Log Relative Risk Research Articles

Simple or complex statistical models: Non-traditional regression models with intuitive interpretations

The evolution of statistical modelling has historically been constrained by the practical limitations of computation; early statistical modelling favoured models which could feasibly be estimated. As increased mathematical complexity often implies more intricate computation, statistical models have grown both mathematically and computationally more complex. However, paradoxically, sometimes conceptually simpler models present more computational challenges than complex ones, and these have historically been neglected. In the case of binary responses, logistic regression models are the gold standard; covariates are modelled additively on the log-odds scale. In the case of time-to-event responses, the Cox proportional hazards regression model has additivity on the log-hazard rate scale. Both of these methods are computationally convenient, and yet the scales on which covariates are modelled are far from intuitive. We demonstrate the use of alternative models which are computationally more complex, yet feasible, but in which modelling is on a more interpretable scale. In the case of binary responses, a more intuitive alternative is log-binomial regression, in which modelling is on the log-relative risk scale. In the case of time-to-event responses, distributional regression enables modelling on the time scale. While logistic regression and proportional hazards regression qualitatively both deliver the same conclusions as the alternative models, log-binomial and distributional regression provide more interpretable coefficients, which are readily estimated.

The performance of marginal structural models for estimating risk differences and relative risks using weighted univariate generalized linear models.

We used Monte Carlo simulations to compare the performance of marginal structural models (MSMs) based on weighted univariate generalized linear models (GLMs) to estimate risk differences and relative risks for binary outcomes in observational studies. We considered four different sets of weights based on the propensity score: inverse probability of treatment weights with the average treatment effect as the target estimand, weights for estimating the average treatment effect in the treated, matching weights and overlap weights. We considered sample sizes ranging from 500 to 10,000 and allowed the prevalence of treatment to range from 0.1 to 0.9. We examined both the robust variance estimator when using generalized estimating equations with an independent working correlation matrix and a bootstrap variance estimator for estimating the standard error of the risk difference and the log-relative risk. The performance of these methods was compared with that of direct weighting. Both the direct weighting approach and MSMs based on weighted univariate GLMs resulted in the identical estimates of risk differences and relative risks. When sample sizes were small to moderate, the use of an MSM with a bootstrap variance estimator tended to result in the most accurate estimates of standard errors. When sample sizes were large, the direct weighting approach and an MSM with a bootstrap variance estimator tended to produce estimates of standard error with similar accuracy. When using a MSM to estimate risk differences and relative risks, in general it is preferable to use a bootstrap variance estimator than the robust variance estimator. We illustrate the application of the different methods for estimating risks differences and relative risks using an observational study on the effect on mortality of discharge prescribing of a beta-blocker in patients hospitalized with acute myocardial infarction.

Shrinkage estimators of the spatial relative risk function.

The spatial relative risk function describes differences in the geographical distribution of two types of points, such as locations of cases and controls in an epidemiological study. It is defined as the ratio of the two underlying densities. Estimation of spatial relative risk is typically done using kernel estimates of these densities, but this procedure is often challenging in practice because of the high degree of spatial inhomogeneity in the distributions. This makes it difficult to obtain estimates of the relative risk that are stable in areas of sparse data while retaining necessary detail elsewhere, and consequently difficult to distinguish true risk hotspots from stochastic bumps in the risk function. We study shrinkage estimators of the spatial relative risk function to address these problems. In particular, we propose a new lasso-type estimator that shrinks a standard kernel estimator of the log-relative risk function towards zero. The shrinkage tuning parameter can be adjusted to help quantify the degree of evidence for the existence of risk hotspots, or selected to optimize a cross-validation criterion. The performance of the lasso estimator is encouraging both on a simulation study and on real-world examples.

On the Q statistic with constant weights in meta-analysis of binary outcomes

BackgroundCochran’s Q statistic is routinely used for testing heterogeneity in meta-analysis. Its expected value (under an incorrect null distribution) is part of several popular estimators of the between-study variance, tau ^2. Those applications generally do not account for use of the studies’ estimated variances in the inverse-variance weights that define Q (more explicitly, Q_{IV}). Importantly, those weights make approximating the distribution of Q_{IV} rather complicated.MethodsAs an alternative, we are investigating a Q statistic, Q_F, whose constant weights use only the studies’ arm-level sample sizes. For log-odds-ratio (LOR), log-relative-risk (LRR), and risk difference (RD) as the measures of effect, we study, by simulation, approximations to distributions of Q_{IV} and Q_F, as the basis for tests of heterogeneity.ResultsThe results show that: for LOR and LRR, a two-moment gamma approximation to the distribution of Q_F works well for small sample sizes, and an approximation based on an algorithm of Farebrother is recommended for larger sample sizes. For RD, the Farebrother approximation works very well, even for small sample sizes. For Q_{IV}, the standard chi-square approximation provides levels that are much too low for LOR and LRR and too high for RD. The Kulinskaya et al. (Res Synth Methods 2:254–70, 2011) approximation for RD and the Kulinskaya and Dollinger (BMC Med Res Methodol 15:49, 2015) approximation for LOR work well for n ge 100 but have some convergence issues for very small sample sizes combined with small probabilities.ConclusionsThe performance of the standard chi ^2 approximation is inadequate for all three binary effect measures. Instead, we recommend a test of heterogeneity based on Q_F and provide practical guidelines for choosing an appropriate test at the .05 level for all three effect measures.

Does mobility restriction significantly control infectious disease transmission? Accounting for non-stationarity in the impact of COVID-19 based on Bayesian spatially varying coefficient models.

COVID-19 is the most severe health crisis of the 21st century. COVID-19 presents a threat to almost all countries worldwide. The restriction of human mobility is one of the strategies used to control the transmission of COVID-19. However, it has yet to be determined how effective this restriction is in controlling the rise in COVID-19 cases, particularly in small areas. Using Facebook's mobility data, our study explores the impact of restricting human mobility on COVID-19 cases in several small districts in Jakarta, Indonesia. Our main contribution is showing how the restriction of human mobility data can give important information about how COVID-19 spreads in different small areas. We proposed modifying a global regression model into a local regression model by accounting for the spatial and temporal interdependence of COVID-19 transmission across space and time. We applied Bayesian hierarchical Poisson spatiotemporal models with spatially varying regression coefficients to account for non-stationarity in human mobility. We estimated the regression parameters using an Integrated Nested Laplace Approximation. We found that the local regression model with spatially varying regression coefficients outperforms the global regression model based on DIC, WAIC, MPL, and R2 criteria for model selection. In Jakarta's 44 districts, the impact of human mobility varies significantly. The impacts of human mobility on the log relative risk of COVID-19 range from -4.445 to 2.353. The prevention strategy involving the restriction of human mobility may be beneficial in some districts but ineffective in others. Therefore, a cost-effective strategy had to be adopted.

S256 Risk Factors of Colorectal Cancer in Africa: A Systematic Review and Meta-Analysis

Introduction: Colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide. A recent meta-analysis estimated a pooled CRC age-standardized incidence rate of 5.25 per 100,000, though suggested this to be an under-estimate of the true rate. Due to the heterogeneity of dietary and lifestyle practices throughout the continent, our work sought to define risk factors for the development of CRC in Africa. Methods: We systematically searched PubMed, Embase, Global Health, CINAHL, Cochrane CENTRAL, and African Index Medicus for studies written in English, examining risk factors of CRC in Africa. Meta-analysis was performed to compare different risk factors in constituent studies. Jamovi software was used for statistical analysis utilizing a random-effects model. Analysis of CRC studies was supplemented by estimated relative risk (RR) comparing various risk factors. Results: Of 2479 studies screened, 149 were included for the quantitative analysis (n=93707). Family history of CRC was associated with a RR of 2.14 and 95% RR CI [1.68-2.72], n=340. Individuals with diets based on high calcium, or vegetable consumption had 45% and 8% lower chances of having CRC, with respective RR of 0.55 [0.44-0.69] and 0.92 [0.84-0.99]. Diets based on carbohydrate, dairy, sugary food/drinks, or meat consumption indicated 14, 31, 43, or 45% higher chances of CRC, and 1.14 [1.03-1.26], 1.31 [1.21-1.42], 1.43 [1.32-1.57], 1.45 [1.36-1.54], n=5303. Physical activity was associated with lower RR of having CRC (81% less), 0.19 [0.15-.26]. Individuals that were obese, have been exposed to carcinogenic chemicals, have history of alcohol use, or tobacco use indicated 43, 45, 54, 65% higher chance of CRC, with 1.43 [1.02-2.03], 1.45 [1.23-1.68], 1.54 [1.28-1.84], 1.65 [1.45-1.9], n=8995. With the exception of family history, there was considerable heterogeneity among studies (I2 > 80%). (Figure) Conclusion: There are both modifiable and non-modifiable risk factors that are distinct to Africa and vary across the continent. Our review revealed that obesity, carcinogen exposure, tobacco or alcohol use, and diets high in carbohydrates, dairy, and red meat increase CRC risk. On the contrary, high calcium or vegeTable-based diets, and physical activity are protective against the development of CRC. Further work is needed to characterize CRC risk factors by region and to understand the impact of risk factor mitigation efforts on the overall incidence of CRC.Figure 1.: Family history (panel a), type of diet (panel b), and type of lifestyle (panel c) risk factors of CRC in Africa, log relative-risk by case-control studies comparison

Spline Analysis of Biomarker Data Pooled from Multiple Matched/Nested Case-Control Studies.

Simple SummaryThis paper presents methods to pool continuous biomarker measurements from multiple studies to estimate the dose–response curves that allow for the nonlinear association between biomarker values and disease risks in matched/nested case–control studies. The approach can be easily applied to pooling projects of cancer studies and user-friendly software for implementing the method can be found on the corresponding author’s website.Pooling biomarker data across multiple studies enables researchers to obtain precise estimates of the association between biomarker measurements and disease risks due to increased sample sizes. However, biomarker measurements often vary significantly across different assays and laboratories; therefore, calibration of the local laboratory measurements to a reference laboratory is necessary before pooling data. We propose two methods for estimating the dose–response curves that allow for a nonlinear association between the continuous biomarker measurements and log relative risk in pooling projects of matched/nested case–control studies. Our methods are based on full calibration and internalized calibration methods. The full calibration method uses calibrated biomarker measurements for all subjects, even for people with reference laboratory measurements, while the internalized calibration method uses the reference laboratory measurements when available and otherwise uses the calibrated biomarker measurements. We conducted simulation studies to compare these methods, as well as a naive method, where data are pooled without calibration. Our simulation and theoretical results suggest that, in estimating the dose–response curves for biomarker-disease relationships, the internalized and full calibration methods perform substantially better than the naive method, and the full calibration approach is the preferred method for calibrating biomarker measurements. We apply our methods in a pooling project of nested case–control studies to estimate the association of circulating Vitamin D levels with risk of colorectal cancer.

Prognostic value of node-to-primary tumor maximum standardized uptake value ratio in T1-4N1-3M0 non-small cell lung cancer patients treated with concurrent chemo-radiotherapy.

BackgroundThis study aimed to identify whether NTR is the independent risk factor for progression-free survival (PFS) and overall survival (OS) in patients treated with concurrent chemo-radiotherapy (cCRT).MethodsWe retrospectively studied 106 T1-4N1-3M0 non-small cell lung cancer patients treated with cCRT. The maximum standardized uptake value (SUVTumor) of the primary tumor and the metastatic lymph nodes (SUVLN) were measured. The prognostic significance of NTR for predicting PFS and OS was assessed. A multi-adjusted spline regression model was conducted to provide more precise estimates and examine the shape of the associations between NTR and the risk of progression.ResultsFrom 2012 to 2017, 106 eligible patients were analyzed. The median follow-up time was 15.3 months (3.5–44.6 months). We determined the maximizing area under the time-dependent receiver operating characteristic curve was at an NTR of 0.73 for predicting PFS. The two-year PFS was significantly lower in the high-NTR group (35.7% vs. 55.4%, P = 0.02) and two-year OS (43.4% vs. 61.1%, P = 0.03 was also significantly worse. Multivariable analysis revealed that only NTR was an independent prognostic factor for PFS (hazard ratio [HR]: 10.04, P < 0.001) and OS (HR: 4.19, P = 0.03). The restricted cubic spline regression model showed that NTR had a non-linear relationship with log relative risk for progression.ConclusionNTR was an independent risk factor for predicting PFS and OS in T1-4N1-3M0 non-small cell lung cancer patients treated with cCRT.

MO700: Outcomes of Remote Patient Monitoring among Peritoneal Dialysis Population in the Covid-19 ERA

Abstract BACKGROUND AND AIMS Automated peritoneal dialysis (APD) is a growing PD modality but as with other home dialysis methods, the lack of monitoring of patients' adherence to prescriptions is a limitation with potential negative impact on clinical outcome parameters. Remote patient monitoring (RPM-PD) allowing the clinical team to have access to dialysis data and adjust the treatment may overcome this limitation. As a result of the coronavirus disease 2019 (COVID-19) pandemic, the importance of RPM programs has raised to allow the physicians ensure optimal care of PD patients. In addition, to avoid the increased risk of complications or technique failure, the present study sought to determine clinical outcomes associated with RPM use in patients on APD therapy. METHOD We performed a systematic review in PubMed, MEDLINE, Embase and Cochrane databases to select studies that met the inclusion criteria. The search terms used were: peritoneal dialysis, remote monitoring, sharesource, outcomes, peritonitis, hospitalization, technique failure and adherence. These search terms were individually used and then combined in different databases. References within the chosen studies were reviewed. We followed the recommendations of Cochrane collaboration and the Quality of Reporting of Meta-analyses guidelines. STATA package-15 was used. We combined all study-specific estimates using inverse-variant weighted averages of logarithmic relative risk in random effects model. Confidence interval including the value of 1 was used evident for statistically significant estimate. Heterogeneity was evaluated using the Higgins I² statistic. Heterogeneity was estimated when the level of P-value was &amp;lt; 0.1. Results of the random effects model were spread out on the forest plot graph. RESULTS Twenty-two studies were included in our meta-analysis. In qualitative analysis: five studies showed that RPM in APD patients had lower hospitalization rates compared to traditional PD. Five studies showed better adherence in the RPM-PD group. Five studies showed better outcomes among RPM-APD patients in terms of symptom control, management of fluid balance, blood pressure control, dialysis prescription and electrolyte management. Five studies showed that RPM-APD had better outcomes in terms of patient independence, quality of life, patient and caregiver satisfaction. Five studies showed better cost-effectiveness in RPM-PD compared to traditional PD. Four studies showed better cost-effectiveness in RMP-PD. Three studies showed lower technique failure rates in RPM-PD compared to traditional PD. Three studies showed lower mortality rates in RPM-PD compared to traditional PD. Three studies showed better quality of life and patient satisfaction in RPM-PD. In quantitative analysis, RPM-PD patients had lower rates of technique failure (log relative risk = −0.32, 95% CI: −0.59 to −0.04), lower hospitalization rates (SMD = −0.84, 95% CI: −1.24 to −0.45), lower mortality rates (log RR = −0.26, 95% CI: −0.44 to −0.08) in comparison to traditional PD. CONCLUSION RPM-PD has better outcomes in terms of cost-effectiveness, patient adherence, hospital admissions, rate of peritonitis, technique failure, mortality rates, symptom control, quality of life, patient and caregiver satisfaction.

Polygenic risk for prostate cancer: Decreasing relative risk with age but little impact on absolute risk

Polygenic risk scores (PRSs) for a variety of diseases have recently been shown to have relative risks that depend on age, and genetic relative risks decrease with increasing age. A refined understanding of the age dependency of PRSs for a disease is important for personalized risk predictions and risk stratification. To further evaluate how the PRS relative risk for prostate cancer depends on age, we refined analyses for a validated PRS for prostate cancer by using 64,274 prostate cancer cases and 46,432 controls of diverse ancestry (82.8% European, 9.8% African American, 3.8% Latino, 2.8% Asian, and 0.8% Ghanaian). Our strategy applied a novel weighted proportional hazards model to case-control data to fully utilize age to refine how the relative risk decreased with age. We found significantly greater relative risks for younger men (age 30-55 years) compared with older men (70-88 years) for both relative risk per standard deviation of the PRS and dichotomized according to the upper 90th percentile of the PRS distribution. For the largest European ancestral group that could provide reliable resolution, the log-relative risk decreased approximately linearly from age 50 to age 75. Despite strong evidence of age-dependent genetic relative risk, our results suggest that absolute risk predictions differed little from predictions that assumed a constant relative risk over ages, from short-term to long-term predictions, simplifying implementation of risk discussions into clinical practice.

Investigating weighted fishing hooks for seabird bycatch mitigation

Fisheries bycatch threatens the viability of some seabird populations and reduces fishing efficiency. Albatross bycatch in a US North Pacific tuna longline fishery has increased over the past decade and now exceeds 1000 annual captures. Seabirds interacting with this fishery reach hooks at depths up to 1 m. A branchline weight’s mass and distance from the hook affect seabird catch rates. We conducted experimental fishing to compare the commercial viability of a weighted hook relative to conventional gear with weights attached 0.75 m from the hook. We used a Bayesian random effects meta-analytic regression modelling approach to estimate pooled expected species-specific log relative risk of capture on conventional versus experimental gear. There was a significant 53% (95% HDI: − 75 to − 25%) decrease in retained species’ catch rates on experimental hooks, indicating an unacceptable economic cost, and no significant effect for discarded species. Using a Bayesian general linear mixed regression modelling approach, experimental hooks sank to 85 cm ca. 1.4 times (95% HDI: 1.37–1.48) faster than control hooks. Given their potential to reduce seabird catch rates, eliminate safety risks from bite-offs and facilitate robust compliance monitoring, it is a priority to find a weighted hook design with acceptable catch rates.

Evaluation of pathological complete response as surrogate endpoint in neoadjuvant randomised clinical trials of early stage breast cancer: systematic review and meta-analysis

ObjectiveTo evaluate pathological complete response as a surrogate endpoint for disease-free survival and overall survival in regulatory neoadjuvant trials of early stage breast cancer.DesignSystematic review and meta-analysis.Data sourcesMedline, Embase, and...

Confidence interval estimation for the Mantel–Haenszel estimator of the risk ratio and risk difference in rare event meta-analysis with emphasis on the bootstrap

This paper takes a deeper look into uncertainty assessment of the Mantel–Haenszel estimator (MHE). In the homogeneity case, all developed confidence intervals for the risk ratio and risk difference behave acceptably, even in therare events situation. For heterogeneity, the non-parametric bootstrap approachprovides confidence intervals for the risk difference with acceptable coverage,depending on the number of studies. For the risk ratio, the situation is morecomplex as typically distributions for the log-relative risk are considered. TheMHE overestimates the expected value of the distribution of the log-relativerisk whatever it may be. However, if we consider as true value the estimand ofMHE, reasonable coverage probabilities can be achieved with the bootstrap. Asource of this problem is that the moments of a non-linearly transformedrelative risk variable are not equal to the non-linearly transformed moments ofthe respective relative risk variable.

The Spatial Overlap of Police Calls Reporting Street-Level and Behind-Closed-Doors Crime: A Bayesian Modeling Approach.

Traditionally, intimate-partner violence has been considered a special type of crime that occurs behind closed doors, with different characteristics from street-level crime. The aim of this study is to analyze the spatial overlap of police calls reporting street-level and behind-closed-doors crime. We analyzed geocoded police calls in the 552 census-block groups of the city of Valencia, Spain, related to street-level crime (N = 26,624) and to intimate-partner violence against women (N = 11,673). A Bayesian joint model was run to analyze the spatial overlap. In addition, two Bayesian hierarchical models controlled for different neighborhood characteristics to analyze the relative risks. Results showed that 66.5% of the total between-area variation in risk of reporting street-level crime was captured by a shared spatial component, while for reporting IPVAW the shared component was 91.1%. The log relative risks showed a correlation of 0.53, with 73.6% of the census-block groups having either low or high values in both outcomes, and 26.4% of the areas with mismatched risks. Maps of the shared component and the relative risks are shown to detect spatial differences. These results suggest that although there are some spatial differences between police calls reporting street-level and behind-closed-doors crime, there is also a shared distribution that should be considered to inform better-targeted police interventions.

Effects of short-term exposure to ambient airborne pollutants on COPD-related mortality among the elderly residents of Chengdu city in Southwest China

BackgroundChronic obstructive pulmonary disease (COPD) has become a severe global burden in terms of both health and the economy. Few studies, however, have thoroughly assessed the influence of air pollution on COPD-related mortality among elderly people in developing areas in the hinterland of southwestern China. This study is the first to examine the association between short-term exposure to ambient airborne pollutants and COPD-related mortality among elderly people in the central Sichuan Basin of southwestern China.MethodsData on COPD-related mortality among elderly people aged 60 and older were obtained from the Population Death Information Registration and Management System (PDIRMS). Data on airborne pollutants comprised of particulate matter < 2.5 μm in aerodynamic diameter (PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) were derived from 23 municipal environmental monitoring sites. Data on weather conditions, including daily mean temperature and relative humidity, were obtained from the Chengdu Meteorological Bureau. All data were collected from January 1, 2015, to December 31, 2018. A quasi-Poisson general additive model (GAM) was utilized to assess the effects of short-term exposure to airborne pollutants on COPD-related mortality among elderly people.ResultsA total of 61,058 COPD-related deaths of people aged 60 and older were obtained. Controlling the influences of daily temperature and relative humidity, interquartile range (IQR) concentration increases of PM2.5 (43 μg/m3), SO2 (8 μg/m3), NO2 (18 μg/m3), CO (0.4 mg/m3), and O3 (78 μg/m3) were associated with 2.7% (95% CI 1.0–4.4%), 4.3% (95% CI 2.1–6.4%), 3.6% (95% CI 1.7–5.6%), 2.7% (95% CI 0.6–4.8%), and 7.4% (95% CI 3.6–11.3%) increases in COPD-related mortality in people aged 60 and older, respectively. The exposure-response curves between each pollutant and the log-relative risk of COPD-related mortality exhibited linear relationships. Statistically significant differences in the associations between pollutants and COPD-related mortality were not observed among sociodemographic factors including age, gender, and marital status. The effects of O3 remained steady after adjusting for PM2.5, SO2, NO2, and CO each time in the two-pollutant models.ConclusionsIncreased concentrations of ambient airborne pollutants composed of PM2.5, SO2, NO2, O3, and CO were significantly and positively associated with COPD-related mortality in the central Sichuan Basin, which is located in the hinterland of southwestern China. The adverse effects of O3 were stable, a finding that should receive more attention.

Spatiotemporal analysis, hotspot mapping, and clustering of confirmed cases of COVID-19 in the initial phase of the pandemic in Qom province, Iran

Objective: To identify the incidence rate, relative risk, hotspot regions and incidence trend of COVID-19 in Qom province, northwest part of Iran in the first stage of the pandemic.Methods: The study included 1 125 officially reported PCR-confirmed cases of COVID-19 from 20 February 2020 to 20 April 2020 in 90 regions in Qom city, Iran. The Bayesian hierarchical spatial model was used to model the relative risk of COVID-19 in Qom city, and the segmented regression model was used to estimate the trend of COVID-19 incidence rate. The Poisson distribution was applied for the observed number of COVID-19, and independent Gamma prior was used for inference on log-relative risk parameters of the model.Results: The total incidence rate of COVID-19 was estimated at 89.5 per 100 000 persons in Qom city (95% CI: 84.3, 95.1). According to the results of the Bayesian hierarchical spatial model and posterior probabilities, 43.33% of the regions in Qom city have relative risk greater than 1; however, only 11.11% of them were significantly greater than 1. Based on Geographic Information Systems (GIS) spatial analysis, 10 spatial clusters were detected as active and emerging hotspot areas in the south and central parts of the city. The downward trend was estimated 10 days after the reporting of the first case (February 7, 2020); however, the incidence rate was decreased by an average of 4.24% per day (95%CI:-10.7, -3.5).Conclusions: Spatial clusters with high incidence rates of COVID-19 in Qom city were in the south and central regions due to the high population density. The GIS could depict the spatial hotspot clusters of COVID-19 for timely surveillance and decision-making as a way to contain the disease.

Disparities in Risks of Malaria Associated with Climatic Variability among Women, Children and Elderly in the Chittagong Hill Tracts of Bangladesh.

Malaria occurrence in the Chittagong Hill Tracts in Bangladesh varies by season and year, but this pattern is not well characterized. The role of environmental conditions on the occurrence of this vector-borne parasitic disease in the region is not fully understood. We extracted information on malaria patients recorded in the Upazila (sub-district) Health Complex patient registers of Rajasthali in Rangamati district of Bangladesh from February 2000 to November 2009. Weather data for the study area and period were obtained from the Bangladesh Meteorological Department. Non-linear and delayed effects of meteorological drivers, including temperature, relative humidity, and rainfall on the incidence of malaria, were investigated. We observed significant positive association between temperature and rainfall and malaria occurrence, revealing two peaks at 19 °C (logarithms of relative risks (logRR) = 4.3, 95% CI: 1.1–7.5) and 24.5 °C (logRR = 4.7, 95% CI: 1.8–7.6) for temperature and at 86 mm (logRR = 19.5, 95% CI: 11.7–27.3) and 284 mm (logRR = 17.6, 95% CI: 9.9–25.2) for rainfall. In sub-group analysis, women were at a much higher risk of developing malaria at increased temperatures. People over 50 years and children under 15 years were more susceptible to malaria at increased rainfall. The observed associations have policy implications. Further research is needed to expand these findings and direct resources to the vulnerable populations for malaria prevention and control in the Chittagong Hill Tracts of Bangladesh and the region with similar settings.

Association between bone marrow fluorodeoxyglucose uptake and recurrence after curative surgical resection in patients with T1–2N0M0 lung adenocarcinoma: a retrospective cohort study

Evidence regarding the relationship between fluorodeoxyglucose (FDG) uptake in the bone marrow of patients with lung adenocarcinoma and prognosis is limited. This study aimed to identify whether bone marrow FDG uptake is a risk factor for recurrence in patients after curative surgical resection of T1-2N0M0 lung adenocarcinoma. From January 2012 to December 2016, we retrospectively enrolled 195 pT1-2N0M0 lung adenocarcinoma patients who underwent both preoperative FDG positron emission tomography/computed tomography (PET/CT) and surgical resection from the lung adenocarcinoma database maintained by the PET/CT department at our hospital. After surgical resection, patients were followed up mainly through regular outpatient examinations. The maximum standardized uptake value (SUVmax) of the primary tumor, the mean FDG uptake of bone marrow (BM SUV), bone marrow-liver uptake ratio (BLR), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were measured from the pretreatment FDG PET/CT images. Multi-adjusted Cox proportional hazards models were built to evaluate the independent prognostic value of BLR in predicting recurrence-free survival (RFS). A restricted cubic spline regression model was conducted to provide more precise estimates and examine the shape of the associations between BLR and the risk of recurrence. The follow-up results showed that 30 of the 195 patients (15.4%) had tumor recurrence. Compared with non-recurrent patients, the primary tumor size in recurrent patients was larger, and the SUVmax, TLG, and serum C-reactive protein (CRP) levels were higher. Univariate analysis showed that BLR, tumor size, SUVmax, TLG, and CRP were significantly correlated with postoperative tumor recurrence. After adjustment for conventional confounding factors, the hazard ratio of BLR was 5.01 (95% CI, 1.32, 18.98) for the highest tertile of BLR compared with the lowest tertile. The multi-adjusted spline regression showed that BLR had a linear relationship with log relative risk (RR) for recurrence when BLR was lower than 0.7. Over this level, the effect stabilized, suggesting a saturation effect for BLR at a level of approximately 0.7 at recurrence. BLR was an independent risk factor for predicting RFS in T1-2N0M0 lung adenocarcinoma patients after curative surgical resection. BLR can be used as a biomarker for evaluating the risk of lung cancer recurrence.

Validation of the Surrogate Threshold Effect for Change in Bone Mineral Density as a Surrogate Endpoint for Fracture Outcomes: The FNIH-ASBMR SABRE Project.

ABSTRACTThe surrogate threshold effect (STE) is defined as the minimum treatment effect on a surrogate that is reliably predictive of a treatment effect on the clinical outcome. It provides a framework for implementing a clinical trial with a surrogate endpoint. The aim of this study was to update our previous analysis by validating the STE for change in total hip (TH) BMD as a surrogate for fracture risk reduction; the novelty of this study was this validation. To do so, we used individual patient data from 61,415 participants in 16 RCTs that evaluated bisphosphonates (nine trials), selective estrogen receptor modulators (four trials), denosumab (one trial), odanacatib (one trial), and teriparatide (one trial) to estimate trial‐specific treatment effects on TH BMD and all, vertebral, hip, and nonvertebral fractures. We then conducted a random effects meta‐regression of the log relative fracture risk reduction against 24‐month change in TH BMD, and computed the STE as the intersection of the upper 95% prediction limit of this regression with the line of no fracture reduction. We validated the STE by checking whether the number of fractures in each trial provided 80% power and determining what proportion of trials with BMD changes ≥ STE reported significant reductions in fracture risk. We applied this analysis to (i) the trials on which we estimated the STE; and (ii) trials on which we did not estimate the STE. We found that the STEs for all, vertebral, hip, and nonvertebral fractures were 1.83%, 1.42%, 3.18%, and 2.13%, respectively. Among trials used to estimate STE, 27 of 28 were adequately powered, showed BMD effects exceeding the STE, and showed significant reductions in fracture risk. Among the validation set of 11 trials, 10 met these criteria. Thus STE differs by fracture type and has been validated in trials not used to develop the approach. © 2021 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Commercial viability of locating pelagic longline branchline weights at the hook to reduce seabird bycatch

Bycatch in pelagic longline fisheries threatens the viability of some seabird populations and reduces fishing efficiency. The mass of a branchline fishing weight and its distance from the hook significantly explain seabird catch risk during setting and hauling. We surveyed Hawaii tuna longline fishers to obtain their perspectives on the practicality, safety and economic viability of alternative designs which place weights adjacent to hooks instead of conventional designs with weighted swivels attached 0.6 m from the hook. We conducted experimental fishing to explore the commercial viability of a combination weighted swivel attached to the eye of the hook. We used a Bayesian random effects meta-analytic regression modeling approach to estimate pooled expected species-specific log relative risk of capture on conventional, as opposed to experimental, branchlines. There were significant decreases in catch rates on experimental branchlines of 54% (95% highest posterior density interval [HDI]: 34.3 to 73.8) for retained species and 28% (95% HDI: -7.5 to 93.3) for discarded species. For about 10% of experimental weighted hooks, the point of the hook tangled on the swivel, likely contributing to lower catch rates and making it more difficult for crew to coil branchlines. We identify possible factors explaining why the experimental gear reduced catch rates. The study demonstrates the benefits of stakeholder scoping to determine bycatch mitigation options with the highest promise for practicality, economic viability and safety, as well as benefits of conducting a pilot study to assess commercial viability to determine if a full experiment to assess bycatch mitigation efficacy is warranted.