A tale of two cities: The role of neighborhood socioeconomic status in spatial clustering of bystander CPR in Austin and Houston

Background

Despite evidence to suggest significant spatial variation in out-of-hospital cardiac arrest (OHCA) and bystander cardiopulmonary resuscitation (BCPR) rates, geographic information systems (GIS) and spatial analysis have not been widely used to understand the reasons behind this variation. This study employs spatial statistics to identify the location and extent of clusters of bystander CPR in Houston and Travis County, TX.

Methods

Data were extracted from the Cardiac Arrest Registry to Enhance Survival for two U.S. sites – Austin-Travis County EMS and the Houston Fire Department – between October 1, 2006 and December 31, 2009. Hierarchical logistic regression models were used to assess the relationship between income and racial/ethnic composition of a neighborhood and BCPR for OHCA and to adjust expected counts of BCPR for spatial cluster analysis. The spatial scan statistic was used to find the geographic extent of clusters of high and low BCPR.

Results

Results indicate spatial clusters of lower than expected BCPR rates in Houston. Compared to BCPR rates in the rest of the community, there was a circular area of 4.2 km radius where BCPR rates were lower than expected (RR = 0.62; p < 0.0001 and RR = 0.55; p = 0.037) which persist when adjusted for individual-level patient characteristics (RR = 0.34; p = 0.027) and neighborhood-level race (RR = 0.34; p = 0.034) and household income (RR = 0.34; p = 0.046). We also find a spatial cluster of higher than expected BCPR in Austin. Compared to the rest of the community, there was a 23.8 km radius area where BCPR rates were higher than expected (RR = 1.75; p = 0.07) which disappears after controlling for individual-level characteristics.

Conclusions

A geographically targeted CPR training strategy which is tailored to individual and neighborhood population characteristics may be effective in reducing existing disparities in the provision of bystander CPR for out-of-hospital cardiac arrest.     

Nurses’ practice environment and satisfaction with schedule flexibility is related to intention to leave due to dissatisfaction: A multi-country,multilevel study

BackgroundNursing turnover is a major issue for health care managers, notably during the global nursing workforce shortage. Despite the often hierarchical structure of the data used in nursing studies, few studies have investigated the impact of the work environment on intention to leave using multilevel techniques. Also, differences between intentions to leave the current workplace or to leave the profession entirely have rarely been studied.ObjectiveThe aim of the current study was to investigate how aspects of the nurse practice environment and satisfaction with work schedule flexibility measured at different organisational levels influenced the intention to leave the profession or the workplace due to dissatisfaction.DesignMultilevel models were fitted using survey data from the RN4CAST project, which has a multi-country, multilevel, cross-sectional design. The data analysed here are based on a sample of 23,076 registered nurses from 2020 units in 384 hospitals in 10 European countries (overall response rate: 59.4%). Four levels were available for analyses: country, hospital, unit, and individual registered nurse. Practice environment and satisfaction with schedule flexibility were aggregated and studied at the unit level. Gender, experience as registered nurse, full vs. part-time work, as well as individual deviance from unit mean in practice environment and satisfaction with work schedule flexibility, were included at the individual level. Both intention to leave the profession and the hospital due to dissatisfaction were studied.ResultsRegarding intention to leave current workplace, there is variability at both country (6.9%) and unit (6.9%) level. However, for intention to leave the profession we found less variability at the country (4.6%) and unit level (3.9%). Intention to leave the workplace was strongly related to unit level variables. Additionally, individual characteristics and deviance from unit mean regarding practice environment and satisfaction with schedule flexibility were related to both outcomes. Major limitations of the study are its cross-sectional design and the fact that only turnover intention due to dissatisfaction was studied.ConclusionsWe conclude that measures aiming to improve the practice environment and schedule flexibility would be a promising approach towards increased retention of registered nurses in both their current workplaces and the nursing profession as a whole and thus a way to counteract the nursing shortage across European countries.     

Magnet Hospital attributes in European hospitals: A multilevel model of job satisfaction

Background

The Magnet Recognition Program in the USA has been based on 14 hospital characteristics that were common in hospitals that were “magnets” for professional nurses. While the program has expanded to other countries, no research has explored how the concept translates to other cultures and healthcare systems, nor have multilevel approaches been used.

Objective

The primary aim of this study was to explore the presence of Magnet Hospital attributes in hospitals in two European countries. In addition, the relationship between Magnet Hospital attributes and nurses’ job satisfaction was examined at both the nurse and the hospital level.

Design

A secondary data analysis with cross-sectional design was conducted. A multilevel approach was taken to account for the hospital effect due to the nested nature of the data.

Settings

Nurses practicing in acute care hospitals in Germany (16) and Belgium (15) were examined. Hospitals that had less than five respondents were excluded.

Participants

Survey responses from 2303 registered nurses (RNs) from Belgium and 2646 RNs from Germany were included. Non-RN providers, RNs with administrative positions, and those working in non-inpatient areas were excluded. The final sample was 3182 staff nurses working in acute care hospitals.

Methods

Magnet Hospital attributes that might be represented in NEXT survey items were reviewed by an expert panel before psychometric testing. Only six Magnet Forces could be measured. Latent constructs of these forces and job satisfaction were established. The measurement models and structural regression models were estimated using multilevel modeling in Mplus 4.21.

Results

Six Magnet Forces were validated by two-level confirmatory factor analyses, with good fit to the data as demonstrated by the fit indices. All six Magnet Forces significantly predicted job satisfaction at the nurse level, with personnel policies having the strongest effect (b = 0.96). At the hospital level, management style had the strongest effect (b = 0.84) in predicting job satisfaction, followed by professional development, interdisciplinary relationship, and autonomy.

Conclusions

Magnet Hospital attributes are evident in hospitals in two European countries and were found to be associated with job satisfaction. Further multilevel research should explore these attributes particularly at the nursing unit-level where work environment is experienced.     

Studying place effects on health by synthesising individual and area-level outcomes

It is well established that there exist substantial area-level socio-demographic variations in population health. However, area-level associations between deprivation and health cannot necessarily be interpreted as place effects on individual health. We demonstrate how recently developed statistical models for combining individual and aggregate data can help to separate the effects of place of residence and personal circumstances. We apply these to two health outcomes: risk of hospitalisation for cardiovascular disease (CVD) and risk of self-reported limiting long-term illness (LLTI). A combination of small-area data from UK hospital episode statistics and the UK census and individual data from the Health Survey for England are analysed, using a new multilevel modelling method termed hierarchical related regression (HRR). The standard multilevel model for place and health explains outcomes from individual data in terms of individual and area-level characteristics. HRR models increase precision by also explaining population aggregate outcomes, in terms of the same predictors. Aggregate outcomes are modelled by averaging the individual-level exposure-outcome relationship over the area, which can alleviate the ecological bias associated with interpreting the relationship between aggregate quantities as an individual-level relationship. We find that there are associations between area-level deprivation indicators and both area-level rates of hospital admission for CVD and area-level rates of LLTI. Multilevel models fitted to the individual data alone had insufficient power to determine whether these associations were due to compositional or contextual effects. Using HRR models which incorporate area-level outcomes in addition to individual outcomes, we found that for CVD, the area-level differences were mostly explained by individual-level effects, in particular the increased risk for individuals from non-white ethnic backgrounds. In contrast, there remained a significant association between LLTI and area-level deprivation even after adjusting for the significant increased risk associated with individual-level ethnicity and income. Our study illustrates that extending multilevel models to incorporate both individual and area-level outcomes increases power to distinguish between contextual and compositional effects.     

A multilevel analysis of socioeconomic status and prostate cancer risk

PURPOSE: We investigated whether prostate cancer was associated with socioeconomic status (SES) at the individual level, area level, or a combination of both levels. METHODS: This population-based case-control study of prostate cancer in men aged 65 to 79 years was conducted between 2000 and 2002 in South Carolina. Complete interviews were available for 407 incident prostate cancer cases and 393 controls (with respective response rates of 61% and 64%). We used educational level to measure individual-level SES and a composite variable capturing income and education from 2000 Census data to measure area-level SES. RESULTS: After adjustment for race, age, geographic region, and prostate-specific antigen testing, men with some college were at reduced risk for prostate cancer (odds ratio [OR], 0.44; 95% confidence interval [CI], 0.27-0.72), as were men in the highest quartile of area-level SES (OR, 0.52; 95% CI, 0.34-0.80). When assessing individual-level and area-level SES simultaneously and accounting for their nonindependence, the independent negative associations persisted and appeared to be more striking for men with a diagnosis of localized disease, rather than advanced disease. CONCLUSION: The independent effects of area-level and individual-level SES on prostate cancer risk seen in our study may help explain the conflicting results of previous studies conducted at both levels.     

Social capital and neo-materialist contextual determinants of sense of insecurity in the neighbourhood: a multilevel analysis in Southern Sweden

BACKGROUND: The aim of this study was to investigate the influence of contextual (social capital and neo-materialist) and individual factors on sense of insecurity in the neighbourhood. METHODS: The 2000 public health survey in Scania is a cross-sectional study. A total of 13,715 persons answered a postal questionnaire, which is 59% of the random sample. A multilevel logistic regression model, with individuals at the first level and municipalities at the second, was performed. The effect (median odds ratios, intra-class correlation, cross-level modification and odds ratios) of individual and municipality/city quarter (social capital and police district) factors on sense of insecurity was analysed. RESULTS: The crude variance between municipalities/city quarters was not affected by individual factors. The introduction of administrative police district in the model reduced the municipality variance, although some of the significant variance between municipalities remained. The introduction of social capital did not affect the municipality variance. CONCLUSIONS: This study suggests that the neo-materialist factor administrative police district may partly explain the individual's sense of insecurity in the neighbourhood.     

A multi-level analysis of counselor attitudes toward the use of buprenorphine in substance abuse treatment

Despite evidence that buprenorphine is effective and safe and offers greater access as compared with methadone, implementation for treatment of opiate dependence continues to be weak. Research indicates that legal and regulatory factors, state policies, and organizational and provider variables affect adoption of buprenorphine. This study uses hierarchical linear modeling to examine National Treatment Center Study data to identify counselor characteristics (attitudes, training, and beliefs) and organizational factors (accreditation, caseload, access to buprenorphine, and other evidence-based practices) that influence implementation of buprenorphine for treatment of opiate dependence. Analyses showed that provider training about buprenorphine, higher prevalence of opiate-dependent clients, and less treatment program emphasis on a 12-step model predicted greater counselor acceptance and perceived effectiveness of buprenorphine. Results also indicate that program use of buprenorphine for any treatment purpose (detoxification, maintenance, and/or pain management) and time (calendar year in data collection) was associated with increased diffusion of knowledge about buprenorphine among counselors and with more favorable counselor attitudes toward buprenorphine.     

Individual factors explain neighbourhood variations in accidents to children under 5 years of age

Previous studies have identified possible neighbourhood-level influences on the risk of injuries to preschool children, but none have had sufficient data at both household and area level to explain these neighbourhood effects. We used data from the Avon Longitudinal Study of Parents and Children, which recruited over 14,062 children at birth in the former county of Avon, UK, and collected information about accidents, as well as extensive social, health and developmental data throughout the first 5 years of life. This information was combined with census and geographical data in order to identify neighbourhood influences on accident risks and then attempt to explain these using multilevel regression modelling. A small but statistically significant amount of between-neighbourhood variance in accident risk was found, with neighbourhood intraclass correlation coefficients of 0.82% for any accident, and 0.84% for accidents resulting in injury requiring medical attention. This was entirely accounted for by a variety of child, parental and household level variables. Independent risk factors for both outcomes were children who were developmentally more advanced or displayed greater conduct and behavioural problems, mothers who were of younger age, who were without work, who were smokers, whose partners were unemployed or drank alcohol excessively, and households in which there had recently been adverse life events, or which were under financial stress. The mother's perceptions of neighbourhood quality also explained some of the risks for any accident, but not for medically attended accidents, and this was a variable that operated at the level of individual households rather than at the level of neighbourhoods. The implications of this study are that differences in accident risk between neighbourhoods are explained by geographical clustering of similar types of children, families and households. Interventions should focus more on parental factors and household social circumstances than on the physical environment or community based risks. However, many of these factors are those most resistant to modification without broader societal change.     

Socioeconomic domains and associations with preterm birth

Neighborhood socioeconomic effects on health have been estimated using multiple variables and indices. This inconsistent estimation approach makes comparison across geographic areas challenging. In this paper, we developed indices representing specific socioeconomic domains that can be reproduced in other areas to estimate elements of the neighborhood socioeconomic environment on health outcomes, specifically preterm birth. Using year 2000 U.S. census data and principal components analysis, socioeconomic indices were developed representing a priori - defined domains of education, employment, housing, occupation, poverty and residential stability. These socioeconomic indices were subsequently used in race-stratified multilevel logistic regression models of preterm birth in eight socioeconomically distinct study areas in the U.S. Maternal residence was obtained from birth records and was geocoded to census tracts. In maternal age and education adjusted models, living in tracts with high unemployment, low education, poor housing, low proportion of managerial or professional occupation and high poverty was associated with increased odds of preterm birth for non-Hispanic white women at most sites. Among non-Hispanic black women, similar associations were noted for tract-level low education, high unemployment, low occupation, and high poverty, but the effect estimates were generally smaller than those seen for white women. Increasing amounts of residential stability were not associated with preterm birth in these analyses. We combined the domain estimates across the eight study sites to produce pooled effect estimates for the socioeconomic domains on preterm birth. The research reported here suggests that specific neighborhood-level socioeconomic features may be especially influential to health outcomes. These socioeconomic domains represent potential targets for intervention or policy efforts designed to improve maternal and child health and reduce health disparities.     

Corporation-induced Diseases,Upstream Epidemiologic Surveillance,and Urban Health

Corporation-induced diseases are defined as diseases of consumers, workers, or community residents who have been exposed to disease agents contained in corporate products. To study the epidemiology and to guide expanded surveillance of these diseases, a new analytical framework is proposed. This framework is based on the agent-host-environment model and the upstream multilevel epidemiologic approach and posits an epidemiologic cascade starting with government-sanctioned corporate profit making and ending in a social cost, i.e., harm to population health. Each of the framework's levels addresses a specific level of analysis, including government, corporations, corporate conduits, the environment of the host, and the host. The explained variable at one level is also the explanatory variable at the next lower level. In this way, a causal chain can be followed along the epidemiologic cascade from the site of societal power down to the host. The framework thus describes the pathways by which corporate decisions filter down to disease production in the host and identifies opportunities for epidemiologic surveillance. Since the environment of city dwellers is strongly shaped by corporations that are far upstream and several levels away, the framework has relevance for the study of urban health. Corporations that influence the health of urban populations include developers and financial corporations that determine growth or decay of urban neighborhoods, as well as companies that use strategies based on neighborhood characteristics to sell products that harm consumer health. Epidemiological inquiry and surveillance are necessary at all levels to provide the knowledge needed for action to protect the health of the population. To achieve optimal inquiry and surveillance at the uppermost levels, epidemiologists will have to work with political scientists and other social scientists and to utilize novel sources of information.