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INTRODUCTION: The relationship between smoking and increased morbidity/mortality with COVID19 is well documented;however, there is little data on the relationship between former smokers (FS) and COVID19. Risk stratification of FS is dependent on years since smoking cessation (SC). We conducted a study to determine, if years since SC influenced COVID19 outcomes. METHOD(S): A retrospective cohort study at a single institution. Inclusion criteria: age >= 18 and admitted to the hospital from Jan 2020 to Nov 2021 for COVID19. Patients were divided into two categories;SC < 15 years, and SC > 15 years. The primary outcome was mortality, with secondary outcomes: requirement of nasal cannula (NC), NIV, and mechanical ventilation (MCV). Odds ratios (OR) were calculated for all outcomes. Post-hoc age-adjusted odds ratios (AOR) for age < 75, were calculated for all outcomes. Mortality data was collected on all patients, and OR were calculated between FS, smokers (CS), and non-smokers (NS). RESULT(S): The total number of patients was 608 with 256 being FS, 308 non-smokers, 30 CS, and 14 unknown smoking status. The mean age of FS was 70. There were 154 males (60%) 102 females (40%). Total FS < 75 was 152. Obesity rates were 64.1% and 67.6%, T2DM (Diabetes Mellitus) rates were 34.3% and 59.5% in SC < 15 and SC > 15, respectively. There was no difference in mortality between the two groups, OR (OR 0.81, CI 0.46-1.40, p=0.45) and AOR (AOR 0.79, 0.37-1.69, p=0.54). There was an increased risk of MCV for the SC < 15 group (OR 2.1, CI 1.02-4.57, p=0.04). AOR did not replicate this trend. There was no difference in patients requiring NC (AOR 1.38, CI 0.70-2.74, p=0.36) or MCV (AOR 1.65, CL 0.69- 3.91, p=0.25) between the two groups. SC < 15 had lower rates of NIV (AOR 0.36, CI 0.15-0.90, p=0.029). There was no difference in mortality between FS and CS (OR 1.69, CI 0.70-4.19, p=0.24). The FS group had higher rates of mortality than NS (OR 1.43, CI 1.00-2.05, p=0.048). CONCLUSION(S): Regardless of the timing of SC, FS have the same mortality and MCV rates with COVID19. High comorbidity burden was noted in both population groups, with the SC > 15 group having higher rates of T2DM. Further studies are needed to determine the full effect of SC on COVID19 outcomes, including effect of pack years.
ABSTRACT
Social activities, economic benefits, and environmental friendly approach are very much essential for a sustainable production system. This is widely observed during the Covid-19 pandemic situation. The demand for essential goods in the business sector is always changing due to different unavoidable situations. The proposed study introduces a variable demand for controlling the fluctuating demand. However, a reworking of produced imperfect products makes the production model more profitable. Partial outsourcing of the good quality products has made the production system more popular and profitable. Separate holding cost for the reworked and produced products are very helpful idea for the proposed model. Moreover, consumption of energy during various purpose are considered. Separate green investment make the model more sustainable and eco-friendly. The main focus of the model is to find the maximum profit through considering optimum value of lot size quantity, average selling price, and green investment. The classical optimization technique is utilized here for optimizing the solution theoretically. The use of concave 3D graphs, different examples, and sensitivity analyses are considered here. Furthermore, managerial insights from this study can be used for industry improvement. © 2022 the Author(s), licensee AIMS Press.
ABSTRACT
Introduction: Length of stay (LoS) in hospitalized COVID-19 patients is highly variable. There is limited data on LoS as a predictive factor of mortality in hospitalized older patients. Methods: A retrospective cohort study at a single 158 bed rural hospital. We identified patients admitted to the hospital from Jan 2020 to Nov 2021 with a Dx of COVID-19 based on PCR testing. Inclusion criteria: age >18 and admission to hospital. Groups were divided into ages 18-64, 65-79, ≥80 and gender (male/female). Primary outcome was age-adjusted all-cause mortality based on LoS. Age-adjusted relative risk (AARR) was used with LoS < 7d as the baseline. Results: Total admissions over the period were 623 patients. 621 met the inclusion criteria with a total of 322 males (M) (51.7%) and 301 females (F) (48.3%). In ages 18-64: 206 patients (115 M, 91 F);with mortality for M 7.8% (9) and F 9.9% (9). In ages 65-79: 274 patients (146 M, 128 F);with mortality for M 24.7% (36) and F 14.1% (19). In ages ≥ 80: 141 patients (61 M, 80 F) (22.9%);with mortality for M 42.6% (26) and F 32.5% (26). Conclusion: We observed a clear difference regarding LoS and mortality for those <80 and ≥80, regardless of gender. Most patients aged ≥80 had shorter hospitalization courses < 14 days with higher mortality rates. However, for patients aged 18-64 and 64-79 hospitalized with COVID for >14 days, LoS was a strong predictive factor for all-cause mortality.
ABSTRACT
Introduction: The COVID-19 pandemic has disproportionately affected older adults and severely strained healthcare resources across the world. There are gaps in the literature with regards to the needs of patients who survive COVID-19. We performed a study to investigate the post-discharge disposition and trends across all adult age groups in our hospital. Methods: A retrospective cohort study at a single 158 bed rural hospital. We identified patients admitted to the hospital from January 2020 to November 2021 with a diagnosis of COVID-19 based on PCR testing. Inclusion criteria: age >18 and admission to hospital. Groups were divided into ages 18-64, 65-79, ≥80. Primary outcome was disposition to home (H), home health (HH), skilled services facility (SSF) (SNF, IPR, LTAC), and death (D). Odds ratios were used with home as the expected outcome. Results: Total admissions over the period were 623 patients. 621 met the inclusion criteria with a total of 320 males (M) (51.7%) and 301 females (F) (48.3%). In ages 18-64 there were 206 patients, ages 65-79, 274 patients and in ages ≥80 141 patients were identified. Age: 18-64 H 153 (55.8%), HH 27 (9.9%) SSF 33 (16%) & D 30 (10.9%). Age 65-79: H 105 (38.3%), HH 53 (19.3%), SSF 57 (16%) & D 54 (19.7%). Age ≥80: H 19 (13.5%), HH 32 (22.7%), SSF 34 (16%) and D 52 (36.9%). Please see table 1 for age-adjusted odds ratio of disposition. Conclusion: With increasing age, we observed higher mortality rates and post-discharge care;with the greatest needs for patients aged ≥80. This increased demand for healthcare resources in older adults creates unique challenges for healthcare providers and policy-makers.