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Social networks, wherein the edges represent nonbehavioral relations such as friendship, power, and influence, can be difficult to measure and model. A powerful tool to address this is cognitive social structures (Krackhardt, D. (1987). Cognitive social structures. Social Networks, 9(2), 109–134.), where the perception of the entire network is elicited from each actor. We provide a formal statistical framework to analyze informants’ perceptions of the network, implementing a latent space network model that can estimate, e.g., homophilic effects while accounting for informant error. Our model allows researchers to better understand why respondents’ perceptions differ. We also describe how to construct a meaningful single aggregated network that ameliorates potential respondent error. The proposed method provides a visualization method, an estimate of the informants’ biases and variances, and we describe a method for sidestepping forced-choice designs.
To estimate the burden of Clostridium difficile infections (CDIs) due to interfacility patient sharing at regional and hospital levels.
Retrospective observational study.
We used data from the Healthcare Cost and Utilization Project California State Inpatient Database (2005–2011) to identify 26,878,498 admissions and 532,925 patient transfers. We constructed a weighted, directed network among the hospitals by defining an edge between 2 hospitals to be the monthly average number of patients discharged from one hospital and admitted to another on the same day. We then used a network autocorrelation model to study the effect of the patient sharing network on the monthly average number of CDI cases per hospital, and we estimated the proportion of CDI cases attributable to the network.
We found that 13% (95% confidence interval [CI], 7.6%–18%) of CDI cases were due to diffusion through the patient-sharing network. The network autocorrelation parameter was estimated at 5.0 (95% CI, 3.0–6.9). An increase in the number of patients transferred into and/or an increased CDI rate at the hospitals from which those patients originated led to an increase in the number of CDIs in the receiving hospital.
A minority but substantial burden of CDI infections are attributable to hospital transfers. A hospital’s infection control may thus be nontrivially influenced by its neighboring hospitals. This work adds to the growing body of evidence that intervention strategies designed to minimize HAIs should be done at the regional rather than local level.
To develop an educational video to reach elderly Latinos in order to improve understanding and encourage evaluation of cognitive changes by 1) using focus groups to identify dementia knowledge gaps, health communication preferences and trusted advisors for health concerns; 2) collaborating with elderly Latino community members to create a video; and 3) collecting survey data regarding community response to the video.
Grounded theory qualitative approach using focus groups; collaborative community based model to create the video and anonymous survey at community screenings.
Community senior centers in East Harlem, New York.
A team of low-income mono and bilingual elderly Latino community residents, researchers, clinicians, and a film professional.
Thematic analysis of focus group transcripts; three item survey.
A collaboratively produced video and initial assessment in 49 Latino elders that indicated the video had a positive effect on interest in obtaining a brief memory screening at outreach events (71%).
The project demonstrates the feasibility of this interdisciplinary partnership to create a culturally and linguistically sensitive video to promote service use concerning memory loss and cognitive evaluations among elderly Latinos. Initial survey results suggested a positive response and an increase in interest in memory screening.
The anterior cingulate cortex (ACC) and the dorsolateral prefrontal cortex (DLPFC) are involved in conflict detection and conflict resolution, respectively. Here, we investigate how lifelong bilingualism induces neuroplasticity to these structures by employing a novel analysis of behavioural performance. We correlated grey matter volume (GMV) in seniors reported by Abutalebi et al. (2015) with behavioral Flanker task performance fitted using the diffusion model (Ratcliff, 1978). As predicted, we observed significant correlations between GMV in the DLPFC and Flanker performance. However, for monolinguals the non-decision time parameter was significantly correlated with GMV in the left DLPFC, whereas for bilinguals the correlation was significant in the right DLPFC. We also found a significant correlation between age and GMV in left DLPFC and the non-decision time parameter for the conflict effect for monolinguals only.
We submit that this is due to cumulative demands on cognitive control over a lifetime of bilingual language processing.
While logistic regression models are easily accessible to researchers, when applied to network data there are unrealistic assumptions made about the dependence structure of the data. For temporal networks measured in discrete time, recent work has made good advances (Almquist & Butts, 2014), but there is still the assumption that the dyads are conditionally independent given the edge histories. This assumption can be quite strong and is sometimes difficult to justify. If time steps are rather large, one would typically expect not only the existence of temporal dependencies among the dyads across observed time points but also the existence of simultaneous dependencies affecting how the dyads of the network co-evolve. We propose a general observation-driven model for dynamic networks that overcomes this problem by modeling both the mean and the covariance structures as functions of the edge histories using a flexible autoregressive approach. This approach can be shown to fit into a generalized linear mixed model framework. We propose a visualization method that provides evidence concerning the existence of simultaneous dependence. We describe a simulation study to determine the method's performance in the presence and absence of simultaneous dependence, and we analyze both a proximity network from conference attendees and a world trade network. We also use this last data set to illustrate how simultaneous dependencies become more prominent as the time intervals become coarser.
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.
Active participation in social activities is important for the wellbeing of older adults. This study explored benefits of active social engagement by evaluating whether relationships that comprise active involvement (e.g. co-engagement in activities) bring more social benefits (i.e. social support, companionship, positive social influence) than other relationships that do not involve co-engagement. A total of 133 adults ages 60 years and older living in a rural Midwestern city in the United States of America were interviewed once and provided information on 1,740 social network members. Among 1,506 social relationships in which interactions occurred at least once a month, 52 per cent involved engagement in social activities together and 35 per cent involved eating together regularly. Results of the generalised linear mixed model showed that relationships involving co-engagement were significantly more likely to also convey social support (i.e. emotional, instrumental, informational), companionship and social influence (encouragement for healthy behaviours) than relationships that do not involve co-engagement. Having more network members who provide companionship was associated with higher sense of environmental mastery, positive relations with others and satisfaction with social network. Interventions may focus on maintaining and developing such social relationships and ensuring the presence of social settings in which co-engagement can occur. Future research may explore whether increasing co-engagement leads to an enhanced sense of companionship and psychological wellbeing.
The relationship between depression and sexual behaviour among men who have sex with men (MSM) is poorly understood.
To investigate prevalence and correlates of depressive symptoms (Patient Health Questionnaire-9 score ≥10) and the relationship between depressive symptoms and sexual behaviour among MSM reporting recent sex.
The Attitudes to and Understanding of Risk of Acquisition of HIV (AURAH) is a cross-sectional study of UK genitourinary medicine clinic attendees without diagnosed HIV (2013–2014).
Among 1340 MSM, depressive symptoms (12.4%) were strongly associated with socioeconomic disadvantage and lower supportive network. Adjusted for key sociodemographic factors, depressive symptoms were associated with measures of condomless sex partners in the past 3 months (≥2 (prevalence ratio (PR) 1.42, 95% CI 1.17–1.74; P=0.001), unknown or HIV-positive status (PR 1.43, 95% CI 1.20–1.71; P<0.001)), sexually transmitted infection (STI) diagnosis (PR 1.46, 95% CI 1.19–1.79; P<0.001) and post-exposure prophylaxis use in the past year (PR 1.83, 95% CI 1.33–2.50; P<0.001).
Management of mental health may play a role in HIV and STI prevention.
To determine whether the seasonality of surgical site infections (SSIs) can be explained by changes in temperature.
Retrospective cohort analysis.
The National Inpatient Sample database.
All hospital discharges with a primary diagnosis of SSI from 1998 to 2011 were considered cases. Discharges with a primary or secondary diagnoses of specific surgeries commonly associated with SSIs from the previous and current month served as our “at risk” cohort.
We modeled the national monthly count of SSI cases both nationally and stratified by region, sex, age, and type of institution. We used data from the National Climatic Data Center to estimate the monthly average temperatures for all hospital locations. We modeled the odds of having a primary diagnosis of SSI as a function of demographics, payer, location, patient severity, admission month, year, and the average temperature in the month of admission.
SSI incidence is highly seasonal, with the highest SSI incidence in August and the lowest in January. During the study period, there were 26.5% more cases in August than in January (95% CI, 23.3–29.7). Controlling for demographic and hospital-level characteristics, the odds of a primary SSI admission increased by roughly 2.1% per 2.8°C (5°F) increase in the average monthly temperature. Specifically, the highest temperature group, >32.2°C (>90°F), was associated with an increase in the odds of an SSI admission of 28.9% (95% CI, 20.2–38.3) compared to temperatures <4.4°C (<40°F).
At population level, SSI risk is highly seasonal and is associated with warmer weather.
Recent immigration and migration patterns have altered the ethnoracial composition of Alameda County, California. Sociopolitical leaders have struggled to adjust to these changes. In an effort to facilitate limited English speakers’ access to critical municipal services, Oakland—the largest municipal in Alameda County—passed an Equal Access to Services Ordinance on May 8, 2001, which is a groundbreaking language access legislation for the City of Oakland’s public administration. Using data from the 2000 Census and the 2005–2011 American Community Survey, this study examines the impact of bilingual employment policies on the ethnoracial segmentation of Alameda County workers. Logistic regression reveals that bilingual employment policies have reorganized both targeted (i.e., public contact) and non-targeted occupations within the local government public administration sector. Specifically, Spanish/Chinese bilingual speakers made gains in the public administration sector (the intended effects), while Black monolingual English speakers experienced losses (the unintended effects). The representation of Black monolingual English speakers in public contact jobs within the local government public administration sector declined by as much as 18 percentage points after the implementation of the nation’s first municipal-level bilingual employment policy. The impact of bilingual employment policies on the East Bay’s Black/Brown relations and African American’s hold on low-skilled jobs in service industries is examined.