Ramal Moonesinghe

Centers for Disease Control and Prevention

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Muin J. Khoury

Office of Public Health Genomics

Benedict I. Truman

Centers for Disease Control and Prevention

Man‐Huei Chang

Centers for Disease Control and Prevention

Ana Penman‐Aguilar

Office of Minority Health and Health Equity

Peggy A. Honoré

Louisiana State University Health Sciences Center New Orleans

Gloria L. Beckles

Centers for Disease Control and Prevention

Quanhe Yang

CDC Foundation

Karen Bouye

Centers for Disease Control and Prevention

Nicole F. Dowling

National Center for Immunization and Respiratory Diseases

Ajay Yesupriya

Albany State University

Cooperative Institutions

Centers for Disease Control and Prevention

Center for Surveillance, Epidemiology, and Laboratory Services

National Center for Chronic Disease Prevention and Health Promotion

National Center for HIV/AIDS Viral Hepatitis STD and TB Prevention

Emory University

The Centers

Office of Public Health Genomics

National Center on Birth Defects and Developmental Disabilities

AID Atlanta

United States Department of Health and Human Services

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A Scoping Review of Electronic Health Records–Based Screening Algorithms for Familial Hypercholesterolemia

JACC Advances (2024)

Jeffery Osei Alexander C. Razavi Baffour Otchere Gracelove Bonful Natalie Akoto

Familial hypercholesterolemia (FH) is a common genetic disorder that is strongly associated with premature cardiovascular disease. Effective diagnosis and appropriate treatment of FH can reduce cardiovascular disease risk; however, FH is underdiagnosed. Electronic health record (EHR)-based FH screening tools have been previously described to enhance the detection of FH. This scoping review explored the available literature on the performance and utility of existing EHR-based FH screening algorithms or tools. We searched PubMed, CINAHL, and Embase from inception to October 2023 for relevant literature on the performance, utility, and/or implementation of EHR-based screening algorithms for FH. Of 14 screening algorithms and/or tools identified in the 27 studies included in this review, Familial Hypercholesterolemia Case Ascertainment Tool (1, 2, and ML), FIND FH algorithm, Mayo SEARCH, and TARB-Ex demonstrated the highest performance metrics for identifying patients with FH. EHR-based screening tools hold great potential for improving population-level FH detection. Lack of established diagnostic criteria that can be applied across diverse populations and the lack of information about the performance, utility, and implementation of current EHR-based screening tools across diverse populations limit the current use of these tools.

Health records

Electronic health record

10.1016/j.jacadv.2024.101297

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Citations (2)

Measuring health disparities: a comparison of absolute and relative disparities

PeerJ (2015)

Ramal Moonesinghe Gloria L. Beckles

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.

Absolute risk reduction

Absolute (philosophy)

10.7717/peerj.1438

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Citations (31)

Racial/Ethnic Disparities in Mortality: Contributions and Variations by Rurality in the United States, 2012–2015

International Journal of Environmental Research and Public Health (2019)

Jeffrey E. Hall Ramal Moonesinghe Karen Bouye Ana Penman‐Aguilar

The value of disaggregating non-metropolitan and metropolitan area deaths in illustrating place-based health effects is evident. However, how place interacts with characteristics such as race/ethnicity has been less firmly established. This study compared socioeconomic characteristics and age-adjusted mortality rates by race/ethnicity in six rurality designations and assessed the contributions of mortality rate disparities between non-Hispanic blacks (NHBs) and non-Hispanic whites (NHWs) in each designation to national disparities. Compared to NHWs, age-adjusted mortality rates for: (1) NHBs were higher for all causes (combined), heart disease, malignant neoplasms, and cerebrovascular disease; (2) American Indian and Alaska Natives were significantly higher for all causes in rural areas; (3) Asian Pacific islanders and Hispanics were either lower or not significantly different in all areas for all causes combined and all leading causes of death examined. The largest contribution to the U.S. disparity in mortality rates between NHBs and NHWs originated from large central metropolitan areas. Place-based variations in mortality rates and disparities may reflect resource, and access inequities that are often greater and have greater health consequences for some racial/ethnic populations than others. Tailored, systems level actions may help eliminate mortality disparities existing at intersections between race/ethnicity and place.

Rurality

10.3390/ijerph16030436

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Citations (11)

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

PubMed (2011)

Benedict I. Truman Kay C Smith Kakoli Roy Zhuo Chen Ramal Moonesinghe

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.

Race and health

Population Health

Social Inequality

Disadvantage

Source

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Citations (78)

The association between family history of asthma and the prevalence of asthma among US adults: National Health and Nutrition Examination Survey, 1999–2004

Genetics in Medicine (2009)

Tiebin Liu Rodolfo Valdéz Paula W. Yoon Deidre Crocker Ramal Moonesinghe

Association (psychology)

10.1097/gim.0b013e31819d3015

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Citations (50)

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

MMWR Surveillance Summaries (2017)

Cara James Ramal Moonesinghe Shondelle M. Wilson‐Frederick Jeffrey E. Hall Ana Penman‐Aguilar

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.2012-2015.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.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.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.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.

Rurality

Rural Health

10.15585/mmwr.ss6623a1

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Citations (243)

Measuring the Magnitude of Health Inequality Between 2 Population Subgroup Proportions

American Journal of Epidemiology (2020)

Makram Talih Ramal Moonesinghe David T. Huang

Abstract 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.

Complement

Percentage point

10.1093/aje/kwaa050

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Citations (1)

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

Emerging Themes in Epidemiology (2008)

Ramal Moonesinghe Quanhe Yang Muin J. Khoury

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.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.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.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.

Sample (material)

Statistic

genetic model

10.1186/1742-7622-5-24

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Citations (5)

A refinement to ‘how many genes underlie the occurrence of common complex diseases in the population?’

International Journal of Epidemiology (2005)

Ramal Moonesinghe

10.1093/ije/dyi288

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Citations (2)