Top

BMC Medical Informatics and Decision Making

Published in:

Open Access 01-12-2023 | Nutrition | Research article

Associations between blood pressure control and clinical events suggestive of nutrition care documented in electronic health records of patients with hypertension

Authors: April R. Williams, Maria D. Thomson, Erin L. Britton

Published in: BMC Medical Informatics and Decision Making | Issue 1/2023

Abstract

Background

Clinical events suggestive of nutrition care found in electronic health records (EHRs) are rarely explored for their associations with hypertension outcomes.

Methods

Longitudinal analysis using structured EHR data from primary care visits at a health system in the US from December 2017—December 2020 of adult patients with hypertension (n = 4,237) tested for associations between last visit blood pressure (BP) control (≤ 140 Systolic BP and ≤ 90 Diastolic BP) and ≥ 1 nutrition care clinical event operationalized as (overweight or obesity (BMI > 25 or 30, respectively) diagnoses, preventive care visits, or provision of patient education materials (PEM)). Descriptive statistics and longitudinal targeted maximum likelihood estimation (LTMLE) models were conducted to explore average treatment effects (ATE) of timing and dose response from these clinical events on blood pressure control overall and by race.

Results

The median age was 62 years, 29% were male, 52% were Black, 25% were from rural areas and 50% had controlled BP at baseline. Annual documentation of overweight/obesity diagnoses ranged 3.0–7.8%, preventive care visits ranged 6.2–15.7%, and PEM with dietary and hypertension content were distributed to 8.5–28.8% patients. LTMLE models stratified by race showed differences in timing, dose, and type of nutrition care. Black patients who had nutrition care in Year 3 only compared to none had lower odds for BP control (ATE -0.23, 95% CI: -0.38,-0.08, p = 0.003), preventive visits in the last 2 years high higher odds for BP control (ATE 0.31, 95% CI: 0.07,0.54, p = 0.01), and early or late PEMs had lower odds for BP control (ATE -0.08, 95% CI: -0.15,-0.01, p = 0.03 and ATE -0.23, 95% CI: -0.41,-0.05, p = 0.01, respectively).

Conclusions

In this study, clinical events suggestive of nutrition care are significantly associated with BP control, but are infrequent and effects differ by type, timing, and patient race. Preventive visits appear to have the most effect; additional research should include examining clinical notes for evidence of nutrition care among different populations, which may uncover areas for improving nutrition care for patients with chronic disease.

Available only for authorised users

Ostchega Y, Fryar CD, Nwankwo T, Nguyen DT. Hypertension prevalence among adults aged 18 and over: United States, 2017–2018. NCHS Data Brief. 2020;364:1–8.

Whelton PK, Carey RM, Aronow WS, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults a report of the American College of Cardiology/American Heart Association Task Force on Clinical pr. Hypertension. 2018;71(6):E13–115. https://doi.org/10.1161/HYP.0000000000000065.CrossRefPubMed

Lacey K, Pritchett E. Nutrition care process and model: ADA adopts road map to quality care and outcomes management. J Am Diet Assoc. 2003;103(8):1061–72. https://doi.org/10.1016/S0002-8223(03)00971-4.CrossRefPubMed

Coa KI, Smith KC, Klassen AC, et al. Capitalizing on the “teachable moment” to promote healthy dietary changes among cancer survivors: the perspectives of health care providers. Support Care Cancer. 2015;23(3):679–86. https://doi.org/10.1007/s00520-014-2412-z.CrossRefPubMed

Grabovac I, Smith L, Stefanac S, et al. Health Care Providers’ Advice on Lifestyle Modification in the US Population: Results from the NHANES 2011–2016. Am J Med. 2019;132(4):489-497.e1. https://doi.org/10.1016/j.amjmed.2018.11.021.CrossRefPubMed

Williams AR, Wilson-Genderson M, Thomson MD. A cross-sectional analysis of associations between lifestyle advice and behavior changes in patients with hypertension or diabetes: NHANES 2015–2018. Prev Med. 2021;145:106426. https://doi.org/10.1016/j.ypmed.2021.106426.CrossRefPubMed

Lyles CR, Gupta R, Tieu L, Fernandez A. After-visit summaries in primary care: mixed methods results from a literature review and stakeholder interviews. Fam Pract. 2019;36(2):206–13. https://doi.org/10.1093/fampra/cmy045.CrossRefPubMed

Kravetz JD, Walsh RF. Team-based hypertension management to improve blood pressure control. J Prim Care Community Health. 2016;7(4):272–5. https://doi.org/10.1177/2150131916645580.CrossRefPubMedPubMedCentral

Kaboli PJ, Howren MB, Ishani A, Carter B, Christensen AJ, Vander Weg MW. Efficacy of Patient Activation Interventions With or Without Financial Incentives to Promote Prescribing of Thiazides and Hypertension Control. JAMA Netw Open. 2018;1(8):e185017. https://doi.org/10.1001/jamanetworkopen.2018.5017.CrossRefPubMedPubMedCentral

10.

Kushner RFF. Barriers to Providing Nutrition Counseling by Physicians: A Survey of Primary Care Practitioners. Prev Med. 1995;24(6):546–52. https://doi.org/10.1006/PMED.1995.1087.CrossRefPubMed

11.

Bardach SH, Schoenberg NE. Primary care physicians’ prevention counseling with patients with multiple morbidity. Qual Health Res. 2012;22(12):1599–611. https://doi.org/10.1177/1049732312458183.CrossRefPubMedPubMedCentral

12.

Aspry KE, Linda Van Horn C, Chair Jo Ann Carson VS, et al. Medical Nutrition Education, Training, and Competencies to Advance Guideline-Based Diet Counseling by Physicians. Circulation. 2018;137(1):821–41. https://doi.org/10.1161/CIR.0000000000000563

13.

Hosomura N, Goldberg SI, Shubina M, Zhang M, Turchin A. Electronic documentation of lifestyle counseling and glycemic control in patients with diabetes. Diabetes Care. 2015;38(7):1326–32. https://doi.org/10.2337/dc14-2016.CrossRefPubMed

14.

Bae J, Hockenberry JM, Rask KJ, Becker ER. Evidence that electronic health records can promote physician counseling for healthy behaviors. Health Care Manage Rev. 2017;42(3):258–68. https://doi.org/10.1097/HMR.0000000000000108.CrossRefPubMed

15.

Watterson JL, Rodriguez HP, Aguilera A, Shortell SM. Ease of use of electronic health records and relational coordination among primary care team members. Health Care Manage Rev. 2020;45(3):267–75. https://doi.org/10.1097/HMR.0000000000000222.CrossRefPubMed

16.

CQF. 2019 HEDIS ® Measures Controlling High Blood Pressure (CBP) HEDIS Measure Description. www.ncqa.org. Accessed 21 Dec 2019.

17.

USDA ERS - Rural-Urban Continuum Codes. https://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx. Accessed 23 Oct 2019.

18.

HCUP Methods Series Comorbidity Software Documentation. https://www.hcup-us.ahrq.gov/reports/methods/2004_1.jsp.

19.

HEDIS MY 2020 Medication List Directory. https://store.ncqa.org/hedis-my-2020-medication-list-directory.html. Accessed 18 June 2021.

20.

Obesity in adults: Overview of management - UpToDate. https://www-uptodate-com.proxy.library.vcu.edu/contents/obesity-in-adults-overview-of-management?search=obesity%20management&source=search_result&selectedTitle=1~150&usage_type=default&display_rank=1. Accessed 16 July 2022.

21.

US Preventive Services Task Force, Curry SJ, Krist AH, et al. Behavioral weight loss interventions to prevent obesity-related morbidity and mortality in adults: US Preventive services task force recommendation Statement. JAMA. 2018;320(11):1163. https://doi.org/10.1001/jama.2018.13022.CrossRef

22.

Diet in the treatment and prevention of hypertension - UpToDate. https://www.uptodate.com/contents/diet-in-the-treatment-and-prevention-of-hypertension. Accessed 16 Aug 2019.

23.

Casey DE, Thomas RJ, Bhalla V, et al. 2019 AHA/ACC clinical performance and quality measures for adults with high blood pressure: a report of the American college of cardiology/American heart association task force on performance measures. Circ Cardiovasc Qual Outcomes. 2019;12(11):e000057. https://doi.org/10.1161/HCQ.0000000000000057.CrossRefPubMedPubMedCentral

24.

Petersen M, Schwab J, Gruber S, Blaser N, Schomaker M, van der Laan M. Targeted maximum likelihood estimation for dynamic and static longitudinal marginal structural working models. J Causal Inference. 2014;2(2):147–85. https://doi.org/10.1515/jci-2013-0007.CrossRefPubMedPubMedCentral

25.

Schnitzer ME, van der Laan MJ, Moodie EEM, Platt RW. LTMLE with Clustering. In: van der Laan MJ, Rose S, eds. Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies. Springer Series in Statistics. Springer International Publishing; 2018:233–251. https://doi.org/10.1007/978-3-319-65304-4_15.

26.

Lendle SD, Schwab J, Petersen ML, van der Laan MJ. ltmle: an R package implementing targeted minimum loss-based estimation for longitudinal data. J Stat Softw. 2017;81:1–21. https://doi.org/10.18637/jss.v081.i01.CrossRef

27.

Ward JB, Gartner DR, Keyes KM, Fliss MD, McClure ES, Robinson WR. How do we assess a racial disparity in health? Distribution, interaction, and interpretation in epidemiological studies. Ann Epidemiol. 2019;29:1–7. https://doi.org/10.1016/j.annepidem.2018.09.007.CrossRefPubMed

28.

HEDIS MY 2020 Medication List Directory. https://store.ncqa.org/hedis-my-2020-medication-list-directory.html. Accessed 18 June 2021

29.

Stange KC, Etz RS, Gullett H, et al. Metrics for assessing improvements in primary health care. Annu Rev Public Health. 2014;35:423–42. https://doi.org/10.1146/annurev-publhealth-032013-182438.CrossRefPubMedPubMedCentral

30.

Foti K, Hardy ST, Chang AR, Selvin E, Coresh J, Muntner P. BMI and blood pressure control among United States adults with hypertension. J Hypertens. 2022;40(4):741–8. https://doi.org/10.1097/HJH.0000000000003072.CrossRefPubMedPubMedCentral

31.

Mattar A, Carlston D, Sariol G, et al. The prevalence of obesity documentation in primary care electronic medical records: Are we acknowledging the problem? Appl Clin Inform. 2017;8(1):67–79. https://doi.org/10.4338/ACI-2016-07-RA-0115.CrossRefPubMedPubMedCentral

32.

Antognoli EL, Seeholzer EL, Gullett H, Jackson B, Smith S, Flocke SA. Primary Care Resident Training for Obesity, Nutrition, and Physical Activity Counseling: A Mixed-Methods Study. Health Promot Pract. 2017;18(5):672–80. https://doi.org/10.1177/1524839916658025.CrossRefPubMed

33.

Nutrition and Weight Status | Healthy People 2020. https://www.healthypeople.gov/2020/topics-objectives/topic/nutrition-and-weight-status. https://www.healthypeople.gov/2020/topics-objectives/topic/nutrition-and-weight-status. Accessed 16 Mar 2020.

34.

Powell W, Richmond J, Mohottige D, Yen I, Joslyn A, Corbie-Smith G. Medical mistrust, racism, and delays in preventive health screening among African-American men. Behav Med Wash DC. 2019;45(2):102–17. https://doi.org/10.1080/08964289.2019.1585327.CrossRef

35.

Ravenell JE, Whitaker EE, Johnson WE. According to him: barriers to healthcare among African-American men. J Natl Med Assoc. 2008;100(10):1153–60. https://doi.org/10.1016/s0027-9684(15)31479-6.CrossRefPubMed

36.

Hammond WP, Matthews D, Mohottige D, Agyemang A, Corbie-Smith G. Masculinity, medical mistrust, and preventive health services delays among community-dwelling African-American Men. J Gen Intern Med. 2010;25(12):1300–8. https://doi.org/10.1007/s11606-010-1481-z.CrossRefPubMedPubMedCentral

37.

Cheatham CT, Barksdale DJ, Rodgers SG. Barriers to health care and health-seeking behaviors faced by Black men. J Am Acad Nurse Pract. 2008;20(11):555–62. https://doi.org/10.1111/j.1745-7599.2008.00359.x.CrossRefPubMed

38.

Krist AH, Peele E, Woolf SH, et al. Designing a patient-centered personal health record to promote preventive care. BMC Med Inform Decis Mak. 2011;11(1):1–11. https://doi.org/10.1186/1472-6947-11-73.CrossRef

39.

McGuinness JE, Zhang TM, Cooper K, et al. Extraction of Electronic Health Record Data using Fast Healthcare Interoperability Resources for Automated Breast Cancer Risk Assessment. AMIA Annu Symp Proc. 2022;2021:843–52.PubMedPubMedCentral

40.

Krist AH. Electronic Health Record Innovations for Healthier Patients and Happier Doctors. J Am Board Fam Med JABFM. 2015;28(3):299–302. https://doi.org/10.3122/jabfm.2015.03.150097.CrossRefPubMed

41.

Schooley B, Singh A, Hikmet N, Brookshire R, Patel N. Integrated digital patient education at the bedside for patients with chronic conditions: observational study. JMIR MHealth UHealth. 2020;8(12):e22947. https://doi.org/10.2196/22947.CrossRefPubMedPubMedCentral

42.

Brownson RC, Eyler AA, Harris JK, Moore JB, Tabak RG. Getting the word out: new approaches for disseminating public health science. J Public Health Manag Pract. 2018;24(2):102–11. https://doi.org/10.1097/PHH.0000000000000673.CrossRefPubMedPubMedCentral

43.

Casey JA, Schwartz BS, Stewart WF, Adler NE. Using electronic health records for population health research: a review of methods and applications HHS public access. Annu Rev Public Health. 2016;37:61–81. https://doi.org/10.1146/annurev-publhealth-032315-021353.CrossRefPubMed

Title: Associations between blood pressure control and clinical events suggestive of nutrition care documented in electronic health records of patients with hypertension
Authors: April R. Williams
Maria D. Thomson
Erin L. Britton
Publication date: 01-12-2023
Publisher: BioMed Central
Keywords: Nutrition
Care
Hypertension
Overweight
Obesity
Obesity
Published in: BMC Medical Informatics and Decision Making / Issue 1/2023
Electronic ISSN: 1472-6947
DOI: https://doi.org/10.1186/s12911-023-02311-3

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Associations between blood pressure control and clinical events suggestive of nutrition care documented in electronic health records of patients with hypertension

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2023

The effect of capacity building evidence-based medicine training on its implementation among healthcare professionals in Southwest Ethiopia: a controlled quasi-experimental outcome evaluation

Decision curve analysis confirms higher clinical utility of multi-domain versus single-domain prediction models in patients with open abdomen treatment for peritonitis

A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability

Artificial intelligence for non-mass breast lesions detection and classification on ultrasound images: a comparative study

Shared decision making for anticoagulation reduces anxiety and improves adherence in patients with atrial fibrillation

Anomaly prediction of CT equipment based on IoMT data