Oliver Gruebner

Psychological Resilience after Hurricane Sandy: The Influence of Individual- and Community-Level Factors on Mental Health after a Large-Scale Natural Disaster

Several individual-level factors are known to promote psychological resilience in the aftermath of disasters. Far less is known about the role of community-level factors in shaping postdisaster mental health. The purpose of this study was to explore the influence of both individual- and community-level factors on resilience after Hurricane Sandy. A representative sample of household residents (N = 418) from 293 New York City census tracts that were most heavily affected by the storm completed telephone interviews approximately 13–16 months postdisaster. Multilevel multivariable models explored the independent and interactive contributions of individual- and community-level factors to posttraumatic stress and depression symptoms. At the individual-level, having experienced or witnessed any lifetime traumatic event was significantly associated with higher depression and posttraumatic stress, whereas demographic characteristics (e.g., older age, non-Hispanic Black race) and more disaster-related stressors were significantly associated with higher posttraumatic stress only. At the community-level, living in an area with higher social capital was significantly associated with higher posttraumatic stress. Additionally, higher community economic development was associated with lower risk of depression only among participants who did not experience any disaster-related stressors. These results provide evidence that individual- and community-level resources and exposure operate in tandem to shape postdisaster resilience.

A spatial epidemiological analysis of self-rated mental health in the slums of Dhaka

BackgroundThe deprived physical environments present in slums are well-known to have adverse health effects on their residents. However, little is known about the health effects of the social environments in slums. Moreover, neighbourhood quantitative spatial analyses of the mental health status of slum residents are still rare. The aim of this paper is to study self-rated mental health data in several slums of Dhaka, Bangladesh, by accounting for neighbourhood social and physical associations using spatial statistics. We hypothesised that mental health would show a significant spatial pattern in different population groups, and that the spatial patterns would relate to spatially-correlated health-determining factors (HDF).MethodsWe applied a spatial epidemiological approach, including non-spatial ANOVA/ANCOVA, as well as global and local univariate and bivariate Morans I statistics. The WHO-5 Well-being Index was used as a measure of self-rated mental health.ResultsWe found that poor mental health (WHO-5 scores < 13) among the adult population (age ≥15) was prevalent in all slum settlements. We detected spatially autocorrelated WHO-5 scores (i.e., spatial clusters of poor and good mental health among different population groups). Further, we detected spatial associations between mental health and housing quality, sanitation, income generation, environmental health knowledge, education, age, gender, flood non-affectedness, and selected properties of the natural environment.ConclusionsSpatial patterns of mental health were detected and could be partly explained by spatially correlated HDF. We thereby showed that the socio-physical neighbourhood was significantly associated with health status, i.e., mental health at one location was spatially dependent on the mental health and HDF prevalent at neighbouring locations. Furthermore, the spatial patterns point to severe health disparities both within and between the slums. In addition to examining health outcomes, the methodology used here is also applicable to residuals of regression models, such as helping to avoid violating the assumption of data independence that underlies many statistical approaches. We assume that similar spatial structures can be found in other studies focussing on neighbourhood effects on health, and therefore argue for a more widespread incorporation of spatial statistics in epidemiological studies.

Mental health in the slums of Dhaka - a geoepidemiological study

BackgroundUrban health is of global concern because the majority of the worlds population lives in urban areas. Although mental health problems (e.g. depression) in developing countries are highly prevalent, such issues are not yet adequately addressed in the rapidly urbanising megacities of these countries, where a growing number of residents live in slums. Little is known about the spectrum of mental well-being in urban slums and only poor knowledge exists on health promotive socio-physical environments in these areas. Using a geo-epidemiological approach, the present study identified factors that contribute to the mental well-being in the slums of Dhaka, which currently accommodates an estimated population of more than 14 million, including 3.4 million slum dwellers.MethodsThe baseline data of a cohort study conducted in early 2009 in nine slums of Dhaka were used. Data were collected from 1,938 adults (≥ 15 years). All respondents were geographically marked based on their households using global positioning systems (GPS). Very high-resolution land cover information was processed in a Geographic Information System (GIS) to obtain additional exposure information. We used a factor analysis to reduce the socio-physical explanatory variables to a fewer set of uncorrelated linear combinations of variables. We then regressed these factors on the WHO-5 Well-being Index that was used as a proxy for self-rated mental well-being.ResultsMental well-being was significantly associated with various factors such as selected features of the natural environment, flood risk, sanitation, housing quality, sufficiency and durability. We further identified associations with population density, job satisfaction, and income generation while controlling for individual factors such as age, gender, and diseases.ConclusionsFactors determining mental well-being were related to the socio-physical environment and individual level characteristics. Given that mental well-being is associated with physiological well-being, our study may provide crucial information for developing better health care and disease prevention programmes in slums of Dhaka and other comparable settings.

Mapping the Slums of Dhaka from 2006 to 2010

Background. Rapid urban growth in low and middle income countries is frequently characterized by informal developments. The resulting social segregation and slums show disparities in health outcomes for the populations of the world’s megacities. To address these challenges, information on the spatial distribution of slums is necessary, yet the data are rarely available. The goal of this study was to use a remote sensing based approach to map urban slums in Dhaka, the second fastest growing megacity in the world. Methods. Slums were mapped through the visual interpretation of Quickbird satellite imagery between the years 2006 and 2010. Ancillary references included the 2005 census and mapping of slums, Google Earth, and geolocated photographs. The 2006 slums were first delineated and filtered in GIS to avoid small, isolated slums. For 2010, changes to the 2006 slums were defined over the latter’s polygons to retain border consistency. Conclusions. The dataset presented here can be considered a stepping stone for further research on slums and urban expansion in Dhaka. The slum distribution dataset is useful to be pooled with other data to reveal trends of informal settlement growth for local health policy advice in Dhaka.

Is area affected by flood or stagnant water independently associated with poorer health outcomes in urban slums of Dhaka and adjacent rural areas

Abstract This study investigated the association of flood/stagnant water (FSW) with various health outcomes among respondents living in urban slums of Dhaka and adjacent rural areas. We also assessed the differences of individual-, household- and area-level characteristics between the FSW-affected and non-affected areas. Bangladesh as a whole and slums in the megacity of Dhaka in particular are severely affected by the FSW. Data were collected from 3,207 subjects (aged 10+ years) through baseline surveys conducted in March 2008 and 2009. Twelve big slums in Dhaka and three adjacent villages were selected as study areas. Face-to-face interviews using a multidimensional pre-tested questionnaire were conducted by the trained university graduates. We performed various types of analyses ranging from the simple frequency analysis to the multivariable-adjusted logistic regression modelling. Our empirical findings suggest that slums were more affected by the FSW as compared to the rural areas. People living in the FSW-affected areas were more vulnerable in terms of individual-, household- and area-level characteristics than non-affected people. Age was also significantly associated with various health outcomes. According to multivariable analyses controlled for various factors, the FSW-affected people reported significantly higher likelihoods of health symptoms (namely fever, cold/cough, weakness), communicable diseases (namely diarrhoea and gastric disease) and poor mental well-being as compared to the non-affected people. Only the burden of non-communicable diseases was lower in the FSW-affected areas than the non-affected areas. Our findings lead us to conclude that the FSW-affected area is an independent risk factor for various physical and mental health problems. Urban slums are more affected than rural areas by the FSW. Therefore, we underscore the necessities of well-designed and comprehensive public health interventions focusing on individual, community and higher levels of interventions to reduce the FSW-related health and other consequences among the people living in the FSW-affected areas and urban slums in the rapidly growing city of Dhaka, Bangladesh.

Cities and Mental Health.

BACKGROUND More than half of the global population currently lives in cities, with an increasing trend for further urbanization. Living in cities is associated with increased population density, traffic noise and pollution, but also with better access to health care and other commodities. METHODS This review is based on a selective literature search, providing an overview of the risk factors for mental illness in urban centers. RESULTS Studies have shown that the risk for serious mental illness is generally higher in cities compared to rural areas. Epidemiological studies have associated growing up and living in cities with a considerably higher risk for schizophrenia. However, correlation is not causation and living in poverty can both contribute to and result from impairments associated with poor mental health. Social isolation and discrimination as well as poverty in the neighborhood contribute to the mental health burden while little is known about specific interactions between such factors and the built environment. CONCLUSION Further insights on the interaction between spatial heterogeneity of neighborhood resources and socio-ecological factors is warranted and requires interdisciplinary research.

Mental health surveillance after the terrorist attacks in Paris

www.thelancet.com Vol 387 May 28, 2016 2195 mental health services in aff ected areas. As suggested in other research, early emotional reactions are also predictive of long-term mental health needs, and therefore our approach could assist in the allocation of services over time as well. Moreover, in countries with limited formal surveillance infrastructure, the approach could potentially identify mass trauma and guide emergency care into those areas most affl icted.

Estimating the spatial distribution of acute undifferentiated fever (AUF) and associated risk factors using emergency call data in India. A symptom-based approach for public health surveillance

The System for Early-warning based on Emergency Data (SEED) is a pilot project to evaluate the use of emergency call data with the main complaint acute undifferentiated fever (AUF) for syndromic surveillance in India. While spatio-temporal methods provide signals to detect potential disease outbreaks, additional information about socio-ecological exposure factors and the main population at risk is necessary for evidence-based public health interventions and future preparedness strategies. The goal of this study is to investigate whether a spatial epidemiological analysis at the ecological level provides information on urban-rural inequalities, socio-ecological exposure factors and the main population at risk for AUF. Our results displayed higher risks in rural areas with strong local variation. Household industries and proximity to forests were the main socio-ecological exposure factors and scheduled tribes were the main population at risk for AUF. These results provide additional information for syndromic surveillance and could be used for evidence-based public health interventions and future preparedness strategies.

Spatial Epidemiological Applications in Public Health Research: Examples from the Megacity of Dhaka

Public health researchers are increasingly shifting their focus from models of disease epidemiology that focus exclusively on individual risk factors to models that also consider the complex and important effects of the socio-physical environment (Geanuracos et al. 2007). The application of spatial analysis in the context of epidemiological surveillance and research has increased exponentially (Pfeiffer et al. 2009). Geographic information systems (GIS), global positioning systems (GPS) and remote sensing (RS) have been increasingly used in public health research since the 1990s (Kaiser et al. 2003). At the same time, geographers have started to extend their collaborations with public health researchers leading to the still young discipline of health geography that uses geographical concepts and techniques to investigate health-related topics (Meade and Earickson 2005; Gatrell and Elliott 2009).

A novel surveillance approach for disaster mental health

Background Disasters have substantial consequences for population mental health. Social media data present an opportunity for mental health surveillance after disasters to help identify areas of mental health needs. We aimed to 1) identify specific basic emotions from Twitter for the greater New York City area during Hurricane Sandy, which made landfall on October 29, 2012, and to 2) detect and map spatial temporal clusters representing excess risk of these emotions. Methods We applied an advanced sentiment analysis on 344,957 Twitter tweets in the study area over eleven days, from October 22 to November 1, 2012, to extract basic emotions, a space-time scan statistic (SaTScan) and a geographic information system (QGIS) to detect and map excess risk of these emotions. Results Sadness and disgust were among the most prominent emotions identified. Furthermore, we noted 24 spatial clusters of excess risk of basic emotions over time: Four for anger, one for confusion, three for disgust, five for fear, five for sadness, and six for surprise. Of these, anger, confusion, disgust and fear clusters appeared pre disaster, a cluster of surprise was found peri disaster, and a cluster of sadness emerged post disaster. Conclusions We proposed a novel syndromic surveillance approach for mental health based on social media data that may support conventional approaches by providing useful additional information in the context of disaster. We showed that excess risk of multiple basic emotions could be mapped in space and time as a step towards anticipating acute stress in the population and identifying community mental health need rapidly and efficiently in the aftermath of disaster. More studies are needed to better control for bias, identify associations with reliable and valid instruments measuring mental health, and to explore computational methods for continued model-fitting, causal relationships, and ongoing evaluation. Our study may be a starting point also for more fully elaborated models that can either prospectively detect mental health risk using real-time social media data or detect excess risk of emotional reactions in areas that lack efficient infrastructure during and after disasters. As such, social media data may be used for mental health surveillance after large scale disasters to help identify areas of mental health needs and to guide us in our knowledge where we may most effectively intervene to reduce the mental health consequences of disasters.