Long-chain per-and polyfluoroalkyl substances (PFASs) are being replaced by short-chain PFASs and fluorinated alternatives. For ten legacy PFASs and seven recently discovered perfluoroalkyl ether carboxylic acids (PFECAs), we report (1) their occurrence in the Cape Fear River (CFR) watershed, (2) their fate in water treatment processes, and (3) their adsorbability on powdered activated carbon (PAC). In the headwater region of the CFR basin, PFECAs were not detected in raw water of a drinking water treatment plant (DWTP), but concentrations of legacy PFASs were high. The U.S. Environmental Protection Agency's lifetime health advisory level (70 ng/L) for perfluorooctanesulfonic acid and perfluorooctanoic acid (PFOA) was exceeded on 57 of 127 sampling days. In raw water of a DWTP downstream of a PFAS manufacturer, the mean concentration of perfluoro-2-propoxypropanoic acid (PFPrOPrA), a replacement for PFOA, was 631 ng/L (n = 37). Six other PFECAs were detected, with three exhibiting chromatographic peak areas up to 15 times that of PFPrOPrA. At this DWTP, PFECA removal by coagulation, ozonation, biofiltration, and disinfection was negligible. The adsorbability of PFASs on PAC increased with increasing chain length. Replacing one CF 2 group with an ether oxygen decreased the affinity of PFASs for PAC, while replacing additional CF 2 groups did not lead to further affinity changes.

This journal is a member of and subscribes to the principles of the Committee on Publication Ethics (COPE) (www.publicationethics.org/).Editorial contact: journals.library@nihr.ac.ukThe full HTA archive is freely available to view online at www.journalslibrary.nihr.ac.uk/hta. Print-on-demand copies can be purchased from the report pages of the NIHR Journals Library website: www.journalslibrary.nihr.ac.uk Criteria for inclusion in the Health Technology Assessment journalReports are published in Health Technology Assessment (HTA) if (1) they have resulted from work for the HTA programme, and (2) they are of a sufficiently high scientific quality as assessed by the reviewers and editors.Reviews in Health Technology Assessment are termed 'systematic' when the account of the search appraisal and synthesis methods (to minimise biases and random errors) would, in theory, permit the replication of the review by others. HTA programmeThe HTA programme, part of the National Institute for Health Research (NIHR), was set up in 1993. It produces high-quality research information on the effectiveness, costs and broader impact of health technologies for those who use, manage and provide care in the NHS. 'Health technologies' are broadly defined as all interventions used to promote health, prevent and treat disease, and improve rehabilitation and long-term care.The journal is indexed in NHS Evidence via its abstracts included in MEDLINE and its Technology Assessment Reports inform National Institute for Health and Care Excellence (NICE) guidance. HTA research is also an important source of evidence for National Screening Committee (NSC) policy decisions.For more information about the HTA programme please visit the website: http://www.nets.nihr.ac.uk/programmes/hta This reportThe research reported in this issue of the journal was funded by the HTA programme as project number 04/33/01. The contractual start date was in September 2006. The draft report began editorial review in March 2015 and was accepted for publication in December 2015. The authors have been wholly responsible for all data collection, analysis and interpretation, and for writing up their work. The HTA editors and publisher have tried to ensure the accuracy of the authors' report and would like to thank the reviewers for their constructive comments on the draft document. However, they do not accept liability for damages or losses arising from material published in this report.This report presents independent research funded by the National Institute for Health Research (NIHR). The views and opinions expressed by authors in this publication are those of the authors and do not necessarily reflect those of the NHS, the NIHR, NETSCC, the HTA programme or the Department of Health. If there are verbatim quotations included in this publication the views and opinions expressed by the interviewees are those of the interviewees and do not necessarily reflect those of the authors, those of the NHS, the NIHR, NETSCC, the HTA programme or the Department of Health. Published by...

Background: We sought to quantify the relationship between body mass index (BMI) and health-related quality (HRQoL) of life, as measured by the EQ-5D, whilst controlling for potential confounders. In addition, we hypothesised that certain long-term conditions (LTCs), for which being overweight or obese is a known risk factor, may mediate the association between BMI and HRQoL. Hence the aim of our study was to explore the association between BMI and HRQoL, first controlling for confounders and then exploring the potential impact of LTCs.

BackgroundMultimorbidity is increasingly being recognized as a serious public health concern. Research into its determinants, prevalence, and management is needed and as the risk of experiencing multiple chronic conditions increases over time, attention should be given to investigating the development of multimorbidity through prospective cohort design studies. Here we examine the baseline patterns of multimorbidity and their association with health outcomes for residents in Yorkshire, England using data from the Yorkshire Health Study.MethodsBaseline data from the Yorkshire Health Study (YHS) was collected from 27,806 patients recruited between 2010 and 2012. A two-stage sampling strategy was implemented which first involved recruiting 43 general practice surgeries and then having them consent to mailing invitations to their patients to complete postal or online questionnaires. The questionnaire collected information on chronic health conditions, demographics, health-related behaviours, healthcare and medication usage, and a range of other health related variables. Descriptive statistics (chi-square and t tests) were used to examine associations between these variables and multimorbidity.ResultsIn the YHS cohort, 10,332 participants (37.2 %) reported having at least two or more long-term health conditions (multimorbidity). Older age, BMI and deprivation were all positively associated with multimorbidity. Nearly half (45.7 %) of participants from the most deprived areas experienced multimorbidity. Based on the weighted sample, average health-related quality of life decreased with the number of health conditions reported; the mean EQ-5D score for participants with no conditions was 0.945 compared to 0.355 for participants with five or more. The mean number of medications used for those without multimorbidity was 1.81 (range 1-13, SD = 1.25) compared to 3.81 (range 1-14, SD = 2.44) for those with at least two long-term conditions and 7.47 (range 1-37, SD = 7.47) for those with 5+ conditions.ConclusionPatterns of multimorbidity within the Yorkshire Health Study support research on multimorbidity within previous observational cross-sectional studies. The YHS provides both a facility for participant recruitment to intervention trials, and a large population-based longitudinal cohort for observational research. It is planned to continue to record chronic conditions and other health related behaviours in future waves which will be useful for examining determinants and trends in chronic disease and multimorbidity.

Objectives Health-related quality of life (HRQoL) measures have been increasingly used in economic evaluations for policy guidance. We investigate the impact of 11 self-reported long-standing health conditions on HRQoL using the EQ-5D in a UK sample.MethodsWe used data from 13,955 patients in the South Yorkshire Cohort study collected between 2010 and 2012 containing the EQ-5D, a preference-based measure. Ordinary least squares (OLS), Tobit and two-part regression analyses were undertaken to estimate the impact of 11 long-standing health conditions on HRQoL at the individual level.ResultsThe results varied significantly with the regression models employed. In the OLS and Tobit models, pain had the largest negative impact on HRQoL, followed by depression, osteoarthritis and anxiety/nerves, after controlling for all other conditions and sociodemographic characteristics. The magnitude of coefficients was higher in the Tobit model than in the OLS model. In the two-part model, these four long-standing health conditions were statistically significant, but the magnitude of coefficients decreased significantly compared to that in the OLS and Tobit models and was ranked from pain followed by depression, anxiety/nerves and osteoarthritis.ConclusionsPain, depression, osteoarthritis and anxiety/nerves are associated with the greatest losses of HRQoL in the UK population. The estimates presented in this article should be used to inform economic evaluations when assessing health care interventions, though improvements can be made in terms of diagnostic information and obtaining longitudinal data.