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Background:\ud The cardiovascular safety profile of biologic therapies used for psoriasis is unclear.\ud \ud Objectives:\ud To compare the risk of major cardiovascular events (CVEs; acute coronary syndrome, unstable angina, myocardial infarction and stroke) in patients with chronic plaque psoriasis treated with adalimumab, etanercept or ustekinumab in a large prospective cohort.\ud \ud Methods:\ud Prospective cohort study examining the comparative risk of major CVEs was conducted using the British Association of Dermatologists Biologics and Immunomodulators Register. The main analysis compared adults with chronic plaque psoriasis receiving ustekinumab with tumour necrosis‐α inhibitors (TNFi: etanercept and adalimumab), whilst the secondary analyses compared ustekinumab, etanercept or methotrexate against adalimumab. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated using overlap weights by propensity score to balance baseline covariates among comparison groups.\ud \ud Results:\ud We included 5468 biologic‐naïve patients subsequently exposed (951 ustekinumab; 1313 etanercept; and 3204 adalimumab) in the main analysis. The secondary analyses also included 2189 patients receiving methotrexate. The median (p25–p75) follow‐up times for patients using ustekinumab, TNFi, adalimumab, etanercept and methotrexate were as follows: 2.01 (1.16–3.21), 1.93 (1.05–3.34), 1.94 (1.09–3.32), 1.92 (0.93–3.45) and 1.43 (0.84–2.53) years, respectively. Ustekinumab, TNFi, adalimumab, etanercept and methotrexate groups had 7, 29, 23, 6 and 9 patients experiencing major CVEs, respectively. No differences in the risk of major CVEs were observed between biologic therapies [adjusted HR for ustekinumab vs. TNFi: 0.96 (95% CI 0.41–2.22); ustekinumab vs. adalimumab: 0.81 (0.30–2.17); etanercept vs. adalimumab: 0.81 (0.28–2.30)] and methotrexate against adalimumab [1.05 (0.34–3.28)].\ud \ud Conclusions:\ud In this large prospective cohort study, we found no significant differences in the risk of major CVEs between three different biologic therapies and methotrexate. Additional studies, with longer term follow‐up, are needed to investigate the potential effects of biologic therapies on incidence of major CVEs.

Variation in response to biologic therapy for inflammatory diseases, such as psoriasis, is partly driven by variation in drug exposure. Real‐world psoriasis data were used to develop a pharmacokinetic/pharmacodynamic (PK/PD) model for the first‐line therapeutic antibody ustekinumab. The impact of differing dosing strategies on response was explored. Data were collected from a UK prospective multicenter observational cohort (491 patients on ustekinumab monotherapy, drug levels, and anti‐drug antibody measurements on 797 serum samples, 1,590 measurements of Psoriasis Area Severity Index (PASI)). Ustekinumab PKs were described with a linear one‐compartment model. A maximum effect (Emax) model inhibited progression of psoriatic skin lesions in the turnover PD mechanism describing PASI evolution while on treatment. A mixture model on half‐maximal effective concentration identified a potential nonresponder group, with simulations suggesting that, in future, the model could be incorporated into a Bayesian therapeutic drug monitoring “dashboard” to individualize dosing and improve treatment outcomes.

BackgroundPatients with psoriasis are often concerned about the risk of serious infection associated with systemic psoriasis treatments.ObjectivesTo develop and externally validate a prediction model for serious infection in patients with psoriasis within 1 year of starting systemic therapies.MethodsThe risk prediction model was developed using the British Association of Dermatologists Biologic Interventions Register (BADBIR), and the German Psoriasis Registry PsoBest was used as the validation dataset. Model discrimination and calibration were assessed internally and externally using the C‐statistic, the calibration slope and the calibration in the large.ResultsOverall 175 (1·7%) out of 10 033 participants from BADBIR and 41 (1·7%) out of 2423 participants from PsoBest developed a serious infection within 1 year of therapy initiation. Selected predictors in a multiple logistic regression model included nine baseline covariates, and starting infliximab was the strongest predictor. Evaluation of model performance showed a bootstrap optimism‐corrected C‐statistic of 0·64 [95% confidence interval (CI) 0·60–0·69], calibration in the large of 0·02 (95% CI −0·14 to 0·17) and a calibration slope of 0·88 (95% CI 0·70–1·07), while external validation performance was poor, with C‐statistic 0·52 (95% CI 0·42–0·62), calibration in the large 0·06 (95% CI −0·25 to 0·37) and calibration slope 0·36 (95% CI −0·24 to 0·97).ConclusionsWe present the first results of the development of a multivariable prediction model. This model may help patients and dermatologists in the U.K. and the Republic of Ireland to identify modifiable risk factors and inform therapy choice in a shared decision‐making process.

BackgroundPatients with psoriasis and clinicians are concerned that infliximab may be associated with a risk of serious infections.ObjectivesTo compare the risk of serious infections associated with infliximab in patients with chronic plaque psoriasis against a cohort on nonbiologic systemic therapies.MethodsA prospective cohort study was performed using data from the British Association of Dermatologists Biologic Interventions Register (BADBIR). Infliximab was compared with nonbiologic systemic therapies, inclusive of any exposure to methotrexate, ciclosporin, acitretin, fumaric acid esters, psoralen‐ultraviolet A or hydroxycarbamide. Serious infections were those associated with hospitalization, the use of intravenous antimicrobial therapy and/or those that led to death. Propensity score inverse probability treatment weights were used to adjust for potential confounding from a priori identified covariates. Cox proportional hazards models were calculated to obtain hazard ratios (HRs).ResultsIn total, 3843 participants were included for analysis up to October 2016. The incidence rates were significantly higher in the infliximab cohort (47·8 per 1000 person‐years) [95% confidence interval (CI) 35·7–64·0], compared with 14·2 per 1000 person‐years (95% CI 11·5–17·4) in the nonbiologic systemic cohort. Infliximab was associated with an overall increase in the risk of serious infection compared with nonbiologics [adjusted HR (adjHR) 1·95, 95% CI 1·01–3·75] and methotrexate only (adjHR 2·96, 95% CI 1·58–5·57) and a higher risk of serious infection in the first 6 months of therapy (adjHR 3·49, 95% CI 1·14–10·70).ConclusionsInfliximab is associated with an increased risk of serious infections compared with nonbiologic systemic therapies in patients with psoriasis in the U.K. and the Republic of Ireland.

Medication non-adherence is a missed opportunity for therapeutic benefit. We assessed “real-world” levels of self-reported non-adherence to conventional and biologic systemic therapies used for psoriasis and evaluated psychological and biomedical factors associated with non-adherence using multivariable analyses. Latent profile analysis was used to investigate whether patients can be categorized into groups with similar medication beliefs. Latent profile analysis categorizes individuals with similar profiles on a set of continuous variables into discrete groups represented by a categorical latent variable. Eight hundred and eleven patients enrolled in the British Association of Dermatologists Biologic Interventions Register were included. Six hundred and seventeen patients were using a self-administered systemic therapy; 22.4% were classified as “non-adherent” (12% intentionally and 10.9% unintentionally). Patients using an oral conventional systemic agent were more likely to be non-adherent compared to those using etanercept or adalimumab (29.2% vs. 16.4%; P ≤ 0.001). Latent profile analysis supported a three-group model; all groups held strong beliefs about their need for systemic therapy but differed in levels of medication concerns. Group 1 (26.4% of the sample) reported the strongest concerns, followed by Group 2 (61%), with Group 3 (12.6%) reporting the weakest concerns. Group 1 membership was associated with intentional non-adherence (odds ratio = 2.27, 95% confidence interval = 1.16−4.47) and weaker medication-taking routine or habit strength was associated with unintentional non-adherence (odds ratio = 0.92, 95% confidence interval = 0.89−0.96). Medication beliefs and habit strength are modifiable targets for strategies to improve adherence in psoriasis.

The National Agricultural Statistics Service (NASS) conducts the June Area Survey (JAS), which is based on an area frame. Segments of land comprise the sampling units for the JAS. Building and constructing an area frame is expensive and time consuming. The agency is evaluating the use of a permanent grid sampling frame as a cost-saving initiative. The proposed grid frame would include sample units having roughly equal-sized and shaped areas called grid cells. The grid cells would be stratified by agricultural intensity and content and then a stratified random sample drawn. A challenge associated with this proposed approach is that only a portion of an agricultural field may lie within the selected grid cells whereas currently sampled segments have boundaries that follow roads or other physical features. Because of the presence of partial fields, mobile mapping technology may be critical for proper identification of the areas to be included in a sampled grid cell. Using mobile mapping technology would also allow for replacing the aerial photo and paper questionnaires, thus modernizing NASS’s data collection efforts and improving data quality. To test the concept of permanent grid cells in conjunction with the mobile mapping instrument, enumerators (field interviewers) in North Carolina, Pennsylvania and South Dakota visited with farm operators during the summer of 2014. For each sampled grid cell, the enumerators completed an evaluation form to obtain information on a variety of issues including 1) those associated with the grid concept, 2) use of a mobile mapping instrument, 3) connectivity and 4) visualization problems associated with the iPad (e.g. sun glare). This report documents the challenges faced, as well as the lessons learned, from collecting data using grid cells with the mobile mapping prototype and discusses future instrument enhancements.

The National Agricultural Statistics Service (NASS) conducts the annual June Area Survey (JAS), which is based on an area sampling frame. Segments of land comprise the JAS sampling units. Field enumerators use hard copy aerial photos that contain outlines of the sample unit or segment boundaries to locate and to interview all farmers within each sampled unit. Paper questionnaires are used to record the farmer’s agricultural activity occurring within each segment and additional information about their entire operation. June Area Survey sampled segment boundaries follow physical features (roads, railroads, rivers) on the ground. As a cost saving initiative, NASS is evaluating the use of a permanent grid area frame with sampling units of roughly equal size and area with data collection conducted using a mobile mapping instrument. The permanent grid frame would be based on the Public Land Survey System’s (PLSS’s) one-square-mile sections in the 30 states in which the PLSS is the primary surveying method. The sample units of the permanent grid frame are referred to as grid cells in this paper. In these 30 states, roads are often aligned with the PLSS section lines. However, exceptions to this rule and gaps in PLSS coverage exist. In states where land surveying is based on alternate systems (non-PLSS states), a grid frame, with one-square-mile sections would be generated using ESRI’s ArcGIS software. These grid cells would not be aligned with physical features on the ground. Due to these characteristics and the fact that grids cells commonly cut across agricultural fields, a mobile mapping instrument was developed to use Geographic Information Systems (GIS) technology to calculate the acreage of the fields located in the grid cells. The primary objective of this study is to determine whether a GIS calculation of acreage is comparable to the acreage reported by JAS farmers. If so, research into the use of a permanent grid sampling frame can move forward. Additionally, research into the use of a mobile mapping instrument for JAS data collection, based on grid cells or JAS segments, can advance with increased reliance on the GIS calculation of acreage. For this study, field enumerators used the aerial photos from previously collected 2013 JAS data to delineate field boundaries in the prototype mobile mapping instrument. The calculated GIS acreage, at the segment and crop field level, was then compared to the acreage reported by JAS 2013 farmers. Ninety segments in Indiana, Pennsylvania and Washington were included to assess segment and crop field acreage differences. Summary statistics and p-values from Wilcoxon signed-rank tests for total segment and crop field acreage differences between GIS calculated values and JAS farmer reported values are reported. To determine whether there was any bias in segment-level and crop-field-level acreage, estimated using the GIS software, the median difference in acreage was examined and was found to be zero. Wilcoxon signed-rank tests were performed for segment totals and at field level for three crop field categories. Results indicated that, for segment totals and for all crop field types in all states, with the exception of Indiana’s boundary fields (p-value = 0.04), the GIS calculation of acreage and the JAS farmer reported acreages were not statistically different.

The National Agricultural Statistics Service (NASS) surveys farmers and ranchers across the United States and Puerto Rico in order to estimate crop production and number of livestock, to assess production practices, and to identify economic trends. The June Agricultural Survey (JAS) is an annual survey that provides information on U.S. crops, livestock, grain storage capacity, and number, type and size of farms. The JAS is comprised of two components, the List Survey and the Area Survey. The List Survey is comprised of agricultural operations known to NASS. The Area Survey is comprised of designated land areas known as segments and is utilized in measuring the incompleteness of the List. This study is focused on the Area portion, which will be abbreviated as JAS. The JAS sample is comprised of nearly 11,000 designated land areas known as segments. A typical segment is about one square mile -- equivalent to 640 acres. Each segment is outlined on an aerial photo (typically 2’ by 2’ in size) and provided to NASS’s field interviewers. Field interviewers visit these segments and identify the owners/operators of all land within the segment. Land is then categorized as agricultural or non-agricultural. For land where agricultural activity is occurring, a separate paper questionnaire is completed for each agricultural operation operating land within the segment. A team composed of staff from NASS and Iowa State University Center for Survey Statistics and Methodology developed a Computer Assisted Personal Interview (CAPI) instrument to conduct the JAS aerial imagery portion and collect field level information. Also, the team was tasked with testing field enumeration of grid segments (a new type of segment) that could make the JAS sample preparation process more efficient. The JAS-CAPI instrument was field tested in Pennsylvania, Indiana and Washington.