Risk Factors for Concussion in Under 18, Under 22 and Professional Men's Rugby Union: A Video Analysis of 14,809 Tackles.

OBJECTIVE: This study aimed to identify the risk factors for tackle-related concussion observed in matches involving under (U) 18, U 22 and professional men's Rugby Union players through video analysis. STUDY DESIGN: Descriptive epidemiology study. METHODS: Twenty Rugby Union matches each for high school (U18), university/college (U22) and professional (Elite) were randomly selected from 202 matches in the 2018/2019 season. Both one-on-one and tackles involving multiple tacklers were analyzed for the 60 matches. The 28 categorical and continuous variables (e.g., tackle characteristics and duration before the tackle) were applied as risk factors to a least absolute shrinkage and selection operator (Lasso) regression analysis. To identify high-risk situations, a simulation model with coefficients obtained from the Lasso regression was used. Statistical analysis was conducted according to tackle direction. RESULTS: A total of 14,809 tackles and 41 concussions involving 1800 players were included in the analyses. The incidence rate of concussions (injuries/1000 tackles) was greater in Elite players (4.0) compared with U18 (1.9) and U22 (2.4) players. The factors most highly associated with concussions were head-in-front tackles (where the tackler's head is placed forward, impeding a ball carrier's forward movements, 11.26/1000 tackles), and were more often observed among U18 players. A simulation model predicted that the highest risk tackle situation in Elite players was a head-in-front, side-on tackle below the hip of the ball carrier (predicted incidence rate 18.07/1000 tackles). CONCLUSION: The risk factors associated with concussion need to be assessed cautiously. Avoiding head-in-front, side-on tackles to the lower extremities of a ball carrier should be considered to reduce injury risks.

Association between number of medications and hip fractures in Japanese elderly using conditional logistic LASSO regression.

To examine the association between hip fracture and associated factors, including polypharmacy, and develop an optimal predictive model, we conducted a population-based matched case-control study using the health insurance claims data on hip fracture among Japanese patients. We included 34,717 hospitalized Japanese patients aged ≥ 65 years with hip fracture and 34,717 age- and sex- matched controls who were matched 1:1. This study included 69,434 participants. Overall, 16 variable comorbidities and 60 variable concomitant medications were used as explanatory variables. The participants were added to early elderly and late elderly categories for further analysis. The odds ratio of hip fracture increased with the number of medications only in the early elderly. AUC was highest for early elderly (AUC, 0.74, 95% CI 0.72-0.76). Use of anti-Parkinson's drugs had the largest coefficient and was the most influential variable in many categories. This study confirmed the association between risk factors, including polypharmacy and hip fracture. The risk of hip fracture increased with an increase in medication number taken by the early elderly and showed good predictive accuracy, whereas there was no such association in the late elderly. Therefore, the early elderly in Japan should be an active target population for hip fracture prevention.

Case fatality rate considering the lag time from the onset of COVID-19 infection to related death from 2020 to 2022 in Japan.

Importance: On an ecological scale, the lag time between coronavirus disease 2019 (COVID-19) infection and related fatality has varied between epidemic waves and prefectures in Japan. The variability in lag time across areas of Japan during the seven distinct waves can help derive a more appropriate estimation of the weekly confirmed case fatality rate (CFR) of COVID-19. Objective: To estimate the 7-day moving average CFR across area block levels in Japan from February 2020 to July 2022 using the lag time between COVID-19 infection and related fatality. Main outcomes and measures: The 7-day moving average CFR of COVID-19 for area blocks in Japan considering the lag time between infection and death (total and subgroup analysis of elderly). Results: Lag time was found to vary substantially among prefectures in Japan from the first wave to the seventh wave of the COVID-19 epidemic. The estimated 7-day moving average CFR based on the lag time reflects the Japanese COVID-19 pandemic and related policy interventions (e.g. vaccination of elderly people) rather than other standard CFR estimations. Conclusions and relevance: The variation in estimated lag time across prefectures in Japan for different epidemic waves indicates that it is inadequate to use the clinical results of the period from the start of infection to death for evaluation of the ecological scale of the CFR. Moreover, the lag time between infection and related fatality was found to be either shorter or longer than the clinically reported period. This revealed that preliminary reports of CFR may be overestimated or underestimated, even if they consider the lag based on clinical reports.

Novel indicator for the spread of new coronavirus disease 2019 and its association with human mobility in Japan.

The Japanese government adopted policies to control human mobility in 2020 to prevent the spread of severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19). The present study examined the impact of human mobility on COVID-19 cases at the prefectural level in Japan by devising an indicator to have a relationship between the number of infected people and on human mobility. We calculated origin-destination travel mobility within prefectures in Japan from March 1st to December 31st, 2020, using mobile phone data. A cross-correlation function (CCF) was used to examine the relationship between human mobility and a COVID-19 infection acceleration indicator (IAI), which represents the rate of change in the speed of COVID-19 infection. The CCF of intraprefectural human mobility and the IAI in Tokyo showed a maximum value of 0.440 at lag day 12, and the IAI could be used as an indicator to predict COVID-19 cases. Therefore, the IAI and human mobility during the COVID-19 pandemic were useful for predicting infection status. The number of COVID-19 cases was associated with human mobility at the prefectural level in Japan in 2020. Controlling human mobility could help control infectious diseases in a pandemic, especially prior to starting vaccination.

The impact of declaring the state of emergency on human mobility during COVID-19 pandemic in Japan.

Background/objectives: Japan has responded to the spread of COVID-19 through declaration of a state of emergency to regulate human mobility. Although the declaration was enforced by the government for prefectures, there is limited evidence as to whether the public complied with requests for voluntary stay at home. In this study, we evaluated the impact of declaring a state of emergency on human mobility during the COVID-19 pandemic in Japan. Methods: We utilized daily human mobility data for 47 prefectures in Japan. Data were collected via mobile phone from February 1, 2020 to April 30, 2021. Difference-in-difference analysis was utilized to estimate the effects of the declaration of a state of emergency on prefectures in the Tokyo Metropolitan Area (Tokyo, Kanagawa, Saitama, and Chiba) in comparison to other prefectures where the state of emergency was first lifted (Osaka, Hyogo, Fukuoka, and Aichi). Results: Human mobility was suppressed during the second state of emergency, from January 8 to March 21, 2021. However, the impact was weaker for the second state of emergency compared to the first. Conclusion: In Japan, government requests for stay at home, such as the declaration of a state of emergency, were temporarily able to control human mobility. However, the second state of emergency was not as effective as the first. If additional need to regulate human mobility arises, self-restraint with stronger enforcement should be considered.

Changes in social environment due to the state of emergency and Go To campaign during the COVID-19 pandemic in Japan: An ecological study.

BACKGROUND: During the coronavirus disease 2019 (COVID-19) pandemic in Japan, the state of emergency, as a public health measure to control the spread of COVID-19, and the Go To campaign, which included the Go To Travel and Go To Eat campaigns and was purposed to stimulate economic activities, were implemented. This study investigated the impact of these government policies on COVID-19 spread. METHODS: This ecological study included all 47 prefectures in Japan as samples between February 3 and December 27, 2020. We used COVID-19 cases and mobility as variables. Additionally, places where social contacts could accrue, defined as restaurants, companies, transportation, and tourist spots; mean temperature and humidity; the number of inhabitants in their twenties to fifties; and the number of COVID-19 cases in the previous period, which were factors or covariates in the graphical modeling analysis, were divided into five periods according to the timing of the implementation of the state of emergency and Go To campaign. RESULTS: Graphical changes occurred throughout all five periods of COVID-19. During the state of emergency (period 2), a correlation between COVID-19 cases and those before the state of emergency (period 1) was observed, although this correlation was not significant in the period after the state of emergency was lifted (period 3). During the implementation of Go To Travel and the Go To Eat campaigns (period 5), the number of places where social contacts could accrue was correlated with COVID-19 cases, with complex associations and mobility. CONCLUSIONS: This study confirms that the state of emergency affected the control of COVID-19 spread and that the Go To campaign led to increased COVID-19 cases due to increased mobility by changing behavior in the social environment where social contacts potentially accrue.

Proportion of treatment effect mediated by surrogate endpoints.

One of the central aims in randomized clinical trials is to find well-validated surrogate endpoints to reduce the sample size and/or duration of trials. Clinical researchers and practitioners have proposed various surrogacy measures for assessing candidate surrogate endpoints. However, most existing surrogacy measures have the following shortcomings: (i) they often fall outside the range [0,1], (ii) they are imprecisely estimated, and (iii) they ignore the interaction associations between a treatment and candidate surrogate endpoints in the evaluation of the surrogacy level. To overcome these difficulties, we propose a new surrogacy measure, the proportion of treatment effect mediated by candidate surrogate endpoints (PMS), based on the decomposition of the treatment effect into direct, indirect, and interaction associations mediated by candidate surrogate endpoints. In addition, we validate the advantages of PMS through Monte Carlo simulations and the application to empirical data from ORIENT (the Olmesartan Reducing Incidence of Endstage Renal Disease in Diabetic Nephropathy Trial).

The Development of Statistical Models for Predicting Surgical Site Infections in Japan: Toward a Statistical Model-Based Standardized Infection Ratio.

OBJECTIVE: To develop and internally validate a surgical site infection (SSI) prediction model for Japan. DESIGN: Retrospective observational cohort study. METHODS: We analyzed surveillance data submitted to the Japan Nosocomial Infections Surveillance system for patients who had undergone target surgical procedures from January 1, 2010, through December 31, 2012. Logistic regression analyses were used to develop statistical models for predicting SSIs. An SSI prediction model was constructed for each of the procedure categories by statistically selecting the appropriate risk factors from among the collected surveillance data and determining their optimal categorization. Standard bootstrapping techniques were applied to assess potential overfitting. The C-index was used to compare the predictive performances of the new statistical models with those of models based on conventional risk index variables. RESULTS: The study sample comprised 349,987 cases from 428 participant hospitals throughout Japan, and the overall SSI incidence was 7.0%. The C-indices of the new statistical models were significantly higher than those of the conventional risk index models in 21 (67.7%) of the 31 procedure categories (P<.05). No significant overfitting was detected. CONCLUSIONS: Japan-specific SSI prediction models were shown to generally have higher accuracy than conventional risk index models. These new models may have applications in assessing hospital performance and identifying high-risk patients in specific procedure categories.

A new proportion measure of the treatment effect captured by candidate surrogate endpoints.

The use of surrogate endpoints is expected to play an important role in the development of new drugs, as they can be used to reduce the sample size and/or duration of randomized clinical trials. Biostatistical researchers and practitioners have proposed various surrogacy measures; however, (i) most of these surrogacy measures often fall outside the range [0,1] without any assumptions, (ii) these surrogacy measures do not provide a cut-off value for judging a surrogacy level of candidate surrogate endpoints, and (iii) most surrogacy measures are highly variable; thus, the confidence intervals are often unacceptably wide. In order to solve problems (i) and (ii), we propose a new surrogacy measure, a proportion of the treatment effect captured by candidate surrogate endpoints (PCS), on the basis of the decomposition of the treatment effect into parts captured and non-captured by the candidate surrogate endpoints. In order to solve problem (iii), we propose an estimation method based on the half-range mode method with the bootstrap distribution of the estimated surrogacy measures. Finally, through numerical experiments and two empirical examples, we show that the PCS with the proposed estimation method overcomes these difficulties. The results of this paper contribute to the reliable evaluation of how much of the treatment effect is captured by candidate surrogate endpoints.

Sharp bounds on causal effects using a surrogate endpoint.

This paper considers a problem of evaluating the causal effect of a treatment X on a true endpoint Y using a surrogate endpoint S, in the presence of unmeasured confounders between S and Y. Such confounders render the causal effect of X on Y unidentifiable from the causal effect of X on S and the joint probability of S and Y. To evaluate the causal effect of X on Y in such a situation, this paper derives closed-form formulas for the sharp bounds on the causal effect of X on Y based on both the causal effect of X on S and the joint probability of S and Y under various assumptions. In addition, we show that it is not always necessary to observe Y to test the null causal effect of X on Y under the monotonicity assumption between X and S. These bounds enable clinical practitioners and researchers to assess the causal effect of a treatment on a true endpoint using a surrogate endpoint with minimum computational effort.

Toward the rational use of standardized infection ratios to benchmark surgical site infections.

BACKGROUND: The National Healthcare Safety Network transitioned from surgical site infection (SSI) rates to the standardized infection ratio (SIR) calculated by statistical models that included perioperative factors (surgical approach and surgery duration). Rationally, however, only patient-related variables should be included in the SIR model. METHODS: Logistic regression was performed to predict expected SSI rate in 2 models that included or excluded perioperative factors. Observed and expected SSI rates were used to calculate the SIR for each participating hospital. The difference of SIR in each model was then evaluated. RESULTS: Surveillance data were collected from a total of 1,530 colon surgery patients and 185 SSIs. C-index in the model with perioperative factors was statistically greater than that in the model including patient-related factors only (0.701 vs 0.621, respectively, P < .001). At one particular hospital, for which the percentage of open surgery was lowest (33.2%), SIR estimates changed considerably from 0.92 (95% confidence interval: 0.84-1.00) for the model with perioperative variables to 0.79 (0.75-0.85) for the model without perioperative variables. In another hospital with a high percentage of open surgery (88.6%), the estimate of SIR was decreased by 12.1% in the model without perioperative variables. CONCLUSION: Because surgical approach and duration of surgery each serve as a partial proxy of the operative process or the competence of surgical teams, these factors should not be considered predictive variables.

Formulating tightest bounds on causal effects in studies with unmeasured confounders.

This paper considers the problem of evaluating the causal effect of an exposure on an outcome in observational studies with both measured and unmeasured confounders between the exposure and the outcome. Under such a situation, MacLehose et al. (Epidemiology 2005; 16:548-555) applied linear programming optimization software to find the minimum and maximum possible values of the causal effect for specific numerical data. In this paper, we apply the symbolic Balke-Pearl linear programming method (Probabilistic counterfactuals: semantics, computation, and applications. Ph.D. Thesis, UCLA Cognitive Systems Laboratory, 1995; J. Amer. Statist. Assoc. 1997; 92:1172-1176) to derive the simple closed-form expressions for the lower and upper bounds on causal effects under various assumptions of monotonicity. These universal bounds enable epidemiologists and medical researchers to assess causal effects from observed data with minimum computational effort, and they further shed light on the accuracy of the assessment.

Bounds on direct effects in the presence of confounded intermediate variables.

This article considers the problem of estimating the average controlled direct effect (ACDE) of a treatment on an outcome, in the presence of unmeasured confounders between an intermediate variable and the outcome. Such confounders render the direct effect unidentifiable even in cases where the total effect is unconfounded (hence identifiable). Kaufman et al. (2005, Statistics in Medicine 24, 1683-1702) applied a linear programming software to find the minimum and maximum possible values of the ACDE for specific numerical data. In this article, we apply the symbolic Balke-Pearl (1997, Journal of the American Statistical Association 92, 1171-1176) linear programming method to derive closed-form formulas for the upper and lower bounds on the ACDE under various assumptions of monotonicity. These universal bounds enable clinical experimenters to assess the direct effect of treatment from observed data with minimum computational effort, and they further shed light on the sign of the direct effect and the accuracy of the assessments.

Non-parametric bounds on treatment effects with non-compliance by covariate adjustment.

Consider a clinical trial where subjects are randomized to two treatment arms but compliance to the assignment is not perfect. Concerning this problem, this paper derives non-parametric bounds on treatment effects by making use of the observed covariates information. The new bounds are narrower and more informative than the existing ones. In addition, a new non-parametric point estimation approach is proposed based on stratified analysis. Furthermore, to examine the accuracy of estimating the proposed bounds, we provide variance estimators for the proposed approach. The results of this paper can yield credible information on treatment effects, which will be useful for medical research and public health policy analysis.

Variance estimators for three "probabilities of causation".

This article introduces the definitions of three "probabilities of causation" suggested by Pearl (1999), which are used to evaluate the causal effect of an exposure on a disease in epidemiological studies. Pearl (1999) and Tian and Pearl (2000a, 2000b) provided identification formulas for three "probabilities of causation" from statistical data under some assumptions. In order to examine the estimation accuracy problem, this article derives variance estimators for three "probabilities of causation" correspondent to each case in Pearl (1999) and at the same time clarify their properties. In addition, we conduct simulation experiments and show that the proposed method can approximate sufficiently to the variance of "probabilities of causation." The results of this article provide a complete framework for using "probabilities of causation" effectively in order to analyze responsibility and susceptibility in epidemiological studies.