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Presenting patient cases is a key part of everyday clinical practice. A well delivered presentation has the potential to facilitate patient care and improve efficiency on ward rounds, as well as a means of teaching and assessing clinical competence. 1
The purpose of a case presentation is to communicate your diagnostic reasoning to the listener, so that he or she has a clear picture of the patient’s condition and further management can be planned accordingly. 2 To give a high quality presentation you need to take a thorough history. Consultants make decisions about patient care based on information presented to them by junior members of the team, so the importance of accurately presenting your patient cannot be overemphasised.
As a medical student, you are likely to be asked to present in numerous settings. A formal case presentation may take place at a teaching session or even at a conference or scientific meeting. These presentations are usually thorough and have an accompanying PowerPoint presentation or poster. More often, case presentations take place on the wards or over the phone and tend to be brief, using only memory or short, handwritten notes as an aid.
Everyone has their own presenting style, and the context of the presentation will determine how much detail you need to put in. You should anticipate what information your senior colleagues will need to know about the patient’s history and the care he or she has received since admission, to enable them to make further management decisions. In this article, I use a fictitious case to …
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A, Distribution of reports of SSNHL by week of vaccination and time to onset after vaccination. The rate of SSNHL reports per 100 000 vaccine doses (blue line) is overlaid. B, Mean age of people reporting SSNHL to VAERS according to the time period reported who met the definition of probable SSNHL (n = 555). Note that the weekly time periods are identical in A and B. VAERS indicates Vaccine Adverse Events Reporting System.
A, Includes 555 cases reported to the VAERS database that met the definition of probable SSNHL during the period examined. B, Includes 21 patients in multi-institutional case series. The x-axis extends to only 15 days after vaccination because no new cases were observed after day 15. VAERS indicates Vaccine Adverse Events Reporting System.
eTable 1. Representative Examples of VAERS Incident Reports Meeting Criteria for Probable SSNHL Compared With Those Unlikely to Represent True SSNHL
eTable 2. Rate of SSNHL Reports in VAERS by Vaccine Manufacturer
eFigure 1. Scattergrams of Pretreatment and Posttreatment Hearing Results
eFigure 2. Audiogram Revealing Unilateral Sensorineural Hearing Loss Occurring 14 Days After Each of 2 COVID-19 Vaccine Doses in 1 Patient
eFigure 3. Number of People in the US With at Least 1 COVID-19 Vaccine Dose According to Age Group at 3 Points During the Initial COVID-19 Vaccination Rollout
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Formeister EJ , Wu MJ , Chari DA, et al. Assessment of Sudden Sensorineural Hearing Loss After COVID-19 Vaccination. JAMA Otolaryngol Head Neck Surg. 2022;148(4):307–315. doi:10.1001/jamaoto.2021.4414
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Question Is COVID-19 vaccination associated with sudden sensorineural hearing loss (SSNHL)?
Findings In this cross-sectional study and case series involving 555 cases of SSNHL among adults reported to the Centers for Disease Control and Prevention Vaccine Adverse Events Reporting System, no increase in the rate of hearing loss after COVID-19 vaccination was found compared with the incidence in the general population. Assessment of 21 adult patients who presented to tertiary care centers with SSNHL after COVID-19 vaccination did not reveal any apparent associations with respect to clinical or demographic factors.
Meaning These results suggest that there is no association between vaccination and the development of SSNHL among adults who received a COVID-19 vaccine.
Importance Emerging reports of sudden sensorineural hearing loss (SSNHL) after COVID-19 vaccination within the otolaryngological community and the public have raised concern about a possible association between COVID-19 vaccination and the development of SSNHL.
Objective To examine the potential association between COVID-19 vaccination and SSNHL.
Design, Setting, and Participants This cross-sectional study and case series involved an up-to-date population-based analysis of 555 incident reports of probable SSNHL in the Centers for Disease Control and Prevention Vaccine Adverse Events Reporting System (VAERS) over the first 7 months of the US vaccination campaign (December 14, 2020, through July 16, 2021). In addition, data from a multi-institutional retrospective case series of 21 patients who developed SSNHL after COVID-19 vaccination were analyzed. The study included all adults experiencing SSNHL within 3 weeks of COVID-19 vaccination who submitted reports to VAERS and consecutive adult patients presenting to 2 tertiary care centers and 1 community practice in the US who were diagnosed with SSNHL within 3 weeks of COVID-19 vaccination.
Exposures Receipt of a COVID-19 vaccine produced by any of the 3 vaccine manufacturers (Pfizer-BioNTech, Moderna, or Janssen/Johnson & Johnson) used in the US.
Main Outcomes and Measures Incidence of reports of SSNHL after COVID-19 vaccination recorded in VAERS and clinical characteristics of adult patients presenting with SSNHL after COVID-19 vaccination.
Results A total of 555 incident reports in VAERS (mean patient age, 54 years [range, 15-93 years]; 305 women [55.0%]; data on race and ethnicity not available in VAERS) met the definition of probable SSNHL (mean time to onset, 6 days [range, 0-21 days]) over the period investigated, representing an annualized incidence estimate of 0.6 to 28.0 cases of SSNHL per 100 000 people per year. The rate of incident reports of SSNHL was similar across all 3 vaccine manufacturers (0.16 cases per 100 000 doses for both Pfizer-BioNTech and Moderna vaccines, and 0.22 cases per 100 000 doses for Janssen/Johnson & Johnson vaccine). The case series included 21 patients (mean age, 61 years [range, 23-92 years]; 13 women [61.9%]) with SSNHL, with a mean time to onset of 6 days (range, 0-15 days). Patients were heterogeneous with respect to clinical and demographic characteristics. Preexisting autoimmune disease was present in 6 patients (28.6%). Of the 14 patients with posttreatment audiometric data, 8 (57.1%) experienced improvement after receiving treatment. One patient experienced SSNHL 14 days after receiving each dose of the Pfizer-BioNTech vaccine.
Conclusions and Relevance In this cross-sectional study, findings from an updated analysis of VAERS data and a case series of patients who experienced SSNHL after COVID-19 vaccination did not suggest an association between COVID-19 vaccination and an increased incidence of hearing loss compared with the expected incidence in the general population.
Anecdotal reports of sudden sensorineural hearing loss (SSNHL) occurring after COVID-19 vaccination have emerged in otolaryngologic professional societies and have important public health implications. Tinnitus, dizziness, and vertigo have also been reported within 2 weeks of vaccination in a recent single-institution case series. 1 Otolaryngologists encounter increasing challenges to promoting public health conduct recommended during the pandemic when they are counseling and evaluating patients who have developed SSNHL and reported a temporal association with COVID-19 vaccination.
Other large-scale vaccination campaigns, such as those for the measles-mumps-rubella and influenza vaccines, have previously been investigated after anecdotal reports of SSNHL emerged among vaccinated individuals. In each campaign, epidemiologic studies 2 , 3 did not show an association between vaccination and SSNHL. Although data from similar epidemiologic studies are not yet available for COVID-19 vaccination, a preliminary analysis 4 of incident reports from the Centers for Disease Control and Prevention (CDC) Vaccine Adverse Events Reporting System (VAERS) during the early phase of public COVID-19 vaccination did not identify an association between vaccination and SSNHL. However, as vaccination campaigns have expanded across the US and currently include vaccines from 3 manufacturers (Pfizer-BioNTech [BNT162b2], Moderna [mRNA-1273], and Janssen/Johnson & Johnson [Ad26.COV2.S]), questions remain regarding whether an association exists between COVID-19 vaccination and SSNHL. In addition, VAERS does not provide detailed patient-level clinical data that may be valuable in evaluating specific patient cofactors.
The purposes of the present study were to (1) extend the preliminary incidence estimate of SSNHL after COVID-19 vaccination 4 to the present phase of vaccination across 3 manufacturers and (2) examine whether emerging patterns in VAERS incident reports suggest an association between COVID-19 vaccination and SSNHL. In addition, we sought to augment this public database evaluation with an in-depth analysis of clinical characteristics among a multi-institutional series of patients who presented with confirmed SSNHL after COVID-19 vaccination.
This study was approved by the institutional review boards of Johns Hopkins University School of Medicine and the Massachusetts Eye and Ear Infirmary/Harvard Medical School. Because the VAERS records review obtained data from a publicly available deidentified database, this portion of the study was deemed exempt from review; similarly, the case series was deemed exempt because the patients’ files did not contain identifiable data.
The study was performed in 2 phases. In the first phase, VAERS was queried for reports of SSNHL after COVID-19 vaccination between December 14, 2020, and July 16, 2021. Cases deemed to represent probable SSNHL were compiled for analysis using previously dfescribed methods. 4 In brief, the search terms sudden hearing loss , deafness , deafness neurosensory , deafness unilateral , deafness bilateral , and hypoacusis were selected as adverse events (AEs) for data extraction. Because multiple symptoms could be selected for each incident report, deduplication was performed to ensure there was only 1 unique VAERS identification number per report. Narratives and laboratory data from all reports were reviewed to assess the likelihood of a report representing probable SSNHL. Inclusion criteria for probable SSNHL consisted of a temporal association with COVID-19 vaccination (defined as onset within 21 days after vaccination) and a high credibility of reporting. A report was deemed credible if it could demonstrate at least 1 of the following: (1) reference to an audiographic test result confirming hearing loss, (2) evaluation by an otolaryngologist, audiologist, or other physician resulting in a diagnosis of sudden hearing loss, or (3) evaluation by an otolaryngologist resulting in treatment with systemic steroid or intratympanic steroid medications, performance of magnetic resonance imaging, or any combination thereof. Incident reports were excluded if they did not reference evaluation by a physician or audiologist leading to a diagnosis of hearing loss, did not contain details within the report or laboratory results section to indicate that a diagnosis of sudden hearing loss was provided (eg, no mention of audiologic testing, no receipt of systemic or intratympanic steroid medications, or no magnetic resonance imaging scan), or indicated that hearing loss onset occurred more than 21 days after vaccination. In addition, reports that described the discovery of an alternative origin for hearing loss (eg, vestibular schwannoma or stroke) were excluded. Examples of narratives and their classifications are shown in eTable 1 in the Supplement .
The number of vaccine doses administered in the US during the study period was obtained from the CDC. 5 An incidence estimate of probable SSNHL on a per-person basis during the study period was obtained and annualized. To account for intrinsic uncertainties, such as the number of unique individuals receiving a vaccine relative to the number of doses administered, the true case numbers of SSNHL based on VAERS incident reports, and potential underreporting bias in VAERS, we conducted a sensitivity analysis that adjusted these assumptions to achieve a range estimate of the incidence of SSNHL. The maximum incidence estimate was produced based on the assumptions that (1) all reports submitted to VAERS represented true cases of SSNHL (eTable 1 in the Supplement ); (2) the number of reports submitted to VAERS was likely subject to a 50% underreporting bias based on previous studies of VAERS sensitivity for rare AEs, such as Guillain-Barré syndrome and anaphylaxis 6 ; and (3) each vaccinated individual received 2 doses, resulting in the smallest possible population size given the number of vaccine doses administered (ie, the highest possible incidence).
Because VAERS reports are unverified and lack detailed clinical data, 6 an in-depth record review of a multi-institutional consecutive series of all adult patients with audiometrically confirmed SSNHL after COVID-19 vaccination was performed in the second phase of the study. The study sites comprised 2 large academic neurotologic centers and 1 community otolaryngological practice. Cases were included if audiometrically confirmed SSNHL occurred within 3 weeks of vaccination and was contemporaneous with VAERS reports of SSNHL (ie, occurring between January 1 and June 30, 2021). Patients with a history of Ménière disease were excluded.
Reports of SSNHL were exported from VAERS into Excel software, version 16.57 (Microsoft Corporation). Simple descriptive statistics (means, ranges, and percentages) were calculated using this software for both the VAERS reports and the case series.
Between December 14, 2020, and July 16, 2021, 185 424 899 COVID-19 vaccine doses were administered in the US across the 3 manufacturers. 5 After deduplication, 2170 VAERS reports of hearing loss based on search criteria and occurring within 21 days of vaccination were extracted and compiled. In total, 555 of the 2170 reports met our definition of probable SSNHL. A total of 305 incidents (55.0%) occurred among women, and 250 incidents (45.0%) occurred among men, with a mean age of 54 years (range, 15-93 years) ( Table 1 ). Data on race and ethnicity were not available in VAERS. Overall, 305 incidents (55.0%) involved the Pfizer-BioNTech vaccine, 222 (40.0%) involved the Moderna vaccine, and 28 (5.0%) involved the Janssen/Johnson & Johnson vaccine.
A sensitivity analysis was then performed to estimate the incidence range on an annualized basis, revealing 0.6 to 28.0 cases of SSNHL per 100 000 people per year ( Table 2 ). In comparison, the annual incidence of idiopathic SSNHL was estimated to be 11 to 77 cases per 100 000 people per year, depending on age. 7 Because speculation has occurred regarding the novel lipid nanoparticle delivery vehicle and the messenger RNA (mRNA) technologies that underlie the Moderna and Pfizer-BioNTech vaccines, we next investigated whether vaccines produced by these 2 manufacturers accounted for a disproportionate number of reports of SSNHL. A total of 186.88 million doses of the Pfizer-BioNTech vaccine were administered, 136.48 million doses of the Moderna vaccine were administered, and 12.97 million doses of the Janssen/Johnson & Johnson vaccine were administered over the period examined. The VAERS reporting rate of probable SSNHL was similar across manufacturers, with 0.16 cases per 100 000 doses administered for both the Pfizer-BioNTech and Moderna vaccines, and 0.22 cases per 100 000 doses administered for the Janssen/Johnson & Johnson vaccine (eTable 2 in the Supplement ).
To further investigate whether reports of SSNHL were associated with COVID-19 vaccination, we examined the total number of reports of the condition submitted to VAERS over each weekly period from the beginning of the public vaccination campaign ( Figure 1 A). The number of submitted reports peaked in the last week of March 2021, which corresponded to the largest number of vaccine doses (16 177 521) administered during a 1-week period since the vaccination campaign began. 5 However, over each weekly period, the relative number of SSNHL reports decreased when accounting for the number of doses administered nationally, from 1.10 reports per 100 000 doses at the beginning of the campaign in December 2020 to 0.01 reports per 100 000 doses by June 2021.
Because the risk of idiopathic SSNHL is highly dependent on age, 7 we specifically examined the mean ages of patients who submitted reports of probable SSNHL, which remained relatively stable over the study period (eg, mean age, 45.9 years [range, 34.0-79.0 years] in December 2020 and 41.6 years [range, 19.0-54.0 years] in June 2021) ( Figure 1 B). We also estimated the age of the overall vaccinated population using publicly available data from the CDC 8 (eFigure 3 in the Supplement ). In the early phases of the vaccination campaign, no preponderance of older individuals (who may have been at higher risk of idiopathic SSNHL) receiving vaccine doses was apparent. In addition, in the later phases of the campaign, no preponderance of younger individuals (who may have been at lower risk of idiopathic SSNHL) was seen.
We then evaluated the possible temporal association between COVID-19 vaccination and the onset of idiopathic SSNHL as documented in VAERS incident reports ( Figure 2 A). The mean time to onset of SSNHL was 6 days (range, 0-21 days), with the highest incidence occurring at 0 days (70 reports), 1 day (104 reports), and 2 days (72 reports) after vaccination and a smaller second peak occurring at 7 days (38 reports) after vaccination.
To better understand the clinical profiles of patients who reported SSNHL after COVID-19 vaccination, we examined the detailed clinical characteristics of patients with confirmed hearing loss occurring after COVID-19 vaccination in a multi-institutional case series. A total of 21 patients were identified across study sites, with a mean age of 61 years (range, 23-92 years; 13 women [61.9%]). Demographic, clinical, and audiometric characteristics of patients are shown in Table 3 . Six patients (28.6%) had a history of autoimmune disease, including eczema, episcleritis, Hashimoto thyroiditis, multiple sclerosis, and rheumatoid arthritis. The mean time to onset of SSNHL was 6 days (range, 0-15 days) after vaccination, with the highest number of cases (6) occurring at 7 days after vaccination ( Figure 2 B). Overall, 18 of 21 patients (85.7%) received treatment; of those, 9 patients (50.0%) received intratympanic steroids, 5 (27.8%) received oral corticosteroids, and 4 (22.2%) received both. No adjuvant therapies were prescribed. Complete posttreatment audiometric data were available for 14 patients, 8 of whom (57.1%) experienced audiometric improvement ( Table 3 ; eFigure 1 in the Supplement ).
One patient without any history of Ménière disease or autoimmune inner ear disease experienced new-onset low-frequency (250-500 Hz) SSNHL at 14 days after the first vaccine dose; the condition improved with oral and intratympanic steroid treatment but worsened again at 14 days after the second vaccine dose (15-dB threshold increase in hearing loss at 500 Hz) (eFigure 2 in the Supplement ).
This comprehensive cross-sectional study of CDC VAERS reports of SSNHL after COVID-19 vaccination during the first 7 months of the national vaccination campaign included 185 million doses across all 3 manufacturers. Although VAERS reports contain raw data that are unverified, they present a national snapshot of potential AEs occurring after vaccination. Our analysis found that, based on VAERS reports, the estimated incidence of SSNHL after COVID-19 vaccination did not exceed the reported incidence of idiopathic SSNHL in the general population. 7 Furthermore, despite the novel delivery vehicle and immunologic mechanism of the mRNA-based vaccines manufactured by Pfizer-BioNTech and Moderna, we did not find an increased reporting rate of SSNHL associated with lipid nanoparticle mRNA vaccines compared with the adenoviral platform used in the Janssen/Johnson & Johnson vaccine.
We also hypothesized that if an association existed between COVID-19 vaccination and SSNHL, we would find an association between the number of reports of SSNHL submitted to VAERS and the number of vaccine doses administered. However, we found the rate of reports per 100 000 doses decreased across the vaccination period, despite large concomitant increases in the absolute number of vaccine doses administered per week ( Figure 1 A).
We also tested the hypothesis that the increased rate of reports of SSNHL in the initial vaccination phase could be associated with older individuals being vaccinated first 9 ; our analysis of the mean ages of people reporting SSNHL after vaccination to VAERS ( Figure 1 B) and the CDC COVID-19 tracking data on the number of individuals vaccinated in each age group over time (eFigure 3 in the Supplement ) did not support this hypothesis. Given that health care professionals were also included in the first phase of vaccination, one might assume that this group would be more attuned to AEs and more likely to report SSNHL; however, the relative number of health care professionals who initially experienced SSNHL was impossible to ascertain based on VAERS data. Taken together, these data suggest that an association between COVID-19 vaccination and SSNHL during the first 7 months of vaccination was unlikely at the population level.
Because VAERS incident reports lack clinical detail, conclusions regarding specific risk factors associated with SSNHL after COVID-19 vaccination cannot be reached. Narrative information within VAERS is self-reported and highly variable, ranging from no information on medical history to detailed information on both medical history and medication use. Thus, we assessed the clinical characteristics of patients with confirmed SSNHL at 3 large otolaryngological practices. The demographic and clinical characteristics of patients examined in our multi-institutional case series ( Table 3 ) did not clearly identify any specific cofactors among those experiencing SSNHL after vaccination, and patient characteristics appeared similar to the highly heterogeneous profiles observed among those with idiopathic SSNHL and those included in case series conducted at other institutions. 1 A previous study suggested that autoimmune disease may increase the risk of idiopathic SSNHL, 10 and we observed that autoimmune disease was present in 28.6% of the 21 patients in the case series reporting SSNHL after COVID-19 vaccination. Autoimmune disease as a risk factor for SSNHL with or without vaccination remains speculative, and further research is needed.
Both the mRNA payload and the lipid nanoparticle delivery vehicle have been suggested to be potential mechanisms of autoimmunogenicity. 11 Notably, the patient in the case series who reported having normal hearing before vaccination (no prevaccination audiometric data were available) and no history of autoimmune disease ( Table 3 ) was found to have low-frequency unilateral SSNHL at 14 days after the first vaccine dose. The patient received treatment with a course of oral steroid medication and experienced partial recovery of hearing; however, the patient subsequently reported new hearing deficit at 14 days after the second vaccine dose and was found to have a 15-dB threshold increase in hearing loss at 500 Hz (eFigure 2 in the Supplement ). Although not meeting the American Academy of Otolaryngology–Head and Neck Surgery criteria for SSNHL, 12 the observed audiometric changes were nonetheless concerning. Sudden sensorineural hearing loss after each COVID-19 vaccine dose was also reported among 3 patients in a recent case series, although 2 of those 3 patients had autoimmune inner ear disease, Ménière disease, or both. 1 Thus, our findings suggested that although no association between COVID-19 vaccination and SSNHL was found at the population level, an association among some individuals cannot be excluded without further research.
We also considered the timing of SSNHL after COVID-19 vaccination because this timing may have offered insight into the mechanistic basis of any potential biological association. For instance, Wichova et al 1 hypothesized that otologic symptoms, such as dizziness or SSNHL occurring 10 to 14 days after vaccination, could coincide with the production of immunoglobulin G at 10 to 14 days after vaccine administration. In both the national VAERS reports and our multi-institutional case series, we found that the mean time to onset of SSNHL was 6 days, with the highest incidence at 0 to 2 days and 7 days after vaccination ( Figure 2 A and B). These temporal patterns were consistent with the timing of onset for other COVID-19 vaccine–associated AEs, such as myocarditis (2-4 days) 13 - 15 and vaccine-induced immune thrombotic thrombocytopenia (7-10 days). 16 In a large epidemiologic study, Baxter et al 3 reported that the mean time to onset of reported SSNHL after influenza vaccination was also 2 days.
Observed peaks in reports of SSNHL at 1 and 7 days after vaccination in both VAERS and our case series could be partly accounted for by recall bias, which has been well documented in studies of passive vaccine AE reporting. 17 , 18 For example, an analysis of AEs associated with the hepatitis B vaccine, in which patient self-reports were cross-referenced with specific vaccination records, found substantial recall bias that produced an inaccurate association between vaccination and the development of multiple sclerosis. 19 The VAERS data may have been especially sensitive to recall bias because a substantial number of reports were submitted in a delayed manner, sometimes weeks to months after the onset of SSNHL. In particular, it is possible that patients, or health care professionals reporting on their behalf, may have estimated “about 1 day” or “about 1 week” when asked about the timing to onset of hearing loss because these are intuitive intervals for estimation. Bias in the perception of vaccine-associated AEs has substantial implications for an individual’s decision to receive a vaccine, as Betsch et al 20 reported in a study of a simulated online social network. Participants in that study were more likely to overestimate true vaccine-associated AE rates if presented with narratives from others that suggested a higher risk of experiencing a vaccine-associated AE, and they were subsequently less likely to receive a vaccine. 20 Notably, narrative information included in reports of AEs was more meaningful in influencing participants’ decisions to receive a vaccine than were statistical summaries. 20
Similar to recommendations provided by other reports of AE clusters, including cerebral venous sinus thrombosis 21 and myocarditis, 13 - 15 after COVID-19 vaccination, long-term epidemiologic and vaccine safety studies supported by mechanistic research are needed to more definitively address any potential association between COVID-19 vaccination and SSNHL. Reports of recovery of SARS-CoV-2 RNA in the middle ear of individuals who died of COVID-19 22 and recent findings of the ability of SARS-CoV-2 to directly infect human vestibular hair and Schwann cells 23 provide plausible biological mechanisms for COVID-19–associated hearing loss and may open avenues of investigation into immune mechanisms in the inner ear.
This study has several limitations. One limitation of the case series is its lack of a comparison group (eg, a group of patients who did not receive a COVID-19 vaccine but experienced SSNHL within the same period examined). Nonetheless, the detailed patient data in this series may serve as a supplement to the national patterns identified through analysis of SSNHL reports in the VAERS database.
Although an important tool for systematic vaccine safety studies, 24 the VAERS incident reports used in the present study are not yet verified by the CDC and therefore need to be interpreted with caution. We specifically focused on SSNHL, which is a well-defined clinical condition with a known population-level incidence, in contrast to other otolaryngological conditions, such as tinnitus or Ménière disease. To account for inherent uncertainties associated with raw report data, we developed a standardized case definition for probable SSNHL to identify the most credible incident reports. Few data exist to guide selection of the risk interval for SSNHL after vaccination. The 3-week interval used in the present study was designed to be longer than the primary interval used in previous studies 3 to balance considerations of temporal association with the risk of overexclusion.
It was also not possible to apply American Academy of Otolaryngology–Head and Neck Surgery criteria for SSNHL (loss of 30 dB over 3 consecutive frequencies) 12 to VAERS reports given the lack of numerical audiometric testing results contained within those reports. Using a sensitivity analysis, the maximum incidence estimate was produced based on the assumptions that (1) all submitted reports represented true SSNHL, which was unlikely (eTable 1 in the Supplement ), and (2) reports were subject to an additional 50% underreporting bias based on previous studies of VAERS sensitivity to detect rare AEs, such as Guillain-Barré syndrome and anaphylaxis. 6 Therefore, our calculated maximum incidence is likely an overestimate of the true incidence of SSNHL, especially given that our 3-week time to onset interval was substantially longer than the interval of 0 to 72 hours endorsed by the American Academy of Otolaryngology–Head and Neck Surgery. 12 In the absence of incident report verification and large-scale vaccine safety studies using verified reports, the estimation strategies used in this study nonetheless provide a snapshot and a potential tool that can be used by otolaryngologists challenged by this difficult clinical issue and its important public health implications.
This cross-sectional study and case series used an up-to-date analysis of VAERS case reports during the first 7 months of the US COVID-19 vaccination campaign across 3 vaccine manufacturers along with retrospective data from a series of patients with confirmed SSNHL, finding no population-level association between COVID-19 vaccination and SSNHL. Assessment of verified cases of SSNHL revealed heterogeneity in patient demographic characteristics, risk factors, and audiologic patterns. Further prospective investigation is needed to identify any potential associations between COVID-19 vaccination and SSNHL in some individuals. It is important that clinicians report all suspected COVID-19 vaccine–associated AEs rigorously and accurately to VAERS to allow verification and future performance of systematic vaccine safety studies.
Accepted for Publication: January 3, 2022.
Published Online: February 24, 2022. doi:10.1001/jamaoto.2021.4414
Corresponding Author: Eric J. Formeister, MD, MS, Department of Otolaryngology–Head and Neck Surgery, Johns Hopkins University School of Medicine, 601 N Caroline St, 6th Floor, Baltimore, MD 21287 ( [email protected] ).
Author Contributions: Drs Formeister and Sun had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Galaiya, Kozin, and Sun contributed equally.
Concept and design: Formeister, Wu, Chari, Rauch, Remenschneider, Quesnel, Stewart, Galaiya, Kozin, Sun.
Acquisition, analysis, or interpretation of data: Formeister, Wu, Chari, Meek, Rauch, Remenschneider, Quesnel, de Venecia, Lee, Chien, Stewart, Kozin, Sun.
Drafting of the manuscript: Formeister, Wu, Chari, Stewart, Kozin, Sun.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Formeister, Wu, Sun.
Obtained funding: Sun.
Administrative, technical, or material support: Wu, Chari, Meek, Remenschneider, de Venecia, Galaiya, Sun.
Supervision: Chari, Remenschneider, Quesnel, Chien, Stewart, Galaiya, Kozin, Sun.
Conflict of Interest Disclosures: Dr Lee reported receiving personal fees from 3NT Medical and income and personal fees from Frequency Therapeutics outside the submitted work. Dr Quesnel reported receiving grants from Frequency Therapeutics and Grace Medical and personal fees from Frequency Therapeutics, and owning a patent for a protective drape to mitigate aerosol spread during otologic surgery (licensed to Grace Medical) outside the submitted work. No other disclosures were reported.
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Hormonal imbalances can wreak havoc on a woman’s body, causing everything from mood swings and fatigue to weight gain and irregular periods. Traditional medicine often focuses on treating symptoms, but functional medicine aims to address the root cause. In this article, we’ll dive deep into how functional medicine can help women with hormonal imbalances, offering a holistic approach to health and wellness.
Before we get into the specifics of how functional medicine tackles hormonal imbalances, let’s break down what functional medicine is all about.
Functional medicine is all about looking at the body as a whole. Instead of just treating symptoms, it aims to understand the underlying causes of health issues. It’s like being a detective—functional medicine practitioners gather clues from your lifestyle, environment, genetics, and biochemistry to figure out what’s going on.
No two people are the same, and functional medicine recognizes that. Treatments are tailored to the individual, taking into account unique factors like diet, stress levels, sleep patterns, and more. This personalized approach ensures that you get the care that’s right for you.
Hormones play a crucial role in regulating many of the body’s processes, from metabolism and mood to reproductive health. When these hormones are out of balance, it can lead to a variety of issues.
Hormonal imbalances can manifest in many ways. Here are some common symptoms that women might experience:
Several factors can contribute to hormonal imbalances, including:
Functional medicine addresses hormonal imbalances by looking at the bigger picture. Here’s how it works:
Functional medicine practitioners start with comprehensive testing to get a clear picture of what’s happening inside your body. This might include blood tests, saliva tests, and even stool tests to check for gut health issues. These tests help identify specific hormonal imbalances and other underlying issues.
Once the testing is done, a personalized treatment plan is created. This plan might include:
Let’s take a closer look at how diet and nutrition play a key role in managing hormonal imbalances.
A diet rich in whole foods can help stabilize blood sugar levels and support hormone production. Here are some particularly beneficial foods:
Certain foods can disrupt hormone balance and should be limited or avoided:
While a healthy diet is crucial, supplements can also play a key role in supporting hormonal health. Here are some commonly recommended supplements in functional medicine:
Omega-3s are anti-inflammatory and can help balance hormones. They are found in fish oil, flaxseeds, and walnuts.
Vitamin D is important for hormone production and overall health. Many people are deficient in this vitamin, especially those who live in areas with limited sunlight.
Magnesium helps regulate cortisol and supports overall hormonal balance. It’s found in foods like nuts, seeds, and leafy greens, but many people benefit from a supplement.
Adaptogens like ashwagandha, Rhodiola, and maca can help the body adapt to stress and support adrenal health, which in turn can help balance hormones.
Managing stress is a key component of balancing hormones. Here are some effective stress management techniques:
Practicing mindfulness and meditation can help reduce stress and lower cortisol levels. Even a few minutes a day can make a big difference.
These gentle forms of exercise not only promote physical health but also help reduce stress and improve mental clarity.
Taking time for self-care is crucial. This might include activities like reading, taking a bath, or spending time in nature.
Regular exercise is essential for maintaining hormonal balance. Here are some tips for incorporating exercise into your routine:
A combination of cardio, strength training, and flexibility exercises is ideal. Cardio helps burn calories and improve heart health, strength training builds muscle, and flexibility exercises like yoga promote relaxation and stress reduction.
Consistency is more important than intensity. Aim for at least 30 minutes of exercise most days of the week.
It’s important to listen to your body and not overdo it. Over-exercising can lead to hormonal imbalances, so find a balance that works for you.
Good sleep is crucial for hormone health. Here are some tips for improving sleep:
Creating a calming bedtime routine can signal to your body that it’s time to wind down. This might include activities like reading, taking a warm bath, or practicing relaxation techniques.
Reducing screen time before bed can help improve sleep quality. The blue light from screens can interfere with the production of melatonin, a hormone that regulates sleep.
A comfortable sleep environment can make a big difference. This might include investing in a good mattress and pillows, keeping the room cool and dark, and reducing noise.
Let’s look at real-life examples of how functional medicine has helped women with hormonal imbalances.
Sarah was in her mid-30s and struggling with fatigue, weight gain, and irregular periods. Traditional treatments weren’t helping, so she turned to functional medicine. After comprehensive testing, it was found that Sarah had high cortisol levels and insulin resistance. Her personalized treatment plan included dietary changes, stress management techniques, and targeted supplements. Within six months, Sarah’s energy levels improved, she lost weight, and her periods became regular.
Emily, a 45-year-old mother of two, was experiencing hot flashes, mood swings, and trouble sleeping. A functional medicine approach revealed that Emily’s estrogen levels were low, and she was also dealing with chronic stress. Her treatment plan included hormone-supporting foods, mindfulness practices, and herbal supplements. Over time, Emily’s symptoms lessened, and she regained a sense of well-being.
Functional medicine offers a comprehensive, personalized approach to addressing hormonal imbalances in women. By looking at the whole picture and addressing the root causes, functional medicine can help women achieve better health and well-being. Whether through dietary changes, stress management, supplements, or lifestyle adjustments, functional medicine provides the tools and strategies to restore hormonal balance and improve quality of life.
Remember, it’s important to work with a qualified functional medicine practitioner to develop a plan tailored to your unique needs. With the right support, you can take control of your health and feel your best.
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BMC Medicine volume 22 , Article number: 343 ( 2024 ) Cite this article
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Racial and ethnic disparities in mortality persist among US cancer survivors, with social determinants of health (SDoH) may have a significant impact on these disparities.
A population-based cohort study of a nationally representative sample of adult cancer survivors, who participated in the US National Health and Nutrition Examination Survey from 1999 to 2018 was included. Sociodemographic characteristics and SDoH were self-reported using standardized questionnaires in each survey cycle. The SDoH was examined by race and estimated for associations with primary outcomes, which included all-cause and cancer-specific mortality. Multiple mediation analysis was performed to assess the contribution of each unfavorable SDoH to racial disparities to all-cause and cancer-specific mortality.
Among 5163 cancer survivors (2724 [57.7%] females and 3580 [69.3%] non-Hispanic White individuals), only 881 (24.9%) did not report an unfavorable SDoH. During the follow-up period of up to 249 months (median 81 months), 1964 deaths were recorded (cancer, 624; cardiovascular, 529; other causes, 811). Disparities in all-cause and cancer-specific mortality were observed between non-Hispanic Black and White cancer survivors. Unemployment, lower economic status, education less than high school, government or no private insurance, renting a home or other arrangements, and social isolation were significantly and independently associated with worse overall survival. Unemployment, lower economic status, and social isolation were significantly associated with cancer-specific mortality. Compared to patients without an unfavorable SDoH, the risk of all-cause mortality was gradually increased in those with a cumulative number of unfavorable SDoHs (1 unfavorable SDoH: hazard ratio [HR] = 1.54, 95% CI 1.25–1.89; 2 unfavorable SDoHs: HR = 1.81, 95% CI 1.46–2.24; 3 unfavorable SDoHs: HR = 2.42, 95% CI 1.97–2.97; 4 unfavorable SDoHs: HR = 3.22, 95% CI 2.48–4.19; 5 unfavorable SDoHs: HR = 3.99, 95% CI 2.99–5.33; 6 unfavorable SDoHs: HR = 6.34 95% CI 4.51–8.90). A similar trend existed for cancer-specific mortality.
In this cohort study of a nationally representative sample of US cancer survivors, a greater number of unfavorable SDoH was associated with increased risks of mortality from all causes and cancer. Unfavorable SDoH levels were critical risk factors for all-cause and cancer-specific mortality, as well as the underlying cause of racial all-cause mortality disparities among US cancer survivors.
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An increase in the cancer survivor population poses a significant health care and economic burden worldwide, and cancer is the second leading cause of death in the United States (US). Specifically, there will be approximately 2,001,140 new cancer cases and an estimated 611,720 deaths from cancer in the US in 2024 [ 1 ]. Although cancer mortality has declined overall by 33% since 1991, improved survival outcomes have not benefitted equally for all cancer populations [ 1 , 2 ]. Substantial racial and ethnic disparities in all-cause and cancer-related mortality rates persist in US cancer survivors [ 1 , 2 , 3 , 4 ]. For example, Black individuals have lower relative cancer survival rates than White individuals for almost every cancer type [ 1 , 5 ]. Interestingly, the most striking gaps in survival involve cancers that are most amenable to prevention and early detection, such as cervical cancer [ 5 ]. Recently, the racial and ethnic disparities in cancer mortality have slowly narrowed; however, these disparities in cancer health have become increasingly understood in the context of social determinants of health (SDoH) [ 2 , 5 , 6 ], which are responsible for an extremely important factor associated with cancer risk and treatment [ 7 ]. The World Health Organization (WHO) defined SDoH as non-medical factors that affect health outcomes, including the conditions in which people are born, grow, live, work, and age, and a wider set of forces and systems shaping daily life conditions [ 8 ]. The SDoH included factors related to economic stability, education, health care access, residential environment, and social context and support [ 9 , 10 , 11 ], associated with the health outcomes of cancer survivors [ 12 , 13 ]. Addressing social disparities in cancer health is essential in the quest to improve survival outcomes among cancer survivors, which reflects a commitment to health equity to achieve optimal health for everyone.
Previous studies have tended to examine the contribution of individual variables involving unfavorable SDoHs in the separate associations with mortality or morbidity, most of which focused on the direct and indirect influence of socioeconomic factors on the disparity in survival [ 9 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. Among the general population, a large multicohort study and meta-analysis with more than 1.7 million individuals from 7 WHO member countries reported that low socioeconomic status was associated with a 46% (95% CI, 39–53%) and 43% (95% CI, 34–52%) greater risk of all-cause and cancer mortality, respectively, compared to high socioeconomic status [ 15 ]. A low level of education, poverty, and a lack of health insurance coverage explain in part the continuous widening in mortality inequities across some adult sociodemographic groups in the US [ 14 , 17 , 20 , 24 ]. Additionally, a recent analysis demonstrated that the cumulative SDoH count was associated with an increased premature mortality risk [ 25 ]. However, limited evidence has been reported on the effect of SDoH in cancer survivors. Although previous cohort studies have shown that disadvantaged SDoH are associated with poor mental and physical health [ 26 ], resulting in a delay in medical and surgical treatment [ 27 ], and an increased risk of all-cause and cancer-related mortality among patients with cancers (such as breast and pancreatic cancer) [ 12 , 13 ]. To the best of our knowledge, few studies have examined the impact of the comprehensive and accumulating burden of SDoH on all-cause and cause-specific mortality, using methods published previously [ 13 , 25 ]. There is no study that has reported the relative contributions of these SDoH on racial disparities in the all-cause and cancer-specific mortality rates among the US cancer survivors at the population level.
The objective of the present study was to evaluate the relationships of multiple SDoH with all-cause, cancer-specific, and non-cancer mortality, and to investigate how SDoH mediates racial differences in all-cause and cancer-specific mortality among cancer survivors. We hypothesized that disparities exist in the cumulative number of unfavorable SDoH across racial and ethnic groups and that a higher number of these unfavorable SDoH is associated with higher mortality rates.
In this retrospective study, 10 cycles of cross-sectional data were collected from the National Health and Nutrition Examination Survey (NHANES) database, which used a complex, multistage, and probability sampling design to recruit participants representative of the civilian non-institutionalized US population [ 28 ]. Each participant was invited to attend an in-person or in-home interview to complete the questionnaire. The present study examined and analyzed existing data involving sociodemographic characteristics and several SDoH co-variables among cancer survivors of 20 years or older with information linked to the National Death Index through 31 December 2019 for 10 survey cycles of NHANES from 1999–2000 to 2017–2018. All the NHANES protocols were approved by the Research Ethics Review Board of the National Center for Health Statistics (NCHS), and written informed consent was provided by all participants at the time of recruitment.
In each 2-year survey, age, gender, and racial or ethnic groups (non-Hispanic White [NHW], Hispanic, non-Hispanic Black [NHB], and other [American Indian/Alaska Native/Pacific Islander, Asian, and multiracial]) were obtained from standardized questionnaires of in-home interviews by self or parent/guardian report from provided categories.
We included several variables that reflected SDoH information from standardized questionnaires, which were defined according to the Healthy People 2030 [ 11 ] and World Health Organization [ 29 ] by the following factors: economic stability; education access and quality; health access and quality; neighborhood and built environment; and social and community context. In the present study, we finally chose eight SDoH variables (employment status, family poverty income ratio, food security, education level, regular health care access, type of health insurance, home ownership, and marital status) in each NHANES cycles from 1999 to 2018, according to previously published studies [ 25 , 30 ]. Social support was excluded because it was only visible in surveys conducted between 1999 and 2008. More detailed description information on SDoH was provided in the supplement (Additional file 1: Table S1) [ 31 , 32 , 33 ], and the definition for unfavorable SDoH was based on the conventional cutoff points [ 10 , 11 , 23 , 34 , 35 ]. Furthermore, the associations between several single SDoH measures and all-cause mortality were investigated using various categorizations with adjustment for age, gender, race, and ethnicity regardless of survey weights (Additional file 1: Table S2). Each SDoH was divided into two levels based on the conventional cut-off points [ 11 , 23 , 34 , 35 ]. Unfavorable SDoH was significantly associated with a lower survival rate. During the in-person interview, participants were asked to respond to several questions about these SDoH. Economic stability was operationalized using self-reported measures of the family poverty income ratio (PIR, less than 2.4 [unfavorable SDoH] and more than 2.4 [favorable SDoH]), employment status (employed, student, or retired [favorable SDoH] and unemployed [unfavorable SDoH]) and household food security category, which was dichotomized as fully food security (no affirmative response) or marginal, low, or very low security (1–10 affirmative responses) based on the responses to the US Food Security Survey Module questions (Bickel et al. [ 36 ]). Education access and quality measurement used the highest grade or level of schooling completed or the highest degree received, dichotomized as less than high school (unfavorable SDoH) and high school graduate or higher (favorable SDoH). Health care access and quality were assessed by self-reported questionnaire about routine places for health care (at least one regular health care facility [favorable SDoH] and none or hospital emergency room [unfavorable SDoH]) and health insurance type (private [favorable SDoH] and none or government [unfavorable SDoH]). The residential environment was assessed by home ownership (owned or being bought [favorable SDoH] and rental or other arrangement [unfavorable SDoH]). Social community context was assessed by self-reported marital status (defined as married or living with a partner [favorable SDoH] and not married nor living with a partner [unfavorable SDoH]).
The cumulative number of unfavorable SDoH variables with a range from 0 (no unfavorable SDoH) to 6 or more (≥ 6 unfavorable SDoH) was calculated to explore the cumulative effect of unfavorable SDoH on all-cause and cancer-specific mortality. Because only a small proportion of participants reported having 6, 7, or 8 unfavorable SDoH variables simultaneously, thus we created a category of six or more, indicating the combination of 6, 7, or 8 unfavorable SDoH variables.
Information on cancer diagnosis was collected from survey questionnaires during the in-person interview using the computer-assisted personal interview system, including cancer type(s), with up to three cancer diagnoses recorded and the age at first diagnosis for each cancer. Participants were asked, “Have you ever been told by a doctor or other health professional that you had cancer or a malignancy of any kind?” If individuals who answered “yes” were defined as cancer survivors and were asked further, “What kind of cancer was it?” and “How old were you when this cancer was first diagnosed?”.
The NCHS provided mortality data that were linked to the National Death Index, with follow-up until 31 December 2019 [ 37 ]. Cause-of-death coding for all US deaths occurring after 1998 followed the 10th revision of the International Statistical Classification of Diseases, Injuries, and Causes of Death (ICD-10) guidelines. Cancer-related mortality was classified as death due to malignant neoplasms (ICD-10, codes C00-C97). The follow-up duration was defined as the interval elapsing from the date of the baseline interview to the date of death or the follow-up cut-off (31 December 2019) for those participants who did not have a death event in the whole study. We investigated the association between SDoH and all-cause, cancer-related, and non-cancer mortality (mortality instead of cancer, ICD-10 codes instead of C00-97). All-cause and cancer-specific mortality were the main outcomes of this study. The all-cause mortality was measured from the date of the baseline interview to the date of death from any cause or the follow-up cut-off. The cancer-specific mortality was calculated from the date of the baseline interview to the date of death from cancer.
All statistical analyses were conducted with the use of R (version 4.3.1) following the NHANES analysis guidance. The survey interview weights were used for analysis as appropriate to obtain nationally representative estimates. We calculated weighted sample sizes to be nationally representative and population-weighted percentages according to race and ethnicity. The chi-square test was used to determine the differences in participants’ sociodemographic characteristics and SDoH variables across four classifications of racial and ethnic groups. The pairwise correlation among the eight dichotomous SDoH was evaluated using the Spearman method. The weighted proportions of cancer survivors in each number of unfavorable SDoH category were estimated by gender, race, and ethnicity. Kaplan–Meier survival curves were examined to determine the all-cause cumulative mortality and cancer-specific cumulative mortality rates among cancer survivors stratified by SDoH. Furthermore, Kaplan–Meier analysis was used to plot the cumulative hazard for all-cause and cancer-specific mortality in entire and gender subgroups and race and ethnicity subgroups using age as the timescale [ 38 ].
Multivariable Cox proportional hazards regression models with the use of imputation-adjusted survey weights were applied to estimate the mortality risks (hazard ratio [HR]) and 95% confidence interval (CI) for the associations between cumulative SDoH variables and race with all-cause, cancer-specific, and non-cancer mortality. Final stage multivariable Cox models were adjusted for age, gender, race, and ethnicity, and additionally included the other SDoHs to identify independent, indirect associations. We plotted the HRs of the cumulative SDoH variables to visualize whether the relationship with all-cause and cancer-specific mortality was linear or non-linear. Sensitivity analyses were performed by excluding participants of deaths that occurred within the first 2-year follow-up to lessen the probability of reverse causation [ 39 ]. All statistical tests were 2-sided and P < 0.05 was considered statistically significant. Data analyses were performed from 1 June to 1 August 2023.
Because of racial disparity in all-cause and cancer-specific mortality between NHW and NHB among cancer survivors in the US, therefore mediation analysis was performed to explore whether SDoH factors contributed to White-Black disparity in mortality or not. We estimated the relative effect (corresponding direct or indirect effect divided by the total effect) of each SDoH variable to explain the racial and ethnic difference in mortality using R package mma [ 31 , 32 , 33 ]. More detailed information was contained in the Supplementary material (Additional file 1: Methods S1).
NHANES (1999–2018) data from 5163 individuals were enrolled in the final analysis (Additional file 1: Fig. S1). A total of 101,316 persons ≥ 1 year of age who participated in the in-person or in-home interview and 96,153 were excluded, as follows: (1) 46 235 participants < 20 years of age; (2) 49,915 whom were not diagnosed with cancer, and (3) 3 individuals who did not have unique identifiers to allow linkage to the National Death Index. Of the 5163 cancer survivors (weighted population, 32,623 176; 57.7% female) in this study cohort, 3580 (69.3%) were NHW, 631 (12.2%) were Hispanic, 718 (13.9%) were NHB, and 234 (4.5%) individuals of were classified as race and ethnicity, including American Indian/native Alaskan, Pacific Islander, Asian, and multiracial (Table 1 ). Compared to NHW, Hispanic, NHB and other race and ethnic cancer survivors were more likely to have unfavorable SDoH factors, including not being married nor living with a partner, education less than high school, a PIR < 2.4, renting a home or other arrangement, unemployment, government or none health insurance, and marginal, low, or very low security. However, a lower proportion of NHB participants had no place routine place when sick or in need of advice about healthcare compared with cancer survivors from all other racial and ethnic subgroups. Approximately 24.9% of cancer survivors did not have a cumulative number of unfavorable SDoH. The higher proportion of NHW cancer survivors with 0 and 1 cumulative unfavorable SDoH was observed compared to patients from all other race and ethnic subgroups. In addition, a higher proportion of Hispanic cancer survivors with 3, 4, 5, and 6 or more unfavorable SDoH was observed compared to patients from all other race and ethnic subgroups. NHB and Hispanic individuals had a higher prevalence of multiple unfavorable SDoH (cumulative of 3 or more) compared to NHW cancer survivors.
Then, we analyzed the relationship between the eight SDoH variables. The results showed that all eight SDoH variables were significantly correlated with each other (Additional file 1: Fig. S2). Furthermore, the proportion of male participants decreased stepwise from 34.8% (0 unfavorable SDoH) to 2.2% (6 or more number of unfavorable SDoH), whereas the proportion of female participants increased from 22.6% (0 unfavorable SDoH) to 24.8% (1 unfavorable SDoH), and then gradually decreased to 4.7% (6 or more number of unfavorable SDoH; Additional file 1: Fig. S3). Breast and prostate cancer were the most common malignant neoplasm type in males and females, respectively (Additional file 1: Table S3).
During the median follow-up of 81 months (ranged 0–249 months) in the 10 NHANES cycles linked mortality file cohort, a total of 1964 deaths occurred (all-cause), including 624 cancer patients who died from cancer (cancer-related mortality), 529 who died from cardiovascular disease, and 811 who died from other cause. Compared to participants who were NHW, NHB adults with cancer had a significantly higher overall mortality rate (HR, 1.57; 95% CI, 1.34–1.89) and cancer-specific mortality (HR, 2.03; 95% CI, 1.60–2.59; Fig. 1 ). Cancer survivors with each unfavorable SDoH variable, except access to regular health care, was significantly associated with higher all-cause, cancer-specific, and non-cancer mortality in the multivariable model adjusted for age (MV model 1), and adjusted for age, gender, race and ethnicity (MV model 2; Table 2 ). After adjustment for age, gender, race and ethnicity and other SDoHs, including unemployment status (HR, 1.83; 95% CI, 1.58–2.12; P < 0.001), family income-to-poverty less than 2.4 (HR, 1.51; 95% CI, 1.32–1.72; P < 0.001), education less than high school attached (HR, 1.23; 95% CI, 1.05–1.44; P = 0.012), government or none of health insurance (HR, 1.19; 95% CI, 1.05–1.36; P = 0.007), renting a home or other housing arrangement environment (HR, 1.39; 95% CI, 1.20–1.62; P < 0.001), and not being married nor living with a partner (HR, 1.22; 95% CI, 1.08–1.38; P < 0.001) were significantly associated with an increased risk for all-cause mortality, which was similar to non-cancer mortality (Table 2 ). Furthermore, unemployed individuals (HR, 2.13; 95% CI, 1.62–2.79; P < 0.001), family income-to-poverty less than 2.4 (HR, 1.35; 95% CI, 1.09–1.66; P = 0.006), and not being married nor living with a partner (HR, 1.22; 95% CI, 1.08–1.38; P < 0.001) were significantly associated with an increased cancer-specific mortality risk compared to those with favorable SDoH (Table 2 ). Specifically, individuals of being unemployed status were associated with almost more than 1.9- and 2.2-fold higher all-cause mortality and cancer-specific mortality rates, respectively.
All-cause mortality ( A ), cancer-specific mortality ( B ), and hazard ratios in US adults diagnosed with cancers aged 20 years or older by race and ethnicity. Note: Kaplan–Meier curves showed cumulative mortality probability race and ethnicity using age as the timescale. The number at risk was unweighted observed frequencies. Cumulative mortality rates were estimated with the use of survey weights. The bar chart showed HRs of all-cause and cancer-specific mortality associated with race and ethnicity, adjusted for age, and gender. Error bars were 95% CIs. NHW indicated non-Hispanic White; NHB indicated non-Hispanic Black; HR indicated hazard ratio; ns was the abbreviation of no significance; *** meant p < 0.001, ** meant p < 0.01, and * meant p < 0.05
Cancer survivors with a greater cumulative number of SDoHs were significantly associated with an increased risk of death from all-cause and cancer (Additional file 1: Fig. S4; P < 0.001). In the multivariable of MV model 1 (adjusted for age, gender, race, and ethnic), the HRs for all-cause and cancer-specific mortality were 1.54 (95% CI, 1.25–1.89) and 1.52 (95% CI, 1.04–2.22) for cancer survivors with 1 unfavorable SDoH, 1.81 (95% CI, 1.46–2.24) and 1.70 (95% CI, 1.20–2.24) for those with 2 unfavorable SDoHs, 2.42 (95% CI, 1.97–2.97) and 2.22 (95% CI, 1.51–3.26) for those with 3 unfavorable SDoHs, 3.22 (95% CI, 2.48–4.19) and 2.44 (95% CI, 1.60–3.72) for those with 4 unfavorable SDoHs, 3.99 (95% CI, 2.99–5.33) and 3.60 (95% CI, 2.25–5.75) for those with 5 unfavorable SDoHs, and 6.34 (95% CI, 4.51–8.90) and 5.00 (95% CI, 3.00–8.31) for those with 6 or more unfavorable SDoHs, respectively, compared with of whom without unfavorable SDoH (Fig. 2 ). Kaplan–Meier curves were used to estimate the cumulative probability of all-cause and cancer-specific mortality using age as the timescale. The all-cause and cancer-specific mortality rates were significant across the several groups with a cumulative number of unfavorable SDoHs (Fig. 2 , P < 0.001). Pairwise comparison using log-rank showed that the all-cause mortality rate was similar and not significantly different among cancer survivors with 0, 1, 2, and 3 cumulative number of unfavorable SDoH across the entire age cohort (Additional file 1: Table S4). There was no significant difference in cancer-specific mortality among cancer survivors with 0, 1, 2, 3, and 4 cumulative number of unfavorable SDoH (Additional file 1: Table S5). Based on the linear dose–response analysis fitted curves (unfavorable SDoH ranged from 0 to 8), every cumulative unfavorable SDoH increase was significantly associated with 64% increased risks of death from all-cause (HR per 1-number increase, 1.64 [95% CI, 1.50–1.78]), and 53% of cancer (HR per 1-number increase, 1.53 [95% CI, 1.45–1.60]) (Additional file 1: Fig. S5 and Table 2 ; P < 0.001 for linear trend).
All-cause mortality ( A ), cancer-specific mortality ( B ), and hazard ratios in US adults diagnosed with cancer aged 20 years or older according to the cumulative number of unfavorable SDoH. Note: Kaplan–Meier curves showed cumulative mortality probability by age and a cumulative number of unfavorable SDoH using age as the timescale. The number at risk is unweighted observed frequencies. Cumulative mortality rates were estimated with the use of survey weights. Bar chart showed hazard ratios of all-cause mortality and cancer-specific mortality associated with a number of unfavorable SDoH, adjusted for age, gender, and race and ethnicity; error bars were 95% CIs. A Compared to those with 0 unfavorable SDoH, all-cause mortality of hazard ratios (95% CI) for cancer survivors with 1, 2, 3, 4, 5, or ≥ 6 unfavorable SDoH were 1.54 (1.25–1.89), 1.81 (1.46–2.24), 2.42 (1.97–2.97), 3.22 (2.48–4.19), 3.99 (2.99–5.33), and 6.34 (4.51–8.90), respectively. B Compared to those with 0 unfavorable SDoH, cancer-specific mortality of hazard ratios (95% CI) for cancer survivors with 1, 2, 3, 4, 5, or ≥ 6 unfavorable SDoH were 1.52 (1.04–2.22), 1.70 (1.20–2.24), 2.22 (1.51–3.26), 2.44 (1.60–3.72), 3.60 (2.25–5.75), and 5.00 (3.00–8.31), respectively. ns was the abbreviation of no significance; *** meant p < 0.001, ** meant p < 0.01, and * meant p < 0.05
Age-adjusted/ age-gender-adjusted all-cause mortality and cancer-specific mortality risk were significantly higher in NHB cancer survivors when compared with NHW. Further adjustment for all SDoH factors, black-white disparity in cancer-specific mortality was still observed (HR 1.45, 95% CI 1.07–1.96), and the all-cause mortality did not show a statistically significant difference (HR, 1.08; 95% CI 0.89–1.30; Table 3 ). In the mediation analysis, the socioeconomic factor of unemployment (17.5% for all-cause mortality; 15.3% for cancer-specific mortality) can mostly explain the racial disparity in all-cause and cancer-specific mortality, and unemployment was associated with a nearly 90% and 120% greater all-cause and cancer-specific mortality, respectively. A family income-to-poverty ratio less than 2.4 (15.7%), an education less than high school (8.1%), government health insurance (6.9%), renting a home or other housing arrangement (15.4%), and not being married nor living with a partner (13.4%) indicated effective relative contribution to the disparity of all-cause mortality between NHB and NHW cancer survivors. An additional factor (not being married nor living with a partner [10.2%]) contributed significantly to the racial difference in cancer-specific mortality (Table 3 ).
In the subgroup analysis, NHW cancer survivors who were unemployed, a lower level of PIR, an education less than high school, government or none of health insurance, renting a home or other housing arrangement, and not being married nor living with a partner were significantly more likely to die of all-cause mortality compared to NHW cancer survivors without unfavorable SDoH. Unemployment and not being married nor living with a partner were significantly associated with a higher risk of cancer-specific mortality (Additional file 1: Table S6). Being unemployed and having no access to a regular health care facility or emergency room was significantly associated with all-cause mortality in NHB cancer survivors. Only unemployed status was associated with cancer-specific mortality (Additional file 1: Table S6). In the stratified analysis by gender (female and male), almost all unfavorable SDoH were significantly associated with greater all-cause and cancer-specific mortality for female and male subgroups after adjusting for age, except for cancer-specific mortality for unfavorable home ownership (Additional file 1: Table S7). In all sensitivity analyses excluding mortalities that happened during the first 2-year follow-up since the baseline interview, all results remained similar in association with unfavorable SDoH with all-cause, cancer-specific, and non-cancer mortality (Additional file 1: Table S8).
In this US nationally representative cohort study of cancer survivors, we found that NHB and Hispanic adult cancer survivors self-reported a higher proportion of multiple unfavorable SDoHs compared to NHW adults diagnosed with cancer. Compared to NHW cancer survivors, NHB cancer survivors had significantly higher all-cause and cancer-specific mortality after adjusting for age and gender. In addition, after further adjusting for all SDoH, there was no longer a difference between NHB and NHW cancer survivors in all-cause mortality, but a significant difference in cancer-specific mortality was still observed. These findings suggest that racial differences in all-cause mortality between NHW and NHB cancer survivors were largely attributable to the explained by differences in SDoH, while cancer-specific mortality disparities were partly explained by differences in SDoH. Furthermore, unfavorable SDoH were associated with a higher risk of all-cause and cancer-specific mortality for cancer survivors. During the 20 years of follow-up, an increasing number of unfavorable SDoHs in the same individual was associated with an increased risk of dying from all causes, cancer, and noncancer causes, even after adjusting for demographic factors, such as age, gender, and race. Of note, there were significantly linear dose–response relationships between the cumulative number of unfavorable SDoHs and all-cause and cancer-specific mortality among cancer survivors, and cancer survivors having six or more unfavorable SDoH increased the HR for mortality of 6.34 and 5.00 compared to those having no unfavorable SDoH, respectively.
NHB cancer survivors were more likely than NHW patients to have unfavorable levels of all SDoH. Compared to NHW cancer survivors, NHB and Hispanic cancer survivors were 3.0 times and 3.9 times more likely to experience six or more unfavorable SDoHs, respectively, which may partly explain the racial disparity in mortality. Most predominantly, NHB cancer survivors were 1.6 times more likely than NHW cancer survivors to have family PIR less than 2.4, which was associated with almost 50% and 25% greater all-cause mortality and cancer-specific mortality, respectively. Most recently, Connolly et al. [ 30 ] conducted a study involving a cohort of 3590 participants from NHANES between 1999 and 2014, and demonstrated that the SDoH level was more favorable for NHW compared to NHB adolescents. Our finding was consistent with another previous study that reported a lower level of PIR, lower level of education attachment, lack of health insurance coverage, dietary insecurity, and limited health access were more common in NHB compared to NHW, which was a key mediator in explaining race disparity in all-cause and cause-specific mortality, especially cardiovascular disease and neoplasms [ 17 , 40 ].
The persistent disparities in survival by race and ethnicity among cancer patients have been well-documented [ 2 , 3 , 4 , 6 , 41 ], and these disparities between NHB and NHW cancer survivors were particularly stark [ 42 ]. Indeed, the overall cancer mortality in 2022 for male and female together was 12% (166.8 vs. 149.3 per 100,000 persons, respectively) higher in NHB compared to NHW cancer survivors [ 6 ]. However, racial differences were not the only factor that contributed to observed mortality disparity and the underlying causes attributed to these disparities have not been well established [ 43 ]. Various factors have been suggested as contributors to these racial and ethnic disparities in survival outcomes among cancer survivors, including differences in tumor characteristics [ 44 , 45 ], neighborhood socioeconomic deprivation [ 42 ], and accessibility to health care. In the current study, disparities in the all-cause mortality HR for NHB cancer survivors compared to NHW cancer survivors decreased from 1.59 (95% CI, 1.36–1.86) to 1.09 (95% CI, 0.91–1.31) after adjusting for all SDoHs, which mostly mediated the racial disparity in all-cause mortality. With respect to cancer-specific mortality, the HR for NHB cancer survivors compared to NHW cancer survivors decreased from 2.04 (95% CI, 1.60–2.62) to 1.45 (95% CI, 1.07–1.96) after adjusting for all SDoHs, which has a partly mediator role in the racial difference. We found that cancer survivors with employed, student or retired status (17.5% relative contribution), and PIR more than 2.4 (15.7% relative contribution) explained the greatest percentage of disparities in all-cause mortality. Furthermore, we also showed that employed, student, or retired status (15.3% relative contribution) and being married or living with a partner (10.2% relative contribution) explained the largest portions of disparities in cancer-specific mortality. Taken together, the traditional socioeconomic factors consisting of household income, level of education completed, and unemployment status were important explanatory factors, that mediated around 45% and 25% of all-cause and cancer-specific mortality in survival inequities between NHB and NHW cancer survivors, respectively, which was consistent with the findings of Bundy et al. (nearly 50% mediated the differential in all-cause premature mortality) [ 25 ]. The SDoH, through an impact on occupational opportunities and income levels, have a substantial influence on insurance coverage, which was one of the main factors determining access to and delivery of health care services in the US as well as associated disparities in survival [ 40 ]. Conversely, these traditional economic factors have a greater effect on the racial/ethnic disparities in the general population compared to cancer patients. Specifically, Luo et al. [ 20 ] suggested that income mediated 62% of the association in mortality between NHB and NHW, which was consistent with the dominant contributors to family income (40%) and education (19%) to the gap between NHB and NHW adult populations [ 17 ]. Interestingly, NHW cancer survivors were approximately 25% more likely to be married or living with a partner compared to NHB cancer survivors. Being married or living with a partner was associated with the cancer-related survival benefits, possibly due to increased social support and higher psychological well-being and instrumental support, helping navigate the health care system [ 46 , 47 ]. According to Fuzzel et al. [ 48 ], barriers to health care accessibility and insurance coverage have a significant impact on rates of cancer screening, as well as the burden and attributions of the disease. These findings suggested SDoH factors, as an important mediator, drive racial health disparities, as well as all-cause and cancer-specific mortality, highlighting the necessity of the level of SDoH contexts for all people, especially those who are more vulnerable to unfavorable SDoH.
The cumulative adverse SDoHs were associated with poor all-cause survival and cause-specific survival rates among the cancer-free population have been previously reported, e.g., among patients with cardiovascular disease. Sameroff et al. [ 49 ] reported that cumulative unfavorable social risk factors, such as food insecurity combined with social isolation and loneliness, have a higher relevance to poor health outcomes than single social risk factors. Jilani et al. [ 50 ] suggested that greater SDoH adversity was linked to a higher burden of cardiovascular risk factors and poor health outcomes, such as stroke, myocardial infarction, coronary heart disease, heart failure, and mortality. Similarly, Zhang et al. [ 16 ] combined family income level, occupation, education level, and health insurance to measure socioeconomic status, and reported that participants who met low socioeconomic status had higher risks of all-cause mortality (HR, 2.13 and 95% CI, 1.90–2.38 in the US NHANES; HR, 1.96 and 95% CI, 1.87–2.06 in the UK Biobank), cardiovascular disease mortality (HR, 2.25; 95% CI, 2.00–2.53), and incident cardiovascular disease (HR, 1.65; 95% CI, 1.52–1.79) in UK Biobank, compared to high socioeconomic status. Our results were consistent with the findings of a study in which each additional SDoH conferred additional cancer-related mortality, compared to cancer survivors without any SDoH (1 SDoH [HR, 1.39; 95% CI, 1.11–1.75], 2 SDoHs [HR, 1.61; 95% CI, 1.26–2.07], and ≥ 3 SDoHs [HR, 2.09; 95% CI, 1.58–2.75]) [ 13 ]. In contrast, Weires et al. [ 51 ] observed that women with a higher socioeconomic status showed increased mortality due to breast cancer in Sweden. This finding may be due to the structure of the Swedish family cancer database (Swedes born after 1931 and their biological parents), as well as analytical restrictions on individuals 30–60 years of age in 1960, which may exclude low-socioeconomic adults with severe health problems. Previous studies have shown that these unfavorable SDoH have a tendency to cluster in individuals [ 13 , 23 ]. For example, individuals in the general US population who self-reported food insecurity were more likely to be combined with a low level of education attachment, not being married, a low level of family income, and a bad lifestyle. This finding was consistent with our observation that these unfavorable SDoH were not isolated but interrelated, and each unfavorable SDoH included in our study has been found to independently increase the risk of mortality. Compared to most previous studies based on a single SDoH, we found that there was a simple linear dose–response relationship reflecting the cumulative effect of multiple unfavorable SDoHs on all-cause and cancer-specific mortality. Collectively, these SDoH appear to synergistically increase the risk of all-cause and cancer-specific mortality among cancer survivors. However, the cumulative risk derived from a sum of the number of unfavorable SDoH assumed that all SDoH had equal and independent effects on survival outcomes, which might not be precise. We suggest that future research may need to use more complex models, such as interaction models, to more accurately capture the complex interactions of unfavorable SDoH.
The major strength of this study was the use of large sample size data from the NHANES, which provides an opportunity to comprehensively evaluate the complex relations of SDoH with all-cause and cancer-specific mortality among cancer survivors. In addition, we focused on multiple SDoH factors and estimated the effect of accumulating unfavorable SDoH burden on mortality. We also performed mediation analysis to show the contribution of SDoH to disparities in all-cause and cancer-specific mortality. There were some limitations in the present study. First, we conducted the analyses based on the follow-up of time-to-event, however, all data on SDoH variables were only assessed at the baseline interview, which may not reflect factors that changed during the follow-up period. Therefore, our study was not able to quantify the effect of changes in eight SDoH on the mortality of cancer survivors over time. It is essential to conduct several repeated interviews about the level of SDoH during the follow-up period to reveal the influence of SDoH factors on survival among cancer survivors. Second, the assessment of SDoH was limited by the availability of variables in the NHANES database. Some SDoH such as neighborhood environment, social support, and exposure to racism, were not widely available, which may also contribute to the all-cause and cancer-specific mortality. Third, the follow-up duration was relatively short (median, 81 months) and an important bias among these cancer survivors such that socially disadvantaged who died during the study period might have had severe disease at baseline.
In conclusion, in this cohort study of a nationally representative sample of US cancer survivors between 1999 and 2018, there were significant differences in SDoH and mortality rates across self-reported racial and ethnic groups. Unfavorable SDoH were more common among NHB cancer survivors than NHW cancer survivors, were strongly associated with an increased risk of all-cause and cancer-specific mortality, and largely explained the difference between NHB and NHW cancer survivors in all-cause mortality, as well as partially explained these racial disparities in cancer-specific mortality. In addition, the cancer participants with a greater cumulative number of unfavorable SDoHs also appeared to be associated with higher risks of death from all-causes, and cause-specific (cancer and non-cancer). Taken together with previous findings, the unfavorable SDoH levels were the major risk factors for all-cause and cancer-specific mortality and were the underlying causes in all-cause racial health disparities among US cancer survivors. The entire government, civil society, local communities, businesses, and international agencies must pay more attention to the upstream SDoH, such as economic resources, employment, education quality, and racial discrimination [ 52 ]. We believe that these findings shed highlight on the cumulative burden of SDoHs on all-cause and cancer-specific mortality among cancer survivors, providing insight for ongoing and future initiatives aimed at mitigating mortality rates within vulnerable populations, including racial/ethnic minorities and individuals with an unfavorable level of SDoH status. Addressing social disparities in cancer health is a very important part of improving survival outcomes for cancer survivors, reflecting a commitment to health equity—aimed at achieving the optimal health for everyone.
The US NHANES are publicly available database and all data can be accessed from https://wwwn.cdc.gov/nchs/nhanes/ . The statistical code and data required to reproduce the results presented in this article can be requested from Hongbo Huang ([email protected]) or Fan Li ([email protected]).
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We thank Dr Jing Yi (School of Public Health, Chongqing Medical University, Chongqing, 400016, China) for suggestions on the used in mediation analysis and International Science Editing ( http://www.internationalscienceediting.com ) for editing this manuscript.
This study was supported by grant 82202913 (Dr Yunhai Li) and 82372996 (Dr Fan Li) from the National Natural Science Foundation of China.
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HBH, YHL and FL designed the study. HBH, YHL, TTW, and YH conducted the statistical analyses. HBH, TTW, YH, AJZ, ZZ, HZ, YJX, HNP, YHL, and FL drafted the original manuscript. HBH, YHL, LQK and FL review the manuscript. All authors approved the final version of manuscript.
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Additional files 1: Method S1. Details about mediation analysis. Table S1. SDoH based on the Office of Disease Prevention and Health Promotion’s Healthy People 2030 and World Health Organization in NHANES 1999–2018. Table S2. Candidate social determinants of healthand associations with all-cause mortality in the US cancer survivors, NHANES 1999–2018. Table S3. Number of cancer survivor by cancer type and gender, NHANES 1999–2018. Table S4. P value for overall survival outcomes pairwise comparisons using log-rank test with Bonferroni adjustment. Table S5. P value for cancer-specific survival outcomes pairwise comparisons using log-rank test with Bonferroni adjustment. Table S6. Associations of social determinants of healthwith all-cause and cancer-specific mortality in US cancer survivors by race/ethnicity, NHANES 1999–2018. Table S7. Associations of social determinants of healthwith all-cause and cancer-specific mortality in US cancer survivors by gender, NHANES 1999–2018. Table S8. Sensitivity analyses of association social determinants of healthand all-cause, cancer-specific and non-cancer mortality in weighted and fully adjusted multivariable analysis among cancer survivors, NHANES 1999–2018. Fig. S1 Flowchart of participants selection for current analysis from NHANES 1999–2018. Fig. S2 The pairwise correlation between social determinants of healthusing Spearman method. Fig. S3 The proportion for each cumulative number of social determinants of healthby gender. Fig. S4 All-cause mortalityand cancer-specific mortalityfor cancer survivors aged 20 years or older in US between 1999 and 2018 stratified by cumulative number of unfavorable social determinants of health. Fig. S5 Linear dose–response association between cumulative number of unfavorable social determinants of healthand all-cause of death, and cancer deathamong US cancer survivors.
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Huang, H., Wei, T., Huang, Y. et al. Association between social determinants of health and survival among the US cancer survivors population. BMC Med 22 , 343 (2024). https://doi.org/10.1186/s12916-024-03563-0
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B lood sugar monitors (aka continuous glucose monitors or CGMs) have been ubiquitous for people with diabetes. But now they are a fad for elite athletes and even some weekend warriors without the disease. Endurance athletes in particular have been quick to adopt this new technology to improve training and racing performance. They monitor their glucose levels with a small sensor attached to the arm or belly.
They are now widely used for training and are ubiquitous in triathlon competitions. But in 2021, cycling’s international governing body, the UCI, banned their use during competition. This ban includes, at a minimum, glucose and lactate measurement. Meanwhile, athletes in other sports are experimenting.
The fad has also crossed over to healthy people without diabetes. Fitness enthusiasts have been using smart wearables for years to track a variety of parameters such as sleep, heart rate, stress levels, and step count over the day. According to the companies that are pitching the gadget to workout enthusiasts, CGMs add another dimension, a way to improve metabolic health by optimizing blood glucose levels. Their pitch is that, even in the absence of diabetes, minimizing blood glucose spikes could prevent diabetes or other diseases.
Do CGMs work? Are they safe?
If you suffer from diabetes it’s often difficult to know the cause. Both Type 1 and Type 2 diabetes are linked in part to genetics, although the family connections are murkier in Type 1. In people with diabetes, after eating, their blood sugar, also known as blood glucose, can shoot up. That could cause organ damage if not contained.
In type 1 diabetes the immune system attacks the beta cell that produces insulin in your pancreas. The attack causes permanent damage and leaves your pancreas unable to produce insulin. Unless sufferers have access to insulin pumps and wearable devices that automatically deliver insulin, they need to inject themselves with insulin a couple of times every day.
For many, that requires pricking their finger to evaluate blood glucose levels to avoid injecting insulin when not needed. “I used to prick my fingers a couple of times a day and it was very painful,“ said Siddharth Kankaria, a science communicator who lives with type 1 diabetes.
There are multiple problems with that kind of test. Sufferers like Kankaria could only measure glucose levels up to four times a day. Plus, most monitors impose a ‘one-size-fits-all” standard. However, research has shown that two healthy people can have different responses to the same food; for example, one person’s blood sugar might spike and dip more after eating carbohydrates than another person’s.
Because of these complications, Kankaria has recently started wearing a CGM patch that connects to his phone, so he can track his glucose levels throughout the day.
Are there benefits for individuals with type 1 diabetes?
People with type 1 diabetes need to make dozens of decisions every day to keep their blood glucose in check: what to eat and when, the carbohydrate content of their meals, when to take insulin, whether and how to exercise, and so on. CGMs can reduce the cognitive load for some of these decisions.
“With a CGM, I can figure out how my body is reacting to different kinds of food, environments, and stimuli,” said Kankaria.
Advocates say they are also life-saving devices. If people with diabetes take insulin when their blood glucose level is already low, it can fall below what is healthy for them. These episodes of hypoglycemia (or low blood glucose) can cause drowsiness, anxiety, increased heart rate, and even death in extreme cases. People with diabetes can have multiple episodes per week.
Over time, repeat episodes impair awareness of early symptoms, increasing the risk of severe episodes. A PLOS One study on South Indian patients noted that 44 of 73 patients were unaware of their hypoglycemia .
In the absence of early symptoms, there is some evidence that CGMs can prevent low blood sugar episodes. Conversely, not taking sufficient insulin or eating carb-heavy foods can cause hyperglycemia. While blood sugar levels quickly revert to normal in healthy individuals, they may stay outside the healthy range for a prolonged time in people with type 1 diabetes.
“I use the CGM to check if I have hypoglycemia,” said Kankaria. “I can see it going down in real-time and intervene before the blood glucose level is too low.”
Some experts are skeptical of the utility of CGMs
That’s debatable. The use of glucose monitors for those with the disease remains controversial in some medical circles. Hyperglycemia (or high blood glucose) is a symptom of diabetes, not its cause. CGMs also do not measure glucose levels in the blood but in the interstitial fluid surrounding cells. Changes in blood glucose levels reflect changes in interstitial glucose levels after 5-20 minutes. In other words, if a CGM shows higher or lower than normal glucose levels, that’s likely old news. This lag is more pronounced when blood glucose levels change rapidly, such as immediately after large meals or during exercise.
CGM readings can be inaccurate for a variety of other reasons. For example, sleeping on the arm where the sensor is can cause pressure-induced errors, producing false lows. Conversely, drugs like acetaminophen (Tylenol) that cause chemical interference in the interstitial fluid can produce false highs.
These monitors often tell what we already know or do not need to know, some experts say. Digestion breaks down carbohydrates in the food into glucose and other sugars, causing a spike in blood sugar levels in all of us. In response, the body produces insulin which usually brings the glucose levels down to pre-meal levels within two hours. In other words,
Glucose levels straying outside what is considered the normal range for a healthy individual is physiologically typical. In any case, when it occurs it’s for brief periods. In a study published in The Journal of Clinical Endocrinology & Metabolism , researchers found that the blood glucose levels for healthy individuals were normal 96% of the time .
In some people with type 1 diabetes, the anxiety of hypoglycemic episodes can cause excessive focus on correcting glucose levels. This can sometimes lead to orthorexia nervosa, an unhealthy fixation with healthy eating. Almas Fatma, a Mumbai-based diabetologist, added that non-diabetic individuals can also develop orthorexia nervosa if they let glucose readings to obsessively monitor their diet. In one case, a patient avoided all food that would cause even minor spikes in their CGM reading. It grew to a point where this individual ate only salads. “Over time, the restrictive diet led to multiple nutritional deficiencies and a significant loss of muscle mass,” said Fatma.
Use in sports?
Their use by those without diabetes crosses a line for many medical practitioners.
“There is a lack of clinical evidence to support the use of CGM in healthy individuals, Fatima warned. Companies selling CGMs to healthy people are pathologizing clinically insignificant fluctuations. With constant access to blood glucose levels, “even healthy people can get fixated on minor fluctuations and readings and it can have a negative impact on their mental health and quality of life”, she said.
Another example. While weight management is a common fitness goal, obsessing over glucose spikes can ironically impede it. If a user takes corrective steps every time CGM says their glucose is approaching lower or higher than the normal range, they might end up eating more or less than they need to.
Many other health officials agree that the benefits do not clearly outweigh the potential downside. “There is no strong evidence the gadgets help people without the condition,” UK National Health Service diabetes advisor Prof Partha Kar recently said .
But an increasing number of elite athletes and their coaches swear by them. “Having insight into the body’s individual response to carbohydrate and protein intake while racing will help you minimize unneeded fueling and excessive exposure to sugars,” said Suzanne Atkinson , an emergency medicine physician and elite-level triathlon coach.
Supporters maintain that CGMs can tell athletes what carbs they should eat to optimally refuel glycogen reserves. Additionally, pro athletes need to maintain their weight in a narrow range. A persistently low glucose level could hint at insufficient calorie intake before it’s evident as weight loss.
However, just like those with type 1 diabetes, elite athletes who use CGMs are best monitored by physicians, who can contextualize their data. In the absence of this important distinction, CGM use among non-diabetic fitness enthusiasts could cause mental and physical harm that supersedes any potential benefits . I n sum, people with type 1 diabetes, episodes of hypoglycemia or hyperglycemia can be a matter of life and death and, understandably, cause significant mental distress. For those individuals, CGM provides one more information resource.
But for non-diabetic users, the measurements from a CGM, more so if they are tracking other vitals, can cause an information overload. Some healthy people using CGMs worry unnecessarily about minor glucose spikes, even if it’s after eating a healthy meal. The use of CGM among healthy people can cause harm if a user tries to obsessively keep the glucose levels in the “healthy” range.
Sachin Rawat is a freelance science and tech writer based in Bangalore. He holds a master’s degree in biotechnology. Follow him on Twitter at @sachinxr .
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