Evaluating movements of opakapaka (Pristipomoides filamentosus) relative to a restricted fishing area by using acoustic telemetry and a depth-constrained estimator of linear home ranges

Networks of no-take fishery reserves have emerged as a tool for managing deepwater fish species. In Hawaii and elsewhere, such areas are used to manage deepwater snapper species. However, little is known regarding the movements of these species relative to protected areas. We used passive acoustic telemetry to track crimson jobfish (Pristipomoides filamentosus), also known as opakapaka, in one of Hawaii’s bottomfish restricted fishing areas to understand the size required for a reserve to protect this species. From January 2017 through January 2018, 179 fish were tagged. Only 10 fish were classified as alive on the basis of movements indicated by detections in tracking data (tracks). For these fish, the median time between the first and last detection of an individual on an acoustic receiver array was 414.5 d with a mean number of detections per individual of 28,321. Linear estimates of home range averaged 3.7 and 6.0 km in conservative and optimistic scenarios, smaller than the median linear habitat dimension of Hawaii’s reserves. Fish were detected within the reserve on 97% or more of the days they were tracked. These results indicate that current reserves in Hawaii are likely sufficient in scale to confer positive biological benefits to opakapaka that reside within their borders

The health and economic costs of smoking in the workforce: premature mortality, sickness absence and workplace interventions for smoking cessation

Background The common argument used against the implementation of tobacco control policies is that revenue from tobacco duty is considerably higher than the health care costs smoking imposes on society. This point is true as revenue in the United Kingdom (UK) totalled £9.1 billion while recent costs estimates for the treatment of smoking-attributable disease totalled £5.2 billion to the UK National Health Service. However, this argument becomes unclear when indirect costs such as productivity loss or cost of absenteeism are incorporated. In the UK, there were 29.2 million employed adults in 2011 of which 20% were current smokers. This equates to approximately 5.84 million employed adult smokers. There are currently no studies which have quantified the economic impact of smoking-attributable indirect costs to both employers and the wider society in the UK. These costs are suspected to impose a large economic burden to society but the best practice methodology for estimating indirect costs and the magnitude of these costs are still unknown. Therefore, the aims of this thesis were to quantify the economic impact of smoking-attributable indirect costs due to productivity loss from premature mortality and absenteeism of workforce and to evaluate workplace interventions which could potentially decrease the burden of smoking in the workforce in the UK. Methods A number of methods were used along with a range of data sources which provided the information to quantify the economic impact of smoking in the workforce. Cost-of-illness methodology based on the human capital method was utilised to quantify the monetary burden of smoking in the workforce due to premature mortality in the UK. Systematic review and meta-analysis was used to examine the epidemiological association between smoking and absenteeism while also providing the necessary parameters to estimate costs of absence in the UK. Finally, decision analysis and Markov simulation modelling was used to conduct both cost-benefit analysis and cost-effectiveness analysis from the employer's perspective for evaluating workplace smoking cessation interventions of brief advice, individual counselling and nicotine replacement therapy with individual counselling. Results Cost-of-smoking modelling estimated that smoking was responsible for 96,105 deaths (58% male) in adults aged 35 years and over (17% of all deaths) in the UK annually, resulting in 1.2 million years of total life lost and 357,831 years of productive life lost valued at £4.93 billion in 2010. From the systematic review of 29 longitudinal studies, current smokers had a 33% increase in risk of absenteeism and were absent for an average of 2.74 more days per year compared with non-smokers. Compared with never smokers, ex-smokers had a 14% increase in risk of absenteeism; however, no increase in duration of absence could be detected. Indirect comparison meta-analysis showed that current smokers also had a 19% increase in risk of absenteeism compared with ex-smokers. Consequently, smoking was estimated to cost UK employers £1.46 billion in 2011 from absenteeism in the workplace. Workplace interventions for smoking cessation provide a possible method for reducing the burden of smoking in the workforce. Cost-benefit analysis of workplace interventions resulted in brief advice being the optimal decision strategy for women while brief advice and individual counselling both were optimal decision strategies for men in terms of minimising total costs and maximising return on investment for the employer. If the employer valued maximising quitting instead, cost-effectiveness analysis showed that nicotine replacement therapy with individual counselling would be the optimal strategy given a maximised budget constraint. Conclusion This thesis has provided the first indirect cost-of-smoking study quantifying the productivity loss due to premature mortality and absenteeism in UK; the first systematic review and meta-analysis which has explored the association between smoking and absence from work; and the first cost-benefit and cost-effectiveness analyses of workplace interventions for smoking cessation in the UK. The implications of this research have particular relevance for UK policy makers and employers to justify stronger tobacco control policy which promotes smoking cessation. However, these findings are not unique to the UK. The thesis has provided the framework and methodology for studies that can strengthen the evidence-base around the economics of smoking in other countries as well

The role of cost-effectiveness analysis in the development of indicators to support incentive-based behaviour in primary care in England

In England, general practitioners are incentivized through a national pay-for-performance scheme to adopt evidence-based quality improvement initiatives using a portfolio of Quality and Outcomes Framework (QOF) indicators. We describe the development of the methods used to assess the cost-effectiveness of these pay-for-performance indicators and how they have contributed to the development of new indicators. Prior to analysis of new potential indicators, an economic subgroup of the National Institute for Health and Care Excellence (NICE) Indicator Advisory Committee is formed to assess evidence on the cost-effectiveness of potential indicators in terms of the health benefits gained, compared to the cost of the intervention and the cost of the incentive. The expert subgroup is convened to reach consensus on the amounts that could potentially be paid to general practices for achieving new indicators. Indicators are also piloted in selected general practices and evidence gathered about their practical implementation. The methods used to assess economic viability of new pilot indicators represent a pragmatic and effective way of providing information to inform recommendations. Current policy to reduce QOF funding could shift the focus from national (QOF) to local schemes, with economic appraisal remaining central

Competition alters predicted forest carbon cycle responses to nitrogen availability and elevated CO2: simulations using an explicitly competitive, game-theoretic vegetation demographic model

Competition is a major driver of carbon allocation to different plant tissues (e.g., wood, leaves, fine roots), and allocation, in turn, shapes vegetation structure. To improve their modeling of the terrestrial carbon cycle, many Earth system models now incorporate vegetation demographic models (VDMs) that explicitly simulate the processes of individual-based competition for light and soil resources. Here, in order to understand how these competition processes affect predictions of the terrestrial carbon cycle, we simulate forest responses to elevated atmospheric CO2 concentration [CO2] along a nitrogen availability gradient, using a VDM that allows us to compare fixed allocation strategies vs. competitively optimal allocation strategies. Our results show that competitive and fixed strategies predict opposite fractional allocation to fine roots and wood, though they predict similar changes in total net primary production (NPP) along the nitrogen gradient. The competitively optimal allocation strategy predicts decreasing fine root and increasing wood allocation with increasing nitrogen, whereas the fixed strategy predicts the opposite. Although simulated plant biomass at equilibrium increases with nitrogen due to increases in photosynthesis for both allocation strategies, the increase in biomass with nitrogen is much steeper for competitively optimal allocation due to its increased allocation to wood. The qualitatively opposite fractional allocation to fine roots and wood of the two strategies also impacts the effects of elevated [CO2] on plant biomass. Whereas the fixed allocation strategy predicts an increase in plant biomass under elevated [CO2] that is approximately independent of nitrogen availability, competition leads to higher plant biomass response to elevated [CO2] with increasing nitrogen availability. Our results indicate that the VDMs that explicitly include the effects of competition for light and soil resources on allocation may generate significantly different ecosystem-level predictions of carbon storage than those that use fixed strategies

Guideline for UK midwives/health visitors to use with parents of infants at risk of developing childhood overweight/obesity

A guideline for members of the health visiting team to use with parents of infants at risk of overweight/obesity has been developed. The guideline contains recommendations about identification of infants at risk as well as a number of strategies that could be used for prevention of overweight/obesity. The guideline needs to be applied alongside health visitors’ professional judgement. It is not intended to replace normal UK clinical practice which is guided by the Healthy Child Programme and complements existing guidance such as the Framework for Action for tackling obesity

Performance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care

Familial hypercholesterolaemia (FH) is a common inherited disorder, causing lifelong elevated low-density lipoprotein cholesterol (LDL-C). Most individuals with FH remain undiagnosed, precluding opportunities to prevent premature heart disease and death. Some machine-learning approaches improve detection of FH in electronic health records, though clinical impact is under-explored. We assessed performance of an array of machine-learning approaches for enhancing detection of FH, and their clinical utility, within a large primary care population. A retrospective cohort study was done using routine primary care clinical records of 4,027,775 individuals from the United Kingdom with total cholesterol measured from 1 January 1999 to 25 June 2019. Predictive accuracy of five common machine-learning algorithms (logistic regression, random forest, gradient boosting machines, neural networks and ensemble learning) were assessed for detecting FH. Predictive accuracy was assessed by area under the receiver operating curves (AUC) and expected vs observed calibration slope; with clinical utility assessed by expected case-review workload and likelihood ratios. There were 7928 incident diagnoses of FH. In addition to known clinical features of FH (raised total cholesterol or LDL-C and family history of premature coronary heart disease), machine-learning (ML) algorithms identified features such as raised triglycerides which reduced the likelihood of FH. Apart from logistic regression (AUC, 0.81), all four other ML approaches had similarly high predictive accuracy (AUC > 0.89). Calibration slope ranged from 0.997 for gradient boosting machines to 1.857 for logistic regression. Among those screened, high probability cases requiring clinical review varied from 0.73% using ensemble learning to 10.16% using deep learning, but with positive predictive values of 15.5% and 2.8% respectively. Ensemble learning exhibited a dominant positive likelihood ratio (45.5) compared to all other ML models (7.0–14.4). Machine-learning models show similar high accuracy in detecting FH, offering opportunities to increase diagnosis. However, the clinical case-finding workload required for yield of cases will differ substantially between models

Sub-optimal cholesterol response to initiation of statins and future risk of cardiovascular disease

Objective:To assess low-density lipoprotein cholesterol (LDL-C) response in patients after initiation of statins, and future risk of CVD.Method:Prospective cohort study of 165,411 primary care patients, from the UK Clinical Practice Research Datalink, who were free from CVD prior to statin initiation, and had at least one pre-treatment LDL-C within 12 months prior to, and one post-treatment LDL-C within 24 months after, statin initiation. Based on current national guidelines, a less than 40% reduction in baseline LDL-cholesterol level within 24 months was classified as sub-optimal statin response. Cox proportional regression and competing-risks survival regression models were used to determine adjusted hazard ratios and sub-hazard ratios for incident CVD outcomes for LDL-C response to statins.Results: 84,609 (51.2%) patients had sub-optimal LDL-cholesterol response to initiated statin therapy within 24 months. During 1,077,299 person-years of follow-up (median follow-up 6.2 years), there were 22,798 CVD events (12,142 in sub-optimal responders and 10,656 in optimal responders). In sub-optimal responders, compared to optimal responders, the hazard ratio (95% CI) for incident CVD was 1.17 (1.13–1.20) and 1.22 (1.19–1.25) after adjusting for age and baseline untreated LDL-cholesterol level. Considering competing risks resulted in lower but similar sub-hazards ratios for both unadjusted 1.13 (1.10–1.16) and adjusted cumulative incidence function, 1.19 (1.16–1.23) of CVD.Conclusions:Optimal lowering of LDL-cholesterol is not achieved within two years in over half of patients in the general population initiated on statin therapy, and these patients will experience significantly increased risk of future cardiovascular disease

Development of an evidence-based practice guideline for UK public health nurses (health visitors) to use with parents of infants at risk of obesity

Introduction: Evidence about effective interventions that reduce obesity risk during infancy is needed. This project aimed to systematically review published Randomised Controlled Trials (RCTs) of behavioural and non-behavioural interventions which address potential risk factors for obesity to inform a guideline for UK health visitors. Methods: A multiprofessional Guideline Development Group (GDG) was convened to undertake a systematic review, based on the National Institute for Health and Clinical Excellence (NICE) guidelines. Findings from the review were used to develop a guideline which was subsequently externally reviewed by national experts and practitioners. Results: We identified 28 RCTs reporting behavioural and non-behavioural interventions delivered during infancy with breastfeeding and/or weight outcomes measured during the first two years of life. A number of on-going studies were also identified. Inclusion criteria for intervention studies included parental breastfeeding intentions and first time parents. Good evidence exists for breastfeeding promotion and support interventions. Evidence exists for parental education around responsive feeding, aspects of infant diet and soothing/sleep expectations. These behavioural components informed the guideline. Despite good evidence that infants fed lower protein formula milk gained less weight compared to milk with higher protein levels, it was not possible to incorporate the evidence from the non-behavioural studies into the guideline. Conclusion: Further research is needed to establish clinically effective interventions for obesity prevention during infancy. Continuous dialogue between commissioners, policy makers, health visitors and parents is essential to ensure existing UK policies are not a barrier to implementing obesity prevention strategies in the first year of life

Characteristics predicting recommendation for familial breast cancer referral in a cohort of women from primary care

© 2020, The Author(s). Family history of breast and related cancers can indicate increased breast cancer (BC) risk. In national familial breast cancer (FBC) guidelines, the risk is stratified to guide referral decisions. We aimed to identify characteristics associated with the recommendation for referral in a large cohort of women undergoing FBC risk assessment in a recent primary care study. Demographic, family history, psychological and behavioural factors were collected with family history questionnaires, psychological questionnaires and manual data extraction from general practice electronic health records. Participants were women aged 30–60 with no previous history of breast or ovarian cancer. Data from 1127 women were analysed with stepwise logistic regression. Two multivariable logistic models were developed to predict recommendations for referral: using the entire cohort (n = 1127) and in a subgroup with uncertain risks (n = 168). Model performance was assessed by the area under the receiver operating curve (AUC). In all 1127 women, a multivariable model incorporating five family history components (BC aged < 40, bilateral BC, prostate cancer, first degree relative with ovarian cancer, paternal family history of BC) and having a mammogram in the last 3years, performed well (AUC = 0.86). For the 168 uncertain risk women, only paternal family history of BC remained significant (AUC = 0.71). Clinicians should pay particular attention to these five family history components when assessing FBC risk, especially prostate cancer which is not in the current national guidelines