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Helping the world's coastal communities adapt to climate change impacts requires evaluating the vulnerability of coastal communities and assessing adaptation options. This includes understanding the potential for 'natural' infrastructure (ecosystems and the biodiversity that underpins them) to reduce communities' vulnerability, alongside more traditional 'hard' infrastructure approaches. Here we present a spatially explicit global evaluation of the vulnerability of coastal-dwelling human populations to key climate change exposures and explore the potential for coastal ecosystems to help people adapt to climate change (ecosystem-based adaptation (EbA)). We find that mangroves and coral reefs are particularly well situated to help people cope with current weather extremes, a function that will only increase in importance as people adapt to climate change now and in coming decades. We find that around 30.9 million people living within 2km of the coast are highly vulnerable to tropical storms and sea-level rise (SLR). Mangroves and coral reefs overlap these threats to at least 5.3 and 3.4 million people, respectively, with substantial potential to dissipate storm surges and improve resilience against SLR effects. Significant co-benefits from mangroves also accrue, with 896 million metric tons of carbon stored in their soils and above- and below-ground biomass. Our framework offers a tool for prioritizing 'hotspots' of coastal EbA potential for further, national and local analyses to quantify risk reduction and, thereby, guide investment in coastal ecosystems to help people adapt to climate change. In doing so, it underscores the global role that conserving and restoring ecosystems can play in protecting human lives and livelihoods, as well as biodiversity, in the face of climate change.

Between 1996 and 2009 the annual number of emergency department (ED) visits in the United States increased by 51 percent while the number of EDs nationwide decreased by 6 percent, which placed unprecedented strain on the nation's EDs. To investigate the effects of an ED's closing on surrounding communities, we identified all ED closures in California during the period 1999-2010 and examined their association with inpatient mortality rates at nearby hospitals. We found that one-quarter of hospital admissions in this period occurred near an ED closure and that these admissions had 5 percent higher odds of inpatient mortality than admissions not occurring near a closure. This association persisted whether we considered ED closures as affecting all future nearby admissions or only those occurring in the subsequent two years. These results suggest that ED closures have ripple effects on patient outcomes that should be considered when health systems and policy makers decide how to regulate ED closures.

BACKGROUND: Trauma centers are an effective but costly element of the US health care infrastructure. Some Level I and II trauma centers regularly incur financial losses when these high fixed costs are coupled with high burdens of uncompensated care for disproportionately young and uninsured trauma patients. As a result, they are at risk of reducing their services or closing. The impact of these closures on patient outcomes, however, has not been previously assessed. METHODS: We performed a retrospective study of all adult patient visits for injuries at Level I and II, nonfederal trauma centers in California between 1999 and 2009. Within this population, we compared the in-hospital mortality of patients whose drive time to their nearest trauma center increased as the result of a nearby closure with those whose drive time did not increase using a multivariate logit-linked generalized linear model. Our sensitivity analysis tested whether this effect was limited to a 2-year period following a closure. RESULTS: The odds of inpatient mortality increased by 21% (odds ratio, 1.21; 95% confidence interval, 1.04-1.40) among trauma patients who experienced an increased drive time to their nearest trauma center as a result of a closure. The sensitivity analyses showed an even larger effect in the 2 years immediately following a closure, during which patients with increased drive time had 29% higher odds of inpatient death (odds ratio, 1.29; 95% confidence interval, 1.11-1.51). CONCLUSION: Our results show a strong association between closure of trauma centers in California and increased mortality for patients with injuries who have to travel further for definitive trauma care. These adverse impacts were intensified within 2 years of a closure. LEVEL OF EVIDENCE: Prognostic and epidemiologic, level III.

Purpose: The purpose of this study is to map and analyze sustainability activities and relationships at the seven Claremont Colleges and graduate institutions using social network analysis (SNA) to inform sustainability planning and programming. Design/methodology/approach: Online surveys and interviews were conducted among faculty, staff and students, and a network map was created and analyzed using network statistics to identify network characteristics. Findings: The mapped sustainability network has 291 one- and bi-directional connections but with substantial differences among institutions. Pomona and Pitzer colleges have the highest number of sustainability-related courses because of their popular Environmental Analysis programs. The two graduate schools and Scripps College are comparatively isolated. Scripps' network is small but highly interconnected and resilient. Pomona's network is extensive but concentrated on a single node. Several other key actors were identified based on the number of nodes extending from or connecting to them. Several new sustainability initiatives were recently launched in response to the study. Practical implications: SNA and mapping for campus sustainability can highlight network gaps and network vulnerabilities. To increase completeness, a representative and sufficiently large data sample is needed, requiring multiple, coordinated forms of contact. Interviews yield more detailed and comprehensive information than online surveys but are more time-consuming. Thus, the combination of electronic surveys and in-person interviews can be a successful strategy for maximizing information collection. Originality/value: The case study was the first of its kind conducted at the Claremont Colleges and one of the first in higher education. It informs sustainability planning, coordination and integration efforts.

STUDY OBJECTIVE: We seek to determine whether patients living in areas affected by emergency department (ED) closure, with subsequent increased distance to the nearest ED, have a higher risk of inpatient death from time-sensitive conditions. METHODS: Using the California Office of Statewide Health and Planning Development database, we performed a nonconcurrent cohort study of hospital admissions in California between 1999 and 2009 for patients admitted for acute myocardial infarction, stroke, sepsis and asthma or chronic obstructive pulmonary disease. We used generalized linear mixed-effects models comparing adjusted inpatient mortality for patients experiencing increased distance to the nearest ED versus no change in distance. RESULTS: Of 785,385 patient admissions, 67,577 (8.6%) experienced an increase in distance to ED care because of an ED closure. The median change for patients experiencing an increase in distance to the nearest ED was only 0.8 miles, with a range of 0.1 to 33.4 miles. Patients with an increase did not have a significantly higher mortality (adjusted odds ratio 1.04; 95% confidence interval 0.99 to 1.09). In subgroups, we also observed no statistically significant differences in adjusted mortality among patients with acute myocardial infarction, stroke, asthma or chronic obstructive pulmonary disease, and sepsis. We did not observe any significant variations in mortality for time-sensitive conditions in sensitivity analyses that incorporated a lag effect of time after change in distance, allowance for a larger affected population, or removal of ST-segment elevation myocardial infarction from the acute myocardial infarction subgroup. CONCLUSION: In this large population-based sample, less than 10% of the patients experienced an increase in distance to the nearest ED, and of that group, the majority had less than a 1-mile increase. These small increased distances to the nearest ED were not associated with higher inpatient mortality among time-sensitive conditions.

Abstract Background Local measurements of health behaviors, diseases, and use of health services are critical inputs into local, state, and national decision-making. Small area measurement methods can deliver more precise and accurate local-level information than direct estimates from surveys or administrative records, where sample sizes are often too small to yield acceptable standard errors. However, small area measurement requires careful validation using approaches other than conventional statistical methods such as in-sample or cross-validation methods because they do not solve the problem of validating estimates in data-sparse domains. Methods A new general framework for small area estimation and validation is developed and applied to estimate Type 2 diabetes prevalence in US counties using data from the Behavioral Risk Factor Surveillance System (BRFSS). The framework combines the three conventional approaches to small area measurement: (1) pooling data across time by combining multiple survey years; (2) exploiting spatial correlation by including a spatial component; and (3) utilizing structured relationships between the outcome variable and domain-specific covariates to define four increasingly complex model types - coined the Naive, Geospatial, Covariate, and Full models. The validation framework uses direct estimates of prevalence in large domains as the gold standard and compares model estimates against it using (i) all available observations for the large domains and (ii) systematically reduced sample sizes obtained through random sampling with replacement. At each sampling level, the model is rerun repeatedly, and the validity of the model estimates from the four model types is then determined by calculating the (average) concordance correlation coefficient (CCC) and (average) root mean squared error (RMSE) against the gold standard. The CCC is closely related to the intraclass correlation coefficient and can be used when the units are organized in groups and when it is of interest to measure the agreement between units in the same group (e.g., counties). The RMSE is often used to measure the differences between values predicted by a model or an estimator and the actually observed values. It is a useful measure to capture the precision of the model or estimator. Results All model types have substantially higher CCC and lower RMSE than the direct, single-year BRFSS estimates. In addition, the inclusion of relevant domain-specific covariates generally improves predictive validity, especially at small sample sizes, and their leverage can be equivalent to a five- to tenfold increase in sample size. Conclusions Small area estimation of important health outcomes and risk factors can be improved using a systematic modeling and validation framework, which consistently outperformed single-year direct survey estimates and demonstrated the potential leverage of including relevant domain-specific covariates compared to pure measurement models. The proposed validation strategy can be applied to other disease outcomes and risk factors in the US as well as to resource-scarce situations, including low-income countries. These estimates are needed by public health officials to identify at-risk groups, to design targeted prevention and intervention programs, and to monitor and evaluate results over time.

Byline: Giuseppe Munda, Michela Nardo, Michaela Saisana, Tanja Srebotnjak Composite indicators are very common in economic and business statistics for benchmarking the relative progress of countries in a variety of policy domains such as industrial competitiveness, sustainable development, globalisation and innovation. This article provides an analysis of the key uncertainties underlying the mathematical modelling used to construct composite indicators within the broader context of sustainability measurement. A case study of the Environmental Sustainability Index illustrates how these uncertainties can be modelled and estimated.

Between 1996 and 2009 the annual number of emergency department (ED) visits in the United States increased by 51 percent while the number of EDs nationwide decreased by 6 percent, which placed unprecedented strain on the nation's EDs. To investigate the effects of an ED's closing on surrounding communities, we identified all ED closures in California during the period 1999-2010 and examined their association with inpatient mortality rates at nearby hospitals. We found that one-quarter of hospital admissions in this period occurred near an ED closure and that these admissions had 5 percent higher odds of inpatient mortality than admissions not occurring near a closure. This association persisted whether we considered ED closures as affecting all future nearby admissions or only those occurring in the subsequent two years. These results suggest that ED closures have ripple effects on patient outcomes that should be considered when health systems and policy makers decide how to regulate ED closures. [ABSTRACT FROM AUTHOR]

Many human populations are dependent on marine ecosystems for a range of benefits, but we understand little about where and to what degree people rely on these ecosystem services. We created a new conceptual model to map the degree of human dependence on marine ecosystems based on the magnitude of the benefit, susceptibility of people to a loss of that benefit, and the availability of alternatives. We focused on mapping nutritional, economic, and coastal protection dependence, but our model is repeatable, scalable, applicable to other ecosystems, and designed to incorporate additional services and data. Here we show that dependence was highest for Pacific and Indian Ocean island nations and several West African countries. More than 775 million people live in areas with relatively high dependence scores. By identifying where and how people are dependent on marine ecosystems, our framework can be used to design more effective large‐scale management and policy interventions. [ABSTRACT FROM AUTHOR]