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Mental health problems clustered predominantly in neighborhoods that are geographically more exposed towards the ocean indicating a spatial variation of risk within and across the boroughs. We further found significant variation in associations between vulnerability and resilience factors and mental health. Race/ethnicity (being Asian or non-Hispanic black) and disaster-related stressors were vulnerability factors for mental health symptoms in Queens, and being employed and married were resilience factors for these symptoms in Manhattan and Staten Island. In addition, parental status was a vulnerability factor in Brooklyn and a resilience factor in the Bronx.
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In addition to Asian ethnicity, we also noted that associations between older age, female gender, Hispanic ethnicity, and being non-Hispanic Black and post-disaster mental health problems also varied across the boroughs (cf. Tables 3 and 4). Older age was positively associated with PTS in Brooklyn and female gender was positively associated with both PTS and depression in Staten Island. Being Hispanic was associated with higher PTS in Manhattan, Asian in the Bronx, and non-Hispanic black in Queens. Being Asian was also positively associated with depression in Queens.
We sought to identify spatial clusters of PTS and depression among residents of New York City neighborhoods that were affected by Hurricane Sandy. We also aimed to explore whether psychological vulnerability and resilience factors varied among the five New York City boroughs. We found that mental health outcomes were spatially clustered in neighborhoods within the boroughs, with local clusters of high PTS in neighborhoods within Queens and Brooklyn and clusters of low PTS in neighborhoods within Manhattan and also within Queens and Brooklyn. High depression clustered spatially in neighborhoods within Queens and low depression clustered in neighborhoods on Staten Island. In addition, we found variation in vulnerability and resilience factors for higher post-disaster PTS and depression among the boroughs. In particular, we found significant variation in associations between demographic characteristics (e.g., age, race/ethnicity), living arrangements (e.g., parent status), and exposure to hurricane-related trauma and mental health outcomes across the five boroughs.
Our analysis provided evidence that there were spatial clusters of both high and low PTS and depression within the study area, indicating geographic variation in the risk for post-disaster mental health problems across the boroughs (all but the Bronx). Notably, the hotspots of PTS and depression did not overlap entirely. This could be due in part to differences in how they were assessed, with PTS assessed specifically in reference to the hurricane and its aftermath, and depression assessed more generally. As such, the spatial patterns of PTS could be seen as more hurricane-specific, whereas those of depression could reflect both the impact of the hurricane and ongoing mental health problems. In interpreting these results, it is important to note that these findings are unlikely to be due to local spillover among the respondents in the study, as in the context of a large, urban area like NYC it is unlikely that the respondents were interacting with each other. Rather, it is more likely that shared vulnerability and resilience factors among residents of the same neighborhoods account for the spatial clusters we observed. For example, we found spatial clusters of above average mental health outcomes in neighborhoods that are geographically more exposed towards the ocean (Southeastern Queens and Brooklyn) assuming that vulnerability factors were more pronounced in those areas [1,19]. In contrast, resilience factors could have played a crucial role in shaping the clusters of lower than average mental health outcomes in neighborhoods within Manhattan and Northwestern Brooklyn and Queens. However, the large number of census tracts represented and the small number of respondents within each of them prohibited exploration of variation in vulnerability and resilience factors across census tracts. Instead, we explored spatial variation in associations between vulnerability and resilience factors and mental health across the five NYC boroughs. There was significant variation in the extent to which Asian ethnicity and trauma exposure in addition to Hurricane Sandy were associated with PTS, and Asian ethnicity, parent status, and hurricane-related trauma were associated with depression. Although variation in the coefficients for other vulnerability and resilience factors across the boroughs did not reach statistical significance, we observed several other instances in which factors were predictive of outcomes in one or more, but not all, of the boroughs.
Furthermore, we noted that some vulnerability and resilience factors that were not significant predictors of mental health outcomes in the full sample reached statistical significance within one or more of the boroughs. For example, Asian ethnicity was a significant vulnerability factor for higher depression in Queens and for higher PTS in the Bronx. On the other hand, factors that reached statistical significance in the full sample were not significant predictors across all of the boroughs. We found, for example, that exposure to more hurricane-related stressors, a robust predictor of both higher PTS and depression in the full sample and in prior research (e.g., [10, 11]), was not associated with PTS in the Bronx or with depression in Queens. Finally, the results showed that a vulnerability factor in one geographic area could be a resilience factor in another. In this case, being the parent or legal guardian of a child during the time of Sandy was associated with higher depression in Brooklyn, but lower depression in the Bronx.
Although further research in this area is needed to understand what factors account for geographic variation in vulnerability and resilience factors, there are several possibilities that could account for the findings. First, the strength of associations might depend on the distribution of both predictors and outcomes in the given area. For example, the risk associated with membership in a racial or ethnic minority group could depend in part on the proportion of residents in the group within a given neighborhood [20,21]. Along similar lines, other variables could influence the distribution, and risk associated with, a given factor. The presence of low quality housing or limited sheltering provision within a neighborhood [22], in combination with geographic exposure towards higher storm surges [19], for example, could shift the distribution of hurricane-related stressors and trauma (e.g., housing damage, displacement, bereavement) thereby strengthening their association with mental health outcomes. Second, the level of resources within a community could enhance or attenuate the influence of a vulnerability or resilience factor. For example, a lack of basic infrastructure or affordable and accessible health and childcare in the aftermaths of the disaster [23,24] might pose a higher risk for mental health problems for parents. Finally, geographic variation in more subtle cultural factors, such as informal social support networks and attitudes toward mental health service use, could influence associations between vulnerability and resilience factors and outcomes.
To assess Sandy-related exposure, we used scales that were successfully applied in other epidemiological surveys on mental health in the aftermath of major hurricanes [10,39]. Hurricane Sandy-related stressors included whether participants (1) were displaced from their pre-hurricane home for over a week, (2) went without electricity, heat, or water for over a week, (3) experienced decline in income due to the hurricane and its aftermath, and (4) had damage to their pre-hurricane home. The four questions were summed up for an affirmative response count for Hurricane Sandy-related stressors. Hurricane Sandy-related traumatic events included whether the respondent (1) was personally injured, (2) had a close friend or family member who was injured, and (3) had a close friend or family member who was killed, each as a direct result of the hurricane and its aftermath. Again, a sum of affirmative responses was included. Table 1 includes descriptive statistics for all study variables, both for the full sample and for subsamples residing in each borough at the time of the hurricane.
This research was supported by the Assistant Secretary of Preparedness and Response of the US Department of Health and Human Services, Grant HITEP130003-01-00 (to S.G.), the National Institute for Mental Health (T32-MH-13043 to S.R.L.), and the German Research Foundation (DFG, GR 4302/1-1 to O.G.). We also thank Julia Koschinsky and Sven Lautenbach for their helpful thoughts on this study.
Many interventions found to be effective in health services research studies fail to translate into meaningful patient care outcomes across multiple contexts. Health services researchers recognize the need to evaluate not only summative outcomes but also formative outcomes to assess the extent to which implementation is effective in a specific setting, prolongs sustainability, and promotes dissemination into other settings. Many implementation theories have been published to help promote effective implementation. However, they overlap considerably in the constructs included in individual theories, and a comparison of theories reveals that each is missing important constructs included in other theories. In addition, terminology and definitions are not consistent across theories. We describe the Consolidated Framework For Implementation Research (CFIR) that offers an overarching typology to promote implementation theory development and verification about what works where and why across multiple contexts. be457b7860
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