Determinants of COVID-19 Outcome as Predictors of Delayed Healthcare Services among Adults ≥50 Years during the Pandemic: 2006–2020 Health and Retirement Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Source
2.2. Study Participants
2.3. Study Variables
2.3.1. COVID-19 Determinants
Demographic, Socioeconomic and Lifestyle Characteristics
Health Characteristics
2.3.2. Delays in Healthcare Services
2.4. Statistical Analysis
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Sankrityayan, H.; Kale, A.; Sharma, N.; Anders, H.J.; Gaikwad, A.B. Evidence for Use or Disuse of Renin-Angiotensin System Modulators in Patients Having COVID-19 With an Underlying Cardiorenal Disorder. J. Cardiovasc. Pharmacol. Ther. 2020, 25, 299–306. [Google Scholar] [CrossRef] [PubMed]
- Raisi-Estabragh, Z.; McCracken, C.; Ardissino, M.; Bethell, M.S.; Cooper, J.; Cooper, C.; Harvey, N.C.; Petersen, S.E. Ren-in-Angiotensin-Aldosterone System Blockers Are Not Associated with Coronavirus Disease 2019 (COVID-19) Hospi-talization: Study of 1439 UK Biobank Cases. Front. Cardiovasc. Med. 2020, 7, 138. [Google Scholar] [CrossRef] [PubMed]
- Raisi-Estabragh, Z.; McCracken, C.; Bethell, M.S.; Cooper, J.; Cooper, C.; Caulfield, M.J.; Munroe, P.B.; Harvey, N.C.; E Petersen, S. Greater risk of severe COVID-19 in Black, Asian and Minority Ethnic populations is not explained by cardiometabolic, socioeconomic or behavioural factors, or by 25(OH)-vitamin D status: Study of 1326 cases from the UK Biobank. J. Public Heal. 2020, 42, 451–460. [Google Scholar] [CrossRef]
- Aung, N.; Khanji, M.Y.; Munroe, P.B.; Petersen, S.E. Causal Inference for Genetic Obesity, Cardiometabolic Profile and COVID-19 Susceptibility: A Mendelian Randomization Study. Front. Genet. 2020, 11, 586308. [Google Scholar] [CrossRef] [PubMed]
- Bello-Chavolla, O.Y.; Bahena-Lopez, J.P.; Antonio-Villa, N.E.; Vargas-Vazquez, A.; Gonzalez-Diaz, A.; Marquez-Salinas, A.; Fer-min-Martinez, C.A.; Naveja, J.J.; Aguilar-Salinas, C.A. Predicting Mortality Due to SARS-CoV-2: A Mechanistic Score Relating Obesity and Diabetes to COVID-19 Outcomes in Mexico. J. Clin. Endocrinol. Metab. 2020, 105, 2752–2761. [Google Scholar] [CrossRef] [PubMed]
- Czernichow, S.; Beeker, N.; Rives-Lange, C.; Guerot, E.; Diehl, J.L.; Katsahian, S.; Hulot, J.S.; Poghosyan, T.; Carette, C.; Jannot, A.S.; et al. Obesity Doubles Mortality in Patients Hospitalized for Severe Acute Respiratory Syndrome Coronavirus 2 in Paris Hospitals, France: A Cohort Study on 5795 Patients. Obesity 2020, 28, 2282–2289. [Google Scholar] [CrossRef]
- Camacho-Rivera, M.; Islam, J.Y.; Vidot, D.C. Associations Between Chronic Health Conditions and COVID-19 Preventive Behaviors Among a Nationally Representative Sample of U.S. Adults: An Analysis of the COVID Impact Survey. Health Equity 2020, 4, 336–344. [Google Scholar] [CrossRef]
- Mechanick, J.I.; Rosenson, R.S.; Pinney, S.P.; Mancini, D.M.; Narula, J.; Fuster, V. Coronavirus and Cardiometabolic Syndrome: JACC Focus Seminar. J. Am. Coll. Cardiol. 2020, 76, 2024–2035. [Google Scholar] [CrossRef]
- Singh, A.K.; Gillies, C.L.; Singh, R.; Singh, A.; Chudasama, Y.; Coles, B.; Seidu, S.; Zaccardi, F.; Davies, M.J.; Khunti, K. Prevalence of co-morbidities and their association with mortality in patients with COVID-19: A systematic review and meta-analysis. Diabetes Obes. Metab. 2020, 22, 1915–1924. [Google Scholar] [CrossRef]
- Tian, W.; Jiang, W.; Yao, J.; Nicholson, C.J.; Li, R.H.; Sigurslid, H.H.; Wooster, L.; Rotter, J.I.; Guo, X.; Malhotra, R. Predictors of mortality in hospitalized COVID-19 patients: A systematic review and meta-analysis. J. Med. Virol. 2020, 92, 1875–1883. [Google Scholar] [CrossRef]
- Parekh, N.; Deierlein, A.L. Health behaviours during the coronavirus disease 2019 pandemic: Implications for obesity. Public Health Nutr. 2020, 23, 3121–3125. [Google Scholar] [CrossRef] [PubMed]
- Chowdhury, R.; van Daalen, K.R.; Franco, O.H. Cardiometabolic Health: Key in Reducing Adverse COVID-19 Outcomes. Glob. Heart 2020, 15, 58. [Google Scholar] [CrossRef] [PubMed]
- King, A.J.; Burke, L.M.; Halson, S.L.; Hawley, J.A. The Challenge of Maintaining Metabolic Health During a Global Pandemic. Sports Med. 2020, 50, 1233–1241. [Google Scholar] [CrossRef] [PubMed]
- Granström, J.; Lantz, P.; Lidin, M.; Wahlström, M.; Nymark, C. Perceptions of delay when afflicted by an acute myocardial in-farction during the first wave of the COVID-19 pandemic. Eur. J. Cardiovasc. Nurs. 2022. [Google Scholar] [CrossRef]
- Greco, A.; Spagnolo, M.; Capodanno, D. Unmasking psychological reasons of delay in acute coronary syndromes presentation during the COVID-19 pandemic. Catheter. Cardiovasc. Interv. 2021, 98, 407–408. [Google Scholar] [CrossRef]
- Karagöz, A.; Keskin, B.; Kültürsay, B.; Ceneli, D.; Akbal, O.Y.; Tokgoz, H.C.; Tanyeri, S.; Efe, S.; Dogan, C.; Bayram, Z.; et al. Temporal association of contamination obsession on the prehospital delay of STEMI during COVID-19 pandemic. Am. J. Emerg. Med. 2021, 43, 134–141. [Google Scholar] [CrossRef]
- Park, D.W.; Yang, Y. Delay, Death, and Heterogeneity of Primary PCI During the COVID-19 Pandemic: An International Perspective. J. Am. Coll. Cardiol. 2020, 76, 2331–2333. [Google Scholar] [CrossRef]
- Sturkenboom, H.N.; van Hattem, V.A.E.; Nieuwland, W.; Paris, F.M.A.; Magro, M.; Anthonio, R.L.; Algin, A.; Lipsic, E.; Bruwiere, E.; Van den Branden, B.J.L.; et al. COVID-19-mediated patient delay caused increased total ischaemic time in ST-segment elevation myocardial infarction. Neth. Heart J. 2022, 30, 96–105. [Google Scholar] [CrossRef]
- Tokarek, T.; Dziewierz, A.; Malinowski, K.P.; Rakowski, T.; Bartuś, S.; Dudek, D.; Siudak, Z. Treatment Delay and Clinical Outcomes in Patients with ST-Segment Elevation Myocardial Infarction during the COVID-19 Pandemic. J. Clin. Med. 2021, 10, 3920. [Google Scholar] [CrossRef]
- Zheng, N.S.; Warner, J.L.; Osterman, T.J.; Wells, Q.S.; Shu, X.O.; Deppen, S.A.; Karp, S.J.; Dwyer, S.; Feng, Q.; Cox, N.J.; et al. 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. J. Biomed. Inform. 2021, 113, 103657. [Google Scholar] [CrossRef]
- Bhatt, A.S.; Moscone, A.; McElrath, E.E.; Varshney, A.S.; Claggett, B.L.; Bhatt, D.L.; Januzzi, J.L.; Butler, J.; Adler, D.S.; Solomon, S.D.; et al. Fewer Hospitalizations for Acute Cardiovascular Conditions During the COVID-19 Pandemic. J. Am. Coll. Cardiol. 2020, 76, 280–288. [Google Scholar] [CrossRef]
- Arduino, P.G.; Conrotto, D.; Broccoletti, R. The outbreak of Novel Coronavirus disease (COVID-19) caused a worrying delay in the diagnosis of oral cancer in north-west Italy: The Turin Metropolitan Area experience. Oral. Dis. 2021, 27 (Suppl. S3), 742–743. [Google Scholar] [CrossRef]
- Cantini, L.; Mentrasti, G.; Russo, G.L.; Signorelli, D.; Pasello, G.; Rijavec, E.; Russano, M.; Antonuzzo, L.; Rocco, D.; Giusti, R.; et al. Evalu-ation of COVID-19 impact on DELAYing diagnostic-therapeutic pathways of lung cancer patients in Italy (COVID-DELAY study): Fewer cases and higher stages from a real-world scenario. ESMO Open 2022, 7, 100406. [Google Scholar] [CrossRef]
- De Luca, P.; Bisogno, A.; Colacurcio, V.; Marra, P.; Cassandro, C.; Camaioni, A.; Cassandro, E.; Scarpa, A. Diagnosis and treatment delay of head and neck cancers during COVID-19 era in a tertiary care academic hospital: What should we expect? Eur. Arch. Otorhinolaryngol 2022, 279, 961–965. [Google Scholar] [CrossRef]
- Dessinioti, C.; Garbe, C.; Stratigos, A.J. The impact of the COVID-19 pandemic on diagnostic delay of skin cancer: A call to restart screening activities. J. Eur. Acad. Derm. Venereol 2021, 35, e836–e837. [Google Scholar] [CrossRef]
- Ehrlich, M.I.; Saif, M.W. Potential Impact of Delay in Cancer Screening due to COVID-19. Cancer Med. J. 2021, 4, 44–47. [Google Scholar]
- Hamilton, W. Cancer diagnostic delay in the COVID-19 era: What happens next? Lancet Oncol. 2020, 21, 1000–1002. [Google Scholar] [CrossRef]
- Hartman, H.E.; Sun, Y.; Devasia, T.P.; Chase, E.C.; Jairath, N.K.; Dess, R.T.; Jackson, W.C.; Morris, E.; Li, P. Hochstedler KA et al: Integrated Survival Estimates for Cancer Treatment Delay Among Adults With Cancer During the COVID-19 Pandemic. JAMA Oncol. 2020, 6, 1881–1889. [Google Scholar] [CrossRef]
- Larson, D.W.; Abd El Aziz, M.A.; Mandrekar, J.N. How Many Lives Will Delay of Colon Cancer Surgery Cost During the COVID-19 Pandemic? An Analysis Based on the US National Cancer Database. Mayo Clin. Proc. 2020, 95, 1805–1807. [Google Scholar] [CrossRef]
- Lee, T.; Cheng, D.Z.; Foo, F.J.; Sivarajah, S.S.; Ho, L.M.L.; Aw, D.; Chong, C.X.Z.; Ng, J.L.; Tan, W.J.H.; Koh, F.H. Did the COVID-19 lockdown result in a delay of colorectal cancer presentation and outcomes? A single centre review. Langenbecks Arch. Surg. 2022, 407, 739–745. [Google Scholar] [CrossRef]
- Liu, Y.; Yao, Y.; Wang, Q.; Liu, Z.; Gu, Y.; Zhang, H.; Yi, F.; Cao, B. Dilemma and solutions of treatment delay in cancer patients during the COVID-19 pandemic: A single-center, prospective survey. Asia Pac. J. Clin. Oncol. 2021, 18, e338–e345. [Google Scholar] [CrossRef]
- Lucidi, D.; Valerini, S.; Federici, G.; Miglio, M.; Cantaffa, C.; Alicandri-Ciufelli, M. Head and Neck Cancer During Covid-19 Pan-demic: Was there a Diagnostic Delay? Indian J. Otolaryngol. Head Neck Surg. 2022, 18, 1–7. [Google Scholar] [CrossRef]
- Mahl, C.; Melo, L.R.S.; Almeida, M.H.A.; Carvalho, C.S.; Santos, L.L.S.; Nunes, P.S.; Quintans-JÚnior, L.J.; AraÚjo, A.A.S.; Santos, V.S.; Mar-tins-Filho, P.R. Delay in head and neck cancer care during the COVID-19 pandemic and its impact on health outcomes. Braz. Oral Res. 2020, 34, e126. [Google Scholar] [CrossRef]
- Mayne, N.R.; Bajaj, S.S.; Powell, J.; Elser, H.C.; Civiello, B.S.; Fintelmann, F.J.; Li, X.; Yang, C.J. Extended Delay to Treatment for Stage III-IV Non-Small-Cell Lung Cancer and Survival: Balancing Risks During the COVID-19 Pandemic. Clin. Lung Cancer 2022, 23, e362–e376. [Google Scholar] [CrossRef]
- Metzger, K.; Mrosek, J.; Zittel, S.; Pilz, M.; Held, T.; Adeberg, S.; Ristow, O.; Hoffmann, J.; Engel, M.; Freudlsperger, C.; et al. Treatment delay and tumor size in patients with oral cancer during the first year of the COVID-19 pandemic. Head Neck 2021, 43, 3493–3497. [Google Scholar] [CrossRef]
- Miranda, D.L.P.; Nogueira-Rodrigues, A.; Fagundes, T.P.; Albuquerque, R.M.; Landeiro, L.C.G. COVID-19 threatens to cause collat-eral delay in cancer diagnosis. Sao Paulo Med. J. 2020, 138, 347–348. [Google Scholar] [CrossRef]
- Quarello, P.; Ferrari, A.; Mascarin, M.; Milano, G.M.; Tornesello, A.; Bertolotti, M.; Spinelli, M.; Pierobon, M.; Perillo, T.; Maule, M.; et al. Diagnostic Delay in Adolescents with Cancer During COVID-19 Pandemic: A New Price for Our Patients to Pay. J. Adolesc. Young Adult Oncol. 2022, 11, 316–319. [Google Scholar] [CrossRef]
- Rao, V.; Arakeri, G.; Subash, A.; Ajaikumar, B.S.; Patil, R.; Hale, B.; Amaral Mendes, R. Decreased Cancer Consultations in the COVID-19 Era: A Concern for Delay in Early Cancer Diagnosis in India. JCO Glob. Oncol. 2021, 7, 408–409. [Google Scholar] [CrossRef]
- Seretis, K.; Boptsi, E.; Boptsi, A.; Lykoudis, E.G. The impact of treatment delay on skin cancer in COVID-19 era: A case-control study. World J. Surg. Oncol. 2021, 19, 350. [Google Scholar] [CrossRef]
- Tachibana, B.M.T.; Ribeiro, R.L.M.; Federicci É, E.F.; Feres, R.; Lupinacci, F.A.S.; Yonekura, I.; Racy, A.C.S. The delay of breast cancer diagnosis during the COVID-19 pandemic in São Paulo, Brazil. Einstein 2021, 19, eAO6721. [Google Scholar] [CrossRef]
- Tan, W.S.; Marchese, M.; Paciotti, M.; Nguyen, D.D.; Cone, E.B.; Mossanen, M.; Webster, J.J.; Barod, R.; Bex, A.; Kibel, A.S.; et al. Delay in surgery for cT1b-2 kidney cancer beyond 90 days is associated with poorer survival: Implications for prioritization during the COVID-19 pandemic. Minerva Urol. Nephrol. 2021, 73, 404–406. [Google Scholar] [CrossRef]
- Valenti, M.; Pavia, G.; Gargiulo, L.; Facheris, P.; Nucca, O.; Mancini, L.; Sacrini, F.; Borroni, R.G.; Narcisi, A.; Costanzo, A. Impact of delay in follow-up due to COVID-19 pandemic on skin cancer progression: A real-life experience from an Italian hub hospital. Int. J. Dermatol. 2021, 60, 860–863. [Google Scholar] [CrossRef]
- Vanni, G.; Tazzioli, G.; Pellicciaro, M.; Materazzo, M.; Paolo, O.; Cattadori, F.; Combi, F.; Papi, S.; Pistolese, C.A.; Cotesta, M.; et al. Delay in Breast Cancer Treatments During the First COVID-19 Lockdown. A Multicentric Analysis of 432 Patients. Anticancer Res. 2020, 40, 7119–7125. [Google Scholar] [CrossRef]
- Vose, J.M. Delay in Cancer Screening and Diagnosis During the COVID-19 Pandemic: What Is the Cost? Oncology 2020, 34, 343. [Google Scholar] [CrossRef]
- Ye, Y.; Wang, J.; Cai, S.; Fu, X.; Ji, Y. Psychological distress of cancer patients caused by treatment delay during the COVID-19 pandemic in China: A cross-sectional study. Psychooncology 2022, 31, 1607–1615. [Google Scholar] [CrossRef]
- Czeisler, M.; Kennedy, J.L.; Wiley, J.F.; Facer-Childs, E.R.; Robbins, R.; Barger, L.K.; Czeisler, C.A.; Rajaratnam, S.M.W.; Howard, M.E. Delay or avoidance of routine, urgent and emergency medical care due to concerns about COVID-19 in a region with low COVID-19 prevalence: Victoria, Australia. Respirology 2021, 26, 707–712. [Google Scholar] [CrossRef]
- Davis, A.L.; Sunderji, A.; Marneni, S.R.; Seiler, M.; Hall, J.E.; Cotanda, C.P.; Klein, E.J.; Brown, J.C.; Gelernter, R.; Griffiths, M.A.; et al. Care-giver-reported delay in presentation to pediatric emergency departments for fear of contracting COVID-19: A multi-national cross-sectional study. Can. J. Emerg. Med. 2021, 23, 778–786. [Google Scholar] [CrossRef]
- Goyal, M.; Singh, P.; Singh, K.; Shekhar, S.; Agrawal, N.; Misra, S. The effect of the COVID-19 pandemic on maternal health due to delay in seeking health care: Experience from a tertiary center. Int. J. Gynaecol. Obstet. 2021, 152, 231–235. [Google Scholar] [CrossRef]
- Kaur, H.; Pranesh, G.T.; Rao, K.A. Emotional Impact of Delay in Fertility Treatment due to COVID-19 Pandemic. J. Hum. Reprod. Sci. 2020, 13, 317–322. [Google Scholar] [CrossRef]
- Lee, D.I.D.; Vanderhout, S.; Aglipay, M.; Birken, C.S.; Morris, S.K.; Piché-Renaud, P.P.; Keown-Stoneman, C.D.G.; Maguire, J.L. Delay in childhood vaccinations during the COVID-19 pandemic. Can. J. Public Health 2022, 113, 126–134. [Google Scholar] [CrossRef]
- Mungmunpuntipantip, R.; Wiwanitkit, V. COVID-19 and delay to care in pediatric trauma. J. Pediatr. Orthop. B 2022, 31, e111. [Google Scholar] [CrossRef]
- Parasole, R.; Stellato, P.; Conter, V.; De Matteo, A.; D’Amato, L.; Colombini, A.; Pecoraro, C.; Bencivenga, C.; Raimondo, M.; Silvestri, S.; et al. Collateral effects of COVID-19 pandemic in pediatric hematooncology: Fatalities caused by diagnostic delay. Pediatr. Blood Cancer 2020, 67, e28482. [Google Scholar] [CrossRef]
- Shaw, K.G.; Salton, R.L.; Carry, P.; Hadley-Miller, N.; Georgopoulos, G. Multi-day delay to care identified in pediatric trauma cases during COVID-19. J. Pediatr. Orthop. B 2022, 31, e56–e64. [Google Scholar] [CrossRef]
- Bagheripour, M.H.; Zakeri, M.A. Acute Mesenteric Ischemia in a COVID-19 Patient: Delay in Referral and Recommendation for Surgery. Case Rep. Gastrointest Med. 2021, 2021, 1999931. [Google Scholar] [CrossRef]
- Gangbe, E.; Cai, E.; Penta, R.; Mansour, F.W.; Krishnamurthy, S. Effects of Surgical Delay Due to COVID-19 on Women Requiring Emergency Gynaecological Surgery. J. Obstet. Gynaecol. Can. 2021, 43, 1296–1300. [Google Scholar] [CrossRef]
- Ginsburg, K.B.; Curtis, G.L.; Patel, D.N.; Chen, W.M.; Strother, M.C.; Kutikov, A.; Derweesh, I.H.; Cher, M.L. Association of Surgical Delay and Overall Survival in Patients With T2 Renal Masses: Implications for Critical Clinical Decision-making During the COVID-19 Pandemic. Urology 2021, 147, 50–56. [Google Scholar] [CrossRef]
- Ginsburg, K.B.; Curtis, G.L.; Timar, R.E.; George, A.K.; Cher, M.L. Delayed Radical Prostatectomy is Not Associated with Adverse Oncologic Outcomes: Implications for Men Experiencing Surgical Delay Due to the COVID-19 Pandemic. J. Urol. 2020, 204, 720–725. [Google Scholar] [CrossRef]
- Piltcher-da-Silva, R.; Castro, T.L.; Trapp, A.G.; Bohnenberger, S.; Kroth, E.C.; Pinto, J.A.R.; Grehs, C.; Tomasi, D.C.; Diemen, V.V.; Cavazzola, L.T. The impact of COVID-19 and social avoidance in urgent and emergency surgeries-will a delay in diagnosis result in perioperative complications? Rev. Assoc. Med. Bras. 2021, 67, 355–359. [Google Scholar] [CrossRef]
- Schöni, D.; Halatsch, M.E.; Alfieri, A. The impact of reduced operating room capacity on the time delay of urgent surgical care for neurosurgical patients during the COVID-19 pandemic. Interdiscip. Neurosurg. 2022, 29, 101544. [Google Scholar] [CrossRef]
- Sean Ong, X.R.; Condon, B.; Bagguley, D.; Lawrentschuk, N.; Azad, A.; Murphy, D. Safety first: Evidence for delay of radical prostatectomy without use of androgen deprivation therapy during COVID-19. Future Oncol. 2020, 16, 1409–1411. [Google Scholar] [CrossRef]
- Smith, K.M.; Wheelwright, J.C.; Christensen, G.V.; Ishikawa, H.; Tashjian, R.Z.; Chalmers, P.N. COVID-19-related rotator cuff repair delay. JSES Int. 2022, 6, 79–83. [Google Scholar] [CrossRef]
- Tan, H.; Preston, J.; Hunn, S.; Kwok, M.; Borschmann, M. COVID-19 did not delay time from referral to definitive management for head and neck cancer patients in a regional Victorian centre. ANZ J. Surg. 2021, 91, 1364–1368. [Google Scholar] [CrossRef]
- Vedachalam, R.; Yamini, K.; Venkatesh, R.; Kalpana, N.; Shivkumar, C.; Shekhar, M.; Haripriya, A.; Sathya, R. Reasons for delay in cataract surgery in patients with advanced cataracts during the COVID-19 pandemic. Indian J. Ophthalmol. 2022, 70, 2153–2157. [Google Scholar] [CrossRef]
- Wilson, J.M.; Schwartz, A.M.; Grissom, H.E.; Holmes, J.S.; Farley, K.X.; Bradbury, T.L.; Guild, G.N., 3rd. Patient Perceptions of COVID-19-Related Surgical Delay: An Analysis of Patients Awaiting Total Hip and Knee Arthroplasty. Hss J. 2020, 16 (Suppl. S1), 45–51. [Google Scholar] [CrossRef]
- Anderson, K.E.; McGinty, E.E.; Presskreischer, R.; Barry, C.L. Reports of Forgone Medical Care Among US Adults During the Initial Phase of the COVID-19 Pandemic. JAMA Netw. Open 2021, 4, e2034882. [Google Scholar] [CrossRef]
- Giannouchos, T.V.; Brooks, J.M.; Andreyeva, E.; Ukert, B. Frequency and factors associated with foregone and delayed medical care due to COVID-19 among nonelderly US adults from August to December 2020. J. Eval. Clin. Pract. 2022, 28, 33–42. [Google Scholar] [CrossRef]
- Sonnega, A.; Weir, D.R. The Health and Retirement Study: A Public Data Resource for Research on Aging. Open Health Data 2014, 2, e7. [Google Scholar] [CrossRef]
- McGrath, R.; Vincent, B.M.; Hackney, K.J.; Robinson-Lane, S.G.; Downer, B.; Clark, B.C. The Longitudinal Associations of Handgrip Strength and Cognitive Function in Aging Americans. J. Am. Med. Dir. Assoc. 2019, 21, 634–639. [Google Scholar] [CrossRef]
- Hunter, J.C.; Handing, E.P.; Casanova, R.; Kuchibhatla, M.; Lutz, M.W.; Saldana, S.; Plassman, B.L.; Hayden, K.M. Neighborhoods, sleep quality, and cognitive decline: Does where you live and how well you sleep matter? Alzheimer’s & dementia. J. Alzheimer’s Assoc. 2018, 14, 454–461. [Google Scholar]
- Beydoun, H.A.; Beydoun, M.A.; Gautam, R.S.; Alemu, B.T.; Weiss, J.; Hossain, S.; Zonderman, A.B. COVID-19 pandemic impact on trajectories in cardiometabolic health, physical activity and functioning among adults from the 2006–2020 Health and Retirement Study. J. Gerontol. A Biol. Sci. Med. Sci. 2022, 77, 1371–1379. [Google Scholar] [CrossRef]
- Beydoun, H.A.; Beydoun, M.A.; Weiss, J.; Hossain, S.; Huang, S.; Alemu, B.T.; Zonderman, A.B. Insomnia as a predictor of diagnosed memory problems: 2006–2016 Health and Retirement Study. Sleep Med. 2021, 80, 158–166. [Google Scholar] [CrossRef]
- Kaufmann, C.N.; Mojtabai, R.; Hock, R.S.; Thorpe, R.J.; Jr Canham, S.L.; Chen, L.Y.; Wennberg, A.M.; Chen-Edinboro, L.P.; Spira, A.P. Racial/Ethnic Differences in Insomnia Trajectories Among U.S. Older Adults. The American journal of geriatric psychiatry. Off. J. Am. Assoc. Geriatr. Psychiatry 2016, 24, 575–584. [Google Scholar] [CrossRef] [Green Version]
- Kim, E.S.; Hershner, S.D.; Strecher, V.J. Purpose in life and incidence of sleep disturbances. J. Behav. Med. 2015, 38, 590–597. [Google Scholar] [CrossRef]
- Cherifa, M.; Blet, A.; Chambaz, A.; Gayat, E.; Resche-Rigon, M.; Pirracchio, R. Prediction of an Acute Hypotensive Episode During an ICU Hospitalization With a Super Learner Machine-Learning Algorithm. Anesth. Analg. 2020, 130, 1157–1166. [Google Scholar] [CrossRef]
- Li, J.; Gu, J.; Lu, Y.; Wang, X.; Si, S.; Xue, F. Development and validation of a Super learner-based model for predicting survival in Chinese Han patients with resected colorectal cancer. Jpn. J. Clin. Oncol. 2020, 50, 1133–1140. [Google Scholar] [CrossRef]
- van der Laan, M.J.; Polley, E.C.; Hubbard, A.E. Super learner. Stat. Appl. Genet. Mol. Biol. 2007, 6, 25. [Google Scholar] [CrossRef]
- Sinisi, S.E.; Polley, E.C.; Petersen, M.L.; Rhee, S.Y.; van der Laan, M.J. Super learning: An application to the prediction of HIV-1 drug resistance. Stat. Appl. Genet. Mol. 2007, 6, 7. [Google Scholar] [CrossRef]
- Bi, Q.; Goodman, K.E.; Kaminsky, J.; Lessler, J. What is Machine Learning? A Primer for the Epidemiologist. Am. J. Epidemiol. 2019, 188, 2222–2239. [Google Scholar] [CrossRef]
- Petersen, M.L.; LeDell, E.; Schwab, J.; Sarovar, V.; Gross, R.; Reynolds, N.; Haberer, J.E.; Goggin, K.; Golin, C.; Arnsten, J.; et al. Super Learner Analysis of Electronic Adherence Data Improves Viral Prediction and May Provide Strategies for Selective HIV RNA Monitoring. J. Acquir. Immune Defic. Syndr. 2015, 69, 109–118. [Google Scholar] [CrossRef]
- Golmakani, M.K.; Polley, E.C. Super Learner for Survival Data Prediction. Int. J. Biostat. 2020, 16, 20190065. [Google Scholar] [CrossRef]
- Carmassi, C.; Cerveri, G.; Bertelloni, C.A.; Marasco, M.; Dell’Oste, V.; Massimetti, E.; Gesi, C.; Dell’Osso, L. Mental health of frontline help-seeking healthcare workers during the COVID-19 outbreak in the first affected hospital in Lombardy, Italy. PsyChiatry Res. 2021, 298, 113763. [Google Scholar] [CrossRef]
- Chou, Y.C.; Yen, Y.F.; Chu, D.; Hu, H.Y. Impact of the COVID-19 Pandemic on Healthcare-Seeking Behaviors among Frequent Emergency Department Users: A Cohort Study. Int. J. Environ. Res. Public Health 2021, 18, 6351. [Google Scholar] [CrossRef]
- Howley, F.; O’Doherty, L.; McEniff, N.; O’Riordan, R. Late presentation of ‘Lemierre’s syndrome’: How a delay in seeking healthcare and reduced access to routine services resulted in widely disseminated Fusobacterium necrophorum infection during the global COVID-19 pandemic. BMJ Case Rep. 2020, 13, e239269. [Google Scholar] [CrossRef]
- Nab, M.; van Vehmendahl, R.; Somers, I.; Schoon, Y.; Hesselink, G. Delayed emergency healthcare seeking behaviour by Dutch emergency department visitors during the first COVID-19 wave: A mixed methods retrospective observational study. BMC Emerg. Med. 2021, 21, 56. [Google Scholar] [CrossRef]
- Watson, G.; Pickard, L.; Williams, B.; Hargreaves, D.; Blair, M. ‘Do I, don’t I?’ A qualitative study addressing parental perceptions about seeking healthcare during the COVID-19 pandemic. Arch. Dis. Child. 2021, 106, 1118–1124. [Google Scholar] [CrossRef]
- Liu, H.; Chen, S.; Liu, M.; Nie, H.; Lu, H. Comorbid Chronic Diseases are Strongly Correlated with Disease Severity among COVID-19 Patients: A Systematic Review and Meta-Analysis. Aging Dis. 2020, 11, 668–678. [Google Scholar] [CrossRef]
- Bienvenu, L.A.; Noonan, J.; Wang, X.; Peter, K. Higher mortality of COVID-19 in males: Sex differences in immune response and cardiovascular comorbidities. Cardiovasc. Res. 2020, 116, 2197–2206. [Google Scholar] [CrossRef]
- Saban-Ruiz, J.; Ly-Pen, D. COVID-19: A Personalized Cardiometabolic Approach for Reducing Complications and Costs. The Role of Aging beyond Topics. J. Nutr. Health Aging 2020, 24, 550–559. [Google Scholar] [CrossRef]
- Rodilla, E.; Saura, A.; Jimenez, I.; Mendizabal, A.; Pineda-Cantero, A.; Lorenzo-Hernandez, E.; Fidalgo-Montero, M.D.P.; Lopez-Cuervo, J.F.; Gil-Sanchez, R.; Rabadan-Pejenaute, E.; et al. Association of Hypertension with All-Cause Mortality among Hospitalized Patients with COVID-19. J. Clin. Med. 2020, 9, 3136. [Google Scholar] [CrossRef]
- Dhindsa, D.; Wong, N.D.; Sperling, L. Cardiovascular and cardiometabolic prevention: High-level priority in the era of COVID-19. Cardiovasc. Endocrinol. Metab. 2020, 9, 125–127. [Google Scholar] [CrossRef]
- de Wilton, A.; Kilich, E.; Chaudhry, Z.; Bell, L.C.; Gahir, J.; Cadman, J.; Lever, R.A.; Logan, S.A.; Team, U.C.-R. Delayed healthcare seeking and prolonged illness in healthcare workers during the COVID-19 pandemic: A single-centre observational study. BMJ Open 2020, 10, e040216. [Google Scholar] [CrossRef] [PubMed]
Total | Healthcare Service Delays | |||
---|---|---|---|---|
Overall (N = 1413) | Surgical (N = 1413) | Non-Surgical (N = 1413) | ||
% or Mean ± SEM | % OR (95% CI) | % OR (95% CI) | % OR (95% CI) | |
OVERALL: | 100 | 32.7 | 5.8 | 31.4 |
DEMOGRAPHIC/SOCIOECONOMIC: | ||||
Sex: | p = 0.017 | p = 0.15 | p = 0.023 | |
Male | 42.8 | 27.8 Ref. | 7.3 Ref. | 26.7 Ref. |
Female | 57.2 | 36.4 1.48 (1.07, 2.06) | 4.7 0.63 (0.33, 1.18) | 34.9 1.47 (1.05, 2.05) |
Birth cohort: | p = 0.0002 | p = 0.04 | p = 0.0001 | |
Original/AHEAD/Children of the Depression | 14.9 | 22.7 Ref. | 2.6 Ref. | 20.9 Ref. |
War Babies | 13.9 | 25.4 1.16 (0.69, 1.94) | 4.5 1.72 (0.54, 5.46) | 24.9 1.25 (0.74, 2.10) |
Early Baby Boomers | 17.6 | 27.2 1.27 (0.79, 2.02) | 3.8 1.43 (0.51, 4.06) | 25.4 1.28 (0.79, 2.07) |
Mid Baby Boomers | 25.9 | 35.2 1.84 (1.21, 2.80) | 8.7 3.52 (1.51, 8.22) | 34.2 1.96 (1.27, 3.00) |
Late Baby Boomers | 27.5 | 43.0 2.56 (1.67, 3.93) | 6.8 2.69 (1.07, 6.80) | 41.5 2.67 (1.72, 4.12) |
Age (years): | p < 0.0001 | p = 0.02 | p < 0.0001 | |
Mean ± SEM | 67.5 ± 0.3 | 0.96 (0.95, 0.98) | 0.96 (0.93, 0.99) | 0.96 (0.95, 0.98) |
p = 0.003 | p = 0.01 | p = 0.002 | ||
50–54 | 1.2 | 56.1 Ref. | 0.0 Ref. | 56.1 Ref. |
55–59 | 20.0 | 42.1 0.57 (0.09, 3.48) | 8.1 3.02 (1.11, 8.20) | 40.8 0.54 (0.09, 3.31) |
60–64 | 25.0 | 34.7 0.42 (0.07, 2.53) | 7.1 2.61 (1.06, 6.45) | 33.2 0.39 (0.06, 2.37) |
65–69 | 17.2 | 34.4 0.41 (0.07, 2.51) | 6.7 2.46 (0.92, 6.59) | 33.1 0.39 (0.06, 2.37) |
70–74 | 14.9 | 24.8 0.26 (0.04, 1.59) | 5.7 2.07 (0.71, 6.02) | 23.9 0.25 (0.04, 1.53) |
75–79 | 8.3 | 26.4 0.28 (0.04, 1.78) | 1.0 0.34 (0.08, 1.38) | 25.6 0.27 (0.04, 1.72) |
≥80 | 13.3 | 23.3 0.24 (0.04, 1.44) | 2.8 -- | 21.4 0.21 (0.04, 1.29) |
Race: | p = 0.61 | p = 0.28 | p = 0.63 | |
White / Caucasian | 76.5 | 33.2 Ref. | 6.5 Ref. | 32.1 Ref. |
Black / African American | 14.2 | 32.9 0.98 (0.69, 1.41) | 4.0 0.60 (0.22, 1.62) | 30.2 0.91 (0.63, 1.31) |
Other | 9.3 | 27.6 0.77 (0.45, 1.29) | 3.2 0.47 (0.16, 1.40) | 27.1 0.78 (0.46, 1.33) |
Ethnicity: | p = 0.79 | p = 0.04 | p = 0.74 | |
Hispanic | 9.7 | 31.5 0.94 (0.61, 1.46) | 2.5 0.38 (0.15, 0.97) | 29.9 0.93 (0.39, 1.45) |
Non-Hispanic | 90.3 | 32.8 Ref. | 6.2 Ref. | 31.5 Ref. |
Education: | p = 0.04 | p = 0.24 | p = 0.016 | |
No degree | 13.8 | 26.7 Ref. | 4.3 Ref. | 23.8 Ref. |
GED | 6.4 | 35.8 1.53 (0.72, 3.24) | 14.4 3.76 (1.00, 14.11) | 35.8 1.79 (0.84, 3.81) |
High school diploma | 27.5 | 30.2 1.19 (0.74, 1.92) | 4.2 0.99 (0.37, 2.64) | 29.3 1.33 (0.81, 2.18) |
Some college | 29.0 | 29.8 1.17 (0.72, 1.88) | 5.3 1.25 (0.48, 3.22) | 28.3 1.27 (0.77, 2.08) |
College degree or higher | 23.3 | 41.9 1.97 (1.22, 3.19) | 7.0 1.69 (0.67, 4.26) | 40.9 2.22 (1.36, 3.64) |
Marital status: | p = 0.23 | p = 0.63 | p = 0.37 | |
Never married | 8.0 | 32.4 Ref. | 4.1 Ref. | 31.4 Ref. |
Married/Partnered | 56.3 | 31.3 0.95 (0.49, 1.83) | 5.1 1.26 (0.31, 5.07) | 30.1 0.94 (0.48, 1.82) |
Separated/Divorced | 20.2 | 39.5 1.36 (0.68, 2.75) | 7.7 1.97 (0.44, 8.74) | 37.2 1.29 (0.63, 2.63) |
Widowed | 15.4 | 29.0 0.85 (0.42, 1.75) | 6.9 1.72 (0.34, 8.77) | 28.5 0.87 (0.42, 1.80) |
Work status: | p = 0.03 | p = 0.66 | p = 0.02 | |
Working | 33.5 | 38.3 1.46 (1.04, 2.05) | 6.4 1.17 (0.58, 2.39) | 37.3 1.49 (1.06, 2.11) |
Not working | 66.5 | 29.8 Ref. | 5.5 Ref. | 28.4 Ref. |
Federal health insurance coverage: | p = 0.14 | p = 0.34 | p = 0.10 | |
Yes | 69.0 | 30.8 0.76 (0.54, 1.08) | 5.2 0.71 (0.35, 1.44) | 29.4 0.75 (0.52, 1.07) |
No | 30.9 | 36.9 Ref. | 7.2 Ref. | 35.8 Ref. |
Total wealth (USD): | p = 0.79 | p = 0.82 | p = 0.60 | |
<25,000 | 32.4 | 30.5 Ref. | 6.1 Ref. | 28.6 Ref. |
25,000–124,999 | 51.7 | 33.4 1.14 (0.82, 1.59) | 5.7 0.92 (0.46, 1.86) | 32.8 1.22 (0.87, 1.71) |
125,000–299,999 | 11.9 | 33.9 1.17 (0.69, 1.98) | 4.3 0.69 (0.23, 2.10) | 30.9 1.12 (0.65, 1.91) |
≥300,000 | 3.9 | 37.5 1.37 (0.57, 3.27) | 9.4 1.58 (0.33, 7.52) | 37.5 1.49 (0.63, 3.58) |
Number of household members: | p = 0.47 | p = 0.95 | p = 0.44 | |
Mean ± SEM | 2.2 ± 0.04 | 1.04 (0.93, 1.17) | 1.01 (0.76, 1.33) | 1.05 (0.93, 1.18) |
p = 0.39 | p = 0.62 | p = 0.30 | ||
≤3 | 88.3 | 32.2 Ref. | 5.7 Ref. | 30.8 Ref. |
>3 | 11.6 | 36.6 1.22 (0.77, 1.92) | 6.9 1.25 (0.51, 3.04) | 36.1 1.27 (0.81, 1.99) |
Census region of residence: | p = 0.25 | p = 0.84 | p = 0.18 | |
Northeast | 15.7 | 29.4 Ref. | 4.8 Ref. | 27.1 Ref. |
Midwest | 22.2 | 37.9 1.47 (0.86, 2.51) | 7.1 1.52 (0.48, 4.79) | 36.8 1.57 (0.91, 2.68) |
South | 41.1 | 29.7 1.01 (0.64, 1.60) | 5.2 1.09 (0.40, 2.93) | 28.4 1.07 (0.67, 1.72) |
West | 20.9 | 35.3 1.31 (0.79, 2.20) | 6.4 1.34 (0.46, 3.93) | 34.8 1.44 (0.85, 2.43) |
LIFESTYLE: | ||||
Smoking status: | p = 0.38 | p = 0.06 | p = 0.36 | |
Never smoker | 40.8 | 34.2 Ref. | 4.4 Ref. | 33.3 Ref. |
Past smoker | 43.8 | 30.0 0.82 (0.59, 1.14) | 5.2 1.20 (0.59, 2.43) | 28.6 0.80 (0.57, 1.12) |
Current smoker | 15.4 | 36.2 1.09 (0.67, 1.77) | 11.3 2.76 (1.16, 6.56) | 34.1 1.03 (0.62, 1.70) |
Frequency of alcohol consumption: | p = 0.05 | p = 0.09 | p = 0.02 | |
Abstinent | 42.6 | 28.8 Ref. | 4.5 Ref. | 27.3 Ref. |
1–3 days per month | 18.2 | 42.5 1.82 (1.17, 2.80) | 10.2 2.41 (1.06, 5.47) | 41.9 1.92 (1.24, 2.96) |
1–2 days per week | 23.0 | 30.9 1.10 (0.74, 1.65) | 3.7 0.82 (0.33, 2.01) | 29.6 1.12 (0.74, 1.68) |
≥3 days per week | 16.1 | 34.3 1.29 (0.81, 2.05) | 7.3 1.67 (0.70, 3.95) | 32.8 1.30 (0.81, 2.08) |
Frequency of moderate/vigorous physical exercise: | p = 0.77 | p = 0.75 | p = 0.86 | |
Never | 20.3 | 30.5 Ref. | 4.6 Ref. | 29.6 Ref. |
1–4 times per month | 25.1 | 34.2 1.18 (0.75, 1.87) | 6.4 1.42 (0.55, 3.62) | 31.5 1.09 (0.69, 1.75) |
>1 times per week | 54.6 | 32.8 1.11 (0.74, 1.67) | 6.0 1.34 (0.56, 3.21) | 32.0 1.12 (0.74, 1.69) |
HEALTH: | ||||
Body mass index (kg/m2): | p = 0.13 | p = 0.72 | p = 0.22 | |
Mean ± SEM | 30.4 ± 0.4 | 1.01 (0.99, 1.02) | 1.00 (0.99, 1.01) | 1.01 (0.99, 1.02) |
p = 0.87 | p = 0.23 | p = 0.92 | ||
<25 | 24.0 | 34.2 Ref. | 4.1 Ref. | 32.6 Ref. |
25–29.9 | 32.8 | 31.9 0.90 (0.61, 1.34) | 4.8 1.17 (0.47, 2.94) | 31.1 0.93 (0.62, 1.40) |
≥30 | 43.1 | 32.4 0.92 (0.63, 1.36) | 7.6 1.89 (0.81, 4.43) | 30.9 0.92 (0.62, 1.37) |
Cardiovascular and/or metabolic conditions: | ||||
Hypertension: | p = 0.77 | p = 0.50 | p = 0.82 | |
Yes | 62.9 | 33.1 1.05 (0.75, 1.46) | 6.3 1.26 (0.64, 2.46) | 31.7 1.04 (0.74, 1.46) |
No | 37.1 | 31.9 Ref. | 5.1 Ref. | 30.9 Ref. |
Diabetes: | p = 0.30 | p = 0.10 | p = 0.27 | |
Yes | 28.1 | 35.5 1.19 (0.85, 1.67) | 8.3 1.77 (0.89, 3.52) | 34.4 1.21 (0.86, 1.70) |
No | 71.9 | 31.6 Ref. | 4.9 Ref. | 30.2 Ref. |
Heart disease: | p = 0.74 | p = 0.75 | p = 0.84 | |
Yes | 29.6 | 31.8 0.95 (0.68, 1.31) | 5.4 0.89 (0.46, 1.76) | 30.8 0.97 (0.69, 1.34) |
No | 70.4 | 33.0 Ref. | 6.0 Ref. | 31.6 Ref. |
Stroke: | p = 0.06 | p = 0.68 | p = 0.08 | |
Yes | 10.6 | 33.8 0.60 (0.35, 1.03) | 4.8 0.79 (0.26, 2.42) | 22.9 0.62 (0.36, 1.07) |
No | 89.4 | 23.5 Ref. | 5.9 Ref. | 32.4 Ref. |
Number of conditions: | p = 0.21 | p = 0.61 | p = 0.28 | |
0 | 25.0 | 29.3 Ref. | 5.2 Ref. | 28.0 Ref. |
1–2 | 60.4 | 35.1 1.30 (0.87, 1.94) | 5.6 1.07 (0.49, 2.32) | 33.6 1.30 (0.86, 1.96) |
≥3 | 14.1 | 28.3 0.95 (0.56, 1.60) | 7.9 1.55 (0.59, 4.09) | 27.9 0.99 (0.59, 1.69) |
Self-rated health: | p = 0.03 | p = 0.16 | p = 0.07 | |
Excellent/very good/good | 64.1 | 29.8 Ref. | 4.9 Ref. | 28.9 Ref. |
Fair/poor | 35.8 | 37.8 1.43 (1.03, 1.97) | 7.5 1.58 (0.83, 3.03) | 35.7 1.36 (0.98, 1.89) |
Depression symptoms score: | p = 0.0002 | p = 0.01 | p = 0.0005 | |
Mean ± SEM | 2.5 ± 0.07 | 1.15 (1.07, 1.24) | 1.17 (1.04, 1.33) | 1.14 (1.06, 1.24) |
Models I b | Models II c | Models III d | ||||
---|---|---|---|---|---|---|
OR | 95% CI | OR | 95% CI | OR | 95% CI | |
Logistic (N = 1413): | ||||||
Body mass index (continuous) | 1.00 | 0.99, 1.02 | 1.00 | 0.99, 1.02 | 1.01 | 0.99, 1.02 |
Body mass index (categorical): | ||||||
<25 | Ref. | Ref. | Ref. | |||
25–29.9 | 0.86 | 0.57, 1.30 | 0.86 | 0.56, 1.31 | 0.95 | 0.62, 1.45 |
≥30 | 0.76 | 0.51, 1.14 | 0.74 | 0.49, 1.13 | 0.78 | 0.51, 1.19 |
Hypertension | 1.34 | 0.93, 1.93 | 1.33 | 0.92, 1.91 | 1.27 | 0.87, 1.85 |
Diabetes | 1.31 | 0.93, 1.83 | 1.26 | 0.89, 1.77 | 1.22 | 0.86, 1.74 |
Heart disease | 1.17 | 0.82, 1.65 | 1.15 | 0.81, 1.65 | 1.02 | 0.69, 1.51 |
Stroke | 0.74 | 0.43, 1.29 | 0.73 | 0.42, 1.28 | 0.57 | 0.32, 1.02 |
Number of cardiovascular and/or metabolic conditions: | ||||||
0 | Ref. | Ref. | Ref. | |||
1–2 | 1.73 | 1.13, 2.65 | 1.74 | 1.15, 2.65 | 1.62 | 1.06, 2.49 |
≥3 | 1.44 | 0.82, 2.51 | 1.34 | 0.76, 2.36 | 1.15 | 0.63, 2.10 |
Mixed effects logistic (N = 2082): | ||||||
Body mass index (continuous) | 1.00 | 1.00, 1.02 | 1.01 | 0.99, 1.02 | 1.00 | 0.99, 1.01 |
Body mass index (categorical): | ||||||
<25 | Ref. | Ref. | Ref. | |||
25–29.9 | 1.09 | 0.94, 1.25 | 1.09 | 0.93, 1.27 | 1.08 | 0.93, 1.26 |
≥30 | 1.09 | 0.94, 1.26 | 1.07 | 0.92, 1.26 | 1.03 | 0.88, 1.20 |
Hypertension | 0.92 | 0.82, 1.04 | 0.91 | 0.80, 1.03 | 0.85 | 0.75, 0.96 |
Diabetes | 1.21 | 1.05, 1.39 | 1.24 | 1.08, 1.44 | 1.17 | 1.01, 1.36 |
Heart disease | 1.14 | 0.99, 1.31 | 1.13 | 0.98, 1.30 | 1.01 | 0.87, 1.16 |
Stroke | 0.90 | 0.72, 1.14 | 0.89 | 0.70, 1.14 | 0.81 | 0.63, 1.04 |
Number of cardiovascular and/or metabolic conditions: | ||||||
0 | Ref. | Ref. | Ref. | |||
1–2 | 1.05 | 0.92, 1.19 | 1.07 | 0.93, 1.23 | 0.99 | 0.75, 1.31 |
≥3 | 1.19 | 0.96, 1.49 | 1.19 | 0.94, 1.49 | 0.99 | 0.77, 1.26 |
Models I b | Models II c | Models III d | ||||
---|---|---|---|---|---|---|
OR | 95% CI | OR | 95% CI | OR | 95% CI | |
Logistic (N = 1413): | ||||||
Body mass index (continuous) | 0.99 | 0.98, 1.01 | 1.00 | 0.99, 1.01 | 1.00 | 0.98, 1.01 |
Body mass index (categorical): | ||||||
<25 | Ref. | Ref. | Ref. | |||
25–29.9 | 1.05 | 0.43, 2.55 | 1.19 | 0.44, 3.22 | 1.22 | 0.45, 3.28 |
≥30 | 1.64 | 0.72, 3.72 | 1.93 | 0.77, 4.88 | 1.83 | 0.72, 4.63 |
Hypertension | 1.40 | 0.69, 2.86 | 1.33 | 0.65, 2.73 | 1.18 | 0.55, 2.52 |
Diabetes | 1.62 | 0.86, 3.04 | 1.52 | 0.80, 2.88 | 1.43 | 0.76, 2.68 |
Heart disease | 0.84 | 0.44, 1.60 | 0.79 | 0.42, 1.48 | 0.67 | 0.35, 1.29 |
Stroke | 0.79 | 0.27, 2.27 | 0.72 | 0.26, 1.98 | 0.62 | 0.21, 1.83 |
Number of cardiovascular and/or metabolic conditions: | ||||||
0 | Ref. | Ref. | Ref. | |||
1–2 | 1.22 | 0.54, 2.74 | 1.24 | 0.53, 2.89 | 1.14 | 0.48, 2.69 |
≥3 | 1.64 | 0.59, 4.52 | 1.34 | 0.49, 3.68 | 1.10 | 0.39, 3.15 |
Mixed effects logistic (N = 2082): | ||||||
Body mass index (continuous) | 1.02 | 1.00, 1.04 | 1.01 | 0.99, 1.04 | 1.01 | 0.98, 1.02 |
Body mass index (categorical): | ||||||
<25 | Ref. | Ref. | Ref. | |||
25–29.9 | 1.08 | 0.75, 1.55 | 1.17 | 0.79, 1.72 | 1.17 | 0.79, 1.71 |
≥30 | 1.13 | 0.79, 1.60 | 1.03 | 0.70, 1.52 | 0.93 | 0.63, 1.38 |
Hypertension | 0.84 | 0.63, 1.09 | 0.79 | 0.59, 1.04 | 0.69 | 0.52, 0.92 |
Diabetes | 1.73 | 1.28, 2.34 | 1.59 | 1.18, 2.16 | 1.42 | 1.04, 1.92 |
Heart disease | 1.02 | 0.75, 1.39 | 0.88 | 0.64, 1.20 | 0.69 | 0.49, 0.98 |
Stroke | 2.40 | 0.98, 5.91 | 1.95 | 1.29, 2.91 | 1.69 | 1.11, 2.57 |
Number of cardiovascular and/or metabolic conditions: | ||||||
0 | Ref. | Ref. | Ref. | |||
1–2 | 0.98 | 0.69, 1.38 | 0.91 | 0.64, 1.28 | 0.80 | 0.56, 1.15 |
≥3 | 2.49 | 1.62, 1.38 | 1.87 | 1.21, 2.92 | 1.36 | 0.85, 2.19 |
Models I b | Models II c | Models III d | ||||
---|---|---|---|---|---|---|
OR | 95% CI | OR | 95% CI | OR | 95% CI | |
Logistic (N = 1413): | ||||||
Body mass index (continuous) | 1.00 | 0.99, 1.01 | 1.01 | 0.99, 1.02 | 1.00 | 0.99, 1.01 |
Body mass index (categorical): | ||||||
<25 | Ref. | Ref. | Ref. | |||
25–29.9 | 0.89 | 0.58, 1.36 | 0.89 | 0.58, 1.36 | 0.97 | 0.63, 1.48 |
≥30 | 0.76 | 0.50, 1.14 | 0.74 | 0.48, 1.13 | 0.76 | 0.50, 1.18 |
Hypertension | 1.35 | 0.93, 1.94 | 1.34 | 0.93, 1.94 | 1.30 | 0.89, 1.89 |
Diabetes | 1.32 | 0.94, 1.87 | 1.26 | 0.89, 1.79 | 1.22 | 0.85, 1.75 |
Heart disease | 1.21 | 0.85, 1.73 | 1.21 | 0.84, 1.74 | 1.09 | 0.74, 1.62 |
Stroke | 0.78 | 0.45, 1.35 | 0.77 | 0.44, 1.34 | 0.59 | 0.33, 1.06 |
Number of cardiovascular and/or metabolic conditions: | ||||||
0 | Ref. | Ref. | Ref. | |||
1–2 | 1.75 | 1.14, 2.69 | 1.79 | 1.16, 2.72 | 1.68 | 1.08, 2.59 |
≥3 | 1.55 | 0.88, 2.73 | 1.46 | 0.83, 2.58 | 1.26 | 0.69, 2.32 |
Mixed effects logistic (N = 2082): | ||||||
Body mass index (continuous) | 1.01 | 0.99, 1.02 | 1.00 | 0.99, 1.02 | 1.00 | 0.99, 1.01 |
Body mass index (categorical): | ||||||
<25 | Ref. | Ref. | Ref. | |||
25–29.9 | 1.09 | 0.94, 1.26 | 1.08 | 0.93, 1.27 | 1.09 | 0.93, 1.27 |
≥30 | 1.06 | 0.91, 1.22 | 1.05 | 0.89, 1.23 | 1.01 | 0.86, 1.19 |
Hypertension | 0.87 | 0.77, 0.98 | 0.87 | 0.77, 0.99 | 0.81 | 0.71, 0.92 |
Diabetes | 1.17 | 1.01, 1.35 | 1.20 | 1.04, 1.39 | 1.13 | 0.97, 1.31 |
Heart disease | 1.15 | 1.00, 1.32 | 1.14 | 0.99, 1.32 | 1.03 | 0.89, 1.19 |
Stroke | 0.88 | 0.69, 1.11 | 0.88 | 0.68, 1.13 | 0.79 | 0.62, 1.02 |
Number of cardiovascular and/or metabolic conditions: | ||||||
0 | Ref. | Ref. | Ref. | |||
1–2 | 1.00 | 0.88, 1.15 | 1.04 | 0.90, 1.19 | 0.96 | 0.84, 1.11 |
≥3 | 1.09 | 0.87, 1.37 | 1.11 | 0.87, 1.40 | 0.92 | 0.72, 1.18 |
Predictors: | Logistic Regression Models | Mixed-Effects Logistic Regression Models | ||||
---|---|---|---|---|---|---|
Overall | Surgical | Non-Surgical | Overall | Surgical | Non-Surgical | |
OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
Sex: | ||||||
Male | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. |
Female | 1.44 (1.02, 2.02) | 0.59 (0.32, 1.14) | 1.42 (1.01, 2.00) | 1.34 (1.18, 1.53) | 0.51 (0.38, 0.67) | 1.35 (1.19, 1.54) |
Age (years): | 0.97 (0.95, 0.99) | 0.97 (0.94, 1.00) | 0.97 (0.95, 0.99) | 0.98 (0.97, 0.99) | 0.96 (0.94, 0.97) | 0.98 (0.97, 0.99) |
Ethnicity: | ||||||
Hispanic | -- | 0.35 (0.13, 0.92) | -- | -- | 0.29 (0.18, 0.49) | -- |
Non-Hispanic | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. |
Education: | ||||||
No degree | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. |
GED | 1.25 (0.61, 2.55) | -- | 1.46 (0.71, 3.00) | 1.33 (0.98, 1.79) | -- | 1.43 (1.06, 1.94) |
High school diploma | 1.11 (0.68, 1.83) | -- | 1.23 (0.74, 2.07) | 1.03 (0.84, 1.26) | -- | 1.04 (0.85, 1.28) |
Some college | 1.03 (0.63, 1.70) | -- | 1.14 (0.68, 1.90) | 1.24 (1.01, 1.52) | -- | 1.28 (1.04, 1.57) |
College degree or higher | 1.94 (1.14, 3.27) | -- | 2.14 (1.26, 3.66) | 1.77 (1.44, 2.17) | -- | 1.80 (1.46, 2.22) |
Work status: | ||||||
Working | 1.38 (0.89, 2.11) | -- | 1.35 (0.87, 2.08) | 1.21 (1.04, 1.39) | -- | 1.19 (1.03, 1.38) |
Not working | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. |
Federal health insurance: | ||||||
Yes | 1.14 (0.71, 1.82) | -- | 1.10 (0.68, 1.78) | 1.08 (0.90, 1.28) | -- | 1.11 (0.94, 1.32) |
No | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. |
Smoking status: | ||||||
Never smoker | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. |
Past smoker | -- | 1.04 (0.50, 2.15) | -- | -- | 2.04 (1.50, 2.77) | -- |
Current smoker | -- | 1.88 (0.82, 4.33) | -- | -- | 1.97 (1.28, 3.03) | -- |
Alcohol consumption: | ||||||
Abstinent | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. |
1–3 days per month | 1.79 (1.15, 2.77) | 2.39 (1.07, 5.32) | 1.83 (1.18, 2.85) | 1.10 (0.94, 1.29) | 1.34 (0.91, 1.97) | 1.09 (0.93, 1.29) |
1–2 days per week | 0.99 (0.64, 1.55) | 0.74 (0.29, 1.86) | 0.98 (0.63, 1.54) | 1.14 (0.98, 1.34) | 0.92 (0.63, 1.36) | 1.13 (0.97, 1.32) |
≥3 days per week | 1.32 (0.81, 2.17) | 1.78 (0.70, 4.51) | 1.28 (0.78, 2.11) | 1.19 (0.99, 1.43) | 0.86 (0.54, 1.36) | 1.19 (0.99, 1.44) |
Body mass index: | 1.00 (0.99, 1.01) | -- | -- | 1.00 (0.99, 1.01) | -- | -- |
Self-rated health: | ||||||
Excellent/very good/good | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. |
Fair/poor | 1.47 (1.03, 2.11) | 1.06 (0.56, 2.00) | 1.41 (0.97, 2.04) | 1.53 (1.31, 1.78) | 1.60 (1.12, 2.27) | 1.49 (1.27, 1.74) |
Depressive symptoms: | 1.14 (1.05, 1.23) | 1.15 (0.99, 1.34) | 1.14 (1.04, 1.23) | 1.08 (1.04, 1.11) | 1.12 (1.05, 1.20) | 1.08 (1.04, 1.11) |
Diabetes: | ||||||
Yes | -- | 1.57 (0.84, 2.90) | -- | -- | 1.55 (1.09, 2.19) | -- |
No | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. |
Stroke: | ||||||
Yes | 0.57 (0.32, 1.01) | -- | 0.59 (0.34, 1.05) | 0.83 (0.65, 1.06) | -- | 0.80 (0.63, 1.03) |
No | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. |
Healthcare Delays | |||
---|---|---|---|
Overall | Surgical | Non-Surgical | |
Model A: LASSO | |||
cv-Risk | 0.214 | 0.052 | 0.210 |
Model B: Random Forest | |||
cv-Risk | 0.226 | 0.054 | 0.218 |
Model C: XGBOOST | |||
cv-Risk | 0.278 | 0.057 | 0.279 |
Model D: SVM | |||
cv-Risk | 0.224 | 0.224 | 0.216 |
Super Learner: | |||
cv-Risk | 0.214 | 0.052 | 0.211 |
AUC | 0.600 | 0.920 | 0.655 |
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Beydoun, H.A.; Beydoun, M.A.; Alemu, B.T.; Weiss, J.; Hossain, S.; Gautam, R.S.; Zonderman, A.B. Determinants of COVID-19 Outcome as Predictors of Delayed Healthcare Services among Adults ≥50 Years during the Pandemic: 2006–2020 Health and Retirement Study. Int. J. Environ. Res. Public Health 2022, 19, 12059. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph191912059
Beydoun HA, Beydoun MA, Alemu BT, Weiss J, Hossain S, Gautam RS, Zonderman AB. Determinants of COVID-19 Outcome as Predictors of Delayed Healthcare Services among Adults ≥50 Years during the Pandemic: 2006–2020 Health and Retirement Study. International Journal of Environmental Research and Public Health. 2022; 19(19):12059. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph191912059
Chicago/Turabian StyleBeydoun, Hind A., May A. Beydoun, Brook T. Alemu, Jordan Weiss, Sharmin Hossain, Rana S. Gautam, and Alan B. Zonderman. 2022. "Determinants of COVID-19 Outcome as Predictors of Delayed Healthcare Services among Adults ≥50 Years during the Pandemic: 2006–2020 Health and Retirement Study" International Journal of Environmental Research and Public Health 19, no. 19: 12059. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph191912059