Bias Adjustment Research Articles

A non-stationary bias adjustment method for improving the inter-annual variability and persistence of projected precipitation

Hydrological studies depend heavily on environmental variables, such as precipitation and resulting runoff, which exhibit highly seasonal and intermittent behaviour in semiarid basins. In these basins, the use of traditional methods to adjust biases in time series projections can lead to inaccurate results regarding the impacts of climate change. This study introduces a non-stationary bias adjustment methodology (NS) specifically designed for environmental variables characterized by sporadic events and substantial intensity variability, such as precipitation. The methodology involves establishing a probability threshold to adapt the occurrence of precipitation events and utilizes a quantile mapping method based on a non-stationary theoretical and parametric distribution to adjust biases associated with precipitation intensity. The NS method is applied to daily precipitation projections from seven regional climatic models under the RCP 8.5 scenario spanning 2006–2100, alongside historical concurrent data from 1970 to 2005. The present method is compared to the widely used quantile delta mapping approach (QDM), revealing significant differences in performance related to the distribution of precipitation events throughout the year and the behaviour of mean and extreme intensity values. Both approaches show reliable performance, with root mean square errors between the empirical distribution functions of the corrected hindcast time series and the observations being lower than or close to 1 mm for the percentiles smaller than 50th. The error increases with the percentiles, particularly at stations located at higher altitudes. In these locations, QDM doubles or triples the error obtained with the non-stationary approach for percentiles higher than 75th, reaching up to 34 mm. The proposed methodology demonstrates promising potential in reducing uncertainties associated with systematic errors in inter-annual precipitation variability. It is a first step to jointly apply a similar approach to all involved driving variables. Such methods are key to assessing hydrological responses and associated impacts in semi-arid mountainous basins all around the Globe.

Bias analysis on socioeconomic status and lung cancer in a pooled international case-control study

Abstract Background Low socioeconomic status (SES) groups showed increased lung cancer risks even after adjustment for smoking habits and other exposures to lung carcinogens. Although several biases were often indicated and discussed, only few studies quantified the impact of potential biases. Methods We conducted a bias analysis on the association of lung cancer and SES using data from the SYNERGY project, including 12 case-control studies with 18 study centres from Europe and Canada (16,550 cases, 20,147 controls). SES in quartiles was derived from the International Socio-Economic Index of occupational status (ISEI). Odds ratios (OR) with 95% confidence intervals (CI) were estimated by logistic regression adjusting for age, study centre, and smoking. In addition, we estimated natural direct SES effects and natural indirect smoking effects by inverse odds ratio weighting. In a multiple quantitative bias analysis, we considered impacts of misclassification of smoking status, selection bias, and unmeasured mediator-outcome confounding by a protective genetic factor, and created 95% simulation intervals (SI) by bootstrap. All analyses were stratified by sex. Results Adjustment for smoking as well as natural effects estimation showed that nearly half of lung cancer risks of lower SES groups in men and up to one third in women were attributable to smoking. Consideration of all types of bias reduced lung-cancer risks in the fully adjusted logistic regression models, with the strongest impact by selection bias: For the 4th versus 1st (highest) ISEI quartile OR decreased from 1.83 (1.69-1.98 CI) to 1.50 (1.30-1.73 SI) in men, and OR 1.48 (1.27-1.72 CI) to 1.20 (0.96-1.53 SI) in women. Conclusions Smoking is the main target for prevention of lung cancer, along with occupational and environmental exposures, in particular in lower SES groups. This finding remains, though our analysis revealed reduced lung-cancer risks of lower SES groups after multiple bias adjustment. Key messages • Impact of potential biases was quantified in the association of SES and lung cancer. • Lung cancer risks were partially attributable to smoking and multiple biases.

Socioeconomic status, smoking, and lung cancer: mediation and bias analysis in the SYNERGY study.

Increased lung-cancer risks for low socioeconomic status (SES) groups are only partially attributable to smoking habits. Little effort has been made to investigate the persistent risks related to low SES by quantification of potential biases. Based on 12 case-control studies, including 18 centers of the international SYNERGY project (16,550 cases, 20,147 controls), we estimated controlled direct effects (CDE) of SES on lung cancer via multiple logistic regression, adjusted for age, study center, and smoking habits, and stratified by sex. We conducted mediation analysis by inverse odds ratio weighting to estimate natural direct effects (NDE) and natural indirect effects via smoking habits. We considered misclassification of smoking status, selection bias, and unmeasured mediator-outcome confounding by genetic risk, both separately as well as by multiple quantitative bias analysis, using bootstrap to create 95% simulation intervals (SI). Mediation analysis of lung-cancer risks for SES estimated mean proportions of 43% in men and 33% in women attributable to smoking. Bias analyses decreased direct effects of SES on lung cancer, with selection bias showing the strongest reduction in lung-cancer risk in the multiple bias analysis. Lung-cancer risks remained increased for lower SES groups, with higher risks in men [4th versus 1st (highest) SES quartile: CDE 1.50 (SI 1.32-1.69)] than women [CDE 1.20 (SI 1.01-1.45)]. NDE were similar to CDE, particularly in men. Bias adjustment lowered direct lung-cancer risk estimates of lower SES groups. However, risks for low SES remained elevated, likely attributable to occupational hazards or other environmental exposures.

A Forensic Investigation of Climate Model Biases in Teleconnections: The Case of the Relationship Between ENSO and the Northern Stratospheric Polar Vortex

AbstractTeleconnections are crucial in shaping climate variability and regional climate change. The fidelity of teleconnections in climate models is important for reliable climate projections. As the observed sample size is limited, scientific judgment is required when models disagree with observed teleconnections. We illustrate this using the example of the relationship between El Niño‐Southern Oscillation (ENSO) and the northern stratospheric polar vortex (SPV), where the MIROC6 large ensemble exhibits an ENSO‐SPV correlation opposite in sign to observations. Yet the model well captures the upward planetary‐wave propagation pathway through which ENSO is known to affect the SPV. We show that the discrepancy arises from the model showing an additional linkage related to horizontal stratospheric wave propagation. Observations do not provide strong statistical evidence for or against the existence of this linkage. Thus, depending on the research purpose, a choice has to be made in how to use the model simulations. Under the assumption that the additional linkage is spurious, a physically‐based bias adjustment is applied to the SPV, which effectively aligns the modeled ENSO‐SPV relationship with the observations, and thereby removes the model‐observations discrepancy in the surface air temperature response. However, if one believed that the additional linkage was genuine and was undersampled in the observations, a different approach could be taken. Our study emphasizes that caution is needed when concluding that a model is not suitable for studying teleconnections. We propose a forensic approach and argue that it helps to better understand model performance and utilize climate model data more effectively.

Survival Analysis of Conversion Surgery in Borderline Resectable and Locally Advanced Unresectable Pancreatic Ductal Adenocarcinoma Addressing Selection and Immortal Time Bias: A Retrospective Single-Center Study.

The purpose of this study was to provide a detailed evaluation of the oncological advantages of surgery following neoadjuvant chemotherapy (NAC) for patients with borderline resectable (BR) or unresectable (UR) pancreatic ductal adenocarcinoma (PDAC), with a focus on minimizing biases. Recently, NAC has become the standard care for BR or UR locally advanced (UR-LA) PDAC, however, many studies have assessed survival benefits and favorable variables without consideration for biases, particularly immortal time bias. This study included patients diagnosed with BR or UR-LA PDAC at Juntendo University Hospital from 2019 to 2022. To mitigate bias, we applied methods such as propensity score matching (PSM), time-dependent covariate Cox proportional hazard regression analysis (TDC), landmark analysis, and multivariable Cox proportional hazards regression model. The study analyzed 124 patients, dividing them into a surgery group (n = 57) and a chemotherapy-only group (n = 67). After PSM, there were 21 matched pairs. Survival analysis using TDC analysis showed that the surgery group had significantly better overall survival compared with the chemotherapy-only group in both the entire cohort and the matched pairs. Cox regression analysis of the entire cohort also revealed a similar superiority of surgery, while the landmark analysis showed varying results depending on the landmark setting. After careful adjustment for selection and immortal time biases, surgery following NAC appears to significantly extend survival in patients with BR or UR PDAC.

How does survey timing influence apparent wasting trends? A case study from Senegal

BackgroundChild wasting is known to exhibit seasonal patterns but few studies have examined how the seasonality of wasting affects tracking of wasting trends over multi-year periods. ObjectiveWe explored the seasonality of wasting in Senegal relative to multi-year changes and examined implications for tracking. We tested whether month fixed-effects reduced bias in estimating longer-term wasting trends given variation in survey timing. MethodsThe average prevalence of child wasting (weight-for-height z-score < -2) and 95% confidence intervals were calculated by month and year from the continuous Demographic and Health Surveys (DHS) from 2012-2019. Peak and low wasting season estimates were defined as the four highest and the four lowest months of average wasting prevalence. Month-adjusted annual wasting estimates were generated using month-fixed effects linear regression, and the effectiveness of this method of bias adjustment was examined in simulated datasets. ResultsNationally, wasting fluctuated from 2013-2019, with the lowest annual prevalence of 6.0% (95% CI, 5.1 – 7.0%) recorded in 2014 and the highest in 2017 at 9.0% (95% CI, 8.3% - 9.8%). Pooled across years, the peak wasting season prevalence was 8.8% (95% CI, 8.3% - 9.3%), and low wasting season prevalence was 6.4% (95% CI, 5.7% - 7.1%). Month-adjusted wasting estimates did not differ notably from raw wasting prevalence estimates. Simulations demonstrated that adjusting for months reduces bias in wasting when surveys are conducted one or two months apart across waves, but fails to reliably do so when surveys are conducted in different seasons across waves. ConclusionsSeasonal fluctuations in the prevalence of wasting can be large enough to bias the interpretation of multi-year trends. Efforts should be made to conduct national surveys at the same time of year. Seasonality adjustment using month fixed effects works more reliably when the differences in survey periods across waves are minimal.

Downscaling, bias correction, and spatial adjustment of extreme tropical cyclone rainfall in ERA5 using deep learning

Hydrological models that are used to analyse flood risk induced by tropical cyclones often input ERA5 reanalysis data. However, ERA5 precipitation has large systematic biases, especially over heavy precipitation events like Tropical Cyclones, compromising its usefulness in such scenarios. Few studies to date have performed bias correction of ERA5 precipitation and none of them for extreme rainfall induced by tropical cyclones. Additionally, most existing works on bias adjustment focus on adjusting pixel-wise metrics of bias, such as the Mean Squared Error (MSE). However, it is equally important to ensure that the rainfall peaks are correctly located within the rainfall maps, especially if these maps are then used as input to hydrological models. In this paper, we describe a novel machine learning model that addresses both gaps, RA-Ucmpd, based on the popular U-Net model. The key novelty of RA-Ucmpd is its loss function, the compound loss, which optimizes both a pixel-wise bias metric (the MSE) and a spatial verification metric (a modified version of the Fractions Skill Score). Our results show how RA-Ucmpd improves ERA5 in almost all metrics by 3-28%—more than the other models we used for comparison which actually worsen the total rainfall bias of ERA5—at the cost of a slightly increased (3%) error on the magnitude of the peak. We analyse the behaviour of RA-Ucmpd by visualizing accumulated maps of four particularly wet tropical cyclones and by dividing our data according to the Saffir-Simpson scale and to whether they made landfall, and we perform an error analysis to understand under what conditions our model performs best.

Intersensor Calibration of Spaceborne Passive Microwave Radiometers and Algorithm Tuning for Long-Term Sea Ice Trend Analysis Based on AMSR-E Observations

Sea ice monitoring is key to analyzing the Earth’s climate system. Long-term sea ice extent (SIE) has been continuously monitored using various spaceborne passive microwave radiometers (PMRs) since November 1978. As the lifetime of a satellite is usually approximately 5 years, bias caused by differences in PMRs should be eliminated to obtain objective SIE trends. Most sea ice products have been analyzed for long-term trends with a bias adjustment based on the coarse resolution special sensor microwave imager (SSM/I) in operation for the longest period. However, since 2002, Japanese microwave radiometers of the Advanced Microwave Scanning Radiometer (AMSR) series, which have the highest spatial resolution in PMR, have been available. In this study, we developed standardization techniques for processing SIE including calibration of the brightness temperature (TB), tuning the sea ice concentration (SIC) algorithm, and adjusting the SIC threshold to retrieve a consistent SIE trend based on the AMSR for the Earth Observing System (AMSR-E, one of the AMSR that operated from May 2002 to October 2011). Analysis results showed that the root-mean-square error between AMSR-E SICs and those of moderate resolution imaging spectroradiometer (MODIS) was 15%. In this study, SIE was defined as the sum of the areas where the AMSR-E SIC was &gt;15%. When retrieving SIE, we adjusted the SIC threshold for each PMR to be consistent with the SIE calculated based on the 15% SIC threshold for AMSR-E. We then calculated a time-series of the SIE trends over approximately 45 years using the adjusted SIE data. Therefore, we revealed the dramatic decrease in global sea ice extent since 1978. This technique enables retrieval of more accurate long-term sea ice trends for more than half a century in the future.

A simple and robust approach for adapting design storms to assess climate-induced changes in flash flood hazard

Hydrologists and civil engineers often use design storms to assess flash flood hazards in urban, rural, and mountainous catchments. These synthetic storms are not representations of real extreme rainfall events, but rather simplified versions parameterized to mimic extreme precipitation statistics often obtained from intensity–duration–frequency (IDF) curves. To construct design storms for the future climate, it is thus necessary first to recalculate IDF curves to represent rainfall under warmer conditions. We propose a framework for adjusting IDF curves and design storms to future climate conditions using the TENAX model, a novel statistical approach that can provide future short-duration precipitation return levels based on projected temperature changes. For most applications, information from climate models at the daily scale can be used to construct design storms at the sub-hourly scale without any downscaling or bias adjustment. Our approach is illustrated through a re-parameterization of the Chicago Design Storm (CDS) in the context of climate change. As a case study demonstration, we apply the TENAX model to data from the city of Zurich to calculate changes in the historical IDF curve for durations ranging from 10 min to 3 h. We then construct synthetic 100-year return period design storms based on the CDS for present and future climates and use the CAFlood model to produce flood inundation maps to assess changes in flood hazard. The codes for adapting design storms to climate change are simple to implement, easily applicable by practitioners, and made freely available.

Change of global land extreme temperature in the future

Understanding future temperature extremes is pivotal to preparing for and mitigating the impacts of climate change. This study proposed machine learning techniques to develop a multi-model ensemble model for high-resolution projection of global land temperature extremes under different emission scenarios, hence providing enhanced precision over previous climate model projections. By utilizing the NEX-GDDP-CMIP6 dataset with bias adjustment and the Gradient Booster algorithm, we reduced the biases that existed in Global Climate Models. The model significantly reduces the root mean square errors (RMSEs) for both the daily maximum and daily minimum temperature extremes. A future scenario analysis revealed that global temperature extremes would substantially increase under high-emission scenarios, highlighting the urgency for stringent emission reduction commitments. This study also identified regions like Greenland, the Tibetan Plateau, and the regional Arctic Archipelago as potential hotspots of temperature extremes under these scenarios. The multi-model ensemble approach, tuned with machine learning and driven by high-resolution data, contributes to climate science by providing refined insights into future temperature extremes, thereby offering direction to climate change mitigation and adaptation strategies.

The factors associated with the provision of public access defibrillation in Japan − A nationwide cohort study

AimThe association between out-of-hospital cardiac arrest (OHCA) and the appropriate provision of public access defibrillation (PAD) remains unclear. This study aimed to evaluate the factors associated with whether or not PAD was provided. MethodsThis retrospective cohort study utilized the All-Japan Utstein and Emergency Transport Registries in 2021. We included OHCA patients who were applied to automated external defibrillators (AEDs) by bystanders and were deemed eligible for defibrillation by an AED. We defined PAD provided or no PAD provided based on bystander defibrillation. Multivariable logistic regression analysis with the Firth bias adjustment method was employed to estimate the adjusted odds ratios (AORs) and 95% confidence intervals (CIs) for the exploratory evaluation of factors associated with PAD provided. Results1949 patients were eligible for analysis (PAD provided, n = 1696 [87.0%]; no PAD provided, n = 253 [13.0%]). Factors positively associated with PAD provided were male (AOR [95% CI], 1.61 [1.17–2.21]; vs. female), other public place incidence (AOR [95% CI], 10.65 [1.40–1367.54]; vs. public place), non-family member witnessed (AOR [95% CI], 2.51 [1.86–3.42]; vs. unwitnessed) and conventional cardiopulmonary resuscitation (CPR), (AOR [95% CI], 1.75 [1.17–2.67]; vs. hands-only CPR). Conversely, factors negatively associated with no PAD provided were over 65 years old (AOR [95% CI], 0.48 [0.28–0.80]; vs. 19–64 yr), night-time onset (AOR [95% CI], 0.61 [0.45–0.83]; vs. daytime), non-cardiogenic (AOR [95% CI], 0.43 [0.31–0.61]; vs. cardiogenic), home setting (AOR [95% CI], 0.33 [0.14–0.83]; vs. public place), healthcare facility setting (AOR [95% CI], 0.40 [0.23–0.66]; vs. public place), no bystander CPR (AOR [95% CI], 0.31 [0.14–0.71]; vs. hands-only CPR), and dispatcher-assistance (AOR [95% CI], 0.72 [0.53–0.97]; vs. no dispatcher-assistance). ConclusionMale patients, other public place onset, witnessed by non-family and conventional CPR were associated with PAD provide. Therefore, training skilled first responders to use AEDs appropriately is necessary.

A Dynamically Consistent ENsemble of Temperature at the Earth surface since 1850 from the DCENT dataset

Accurate historical records of Earth’s surface temperatures are central to climate research and policy development. Widely-used estimates based on instrumental measurements from land and sea are, however, not fully consistent at either global or regional scales. To address these challenges, we develop the Dynamically Consistent ENsemble of Temperature (DCENT), a 200-member ensemble of monthly surface temperature anomalies relative to the 1982–2014 climatology. Each DCENT member starts from 1850 and has a 5° × 5° resolution. DCENT leverages several updated or recently-developed approaches of data homogenization and bias adjustments: an optimized pairwise homogenization algorithm for identifying breakpoints in land surface air temperature records, a physics-informed inter-comparison method to adjust systematic offsets in sea-surface temperatures recorded by ships, and a coupled energy balance model to homogenize continental and marine records. Each approach was published individually, and this paper describes a combined approach and its application in developing a gridded analysis. A notable difference of DCENT relative to existing temperature estimates is a cooler baseline for 1850–1900 that implies greater historical warming.

Reduced mortality with antimicrobial stewardship guided by BioFire FilmArray Blood Culture Identification 2 panel in critically ill patients with bloodstream infection: A retrospective propensity score-matched study

ObjectivesTo investigate whether using the BioFire® FilmArray® Blood Culture Identification 2 panel (BCID2) leads to timely antimicrobial therapy and improves patient outcomes in critically ill patients with bloodstream infections (BSIs). MethodsThis retrospective observational study included patients with BSIs admitted to the intensive care unit from July 1, 2021, to August 31, 2023. Patients were divided into groups receiving appropriate or inappropriate antimicrobial therapy. Those receiving inappropriate therapy underwent adjustments using standard-of-care (SOC) testing or BCID2. Propensity score matching (PSM) was performed on the original cohort (Model 1) and a time-window bias-adjusted cohort (Model 2). Clinical impact of BCID2-guided antimicrobial adjustment was analysed in both models. ResultsA total of 181 patients received inappropriate antimicrobial therapy, with 33 undergoing BCID2 testing and 148 undergoing SOC testing. Following PSM and time-window bias adjustment, 66 patients were analysed in Model 1 and 46 patients in Model 2. BCID2 significantly reduced the median time to appropriate antimicrobial therapy (40.8 vs. 74.0 h in Model 1; 42.8 vs. 68.9 h in Model 2) and the day-28 mortality rate (27.8% vs. 77.1%, P < 0.001 in Model 1; 23.5% vs. 58.6%, P = 0.021 in Model 2). In multivariate regression analysis, BCID2-guided antimicrobial adjustment was an independent prognostic factor for day-28 mortality (adjusted odds ratio [aOR] 0.07 in Model 1 and aOR 0.12 in Model 2). ConclusionBCID2-guided antimicrobial stewardship was associated with a shorter time to appropriate antimicrobial therapy and reduced day-28 mortality in critically ill patients with BSIs receiving inappropriate antimicrobial therapy.

Anaesthesia modality on endovascular therapy outcomes in patients with large infarcts: a post hoc analysis of the ANGEL-ASPECT trial

ObjectivesEndovascular therapy (EVT) now penetrates the once obscure realm of large infarct core volume acute ischaemic stroke (LICV-AIS). This research aimed to investigate the potential correlation between different anaesthetic approaches...

Frequency Bias Causes Overestimation of Climate Change Impacts on Global Flood Occurrence

AbstractThe frequency change of 100‐year flood events is often determined by fitting extreme value distributions to annual maximum discharge from a historical base period. This study demonstrates that this approach may significantly bias the computed flood frequency change. An idealized experiment shows frequency bias exceeding 100% for a 50‐year base period. Further analyses using Monte Carlo simulations, mathematical derivations, and hydrological model outputs reveal that bias magnitude inversely relates to base period length and is weakly influenced by the generalized extreme value distribution's shape parameter. The bias, persisting across different estimation methods, implies floods may exceed local defenses designed based on short historical records more often than expected, even without climate change. We introduce a frequency bias adjustment method, which significantly reduces the projected rise in global flood occurrence. This suggests a substantial part of the earlier projected increase in flood occurrence and impacts is not attributable to climate change.

Elevation-dependent biases of raw and bias-adjusted EURO-CORDEX regional climate models in the European Alps

Data from the EURO-CORDEX ensemble of regional climate model simulations and the CORDEX-Adjust dataset were evaluated over the European Alps using multiple gridded observational datasets. Biases, which are here defined as the difference between models and observations, were assessed as a function of the elevation for different climate indices that span average and extreme conditions. Moreover, we assessed the impact of different observational datasets on the evaluation, including E-OBS, APGD, and high-resolution national datasets. Furthermore, we assessed the bi-variate dependency of temperature and precipitation biases, their temporal evolution, and the impact of different bias adjustment methods and bias adjustment reference datasets. Biases in seasonal temperature, seasonal precipitation, and wet-day frequency were found to increase with elevation. Differences in temporal trends between RCMs and observations caused a temporal dependency of biases, which could be removed by detrending both observations and RCMs. The choice of the reference observation datasets used for bias adjustment turned out to be more relevant than the choice of the bias adjustment method itself. Consequently, climate change assessments in mountain regions need to pay particular attention to the choice of observational dataset and, furthermore, to the elevation dependence of biases and the increasing observational uncertainty with elevation in order to provide robust information on future climate.

Information disclosure, multifaceted collaborative governance, and carbon total factor productivity—An evaluation of the effects of the ‘environmental information disclosure pilot’ policy based on double machine learning

As an environmental institutional arrangement related to the information factor of the diversified participation of the government, enterprises, the media and the public, the environmental information disclosure pilot policy, can and how to affect the carbon emission efficiency through multiple collaborative governance? This study uses the Environmental Information Disclosure Pilot Policy implemented in China in 2007 as a quasi-natural experiment. It examines 284 prefecture-level cities from 2004 to 2021 and A-share listed companies from 2004 to 2021, constructing an evolutionary game dynamic model involving government, public, enterprises, and media. Through mathematical derivation and assignment analysis, it explores how environmental information impacts carbon emission efficiency under multifaceted collaborative governance, assessing the strategic choices and evolutionary paths of stakeholders before and after policy implementation, using methods like double machine learning for empirical testing. The study highlights several key findings: First, the implementation of the Environmental Information Disclosure Pilot Policy significantly enhanced carbon total factor productivity in pilot cities, as revealed through Double Machine Learning (DML) policy effect evaluation. Second, adjustments for potential estimation biases using Doubly Debiased LASSO (DDL) regression indicated that environmental information disclosure impacts carbon productivity via a governance mechanism involving government, public, media, and enterprises. Third, a causal pathway analysis suggested a sequential logic in governance effectiveness, starting from governmental environmental focus to corporate environmental responsibility. Lastly, integrating DML with a moderation effect model revealed a regulatory role for environmental legislation construction, offering new insights for achieving dual carbon goals and enriching empirical evidence on information's impact on carbon emission efficiency.

Risk of incident gout following exposure to recombinant zoster vaccine in US adults aged ≥65 years

ObjectiveAssess the risk of incident gout following exposure to recombinant zoster vaccine (RZV). MethodsThis case-only, self-controlled risk interval study included a cohort of US fee-for-service Medicare (Part A, B, and D) beneficiaries aged ≥65 years. The exposure was receipt of at least one dose of the two-dose RZV regimen in 2018 or 2019. The risk and control windows were days 1–30 and days 31–60, respectively, following vaccination. Incident gout was defined as the first episode of gout during the risk or control window, with no evidence of gout in the last 365 days. We estimated the relative risk (RR) and 95 % confidence interval (CI) of incident gout in the risk window relative to the control window, using conditional Poisson regression models. Sensitivity analyses included a dose-compliant subanalysis of individuals who received dose 2 60–183 days after dose 1; dose-specific analysis; seasonality adjustment; and COVID-19 adjustment for potential detection bias due to the pandemic. ResultsThe 1290 RZV-exposed individuals with incident gout were primarily White (86.98 %), male (61.16 %), and aged 70–79 years (55.82 %). The RR of incident gout was 1.00 (95 % CI 0.90, 1.12). In the dose-compliant sensitivity analysis (n = 959 cases of incident gout), the RR of incident gout was 0.99 (95 % CI 0.87, 1.13). The findings were unchanged in the dose-specific, seasonality, and COVID-19 sensitivity analyses. ConclusionThe findings suggest that RZV is not significantly associated with an increased risk of incident gout in the Medicare population aged ≥65 years.

Osteochondral allografts for the treatment of shoulder instability.

To systematically review the predominant complication rates and changes to patient-reported outcome measures (PROMs) following osteochondral allograft (OCA) transplantation for shoulder instability. This systematic review, following PRISMA guidelines and registered in PROSPERO, involved a comprehensive literature search using PubMed, Embase, Web of Science, and Scopus. Key search terms included "allograft", "shoulder", "humerus", and "glenoid". The review encompassed 37 studies with 456 patients, focusing on primary outcomes like failure rates and secondary outcomes such as PROMs and functional test results. A meta-analysis of primary outcomes across 17 studies revealed a dislocation rate of 5.1% and an increase in reoperation rates from 9.3% to 13.7% post-publication bias adjustment. There was also a noted rise in conversion to total shoulder arthroplasty and incidence of osteoarthritis/osteonecrosis over longer follow-up periods. Patient-reported outcomes and functional tests generally showed improvement, albeit with notable variability across studies. A concerning observation was the consistent presence of allograft resorption, with rates ranging from 33% to 80%. Comparative studies highlighted similar efficacy between distal tibial allografts and Latarjet procedures in most respects, with some differences in specific tests. OCA transplantation presents a promising treatment option for shoulder instability, effectively addressing both glenoid and humeral head defects with favourable patient-reported outcomes. These findings advocate for the inclusion of OCA transplantation in treatment protocols for shoulder instability, while also emphasizing the need for further high-quality, long-term research to better understand the procedure's efficacy profile.

Adjustment for sparse data bias in odds ratios: Significance to appraisal of risk of diabetes due to occupational trichlorfon insecticide exposure

BackgroundBias away from the null in odds ratios (OR), aggravated by low power, is a well-known phenomenon in statistics (sparse data bias). Such bias increases in presence of selection of “significant” results on the basis of null hypothesis testing (effect size magnification, ESM). ObjectivesWe seek to illustrate these issues and adjust for suspected sparse data bias in the context of a reported more than doubling of the odds of new onset type 2 diabetes in presence of occupational trichlorfon insecticide exposure reported in the Agricultural Health Study. MethodsWe performed ESM analysis on the crude ORs extracted from the contingency table in the published report, which is done by simulating selected OR given a posited true OR. Next, we applied easily accessible methods that adjust for sparse data bias to the extracted contingency tables, including data augmentation, bootstrap, Firth's regression, and Bayesian methods with weakly informative priors. ResultsDuring the ESM analysis, we observed that there was a reasonable chance that a “statistically significant” OR of around 2.5–2.6 would be observed for true OR of 1.2. Adjustment for sparse data bias revealed that Bayesian methods outperformed alternative approaches in terms of yielding more precise inference, while not making unjustified distributional assumptions about estimates of OR. The OR in the original paper of about 2.5–2.6 was reduced on average to OR of 1.9 to 2.2, with 95% (Bayesian) credible intervals that included the null. DiscussionIt is reasonable to adjust ORs for sparse data bias when the reported association has societal importance, because policy must be informed by the least biased estimates of the effect. We think that such adjustment would lead to a more appropriate evaluation of the extent of evidence on the contribution of occupational exposure to trichlorfon pesticide to risk of new onset diabetes.