Nutrition health disparities include differences in incidence, prevalence, morbidity, and mortality of diet-related diseases and conditions. Often, race, ethnicity, and the social determinants of health are associated with dietary intake and related health disparities. This report describes the nutrition health disparities research supported by NIH over the past decade and offers future research opportunities relevant to NIH’s mission as described in the Strategic Plan for NIH Nutrition Research.
Data were extracted from an internal reporting system from FY2010 to FY2019 using the Research, Condition, and Disease Categorization spending categories for Nutrition and Health Disparities.
Over the past decade, NIH-supported nutrition and health disparities research increased, from 860 grants in 2010 to 937 grants in FY2019, whereas total nutrition and health disparities funding remained relatively stable. The top 5 Institutes/Centers that funded nutrition and health disparities research (on the basis of both grant numbers and dollars) were identified. Principal areas of focus included several chronic diseases (e.g., obesity, diabetes, cancer, heart disease) and research disciplines (e.g., clinical research and behavioral and social science). Focus areas related to special populations included pediatrics, minority health, aging, and women’s health.
The gaps and trends identified in this analysis highlight the need for future nutrition and health disparities research, including a focus on American Indian and Asian populations and the growing topics of rural health, maternal health, and food insecurity. In alignment with the Strategic Plan for NIH Nutrition Research, health equity may be advanced through innovative research approaches to develop effective targeted interventions to address these disparities.
In an analysis of the burden of disease in the U.S. by age, sex, geography, and year (1990–2016), poor diet was a leading risk factor of attributable disability-adjusted life years
(1 disability-adjusted life year represents the loss of the equivalent of 1 year of full health). Furthermore, evidence shows that diet-related health disparities vary by race and ethnicity, education, and income level.
Systematic reviews and meta-analyses have documented the associations between dietary factors and nutrients on chronic diseases such as cardiovascular diseases, diabetes, and cancer
and increased risk of mortality associated with diet-related disease.
Many subgroups experience higher rates of diet-related morbidity and mortality than the general population (e.g., across different socioeconomic, racial, and ethnic subpopulations).
For example, American Indians/Alaska Natives (AI/ANs) have a higher prevalence of type 2 diabetes than other groups.
Those living in rural communities are at a greater risk of mortality from heart disease and stroke than those living in urban communities.
In addition, there are racial differences in the incidence and mortality for certain cancers.
, Factors that contribute to NHDs are complex and may individually or synergistically affect inter-relationships impacting dietary intake, nutritional status, and health.
For example, healthy food access and affordability (societal influences) can intersect with cultural norms and practices (individual influences) to affect dietary behaviors and consequent risk for diet-related disease. Synergistic relationships can impact nutrition-related health outcomes and widen health disparities throughout the life course. Therefore, research to better understand the complex nutrition-related inter-relationships may lead to the development of tailored interventions that address these dynamics, promote minority health, and reduce health inequities. Moreover, the recent health-related burden caused by the coronavirus disease 2019 (COVID-19) pandemic on racial/ethnic minority populations (e.g., Black, Hispanic, and AI/AN communities) and the diet-related comorbidities (e.g., obesity, hypertension, diabetes, and some cancers) that exacerbate poor outcomes underscore the importance of exploring multidisciplinary approaches.
Data for extramural projects from FY2010 to FY2019 were extracted from NIH internal reporting systems and included the overlapping grants of 2 official Research, Condition, and Disease Categorization (RCDC) spending categories, Nutrition and Health Disparities. RCDC was utilized for the Nutrition project lists from FY2010 to FY2019 and for the Health Disparities project lists from FY2017 to FY2019. The NIH defines health disparities as a health difference that adversely affects disadvantaged populations on the basis of 1 or more health outcomes and health disparity populations as racial and ethnic minority populations and those less privileged such as low socioeconomic populations, underserved rural populations, sexual and gender minorities, and any subpopulations that can be characterized by 2 or more of these descriptions. Owing to the specific methodology for the development of the Health Disparities RCDC category, the NIH RCDC Process Budget Estimating Tool (R-BET) was used to obtain the Health Disparities project lists from FY2010 to FY2016. The R-BET system is an NIH internal budgeting tool used to collect a variety of funding data on disease categories and is used primarily for budget officers to generate estimates of those categories. Before FY2017, the R-BET system housed the manual categories (e.g., for minority health and health disparities) that were compiled separately by each individual NIH Institutes and Centers. Once these lists were established, the 2 Nutrition and Health Disparities categories were extracted and combined, and the overlap of projects was analyzed for each FY.
To further characterize the NHDs research portfolio, RCDC was also used to identify special populations using predefined NIH official spending categories: Maternal Health, Rural Health, American Indian or Alaska Native, Women’s Health, Aging, Minority Health, and Pediatrics within the combined nutrition and health disparities project list. Additional keyword searches to identify specific populations (African American/Black, Hispanic/Latino, and Asian) were done within the combined project portfolio. In the search, 291 grants were identified for African Americans/Blacks, 208 for Hispanics/Latinos, and 52 for Asians. A subset of authors (12) reviewed the abstracts and specific aims of each grant for relevance. Each grant was assigned 2 independent reviewers, and minor discrepancies were reconciled by the lead authors (AB, TAC).
Descriptive statistics were used to analyze the research portfolio. These included total spending by FY (all grants), number of total and new/renewal competitive grants (Type 1 and Type 2) (Type 1 New Grant: initial request for support of a project that has not yet been funded. Type 2 Renewal Grant: initial request for additional funding for a period subsequent to that provided by a current award) by FY, and budget mechanisms: Research Projects (R01, other Rs), Cooperative Agreements (Us), Program Projects and Center Grants (P01, Other Ps), and Research Career Programs (Ks) (note that not all training grants are shown as Ks consistently made up of 80%–90% of all training grants). The analysis mainly focused on competing Type 1 (new) and Type 2 (renewal) grants. Descriptive statistics are also presented for study type, the special population, and diseases and conditions. To compare shifts in research topics and subtopics between the first 5 years (FY2010–FY2014) and the latter 5 years (FY2015–FY2019), visualizations for each time period were compared using a clustering algorithm that uses words and phrases from funded grant applications (title, abstract, and specific aims fields).
From FY2010 to FY2014, a total of 954 new grants (48%) were identified, compared with 1,037 new grants (52%) from FY2015 to FY2019. On the basis of the NIH iSearch visualization tool, topics common between both time periods included weight loss, weight gain, insulin resistance, African Americans, long term, skeletal muscle, breast cancer, and vitamin D (data not shown). Prevalent NHDs areas that emerged from FY2015 to FY2019 included gut microbiota, food insecurity, AI/ANs, sodium intake, sugar-sweetened beverage consumption, and food allergies (data not shown).
This analysis explored the trends for NIH NHDs research from FY2010 to FY2019. To the authors’ knowledge, this analysis is unique because no previous NIH-wide portfolio analysis focused on NHDs. Over the past decade, there have been minimal changes in the number of new awards (i.e., Type 1 and Type 2) and in total funding for NHDs research. Since FY2012, total NHDs research funding remained stable, yet there was a 21% rise in the number of NHDs awards. This pattern can be explained, in part, by a shift in funding mechanisms over this time period, with a considerable increase in the number of newly funded training grants such as increased K award funding. Comparatively, K awards typically have smaller budgets than other funding mechanisms (e.g., R01s, U grants). These increases in funding for trainees and early-stage investigators beginning in FY2016 could be driven by the release of program announcements specific to stimulating mentored research scientist development awards (K01s) (e.g., PA-16-190). These findings signal the potential for increased NHDs research in the coming years as early-stage investigators advance in their research careers. Fluctuations in cooperative agreement (U mechanism) funding during the past decade have been driven by specific program announcements such as RFA-DK-14-501. There have been fluctuations in R01 funding, particularly from FY2016 to FY2017, as well as variations in the other R mechanisms. These fluctuations are largely owing to the changes in the health disparities category for FY2017. Overall, these findings suggest that NHDs research may be sensitive to the release of program announcements, and researchers are actively responsive to funding opportunities related to this research area. Although NIH funding increased between FY2010 and FY2019, it is not possible to definitively determine the precise factor in any given year that influences the number of relevant grants funded. The factors that influence the number of grants funded vary and include (but are not limited to) Institute and Center budgets and funding pay lines, funding opportunity announcements, Institute and Center priorities, national priorities, and overall NIH funding.
which explains the large percentage of human-funded grants instead of animal-based studies. In addition, foundational mechanistic studies examining the interplay between the various SDHs and genetics may lead to a better understanding of the inter-relationships between these driving factors to address these disparities. Related are the increases in funded research on topics such as the gut microbiome, sodium intake, sugar-sweetened beverage consumption, and food allergies. In addition, food insecurity and AI/ANs are additional opportunities for future research. AI/ANs are at increased risk for food insecurity and numerous diet-related diseases such as obesity, diabetes, and heart disease, warranting further research among this group.
Although Hispanic/Latino and African American/Black populations experience diet-related disparities at disproportionally higher rates than other racial/ethnic groups, the limited number of studies targeting the Asian population suggest the potential for more research, particularly with the growing Asian population in the U.S. and the increased risk of type 2 diabetes among South Asians.
Analyses of the research focus areas, based on the RCDC categories and keyword searches, revealed additional scientific gaps and areas of opportunity for research in the areas of Rural Health and Maternal Health. Accelerating research to prevent disparities in maternal morbidity and mortality is also of particular interest at NIH, and researchers are encouraged to utilize resources provided by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and NIH’s Office of Research on Women’s Health. Diet- and nutrition-related conditions related to pregnancy such as preeclampsia and gestational diabetes are worth further investigation. The National Heart, Lung, and Blood Institute–funded RURAL (Risk Underlying Rural Areas Longitudinal) Cohort Study, which will include dietary measures and several health-related outcomes, could also be leveraged by NHDs researchers for future ancillary studies or secondary data analysis. In addition, the National Cancer Institute Cancer Control Research in Persistent Poverty Areas (U54) provides an opportunity to examine or modify diet behaviors and outcomes among populations (e.g., rural, racial/ethnic) living in persistent poverty census tracts in partnerships with local communities, community-based organizations, and primary/local clinics/hospitals.
Because diet and nutrition play a key role in most chronic diseases and conditions, NHDs research has great potential to further contribute to the reduction of chronic disease morbidity and mortality rates and ultimately enhance population health.
Although this analysis includes many strengths, the results must be interpreted in the context of several limitations. First, the study data sources have had a variety of categorical definitions and methodologic changes in data collection over the years; however, the search categories provided an overall interpretation of year-to-year comparisons and funding trends. Second, this analysis is targeting the external research community and therefore excludes intramural projects, Research and Development contracts, and certain types of grants (e.g., SC1, SC2 mechanisms). Finally, this study was not designed to evaluate the cause of differences in funding (e.g., appropriateness of applications, review process, and award policies). Future analyses could examine unfunded NHDs applications and the success rates of these applications. Analyses of funded grants from other federal agencies could also identify the spectrum of gaps and opportunities in NHDs research.
This characterization and analysis of the NIH NHDs research portfolio from FY2010 to FY2019 highlights research gaps and potential opportunities to advance NHDs research. The higher mortality rates of COVID-19 among racial/ethnic minorities, people with lower SES, the aging population, and individuals with chronic conditions could be related to the higher prevalence of nutrition-related comorbidities, less optimal or constrained food access, and poor food quality among these groups and further underscores the timeliness of this research. In alignment with the SPNR’s cross-cutting theme and ongoing NIH efforts to address health disparities, health equity can be advanced through innovative research examining the influence of biological, behavioral, social, and structural factors on NHDs and developing effective targeted interventions to address these disparities.
The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute (NHLBI); Office of Nutrition Research; National Institute of Environmental Health Sciences; National Institute on Minority Health and Health Disparities; National Cancer Institute; National Institute on Aging (NIA); NIH; or HHS.
All authors were employees of NIH when they were actively engaged in work related to this paper. NIH is the sole source of support for the reported work. JEAB was a staff member at the NHLBI during the preparation of this manuscript but has since left NHLBI. GZ was a staff member at the NIA during the preparation of this manuscript but has since left NIA. NR was a staff member at NIMHD during the preparation of this manuscript but has since joined NHLBI.
No financial disclosures were reported by the authors of this paper.
CRediT AUTHOR STATEMENT
Alison G.M. Brown: Conceptualization, Formal analysis, Project administration, Visualization, Writing – original draft. Scarlet Shi: Data curation, Formal analysis, Visualization, Writing – original draft. Samantha Adas: Conceptualization, Data curation, Writing – review and editing. Josephine E.A. Boyington: Conceptualization, Data curation, Writing – review and editing. Paul A. Cotton: Conceptualization, Data curation, Writing – review and editing, Bill Jirles: Conceptualization, Data curation, Writing – review and editing. Nishadi Rajapakse: Conceptualization, Data curation, Writing – review and editing. Jill Reedy: Conceptualization, Data curation, Writing – review and editing. Karen Regan: Conceptualization, Data curation, Formal analysis, Writing – review and editing. Dan Xi: Conceptualization, Data curation, Writing – review and editing. Giovanna Zappalà: Conceptualization, Data curation, Writing – review and editing. Tanya Agurs-Collins: Data curation, Supervision, Writing – original draft.
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Published online: April 22, 2022
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