Examining Racial Disparities in Criminal Case Outcomes among Indigent Defendants in San Francisco

We reviewed 10,753 complete case records, consisting of cases between 2011 and 2014, from the San Francisco Public Defender's Office. These data were stored in the Public Defender's GIDEON case management system, which draws from data maintained by the San Francisco County Superior Court's larger case management system database. Unlike previous studies that rely solely on arrest and conviction data, these records cover the entire pretrial process, providing a richer portrait of the experiences of defendants in the criminal justice system. These data can help policymakers and stakeholders understand whether racial disparities exist in the outcomes of San Francisco criminal cases, including cases resolved by plea bargains , and how bargaining affects disparities in other areas of the criminal justice system, such as corrections. Where disparities were seen, we sought to understand them and to evaluate what changes could be made to ensure that similarly situate d individuals receive equal and race - neutral treatment in the criminal justice system. Such information could assist the Public Defender, the San Francisco District Attorney, the San Francisco Police Department, and other criminal justice stakeholders to ensure equitable treatment of all San Franciscans

Inferring Energy Bounds via Static Program Analysis and Evolutionary Modeling of Basic Blocks

The ever increasing number and complexity of energy-bound devices (such as the ones used in Internet of Things applications, smart phones, and mission critical systems) pose an important challenge on techniques to optimize their energy consumption and to verify that they will perform their function within the available energy budget. In this work we address this challenge from the software point of view and propose a novel parametric approach to estimating tight bounds on the energy consumed by program executions that are practical for their application to energy verification and optimization. Our approach divides a program into basic (branchless) blocks and estimates the maximal and minimal energy consumption for each block using an evolutionary algorithm. Then it combines the obtained values according to the program control flow, using static analysis, to infer functions that give both upper and lower bounds on the energy consumption of the whole program and its procedures as functions on input data sizes. We have tested our approach on (C-like) embedded programs running on the XMOS hardware platform. However, our method is general enough to be applied to other microprocessor architectures and programming languages. The bounds obtained by our prototype implementation can be tight while remaining on the safe side of budgets in practice, as shown by our experimental evaluation.Comment: Pre-proceedings paper presented at the 27th International Symposium on Logic-Based Program Synthesis and Transformation (LOPSTR 2017), Namur, Belgium, 10-12 October 2017 (arXiv:1708.07854). Improved version of the one presented at the HIP3ES 2016 workshop (v1): more experimental results (added benchmark to Table 1, added figure for new benchmark, added Table 3), improved Fig. 1, added Fig.

Multi-omic prediction of incident type 2 diabetes.

AIMS/HYPOTHESIS: The identification of people who are at high risk of developing type 2 diabetes is a key part of population-level prevention strategies. Previous studies have evaluated the predictive utility of omics measurements, such as metabolites, proteins or polygenic scores, but have considered these separately. The improvement that combined omics biomarkers can provide over and above current clinical standard models is unclear. The aim of this study was to test the predictive performance of genome, proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes. METHODS: We developed sparse interpretable prediction models in a prospective, nested type 2 diabetes case-cohort study (N=1105, incident type 2 diabetes cases=375) with 10,792 person-years of follow-up, selecting from 5759 features across the genome, proteome, metabolome and clinical biomarkers using least absolute shrinkage and selection operator (LASSO) regression. We compared the predictive performance of omics-derived predictors with a clinical model including the variables from the Cambridge Diabetes Risk Score and HbA1c. RESULTS: Among single omics prediction models that did not include clinical risk factors, the top ten proteins alone achieved the highest performance (concordance index [C index]=0.82 [95% CI 0.75, 0.88]), suggesting the proteome as the most informative single omic layer in the absence of clinical information. However, the largest improvement in prediction of type 2 diabetes incidence over and above the clinical model was achieved by the top ten features across several omic layers (C index=0.87 [95% CI 0.82, 0.92], Δ C index=0.05, p=0.045). This improvement by the top ten omic features was also evident in individuals with HbA1c <42 mmol/mol (6.0%), the threshold for prediabetes (C index=0.84 [95% CI 0.77, 0.90], Δ C index=0.07, p=0.03), the group in whom prediction would be most useful since they are not targeted for preventative interventions by current clinical guidelines. In this subgroup, the type 2 diabetes polygenic risk score was the major contributor to the improvement in prediction, and achieved a comparable improvement in performance when added onto the clinical model alone (C index=0.83 [95% CI 0.75, 0.90], Δ C index=0.06, p=0.002). However, compared with those with prediabetes, individuals at high polygenic risk in this group had only around half the absolute risk for type 2 diabetes over a 20 year period. CONCLUSIONS/INTERPRETATION: Omic approaches provided marginal improvements in prediction of incident type 2 diabetes. However, while a polygenic risk score does improve prediction in people with an HbA1c in the normoglycaemic range, the group in whom prediction would be most useful, even individuals with a high polygenic burden in that subgroup had a low absolute type 2 diabetes risk. This suggests a limited feasibility of implementing targeted population-based genetic screening for preventative interventions

Colorectal cancer screening and the role of community pharmacy

Access to colorectal cancer screening varies across the UK. This article describes the various tests and how community pharmacists can promote them

Predictors of intention translation in flexible sigmoidoscopy screening for colorectal cancer

Objective: This prospective study aimed to identify predictors of intention and subsequent attendance of flexible sigmoidoscopy screening using constructs derived from the Health Belief Model (HBM). Method: A total of 4,330 people aged 54 years and registered at 1 of 83 participating English general practices were sent a preinvitation questionnaire to assess sociodemographics, HBM variables including perceived benefits, barriers, seriousness, health motivation, and external cues to action as well a range of other constructs and personal characteristics known to relate to cancer screening. Results: Of the 1,578 respondents (36.4%), 1,555 (98.5%) answered the intention question: 52.9% stated definitely yes, 38.1% probably yes, 6.8% probably not, and 2.2% definitely not. Intentions were positively associated with a higher score on a scale of benefits (odds ratio [OR] = 4.62; 95% confidence intervals [CI; 3.24, 6.59]) and health motivation, that is, interest in other ways of preventing colorectal cancer (OR = 2.61; 95% CI [1.62, 4.22]), while a higher score on perceived barriers (OR = 0.19; 95% CI [0.12, 0.31]) and currently following recommended healthy lifestyle behaviors (OR = 0.31; 95% CI [0.16, 0.59]) were negatively associated. Attendance was verified for 922 intenders (65.2%) of whom 737 (79.9%) attended. Attendance was predicted by health motivation (OR = 1.75; 95% CI [1.07, 2.86]), perceived benefits (OR = 1.82; 95% CI [1.37, 2.43]), perceived barriers (OR = 0.47; 95% CI [0.32, 0.69]), individual-level deprivation (OR = 0.26; 95% CI [0.14, 0.50]), and having diabetes (OR = 0.48; 95% CI [0.25, 0.94]). Conclusion: This study supported the usefulness of the HBM in predicting cancer screening and was further enhanced by adding non-HBM variables such as individual socioeconomic deprivation and comorbidities

Barriers to bowel scope (flexible sigmoidoscopy) screening: a comparison of non-responders, active decliners and non-attenders

Background Participation in bowel scope screening (BSS) is low (43%), limiting its potential to reduce colorectal cancer (CRC) incidence and mortality. This study aimed to quantify the prevalence of barriers to BSS and examine the extent to which these barriers differed according to non-participant profiles: non-responders to the BSS invitation, active decliners of the invitation, and non-attenders of confirmed appointments. Methods Individuals invited for BSS between March 2013 and December 2015, across 28 General Practices in England, were sent a questionnaire. Questions measured initial interest in BSS, engagement with the information booklet, BSS participation, and, where applicable, reasons for BSS non-attendance. Chi-square tests of independence were performed to examine the relationship between barriers, non-participant groups and socio-demographic variables. Results 1478 (45.8%) questionnaires were returned for analysis: 1230 (83.2%) attended screening, 114 (7.7%) were non-responders to the BSS invitation, 100 (6.8%) were active decliners, and 34 (2.3%) were non-attenders. Non-responders were less likely to have read the whole information booklet than active decliners (x2 (2, N = 157) = 7.00, p = 0.008) and non-attenders (x2 (2, N = 101) = 8.07, p = 0.005). Non-responders also had lower initial interest in having BSS than either active decliners (x2 (2, N = 213) = 6.07, p = 0.014) or non-attenders (x2 (2, N = 146) = 32.93, p

Barriers to flexible sigmoidoscopy colorectal cancer screening in low uptake socio-demographic groups: A systematic review.

OBJECTIVE: To synthesise qualitative evidence related to barriers and facilitators of flexible sigmoidoscopy screening (FSS) intention and uptake, particularly within low socio-demographic uptake groups. FSS uptake is lower amongst women, lower socio-economic status (SES), and Asian ethnic groups within the United Kingdom (UK) and United States of America. METHODS: A total of 12 168 articles were identified from searches of four databases: EMBASE, MEDLINE, PsycINFO and Web of Science. Eligibility criteria included: individuals eligible to attend FSS and empirical peer-reviewed studies that analysed qualitative data. The Critical Appraisal Skills Program tool evaluated the methodological quality of included studies, and thematic synthesis was used to analyse the data. RESULTS: Ten qualitative studies met the inclusion criteria. Key barriers to FSS intention and uptake centred upon procedural anxieties. Women, including UK Asian women, reported shame and embarrassment, anticipated pain, perforation risk, and test preparation difficulties to elevate anxiety levels. Religious and cultural-influenced health beliefs amongst UK Asian groups were reported to inhibit FSS intention and uptake. Competing priorities, such as caring commitments, particularly impeded women's ability to attend certain FSS appointments. The review identified a knowledge gap concerning factors especially associated with FSS participation amongst lower SES groups. CONCLUSIONS: Studies mostly focussed on barriers and facilitators of intention to participate in FSS, particularly within UK Asian groups. To determine the barriers associated with FSS uptake, and further understand how screening intention translates to behaviour, it is important that future qualitative research is equally directed towards factors associated with screening behaviour

Synergistic insights into human health from aptamer- and antibody-based proteomic profiling

Affinity-based proteomics has enabled scalable quantification of thousands of protein targets in blood enhancing biomarker discovery, understanding of disease mechanisms, and genetic evaluation of drug targets in humans through protein quantitative trait loci (pQTLs). Here, we integrate two partly complementary techniques-the aptamer-based SomaScan® v4 assay and the antibody-based Olink assays-to systematically assess phenotypic consequences of hundreds of pQTLs discovered for 871 protein targets across both platforms. We create a genetically anchored cross-platform proteome-phenome network comprising 547 protein-phenotype connections, 36.3% of which were only seen with one of the two platforms suggesting that both techniques capture distinct aspects of protein biology. We further highlight discordance of genetically predicted effect directions between assays, such as for PILRA and Alzheimer's disease. Our results showcase the synergistic nature of these technologies to better understand and identify disease mechanisms and provide a benchmark for future cross-platform discoveries