@article{HippBuenning2020,
  author    = {Hipp, Lena and B{\"u}nning, Mareike},
  title     = {Parenthood as a driver of increased genderinequality during COVID-19?},
  series = {European societies},
  volume    = {23},
  journal   = {European societies},
  publisher = {Taylor \& Francis Group},
  address   = {London},
  issn      = {1461-6696},
  doi       = {10.1080/14616696.2020.1833229},
  pages     = {S658 -- S673},
  year      = {2020},
  abstract  = {Drawing on three waves of survey data from a non-probability sample from Germany, this paper examines two opposing expectations about the pandemic's impacts on gender equality: The optimistic view suggests that gender equality has increased, as essential workers in Germany have been predominantly female and as fathers have had more time for childcare. The pessimistic view posits that lockdowns have also negatively affected women's jobs and that mothers had to shoulder the additional care responsibilities. Overall, our exploratory analyses provide more evidence supporting the latter view. Parents were more likely than non-parents to work fewer hours during the pandemic than before, and mothers were more likely than fathers to work fewer hours once lockdowns were lifted. Moreover, even though parents tended to divide childcare more evenly, at least temporarily, mothers still shouldered more childcare work than fathers. The division of housework remained largely unchanged. It is therefore unsurprising that women, in particular mothers, reported lower satisfaction during the observation period. Essential workers experienced fewer changes in their working lives than respondents in other occupations.},
  language  = {en}
}
@article{HippBuenningMunnesetal.2020,
  author    = {Hipp, Lena and B{\"u}nning, Mareike and Munnes, Stefan and Sauermann, Armin},
  title     = {Problems and pitfalls of retrospective survey questions in COVID-19 studies},
  series = {Survey research methods},
  volume    = {14},
  journal   = {Survey research methods},
  number    = {2},
  publisher = {European Survey Research Association},
  address   = {Konstanz},
  issn      = {1864-3361},
  doi       = {10.18148/srm/2020.v14i2.7741},
  pages     = {109 -- 113},
  year      = {2020},
  abstract  = {This paper examines and discusses the biases and pitfalls of retrospective survey questions that are currently being used in many medical, epidemiological, and sociological studies on the COVID-19 pandemic. By analyzing the consistency of answers to retrospective questions provided by respondents who participated in the first two waves of a survey on the social consequences of the COVID-19 pandemic, we illustrate the insights generated by a large body of survey research on the use of retrospective questions and recall accuracy.},
  language  = {en}
}
@article{Kohler2020,
  author    = {Kohler, Ulrich},
  title     = {Survey Research Methods during the COVID-19 Crisis},
  series = {Survey research methods},
  volume    = {14},
  journal   = {Survey research methods},
  number    = {2},
  publisher = {European Survey Research Association},
  address   = {Konstanz},
  issn      = {1864-3361},
  doi       = {10.18148/srm/2020.v14i2.7769},
  pages     = {93 -- 94},
  year      = {2020},
  language  = {en}
}
@article{KrausClaussBreieretal.2020,
  author    = {Kraus, Sascha and Clauss, Thomas and Breier, Matthias and Gast, Johanna and Zardini, Alessandro and Tiberius, Victor},
  title     = {The economics of COVID-19},
  series = {International journal of entrepreneurial behaviour \& research},
  volume    = {26},
  journal   = {International journal of entrepreneurial behaviour \& research},
  number    = {5},
  publisher = {Emerald},
  address   = {Bingley},
  issn      = {1355-2554},
  doi       = {10.1108/IJEBR-04-2020-0214},
  pages     = {1067 -- 1092},
  year      = {2020},
  abstract  = {Purpose Within a very short period of time, the worldwide pandemic triggered by the novel coronavirus has not only claimed numerous lives but also caused severe limitations to daily private as well as business life. Just about every company has been affected in one way or another. This first empirical study on the effects of the COVID-19 crisis on family firms allows initial conclusions to be drawn about family firm crisis management. Design/methodology/approach Exploratory qualitative research design based on 27 semi-structured interviews with key informants of family firms of all sizes in five Western European countries that are in different stages of the crisis. Findings The COVID-19 crisis represents a new type and quality of challenge for companies. These companies are applying measures that can be assigned to three different strategies to adapt to the crisis in the short term and emerge from it stronger in the long run. Our findings show how companies in all industries and of all sizes adapt their business models to changing environmental conditions within a short period of time. Finally, the findings also show that the crisis is bringing about a significant yet unintended cultural change. On the one hand, a stronger solidarity and cohesion within the company was observed, while on the other hand, the crisis has led to a tentative digitalization. Originality/value To the knowledge of the authors, this is the first empirical study in the management realm on the impacts of COVID-19 on (family) firms. It provides cross-national evidence of family firms' current reactions to the crisis.},
  language  = {en}
}
@article{PostClassKohler2020,
  author    = {Post, Julia C. and Class, Fabian and Kohler, Ulrich},
  title     = {Unit nonresponse biases in estimates of SARS-CoV-2 prevalence},
  series = {Survey research methods},
  volume    = {14},
  journal   = {Survey research methods},
  number    = {2},
  publisher = {European Survey Research Association},
  address   = {Duisburg},
  issn      = {1864-3361},
  doi       = {10.18148/srm/2020.v14i2.7755},
  pages     = {115 -- 121},
  year      = {2020},
  abstract  = {Since COVID-19 became a pandemic, many studies are being conducted to get a better understanding of the disease itself and its spread. One crucial indicator is the prevalence of SARS-CoV-2 infections. Since this measure is an important foundation for political decisions, its estimate must be reliable and unbiased. This paper presents reasons for biases in prevalence estimates due to unit nonresponse in typical studies. Since it is difficult to avoid bias in situations with mostly unknown nonresponse mechanisms, we propose the maximum amount of bias as one measure to assess the uncertainty due to nonresponse. An interactive web application is presented that calculates the limits of such a conservative unit nonresponse confidence interval (CUNCI).},
  language  = {en}
}
@article{UrbachAwiszusLeissetal.2020,
  author    = {Urbach, Dietmar and Awiszus, Friedemann and Leiß, Sven and Venton, Tamsin and De Specht, Alexander Vincent and Apfelbacher, Christian},
  title     = {Associations of medications with lower odds of typical COVID-19 symptoms},
  series = {JMIR public health and surveillance},
  volume    = {6},
  journal   = {JMIR public health and surveillance},
  number    = {4},
  publisher = {JMIR Publications},
  address   = {Toronto},
  issn      = {2369-2960},
  doi       = {10.2196/22521},
  pages     = {10},
  year      = {2020},
  abstract  = {Background: As the COVID-19 pandemic continues to spread across the globe, the search for an effective medication to treat the symptoms of COVID-19 continues as well. It would be desirable to identify a medication that is already in use for another condition and whose side effect profile and safety data are already known and approved. Objective: The objective of this study was to evaluate the effect of different medications on typical COVID-19 symptoms by using data from an online surveillance survey. Methods: Between early April and late-July 2020, a total of 3654 individuals in Lower Saxony, Germany, participated in an online symptom-tracking survey conducted through the app covid-nein-danke.de. The questionnaire comprised items on typical COVID-19 symptoms, age range, gender, employment in patient-facing healthcare, housing status, postal code, previous illnesses, permanent medication, vaccination status, results of reverse transcription polymerase chain reaction (RT-PCR) and antibody tests for COVID-19 diagnosis, and consequent COVID-19 treatment if applicable. Odds ratio estimates with corresponding 95\% CIs were computed for each medication and symptom by using logistic regression models. Results: Data analysis suggested a statistically significant inverse relationship between typical COVID-19 symptoms self-reported by the participants and self-reported statin therapy and, to a lesser extent, antihypertensive therapy. When COVID-19 diagnosis was based on restrictive symptom criteria (ie, presence of 4 out of 7 symptoms) or a positive RT-PCR test, a statistically significant association was found solely for statins (odds ratio 0.28, 95\% CI 0.1-0.78). Conclusions: Individuals taking statin medication are more likely to have asymptomatic COVID-19, in which case they may be at an increased risk of transmitting the disease unknowingly. We suggest that the results of this study be incorporated into symptoms-based surveillance and decision-making protocols in regard to COVID-19 management. Whether statin therapy has a beneficial effect in combating COVID-19 cannot be deduced based on our findings and should be investigated by further study.},
  language  = {en}
}
@article{VaidSomaniRussaketal.2020,
  author    = {Vaid, Akhil and Somani, Sulaiman and Russak, Adam J. and De Freitas, Jessica K. and Chaudhry, Fayzan F. and Paranjpe, Ishan and Johnson, Kipp W. and Lee, Samuel J. and Miotto, Riccardo and Richter, Felix and Zhao, Shan and Beckmann, Noam D. and Naik, Nidhi and Kia, Arash and Timsina, Prem and Lala, Anuradha and Paranjpe, Manish and Golden, Eddye and Danieletto, Matteo and Singh, Manbir and Meyer, Dara and O'Reilly, Paul F. and Huckins, Laura and Kovatch, Patricia and Finkelstein, Joseph and Freeman, Robert M. and Argulian, Edgar and Kasarskis, Andrew and Percha, Bethany and Aberg, Judith A. and Bagiella, Emilia and Horowitz, Carol R. and Murphy, Barbara and Nestler, Eric J. and Schadt, Eric E. and Cho, Judy H. and Cordon-Cardo, Carlos and Fuster, Valentin and Charney, Dennis S. and Reich, David L. and B{\"o}ttinger, Erwin and Levin, Matthew A. and Narula, Jagat and Fayad, Zahi A. and Just, Allan C. and Charney, Alexander W. and Nadkarni, Girish N. and Glicksberg, Benjamin S.},
  title     = {Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation},
  series = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR},
  volume    = {22},
  journal   = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR},
  number    = {11},
  publisher = {Healthcare World},
  address   = {Richmond, Va.},
  issn      = {1439-4456},
  doi       = {10.2196/24018},
  pages     = {19},
  year      = {2020},
  abstract  = {Background: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. Objective: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. Methods: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Results: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. Conclusions: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.},
  language  = {en}
}