What association do political interventions, environmental and health variables have with the number of Covid-19 cases and deaths? A linear modeling approach
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Abstract
Background and Question
It is unclear which variables contribute to the variance in corona-virus disease (Covid-19) related deaths and Corono-virus2 (Cov2) cases. We wanted to see which contribution public health variables make in addition to health systems, health, and population variables to explain Covid-19 cases and deaths
Method
We modelled the relationship of various predictors (health systems variables, population and population health indicators) together with variables indicating public health measures (school closures, border closures, country lockdown) in 40 European and other countries, using Generalized Linear Models and minimized information criteria to select the best fitting and most parsimonious models.
Results
We fitted two models with log-linearly linked variables on gamma-distributed outome variables (CoV2 cases and Covid-19 related deaths, standardized on population). CoV2-cases were best predicted by number of tests (b = 2*10 −7 , p =.00005), life-expectancy in a country (b = 0.19, p < .000001), and border closure (b = −0.93, p = .001). Population standardized deaths were best predicted by time, the virus had been in the country (b = 0.02, p = .02), life expectancy (b = 0.2, p = .000005), smoking (b = −0.08, p = .00001), and school closures (b = 2.54, p = .0001). Model fit statistics and model adequacy were good (model 1: Chi 2 /DF = 0.43; model 2: Chi 2 /DF = 0.88).
Discussion and Interpretation
Only few variables were good predictors. Of the public health variables only border closure had the potential of preventing cases and none were predictors for preventing deaths. School closures, likely as a proxy for social distancing in severely ill patients, was associated with increased deaths.
Conclusion
The pandemic seems to run its autonomous course and only border closure has the potential to prevent cases. None of them contributes to preventing deaths.
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SciScore for 10.1101/2020.06.18.20135012: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This limitation has to be borne in mind. We gleaned our data from respectable sources, and inspecting our residuals vs. countries plots (Fig. 4 and 5) we can see that large countries with potential testing and reporting problems (Brazil, Russia, China, Iran) are nearly perfectly accounted for by our model, and hence unreliability of data does not seem to be a major problem. An exploratory modeling approach is always open to critique, as it is …
SciScore for 10.1101/2020.06.18.20135012: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This limitation has to be borne in mind. We gleaned our data from respectable sources, and inspecting our residuals vs. countries plots (Fig. 4 and 5) we can see that large countries with potential testing and reporting problems (Brazil, Russia, China, Iran) are nearly perfectly accounted for by our model, and hence unreliability of data does not seem to be a major problem. An exploratory modeling approach is always open to critique, as it is an observational study trying to infer potentially causal factors from a cross-sectional piece of data. This has to be borne in mind. We decided to build models that are theoretically guided and conceptually informed [15], starting with health systems, structural and population indicator variables and entering political public health variables in a last step, then adapt the model to find the best model fit. We followed a predefined, published protocol which guarded us against aimless fishing, and strove for parsimonious models that could explain the data with a minimum of predictors and good model fit. We avoided computer guided step-down and step-up procedures as they are inefficient or prone to overfitting. [15] Thus, we are quite confident that we did not overlook an important contribution of political actions to an explanatory model: they are not visible in our data except for those we report. More fine-grained, or country specific analyses might eventually unravel some contribution of such procedures, but on a large scale population...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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SciScore for 10.1101/2020.06.18.20135012: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources We thank the students of the master class “Quantitative Research Methods” of the MSc course “Health Promotion” who gathered the data for this study and participated in discussing and initiating this project. Promotion”suggested: NoneResults from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
- This limitation has to be borne in mind.
Results from OddPub: Thank you for sharing your data.
About SciScore
SciScore is an automated tool that is designed to assist expert …
SciScore for 10.1101/2020.06.18.20135012: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources We thank the students of the master class “Quantitative Research Methods” of the MSc course “Health Promotion” who gathered the data for this study and participated in discussing and initiating this project. Promotion”suggested: NoneResults from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
- This limitation has to be borne in mind.
Results from OddPub: Thank you for sharing your data.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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