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      Cost-effectiveness of a Telemonitoring Program for Patients With Heart Failure During the COVID-19 Pandemic in Hong Kong: Model Development and Data Analysis

      research-article
      , BSc, MPhil 1 , , BSc, MPhil 1 , , PharmD 1 ,
      ,
      (Reviewer), (Reviewer)
      Journal of Medical Internet Research
      JMIR Publications
      telemonitoring, mobile health, smartphone, heart failure, COVID-19, health care avoidance, cost-effectiveness

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          Abstract

          Background

          The COVID-19 pandemic has caused patients to avoid seeking medical care. Provision of telemonitoring programs in addition to usual care has demonstrated improved effectiveness in managing patients with heart failure (HF).

          Objective

          We aimed to examine the potential clinical and health economic outcomes of a telemonitoring program for management of patients with HF during the COVID-19 pandemic from the perspective of health care providers in Hong Kong.

          Methods

          A Markov model was designed to compare the outcomes of a care under COVID-19 (CUC) group and a telemonitoring plus CUC group (telemonitoring group) in a hypothetical cohort of older patients with HF in Hong Kong. The model outcome measures were direct medical cost, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio. Sensitivity analyses were performed to examine the model assumptions and the robustness of the base-case results.

          Results

          In the base-case analysis, the telemonitoring group showed a higher QALY gain (1.9007) at a higher cost (US $15,888) compared to the CUC group (1.8345 QALYs at US $15,603). Adopting US $48,937/QALY (1 × the gross domestic product per capita of Hong Kong) as the willingness-to-pay threshold, telemonitoring was accepted as a highly cost-effective strategy, with an incremental cost-effective ratio of US $4292/QALY. No threshold value was identified in the deterministic sensitivity analysis. In the probabilistic sensitivity analysis, telemonitoring was accepted as cost-effective in 99.22% of 10,000 Monte Carlo simulations.

          Conclusions

          Compared to the current outpatient care alone under the COVID-19 pandemic, the addition of telemonitoring-mediated management to the current care for patients with HF appears to be a highly cost-effective strategy from the perspective of health care providers in Hong Kong.

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          Most cited references33

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          Delay or Avoidance of Medical Care Because of COVID-19–Related Concerns — United States, June 2020

          Temporary disruptions in routine and nonemergency medical care access and delivery have been observed during periods of considerable community transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19) ( 1 ). However, medical care delay or avoidance might increase morbidity and mortality risk associated with treatable and preventable health conditions and might contribute to reported excess deaths directly or indirectly related to COVID-19 ( 2 ). To assess delay or avoidance of urgent or emergency and routine medical care because of concerns about COVID-19, a web-based survey was administered by Qualtrics, LLC, during June 24–30, 2020, to a nationwide representative sample of U.S. adults aged ≥18 years. Overall, an estimated 40.9% of U.S. adults have avoided medical care during the pandemic because of concerns about COVID-19, including 12.0% who avoided urgent or emergency care and 31.5% who avoided routine care. The estimated prevalence of urgent or emergency care avoidance was significantly higher among the following groups: unpaid caregivers for adults* versus noncaregivers (adjusted prevalence ratio [aPR] = 2.9); persons with two or more selected underlying medical conditions † versus those without those conditions (aPR = 1.9); persons with health insurance versus those without health insurance (aPR = 1.8); non-Hispanic Black (Black) adults (aPR = 1.6) and Hispanic or Latino (Hispanic) adults (aPR = 1.5) versus non-Hispanic White (White) adults; young adults aged 18–24 years versus adults aged 25–44 years (aPR = 1.5); and persons with disabilities § versus those without disabilities (aPR = 1.3). Given this widespread reporting of medical care avoidance because of COVID-19 concerns, especially among persons at increased risk for severe COVID-19, urgent efforts are warranted to ensure delivery of services that, if deferred, could result in patient harm. Even during the COVID-19 pandemic, persons experiencing a medical emergency should seek and be provided care without delay ( 3 ). During June 24–30, 2020, a total of 5,412 (54.7%) of 9,896 eligible adults ¶ completed web-based COVID-19 Outbreak Public Evaluation Initiative surveys administered by Qualtrics, LLC.** The Human Research Ethics Committee of Monash University (Melbourne, Australia) reviewed and approved the study protocol on human subjects research. This activity was also reviewed by CDC and was conducted consistent with applicable federal law and CDC policy. †† Respondents were informed of the study purposes and provided electronic consent before commencement, and investigators received anonymized responses. The 5,412 participants included 3,683 (68.1%) first-time respondents and 1,729 (31.9%) persons who had completed a related survey §§ during April 2–8, 2020. Among the 5,412 participants, 4,975 (91.9%) provided complete data for all variables in this analysis. Quota sampling and survey weighting ¶¶ were employed to improve cohort representativeness of the U.S. population by gender, age, and race/ethnicity. Respondents were asked “Have you delayed or avoided medical care due to concerns related to COVID-19?” Delay or avoidance was evaluated for emergency (e.g., care for immediate life-threatening conditions), urgent (e.g., care for immediate non–life-threatening conditions), and routine (e.g., annual check-ups) medical care. Given the potential for variation in interpretation of whether conditions were life-threatening, responses for urgent and emergency care delay or avoidance were combined for analysis. Covariates included gender; age; race/ethnicity; disability status; presence of one or more selected underlying medical conditions known to increase risk for severe COVID-19; education; essential worker status***; unpaid adult caregiver status; U.S. census region; urban/rural classification ††† ; health insurance status; whether respondents knew someone who had received a positive SARS-CoV-2 test result or had died from COVID-19; and whether the respondents believed they were at high risk for severe COVID-19. Comparisons within all these subgroups were evaluated using multivariable Poisson regression models §§§ with robust standard errors to estimate prevalence ratios adjusted for all covariates, 95% confidence intervals, and p-values to evaluate statistical significance (α = 0.05) using the R survey package (version 3.29) and R software (version 4.0.2; The R Foundation). As of June 30, 2020, among 4,975 U.S. adult respondents, 40.9% reported having delayed or avoided any medical care, including urgent or emergency care (12.0%) and routine care (31.5%), because of concerns about COVID-19 (Table 1). Groups of persons among whom urgent or emergency care avoidance exceeded 20% and among whom any care avoidance exceeded 50% included adults aged 18–24 years (30.9% for urgent or emergency care; 57.2% for any care), unpaid caregivers for adults (29.8%; 64.3%), Hispanic adults (24.6%; 55.5%), persons with disabilities (22.8%; 60.3%), persons with two or more selected underlying medical conditions (22.7%; 54.7%), and students (22.7%; 50.3%). One in four unpaid caregivers reported caring for adults who were at increased risk for severe COVID-19. TABLE 1 Estimated prevalence of delay or avoidance of medical care because of concerns related to COVID-19, by type of care and respondent characteristics — United States, June 30, 2020 Characteristic No. (%)† Type of medical care delayed or avoided* Urgent or emergency Routine Any %† P-value§ %† P-value§ %† P-value§ All respondents 4,975 (100) 12.0 — 31.5 — 40.9 — Gender Female 2,528 (50.8) 11.7 0.598 35.8 <0.001 44.9 <0.001 Male 2,447 (49.2) 12.3 27.0 36.7 Age group, yrs 18–24 650 (13.1) 30.9 <0.001 29.6 0.072 57.2 <0.001 25–44 1,740 (35.0) 14.9 34.2 44.8 45–64 1,727 (34.7) 5.7 30.0 34.5 ≥65 858 (17.3) 4.4 30.3 33.5 Race/Ethnicity White, non-Hispanic 3,168 (63.7) 6.7 <0.001 30.9 0.020 36.2 <0.001 Black, non-Hispanic 607 (12.2) 23.3 29.7 48.1 Asian, non-Hispanic 238 (4.8) 8.6 31.3 37.7 Other race or multiple races, non-Hispanic¶ 150 (3.0) 15.5 23.9 37.3 Hispanic, any race or races 813 (16.3) 24.6 36.4 55.5 Disability** Yes 1,108 (22.3) 22.8 <0.001 42.9 <0.001 60.3 <0.001 No 3,867 (77.7) 8.9 28.2 35.3 Underlying medical condition†† No 2,537 (51.0) 8.2 <0.001 27.9 <0.001 34.7 <0.001 One 1,328 (26.7) 10.4 33.0 41.2 Two or more 1,110 (22.3) 22.7 37.7 54.7 2019 household income, USD <25,000 665 (13.4) 13.9 0.416 31.2 0.554 42.8 0.454 25,000–49,999 1,038 (20.9) 11.1 30.9 38.6 50,000–99,999 1,720 (34.6) 12.5 30.5 41.1 ≥100,000 1,552 (31.2) 11.2 33.0 41.4 Education Less than high school diploma 65 (1.3) 15.6 0.442 24.7 0.019 37.9 0.170 High school diploma 833 (16.7) 12.3 28.1 38.1 Some college 1,302 (26.2) 13.6 29.7 40.3 Bachelor's degree 1,755 (35.3) 11.2 34.8 43.6 Professional degree 1,020 (20.5) 10.9 31.2 39.5 Employment status Employed 3,049 (61.3) 14.6 <0.001 31.5 0.407 43.3 <0.001 Unemployed 630 (12.7) 8.7 34.4 39.5 Retired 1,129 (22.7) 5.3 29.9 33.8 Student 166 (3.3) 22.7 30.5 50.3 Essential worker status§§ Essential worker 1,707 (34.3) 19.5 <0.001 32.4 0.293 48.0 <0.001 Nonessential worker 1,342 (27.0) 8.4 30.3 37.3 Unpaid caregiver status¶¶ Unpaid caregiver for adults 1,344 (27.0) 29.8 <0.001 41.0 <0.001 64.3 <0.001 Not unpaid caregiver for adults 3,631 (73.0) 5.4 27.9 32.2 U.S. Census region*** Northeast 1,122 (22.6) 11.0 0.008 33.9 0.203 42.5 0.460 Midwest 936 (18.8) 8.5 32.0 38.7 South 1,736 (34.9) 13.9 29.6 40.7 West 1,181 (23.7) 13.0 31.5 41.5 Rural/Urban classification††† Urban 4,411 (88.7) 12.3 0.103 31.5 0.763 41.2 0.216 Rural 564 (11.3) 9.4 30.9 38.2 Health insurance status Yes 4,577 (92.0) 12.4 0.036 32.6 <0.001 42.3 <0.001 No 398 (8.0) 7.8 18.4 24.8 Know someone with positive test results for SARS-CoV-2§§§ Yes 989 (19.9) 8.8 0.004 40.7 <0.001 46.6 <0.001 No 3,986 (80.1) 12.8 29.2 39.5 Knew someone who died from COVID-19 Yes 364 (7.3) 10.1 0.348 41.4 <0.001 46.3 0.048 No 4,611 (92.7) 12.2 30.7 40.5 Believed to be in group at high risk for severe COVID-19 Yes 981 (19.7) 10.0 0.050 42.5 <0.001 49.4 <0.001 No 3,994 (80.3) 12.5 28.8 38.8 Abbreviations: CI = confidence interval; COVID-19 = coronavirus disease 2019; USD = U.S. dollars. * The types of medical care avoidance are not mutually exclusive; respondents had the option to indicate that they had delayed or avoided more than one type of medical care (i.e., routine medical care and urgent/emergency medical care). † Statistical raking and weight trimming were employed to improve the cross-sectional June cohort representativeness of the U.S. population by gender, age, and race/ethnicity according to the 2010 U.S. Census. § The Rao-Scott adjusted Pearson chi-squared test was used to test for differences in observed and expected frequencies among groups by characteristic for avoidance of each type of medical care (e.g., whether avoidance of routine medical care differs significantly by gender). Statistical significance was evaluated at a threshold of α = 0.05. ¶ “Other” race includes American Indian or Alaska Native, Native Hawaiian or Pacific Islander, or Other. ** Persons who had a disability were defined as such based on a qualifying response to either one of two questions: “Are you limited in any way in any activities because of physical, mental, or emotional condition?” and “Do you have any health conditions that require you to use special equipment, such as a cane, wheelchair, special bed, or special telephone?” https://www.cdc.gov/brfss/questionnaires/pdf-ques/2015-brfss-questionnaire-12-29-14.pdf. †† Selected underlying medical conditions known to increase the risk for severe COVID-19 included in this analysis were obesity, diabetes, high blood pressure, cardiovascular disease, and any type of cancer. Obesity is defined as body mass index ≥30 kg/m2 and was calculated from self-reported height and weight (https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html). The remaining conditions were assessed using the question “Have you ever been diagnosed with any of the following conditions?” with response options of 1) “Never”; 2) “Yes, I have in the past, but don’t have it now”; 3) “Yes I have, but I do not regularly take medications or receive treatment”; and 4) “Yes I have, and I am regularly taking medications or receiving treatment.” Respondents who answered that they have been diagnosed and chose either response 3 or 4 were considered as having the specified medical condition. §§ Essential worker status was self-reported. ¶¶ Unpaid caregiver status was self-reported. Unpaid caregivers for adults were defined as having provided unpaid care to a relative or friend aged ≥18 years at any time in the last 3 months. Examples provided to survey respondents included helping with personal needs, household chores, health care tasks, managing a person’s finances, taking them to a doctor’s appointment, arranging for outside services, and visiting regularly to see how they are doing. *** Region classification was determined by using the U.S. Census Bureau’s Census Regions and Divisions. https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf. ††† Rural-urban classification was determined by using self-reported ZIP codes according to the Federal Office of Rural Health Policy definition of rurality. https://www.hrsa.gov/rural-health/about-us/definition/datafiles.html. §§§ For this question, respondents were asked to select the following statement, if applicable: “I know someone who has tested positive for COVID-19.” In the multivariable Poisson regression models, differences within groups were observed for urgent or emergency care avoidance (Figure) and any care avoidance (Table 2). Adjusted prevalence of urgent or emergency care avoidance was significantly higher among unpaid caregivers for adults versus noncaregivers (2.9; 2.3–3.6); persons with two or more selected underlying medical conditions versus those without those conditions (1.9; 1.5–2.4); persons with health insurance versus those without health insurance (1.8; 1.2–2.8); Black adults (1.6; 1.3–2.1) and Hispanic adults (1.5; 1.2–2.0) versus White adults; young adults aged 18–24 years versus adults aged 25–44 years (1.5; 1.2–1.8); and persons with disabilities versus those without disabilities (1.3; 1.1–1.5). Avoidance of urgent or emergency care was significantly lower among adults aged ≥45 years than among younger adults. FIGURE Adjusted prevalence ratios* , † for characteristics § , ¶ , ** , †† associated with delay or avoidance of urgent or emergency medical care because of concerns related to COVID-19 — United States, June 30, 2020 Abbreviation: COVID-19 = coronavirus disease 2019. * Comparisons within subgroups were evaluated using Poisson regressions used to calculate a prevalence ratio adjusted for all characteristics shown in figure. † 95% confidence intervals indicated with error bars. § “Other” race includes American Indian or Alaska Native, Native Hawaiian or Pacific Islander, or Other. ¶ Selected underlying medical conditions known to increase the risk for severe COVID-19 were obesity, diabetes, high blood pressure, cardiovascular disease, and any type of cancer. Obesity is defined as body mass index ≥30 kg/m2 and was calculated from self-reported height and weight (https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html). The remaining conditions were assessed using the question “Have you ever been diagnosed with any of the following conditions?” with response options of 1) “Never”; 2) “Yes, I have in the past, but don’t have it now”; 3) “Yes I have, but I do not regularly take medications or receive treatment”; and 4) “Yes I have, and I am regularly taking medications or receiving treatment.” Respondents who answered that they have been diagnosed and chose either response 3 or 4 were considered as having the specified medical condition. ** Essential worker status was self-reported. For the adjusted prevalence ratios, essential workers were compared with all other respondents (including those who were nonessential workers, retired, unemployed, and students). †† Unpaid caregiver status was self-reported. Unpaid caregivers for adults were defined as having provided unpaid care to a relative or friend aged ≥18 years to help them take care of themselves at any time in the last 3 months. The figure is a forest plot showing the adjusted prevalence ratios for characteristics associated with delay or avoidance of urgent or emergency medical care because of concerns related to COVID-19, in the United States, as of June 30, 2020. TABLE 2 Characteristics associated with delay or avoidance of any medical care because of concerns related to COVID-19 — United States, June 30, 2020 Characteristic Weighted* no. Avoided or delayed any medical care aPR† (95% CI†) P-value† All respondents 4,975 — — — Gender Female 2,528 Referent — — Male 2,447 0.81 (0.75–0.87)§ <0.001 Age group, yrs 18–24 650 1.12 (1.01–1.25)§ 0.035 25–44 1,740 Referent — — 45–64 1,727 0.80 (0.72–0.88)§ <0.001 ≥65 858 0.72 (0.64–0.81)§ <0.001 Race/Ethnicity White, non-Hispanic 3,168 Referent — — Black, non-Hispanic 607 1.07 (0.96–1.19) 0.235 Asian, non-Hispanic 238 1.04 (0.91–1.18) 0.567 Other race or multiple races, non-Hispanic¶ 150 0.87 (0.71–1.07) 0.196 Hispanic, any race or races 813 1.15 (1.03–1.27)§ 0.012 Disability** Yes 1,108 1.33 (1.23–1.43)§ <0.001 No 3,867 Referent — — Underlying medical condition†† No 2,537 Referent — — One 1,328 1.15 (1.05–1.25)§ 0.004 Two or more 1,110 1.31 (1.20–1.42)§ <0.001 Education Less than high school diploma 65 0.72 (0.53–0.98)§ 0.037 High school diploma 833 0.79 (0.71–0.89)§ <0.001 Some college 1,302 0.85 (0.78–0.93)§ 0.001 Bachelor's degree 1,755 Referent — — Professional degree 1,020 0.90 (0.82–0.98)§ 0.019 Essential workers vs others§§ Essential workers 1,707 1.00 (0.92–1.09) 0.960 Other respondents (nonessential workers, retired persons, unemployed persons, and students) 3,268 Referent — — Unpaid caregiver status¶¶ Unpaid caregiver for adults 1,344 1.64 (1.52–1.78)§ <0.001 Not unpaid caregiver for adults 3,631 Referent — — U.S. Census region*** Northeast 1,122 Referent — — Midwest 936 0.93 (0.83–1.04) 0.214 South 1,736 0.90 (0.82–0.99)§ 0.028 West 1,181 0.99 (0.89–1.09) 0.808 Rural/Urban classification††† Urban 4,411 1.00 (0.89–1.12) 0.993 Rural 564 Referent — — Health insurance status Yes 4,577 1.61 (1.31–1.98)§ <0.001 No 398 Referent — — Know someone with positive test results for SARS-CoV-2§§§ Yes 989 1.22 (1.12–1.33)§ <0.001 No 3,986 Referent — — Knew someone who died from COVID-19 Yes 364 0.99 (0.88–1.12) 0.860 No 4,611 Referent — — Believed to be in a group at high risk for severe COVID-19 Yes 981 1.33 (1.23–1.44)§ <0.001 No 3,994 Referent — — Abbreviations: aPR = adjusted prevalence ratio; CI = confidence interval; COVID-19 = coronavirus disease 2019. * Statistical raking and weight trimming were employed to improve the cross-sectional June cohort representativeness of the U.S. population by gender, age, and race/ethnicity according to the 2010 U.S. Census. † Comparisons within subgroups were evaluated using Poisson regressions used to calculate a prevalence ratio adjusted for all characteristics listed, as well as a 95% CI and p-value. Statistical significance was evaluated at a threshold of α = 0.05. § P-value calculated using Poisson regression among respondents within a characteristic is statistically significant at levels of p<0.05. ¶ “Other” race includes American Indian or Alaska Native, Native Hawaiian or Pacific Islander, or Other. ** Persons who had a disability were defined based on a qualifying response to either one of two questions: “Are you limited in any way in any activities because of physical, mental, or emotional condition?” and “Do you have any health conditions that require you to use special equipment, such as a cane, wheelchair, special bed, or special telephone?” https://www.cdc.gov/brfss/questionnaires/pdf-ques/2015-brfss-questionnaire-12-29-14.pdf. †† Selected underlying medical conditions known to increase the risk for severe COVID-19 were obesity, diabetes, high blood pressure, cardiovascular disease, and any type of cancer. Obesity is defined as body mass index ≥30 kg/m2 and was calculated from self-reported height and weight (https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html). The remaining conditions were assessed using the question “Have you ever been diagnosed with any of the following conditions?” with response options of 1) “Never”; 2) “Yes, I have in the past, but don’t have it now”; 3) “Yes I have, but I do not regularly take medications or receive treatment”; and 4) “Yes I have, and I am regularly taking medications or receiving treatment.” Respondents who answered that they have been diagnosed and chose either response 3 or 4 were considered as having the specified medical condition. §§ Essential worker status was self-reported. For the adjusted prevalence ratios, essential workers were compared with all other respondents (including those who were nonessential workers, retired, unemployed, and students). ¶¶ Unpaid caregiver status was self-reported. Unpaid caregivers for adults were defined as having provided unpaid care to a relative or friend aged ≥18 years at any time in the last 3 months. Examples provided to survey respondents included helping with personal needs, household chores, health care tasks, managing a person’s finances, taking them to a doctor’s appointment, arranging for outside services, and visiting regularly to see how they are doing. *** Region classification was determined by using the U.S. Census Bureau’s Census Regions and Divisions. https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf. ††† Rural/urban classification was determined by using self-reported ZIP codes according to the Federal Office of Rural Health Policy definition of rurality. https://www.hrsa.gov/rural-health/about-us/definition/datafiles.html. §§§ For this question, respondents were asked to select the following statement, if applicable: “I know someone who has tested positive for COVID-19.” Discussion As of June 30, 2020, an estimated 41% of U.S. adults reported having delayed or avoided medical care during the pandemic because of concerns about COVID-19, including 12% who reported having avoided urgent or emergency care. These findings align with recent reports that hospital admissions, overall emergency department (ED) visits, and the number of ED visits for heart attack, stroke, and hyperglycemic crisis have declined since the start of the pandemic ( 3 – 5 ), and that excess deaths directly or indirectly related to COVID-19 have increased in 2020 versus prior years ( 2 ). Nearly one third of adult respondents reported having delayed or avoided routine medical care, which might reflect adherence to community mitigation efforts such as stay-at-home orders, temporary closures of health facilities, or additional factors. However, if routine care avoidance were to be sustained, adults could miss opportunities for management of chronic conditions, receipt of routine vaccinations, or early detection of new conditions, which might worsen outcomes. Avoidance of both urgent or emergency and routine medical care because of COVID-19 concerns was highly prevalent among unpaid caregivers for adults, respondents with two or more underlying medical conditions, and persons with disabilities. For caregivers who reported caring for adults at increased risk for severe COVID-19, concern about exposure of care recipients might contribute to care avoidance. Persons with underlying medical conditions that increase their risk for severe COVID-19 ( 6 ) are more likely to require care to monitor and treat these conditions, potentially contributing to their more frequent report of avoidance. Moreover, persons at increased risk for severe COVID-19 might have avoided health care facilities because of perceived or actual increased risk of exposure to SARS-CoV-2, particularly at the onset of the pandemic. However, health care facilities are implementing important safety precautions to reduce the risk of SARS-CoV-2 infection among patients and personnel. In contrast, delay or avoidance of care might increase risk for life-threatening medical emergencies. In a recent study, states with large numbers of COVID-19–associated deaths also experienced large proportional increases in deaths from other underlying causes, including diabetes and cardiovascular disease ( 7 ). For persons with disabilities, accessing medical services might be challenging because of disruptions in essential support services, which can result in adverse health outcomes. Medical services for persons with disabilities might also be disrupted because of reduced availability of accessible transportation, reduced communication in accessible formats, perceptions of SARS-CoV-2 exposure risk, and specialized needs that are difficult to address with routine telehealth delivery during the pandemic response. Increasing accessibility of medical and telehealth services ¶¶¶ might help prevent delay of needed care. Increased prevalences of reported urgent or emergency care avoidance among Black adults and Hispanic adults compared with White adults are especially concerning given increased COVID-19-associated mortality among Black adults and Hispanic adults ( 8 ). In the United States, the age-adjusted COVID-19 hospitalization rates are approximately five times higher among Black persons and four times higher among Hispanic persons than are those among White persons ( 9 ). Factors contributing to racial and ethnic disparities in SARS-CoV-2 exposure, illness, and mortality might include long-standing structural inequities that influence life expectancy, including prevalence and underlying medical conditions, health insurance status, and health care access and utilization, as well as work and living circumstances, including use of public transportation and essential worker status. Communities, health care systems, and public health agencies can foster equity by working together to ensure access to information, testing, and care to assure maintenance and management of physical and mental health. The higher prevalence of medical care delay or avoidance among respondents with health insurance versus those without insurance might reflect differences in medical care-seeking behaviors. Before the pandemic, persons without insurance sought medical care much less frequently than did those with insurance ( 10 ), resulting in fewer opportunities for medical care delay or avoidance. The findings in this report are subject to at least five limitations. First, self-reported data are subject to recall, response, and social desirability biases. Second, the survey did not assess reasons for COVID-19–associated care avoidance, such as adherence to public health recommendations; closure of health care provider facilities; reduced availability of public transportation; fear of exposure to infection with SARS-CoV-2; or availability, accessibility, and acceptance or recognition of telemedicine as a means of providing care in lieu of in-person services. Third, the survey did not assess baseline patterns of care-seeking or timing or duration of care avoidance. Fourth, perceptions of whether a condition was life-threatening might vary among respondents. Finally, although quota sampling methods and survey weighting were employed to improve cohort representativeness, this web-based survey might not be fully representative of the U.S. population for income, educational attainment, and access to technology. However, the findings are consistent with reported declines in hospital admissions and ED visits during the pandemic ( 3 – 5 ). CDC has issued guidance to assist persons at increased risk for severe COVID-19 in staying healthy and safely following treatment plans**** and to prepare health care facilities to safely deliver care during the pandemic. †††† Additional public outreach in accessible formats tailored for diverse audiences might encourage these persons to seek necessary care. Messages could highlight the risks of delaying needed care, especially among persons with underlying medical conditions, and the importance of timely emergency care. Patient concerns related to potential exposure to SARS-CoV-2 in health care settings could be addressed by describing facilities’ precautions to reduce exposure risk. Further exploration of underlying reasons for medical care avoidance is needed, including among persons with disabilities, persons with underlying health conditions, unpaid caregivers for adults, and those who face structural inequities. If care were avoided because of concern about SARS-CoV-2 exposure or if there were closures or limited options for in-person services, providing accessible telehealth or in-home health care could address some care needs. Even during the COVID-19 pandemic, persons experiencing a medical emergency should seek and be provided care without delay ( 3 ). Summary What is already known about this topic? Delayed or avoided medical care might increase morbidity and mortality associated with both chronic and acute health conditions. What is added by this report? By June 30, 2020, because of concerns about COVID-19, an estimated 41% of U.S. adults had delayed or avoided medical care including urgent or emergency care (12%) and routine care (32%). Avoidance of urgent or emergency care was more prevalent among unpaid caregivers for adults, persons with underlying medical conditions, Black adults, Hispanic adults, young adults, and persons with disabilities. What are the implications for public health practice? Understanding factors associated with medical care avoidance can inform targeted care delivery approaches and communication efforts encouraging persons to safely seek timely routine, urgent, and emergency care.
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            The role of telehealth during COVID-19 outbreak: a systematic review based on current evidence

            Background The outbreak of coronavirus disease-19 (COVID-19) is a public health emergency of international concern. Telehealth is an effective option to fight the outbreak of COVID-19. The aim of this systematic review was to identify the role of telehealth services in preventing, diagnosing, treating, and controlling diseases during COVID-19 outbreak. Methods This systematic review was conducted through searching five databases including PubMed, Scopus, Embase, Web of Science, and Science Direct. Inclusion criteria included studies clearly defining any use of telehealth services in all aspects of health care during COVID-19 outbreak, published from December 31, 2019, written in English language and published in peer reviewed journals. Two reviewers independently assessed search results, extracted data, and assessed the quality of the included studies. Quality assessment was based on the Critical Appraisal Skills Program (CASP) checklist. Narrative synthesis was undertaken to summarize and report the findings. Results Eight studies met the inclusion out of the 142 search results. Currently, healthcare providers and patients who are self-isolating, telehealth is certainly appropriate in minimizing the risk of COVID-19 transmission. This solution has the potential to prevent any sort of direct physical contact, provide continuous care to the community, and finally reduce morbidity and mortality in COVID-19 outbreak. Conclusions The use of telehealth improves the provision of health services. Therefore, telehealth should be an important tool in caring services while keeping patients and health providers safe during COVID-19 outbreak.
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              Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association.

              Heart failure (HF) is an important contributor to both the burden and cost of national healthcare expenditures, with more older Americans hospitalized for HF than for any other medical condition. With the aging of the population, the impact of HF is expected to increase substantially. We estimated future costs of HF by adapting a methodology developed by the American Heart Association to project the epidemiology and future costs of HF from 2012 to 2030 without double counting the costs attributed to comorbid conditions. The model assumes that HF prevalence will remain constant by age, sex, and race/ethnicity and that rising costs and technological innovation will continue at the same rate. By 2030, >8 million people in the United States (1 in every 33) will have HF. Between 2012 and 2030, real (2010$) total direct medical costs of HF are projected to increase from $21 billion to $53 billion. Total costs, including indirect costs for HF, are estimated to increase from $31 billion in 2012 to $70 billion in 2030. If one assumes all costs of cardiac care for HF patients are attributable to HF (no cost attribution to comorbid conditions), the 2030 projected cost estimates of treating patients with HF will be 3-fold higher ($160 billion in direct costs). The estimated prevalence and cost of care for HF will increase markedly because of aging of the population. Strategies to prevent HF and improve the efficiency of care are needed.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                March 2021
                3 March 2021
                3 March 2021
                : 23
                : 3
                : e26516
                Affiliations
                [1 ] School of Pharmacy Faculty of Medicine The Chinese University of Hong Kong Hong Kong China (Hong Kong)
                Author notes
                Corresponding Author: Joyce Hoi-Sze You joyceyou@ 123456cuhk.edu.hk
                Author information
                https://orcid.org/0000-0002-6647-2364
                https://orcid.org/0000-0003-2076-9880
                https://orcid.org/0000-0002-5763-7403
                Article
                v23i3e26516
                10.2196/26516
                7931824
                33656440
                c983bd63-f825-4936-ae76-7b822c3d112a
                ©Xinchan Jiang, Jiaqi Yao, Joyce Hoi-Sze You. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.03.2021.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 15 December 2020
                : 23 January 2021
                : 8 February 2021
                : 19 February 2021
                Categories
                Original Paper
                Original Paper

                Medicine
                telemonitoring,mobile health,smartphone,heart failure,covid-19,health care avoidance,cost-effectiveness

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