Association Between a First-Degree Family History and Self-Reported Personal History of Obesity, Diabetes, and Heart and Blood Conditions: Results From the All of Us Research Program

Background Family history reflects the complex interplay of genetic susceptibility and shared environmental exposures and is an important risk factor for obesity, diabetes, and heart and blood conditions (ODHB). However, the overlap in family history associations between various ODHBs has not been quantified. Methods and Results We assessed the association between a self-reported family history of ODHBs and their risk in the adult population (age ≥20 years) of the AoU (All of Us) Research Program, a longitudinal cohort study of diverse participants across the United States. We conducted a family history-wide association study to systematically assess the association of a first-degree family history of 15 ODHBs in AoU. We performed stratified analyses based on racial and ethnic categories, education, household income and gender minority status, and quantified associations by type of affected relatives. Of 125 430 participants, 76.8% reported a first-degree family history of any ODHB, most commonly hypertension (n=64 982, 51.8%), high cholesterol (49 753, 39.7%), and heart attack (29 618, 23.6%). We use the FamWAS method to estimate 225 familial associations among 15 ODHBs. The results include overlapping associations between family history of different types of cardiometabolic conditions (such as type 2 diabetes and coronary artery disease), and their risk factors (obesity, hypertension), where adults with a family history of 1 ODHB exhibited 1.1 to 5.6 times (1.5, on average) the odds of having a different ODHB. Conclusions Our findings inform the utility of family history data as a risk assessment and screening tool for the prevention of ODHBs and to provide additional insights into shared risk factors and pathogenic mechanisms.

Emergency department claims among Medicare beneficiaries with HIV, STDs, viral hepatitis or tuberculosis before and during the COVID-19 pandemic

BACKGROUND

Changes in emergency department (ED) usage among US Medicare beneficiaries (MB) with fee-for-service claims for HIV, viral hepatitis, sexually transmitted diseases (STDs) or tuberculosis (TB) (HHST) services have not been assessed since the COVID-19 pandemic.

METHODS

During 2006-20, we assessed the annual number of MB with each HHST per 1000 persons with ED claims for all conditions, and changes in demographic and geographic distribution of ED claimants for each HHST condition.

RESULTS

Of all persons who attended an ED for any condition, 10.5 million (27.5%) were MB with ≥1 ED claim in 2006; that number (percentage) increased to 11.0 million (26.7%) in 2019 and decreased to 9.2 million (22.7%) in 2020; < 5 MB per 1000 ED population had HHST ED claims in 2020. The percentage increase in ED claims was higher for MB with STDs than for those with other HHST conditions, including a 10% decrease for MB with TB in 2020.

CONCLUSIONS

Trends in ED usage for HHST conditions were associated with changes in demographic and geographic distribution among MB during 2006-20. Updated ED reimbursement policies and primary care practices among MB might improve prevention, diagnosis and treatment of HHST conditions in the future.

Severity Outcomes among Adult Patients with Primary Immunodeficiency and COVID-19 Seen in Emergency Departments, United States, April 2020-August 2021

Primary immunodeficiencies (PIs) are a group of diseases that increase susceptibility to infectious diseases. Few studies have examined the relationship between PI and COVID-19 outcomes. In this study, we used Premier Healthcare Database, which contains information on inpatient discharges, to analyze COVID-19 outcomes among 853 adult PI and 1,197,430 non-PI patients who visited the emergency department. Hospitalization, intensive care unit (ICU) admission, invasive mechanical ventilation (IMV), and death had higher odds in PI patients than in non-PI patients (hospitalization aOR: 2.36, 95% CI: 1.87-2.98; ICU admission aOR: 1.53, 95% CI: 1.19-1.96; IMV aOR: 1.41, 95% CI: 1.15-1.72; death aOR: 1.37, 95% CI: 1.08-1.74), and PI patients spent on average 1.91 more days in the hospital than non-PI patients when adjusted for age, sex, race/ethnicity, and chronic conditions associated with severe COVID-19. Of the largest four PI groups, selective deficiency of the immunoglobulin G subclass had the highest hospitalization frequency (75.2%). This large study of United States PI patients provides real-world evidence that PI is a risk factor for adverse COVID-19 outcomes.

Health Care Use among Medicare Beneficiaries with HIV and Depression during the COVID-19 Pandemic-United States, 2020

Access and use of health care services are essential to health and well-being for people with HIV and HIV-related comorbidities. Health care use during the COVID-19 pandemic among Medicare beneficiaries (MBs) with concurrent HIV and depression has not been investigated. We used 2020 Medicare data to assess the percentage of MBs with claims for HIV and depression who also received hospitalization, outpatient diagnostic services, drug treatment, and outpatient procedures. We assessed person-level association between service receipt and HIV and depression, adjusting for known risk factors. MBs with claims for HIV and depression were more likely than those with neither claim to have claims for short-stay hospitalization, long-stay hospitalization, outpatient diagnostic services, prescription drugs, or outpatient procedures, supplies, and products. Non-White beneficiaries were more likely than White beneficiaries to be hospitalized but were less likely to receive drug treatment, outpatient diagnostic services, or outpatient procedures, supplies, and products during the pandemic. Significant disparities in health care use by race/ethnicity existed among MBs. Policymakers and practitioners can use these findings to implement public health policies and programs that reduce disparities in health care access and optimize use among vulnerable populations during a public health emergency.

Hospitalization Associated With Comorbid Psychiatric and Substance Use Disorders Among Adults With COVID-19 Treated in US Emergency Departments From April 2020 to August 2021

Importance

During the COVID-19 pandemic, US emergency department (ED) visits for psychiatric disorders (PDs) and drug overdoses increased. Psychiatric disorders and substance use disorders (SUDs) independently increased the risk of COVID-19 hospitalization, yet their effect together is unknown.

Objective

To assess how comorbid PD and SUD are associated with the probability of hospitalization among ED patients with COVID-19.

Design, Setting, and Participants

This retrospective cross-sectional study analyzed discharge data for adults (age ≥18 years) with a COVID-19 diagnosis treated in 970 EDs and inpatient hospitals in the United States from April 2020 to August 2021.

Exposures

Any past diagnosis of (1) SUD from opioids, stimulants, alcohol, cannabis, cocaine, sedatives, or other substances and/or (2) PD, including attention-deficit/hyperactivity disorder (ADHD), anxiety, bipolar disorder, major depression, other mood disorder, posttraumatic stress disorder (PTSD), or schizophrenia.

Main Outcomes and Measures

The main outcome was any hospitalization. Differences in probability of hospitalization were calculated to assess its association with both PD and SUD compared with PD alone, SUD alone, or neither condition.

Results

Of 1 274 219 ED patients with COVID-19 (mean [SD] age, 54.6 [19.1] years; 667 638 women [52.4%]), 18.6% had a PD (mean age, 59.0 years; 37.7% men), 4.6% had a SUD (mean age, 50.1 years; 61.7% men), and 2.3% had both (mean age, 50.4 years; 53.1% men). The most common PDs were anxiety (12.9%), major depression (9.8%), poly (≥2) PDs (6.4%), and schizophrenia (1.4%). The most common SUDs involved alcohol (2.1%), cannabis (1.3%), opioids (1.0%), and poly (≥2) SUDs (0.9%). Prevalence of SUD among patients with PTSD, schizophrenia, other mood disorder, or ADHD each exceeded 21%. Based on significant specific PD-SUD pairs (Q < .05), probability of hospitalization of those with both PD and SUD was higher than those with (1) neither condition by a weighted mean of 20 percentage points (range, 6 to 36; IQR, 16 to 25); (2) PD alone by 12 percentage points (range, -4 to 31; IQR, 8 to 16); and (3) SUD alone by 4 percentage points (range, -7 to 15; IQR, -2 to 7). Associations varied by types of PD and SUD. Substance use disorder was a stronger predictor of hospitalization than PD.

Conclusions and Relevance

This study found that patients with both PD and SUD had a greater probability of hospitalization, compared with those with either disorder alone or neither disorder. Substance use disorders appear to have a greater association than PDs with the probability of hospitalization. Overlooking possible coexisting PD and SUD in ED patients with COVID-19 can underestimate the likelihood of hospitalization. Screening and assessment of both conditions are needed.

The Joint Public Health Impact of Family History of Diabetes and Cardiovascular Disease among Adults in the United States: A Population-Based Study

INTRODUCTION

Family history is an established risk factor for both cardiovascular disease (CVD) and diabetes; however, no study has presented population-based prevalence estimates of family histories of CVD and diabetes and examined their joint impact on prevalence of diabetes, CVD, cardiometabolic risk factors, and mortality risk.

METHODS

We analyzed data from a representative sample of the US adult population including 29,440 participants from the National Health and Nutrition Examination Survey (2007-2018) and assessed self-reported first-degree family history of diabetes and CVD (premature heart disease before age of 50 years) as well as meeting criteria and/or having risk factors for CVD and diabetes.

RESULTS

Participants with joint family history exhibit 6.5 greater odds for having both diseases and are diagnosed with diabetes 6.6 years earlier than participants without family history. Healthy participants without prevalent CVD or diabetes but with joint family history exhibit a greater prevalence of diabetes risk factors compared to no family history counterparts. Joint family history is associated with an increase in all-cause mortality, but with no interactive effect.

CONCLUSION

Over 44% of the US adult population has a family history of CVD and/or diabetes that is comparable in risk to common cardiometabolic risk factors. This wide presence of high-risk family history and its simplicity of ascertainment suggests that clinical and public health efforts should collect and act on joint family history of CVD and diabetes to improve population efforts in the prevention and early detection of these common chronic diseases.

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

BACKGROUND

Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications.

METHODS

We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC).

FINDINGS

In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683-0·717]).

INTERPRETATION

In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required.

FUNDING

British Journal of Surgery Society.

Provisional COVID-19 Age-Adjusted Death Rates, by Race and Ethnicity - United States, 2020-2021

Disparities in COVID-19 death rates by race and ethnicity have been reported in the United States (1,2). In response to these disparities, preventive, medical care, and social service assistance programs were implemented to lessen disparities in COVID-19 outcomes, including grants to support state, tribal, local, and territorial health department responses (3). The potential impact of such efforts on annual changes in racial and ethnic disparities in mortality rates that identify COVID-19 as the underlying cause of death has not been previously reported. This analysis used U.S. provisional mortality data from death certificates collected by CDC's National Vital Statistics System (NVSS) to estimate changes in COVID-19-related age-adjusted death rates (AADRs) by race and ethnicity during 2020-2021. Compared with non-Hispanic multiracial persons (the group with the lowest death rate), significant decreases in AADR ratios occurred during 2020-2021 among non-Hispanic American Indian or Alaska Native (AI/AN) persons (34.0%), non-Hispanic Asian (Asian) persons (37.6%), non-Hispanic Black or African American (Black) persons (40.2%), Hispanic persons (37.1%), and non-Hispanic White (White) persons (14%); a non-statistically significant 7.2% increase in AADR ratio occurred among non-Hispanic Native Hawaiian or other Pacific Islander (NH/OPI) persons. Despite reductions in AADR disparities from 2020 to 2021, large disparities in AADR by race and ethnicity remained in 2021. Providing effective preventive interventions, including vaccination and clinical care, to all communities in proportion to their need for these interventions is necessary to reduce racial and ethnic disparities in COVID-19 deaths.

Racial and ethnic differences in COVID-19 hospitalizations by metropolitan status among Medicare beneficiaries, 1 January-31 December 2020

BACKGROUND

Risk for COVID-19 hospitalizations increases with increasing age and presence of underlying medical conditions. However, the burden has not been well-assessed in metropolitan and nonmetropolitan areas by race/ethnicity among Medicare population with chronic conditions.

METHODS

We used the 2020 Medicare data to estimate COVID-19 hospitalization rates by race/ethnicity among Medicare beneficiaries for COVID-19 by metropolitan status and to assess the association of hospitalizations from COVID-19 with each of selected 29 chronic conditions for patients by metropolitan status and by race/ethnicity.

RESULTS

The COVID-19 hospitalization rate was higher among beneficiaries residing in nonmetropolitan counties than those residing in metropolitan counties in 2020. Approximately 1 in 2 AI/AN, 1 in 3 NHB, Hispanic and A/PI, and 1 in 4 NHW beneficiaries with COVID-19 residing in nonmetropolitan counties were hospitalized. Beneficiaries with COVID-19 and chronic conditions were more likely to be hospitalized compared with those without chronic conditions.

CONCLUSIONS

Hospitalization rates among beneficiaries with COVID-19 and chronic conditions were not distributed equally by race/ethnicity and by metropolitan status. Researchers, policymakers and practitioners can use these findings to explore more effective ways of reducing racial/ethnic and geographic disparities among minorities disproportionately affected by COVID-19 and are at highest risk of hospitalization.

COVID-19 Hospitalization by Race and Ethnicity: Association with Chronic Conditions Among Medicare Beneficiaries, January 1-September 30, 2020

OBJECTIVES

We assessed the association between hospitalization for illness from COVID-19 infection and chronic conditions among Medicare beneficiaries (MBs) with fee-for-service (FFS) claims by race and ethnicity for January 1-September 30, 2020.

METHODS

We used 2020 monthly Medicare data from January 1-September 30, 2020, reported to the Centers for Medicare and Medicaid Services to compute hospitalization rates per 100 COVID-19 MBs with FFS claims who were hospitalized (ICD-10-CM codes: B97.29 before April 1, 2020; ICD-10-CM codes: U07.1 from April 1, 2020, onward) with or without selected chronic conditions. We used logistic regression to estimate adjusted odds ratios with 95% confidence intervals for association of person-level rate of being hospitalized with COVID-19 and each of 27 chronic conditions by race/ethnicity, controlling for age, sex, and urban-rural residence among MBs.

RESULTS

COVID-19-related hospitalizations were associated with all selected chronic conditions, except osteoporosis and Alzheimer disease/dementia among COVID-19 MBs. The top five conditions with the highest odds for hospitalization among COVID-19 MBs were end-stage renal disease (adjusted odds ratios (aOR): 2.15; 95% CI: 2.10-2.21), chronic kidney disease (aOR: 1.54; 95% CI: 1.52-1.56), acute myocardial infarction (aOR: 1.45; 95% CI: 1.39-1.53), heart failure (aOR: 1.43; 95% CI: 1.41-1.44), and diabetes (aOR: 1.37; 95% CI: 1.36-1.39).

CONCLUSIONS

Racial/ethnic disparities in hospitalization rate persist among MBs with COVID-19, and associations of COVID-19 hospitalization with chronic conditions differ among racial/ethnic groups in the USA. These findings indicate the need for interventions in racial/ethnic populations at the highest risk of being hospitalized with COVID-19.

COVID-19 Hospitalization by Race and Ethnicity: Association with Chronic Conditions Among Medicare Beneficiaries, January 1-September 30, 2020

Objectives

We assessed the association between hospitalization for illness from COVID-19 infection and chronic conditions among Medicare beneficiaries (MBs) with fee-for-service (FFS) claims by race and ethnicity for January 1–September 30, 2020.

Methods

We used 2020 monthly Medicare data from January 1–September 30, 2020, reported to the Centers for Medicare and Medicaid Services to compute hospitalization rates per 100 COVID-19 MBs with FFS claims who were hospitalized (ICD-10-CM codes: B97.29 before April 1, 2020; ICD-10-CM codes: U07.1 from April 1, 2020, onward) with or without selected chronic conditions. We used logistic regression to estimate adjusted odds ratios with 95% confidence intervals for association of person-level rate of being hospitalized with COVID-19 and each of 27 chronic conditions by race/ethnicity, controlling for age, sex, and urban-rural residence among MBs.

Results

COVID-19-related hospitalizations were associated with all selected chronic conditions, except osteoporosis and Alzheimer disease/dementia among COVID-19 MBs. The top five conditions with the highest odds for hospitalization among COVID-19 MBs were end-stage renal disease (adjusted odds ratios (aOR): 2.15; 95% CI: 2.10–2.21), chronic kidney disease (aOR: 1.54; 95% CI: 1.52–1.56), acute myocardial infarction (aOR: 1.45; 95% CI: 1.39–1.53), heart failure (aOR: 1.43; 95% CI: 1.41–1.44), and diabetes (aOR: 1.37; 95% CI: 1.36–1.39).

Conclusions

Racial/ethnic disparities in hospitalization rate persist among MBs with COVID-19, and associations of COVID-19 hospitalization with chronic conditions differ among racial/ethnic groups in the USA. These findings indicate the need for interventions in racial/ethnic populations at the highest risk of being hospitalized with COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40615-020-00960-y.

Differential Association of HIV Funding With HIV Mortality by Race/Ethnicity, United States, 1999-2017

OBJECTIVE

Federal funds have been spent to reduce the disproportionate effects of HIV/AIDS on racial/ethnic minority groups in the United States. We investigated the association between federal domestic HIV funding and age-adjusted HIV death rates by race/ethnicity in the United States during 1999-2017.

METHODS

We analyzed HIV funding data from the Kaiser Family Foundation by federal fiscal year (FFY) and US age-adjusted death rates (AADRs) by race/ethnicity (Hispanic, non-Hispanic white, non-Hispanic black, and Asian/Pacific Islander and American Indian/Alaska Native [API+AI/AN]) from Centers for Disease Control and Prevention WONDER detailed mortality files. We fit joinpoint regression models to estimate the annual percentage change (APC), average APC, and changes in AADRs per billion US dollars in HIV funding, with 95% confidence intervals (CIs). For 19 data points, the number of joinpoints ranged from 0 to 4 on the basis of rules set by the program or by the user. A Monte Carlo permutation test indicated significant (P < .05) changes at joinpoints, and 2-sided t tests indicated significant APCs in AADRs.

RESULTS

Domestic HIV funding increased from $10.7 billion in FFY 1999 to $26.3 billion in FFY 2017, but AADRs decreased at different rates for each racial/ethnic group. The average rate of change in AADR per US billion dollars was -9.4% (95% CI, -10.9% to -7.8%) for Hispanic residents, -7.8% (95% CI, -9.0% to -6.6%) for non-Hispanic black residents, -6.7% (95% CI, -9.3% to -4.0%) for non-Hispanic white residents, and -5.2% (95% CI, -7.8% to -2.5%) for non-Hispanic API+AI/AN residents.

CONCLUSIONS

Increased domestic HIV funding was associated with faster decreases in age-adjusted HIV death rates for Hispanic and non-Hispanic black residents than for residents in other racial/ethnic groups. Increasing US HIV funding could be associated with decreasing future racial/ethnic disparities in the rate of HIV-related deaths.

Measuring the Magnitude of Health Inequality Between 2 Population Subgroup Proportions

In this paper, we evaluate 11 measures of inequality, d(p1, p2), between 2 proportions p1 and p2, some of which are new to the health disparities literature. These measures are selected because they are continuous, nonnegative, equal to 0 if and only if |p1 - p2| = 0, and maximal when |p1 - p2| = 1. They are also symmetrical [d(p1, p2) = d(p2, p1)] and complement-invariant [d(p1, p2) = d(1 - p2, 1 - p1)]. To study intermeasure agreement, 5 of the 11 measures, including the absolute difference, are retained, because they remain finite and are maximal if and only if |p1 - p2| = 1. Even when the 2 proportions are assumed to be drawn at random from a shared distribution-interpreted as the absence of an avoidable difference-the expected value of d(p1, p2) depends on the shape of the distribution (and the choice of d) and can be quite large. To allow for direct comparisons among measures, we propose a standard measurement unit akin to a z score. For skewed underlying beta distributions, 4 of the 5 retained measures, once standardized, offer more conservative assessments of the magnitude of inequality than the absolute difference. We conclude that, even for measures that share the highlighted mathematical properties, magnitude comparisons are most usefully assessed relative to an elicited or estimated underlying distribution for the 2 proportions.

County-Level Socioeconomic Disparities in Use of Medical Services for Management of Infections by Medicare Beneficiaries With Diabetes-United States, 2012

OBJECTIVE

To assess county-level socioeconomic disparities in medical service usage for infections among Medicare beneficiaries with diabetes (MBWDs) who had fee-for-service health insurance claims during 2012.

DESIGN

We used Medicare claims data to calculate percentage of MBWDs with infections.

SETTING

Medicare beneficiaries.

PARTICIPANTS

We estimated the percentage of MBWDs who used medical services for each of 3 groups of infections by sex and quintiles of the prevalence of social factors in the person's county of residence: anatomic site-specific infections; pathogen-specific infections; and HHST infections (human immunodeficiency virus/acquired immunodeficiency syndrome, viral hepatitis, sexually transmitted diseases, and tuberculosis).

MAIN OUTCOME MEASURES

Using quintiles of county-specific socioeconomic determinants, we calculated absolute and relative disparities in each group of infections for male and female MBWDs. We also used regression-based summary measures to estimate the overall average absolute and relative disparities for each infection group.

RESULTS

Of the 4.5 million male MBWDs, 15.8%, 25.3%, and 2.7% had 1 or more site-specific, pathogen-specific, and HHST infections, respectively. Results were similar for females (n = 5.2 million). The percentage of MBWDs with 1 or more infections in each group increased as social disadvantage in the MBWDs' county of residence increased. Absolute and relative county-level socioeconomic disparities in receipt of medical services for 1 or more infections (site- or pathogen-specific) were 12.9 or less percentage points and 65.5% or less, respectively. For HHST infections, percentage of MBWDs having 1 or more HHST infections for persons residing in the highest quintile (Q5) was 3- to 4-fold higher (P < .001) than persons residing in the lowest quintile (Q1).

CONCLUSIONS

Infection burden among MBWDs is generally associated with county-level contextual socioeconomic disadvantage, and the extent of health disparities varies by infection category, socioeconomic factor, and quintiles of socioeconomic disadvantage. The findings imply ongoing need for efforts to identify effective interventions for reducing county-level social disparities in infections among patients with diabetes.

Multidisciplinary guidance for safe tracheostomy care during the COVID-19 pandemic: the NHS National Patient Safety Improvement Programme (NatPatSIP)

The COVID-19 pandemic is causing a significant increase in the number of patients requiring relatively prolonged invasive mechanical ventilation and an associated surge in patients who need a tracheostomy to facilitate weaning from respiratory support. In parallel, there has been a global increase in guidance from professional bodies representing staff who care for patients with tracheostomies at different points in their acute hospital journey, rehabilitation and recovery. Of concern are the risks to healthcare staff of infection arising from tracheostomy insertion and caring for patients with a tracheostomy. Hospitals are also facing extraordinary demands on critical care services such that many patients who require a tracheostomy will be managed outside established intensive care or head and neck units and cared for by staff with little tracheostomy experience. These concerns led NHS England and NHS Improvement to expedite the National Patient Safety Improvement Programme's 'Safe Tracheostomy Care' workstream as part of the NHS COVID-19 response. Supporting this workstream, UK stakeholder organisations involved in tracheostomy care were invited to develop consensus guidance based on: expert opinion; the best available published literature; and existing multidisciplinary guidelines. Topics with direct relevance for frontline staff were identified. This consensus guidance includes: infectivity of patients with respect to tracheostomy indications and timing; aerosol-generating procedures and risks to staff; insertion procedures; and management following tracheostomy.

Prevalence and Cardiovascular Health Impact of Family History of Premature Heart Disease in the United States: Analysis of the National Health and Nutrition Examination Survey, 2007-2014

Background

Because family history is a known risk factor for heart disease, it is important to characterize its public health impact in terms of population prevalence of family history of heart disease, the burden of heart disease attributable to family history, and whether family history interacts with modifiable risk factors for heart disease.

Methods and Results

We used population data from NHANES (the National Health and Nutrition Examination Survey [2007–2014]) to measure the association of self‐reported family history of premature heart disease (FHPHD) with cardiovascular disease (n=19 253) and to examine the association between cardiovascular health metrics and FHPHD (n=16 248). Using logistic regression and multivariable adjustment, family history odds ratios were 5.91 (95% CI, 3.34–10.44) for ages 20 to 39, 3.02 (95% CI, 2.41–3.79) for ages 40 to 59, and 1.87 (95% CI, 1.54–2.28) for age ≥60 for cardiovascular disease. The prevalence of cardiovascular disease for the population with a FHPHD (15.72%; 95% CI, 13.81–17.64) was more than double the prevalence of cardiovascular disease for those without a family history (6.25%; 95% CI, 5.82–6.69). Compared with participants with optimum cardiovascular health, the prevalence ratio for FHPHD was 1.98 (95% CI, 1.40–2.79) for those with inadequate cardiovascular health.

Conclusions

Millions of people who are at high risk of having cardiovascular disease could be identified using FHPHD. FHPHD can become an important component of public health campaigns that address modifiable risk factors that plan to reduce the overall risk of heart disease.

Racial/Ethnic Health Disparities Among Rural Adults-United States, 2012-2015: <i>MMWR Surveillance Summaries</i> / November 17, 2017 / 66(23);1-9

PROBLEM/CONDITION

Rural communities often have worse health outcomes, have less access to care, and are less diverse than urban communities. Much of the research on rural health disparities examines disparities between rural and urban communities, with fewer studies on disparities within rural communities. This report provides an overview of racial/ethnic health disparities for selected indicators in rural areas of the United States.

REPORTING PERIOD

2012-2015.

DESCRIPTION OF SYSTEM

Self-reported data from the 2012-2015 Behavioral Risk Factor Surveillance System were pooled to evaluate racial/ethnic disparities in health, access to care, and health-related behaviors among rural residents in all 50 states and the District of Columbia. Using the National Center for Health Statistics 2013 Urban-Rural Classification Scheme for Counties to assess rurality, this analysis focused on adults living in noncore (rural) counties.

RESULTS

Racial/ethnic minorities who lived in rural areas were younger (more often in the youngest age group) than non-Hispanic whites. Except for Asians and Native Hawaiians and other Pacific Islanders (combined in the analysis), more racial/ethnic minorities (compared with non-Hispanic whites) reported their health as fair or poor, that they had obesity, and that they were unable to see a physician in the past 12 months because of cost. All racial/ethnic minority populations were less likely than non-Hispanic whites to report having a personal health care provider. Non-Hispanic whites had the highest estimated prevalence of binge drinking in the past 30 days.

INTERPRETATION

Although persons in rural communities often have worse health outcomes and less access to health care than those in urban communities, rural racial/ethnic minority populations have substantial health, access to care, and lifestyle challenges that can be overlooked when considering aggregated population data. This study revealed difficulties among non-Hispanic whites as well, primarily related to health-related risk behaviors. Across each population, the challenges vary.

PUBLIC HEALTH ACTION

Stratifying data by different demographics, using community health needs assessments, and adopting and implementing the National Culturally and Linguistically Appropriate Services Standards can help rural communities identify disparities and develop effective initiatives to eliminate them, which aligns with a Healthy People 2020 overarching goal: achieving health equity.

Unequal Declines in Absolute and Relative Disparities in HIV Diagnoses Among Black Women, United States, 2008 to 2016

OBJECTIVES

To assess changes in disparities of HIV diagnosis rates among Black women aged 18 years or older living in the United States.

METHODS

We calculated estimated annual percent changes (EAPCs) in annual diagnosis rates, rate differences (absolute disparity), and rate ratios (relative disparity) for groups (total, US-born, and non-US-born) of Black women (referent was all White women) with diagnosed HIV infection, using data reported to the National HIV Surveillance System.

RESULTS

Of 39 333 Black women who received an HIV diagnosis during 2008 to 2016, 21.4% were non-US-born. HIV diagnosis rates declined among all Black women, with the smallest decline among non-US-born groups (EAPC = -3.1; P ≤ .001). Absolute disparities declined for both US-born and non-US-born Black women; however, the relative disparity declined for Black women overall and US-born Black women, whereas it increased for non-US-born (including Caribbean- and Africa-born) Black women.

CONCLUSIONS

Differences in disparities in HIV diagnoses exist between US-, and non-US-born (specifically Caribbean- and Africa-born) Black women. Accounting for the heterogeneity of the Black women's population is crucial in measuring and monitoring progress toward eliminating health disparities among Black women.

The contribution of family history to the burden of diagnosed diabetes, undiagnosed diabetes, and prediabetes in the United States: analysis of the National Health and Nutrition Examination Survey, 2009-2014

PURPOSE

Given the importance of family history in the early detection and prevention of type 2 diabetes, we quantified the public health impact of reported family health history on diagnosed diabetes (DD), undiagnosed diabetes (UD), and prediabetes (PD) in the United States.

METHODS

We used population data from the National Health and Nutrition Examination Survey 2009-2014 to measure the association of reported family history of diabetes with DD, UD, and PD.

RESULTS

Using polytomous logistic regression and multivariable adjustment, family history prevalence ratios were 4.27 (confidence interval (CI): 3.57, 5.12) for DD, 2.03 (CI: 1.56, 2.63) for UD, and 1.26 (CI: 1.09, 1.44) for PD. In the United States, we estimate that 10.1 million DD cases, 1.4 million UD cases, and 3.9 million PD cases can be attributed to having a family history of diabetes.

CONCLUSION

These findings confirm that family history of diabetes has a major public health impact on diabetes in the United States. In spite of the recent interest and focus on genomics and precision medicine, family health history continues to be an integral component of public health campaigns to identify persons at high risk for developing type 2 diabetes and early detection of diabetes to prevent or delay complications.

Racial/Ethnic Health Disparities Among Rural Adults - United States, 2012-2015

PROBLEM/CONDITION

Rural communities often have worse health outcomes, have less access to care, and are less diverse than urban communities. Much of the research on rural health disparities examines disparities between rural and urban communities, with fewer studies on disparities within rural communities. This report provides an overview of racial/ethnic health disparities for selected indicators in rural areas of the United States.

REPORTING PERIOD

2012-2015.

DESCRIPTION OF SYSTEM

Self-reported data from the 2012-2015 Behavioral Risk Factor Surveillance System were pooled to evaluate racial/ethnic disparities in health, access to care, and health-related behaviors among rural residents in all 50 states and the District of Columbia. Using the National Center for Health Statistics 2013 Urban-Rural Classification Scheme for Counties to assess rurality, this analysis focused on adults living in noncore (rural) counties.

RESULTS

Racial/ethnic minorities who lived in rural areas were younger (more often in the youngest age group) than non-Hispanic whites. Except for Asians and Native Hawaiians and other Pacific Islanders (combined in the analysis), more racial/ethnic minorities (compared with non-Hispanic whites) reported their health as fair or poor, that they had obesity, and that they were unable to see a physician in the past 12 months because of cost. All racial/ethnic minority populations were less likely than non-Hispanic whites to report having a personal health care provider. Non-Hispanic whites had the highest estimated prevalence of binge drinking in the past 30 days.

INTERPRETATION

Although persons in rural communities often have worse health outcomes and less access to health care than those in urban communities, rural racial/ethnic minority populations have substantial health, access to care, and lifestyle challenges that can be overlooked when considering aggregated population data. This study revealed difficulties among non-Hispanic whites as well, primarily related to health-related risk behaviors. Across each population, the challenges vary.

PUBLIC HEALTH ACTION

Stratifying data by different demographics, using community health needs assessments, and adopting and implementing the National Culturally and Linguistically Appropriate Services Standards can help rural communities identify disparities and develop effective initiatives to eliminate them, which aligns with a Healthy People 2020 overarching goal: achieving health equity.

Measurement of Health Disparities, Health Inequities, and Social Determinants of Health to Support the Advancement of Health Equity

Reduction of health disparities and advancement of health equity in the United States require high-quality data indicative of where the nation stands vis-à-vis health equity, as well as proper analytic tools to facilitate accurate interpretation of these data. This article opens with an overview of health equity and social determinants of health. It then proposes a set of recommended practices in measurement of health disparities, health inequities, and social determinants of health at the national level to support the advancement of health equity, highlighting that (1) differences in health and its determinants that are associated with social position are important to assess; (2) social and structural determinants of health should be assessed and multiple levels of measurement should be considered; (3) the rationale for methodological choices made and measures chosen should be made explicit; (4) groups to be compared should be simultaneously classified by multiple social statuses; and (5) stakeholders and their communication needs can often be considered in the selection of analytic methods. Although much is understood about the role of social determinants of health in shaping the health of populations, researchers should continue to advance understanding of the pathways through which they operate on particular health outcomes. There is still much to learn and implement about how to measure health disparities, health inequities, and social determinants of health at the national level, and the challenges of health equity persist. We anticipate that the present discussion will contribute to the laying of a foundation for standard practice in the monitoring of national progress toward achievement of health equity.

Measuring health disparities: a comparison of absolute and relative disparities

Monitoring national trends in disparities in different diseases could provide measures to evaluate the impact of intervention programs designed to reduce health disparities. In the US, most of the reports that track health disparities provided either relative or absolute disparities or both. However, these two measures of disparities are not only different in scale and magnitude but also the temporal changes in the magnitudes of these measures can occur in opposite directions. The trends for absolute disparity and relative disparity could move in opposite directions when the prevalence of disease in the two populations being compared either increase or decline simultaneously. If the absolute disparity increases but relative disparity declines for consecutive time periods, the absolute disparity increases but relative disparity declines for the combined time periods even with a larger increase in absolute disparity during the combined time periods. Based on random increases or decreases in prevalence of disease for two population groups, there is a higher chance the trends of these two measures could move in opposite directions when the prevalence of disease for the more advantaged group is very small relative to the prevalence of disease for the more disadvantaged group. When prevalence of disease increase or decrease simultaneously for two populations, the increase or decrease in absolute disparity has to be sufficiently large enough to warrant a corresponding increase or decrease in relative disparity. When absolute disparity declines but relative disparity increases, there is some progress in reducing disparities, but the reduction in absolute disparity is not large enough to also reduce relative disparity. When evaluating interventions to reduce health disparities using these two measures, it is important to consider both absolute and relative disparities and consider all the scenarios discussed in this paper to assess the progress towards reducing or eliminating health disparities.

Vital signs: leading causes of death, prevalence of diseases and risk factors, and use of health services among Hispanics in the United States - 2009-2013

BACKGROUND

Hispanics and Latinos (Hispanics) are estimated to represent 17.7% of the U.S. population. Published national health estimates stratified by Hispanic origin and nativity are lacking.

METHODS

Four national data sets were analyzed to compare Hispanics overall, non-Hispanic whites (whites), and Hispanic country/region of origin subgroups (Hispanic origin subgroups) for leading causes of death, prevalence of diseases and associated risk factors, and use of health services. Analyses were generally restricted to ages 18-64 years and were further stratified when possible by sex and nativity.

RESULTS

Hispanics were on average nearly 15 years younger than whites; they were more likely to live below the poverty line and not to have completed high school. Hispanics showed a 24% lower all-cause death rate and lower death rates for nine of the 15 leading causes of death, but higher death rates from diabetes (51% higher), chronic liver disease and cirrhosis (48%), essential hypertension and hypertensive renal disease (8%), and homicide (96%) and higher prevalence of diabetes (133%) and obesity (23%) compared with whites. In all, 41.5% of Hispanics lacked health insurance (15.1% of whites), and 15.5% of Hispanics reported delay or nonreceipt of needed medical care because of cost concerns (13.6% of whites). Among Hispanics, self-reported smoking prevalences varied by Hispanic origin and by sex. U.S.-born Hispanics had higher prevalences of obesity, hypertension, smoking, heart disease, and cancer than foreign-born Hispanics: 30% higher, 40%, 72%, 89%, and 93%, respectively.

CONCLUSION

Hispanics had better health outcomes than whites for most analyzed health factors, despite facing worse socioeconomic barriers, but they had much higher death rates from diabetes, chronic liver disease/cirrhosis, and homicide, and a higher prevalence of obesity. There were substantial differences among Hispanics by origin, nativity, and sex.

IMPLICATIONS FOR PUBLIC HEALTH

Differences by origin, nativity, and sex are important considerations when targeting health programs to specific audiences. Increasing the proportions of Hispanics with health insurance and a medical home (patientcentered, team-based, comprehensive, coordinated health care with enhanced access) is critical. A feasible and systematic data collection strategy is needed to reflect health diversity among Hispanic origin subgroups, including by nativity.

Classification tree analysis of race-specific subgroups at risk for a central venous catheter-related bloodstream infection

BACKGROUND

Studies of racial disparities in patient safety events often do not use race-specific risk adjustment and do not account for reciprocal covariate interactions. These limitations were addressed by using classification tree analysis separately for black patients and white patients to identify characteristics that segment patients who have increased risks for a venous catheter-related bloodstream infection.

METHODS

A retrospective, cross-sectional analysis of 5,236,045 discharges from 103 Florida acute hospitals in 2005-2009 was conducted. Hospitals were rank ordered on the basis of the black/white Patient Safety Indicator (PSI) 7 rate ratio as follows: Group 1 (white rate higher), Group 2, (equivalent rates), Group 3, (black rate higher), and Group 4, (black rate highest). Predictor variables included 26 comorbidities (Elixhauser Comorbidity Index) and demographic characteristics. Four separate classification tree analyses were completed for each race/hospital group.

RESULTS

Individual characteristics and groups of characteristics associated with increased PSI 7 risk differed for black and white patients. The average age for both races was different across the hospital groups (p < .01). Weight loss was the strongest single delineator and common to both races. The black subgroups with the highest PSI 7 risk were Medicare beneficiaries who were either < or = 25.5 years without hypertension or < or = 39.5 years without hypertension but with an emergency or trauma admission. The white subgroup with the highest PSI 7 risk consisted of patients < or = 45.5 years who had congestive heart failure but did not have either hypertension or weight loss.

DISCUSSION

Identifying subgroups of patients at risk for a rare safety event such as PSI 7 should aid effective clinical decisions and efficient use of resources and help to guide patient safety interventions.

Health insurance coverage - United States, 2008 and 2010

One out of four adults aged 19-64 years reported not having health insurance at some time during 2011, with a majority remaining uninsured for ≥1 year. In the first quarter of 2010, an estimated 59.1 million persons had no health insurance for at least part of the year, an increase from 58.7 million in 2009 and 56.4 million in 2008. The unemployment rate increased from 5.8% to 9.3% from 2008 to 2009, the largest 1-year increase on record. Losing or changing jobs was the primary reason persons experienced a gap in health insurance. Employment-based coverage for persons aged <65 years continued to erode for the ninth year in a row, falling 3.0 percentage points from 61.9% in 2008 to 58.9% in 2009. Persons aged 18-64 years with no health insurance during the preceding year were seven times as likely as those continuously insured to forgo needed health care because of cost.

Disparity in race-specific comorbidities associated with central venous catheter-related bloodstream infection (AHRQ-PSI7)

Studies of racial disparities in hospital-level patient safety outcomes typically apply a race-common approach to risk adjustment. Risk factors specific to a minority population may not be identified in a race-common analysis if they represent only a small percentage of total cases. This study identified patient comorbidities and characteristics associated with the likelihood of a venous catheter-related bloodstream infection (Agency for Healthcare Research and Quality Patient Safety Indicator 7 [PSI7]) separately for blacks and whites using race-specific logistic regression models. Hospitals were ranked by the racial disparity in PSI7 and segmented into 4 groups. The analysis identified both black- and white-specific risk factors associated with PSI7. Age showed race-specific reverse association, with younger blacks and older whites more likely to have a PSI7 event. These findings suggest the need for race-specific covariate adjustments in patient outcomes and provide a new context for examining racial disparities.

Linear and non-linear associations of gonorrhea diagnosis rates with social determinants of health

Identifying how social determinants of health (SDH) influence the burden of disease in communities and populations is critically important to determine how to target public health interventions and move toward health equity. A holistic approach to disease prevention involves understanding the combined effects of individual, social, health system, and environmental determinants on geographic area-based disease burden. Using 2006–2008 gonorrhea surveillance data from the National Notifiable Sexually Transmitted Disease Surveillance and SDH variables from the American Community Survey, we calculated the diagnosis rate for each geographic area and analyzed the associations between those rates and the SDH and demographic variables. The estimated product moment correlation (PMC) between gonorrhea rate and SDH variables ranged from 0.11 to 0.83. Proportions of the population that were black, of minority race/ethnicity, and unmarried, were each strongly correlated with gonorrhea diagnosis rates. The population density, female proportion, and proportion below the poverty level were moderately correlated with gonorrhea diagnosis rate. To better understand relationships among SDH, demographic variables, and gonorrhea diagnosis rates, more geographic area-based estimates of additional variables are required. With the availability of more SDH variables and methods that distinguish linear from non-linear associations, geographic area-based analysis of disease incidence and SDH can add value to public health prevention and control programs.

Estimating the contribution of genetic variants to difference in incidence of disease between population groups

Genome-wide association studies have identified multiple genetic susceptibility variants to several complex human diseases. However, risk-genotype frequency at loci showing robust associations might differ substantially among different populations. In this paper, we present methods to assess the contribution of genetic variants to the difference in the incidence of disease between different population groups for different scenarios. We derive expressions for the contribution of a single genetic variant, multiple genetic variants, and the contribution of the joint effect of a genetic variant and an environmental factor to the difference in the incidence of disease. The contribution of genetic variants to the difference in incidence increases with increasing difference in risk-genotype frequency, but declines with increasing difference in incidence between the two populations. The contribution of genetic variants also increases with increasing relative risk and the contribution of joint effect of genetic and environmental factors increases with increasing relative risk of the gene-environmental interaction. The contribution of genetic variants to the difference in incidence between two populations can be expressed as a function of the population attributable risks of the genetic variants in the two populations. The contribution of a group of genetic variants to the disparity in incidence of disease could change considerably by adding one more genetic variant to the group. Any estimate of genetic contribution to the disparity in incidence of disease between two populations at this stage seems to be an elusive goal.

The ACE I/D polymorphism in US adults: limited evidence of association with hypertension-related traits and sex-specific effects by race/ethnicity

BACKGROUND

The insertion/deletion (I/D) variant (rs4646994) of the angiotensin I-converting enzyme (ACE) gene is one of the most studied polymorphisms in relation to blood pressure and essential hypertension in humans. The evidence to date, however, on an association of this variant with blood pressure-related outcomes has been inconclusive.

METHODS

We examined 5,561 participants of the Third National Health and Nutrition Examination Survey (NHANES III), a population-based and nationally representative survey of the United States, who were ≥20 years of age and who self-identified as non-Hispanic white, non-Hispanic black, or Mexican American. Within each race/ethnicity, we assessed genetic associations of the I/D variant with systolic blood pressure (SBP), diastolic blood pressure (DBP), and hypertension, as well as genotype-sex interactions, in four genetic models (additive, dominant, recessive, and codominant).

RESULTS

The frequency of the I/D variant differed significantly by race/ethnicity (P = 0.001). Among non-Hispanic blacks, the D allele was significantly associated (P < 0.05) with increased SBP in additive and dominant covariate-adjusted models and was also associated with increased DBP in dominant models when participants taking ACE inhibitors were excluded from the analyses. No other significant associations were observed in any race/ethnic group. Significant genotype-sex interactions were detected among Mexican Americans, for whom positive associations with SBP and hypertension were seen among females, but not males.

CONCLUSIONS

This study gives limited support for association of the ACE I/D variant with blood pressure and for sex-specific effects among particular race/ethnic groups, though we cannot rule out the role of genetic or environmental interactions.

Short report: Race and rickettsiae: a United States perspective

US surveillance programs for Rocky Mountain spotted fever (RMSF), ehrlichiosis, and anaplasmosis collect demographic data on patients, including race and ethnicity. Reporting of these diseases among race groups is not uniform across the United States. Because a laboratory confirmation is required to meet the national surveillance case definition, reporting may be influenced by a patient's access to healthcare. Determining the association between race and ethnicity with incidence of rickettsial infections requires targeted, active surveillance.

Predictive genetic testing for the identification of high-risk groups: a simulation study on the impact of predictive ability

Background

Genetic risk models could potentially be useful in identifying high-risk groups for the prevention of complex diseases. We investigated the performance of this risk stratification strategy by examining epidemiological parameters that impact the predictive ability of risk models.

Methods

We assessed sensitivity, specificity, and positive and negative predictive value for all possible risk thresholds that can define high-risk groups and investigated how these measures depend on the frequency of disease in the population, the frequency of the high-risk group, and the discriminative accuracy of the risk model, as assessed by the area under the receiver-operating characteristic curve (AUC). In a simulation study, we modeled genetic risk scores of 50 genes with equal odds ratios and genotype frequencies, and varied the odds ratios and the disease frequency across scenarios. We also performed a simulation of age-related macular degeneration risk prediction based on published odds ratios and frequencies for six genetic risk variants.

Results

We show that when the frequency of the high-risk group was lower than the disease frequency, positive predictive value increased with the AUC but sensitivity remained low. When the frequency of the high-risk group was higher than the disease frequency, sensitivity was high but positive predictive value remained low. When both frequencies were equal, both positive predictive value and sensitivity increased with increasing AUC, but higher AUC was needed to maximize both measures.

Conclusions

The performance of risk stratification is strongly determined by the frequency of the high-risk group relative to the frequency of disease in the population. The identification of high-risk groups with appreciable combinations of sensitivity and positive predictive value requires higher AUC.

The effects on population health status of using dedicated property taxes to fund local public health agencies

BACKGROUND

In the United States, a dedicated property tax describes the legal authority given to a local jurisdiction to levy and collect a tax for a specific purpose. We investigated for an association of locally dedicated property taxes to fund local public health agencies and improved health status in the eight states designated as the Mississippi Delta Region.

METHODS

We analyzed the difference in health outcomes of counties with and without a dedicated public health tax after adjusting for a set of control variables using regression models for county level data from 720 counties of the Mississippi Delta Region.

RESULTS

Levying a dedicated public health tax for counties with per capita income above $28,000 is associated with improved health outcomes of those counties when compared to counties without a dedicated property tax for public health. Alternatively, levying a dedicated property tax in counties with lower per capita income is associated with poor health outcomes.

CONCLUSIONS

There are both positive and negative consequences of using dedicated property taxes to fund public health. Policymakers should carefully examine both the positive association of improved health outcomes and negative impact of taxation on poor populations before authorizing the use of dedicated local property tax levies to fund public health agencies.

Rationale for regular reporting on health disparities and inequalities - United States

Most U.S. residents want a society in which all persons live long, healthy lives; however, that vision is yet to be realized fully. As two of its primary goals, CDC aims to reduce preventable morbidity and mortality and to eliminate disparities in health between segments of the U.S. population. The first of its kind, this 2011 CDC Health Disparities and Inequalities Report (2011 CHDIR) represents a milestone in CDC's long history of working to eliminate disparities. Health disparities are differences in health outcomes and their determinants between segments of the population, as defined by social, demographic, environmental, and geographic attributes. Health inequalities, which is sometimes used interchangeably with the term health disparities, is more often used in the scientific and economic literature to refer to summary measures of population health associated with individual- or group-specific attributes (e.g., income, education, or race/ethnicity). Health inequities are a subset of health inequalities that are modifiable, associated with social disadvantage, and considered ethically unfair. Health disparities, inequalities, and inequities are important indicators of community health and provide information for decision making and intervention implementation to reduce preventable morbidity and mortality. Except in the next section of this report that describes selected health inequalities, this report uses the term health disparities as it is defined in U.S. federal laws and commonly used in the U.S. public health literature to refer to gaps in health between segments of the population.

Inflammation gene variants and susceptibility to albuminuria in the U.S. population: analysis in the Third National Health and Nutrition Examination Survey (NHANES III), 1991-1994

BACKGROUND

Albuminuria, a common marker of kidney damage, serves as an important predictive factor for the progression of kidney disease and for the development of cardiovascular disease. While the underlying etiology is unclear, chronic, low-grade inflammation is a suspected key factor. Genetic variants within genes involved in inflammatory processes may, therefore, contribute to the development of albuminuria.

METHODS

We evaluated 60 polymorphisms within 27 inflammatory response genes in participants from the second phase (1991-1994) of the Third National Health and Nutrition Examination Survey (NHANES III), a population-based and nationally representative survey of the United States. Albuminuria was evaluated as logarithm-transformed albumin-to-creatinine ratio (ACR), as ACR ≥ 30 mg/g, and as ACR above sex-specific thresholds. Multivariable linear regression and haplotype trend analyses were conducted to test for genetic associations in 5321 participants aged 20 years or older. Differences in allele and genotype distributions among non-Hispanic whites, non-Hispanic blacks, and Mexican Americans were tested in additive and codominant genetic models.

RESULTS

Variants in several genes were found to be marginally associated (uncorrected P value < 0.05) with log(ACR) in at least one race/ethnic group, but none remained significant in crude or fully-adjusted models when correcting for the false-discovery rate (FDR). In analyses of sex-specific albuminuria, IL1B (rs1143623) among Mexican Americans remained significantly associated with increased odds, while IL1B (rs1143623), CRP (rs1800947) and NOS3 (rs2070744) were significantly associated with ACR ≥ 30 mg/g in this population (additive models, FDR-P < 0.05). In contrast, no variants were found to be associated with albuminuria among non-Hispanic blacks after adjustment for multiple testing. The only variant among non-Hispanic whites significantly associated with any outcome was TNF rs1800750, which failed the test for Hardy-Weinberg proportions in this population. Haplotypes within MBL2, CRP, ADRB2, IL4R, NOS3, and VDR were significantly associated (FDR-P < 0.05) with log(ACR) or albuminuria in at least one race/ethnic group.

CONCLUSIONS

Our findings suggest a small role for genetic variation within inflammation-related genes to the susceptibility to albuminuria. Additional studies are needed to further assess whether genetic variation in these, and untested, inflammation genes alter the susceptibility to kidney damage.

Improvements in ability to detect undiagnosed diabetes by using information on family history among adults in the United States

Family history is an independent risk factor for diabetes, but it is not clear how much adding family history to other known risk factors would improve detection of undiagnosed diabetes in a population. Using the National Health and Nutrition Examination Survey for 1999-2004, the authors compared logistic regression models with established risk factors (model 1) with a model (model 2) that also included familial risk of diabetes (average, moderate, and high). Adjusted odds ratios for undiagnosed diabetes, using average familial risk as referent, were 1.7 (95% confidence interval (CI): 1.2, 2.5) and 3.8 (95% CI: 2.2, 6.3) for those with moderate and high familial risk, respectively. Model 2 was superior to model 1 in detecting undiagnosed diabetes, as reflected by several significant improvements, including weighted C statistics of 0.826 versus 0.842 (bootstrap P = 0.001) and integrated discrimination improvement of 0.012 (95% CI: 0.004, 0.030). With a risk threshold of 7.3% (sensitivity of 40% based on model 1), adding family history would identify an additional 620,000 (95% CI: 221,100, 1,020,000) cases without a significant change in false-positive fraction. Study findings suggest that adding family history of diabetes can provide significant improvements in detecting undiagnosed diabetes in the US population. Further research is needed to validate the authors' findings.

A Hardy-Weinberg equilibrium test for analyzing population genetic surveys with complex sample designs

Testing for deviations from Hardy-Weinberg equilibrium is a widely recommended practice for population-based genetic association studies. However, current methods for this test assume a simple random sample and may not be appropriate for sample surveys with complex survey designs. In this paper, the authors present a test for Hardy-Weinberg equilibrium that adjusts for the sample weights and correlation of data collected in complex surveys. The authors perform this test by using a simple adjustment to procedures developed to analyze data from complex survey designs available within the SAS statistical software package (SAS Institute, Inc., Cary, North Carolina). Using 90 genetic markers from the Third National Health and Nutrition Examination Survey, the authors found that survey-adjusted and -unadjusted estimates of the disequilibrium coefficient were generally similar within self-reported races/ethnicities. However, estimates of the variance of the disequilibrium coefficient were significantly different between the 2 methods. Because the results of the survey-adjusted tests account for correlation among participants sampled within the same cluster, and the possibility of having related individuals sampled from the same household, the authors recommend use of this test when analyzing genetic data originating from sample surveys with complex survey designs to assess deviations from Hardy-Weinberg equilibrium.

Using lifetime risk estimates in personal genomic profiles: estimation of uncertainty

Personal genome tests are now offered direct-to-consumer (DTC) via genetic variants identified by genome-wide association studies (GWAS) for common diseases. Tests report risk estimates (age-specific and lifetime) for various diseases based on genotypes at multiple loci. However, uncertainty surrounding such risk estimates has not been systematically investigated. With breast cancer as an example, we examined the combined effect of uncertainties in population incidence rates, genotype frequency, effect sizes, and models of joint effects among genetic variants on lifetime risk estimates. We performed simulations to estimate lifetime breast cancer risk for carriers and noncarriers of genetic variants. We derived population-based cancer incidence rates from Surveillance, Epidemiology, and End Results (SEER) Program and comparative international data. We used data for non-Hispanic white women from 2003 to 2005. We derived genotype frequencies and effect sizes from published GWAS and meta-analyses. For a single genetic variant in FGFR2 gene (rs2981582), combination of uncertainty in these parameters produced risk estimates where upper and lower 95% simulation intervals differed by more than 3-fold. Difference in population incidence rates was the largest contributor to variation in risk estimates. For a panel of five genetic variants, estimated lifetime risk of developing breast cancer before age 80 for a woman that carried all risk variants ranged from 6.1% to 21%, depending on assumptions of additive or multiplicative joint effects and breast cancer incidence rates. Epidemiologic parameters involved in computation of disease risk have substantial uncertainty, and cumulative uncertainty should be properly recognized. Reliance on point estimates alone could be seriously misleading.

Evaluation of the discriminative accuracy of genomic profiling in the prediction of common complex diseases

Genetic testing for susceptibility to common diseases based on a combination of genetic markers may be needed because the effect size associated with each genetic marker is small. Whether or not a genome profile based on a combination of markers could yield a useful test can be evaluated by assessing the discriminative accuracy. The authors present a simple method to calculate the clinical discriminative accuracy of a genomic profile when the relative risk and genotype frequency of each genotype are known. In addition, the clinical discriminative accuracy of a genetic test is presented for given values of the heritability and prevalence of the disease and for the population-attributable fraction of the combined genetic markers. For given values of relative risk and genotype frequency, the discriminative accuracy increases with increasing heritability but declines with increasing prevalence of the disease. For a given value of population-attributable fraction, the discriminative accuracy increases with increasing relative risks, but declines with increasing genotype frequency. On the basis of population-attributable fraction and estimates of heritability of disease, the number of risk genotypes required to have a reasonable clinical discriminative accuracy is much higher than the genome profiles available at present.

The impact of genotype frequencies on the clinical validity of genomic profiling for predicting common chronic diseases

PURPOSE

Single genetic variants in multifactorial disorders typically have small effects, so major increases in disease risk are expected only from the simultaneous exposure to multiple risk genotypes. We investigated the impact of genotype frequencies on the clinical discriminative accuracy for the simultaneous testing of 40 independent susceptibility genetic variants.

METHODS

In separate simulation scenarios, we varied the genotype frequency from 1% to 50% and the odds ratio for each genetic variant from 1.1 to 2.0. Population size was 1 million and the population disease risk was 10%. Discriminative accuracy was quantified as the area under the receiver-operating characteristic curve. Using an example of genomic profiling for type 2 diabetes, we evaluated the area under the receiver-operating characteristic curve when the odds ratios and genotype frequencies varied between five postulated genetic variants.

RESULTS

When the genotype frequency was 1%, none of the subjects carried more than six of 40 risk genotypes, and when risk genotypes were frequent (> or =30%), all carried at least six. The area under the receiver-operating characteristic curve did not increase above 0.70 when the odds ratios were modest (1.1 or 1.25), but higher genotype frequency increased the area under the receiver-operating characteristic curve from 0.57 to 0.82 and from 0.63 to 0.93 when odds ratios were 1.5 or 2.0. The example of type 2 diabetes showed that the area under the receiver-operating characteristic curve did not change when differences in the odds ratios were ignored.

CONCLUSIONS

Given that the effects of susceptibility genes in complex diseases are small, the feasibility of future genomic profiling for predicting common diseases will depend substantially on the frequencies of the risk genotypes.

Prevalence in the United States of selected candidate gene variants: Third National Health and Nutrition Examination Survey, 1991-1994

Population-based allele frequencies and genotype prevalence are important for measuring the contribution of genetic variation to human disease susceptibility, progression, and outcomes. Population-based prevalence estimates also provide the basis for epidemiologic studies of gene-disease associations, for estimating population attributable risk, and for informing health policy and clinical and public health practice. However, such prevalence estimates for genotypes important to public health remain undetermined for the major racial and ethnic groups in the US population. DNA was collected from 7,159 participants aged 12 years or older in Phase 2 (1991-1994) of the Third National Health and Nutrition Examination Survey (NHANES III). Certain age and minority groups were oversampled in this weighted, population-based US survey. Estimates of allele frequency and genotype prevalence for 90 variants in 50 genes chosen for their potential public health significance were calculated by age, sex, and race/ethnicity among non-Hispanic whites, non-Hispanic blacks, and Mexican Americans. These nationally representative data on allele frequency and genotype prevalence provide a valuable resource for future epidemiologic studies in public health in the United States.

Genomic medicine and racial/ethnic health disparities: promises, perils, and the challenges for health care and public health policy

Scientific and policy debates following new genetic discoveries have been intense and emotional when they have involved questions about the causes of, and solutions for, racial and ethnic health disparities in the United States. The difference in prevalence of diseases, allele frequency and genotype frequency among racial/ethnic groups are well known. The genomic profile for a given disease could have different genetic variants for different racial/ethnic groups. Do these results indicate that we have to consider different genetic tests and different genomic medicine for different racial/ethnic groups? If we do this, what is the impact on ethnic and class disparities in health care services in the United States? Current advances in genetic medicine are very promising; however, we must consider the possible impacts of these findings on health disparities, and how genetic medicine can be extended to everyone, not just those who can pay the often high price. If genomic medicine is to be a valid and reliable technology for all citizens regardless of wealth, race, ethnicity, or other determinants of social disadvantage, public health policymakers have to consider a number of policy issues and implications.

Sample size requirements to detect the effect of a group of genetic variants in case-control studies

Background

Because common diseases are caused by complex interactions among many genetic variants along with environmental risk factors, very large sample sizes are usually needed to detect such effects in case-control studies. Nevertheless, many genetic variants act in well defined biologic systems or metabolic pathways. Therefore, a reasonable first step may be to detect the effect of a group of genetic variants before assessing specific variants.

Methods

We present a simple method for determining approximate sample sizes required to detect the average joint effect of a group of genetic variants in a case-control study for multiplicative models.

Results

For a range of reasonable numbers of genetic variants, the sample size requirements for the test statistic proposed here are generally not larger than those needed for assessing marginal effects of individual variants and actually decline with increasing number of genetic variants in many situations considered in the group.

Conclusion

When a significant effect of the group of genetic variants is detected, subsequent multiple tests could be conducted to detect which individual genetic variants and their combinations are associated with disease risk. When testing for an effect size in a group of genetic variants, one can use our global test described in this paper, because the sample size required to detect an effect size in the group is comparatively small. Our method could be viewed as a screening tool for assessing groups of genetic variants involved in pathogenesis and etiology of common complex human diseases.

Evaluating the ecological association of casino industry economic development on community health status: a natural experiment in the Mississippi delta region

Objectives of this study were to examine for associations of casino industry economic development on improving community health status and funding for public health services in two counties in the Mississippi Delta Region of the United States. An ecological approach was used to evaluate whether two counties with casino gaming had improved health status and public health funding in comparison with two noncasino counties in the same region with similar social, racial, and ethic backgrounds. Variables readily available from state health department records were used to develop a logic model for guiding analytical work. A linear regression model was built using a stepwise approach and hierarchical regression principles with many dependent variables and a set of fixed and nonfixed independent variables. County-level data for 23 variables over an 11-year period were used. Overall, this study found a lack of association between the presence of a casino and desirable health outcomes or funding for public health services. Changes in the environment were made to promote health by utilizing gaming revenues to build state-of-the-art community health and wellness centers and sports facilities. However, significant increases in funding for local public health services were not found in either of the counties with casinos. These findings are relevant for policy makers when debating economic development strategies. Analysis similar to this should be combined with other routine public health assessments after implementation of development strategies to increase knowledge of health outcome trends and shifts in socioeconomic position that may be expected to accrue from economic development projects.

Most published research findings are false-but a little replication goes a long way

While the authors agree with John Ioannidis that "most research findings are false," here they show that replication of research findings enhances the positive predictive value of research findings being true.