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Hurricane Maria caused catastrophic damage in Puerto Rico, increasing the risk for morbidity and mortality in the post-impact period. We aimed to establish a syndromic surveillance system to describe the number and type of visits at 2 emergency health-care settings in the same hospital system in Ponce, Puerto Rico.
We implemented a hurricane surveillance system by interviewing patients with a short questionnaire about the reason for visit at a hospital emergency department and associated urgent care clinic in the 6 mo after Hurricane Maria. We then evaluated the system by comparing findings with data from the electronic medical record (EMR) system for the same time period.
The hurricane surveillance system captured information from 5116 participants across the 2 sites, representing 17% of all visits captured in the EMR for the same period. Most visits were associated with acute illness/symptoms (79%), followed by injury (11%). The hurricane surveillance and EMR data were similar, proportionally, by sex, age, and visit category.
The hurricane surveillance system provided timely and representative data about the number and type of visits at 2 sites. This system, or an adapted version using available electronic data, should be considered in future disaster settings.
The purpose of this chapter is to serve as a bridge between the chapters in the previous three sections and those in this fourth section. Thus far, we have sought to analyse the social bases of mental and physical wellbeing. Now, we turn to the question of how the fruits of these analyses can be applied in practice. That is, we have been reporting and interpreting the way the world impacts individual people for long enough; it is time to consider how we might change the world in order to improve our wellbeing.
This chapter pulls together key matters in this book. Its title is a quote from a line given to one of the characters in Hamlet by Shakespeare. That sentence perfectly outlines the intention of Section 5 of this book and the function of this final chapter in which I endeavour to align theory, research and the practical impacts of the topics covered by this book with the circumstances in which we find health services as we near the close of the second decade of the twenty-first century. But, first, I return to Chapter 1, to recapture some of those circumstances. Then, I look at the matters on which I think we should focus in order to sustain healthcare services and incorporate the social agenda identified in this book.
This book’s roots are in an impactful seminar series hosted by the Royal College of Psychiatrists in which practitioners and scientists from a wide array of disciplines came together in 2014 to explore the social influences on our health and recovery from ill health. This volume echoes the evocative conversations in that College and is intended to rehearse research of potentially great impact. It presents practitioners, researchers, policymakers and students of a wide array of disciplines and roles with the material to support them in better harnessing what we now know about the impact of social factors on health. Thereby, the editors hope to influence how practitioners and the responsible authorities work together with members of the public and communities to design and deliver services. Our aspiration is to contribute to creating better-targeted approaches to promoting health and mental health and more effective and integrated interventions for people who have health problems or disorders.
Public health is defined by the UK’s Faculty of Public Health as ‘The science and art of promoting and protecting health and well being, preventing ill health and prolonging life through the organised efforts of society’.
This definition locates the causes of ill health and the remedies in the realms of personal and societal agency, and not only in the remit of health practitioners. Although the latter have a role as members of society to make prevention a reality for themselves, families and communities, they play a special part in preventing further ill health for people who suffer mental illness and are seeking help for it.
Other chapters in this book attend to the relational and social fabric that enables people to flourish; it is made of good and trusting relationships, and material conditions that permit thought about purpose and meaning beyond survival.
While there is great optimism for healthcare to be gained from developments in neuroscience, genetics and epigenetics, the social contexts and social approaches revealed by research, including much that we cover in this book, are also very powerful contributors to our health and recovery from ill health. As Nestler et al. say, ‘Psychiatric disorders are complex multifactorial illnesses … While genetic factors are important in the etiology of most mental disorders, the relatively high rates of discordance among identical twins … clearly indicate the importance of additional mechanisms’ (Nestler et al., 2016, p. 447).
This book focuses on social and environmental mechanisms; this chapter draws together a selection of the topics raised in Sections 1 and 2. We link facets of the social science that have come up thus far with concepts that are implicit in public physical and mental healthcare, and we summarise the concept of mental health recovery.
Reading the first part of this book presents a striking contrast between current preoccupations in healthcare systems and the science presented here. In other words, between extant public concerns about entitlement to, funding of, and delivering healthcare in the second decade of the twenty-first century and the contents of Chapters 2, 3 and 4.
Healthcare systems and the people who fund, run and deliver them are, arguably, necessarily acutely sensitive to the socio-economic environment in which countries sit. The potential capabilities of healthcare continue to develop at increasingly rapid rates. By contrast, we live in a world in which the resources available are affected by austerity and in which the spread of affluence between the most advantaged people and the least affluent continues to grow. This is contributing to an increasing gap between potential capability and actual capacity, which appears to be expanding rapidly.
This chapter does two things. First, it shows how social identity principles can explain the basic psychological and behavioural effects of crowd membership. Second, it describes some recent research and applied work that shows how these basic effects operate to contribute to harmonious outcomes in potentially dangerous crowd events.
We begin by explaining some of the fundamental psychology of crowd membership in the next section.
Using current societal dilemmas, this book explores how social factors and social identity influence our health and recovery from illness. It includes recent research to present practitioners, researchers, policymakers and students of many disciplines with the material to support them in better harnessing current knowledge of the impact of social factors on health. The contents will influence collaborative working across policy, disciplinary and practice boundaries to design and deliver healthcare services. The book identifies the importance of social connectedness, social support, agency and self and group efficacy in people's health, longevity and resilience after adversity. Core perspectives include the social identity approach and a values framework for taking public health ethics into decision-making, both of which emphasise valuing people and co-productive relationships. Advocating better targeted mental health promotion and integrated interventions, this book strongly argues for a greater emphasis on social factors in evidence-based and cost-effective practice.
A predictive risk stratification tool (PRISM) to estimate a patient's risk of an emergency hospital admission in the following year was trialled in general practice in an area of the United Kingdom. PRISM's introduction coincided with a new incentive payment (‘QOF’) in the regional contract for family doctors to identify and manage the care of people at high risk of emergency hospital admission.
Alongside the trial, we carried out a complementary qualitative study of processes of change associated with PRISM's implementation. We aimed to describe how PRISM was understood, communicated, adopted, and used by practitioners, managers, local commissioners and policy makers. We gathered data through focus groups, interviews and questionnaires at three time points (baseline, mid-trial and end-trial). We analyzed data thematically, informed by Normalisation Process Theory (1).
All groups showed high awareness of PRISM, but raised concerns about whether it could identify patients not yet known, and about whether there were sufficient community-based services to respond to care needs identified. All practices reported using PRISM to fulfil their QOF targets, but after the QOF reporting period ended, only two practices continued to use it. Family doctors said PRISM changed their awareness of patients and focused them on targeting the highest-risk patients, though they were uncertain about the potential for positive impact on this group.
Though external factors supported its uptake in the short term, with a focus on the highest risk patients, PRISM did not become a sustained part of normal practice for primary care practitioners.
New approaches are needed to safely reduce emergency admissions to hospital by targeting interventions effectively in primary care. A predictive risk stratification tool (PRISM) identifies each registered patient's risk of an emergency admission in the following year, allowing practitioners to identify and manage those at higher risk. We evaluated the introduction of PRISM in primary care in one area of the United Kingdom, assessing its impact on emergency admissions and other service use.
We conducted a randomized stepped wedge trial with cluster-defined control and intervention phases, and participant-level anonymized linked outcomes. PRISM was implemented in eleven primary care practice clusters (total thirty-two practices) over a year from March 2013. We analyzed routine linked data outcomes for 18 months.
We included outcomes for 230,099 registered patients, assigned to ranked risk groups.
Overall, the rate of emergency admissions was higher in the intervention phase than in the control phase: adjusted difference in number of emergency admissions per participant per year at risk, delta = .011 (95 percent Confidence Interval, CI .010, .013). Patients in the intervention phase spent more days in hospital per year: adjusted delta = .029 (95 percent CI .026, .031). Both effects were consistent across risk groups.
Primary care activity increased in the intervention phase overall delta = .011 (95 percent CI .007, .014), except for the two highest risk groups which showed a decrease in the number of days with recorded activity.
Introduction of a predictive risk model in primary care was associated with increased emergency episodes across the general practice population and at each risk level, in contrast to the intended purpose of the model. Future evaluation work could assess the impact of targeting of different services to patients across different levels of risk, rather than the current policy focus on those at highest risk.
Emergency admissions to hospital are a major financial burden on health services. In one area of the United Kingdom (UK), we evaluated a predictive risk stratification tool (PRISM) designed to support primary care practitioners to identify and manage patients at high risk of admission. We assessed the costs of implementing PRISM and its impact on health services costs. At the same time as the study, but independent of it, an incentive payment (‘QOF’) was introduced to encourage primary care practitioners to identify high risk patients and manage their care.
We conducted a randomized stepped wedge trial in thirty-two practices, with cluster-defined control and intervention phases, and participant-level anonymized linked outcomes. We analysed routine linked data on patient outcomes for 18 months (February 2013 – September 2014). We assigned standard unit costs in pound sterling to the resources utilized by each patient. Cost differences between the two study phases were used in conjunction with differences in the primary outcome (emergency admissions) to undertake a cost-effectiveness analysis.
We included outcomes for 230,099 registered patients. We estimated a PRISM implementation cost of GBP0.12 per patient per year.
Costs of emergency department attendances, outpatient visits, emergency and elective admissions to hospital, and general practice activity were higher per patient per year in the intervention phase than control phase (adjusted δ = GBP76, 95 percent Confidence Interval, CI GBP46, GBP106), an effect that was consistent and generally increased with risk level.
Despite low reported use of PRISM, it was associated with increased healthcare expenditure. This effect was unexpected and in the opposite direction to that intended. We cannot disentangle the effects of introducing the PRISM tool from those of imposing the QOF targets; however, since across the UK predictive risk stratification tools for emergency admissions have been introduced alongside incentives to focus on patients at risk, we believe that our findings are generalizable.
Sea-ice thickness distributions from 12 submarine cruises under the North Pole are used to evaluate and enhance the results of sea-ice model simulations. The sea-ice models include versions with cavitating fluid and elastic-viscous-plastic rheologies, and versions with a single thickness and with multiple (5–27) thicknesses in each gridcell. A greater portion of the interannual variance of observed mean thickness at the Pole is captured by the multiple-thickness models than by the single-thickness models, although even the highest correlations are only about 0.6. After The observed thickness distributions are used to ˚tune" the model to capture the primary mode of the distribution, the largest model-data discrepancies are in the thin-ice tail of the distribution. In a 41 year simulation ending in 1998, the model results show a pronounced decrease of mean ice thickness at the Pole around 1990; the minimum simulated thickness occurs in summer 1998. The decrease coincides with a shift of the Arctic Oscillation to its positive phase. The smallest submarine-derived mean thickness occurs in 1990, but no submarine data were available after 1992. The submarine-derived thicknesses for 1991 and 1992 are only slightly smaller than the 12–case mean.
For most of the Twentieth Century the angiosperm archetypal flower has been viewed as relatively large, multiparted, with spirally arranged fleshy appendages, and as being probably beetle pollinated as in some extant Magnoliales. However, the preponderance of fossil evidence indicates that flowers with such characters do not appear until the mid-Cretaceous, well after smaller simpler fossil flowers such as platanoids and chloranthoids. Winteraceous and Chloranthaceous pollen appears more or less simultaneously in the Lower Cretaceous, but rapidly mounting evidence for mosaicism in Cretaceous taxa makes it unwise to extrapolate floral structure on the basis of dispersed pollen. Mid-Late Cretaceous fossils illustrate an increasing proportion of simple flowered Rosidae in the angiosperm flora. We report new fossil evidence of charcoalified flowers and fruits representing at least 20–30 diverse angiosperm taxa from the Cenomanian and Turonian deposits of the Atlantic Coastal Plain. These fossil flowers include representatives with hypanthia and floral cups, sympetaly, syncarpy, inferior ovaries, campylotropous ovules, nectaries of various forms, specialized anther dehiscence, epipetalous stamens, and connate filament tubes. Major taxonomic groups (as defined by Cronquist) represented by these fossils include Dilleniidae, Magnoliidae, Rosidae, monocots, and possibly Caryophyllidae. Thus, the early Late Cretaceous angiosperm flora had greater floral diversity than has previously been documented. This array of floral structures includes features that are now associated with bees and other specialized insect pollinators, thus providing a new perspective on the evolution of insect pollination.
Studies have demonstrated that the effects of two well-known predictors of adolescent substance use, family monitoring and antisocial peers, are not static but change over the course of adolescence. Moreover, these effects may differ for different groups of youth. The current study uses time-varying effect modeling to examine the changes in the association between family monitoring and antisocial peers and marijuana use from ages 11 to 19, and to compare these associations by gender and levels of behavioral disinhibition. Data are drawn from the Raising Healthy Children study, a longitudinal panel of 1,040 youth. The strength of association between family monitoring and antisocial peers and marijuana use was mostly steady over adolescence, and was greater for girls than for boys. Differences in the strength of the association were also evident by levels of behavioral disinhibition: youth with lower levels of disinhibition were more susceptible to the influence of parents and peers. Stronger influence of family monitoring on girls and less disinhibited youth was most evident in middle adolescence, whereas the stronger effect of antisocial peers was significant during middle and late adolescence. Implications for the timing and targeting of marijuana preventive interventions are discussed.