Cardiovascular Disease Risk Management During COVID-19: In-Person vs Virtual Visits

ABSTRACT

Objectives: Limited research has assessed how virtual care (VC) affects cardiovascular disease (CVD) risk management, especially in community clinic settings. This study assessed change in community clinic patients’ CVD risk management during the COVID-19 pandemic and CVD risk factor control among patients who had primarily in-person or primarily VC visits.

Study Design: Retrospective interrupted time-series analysis.

Methods: Data came from an electronic health record shared by 52 community clinics for index (March 1, 2019, to February 29, 2020) and follow-up (July 1, 2020, to February 28, 2022) periods. Analyses compared follow-up period changes in slope and level of population monthly means of 10-year reversible CVD risk score, blood pressure (BP), and hemoglobin A_1c (HbA_1c) among patients whose completed follow-up period visits were primarily in person vs primarily VC. Propensity score weighting minimized confounding.

Results: There were 10,028 in-person and 6593 VC patients in CVD risk analyses, 9874 in-person and 5390 VC patients in BP analyses, and 8221 in-person and 4937 VC patients in HbA1c analyses. The VC group was more commonly younger, female, White, and urban. Mean reversible CVD risk, mean systolic BP, and percentage of BP measurements that were 140/90 mm Hg or higher increased significantly from index to follow-up periods in both groups. Rate of change between these periods was the same for all outcomes in both groups, regardless of care modality.

Conclusions: Among community clinic patients with CVD risk, receiving a majority of care in person vs a majority of care via VC was not significantly associated with longitudinal trends in reversible CVD risk score or key CVD risk factors.

Am J Manag Care. 2024;30(1):e11-e18. https://doi.org/10.37765/ajmc.2024.89489

Takeaway Points

Among primary care community clinic patients with high cardiovascular disease (CVD) risk, in the period following the onset of the COVID-19 pandemic, receipt of mostly in-person vs mostly virtual care was not associated with rate of change in CVD risk or CVD risk factor management. No significant difference in these measures over time was associated with mode of care. Furthermore, mean CVD risk was lower at the end of the study period than projected based on prepandemic trends, regardless of care mode. This indicates that virtual and in-person care can yield similar outcomes when managing CVD risk among low-income patients.

• Results add knowledge by focusing on virtual care for community clinic patients, whose rates of CVD and uncontrolled risk factors are high and for whom a virtual care option might mitigate common access barriers (eg, transportation insecurity).
• Results indicate the importance of reimbursing safety net clinics for providing virtual care and of policies that expand equitable access to virtual care.
• More research is needed on the optimal ratio of in-person to virtual visits for CVD risk management and other health elements managed in primary care settings and on the circumstances in which CVD risk management via virtual care is most and least effective.

Management of cardiovascular disease (CVD) risk (ie, risk of having an event such as heart attack or stroke) is a common focus of primary care. In response to the COVID-19 pandemic’s onset, many primary care practices began offering telephone and video visits, a care delivery mode called synchronous virtual care (VC).^1-5 This spike in primary care visits delivered virtually^6-8 provided an opportunity to examine whether CVD risk management delivered in this manner yields different outcomes than care delivered in person. This is important because although VC rates have declined since their peak, VC continues to be a common care delivery mode.⁹

Results of prior studies indicated that VC may be associated with management of CVD risk factors (eg, blood pressure [BP], hemoglobin A_1c [HbA_1c]) similar to in-person care.^10-15 However, we know of no prior evidence on overall CVD risk management associated with VC in primary care settings serving socioeconomically vulnerable populations, such as community-based safety net clinics.¹⁶ Understanding the relationship between VC and CVD risk management is important in these settings, as community clinics’ low-income, medically/socially complex patients have high rates of unmanaged CVD risk^17,18 and transportation-related barriers to accessing care.¹⁹ We examined change over time in CVD risk management when patients received care in person vs through VC in the 2 years post pandemic onset (March 2020 to February 2022) and compared with the year before the pandemic (March 2019 to February 2020).

METHODS

OCHIN, the study setting, is a national network of primary care safety net clinics; its members (96 community clinic organizations running 493 clinic sites in 14 states as of September 2018) share a centrally managed instance of the Epic electronic health record (EHR). Shortly after the pandemic’s onset, OCHIN members’ VC capabilities were expanded to include embedded/facilitated VC functionality within the EHR, including video and telephone options; a rapidly increased number of patients received care via these modalities.²⁰

In 2018, the CV Wizard clinical decision support system was activated in 70 OCHIN clinics for a trial of its effectiveness; the analyses presented here used data from these clinics.^21-23 CV Wizard’s algorithms process EHR data to generate a 10-year reversible CVD risk estimate (hereafter called CVD risk) and evidence-based care recommendations for patients meeting specific risk criteria (ie, aged 40-75 years with [1] a > 10% risk of a CV event in the next 10 years attributable to uncontrolled CVD risk factors, [2] diabetes and ≥ 1 uncontrolled CVD risk factor, or [3] CVD and ≥ 1 uncontrolled CVD risk factor).²³ Study data on CVD risk score came from CV Wizard. All other data were extracted from the parent trial clinics’ EHR and made research ready by the Accelerating Data Value Across a National Community Health Center clinical research network of PCORnet.²⁴

Clinical outcomes related to CVD risk were examined: CVD risk, systolic BP (SBP), diastolic BP (DBP), and HbA_1c. Analyses compared change in these outcomes during a prepandemic index period (March 2019 to February 2020) and a follow-up period (July 2020 to February 2022). March 2020 through June 2020 was considered a washout period during which the study’s clinics established VC capacity and CV Wizard was set up for use in VC.

The analysis population included patients with at least 1 index period primary care visit at 1 of the 70 study clinics at which they met CV Wizard’s risk criteria and at least 1 primary care visit in the follow-up period. For BP and HbA_1c analyses, at least 1 relevant measurement in the index and follow-up periods was also required. BP data could be self-reported during VC or collected during in-person visits. HbA_1c data came from a laboratory test for which results were received in the patient’s EHR from a clinic or external laboratory. Blood draws for HbA_1c (unassociated with an ambulatory visit) were not counted as in-person visits. Clinics with less than 10% of visits coded as VC in the follow-up period were excluded, as was another clinic at which key outcomes of interest were rarely recorded during the follow-up period. Patients who died during the study period were excluded. For each outcome, population-level monthly means were calculated for the index (12 time points) and follow-up (20 time points) periods.

The independent variable was created by categorizing patients based on how many primary care visits in the follow-up period were in person vs VC: (1) the majority of visits (> 50%) were in person or evenly split between in person and VC (in-person group) or (2) the majority were VC (VC group). Analyses compared these groups regarding how index period trends in the monthly mean outcome measures changed in the follow-up period.

Interrupted time-series analysis was used to account for index period trends when assessing VC impacts on follow-up period outcomes. To evaluate the presence of immediate or long-term impacts, models included both a level term and a slope-change term. Models used multilevel segmented regression. A random effect for clinic was included to account for clinic-level clustering. Monthly mean data over time were plotted to assess trends visually and to assess nonstationarity and seasonality. Although some seasonality was apparent in the CVD risk and BP outcomes, these effects were minimal, and Durbin-Watson statistics^25,26 were nonsignificant at 6- and 12-month lags. These statistics and autocorrelation and partial autocorrelation function plots determined the nature of autocorrelation; because strong serial autocorrelation was indicated, a first-order autoregressive structure was added to the model. A log transformation was applied to eliminate skew in outcome variables. Both improved model fit.

Inverse propensity score weighting was used to minimize potential confounding caused by index period differences between comparison groups. Weighting was based on patient age, race/ethnicity, gender, language, number of index period clinic visits, federal poverty level, most common payer across visits, and level of rurality of patient residence (determined using zip codes linked to US Department of Agriculture Economic Research Service Rural-Urban Commuting Area codes and assigned using University of Washington Rural Research Health Center–recommended categories). To create the propensity score, these variables were put into a logistic regression model with the VC/in-person category as the dependent variable. Stabilized inverse propensity score weights were used to reduce type I errors. After applying the weights, there was less than 10% absolute standardized difference between the groups in all covariates, indicating good balance.²⁷

We also assessed COVID-19 status during the follow-up period in the comparison groups. eAppendix Table 1 (eAppendix available at ajmc.com) lists the diagnostic codes used for this purpose.

RESULTS

Patients from 52 clinics were included; 957 patients (0.5% of initial population) died during the study period and were excluded. CVD risk analyses included 10,028 (60.3%) in-person and 6593 (39.7%) VC patients, BP analyses included 9874 (64.7%) in-person and 5390 (35.3%) VC patients, and HbA_1c analyses included 8221 (62.5%) in-person and 4937 (37.5%) VC patients. Characteristics of the CVD risk analysis population are in Table 1 [part A and part B], including distribution of visits that were VC vs in person; those of the BP and HbA_1c subsets are in eAppendix Tables 2 and 3. A higher percentage of the VC group patients were female and White compared with the in-person group. A higher percentage of the VC group visits were by urban residents and were paid by Medicaid. As noted, methods were used to minimize confounding that this might cause.

Table 2 displays unadjusted outcome measures for both comparison groups. In the index period, mean CVD risk and BP were higher in the in-person group than the VC group, mean HbA_1c level was higher in the VC group, the proportion of measurements at which CVD risk was elevated was not different between the groups, rate of uncontrolled BP was higher in the in-person group, and rate of measurements with uncontrolled HbA_1c was higher in the VC group. In most cases, these differences were significant but small. Table 2 also shows the unadjusted changes in outcomes between the index and follow-up periods. After propensity score weighting, the parameter in the regression model representing the difference in index period intercept between groups was not significant.

These results also show the unadjusted overall changes in CVD risk between the index and follow-up periods. Mean CVD risk score increased in both groups (in person: 9.2 to 10.6; VC: 9.0 to 10.1) as did proportion of measurements with more than 10% risk (from 34.9% to 37.8% and from 34.5% to 38.4%, respectively). Mean SBP increased in both groups (from 131.2 to 133.2 mm Hg and from 129.6 to 131.0 mm Hg, respectively), mean DBP was static in both groups, and the proportion of BP measurements 140/90 mm Hg or higher increased in both groups (from 30.9% to 34.0% and from 28.0% to 30.3%, respectively; these differences were significant at P < .0001). In both groups, mean HbA_1c level was static.

The in-person group had a significant upward index period trend in CVD risk (P = .004) with no significant change for other outcome measures (P = .51 for SBP, P = .46 for DBP, and P = .91 for HbA_1c). Between the end of the index period and the start of the follow-up period (ie, the washout period), there was a 2.2% (P = .006) increase in this group’s mean SBP and a 3.4% (P = .04) decrease in mean HbA_1c. Neither mean CVD risk nor mean DBP changed.

Differences in change in the outcomes of interest between the comparison groups during the washout period are relevant to understanding follow-up period results (Figures 1, 2, and 3). During the washout period, change in CVD risk, SBP, and DBP was not significantly different between comparison groups. HbA_1c level increased 5.2% more in the VC group than the in-person group (P = .04).

The difference in slope of change over time between the prepandemic onset index period and the post–pandemic onset follow-up period was the same between groups for all outcomes, with no significant difference in longer-term impact.

Mean CVD risk increased for both groups in the index period but leveled off for the in-person group during follow-up, whereas the VC group mean decreased slightly but not significantly (Figure 1). The counterfactual dashed lines show that both groups had lower mean CVD risk at the end of the study period than that projected based on index period trends. Mean HbA_1c was stable for both groups in the index period, with no significant change in the follow-up period (Figure 2). For BP (Figure 3), little change was seen in the index period; during follow-up, the mean SBP of the in-person group increased initially but then flattened, whereas the VC group’s mean SBP dropped slightly and continued a modest upward trajectory, ultimately ending below the predicted counterfactual. For DBP, both groups trended downward in the follow-up period.

DISCUSSION

Among community clinic patients, mean CVD risk and SBP increased slightly following the pandemic’s onset, which aligns with findings of prior research.^28-32 Pandemic-related drivers of risk include patients having difficulty maintaining CVD risk reduction behaviors.¹ The fact that SBP naturally increases with age³³ likely explains some of the higher SBP over time observed here and some of the CVD risk increase.

The increased CVD risk and SBP in the first few months post pandemic onset gradually corrected after some time; DBP and HbA_1c followed similar patterns. Because DBP increases into one’s 50s and declines thereafter, given the age distribution of this population, we would expect DBP to be flat or slightly decrease over time.³⁴ That these measures improved more than expected based on prepandemic trends might reflect a post–pandemic onset correction to care over time in the study clinics.³⁵

The rates of increase of mean CVD risk and SBP were not significantly different between comparison groups, suggesting that in these circumstances, CVD risk management was of similar quality regardless of care delivery mode. It is possible that VC visits were used selectively to tailor care to patients’ needs and/or used more often for quick assessment/follow-up and to increase care accessibility and that in-person visits were used more often when in-depth physical assessment and more specialized treatment were needed.

These findings align with prior research showing that CVD risk management through VC yields outcomes similar to those associated with in-person visits,³⁶ although little prior research involved community clinic populations.^{10,12,14,37-40} Results from one study found that VC was associated with worse BP control but not among patients with 1 or more recorded BP measurements, perhaps reflecting poor documentation rather than inferior BP control.¹⁵ Synchronous VC has also been associated with outcomes similar to those from in-person care for hospitalizations and emergency department visits among patients with heart failure, but this evidence is limited⁴¹ and results vary.⁴² Research is needed to better understand the circumstances in which CVD risk management via VC is most effective. For example, VC visits may be more effective when patients have an established relationship with a provider and less ideal for new patient visits.

These results add knowledge by focusing on community clinic patients, whose rates of CVD and uncontrolled risk factors are higher than those of the general population.⁴³ The finding that CVD risk management was as effective when delivered mostly via VC vs mostly in person holds particular promise for community clinic patients, as a VC option for care receipt might mitigate some access barriers for these patients (eg, transportation insecurity) without affecting care quality.⁴⁴ Transportation insecurity negatively affects health care access and outcomes, especially among individuals with lower income and those with chronic conditions.⁴⁵ However, VC also has the potential to increase disparities in access and health outcomes because individuals without the resources needed to engage in VC (eg, broadband internet) may not benefit from VC equitably.^46-54 Policies related to VC should ensure that its potential benefits are distributed equitably; this may involve expanding access to high-speed internet and other resources needed to use such technologies.

Limitations

Analysis results can be generalized only to community clinic patients who accessed care during the periods prior to and after the pandemic’s onset. The differences between comparison groups at the start of the follow-up period warrant consideration because the groups were defined by the visits they had during that period, which could have been influenced by their CVD risk management status or practice decisions about when to use in-person or VC visits. Propensity score weighting addressed these differences, but contextual reasons why patients received VC vs in-person care may still have influenced mode of care.

In sum, the in-person group had somewhat higher mean CVD risk and SBP levels at the start of follow-up than the VC group and somewhat lower HbA_1c levels. This might have influenced care received in the follow-up period if clinics emphasized seeing patients in person based on their most recent BP. Yet the mean number of total visits in the follow-up period was higher in the VC group, indicating more points of engagement with the clinic. The interrupted time-series analysis with inverse propensity score weighting is intended to mitigate such potential bias because the in-person group was well balanced with the VC group on outcome variables, as seen through the model output and patient characteristics, through weighting. Furthermore, it was not feasible to assess whether CVD risk management was a focus of these visits. This is a minor limitation, as all included patients had high risk of CVD, and CVD risk management is often discussed in community clinics regardless of the main purpose of the visit.

Analyses were limited to clinics that had previously volunteered for a study on CVD risk management, with the potential that this received particular emphasis in these clinics. However, CVD risk management is of importance to all primary care community clinics because of its health impacts and because management of specific CVD risk factors is a reported quality metric. These analyses also excluded clinics with low VC provision rates, which may differ from those willing and able to implement this change. Analyses also excluded patients who died during follow-up. Results may not be generalizable to the sickest patients.

Comparison group categories reflect whether the majority of a given patient’s visits were in person or VC; results do not reflect the impact of receiving all care virtually. However, these categories reflect real-world practice, where a combination of care modes is likely. Research is needed to assess whether specific proportions of in-person to VC visits optimize outcomes and whether there is a threshold of proportion of VC visits below which care outcomes are less optimized. Furthermore, we were unable to distinguish between different virtual care modalities (eg, video or telephone) because modality is not well documented or easily determined using EHR data.

Results relating to BP were likely affected by practices beginning to document BP from patient-reported measures in the postpandemic period. Home-based measures are known to trend lower than those taken at in-person visits.⁵⁵ Postpandemic BP trends in both groups should be interpreted noting that they differed categorically from the pre–pandemic onset trends. It is also possible that BP measures in VC visits are lower than those taken in the office setting due to preferential patient reporting of lower BP measures.

Although just 10% to 14% of the analysis populations had a COVID-19 diagnosis in the follow-up period, it was statistically more likely in the VC group. In sensitivity analyses excluding persons who received a diagnosis of COVID-19 during follow-up, results were not meaningfully different than those in the original models; details are available upon request.

All follow-up period data came from the first 2 years post pandemic onset, a period in which primary care provision was substantially disrupted. Therefore, results do not indicate the comparability of in-person care vs VC in a more typical period. Nevertheless, this period of rapid change in care delivery provides an opportunity for initial assessments of CVD risk management outcomes in VC. Our team’s companion article published in this issue assesses the quality of CVD preventive care at in-person vs VC visits in the same population analyzed here, which will illuminate this potential; further research is needed to understand these patterns.

CONCLUSIONS

In the 2 years post pandemic onset among community clinic patients with high risk of CVD, having a majority of visits via VC was associated with CVD risk management outcomes that did not differ from having a majority of visits in person. This supports reimbursing safety net clinics for providing VC as part of increasing care accessibility and policies designed to expand equitable access to VC. Research is needed on the optimal ratio of in-person to VC visits in terms of CVD risk management and other health elements in primary care settings.

Acknowledgments

The authors would like to thank Nadia Yosuf, MPH, and Jenny Hauschildt, MPH, for their contributions to this project. This work was conducted with the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) Clinical Research Network. OCHIN leads the ADVANCE network in partnership with Health Choice Network, Fenway Health, and Oregon Health & Science University. ADVANCE is funded through the Patient-Centered Outcomes Research Institute, contract No. RI-CRN-2020-001.

Author Affiliations: Kaiser Permanente Center for Health Research (RG, CRS), Portland, OR; OCHIN (NC, JD, AEL, DB, BMM), Portland, OR; HealthPartners Institute (PJO), Minneapolis, MN; Center for Community Health Integration, Case Western Reserve University (KCS), Cleveland, OH.

Source of Funding: Research reported in this publication was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health (NIH) under award No. R01HL133793 and the National Institute on Minority Health and Health Disparities of the NIH under award No. R01MD016389. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Author Disclosures: Dr O’Connor reported receiving research funding from NIH. Dr Stange reported being a contact investigator with OCHIN and being a principal investigator on 2 related NIH grants. The remaining authors reported no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (RG, NC, AEL, CRS, DB, PJO); analysis and interpretation of data (RG, NC, JD, AEL, DB, BMM, KCS); drafting of the manuscript (RG, NC, JD, AEL, DB); critical revision of the manuscript for important intellectual content (RG, NC, JD, AEL, CRS, PJO, BMM, KCS); statistical analysis (JD, BMM); obtaining funding (RG, PJO); administrative, technical, or logistic support (RG, NC, CRS); supervision (RG); and linkage with a related study (KCS).

Address Correspondence to: Rachel Gold, PhD, MPH, Kaiser Permanente Center for Health Research, 3800 N Interstate Ave, Portland, OR 97227. Email: rachel.gold@kpchr.org.

REFERENCES

1. Alexander GC, Tajanlangit M, Heyward J, Mansour O, Qato DM, Stafford RS. Use and content of primary care office-based vs telemedicine care visits during the COVID-19 pandemic in the US. JAMA Netw Open. 2020;3(10):e2021476. doi:10.1001/jamanetworkopen.2020.21476

2. Hamadi HY, Zhao M, Haley DR, Dunn A, Paryani S, Spaulding A. Medicare and telehealth: the impact of COVID-19 pandemic. J Eval Clin Pract. 2022;28(1):43-48. doi:10.1111/jep.13634

3. Patel SY, Mehrotra A, Huskamp HA, Uscher-Pines L, Ganguli I, Barnett ML. Variation in telemedicine use and outpatient care during the COVID-19 pandemic in the United States. Health Aff (Millwood). 2021;40(2):349-358. doi:10.1377/hlthaff.2020.01786

4. Chang JE, Lai AY, Gupta A, Nguyen AM, Berry CA, Shelley DR. Rapid transition to telehealth and the digital divide: implications for primary care access and equity in a post-COVID era. Milbank Q. 2021;99(2):340-368. doi:10.1111/1468-0009.12509

5. Mehrotra A, Ray K, Brockmeyer DM, Barnett ML, Bender JA. Rapidly converting to “virtual practices”: outpatient care in the era of Covid-19. NEJM Catalyst. April 1, 2020. Accessed July 15, 2023. https://catalyst.nejm.org/doi/full/10.1056/CAT.20.0091

6. Callaghan T, McCord C, Washburn D, et al. The changing nature of telehealth use by primary care physicians in the United States. J Prim Care Community Health. 2022;13:21501319221110418. doi:10.1177/21501319221110418

7. Der-Martirosian C, Chu K, Steers WN, et al. Examining telehealth use among primary care patients, providers, and clinics during the COVID-19 pandemic. BMC Prim Care. 2022;23(1):155. doi:10.1186/s12875-022-01738-3

8. Friedman AB, Gervasi S, Song H, et al. Telemedicine catches on: changes in the utilization of telemedicine services during the COVID-19 pandemic. Am J Manag Care. 2022;28(1):e1-e6. doi:10.37765/ajmc.2022.88771

9. Shaver J. The state of telehealth before and after the COVID-19 pandemic. Prim Care. 2022;49(4):517-530. doi:10.1016/j.pop.2022.04.002

10. Baughman DJ, Jabbarpour Y, Westfall JM, et al. Comparison of quality performance measures for patients receiving in-person vs telemedicine primary care in a large integrated health system. JAMA Netw Open. 2022;5(9):e2233267. doi:10.1001/jamanetworkopen.2022.33267

11. Lewinski AA, Walsh C, Rushton S, et al. Telehealth for the longitudinal management of chronic conditions: systematic review. J Med Internet Res. 2022;24(8):e37100. doi:10.2196/37100

12. Levine DM, Dixon RF, Linder JA. Association of structured virtual visits for hypertension follow-up in primary care with blood pressure control and use of clinical services. J Gen Intern Med. 2018;33(11):1862-1867. doi:10.1007/s11606-018-4375-0

13. McAlister FA, Hsu Z, Dong Y, Tsuyuki RT, van Walraven C, Bakal JA. Frequency and type of outpatient visits for patients with cardiovascular ambulatory-care sensitive conditions during the COVID-19 pandemic and subsequent outcomes: a retrospective cohort study. J Am Heart Assoc. 2023;12(3):e027922. doi:10.1161/JAHA.122.027922

14. Mabeza RMS, Maynard K, Tarn DM. Influence of synchronous primary care telemedicine versus in-person visits on diabetes, hypertension, and hyperlipidemia outcomes: a systematic review. BMC Prim Care. 2022;23(1):52. doi:10.1186/s12875-022-01662-6

15. Ye S, Anstey DE, Grauer A, et al. The impact of telemedicine visits on the controlling high blood pressure quality measure during the COVID-19 pandemic: retrospective cohort study. JMIR Form Res. 2022;6(3):e32403. doi:10.2196/32403

16. Lau J, Knudsen J. Reducing disparities in telemedicine: an equity-focused, public health approach. Health Aff (Millwood). 2022;41(5):647-650. doi:10.1377/hlthaff.2022.00314

17. Understanding health disparities in cardiovascular disease. CDC. September 2014. Accessed December 7, 2023. https://www.cdc.gov/dhdsp/pubs/docs/sib_feature_nov2014.pdf

18. High cholesterol facts. CDC. Updated May 15, 2023. Accessed December 7, 2023. https://www.cdc.gov/cholesterol/facts.htm

19. Acquah I, Hagan K, Valero-Elizondo J, et al. Delayed medical care due to transportation barriers among adults with atherosclerotic cardiovascular disease. Am Heart J. 2022;245:60-69. doi:10.1016/j.ahj.2021.11.019

20. Cook N, McGrath BM, Navale SM, et al. Care delivery in community health centers before, during, and after the COVID-19 pandemic (2019-2022). J Am Board Fam Med. Published online October 19, 2023. doi:10.3122/jabfm.2023.230081R1

21. Gold R, Middendorf M, Heintzman J, et al. Challenges involved in establishing a web-based clinical decision support tool in community health centers. Healthc (Amst). 2020;8(4):100488. doi:10.1016/j.hjdsi.2020.100488

22. Sperl-Hillen JM, Rossom RC, Kharbanda EO, et al. Priorities Wizard: multisite web-based primary care clinical decision support improved chronic care outcomes with high use rates and high clinician satisfaction rates. EGEMS (Wash DC). 2019;7(1):9. doi:10.5334/egems.284

23. Gold R, Larson AE, Sperl-Hillen JM, et al. Effect of clinical decision support at community health centers on the risk of cardiovascular disease: a cluster randomized clinical trial. JAMA Netw Open. 2022;5(2):e2146519. doi:10.1001/jamanetworkopen.2021.46519

24. DeVoe JE, Gold R, Cottrell E, et al. The ADVANCE network: accelerating data value across a national community health center network. J Am Med Inform Assoc. 2014;21(4):591-595. doi:10.1136/amiajnl-2014-002744

25. Wagner AK, Soumerai SB, Zhang F, Ross-Degnan D. Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 2002;27(4):299-309. doi:10.1046/j.1365-2710.2002.00430.x

26. Jandoc R, Burden AM, Mamdani M, Lévesque LE, Cadarette SM. Interrupted time series analysis in drug utilization research is increasing: systematic review and recommendations. J Clin Epidemiol. 2015;68(8):950-956. doi:10.1016/j.jclinepi.2014.12.018

27. Rubin DB. Using propensity scores to help design observational studies: application to the tobacco litigation. Health Serv Outcomes Res Methodol. 2001;2:169-188. doi:10.1023/A:1020363010465

28. Lee MS, Chen A, Zhou H, Herald J, Nayak R, Shen YA. Control of atherosclerotic risk factors during the COVID-19 pandemic in the U.S. Am J Prev Med. 2023;64(1):125-128. doi:10.1016/j.amepre.2022.08.007

29. Gumuser ED, Haidermota S, Finneran P, Natarajan P, Honigberg MC. Trends in cholesterol testing during the COVID-19 pandemic: COVID-19 and cholesterol testing. Am J Prev Cardiol. 2021;6:100152. doi:10.1016/j.ajpc.2021.100152

30. Tison GH, Avram R, Kuhar P, et al. Worldwide effect of COVID-19 on physical activity: a descriptive study. Ann Intern Med. 2020;173(9):767-770. doi:10.7326/M20-2665

31. Lin AL, Vittinghoff E, Olgin JE, Pletcher MJ, Marcus GM. Body weight changes during pandemic-related shelter-in-place in a longitudinal cohort study. JAMA Netw Open. 2021;4(3):e212536. doi:10.1001/jamanetworkopen.2021.2536

32. Lau D, McAlister FA. Implications of the COVID-19 pandemic for cardiovascular disease and risk-factor management. Can J Cardiol. 2021;37(5):722-732. doi:10.1016/j.cjca.2020.11.001

33. Muntner P, Jaeger BC, Hardy ST, et al. Age-specific prevalence and factors associated with normal blood pressure among US adults. Am J Hypertens. 2022;35(4):319-327. doi:10.1093/ajh/hpab154

34. Deepa M, Anjana RM, Unnikrishnan R, et al; ICMR-INDIAB Collaborative Study Group. Variations in glycated haemoglobin with age among individuals with normal glucose tolerance: implications for diagnosis and treatment-results from the ICMR-INDIAB population-based study (INDIAB-12). Acta Diabetol. 2022;59(2):225-232. doi:10.1007/s00592-021-01798-4

35. Quick COVID-19 primary care patient survey. The Larry A. Green Center. 2022. Accessed May 17, 2023. https://www.green-center.org/covid-survey

36. Demaerschalk BM, Pines A, Butterfield R, et al; Diagnostic Accuracy of Telemedicine Utilized at Mayo Clinic Alix School of Medicine Study Group Investigators. Assessment of clinician diagnostic concordance with video telemedicine in the integrated multispecialty practice at Mayo Clinic during the beginning of COVID-19 pandemic from March to June 2020. JAMA Netw Open. 2022;5(9):e2229958. doi:10.1001/jamanetworkopen.2022.29958

37. Nguyen OT, Alishahi Tabriz A, Huo J, Hanna K, Shea CM, Turner K. Impact of asynchronous electronic communication-based visits on clinical outcomes and health care delivery: systematic review. J Med Internet Res. 2021;23(5):e27531. doi:10.2196/27531

38. Salisbury C, O’Cathain A, Thomas C, et al. Telehealth for patients at high risk of cardiovascular disease: pragmatic randomised controlled trial. BMJ. 2016;353:i2647. doi:10.1136/bmj.i2647

39. Timpel P, Oswald S, Schwarz PEH, Harst L. Mapping the evidence on the effectiveness of telemedicine interventions in diabetes, dyslipidemia, and hypertension: an umbrella review of systematic reviews and meta-analyses. J Med Internet Res. 2020;22(3):e16791. doi:10.2196/16791

40. Quinton JK, Ong MK, Sarkisian C, et al. The impact of telemedicine on quality of care for patients with diabetes after March 2020. J Gen Intern Med. 2022;37(5):1198-1203. doi:10.1007/s11606-021-07367-3

41. Piskulic D, McDermott S, Seal L, Vallaire S, Norris CM. Virtual visits in cardiovascular disease: a rapid review of the evidence. Eur J Cardiovasc Nurs. 2021;20(8):816-826. doi:10.1093/eurjcn/zvab084

42. Li KY, Ng S, Zhu Z, McCullough JS, Kocher KE, Ellimoottil C. Association between primary care practice telehealth use and acute care visits for ambulatory care-sensitive conditions during COVID-19. JAMA Netw Open. 2022;5(3):e225484. doi:10.1001/jamanetworkopen.2022.5484

43. Community Health Center Chartbook. National Association of Community Health Centers. March 2023. Accessed December 7, 2023. https://www.nachc.org/wp-content/uploads/2023/07/Community-Health-Center-Chartbook-2023-2021UDS.pdf

44. Murphy AK, McDonald-Lopez K, Pilkauskas N, Gould-Werth A. Transportation insecurity in the United States: a descriptive portrait. Socius. 2022;8:23780231221121060. doi:10.1177/23780231221121060

45. Wolfe MK, McDonald NC, Holmes GM. Transportation barriers to health care in the United States: findings from the national health interview survey, 1997-2017. Am J Public Health. 2020;110(6):815-822. doi:10.2105/AJPH.2020.305579

46. Drake C, Zhang Y, Chaiyachati KH, Polsky D. The limitations of poor broadband internet access for telemedicine use in rural America: an observational study. Ann Intern Med. 2019;171(5):382-384. doi:10.7326/M19-0283

47. Khairat S, Haithcoat T, Liu S, et al. Advancing health equity and access using telemedicine: a geospatial assessment. J Am Med Inform Assoc. 2019;26(8-9):796-805. doi:10.1093/jamia/ocz108

48. Nouri S, Khoong EC, Lyles CR, Karliner L. Addressing equity in telemedicine for chronic disease management during the COVID-19 pandemic. NEJM Catalyst. May 4, 2020. Accessed July 15, 2023. https://catalyst.nejm.org/doi/full/10.1056/CAT.20.0123

49. Weiss D, Eikemo TA. Technological innovations and the rise of social inequalities in health. Scand J Public Health. 2017;45(7):714-719. doi:10.1177/1403494817711371

50. Vogels EA. Some digital divides persist between rural, urban and suburban America. Pew Research Center. August 19, 2021. Accessed May 16, 2023. https://www.pewresearch.org/short-reads/2021/08/19/some-digital-divides-persist-between-rural-urban-and-suburban-america/

51. Wilcock AD, Rose S, Busch AB, et al. Association between broadband internet availability and telemedicine use. JAMA Intern Med. 2019;179(11):1580-1582. doi:10.1001/jamainternmed.2019.2234

52. 2018 Broadband Deployment Report. Federal Communications Commission. February 2, 2018. Accessed May 5, 2020. https://www.fcc.gov/reports-research/reports/broadband-progress-reports/2018-broadband-deployment-report

53. O’Dowd E. Lack of broadband access can hinder rural telehealth programs. HITInfrastructure. October 18, 2018. Accessed May 5, 2020. https://hitinfrastructure.com/news/lack-of-broadband-access-can-hinder-rural-telehealth-programs

54. Mackert M, Mabry-Flynn A, Champlin S, Donovan EE, Pounders K. Health literacy and health
information technology adoption: the potential for a new digital divide. J Med Internet Res. 2016;18(10):e264. doi:10.2196/jmir.6349

55. Weinfeld JM, Hart KM, Vargas JD. Home blood pressure monitoring. Am Fam Physician. 2021;104(3):237-243.