A retrospective approach to evaluating potential adverse outcomes associated with delay of procedures for cardiovascular and cancer-related diagnoses in the context of COVID-19

Leveraging generative AI to prioritize drug repurposing candidates for Alzheimer's disease with real-world clinical validation

Drug repurposing represents an attractive alternative to the costly and time-consuming process of new drug development, particularly for serious, widespread conditions with limited effective treatments, such as Alzheimer's disease (AD). Emerging generative artificial intelligence (GAI) technologies like ChatGPT offer the promise of expediting the review and summary of scientific knowledge. To examine the feasibility of using GAI for identifying drug repurposing candidates, we iteratively tasked ChatGPT with proposing the twenty most promising drugs for repurposing in AD, and tested the top ten for risk of incident AD in exposed and unexposed individuals over age 65 in two large clinical datasets: (1) Vanderbilt University Medical Center and (2) the All of Us Research Program. Among the candidates suggested by ChatGPT, metformin, simvastatin, and losartan were associated with lower AD risk in meta-analysis. These findings suggest GAI technologies can assimilate scientific insights from an extensive Internet-based search space, helping to prioritize drug repurposing candidates and facilitate the treatment of diseases.

Are polypharmacy side effects predicted by public data still valid in real-world data?

Background and Objective

Although interest in predicting drug-drug interactions is growing, many predictions are not verified by real-world data. This study aimed to confirm whether predicted polypharmacy side effects using public data also occur in data from actual patients.

Methods

We utilized a deep learning-based polypharmacy side effects prediction model to identify cefpodoxime-chlorpheniramine-lung edema combination with a high prediction score and a significant patient population. The retrospective study analyzed patients over 18 years old who were admitted to the Asan medical center between January 2000 and December 2020 and took cefpodoxime or chlorpheniramine orally. The three groups, cefpodoxime-treated, chlorpheniramine-treated, and cefpodoxime & chlorpheniramine-treated were compared using inverse probability of treatment weighting (IPTW) to balance them. Differences between the three groups were analyzed using the Kaplan-Meier method and Cox proportional hazards model.

Results

The study population comprised 54,043 patients with a history of taking cefpodoxime, 203,897 patients with a history of taking chlorpheniramine, and 1,628 patients with a history of taking cefpodoxime and chlorpheniramine simultaneously. After adjustment, the 1-year cumulative incidence of lung edema in the patient group that took cefpodoxime and chlorpheniramine simultaneously was significantly higher than in the patient groups that took cefpodoxime or chlorpheniramine only (p=0.001). Patients taking cefpodoxime and chlorpheniramine together had an increased risk of lung edema compared to those taking cefpodoxime alone [hazard ratio (HR) 2.10, 95% CI 1.26-3.52, p<0.005] and those taking chlorpheniramine alone, which also increased the risk of lung edema (HR 1.64, 95% CI 0.99-2.69, p=0.05).

Conclusions

Validation of polypharmacy side effect predictions with real-world data can aid patient and clinician decision-making before conducting randomized controlled trials. Simultaneous use of cefpodoxime and chlorpheniramine was associated with a higher long-term risk of lung edema compared to the use of cefpodoxime or chlorpheniramine alone.

Annual Report to the Nation on the Status of Cancer, part 2: Early assessment of the COVID-19 pandemic's impact on cancer diagnosis

BACKGROUND

With access to cancer care services limited because of coronavirus disease 2019 control measures, cancer diagnosis and treatment have been delayed. The authors explored changes in the counts of US incident cases by cancer type, age, sex, race, and disease stage in 2020.

METHODS

Data were extracted from selected US population-based cancer registries for diagnosis years 2015-2020 using first-submission data from the North American Association of Central Cancer Registries. After a quality assessment, the monthly numbers of newly diagnosed cancer cases were extracted for six cancer types: colorectal, female breast, lung, pancreas, prostate, and thyroid. The observed numbers of incident cancer cases in 2020 were compared with the estimated numbers by calculating observed-to-expected (O/E) ratios. The expected numbers of incident cases were extrapolated using Joinpoint trend models.

RESULTS

The authors report an O/E ratio <1.0 for major screening-eligible cancer sites, indicating fewer newly diagnosed cases than expected in 2020. The O/E ratios were lowest in April 2020. For every cancer site except pancreas, Asians/Pacific Islanders had the lowest O/E ratio of any race group. O/E ratios were lower for cases diagnosed at localized stages than for cases diagnosed at advanced stages.

CONCLUSIONS

The current analysis provides strong evidence for declines in cancer diagnoses, relative to the expected numbers, between March and May of 2020. The declines correlate with reductions in pathology reports and are greater for cases diagnosed at in situ and localized stage, triggering concerns about potential poor cancer outcomes in the coming years, especially in Asians/Pacific Islanders.

PLAIN LANGUAGE SUMMARY

To help control the spread of coronavirus disease 2019 (COVID-19), health care organizations suspended nonessential medical procedures, including preventive cancer screening, during early 2020. Many individuals canceled or postponed cancer screening, potentially delaying cancer diagnosis. This study examines the impact of the COVID-19 pandemic on the number of newly diagnosed cancer cases in 2020 using first-submission, population-based cancer registry database. The monthly numbers of newly diagnosed cancer cases in 2020 were compared with the expected numbers based on past trends for six cancer sites. April 2020 had the sharpest decrease in cases compared with previous years, most likely because of the COVID-19 pandemic.

Understanding disruptions in cancer care to reduce increased cancer burden

Background

This study seeks to understand how and for whom COVID-19 disrupted cancer care to understand the potential for cancer health disparities across the cancer prevention and control continuum.

Methods

In this cross-sectional study, participants age 30+residing in an 82-county region in Missouri and Illinois completed an online survey from June-August 2020. Descriptive statistics were calculated for all variables separately and by care disruption status. Logistic regression modeling was conducted to determine the correlates of care disruption.

Results

Participants (N=680) reported 21% to 57% of cancer screening or treatment appointments were canceled/postponed from March 2020 through the end of 2020. Approximately 34% of residents stated they would need to know if their doctor's office is taking the appropriate COVID-related safety precautions to return to care. Higher education (OR = 1.26, 95% CI:1.11-1.43), identifying as female (OR = 1.60, 95% CI:1.12-2.30), experiencing more discrimination in healthcare settings (OR = 1.40, 95% CI:1.13-1.72), and having scheduled a telehealth appointment (OR = 1.51, 95% CI:1.07-2.15) were associated with higher odds of care disruption. Factors associated with care disruption were not consistent across races. Higher odds of care disruption for White residents were associated with higher education, female identity, older age, and having scheduled a telehealth appointment, while higher odds of care disruption for Black residents were associated only with higher education.

Conclusions

This study provides an understanding of the factors associated with cancer care disruption and what patients need to return to care. Results may inform outreach and engagement strategies to reduce delayed cancer screenings and encourage returning to cancer care.

Funding

This study was supported by the National Cancer Institute's Administrative Supplements for P30 Cancer Center Support Grants (P30CA091842-18S2 and P30CA091842-19S4). Kia L. Davis, Lisa Klesges, Sarah Humble, and Bettina Drake were supported by the National Cancer Institute's P50CA244431 and Kia L. Davis was also supported by the Breast Cancer Research Foundation. Callie Walsh-Bailey was supported by NIMHD T37 MD014218. The content does not necessarily represent the official view of these funding agencies and is solely the responsibility of the authors.

Leveraging Generative AI to Prioritize Drug Repurposing Candidates: Validating Identified Candidates for Alzheimer's Disease in Real-World Clinical Datasets

Drug repurposing represents an attractive alternative to the costly and time-consuming process of new drug development, particularly for serious, widespread conditions with limited effective treatments, such as Alzheimer's disease (AD). Emerging generative artificial intelligence (GAI) technologies like ChatGPT offer the promise of expediting the review and summary of scientific knowledge. To examine the feasibility of using GAI for identifying drug repurposing candidates, we iteratively tasked ChatGPT with proposing the twenty most promising drugs for repurposing in AD, and tested the top ten for risk of incident AD in exposed and unexposed individuals over age 65 in two large clinical datasets: 1) Vanderbilt University Medical Center and 2) the All of Us Research Program. Among the candidates suggested by ChatGPT, metformin, simvastatin, and losartan were associated with lower AD risk in meta-analysis. These findings suggest GAI technologies can assimilate scientific insights from an extensive Internet-based search space, helping to prioritize drug repurposing candidates and facilitate the treatment of diseases.

Leveraging Generative AI to Prioritize Drug Repurposing Candidates: Validating Identified Candidates for Alzheimer's Disease in Real-World Clinical Datasets

Drug repurposing represents an attractive alternative to the costly and time-consuming process of new drug development, particularly for serious, widespread conditions with limited effective treatments, such as Alzheimer's disease (AD). Emerging generative artificial intelligence (GAI) technologies like ChatGPT offer the promise of expediting the review and summary of scientific knowledge. To examine the feasibility of using GAI for identifying drug repurposing candidates, we iteratively tasked ChatGPT with proposing the twenty most promising drugs for repurposing in AD, and tested the top ten for risk of incident AD in exposed and unexposed individuals over age 65 in two large clinical datasets: 1) Vanderbilt University Medical Center and 2) the All of Us Research Program. Among the candidates suggested by ChatGPT, metformin, simvastatin, and losartan were associated with lower AD risk in meta-analysis. These findings suggest GAI technologies can assimilate scientific insights from an extensive Internet-based search space, helping to prioritize drug repurposing candidates and facilitate the treatment of diseases.

The IHI Rochester Report 2022 on Healthcare Informatics Research: Resuming After the CoViD-19

In 2020, the CoViD-19 pandemic spread worldwide in an unexpected way and suddenly modified many life issues, including social habits, social relationships, teaching modalities, and more. Such changes were also observable in many different healthcare and medical contexts. Moreover, the CoViD-19 pandemic acted as a stress test for many research endeavors, and revealed some limitations, especially in contexts where research results had an immediate impact on the social and healthcare habits of millions of people. As a result, the research community is called to perform a deep analysis of the steps already taken, and to re-think steps for the near and far future to capitalize on the lessons learned due to the pandemic. In this direction, on June 09th-11th, 2022, a group of twelve healthcare informatics researchers met in Rochester, MN, USA. This meeting was initiated by the Institute for Healthcare Informatics-IHI, and hosted by the Mayo Clinic. The goal of the meeting was to discuss and propose a research agenda for biomedical and health informatics for the next decade, in light of the changes and the lessons learned from the CoViD-19 pandemic. This article reports the main topics discussed and the conclusions reached. The intended readers of this paper, besides the biomedical and health informatics research community, are all those stakeholders in academia, industry, and government, who could benefit from the new research findings in biomedical and health informatics research. Indeed, research directions and social and policy implications are the main focus of the research agenda we propose, according to three levels: the care of individuals, the healthcare system view, and the population view.

Prevention and screening during the COVID-19 pandemic: qualitative findings from the BETTER WISE project

BACKGROUND

The COVID-19 pandemic challenged healthcare systems worldwide and disrupted primary care, particularly prevention, screening, and lifestyle counselling. BETTER WISE is a comprehensive and structured approach that proactively addresses cancer and chronic disease prevention and screening (CCDPS), including cancer survivorship and screening for poverty and lifestyle risks for patients aged 40 to 65. Patients from 13 primary care clinics (urban, rural, and remote) in Alberta, Ontario, and Newfoundland & Labrador, Canada were invited for a 1-hour visit with a prevention practitioner (PP), a member of the primary care team with specialized training in CCDPS to provide patients an overview of eligible screening and assist with lifestyle counselling. This qualitative sub-study describes how the COVID-19 pandemic impacted BETTER WISE in a constantly changing medical landscape.

METHODS

We conducted 17 focus groups and 48 key informant interviews with a total of 132 primary care providers (PPs, physicians, allied health professionals, and clinic staff) over three different time points to better understand their perspectives on the BETTER WISE project. We also received 585 patient feedback forms of the 1005 patients who agreed to participate in the study. We also collected field notes and memos and employed thematic analysis using a constant comparative method focused on the impact of the pandemic on BETTER WISE.

RESULTS

We identified four themes related to how the COVID-19 pandemic impacted the BETTER WISE study: 1) Switch of in-person visits to visits over the phone; 2) Lack of access to preventive care and delays of screening tests; 3) Changes in primary care providers' availability and priorities; 4) Mental health impacts of the pandemic on patients and primary care providers.

CONCLUSIONS

The COVID-19 pandemic had and, at the time of writing, continues to have an impact on primary care, particularly on prevention, screening, and lifestyle counselling. Despite structural, procedural, and personal challenges throughout different waves of the pandemic, the primary care clinics participating in BETTER WISE were able to complete the study. Our results underscore the importance of the role of primary care providers in adapting to changing circumstances and support of patients in these challenging times.

TRIAL REGISTRATION

This qualitative study is a sub-component of the BETTER WISE pragmatic, cRCT, trial registration ISRCTN21333761 (date of registration 19/12/2016).

Decrease of cancer diagnosis during COVID-19 pandemic: a systematic review and meta-analysis

Many health services, including cancer care, have been affected by the COVID-19 epidemic. This study aimed at providing a systematic review of the impact of the epidemic on cancer diagnostic tests and diagnosis worldwide. In our systematic review and meta-analysis, databases such as Pubmed, Proquest and Scopus were searched comprehensively for articles published between January 1st, 2020 and December 12th, 2021. Observational studies and articles that reported data from single clinics and population registries comparing the number of cancer diagnostic tests and/or diagnosis performed before and during the pandemic, were included. Two pairs of independent reviewers extracted data from the selected studies. The weighted average of the percentage variation was calculated and compared between pandemic and pre-pandemic periods. Stratified analysis was performed by geographic area, time interval and study setting. The review was registered on PROSPERO (ID: CRD42022314314). The review comprised 61 articles, whose results referred to the period January-October 2020. We found an overall decrease of - 37.3% for diagnostic tests and - 27.0% for cancer diagnosis during the pandemic. For both outcomes we identified a U-shaped temporal trend, with an almost complete recovery for the number of cancer diagnosis after May 2020. We also analyzed differences by geographic area and screening setting. We provided a summary estimate of the decrease in cancer diagnosis and diagnostic tests, during the first phase of the COVID-19 pandemic. The delay in cancer diagnosis could lead to an increase in the number of avoidable cancer deaths. Further research is needed to assess the impact of the pandemic measures on cancer treatment and mortality.

Determinants of COVID-19 Outcome as Predictors of Delayed Healthcare Services among Adults ≥50 Years during the Pandemic: 2006-2020 Health and Retirement Study

BACKGROUND

The coronavirus disease 19 (COVID-19) was declared a global pandemic on 11 March 2020. To date, a limited number of studies have examined the impact of this pandemic on healthcare-seeking behaviors of older populations. This longitudinal study examined personal characteristics linked to COVID-19 outcomes as predictors of self-reported delayed healthcare services attributed to this pandemic, among U.S. adults, ≥50 years of age.

METHODS

Secondary analyses were performed using cross-sectional data (1413 participants) and longitudinal data (2881 participants) from Health and Retirement Study (HRS) (2006-2018) linked to the 2020 HRS COVID-19 Project (57% female, mean age: 68 years). Demographic, socioeconomic, lifestyle and health characteristics were evaluated in relation to delayed overall, surgical and non-surgical healthcare services ("Since March 2020, was there any time when you needed medical or dental care, but delayed getting it, or did not get it at all?" and "What type of care did you delay") using logistic regression and Ensemble machine learning for cross-sectional data as well as mixed-effects logistic modeling for longitudinal data.

RESULTS

Nearly 32.7% delayed healthcare services, 5.8% delayed surgical services and 31.4% delayed non-surgical services. Being female, having a college degree or higher and 1-unit increase in depression score were key predictors of delayed healthcare services. In fully adjusted logistic models, a history of 1 or 2 cardiovascular and/or metabolic conditions (vs. none) was associated with 60-70% greater odds of delays in non-surgical services, with distinct findings for histories of hypertension, cardiovascular disease, diabetes and stroke. Ensemble machine learning predicted surgical better than overall and non-surgical healthcare delays.

CONCLUSION

Among older adults, sex, education and depressive symptoms are key predictors of delayed healthcare services attributed to the COVID-19 pandemic. Delays in surgical and non-surgical healthcare services may have distinct predictors, with non-surgical delays more frequently observed among individuals with a history of 1 or 2 cardiovascular and/or metabolic conditions.

An Analysis of the Impact of COVID-19 Pandemic-related Lockdown Measures on a Large Gastrointestinal Pathology Service in the United States

BACKGROUND/AIM

The coronavirus disease 2019 (COVID-19) pandemic prompted global recommendations to delay non-urgent endoscopic procedures to limit the spread of SARS-COV-2, but such delays had unprecedented impact on the delivery of healthcare. Being a large specialty GI Pathology service, we sought to analyze the effect of the pandemic on the frequency of GI malignancies in our department.

PATIENTS AND METHODS

Based on the electronic search of departmental pathology records, we compared the total numbers of cancer diagnoses (primary and metastatic) from various GI biopsy sites during the 12-month pre- and post-pandemic periods. We summarized patient demographics and analyzed pertinent histopathologic data.

RESULTS

For all GI biopsy sites, the number of intramucosal/invasive malignancies reported during the one-year pre-COVID-19 pandemic (pre-COVID) and post-COVID-19 pandemic national lockdown (post-COVID) observation periods were 146 and 218, respectively. Among these, 32 and 70 malignancies were reported for the first quarter (representing the earliest post-lockdown period), 29 and 53 for the second, 41 and 54 for the third, and 44 and 41 for the fourth quarter. During the first two quarters of the post-COVID observation period, the increase in malignant diagnoses was most profound, showing 119% post-COVID increase compared to the pre-COVID levels. Of the two main primary histologic types of large intestinal carcinomas [adenocarcinoma (ADC) and squamous cell carcinoma (SCC)], the most profound post-COVID increase was noted in SCCs (136% vs. 58% for ADCs).

CONCLUSION

Compared to the pre-pandemic baseline, the COVID-19 pandemic caused a major increase in biopsy diagnoses of GI cancers in our department. The most plausible explanations for this trend include inevitable lockdowns to minimize the spread of SAR-COV2, which affected GI endoscopy procedure schedules/re-schedules as well as patient response and adaptation to emerging post-COVID GI healthcare patterns. The COVID-19 pandemic's long-term impact on the health of GI cancer patients will need to be determined through systematic analyses by multi-disciplinary teams.

Delayed Diagnosis and Treatment of Cancer Patients During the COVID-19 Pandemic in Henan, China: An Interrupted Time Series Analysis

Objective

To investigate the possible impact of lockdown policies on the diagnosis and treatment of cancer patients in Henan, China.

Design Setting and Participants

We collected data from the Henan Cancer Hospital, affiliated with Zhengzhou University. The monthly numbers of inpatient admissions from January 2014 to December 2019 were used to forecast the number of inpatient admissions in 2020, which was then compared to the actual number of patients admitted during the pandemic to evaluate how the actual number diverges from this forecast. We conducted an interrupted time series analysis using the autoregressive integrated moving average (ARIMA) model.

Main Outcomes and Measures

For specific diagnoses, treatment modalities, and age groups, we compared the changes in monthly admissions after the pandemic with the forecasted changes from the model.

Results

The observed overall monthly number of inpatient admissions decreased by 20.2% [95% confidence interval (CI), 11.7-27.2%], 78.9% (95% CI, 77.3-80.4%), and 40.9% (95% CI, 35.6-45.5%) in January, February, and March 2020, respectively, as compared with those predicted using the ARIMA model. After the lockdown, visits for all treatment modalities decreased sharply. However, apparent compensation and recovery of the backlog appeared in later surgeries. As a result, the number of patients who underwent surgery in 2020 (30,478) was close to the number forecasted by the ARIMA model (30,185). In the same period, patients who received other treatments or underwent examinations were 106,074 and 36,968, respectively; the respective numbers that were forecasted by ARIMA were 127,775 and 60,025, respectively. These findings depict a decrease of 16.9 and 38.4% in patients who received other treatments or underwent examinations only, respectively. Regarding diagnosis, the reported incidence of various cancers decreased dramatically in February, with varying extent and speed of recovery.

Conclusion and Relevance

The COVID-19 pandemic has significantly delayed the diagnosis and treatment of cancer in Henan, China. Long-term research should be conducted to assess the future effects of lockdown policies.

Impact of the COVID-19 Pandemic on Primary Health Care Disease Incidence Rates: 2017 to 2020

We assessed the impact of the COVID-19 pandemic in Spain on new cases of diseases and conditions commonly seen in primary care. In 2020, there were significant reductions from 2017-2019 in the annual incidences of hypertension (40% reduction), hypercholesterolemia (36%), type 2 diabetes (39%), chronic kidney disease (43%), ischemic heart disease (48%), benign prostatic hypertrophy (38%), osteoporosis (40%), hypothyroidism (46%), chronic obstructive pulmonary disease (50%), alcohol use disorder (46%), benign colon polyps and tumors (42%), and melanomas (45%). Prioritization of COVID-19 care changed the physician-patient relationship to the detriment of face-to-face scheduled visits for chronic disease detection and monitoring, which fell by almost 41%. To return to prepandemic levels of diagnosis and management of chronic diseases, primary health care services should reorganize and carry out specific actions for groups at higher risk.VISUAL ABSTRACTAnnals "Online First" article.

Design and in-vitro testing of a portable patient isolation chamber for bedside aerosol containment and filtration

The emergence of the SARS-CoV-2 pandemic has imposed a multitude of complications on healthcare facilities. Healthcare professionals had to develop creative solutions to deal with resource shortages and isolation spaces when caring for COVID positive patients. Among many other solutions, facilities have utilized engineering strategies to mitigate the spread of viral contamination within the hospital environment. One of the standard solutions has been the use of whole room negative pressurization (WRNP) to turn a general patient room into an infection isolation space. However, this has not always been easy due to many limitations, such as direct access to the outdoors and the availability of WRNP units. In operating rooms where a patient is likely to go through aerosol-generating procedures, other solutions must be considered because most operating rooms use positive pressure ventilation to maintain sterility. The research team has designed, built, and tested a Covering for Operations during Viral Emergency Response (COVER), a low-cost, portable isolation chamber that fits over a patient's torso on a hospital bed to contain and remove the pathogenic agents at the source (i.e., patient's mouth and nose). This study tests the performance of the COVER system under various design and performance scenarios using particle tracing techniques and compares its efficiency with WRNP units. The results show that COVER can dramatically reduce the concentration of particles within the room, while WRNP is only effective in preventing the room-induced particles from migrating to adjacent spaces.

Health Care Utilization Among Texas Veterans Health Administration Enrollees Before and After Hurricane Harvey, 2016-2018

IMPORTANCE

Hurricanes and flooding can interrupt health care utilization. Understanding the magnitude and duration of interruptions, as well as how they vary according to hazard exposure, race, and income, are important for identifying populations in need of greater retention in care.

OBJECTIVE

To determine how the differential exposure to Hurricane Harvey in August 2017 is associated with changes in utilization of Veterans Health Administration health care.

DESIGN, SETTING, AND PARTICIPANTS

This is a retrospective cohort analysis of primary care practitioner (PCP) visits, emergency department visits, and inpatient admissions in the Veterans Health Administration among Texas veterans residing in counties impacted by Hurricane Harvey from 2016 to 2018. Data analysis was performed from September 2020 to May 2021.

EXPOSURES

Residential flooding after Hurricane Harvey.

MAIN OUTCOMES AND MEASURES

Interrupted time series analysis measured changes in health care utilization over time, stratified by residential flood exposure, race, and income.

RESULTS

Of the 99 858 patients in the cohort, 89 931 (90.06%) were male, and their median (range) age was 58 (21 to 102) years. Compared with veterans in nonflooded areas, veterans living in flooded areas were more likely to be Black (24 715 veterans [33.80%] vs 4237 veterans [15.85%]) and low-income (14 895 veterans [20.37%] vs 4853 veterans [18.15%]). Rates of PCP visits decreased by 49.78% (95% CI, -64.52% to -35.15%) for veterans in flooded areas and by 45.89% (95% CI, -61.93% to -29.91%) for veterans in nonflooded areas and did not rebound until more than 8 weeks after the hurricane. Rates of PCP visits in flooded areas remained lower than expected for 11 weeks among White veterans (-6.99%; 95% CI, -14.36% to 0.81%) and for 13 weeks among racial minority veterans (-7.22%; 95% CI, -14.11% to 0.30%). Low-income veterans, regardless of flood status, experienced greater suppression of PCP visits in the 8 weeks following the hurricane (-13.72%; 95% CI, -20.51% to -6.68%) compared with their wealthier counterparts (-9.63%; 95% CI, -16.74% to -2.26%).

CONCLUSIONS AND RELEVANCE

These findings suggest that flood disasters such as Hurricane Harvey may be associated with declines in health care utilization that differ according to flood status, race, and income strata. Patients most exposed to the disaster also had the greatest delay or nonreceipt of care.

Disability-Adjusted Life Years for the COVID-19 Pandemic in the Mexican Population

Mexico is one of the countries most affected by the COVID-19 disease. Although there is vast information on the disease, there still are unknown data on the societal and economic cost of the pandemic. To estimate this impact, the disability-adjusted life years (DALYs) can be a useful tool. Objective: To assess the DALYs due to COVID-19 in Mexico. Methods: We used the data released by the Mexican Ministry of Health to estimate the DALYs by the sum of the years of life lived with disability (YLDs) and the years of life lost (YLLs). Results: A total of 1,152,885 confirmed cases and 324,570 suspected cases of COVID-19 have been registered. Half of the cases were men, with a median age of 43.4 ± 16.9 years. About 8.3% died. A total of 39,202 YLDs were attributable to COVID-19. The total YLLs caused by COVID-19 were 2,126,222. A total of 2,165,424.5 DALYs for COVID-19 were estimated. The total DALYs were the highest in people between 50 and 59 years. The DALYs for each COVID-19 case were the highest in individuals between 60 and 79 years. Conclusion: The DALYs generated by the COVID-19 represent a more significant disease burden than that reported for other causes, such as the 2009 H1N1 influenza pandemic. Although it impacts all age groups in terms of disability, the most affected group are people over 50 years of age, whose risk of death is higher.