To send content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .
To send content items to your Kindle, first ensure email@example.com
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Hurricane Sandy's October 29, 2012 arrival in New York City caused flooding, power disruption, and population displacement. Infectious disease risk may have been affected by floodwater exposure, residence in emergency shelters, overcrowding, and lack of refrigeration or heating. For 42 reportable diseases that could have been affected by hurricane-related exposures, we developed methods to assess whether hurricane-affected areas had higher disease incidence than other areas of NYC.
We identified post-hurricane cases as confirmed, probable, or suspected cases with onset or diagnosis between October 30 and November 26 that were reported via routine passive surveillance. Pre-hurricane cases for the same 4-week period were identified in 5 prior years, 2007–2011. Cases were geocoded to the census tract of residence. Using data compiled by the NYC Office of Emergency Management, we determined (1) the proportion of the population in each census tract living in a flooded block and (2) the subset of flooded tracts severely “impacted”, e.g., by prolonged service outages or physical damage. A separate multivariable regression model was constructed for each disease, modeling the outcome of case counts using a negative binomial distribution. Independent variables were: neighborhood poverty; whether cases were pre- or post-hurricane (time); the proportion of the population flooded in impacted and not impacted tracts; and interaction terms between the flood/impact variables and time. Models used repeated measures to adjust for correlated observations from the same tract and an offset term of the log of the population size. Sensitivity analyses assessed the effects of case count fluctuations and accounted for variations in reporting volume by using an offset term of the log of total cases.
Only legionellosis was statistically significantly associated with increased occurrence in flooded/impacted areas post-hurricane, adjusting for baseline differences (P = .04). However, there was only 1 legionellosis case post-hurricane in a flooded/impacted area.
Hurricane Sandy did not appear to elevate reportable disease incidence in NYC. Defining and acquiring reliable data and meta-data regarding hurricane-affected areas was a challenge in the weeks post-storm. Relevant metrics could be developed during disaster preparedness planning. These methods to detect excess disease can be adapted for future emergencies. (Disaster Med Public Health Preparedness. 2013;7:513-521)
Recommendations for fruit and vegetable consumption are largely unmet. Lower socio-economic status (SES), neighbourhood poverty and poor access to retail outlets selling healthy foods are thought to predict lower consumption. The objective of the present study was to assess the interrelationships between these risk factors as predictors of fruit and vegetable consumption.
Cross-sectional multilevel analyses of data on fruit and vegetable consumption, socio-demographic characteristics, neighbourhood poverty and access to healthy retail food outlets.
Survey data from the 2002 and 2004 New York City Community Health Survey, linked by residential zip code to neighbourhood data.
Adult survey respondents (n 15 634).
Overall 9·9 % of respondents reported eating ≥5 servings of fruits or vegetables in the day prior to the survey. The odds of eating ≥5 servings increased with higher income among women and with higher educational attainment among men and women. Compared with women having less than a high-school education, the OR was 1·12 (95 % CI 0·82, 1·55) for high-school graduates, 1·95 (95 % CI 1·43, 2·66) for those with some college education and 2·13 (95 % CI 1·56, 2·91) for college graduates. The association between education and fruit and vegetable consumption was significantly stronger for women living in lower- v. higher-poverty zip codes (P for interaction < 0·05). The density of healthy food outlets did not predict consumption of fruits or vegetables.
Higher SES is associated with higher consumption of produce, an association that, in women, is stronger for those residing in lower-poverty neighbourhoods.
Email your librarian or administrator to recommend adding this to your organisation's collection.