of this present study is to the analysis of inter-district variation in the health status of Odisha with a special reference to Healthcare. Health is an important view of health care. One needs good health to lead an economical life. The health of individuals and communities depends upon various socio-economic, demographic and environmental components. To understand the situation of health care an analysis of indicators such as infant mortality rate, death rate, birth rate, life expectancy, Sex ratio, Doctor per thousand of population, medical in hospital per thousand of population, Beds per thousand of population, and literacy rate have been employed through various quantitative techniques like composite index, Descriptive Statistics, Correlation etc. The study depends entirely on secondary data. The relevant secondary data have been collected from various dependable sources. Although the level of health status in Odisha has shown substantial improvement over the years, yet there are marked inter-district variations in health achievements of the Odisha and their districts are at a deplorably low level of health status. In this context, the main aim of this objective is to an analysis of Inter-District Variation in Health Status of Odisha with a special reference to Health care with Annual Health Survey (2012-13) presents data and methodology. KEY WORDS: Birth Rate, Death Rate, Infant Mortality, Life Expectancy, Sex Ratio, Literacy rate

Mapping of Household Surveys to Measure Barriers to Access to Maternal and Child Health Services in India

Objective: To map the range of access barrier indicators for which data can be derived from the three most common health related household surveys in India. Methods: A mapping review study was conducted to identify access dimensions and indicators of access barriers for maternal and child health (MCH) services included in three household surveys in India: National Family Health Survey (NFHS), District Level Household and Facility Survey (DLHS) and Annual Health Survey (AHS). Results: The Tanahashi framework for effective coverage of health services was used in this study, and 12 types of access barriers were identified, from which 23 indicators could be generated. These indicators measure self-reported access barriers for unmet healthcare needs through delayed care, as well as forgone care, and unsatisfactory experiences during health service provision. Multiple barriers could be identified, although there was marked heterogeneity in variables included and how barriers were measured. Conclusions: This study identified tracer indicators that could be used in India to monitor the population that experiences healthcare needs but fails to seek and obtain appropriate healthcare, and determine what the main barriers are. The surveys identified are well validated and allow the disaggregation of these indicators by equity stratifiers. Given the variability of the frequency and methodologies used in these surveys, comparability could be limited.

Quality Assessment of Stillbirth Review: A Pilot Study in Ten High-Priority Districts in Odisha.

Background:Stillbirth rate has shown less or no improvement in developing countries. India was estimated to have the largest number of stillbirths globally in 2015. Systematic review of stillbirths is a strategy that helps in identifying gaps in the care of a pregnant mother, and is a useful and comprehensive indicator of the quality of maternity care.Objectives:The objective of this study was to assess the quality of maternal care, and factors causing stillbirth, and to provide some doable plans to reduce its incidence in the Odisha state.Materials and Methods:The stillbirth review was undertaken over 4-month timeline (August to November 2014) in ten high-priority districts (HPDs) of Odisha. It included development of tools, desk reviews, training of staffs, and data handling. The deaths were estimated from Annual Health Survey. It was compared to the estimated stillbirth of each district to get the underreporting/overreporting districts. A report was generated on stillbirth process indicators, and program indicators after completion of assessment.Results:In the selected HPDs of Odisha, 4689 stillbirths were observed during the study period. However, the labor room register stated the reason of death in only 408 cases (8.7%). Further, at the time of admission, a provisional diagnosis could be made for only 3038 (64.7%) cases, of which 11% diagnosed as safe delivery resulted in stillbirth.Conclusions:The present study could contribute to a larger extent to address some of the gaps in the stillbirth review process in Odisha.

Impact of socioeconomic risk factors on carcinoma cervix: Hospital based pap smear screening of 2 years in Bihar

Introduction: Cervical cancer is leading cause of cancer related morbidity and mortality in Indian women with 123,000 new cases and 67,000 deaths annually. Various socioeconomic associated factors like Income, education, parity, early age at marriage and low awareness about risk factors and symptoms play contributory in initiation and progression from dysplasia to frank cervical carcinoma. Aim: According to National Family Health Survey (NFHS) State fact sheet Bihar year 2015-16, National Census of India year 2011 and Annual Health Survey year 2012-13, various above mentioned socioeconomic associated factors etiological are highly prevalent in female population of Bihar leading to growing number of cervical cancer patients. This study was carried out in a 199 bedded zonal hospital in Bihar to assess the impact of these factors on routine screening cervical pap smears and correlate the findings. Materials and Methods: Cross sectional study carried out by analysing 860 cervical pap smears at a 199 bedded hospital in Bihar from January 2015 to December 2017 with pre defined socioeconomic variables. Majority of patients were from low socioeconomic rural background. Result: Result showed linear correlation all the above risk factors to development of cervical cancer other then level of formal education. Conclusion: More awareness specific to cervical cancer is needed for effective control of cervical cancer. Also more access and effective implementation of cervical cancer screening programs nearest to rural patients and overall improvement in socioeconomic parameters will go a long way in reducing the burden of cervical cancer in developing countries. Keywords: Cervical cancer awareness in Bihar, The bethesda system, Socioeconomic factors, Risk factors, Poverty, Illiteracy.

COVID-19 mortality effects of underlying health conditions in India: a modelling study

ObjectiveTo model how known COVID-19 comorbidities affect mortality rates and the age distribution of mortality in a large lower-middle-income country (India), and to identify which health conditions drive differences with...

Effective coverage of medical treatment for hypertension, diabetes and dyslipidaemia in Japan: An analysis of National Health and Nutrition Surveys 2003-2017.

To examine trends in effective medical treatment coverage for hypertension, diabetes and dyslipidaemia in Japan, using a metric to assess effective coverage of health interventions derived from a health system performance assessment framework. We obtained cross-sectional data for 96,863 individuals aged 40-74 years from the 15 annual Japanese National Health and Nutrition Surveys (2003-2017). We defined treatment need for hypertension, diabetes and dyslipidaemia as biomarkers equal to or greater than diagnostic thresholds or medication use. For individuals needing treatment, we conducted nearest-neighbour matching to estimate treatment effects and effective coverage, defined as the fraction of potential reductions in biomarkers actually achieved in treated individuals by medications. The age-standardized prevalence of treatment need for hypertension, diabetes and dyslipidaemia remained around 40%, 7% and 33%, respectively, in 2003-2017. Average treatment effects for those treated in 2013-2017 were 14.8 mmHg (95% confidence interval: 14.2-15.4) for systolic blood pressure, 1.2 percentage points (0.8-1.6) for haemoglobin A1c and 57.9 mg/dl (56.6-59.2) for non-high-density lipoprotein cholesterol. Effective coverage significantly increased between 2003-2007 (hypertension: 48.4% [44.7-52.0], diabetes: 43.8% [35.7-51.8], dyslipidaemia: 86.3% [83.1-89.5]) and 2013-2017 (hypertension: 76.2% [74.2-78.2], diabetes: 74.7% [71.0-78.5], dyslipidaemia: 94.6% [93.3-95.9]). Effective coverage of medical treatment for metabolic risk factors has increased. Most of the potential reductions in non-high-density lipoprotein cholesterol have been achieved by statins. Further efforts are necessary to improve the effectiveness of antihypertensive and antidiabetic drugs.

Estimation of missing values in aggregate level spatial data

BackgroundData can be missing when a survey fails to collect information from certain regions due to feasibility issues, which can impose problems while performing spatial analysis. ObjectiveThe present study aims to estimate missing aggregate level public health spatial data by utilizing the information from neighbouring regions and accounting for spatial autocorrelation inherently present in the data. MethodologyData was simulated for fixed values of various parameters in spatial regression models under low and high autocorrelation scenarios in dependent and independent variables. In dependent variable, 5%–25% of values were assumed to be missing. Stochastic regression imputation using spatial regression models namely spatial lag model, spatial error model, spatial Durbin model, spatial Durbin error model and spatial lag of X model was performed. The performance of these models were also compared using data from Annual Health Survey 2012-13. ResultsThe simulation analysis revealed that for any amount of missing values in the data, irrespective of whether the other variables in the regression model are spatially autocorrelated or not, if autocorrelation in the variable with missing values is high, stochastic regression imputation performed using spatial lag model, spatial Durbin model and spatial Durbin error model gives accurate estimates of missing values. If the autocorrelation is low, in addition to these three models, spatial lag X model was also found to be effective in estimating the missing values. ConclusionThe proposed mechanism results in optimal imputation of missing values in spatial data, which can yield complete data useful for public health professionals for effective interventions.

The association between serum inflammatory biomarkers and incident hypertension among postmenopausal women in the Buffalo OsteoPerio Study.

Several serum inflammatory biomarkers have been associated with blood pressure and hypertension prevalence in cross-sectional studies. Few of these associations have been evaluated prospectively. We examined associations for 10 serum inflammatory biomarkers with incident hypertension among 471 postmenopausal women (mean age = 65) in the Buffalo OsteoPerio Study. Concentrations of C-reactive protein, interleukin (IL)-2, IL-4, IL-6, IL-8, IL-10, tumor necrosis factor (TNF)-α, monocyte chemoattractant protein (MCP)-1, adiponectin, and leptin were measured using multiplexed sandwich immunoassays on fasting serum samples collected at baseline (1997–2001). Incident hypertension (195 cases) was defined as physician-diagnosed hypertension and treatment with medication identified on annual mailed health surveys during follow-up (mean 10 years). Cox regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) between log-transformed biomarkers (per 1-SD) and hypertension. When adjusted for age, leptin was significantly associated with hypertension risk (HR=1.55, 95% CI: 1.04, 2.29), however, the association was attenuated and not significant after adjustment for demographic and lifestyle factors, including BMI. Significant (P<0.10) interactions were observed for smoking (never, ever) with CRP (HR: Never, 1.31; Ever, 0.91; P=0.06) and MCP-1 (HR: Never, 0.59; Ever, 5.11; P=0.004); for BMI (<25, ≥25) with MCP-1(HR: <25, 3.45; ≥25, 0.95; P=0.07); for systolic BP with IL-10 (HR: <120, 0.85; 120–139, 1.11; P=0.07); and for diastolic BP with MCP-1 (HR: <80, 1.29; 80–89, 0.84; P=0.03) and with adiponectin (HR: <80, 0.86; 80–89, 1.50; P=0.03). This study adds needed understanding on prospective associations between several serum inflammatory biomarkers and hypertension risk in older postmenopausal women, among whom hypertension burden is substantial.

Defining the relative contribution of health care environmental components to patient outcomes in the model of 30-day readmission after coronary artery bypass graft (CABG)

BackgroundPatient outcomes following health care interventions may be dependent on a variety of factors: patient, surgeon, hospital, information technology, and temporal, cultural, and socioeconomic factors, among others. In this study, we characterize the relative contribution of each of these factors using a model of 30-day readmission following coronary artery bypass graft. MethodsThe Healthcare Cost and Utilization Project, the American Hospital Association Annual Health Survey Databases, the Healthcare Information and Management Systems Society, and the Distressed Communities Index from 2010 to 2013 were linked for Florida, Iowa, Massachusetts, Maryland, New York, and Washington. Logistic regression, random forest, decision tree, gradient boosting, k-nearest-neighbors classification, and XGBoost tree models were implemented. Modeling results were compared on the basis of predictive accuracy, sensitivity, specificity, and area under the curve. Decision tree performed best and was selected for further analysis. A gradient-boosted model was used to quantify factor contribution. ResultsThe model had 45,352 patients, 54,096 admissions, and a 16.2% 30-day readmission rate after coronary artery bypass graft. The top 10 predictors were disposition at discharge, number of chronic conditions, total procedures, median household income, adults without high school diplomas, primary payer method, Agency for Healthcare Research and Quality comorbidity: renal failure, patient location (urban-rural), admission type, and age categories. The top 3 socioeconomic predictors were estimated state median household income, adults without high school diplomas, and patient location (urban versus rural designation). The relative contribution of patient/temporal, socioeconomic, hospital information technology, and hospital factors to readmission is 83.45%, 5.71%, 6.34%, and 4.31%, respectively. ConclusionIn this model, the contribution of socioeconomic factors is substantive but lags significantly behind patient/temporal factors. With ever increasing availability of data, identification of contributors to patient outcomes within the overall health care macroenvironment will allow prioritization of interventions.

Abstract P128: Age-related Patterns In Systolic And Diastolic Blood Pressure In Adults From The United States And India

The purpose of this cross-sectional, exploratory analysis was to describe age-related patterns of blood pressure (BP) among participants in India (using the 2014 Annual Health Survey) and the United States (using National Health &amp; Nutrition Examination Surveys 2011-2016). We included 10,759 U.S. and 790,641 Indian participants aged ≥20 years with ≥2 BP readings. We plotted mean systolic (SBP) and diastolic BP (DBP) across 5-year age groups and estimated best fit models. SBP increased linearly with age in both sexes and study populations (R 2 : 0.88-0.99; Fig. 1-2). Those with overweight/obese BMI had higher SBP and modestly higher rates of increase in SBP. DBP followed a quadratic curve (R 2 : 0.68-0.99), peaking in the 5-6th decade (45-49 years in U.S. and 50-59 in India) with higher and earlier peaks in those with elevated BMI. The models’ strong fit and similarity between study populations supports the notion that physical processes underly BP’s age-related changes.

Risk factors for maternal mortality among 1.9 million women in nine empowered action group states in India: secondary analysis of Annual Health Survey data

ObjectiveTo examine the risk factors for pregnancy-related death in India’s nine Empowered Action Group (EAG) states.DesignSecondary data analysis of the Indian Annual Health Survey (2010–2013).SettingNine states: Assam, Bihar, Chhattisgarh, Jharkhand,...

Quality Assessment of Maternal Death Review: A Pilot Study in 10 High Priority Districts of Odisha State, India.

Background:Maternal death review (MDR) is a strategy that helps in identifying gaps in the care of a pregnant mother.Objectives:The objective is to assess the quality of MDR, causes of maternal mortality, and finding corrective action in 10 high-priority districts of Odisha.Materials and Methods:MDR was undertaken by our team in 4-month timeline (August to November 2014). It included the development of tools, desk reviews, training of staffs, and data handling. The maternal deaths were estimated from the Annual Health Survey. It was compared to estimated maternal death of each district to get the under reporting/over reporting districts. A report was generated on MDR process indicators and program indicators after completion of the assessment.Results:Only 129 (52%) of the 247 deaths found suitable for community-based MDR. The proportion of maternal death reported versus estimated was 247 versus 367. Correct diagnoses were reported in 120 cases. The classification of deaths was not mentioned in 74 cases. Maximum deaths (55%) were in 18–25 years of age group (the most common cause being anemia). Majority (50%) of the deaths occurred during the postnatal period and majority (67%) at the health facility. Only 61 (47%) had received antenatal check-ups. Facility-based MDR showed, Type 1 delay (denotes about seeking care) being the most common (53%). Inaccurate and incomplete information available was also found to compound the above problems in addition.Conclusions:The present study could contribute to a larger extent to address some of the gaps in the MDR process in the Odisha state.

A Novel Integrated Type 2 Diabetes Prediction Model for Indian Population using Data Mining Techniques

Late diagnosis and undiagnosed type 2 diabetes are the two major concerns for India, which is going to be a diabetes capital shortly. Several diabetes risk score (DRS) tools have been proposed and deployed for detecting the persons with high risk. These DRS tools have been developed using the multiple logistic regression model. But this model is both imperfect and subject to misuse. Another major issue with the DRS tools developed for Indian population is that they are based on the very limited urban population that does not represent the population of India. The objective of current research work is to develop a classification model for type 2 diabetes prediction. Along with this, the building of a novel integrated model for type 2 diabetes risk prediction is discussed consisting of the aggregate classification model and Indian weighted diabetes risk score model. The dataset used to develop and validate the model is obtained from the Annual Health Survey comprising of nearly 0.7 million and nearly 75 thousand adult participants respectively from around 400 districts of India. The proposed integrated diabetes risk prediction model predicts diabetes with 69.89% sensitivity, 56.58% specificity. The positive predictive value of the proposed integrated model is 15.88%, which is a significant improvement as the prevalence of diabetes is only 3.68% for the study population. Developing countries such as India, where undiagnosed diabetes and limited financial resources are a significant concern, the proposed integrated model for diabetes risk prediction can be useful as a cheaper tool useful for mass-screening, which can save up to 30% of the total screening cost.

331 Predictors of Colonoscopy Use Among Asian Indians in New York City, 2003-2016

INTRODUCTION: Asian Americans have the lowest colorectal cancer screening uptake of any racial/ethnic group in the United States. Among Asian Americans, Asian Indians appear to be particularly under-screened, although data for this population is limited. Our objective was to characterize temporal trends in predictors of up-to-date colonoscopy use among Asian Indians in New York City. METHODS: We analyzed data from Indian participants in the 2003-2016 New York City Community Health Survey (NYCCHS), an annual telephone-based health survey. A total of 496 participants aged 50 years or older were identified during the study period. We examined 4 consecutive time periods for factors associated with up-to-date colonoscopy use (defined as a colonoscopy within the last 10 years): 2003-2008, 2009-2012, 2013-2014, and 2015-2016. Age, sex, and any other variable with P &lt; 0.10 on univariate analysis was entered into a multivariable model; stepwise backward selection was performed to create the final model. RESULTS: On univariate analysis, factors associated with up-to-date colonoscopy use in at least two time periods included greater time in the US, non-Manhattan borough of residence, having received a flu shot, and age older than 65 years. Home language other than English, foreign birthplace, and not having a primary care provider were associated with decreased colonoscopy uptake. On multivariable analysis, older age, non-Manhattan borough of residence, BMI 25-29.9, higher income, and having received a flu shot were positively associated with colonoscopy use while Indian language, unemployment, and being non-diabetic were negatively associated in at least one time period. Compared to English, Indian home language was the most consistent negative predictor of colonoscopy uptake (OR 0.12-0.32 from 2003-2014). CONCLUSION: In a large study of Asian Indians in NYC, home language—a measure of acculturation—was the most consistent predictor of up-to-date colonoscopy use. Well-characterized predictors of screening in the general population, including education and insurance status, were not significant factors in the Indian population. These findings suggest targeted intervention for less acculturated members of the Asian Indian community may be warranted, and further research in this under-screened population is needed.

P5294Metabolically healthy obese is associated with better left atrial function and lower risk of adverse cardiovascular events including atrial fibrillation

Abstract Purpose Metabolic syndrome (MetS) and obesity are both crucial risk factors for heart failure with preserved ejection fraction (HFpEF) and atrial fibrillation (AF). However, the prognostic value of utilizing left atrial (LA) deformation imaging in metabolically healthy obese (MHO) or metabolically unhealthy normal weight (MUNW) subjects remains largely unexplored. Methods Speckle-tracking echocardiography was used to evaluate LA strain and strain rate among 3,915 subjects free from clinical heart failure (mean age: 50.0±10.6 years, 66.2% male) undergoing annual cardiovascular health survey. Metabolically unhealthy was defined by the presence of at least one MetS component or history of cardiovascular disease. Participants were categorized as: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight/obese (MHO), metabolically unhealthy overweight/obese (MUO). Results Markedly decreased LA strain, early-diastolic LA strain rate and increased LA stiffness were observed with metabolic disorders (all trend p: &lt;0.05). During a median follow-up period of 3.68 years, significantly higher risk of primary endpoints (death, heart failure, major cardiovascular events) was observed in MUO vs. MHO (HR=1.87, p=0.005) and MUNW vs. MHNW (HR=2.82, p=0.004). Similar patterns with incident AF (MUO vs MHO: HR=3.05, p=0.001, MUNW vs MHNW: HR=4.04, p=0.002) were also observed. After multivariate adjustment, risk of AF remained associated with low LA strain (β=−0.07, p&lt;0.001), systolic LA strain rate (β:−0.05, p=0.007) and high LA stiffness (β=0.18, p&lt;0.001). Comparison between metabolic groups MHNW (N=1107) MUNW (N=255) p (MUNW vs MHNW) MHO (N=1859) MUO (N=694) p (MUO vs MHO) Age 48±10.57 54.29±11.52 &lt;0.001 49±10.02 54.21±10.49 &lt;0.001 LA GLS (%) 40.6±7.68 37.16±7.5 &lt;0.001 36.77±7.66* 33.58±8.01‡ &lt;0.001 LA SRs 1.79±0.41 1.76±0.48 NS 1.63±0.39* 1.55±0.41‡ &lt;0.001 LA SRe 2.11±0.62 1.8±0.63 &lt;0.001 1.68±0.55* 1.48±0.52‡ &lt;0.001 LA stiffness 0.17±0.08 0.22±0.12 &lt;0.001 0.21±0.11* 0.25±0.13‡ &lt;0.001 *p&lt;0.001 for MHO vs MHNW, ‡p&lt;0.001 for MUO vs MUNW. LA GLS = left atrial global longitudinal strain, LA SRs = left atrial systolic strain rate, LA SRe = left atrial early-diastolic strain rate, NS = non-significant. Kaplan-Meier curves of adverse events Conclusion Metabolic abnormality may contribute to certain aspects of mechanistic LA dysfunction tightly linked to clinical events, even with normal lean body mass. Our observations may be insightful for targeted interventions in higher-risk patients with subclinical atrial dysfunction.