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Type II diabetes emergency room visits associated with Hurricane Sandy in New Jersey: implications for preparedness.

Introduction

Type II diabetes mellitus is a leading cause of death and disability in the U.S. (Centers for Disease Control and Prevention [CDC], 2011). Diabetes is a serious chronic health condition that if not properly managed and monitored can lead to health complications and mortality. As diabetes incidence increases, this subpopulation becomes especially vulnerable to disasters and climate change (Cook, Wellik, & Fowke, 2011) because they may require special health monitoring devices and regular medication intake. Improper emergency preparedness during disasters can lead to inappropriate medication storage or a lack of extra battery supplies for monitoring equipment and other devices necessary for appropriate diabetes control (Cefalu, Smith, Blonde, & Fonseca, 2006).

Few studies have examined if natural disasters are associated with increases in diabetes-related visits or hospitalizations during the natural disasters. One study reported aggravated glycemic control due to increased stress after a disaster among populations diagnosed with diabetes (Inui et al., 1998). Fonseca and co-authors (2009) reported a significant adverse effect on diabetes management, resulting in both negative health and economic implications, after Hurricane Katrina. Patient hemoglobin A1C levels, for example, postdisaster increased significantly (p < .001). People with diabetes are also susceptible to experiencing cuts, burns, and amputations as a result of a natural disaster, and some previous studies have suggested increases in emergency room visits (ERVs) and hospitalizations related to accidental injury and trauma after hurricanes for individuals with diabetes (Brewer, Morris, & Cole, 1994; Ford et al., 2006; Platz, Cooper, Silvestri, & Siebert, 2007).

On October 29, 2012, Hurricane Sandy made landfall in New Jersey, causing major flooding (Jonkman, Maaskant, Boyd, & Levitan, 2009); power outages (Sakashita, Matthews, & Yamamoto, 2013; SeungRyong et al., 2008); and closures of community pharmacies (Traynor, 2012), roads, and public transportation. Hurricane Sandy caused 65% of utility customers in New Jersey to lose power (Trinacria, 2012) and the restoration of power to 95% of the population was reached only 11 days after the peak number of outages were reported (Siart, 2012). In New Jersey, the estimated age-adjusted diabetes prevalence for adults was 8.5% in 2010, an increase from 4.5% in 1995 (CDC, 2012). With hurricanes making landfall increasingly more often in the U.S., it is essential not only to document effects of natural disasters on medical care and health outcomes, as previously described (Brewer, Morris, & Cole, 1994; Ford et al., 2006; Jonkman, Maaskant, Boyd, & Levitan, 2009; Platz et al., 2007; Seung-Ryong et al., 2008), but also to geographically map ERVs and determine if there are any spatial patterns of risk to prepare for more prompt and effective emergency clinical care and public health responses.

In this study, we investigated changes in ERVs associated with Hurricane Sandy, pre and postdisaster. There was a special interest to examine diabetes visits in Atlantic, Cape May, and Ocean counties due to their high diabetes prevalence, their spatial location on the Atlantic coast with relation to hurricanes making landfall, and because New Jersey residents did not have mandatory evacuation advisories, with the exception of Cape May County. Specific hypotheses of the study were: a) ERVs for type II diabetes diagnoses will be significantly higher after the arrival of Hurricane Sandy (October 29-December 31, 2012) compared with the same time period the previous year (October 29-December 31, 2011); b) there will be a significant change in the place of residence of patients diagnosed with primary and secondary diagnoses of type II diabetes after Hurricane Sandy, with the majority of cases emanating from flood zone areas after the hurricane; and c) after Hurricane Sandy, individuals who lived in socioeconomically disadvantaged places of residence (i.e., neighborhood of residence) will have a greater number of ERVs for type II diabetes care than those living in more affluent places.

Methods

Study Design

This study was a retrospective analysis of ERV records before and after Hurricane Sandy. Data were extracted from New Jersey Department of Health's (NJDOH) Uniform Bill emergency department discharge data files. This study was approved by the Rutgers Institutional Review Board.

Study Settings and Population

The study population included adults in New Jersey who resided in Atlantic, Cape May, and Ocean counties and who had an ERV at a general acute care hospital in New Jersey during 2011 and 2012. We assessed patients with PDD or SDD not admitted for hospitalization after having been in the emergency room.

Outcomes

Diagnosis was based on the International Classification of Disease, 9th Revision, Clinical Modification [ICD-9-CM] (Medicode, 1996). We included ERV with type II primary and/or secondary diabetes diagnosis (i.e., ICD-9-CM codes 250.x0 or 250.x2).

Exposure

Our main interest was to compare the time period during the week of Hurricane Sandy, October 29-November 4, 2012, with the same period of the previous year, October 29-November 4, 2011. Further, we examined trends across various time periods in an attempt to capture changes due to seasonal trends. The time periods were divided into weekly segments before and after the week of Hurricane Sandy. The four time periods immediately prior to the hurricane included October 1-October 7, October 8-October 14, October 15-October 21, and October 22-October 28. The nine periods of and after the week of the hurricane included October 29-November 4, November 5-November 11, November 12-November 18, November 19-November 25, November 26-December 2, December 3-December 9, December 10December 16, December 17-December 23, and December 24-December 30.

Flooding zone data for New Jersey were acquired from the U.S. Federal Emergency Management Agency (FEMA), Region II, Coastal Analysis and Mapping. Flood hazard data were used to geographically map flood zones to compare municipality level ERV rates pre-Sandy during the week of October 29-November 4, 2011, with the week of October 29-November 4, 2012 (week of Hurricane Sandy).

Potential Confounders

Data on potential confounders available for the present study included age, sex, race, and ethnicity, plus county and municipality of residence. Age was grouped into 20-34 years, 35-49 years, 50-64 years, 65-79 years, and 80+ years. Race was categorized as nonHispanic White; non-Hispanic Black; Asian, non-Hispanic; Multiracial and Other races, non-Hispanic; and Hispanic/Latino. Municipal-level poverty was grouped into 0-10%, 11-20%, and 21-40%.

Municipality-level poverty, as an indicator of socioeconomic status, was obtained from U.S. Census American Community Survey 2006-2010, Selected Population Tables (DP03) by county subdivisions. The variable examined was the percentage of families whose income in the past 12 months fell below the federal poverty level.

Data Analysis

Geographical Analysis and Mapping

ERV data were linked, using county and municipality codes, to Federal Information Processing Standard (FIPS) codes. Patient data were merged with U.S. Census data using their FIPS code and geographical identification (GEO.ID2) The crude rate per 10,000 population was calculated by municipality for PDD and SDD using frequency of ERVs during the week of October 29-November 4 (in 2011 and in 2012) divided by municipality population. Rates were mapped using municipality boundaries and these maps were compared to FEMA flood zone boundaries to spatially identify the difference in ERV rates between the two years by municipality. Due to research staff limitations, we were not able to further determine which specific areas experienced actual flooding and how these areas compared to the flood zones designated by FEMA. This would have allowed us to determine how well emergency response planning corresponded to actual affected areas. Additionally, it should be noted how in each targeted county, the municipalities could be entirely in, partially within, or completely outside FEMA flood zones. Municipalities were defined as inside if they were completely inside the flooding area and outside if they were completely outside of the flooding area. Remaining municipalities were categorized as partially inside of flooding area. Additionally, county maps with municipality divisions were used to spatially map the crude rate of type II diabetes ERVs before and after flooding (October 29-November 4, 2011 versus October 29-November 4, 2012, respectively). (Map not presented; other maps available upon request from the authors.)

Statistical Analysis

Descriptive statistics were calculated for PDD and SDD by weeks, months, and year. Analyses were performed using SAS Version 9.3 and ArcGIS 10.2. Comparisons included weeks and months of the previous year to determine the impact of the storm on ERVs and if observed differences could be due to seasonal trends. The count differences and percent changes were calculated by weeks. The distribution of sex, race, ethnicity, municipality-level poverty percentage, and age were calculated for PDD and SDD during the week of the hurricane in 2011 and in 2012 for residents of each county. We used distributed-lag Poisson generalized linear models to obtain rate ratios examining the association between the week of the hurricane event in 2012 compared with the same week in 2011 and the number of diabetes ERVs. Separate models were fit for PDD and SDD. Poisson distribution was used because it is considered appropriate for ERV count data. Model 1 represented the crude association in the change in number of ERVs for 2012 compared with 2011. Model 2 additionally adjusted for age and sex, and Model 3 added race and ethnicity. To determine if the change in ERVs differed by municipality poverty level, we re-coded the poverty variable into a three-level measure (<10%, 11-20, and >21%). We stratified by this new poverty measure and re-ran Models 1 through 3. The models, however, did not converge due to small sample sizes between race/ethnicity and poverty level; we present results adjusted for municipality poverty level. Data analyses were conducted in 2014.

Results

Table 1 presents distributions of ERVs by week for October 1 through December 30 in 2011 and 2012. A total of 1,748 emergency room visits for PDD and 25,959 for SDD were reported for adult residents of Atlantic, Cape May, and Ocean counties to the NJDOH during the study period monitored in 2012. There were 53 emergency room visits for PDD and 527 for SDD during the week of the hurricane (October 29-November 4, 2012), representing a relative increase of 140.9% and 23.7%, respectively, when compared with the same week in 2011.

Characteristics of the study population are presented in Table 2. Results suggest minor changes in the age, sex, and municipality poverty distribution in ERVs between October 29-November 4, 2011, and October 29November 4, 2012. Also in 2012, there was a decrease in the number of ERVs from Hispanic/Latinos and non-Hispanic Blacks and an increase in the number of ERVs by nonHispanic Whites for both diabetes diagnoses. In Cape May County in 2012, most ERVs resulted from Hispanic/Latinos and Non-Hispanic Whites for PDD, and by non-Hispanic Whites for SDD.

Table 3 presents data on the number of municipalities by county with at least one case of a diabetes ERV during the week of October 29-November 4 by FEMA flood zones (i.e., with the municipalities completely within, completely outside, or partially within or overlapping flood zones). There was no clear pattern and no statistically significant difference, however, when comparing 2012 and 2011.

Spatial analysis revealed no consistent pattern for residents of the three targeted New Jersey counties (Figures 1 and 2 for Ocean County, as an illustrative example; Atlantic County and Cape May County figures are not presented--these other maps are available upon request from the authors). Briefly, in summary, data for Atlantic County showed a decrease for PDD in 2012, and a slight increase for SDD; Cape May County showed an increase for PDD and SDD, especially for the shore area. The Ocean County maps (Figures 1 and 2) were harder to analyze, due to the gap in territory near the shore, where water bodies are between the shore and mainland Ocean County. An increase was observed for PDD in 2012 compared with 2011 (Figure 1) not only for the shore area, but also for areas outside of flood zones, such as Plumsted Township and Jackson Township. An increase was also observed for SDD (Figure 2), mainly along the Ocean County shore area.

The distributed-lag Poisson generalized linear models analysis indicated an 84% increase (1.84, CI = 1.12, 3.04, p = .01) in the rate of ERVs for PDD during the week of the hurricane in 2012 compared with the same week in 2011 (Model 1). In Model 2, ERVs in 2012 were 1.95 times higher than in 2011, after adjusting for age and sex (1.95, CI = 1.18, 3.21, p = .01). After further adjusting for race and ethnicity (Model 3) and municipal poverty (Model 4), the increase in PDD was no longer significant (data not shown). Results for SDD were not significant across the models (data not shown).

[FIGURE 1 OMITTED]

Discussion

The main results of the study showed an increase in PDD in three targeted counties in southern New Jersey from Hurricane Sandy during the week of this storm, compared with the previous year in the same time period. Results remained statistically significant when adjusted for age and sex. There were no statistically significant associations observed for SDD. In general, the geographic analysis of the three targeted counties suggested the areas designated as high flood areas had a higher number of ERVs during the week of the hurricane after accounting for population size.

This study suggested how a natural disaster such as a hurricane can affect individuals living with diabetes (i.e., as suggested by the substantial increase in the number of diabetes-related ERVs during the week of Hurricane Sandy, even if we cannot know the true reason for those ERVs). The observed increase was significant for a primary diagnosis of type II diabetes across three southern New Jersey counties studied. Results remained significant after adjusting for age and sex. Moreover, results suggest that the increased number of ERVs were made by non-Hispanic White individuals. This result is different from a previous study, which indicated African-Americans are more likely to visit emergency departments for diabetes care (Chin, Zhang, & Merrell, 1998). If nonHispanic Whites had more resources to travel after the hurricane, however, this might explain the differences observed. For example, one possible explanation might be racial or ethnic minority populations could have been unable to get to the hospital if roads were closed or public transportation was not functioning or had limited function, as use of roads were suspended until they were cleared of damaged power lines, trees, etc. Although safety issues on roads likely affected entire communities, the extent to which safety issues disproportionately affected racial and ethnic minorities is unclear. On a global level, research has shown the devastating effects of natural disasters in populations already experiencing high levels of poverty (Silbert & Useche, 2012). Given how racial and ethnic minority groups are less likely to receive diabetes care and manage their health (Chin et al., 1998; McCall, Sauaia, Hamman, Reusch, & Barton, 2003; Mullins, Blatt, Gbarayor, Yang, & Baquet, 2005) and are more prone to have comorbidities (Anderson, Freedland, Clouse, & Lustman, 2001; Pan et al., 2012; Piette & Kerr, 2006), future research should explore the disproportionate burden of natural disasters in racially and ethnically diverse and poor communities.

[FIGURE 2 OMITTED]

Most studies to date exploring associations between natural disasters and health have reported a significant association between disasters and chronic disease outcomes (Chulada et al., 2012; Crook, Arrieta, & Foreman, 2010; Ford et al., 2006; Grimsley, Chulada, et al., 2012; Grimsley, Wildfire, et al., 2012; Neria & Shultz, 2012; Rath et al., 2011; Rhodes et al., 2010). Few studies, however, have analyzed diabetes-specific visits, multiple time periods, or the spatial patterning of diabetes-related ERVs. Prior research examining the effect of hurricanes on diabetes management found a significant increase in A1C levels in one out of three hospitals studied (Fonseca et al., 2009). Our study examined ERVs across numerous hospitals and areas most directly affected by Hurricane Sandy. We found significantly higher numbers of ERVs for PDD and SDD during the hurricane period in three counties of southern New Jersey that were most at risk of flooding and thus represent susceptible populations with vulnerable subpopulations during hurricanes. Additionally, other studies in the U.S. (Smith & Graffeo, 2005; Platz et al., 2007) have documented an increase of ERVs in general but only examined this within days after hurricanes made landfall. We extended previous findings by documenting changes in type II diabetes ERVs over several weeks before and after Hurricane Sandy and by comparing changes with the year prior to the hurricane to account for any possible demographic and seasonal trends.

The present study had potential limitations. First, data available only included patients visiting the emergency department who were not admitted for hospitalization. Severe outcomes related with diabetes management, including deaths, were, as a result, not taken into consideration in this study. Second, the study included numbers of visits as an outcome. If the same person went to the ER several times, that person was counted as different visits. This could introduce autocorrelation in the data resulting in potentially biased standard errors and possibly influenced tests of significance. The point estimates (rate ratio) obtained, however, would not have been affected and in the present study showed strong associations. Third, the rate ratios could be underestimated because only general acute care hospitals in New Jersey report ERVs to NJDOH; ERVs coming from specialized hospitals (e.g., Veteran Affairs hospital, skilled nursing facilities) or out-of-state hospitals were not included. Skilled nursing facilities, for example, might have had a large number of elderly people with diabetes management episodes related to Hurricane Sandy. Fourth, neighborhood poverty was measured at the municipality level. Census tracts (i.e., smaller geographic scale) would have been a more appropriate proxy for neighborhood contexts. Similarly, as patient addresses were not available for this study, ERVs were analyzed at the municipality level, which potentially concealed heterogeneity within each municipality. It should be noted that geocoding ERVs using patient addresses would have allowed for a more accurate categorization regarding FEMA flood zone areas. Finally, the data were derived from hospital billing information and so the municipality of residence associated with ERVs after the hurricane could be from a temporary home, potentially misleading the relationship with the flooding zone. Further research is needed to examine the significance of ERVs associated with flood zone areas defined by FEMA.

Strengths of our study include using outcomes based on standard clinical reporting criteria and not self-reported measures. This study targeted three southern counties of New Jersey that experienced detrimental impacts from Hurricane Sandy and have higher diabetes prevalence. Moreover, our study may be generalized to adult populations of those three counties in New Jersey because it included visits to the emergency room at every general acute care hospital.

Conclusion

In conclusion, we observed substantial increases in ERVs for primary type II diabetes diagnoses associated with Hurricane Sandy in New Jersey. Future public health preparedness efforts during storms should include planning for healthcare needs of populations living with diabetes. Specifically, results from our study suggested some targeted interventions hospitals, public health agencies, and community members can undertake to better manage diabetes during natural disasters. First, our findings, as well as recent research conducted by federal agencies (Lurie, Manolio, Patterson, Collins, & Frieden, 2013), suggest the need for hospitals to be prepared with enough medical and staff resources during the week of a natural disaster to care for populations with diabetes. Second, other nonhospital-based personnel such as police officers, firefighters, and volunteer medical and nursing students should be trained on emergency healthcare needs during and after natural disasters. Third, efforts to facilitate the availability of glucose monitoring devices, insulin, syringes, and antibiotics in community-based emergency shelters should also be considered. Finally, educational campaigns are needed to encourage those diagnosed with type II (and type I) diabetes to have adequate battery supplies for glucose monitoring devices during disasters; to prepare medication travel bags, because mandatory evacuations can happen suddenly; to use stress management techniques during and after natural disasters to alleviate anxiety potentially leading to poor glycemic control; and to keep an updated list of medications and doses taken in wallets or purses to be presented to any healthcare provider who may need to provide temporary medical care.

Enid M. Velez-Valle, MPH

Derek Shendell, MPH, DEnv

School of Public Health

Rutgers, The State University of New Jersey

Sandra Echeverria, MPH, PhD

The City University of New York School of Public at Hunter College

Melissa Santorelli, MPH, PhD

Rutgers, The State University of New Jersey

New Jersey Department of Health

Acknowledgements: We thank the New Jersey Office of Information Technology, Office of Geographic Information Systems, New Jersey Department of Health (NJDOH), Trenton, NJ, and the Federal Emergency Management Agency, Region II office for access to data files and maps. This report does not constitute an endorsement of authors, or organizations, by NJDOH. Views and opinions expressed in this manuscript are not necessarily those of NJDOH. First author Enid M. Velez-Valle is now employed by the University of Massachusetts Medical Center.

Corresponding Author: Derek G. Shendell, Associate Professor and Center Co-Director, Rutgers School of Public Health, 683 Hoes Lane West, 3rd Floor, School of Public Health Building, Piscataway, NJ 08854-8020.

E-mail: shendedg@sph.rutgers.edu.

References

Anderson, R.J., Freedland, K.E., Clouse, R.E., & Lustman, PJ. (2001). The prevalence of comorbid depression in adults with diabetes. Diabetes Care, 24, 1069-1078.

Brewer, R.D., Morris, P.D., & Cole, T.B. (1994). Hurricane-related emergency department visits in an inland area: An analysis of the public health impact of Hurricane Hugo in North Carolina. Annals of Emergency Medicine, 23(4), 731-736.

Cefalu, W.T., Smith, S.R., Blonde, L., & Fonseca, V. (2006). The Hurricane Katrina aftermath and its impact on diabetes care. Diabetes Care, 29(1), 158.

Centers for Disease Control and Prevention. (2011). National diabetes fact sheet: National estimates and general information on diabetes and prediabetes in the United States, 2011. Retrieved from http:// www.cdc.gov/diabetes/pubs/pdf/ndfs_2011.pdf

Centers for Disease Control and Prevention. (2012). Increasing prevalence of diagnosed diabetes--United States and Puerto Rico, 1995-2010. Morbidity and Mortality Weekly Report, 61(45), 918-921. Retrieved from http://www.cdc.gov/mmwr/preview/ mmwrhtml/mm6145a4.htm

Chin, M.H., Zhang, J.X., & Merrell, K. (1998). Diabetes in the African-American Medicare population: Morbidity, quality of care, and resource utilization. Diabetes Care, 21(7), 1090-1095.

Chulada, PC., Kennedy, S., Mvula, M.M., Jaffee, K., Wildfire, J., Thornton, E., Cohn, R.D., Grimsley, L.F, Mitchell, H., El-Dahr, J., Sterling, Y., Martin, W.J., White, L., Stephens, K.U., & Lichtveld, M. (2012). The Head-off Environmental Asthma in Louisiana (HEAL) study: Methods and study population. Environmental Health Perspectives, 120(11), 1592-1599.

Cook, C.B., Wellik, K.E., & Fowke, M. (2011). Geoenvironmental diabetology. Journal of Diabetes Science and Technology, 5(4), 834-842.

Crook, E., Arrieta, M., & Foreman R. (2010). Management of hypertension following Hurricane Katrina: A review of issues in management of chronic health conditions following a disaster. Current Cardiovascular Risk Reports, 4(3), 195.

Fonseca, VA., Smith, H., Kuhadiya, N., Leger, S.M., Yau, C.L., Reynolds, K., Shi, L., McDuffie, R.H., Thethi, T., & John-Kalarickal, J. (2009). Impact of a natural disaster on diabetes: Exacerbation of disparities and long-term consequences. Diabetes Care, 32(9), 1632-1638.

Ford, E.S., Mokdad, A.H., Link, M.W., Garvin, W.S., McGuire, L.C., Jiles, R.B., & Balluz, L.S. (2006). Chronic disease in health emergencies: In the eye of the hurricane. Preventing Chronic Disease, 3(2), A46.

Grimsley, L.F., Chulada, PC., Kennedy, S., White, L., Wildfire, J., Cohn, R.D., Mitchell, H., Thornton, E., El-Dahr, J., Mvula, M.M., Sterling, Y., Martin, WJ., Stephens, K.U., & Lichtveld, M. (2012). Indoor environmental exposures for children with asthma enrolled in the HEAL study, post-Katrina New Orleans. Environmental Health Perspectives, 120(11), 1600-1606.

Grimsley, L.F, Wildfire, J., Lichtveld, M., Kennedy, S., El-Dahr, J.M., Chulada, PC., Cohn, R., Mitchell, H., Thornton, E., Mvula, M., Sterling, Y., Martin, W., Stephens, K., & White, L. (2012). Few associations found between mold and other allergen concentrations in the home versus skin sensitivity from children with asthma after Hurricane Katrina in the Head-off Environmental Asthma in Louisiana study. International Journal of Pediatrics, 2012. Retrieved from http://dx.doi.org/10.1155/2012/427358

Inui, A., Kitaoka, H., Majima, M., Takamiya, S., Uemoto, M., Yonenaga, C., Honda, M., Shirakawa, K., Ueno, N., Amano, K., Morita, S., Kawara, A., Yokono, K., Kasuga, M., & Taniguchi, H. (1998). Effect of the Kobe earthquake on stress and glycemic control in patients with diabetes mellitus. Archives of Internal Medicine, 158, 274-278.

Jonkman, S.N., Maaskant, B., Boyd, E., & Levitan, M.L. (2009). Loss of life caused by the flooding of New Orleans after Hurricane Katrina: Analysis of the relationship between flood characteristics and mortality. Risk Analysis: An International Journal, 29(5), 676-698.

Lurie, N., Manolio, T., Patterson, A.P, Collins, F, & Frieden, T. (2013). Research as a part of public health emergency response. New England Journal of Medicine, 368, 1251-1255.

McCall, D.T., Sauaia, A., Hamman, R.F., Reusch, J.E., & Barton, P (2004) . Are low-income elderly patients at risk for poor diabetes care? Diabetes Care, 27(5), 1060-1065.

Medicode (Firm). (1996). ICD-9-CM: International classification of diseases, 9th revision, clinical modification. Salt Lake City, UT: Author.

Mullins, C.D., Blatt, L., Gbarayor, C.M., Yang, H.-W.K., & Baquet, C. (2005) . Health disparities: A barrier to high-quality care. American Journal of Health-System Pharmacy, 62(18), 1873-1882.

Neria, Y., & Shultz, J.M. (2012). Mental health effects of Hurricane Sandy: Characteristics, potential aftermath, and response. Journal of the American Medical Association, 308(24), 2571-2572.

Pan, A., Keum, N., Okereke, O.I., Sun, Q., Kivimaki, M., Rubin, R.R., & Hu, FB. (2012). Bidirectional association between depression and metabolic syndrome. Diabetes Care, 35, 1171-1180.

Piette, J.D., & Kerr, E.A. (2006). The impact of comorbid chronic conditions on diabetes care. Diabetes Care, 29(3), 725-731.

Platz, E., Cooper, H.P, Silvestri, S., & Siebert, C.F (2007). The impact of a series of hurricanes on the visits to two central Florida emergency departments. Journal of Emergency Medicine, 33(1), 39-46.

Rath, B., Young, E.A., Harris, A., Perrin, K., Bronfin, D.R., Ratard, R., Vandyke, R., Goldshore, M., & Magnus, M. (2011). Adverse respiratory symptoms and environmental exposures among children and adolescents following Hurricane Katrina. Public Health Reports, 126(6), 853-860.

Rhodes, J., Chan, C., Paxson, C., Rouse, C.E., Waters, M., & Fussell, E. (2010). The impact of Hurricane Katrina on the mental and physical health of low-income parents in New Orleans. American Journal of Orthopsychiatry, 80(2), 237.

Sakashita, K., Matthews, W.J., & Yamamoto, L.G. (2013). Disaster preparedness for technology and electricity-dependent children and youth with special health care needs. Clinical Pediatrics, 52(6), 549-556.

Seung-Ryong, H., Seth, D.G., Steven, M.Q., Kyung-Ho, L., David, R., & Rachel, A.D. (2008). Estimating the spatial distribution of power outages during hurricanes in the Gulf coast region. Reliability Engineering and System Safety, 94, 199-210.

Siart, J. (2012, October 30). Hurricane Sandy knocks out power for 97 percent of Fairfield as of Tues AM. Fairfield Daily Voice. Retrieved from http://fairfield.dailyvoice.com/news/ hurricanesandy-knocks-out-power-for-97-percent-of-fairfield-as-of-tues-am/545403/

Silbert, M., & Useche, M.P (2012). Repeated natural disasters and poverty in island nations: A decade of evidence from Indonesia. Department of Economics, University of Florida, PURC Working Paper. Retrieved from http://warrington.ufl.edu/centers/purc/ purcdocs/papers/1202_Silbert_Repeated_Natural_Disasters.pdf

Smith, C.M., & Graffeo, C.S. (2005). Regional impact of Hurricane Isabel on emergency departments in coastal southeastern Virginia. Academic Emergency Medicine, 12(12), 1201-1205.

Traynor, K. (2012). New Jersey hospitals come through during Hurricane Sandy. American Journal of Health-System Pharmacy, 69(24), 2120-2121.

Trinacria, J. (2012, November 4). Power back for 65 percent of PSE&G customers in N.J. The Inquirer Daily News. Retrieved from http://articles.philly.com/2012-11-04/news/349 30453_1_electric-customers-pse-g-power
TABLE 1
Number of Weekly Emergency Room Visits (ERVs) for Type II Diabetes
and Percent Change, 2011-2012

Primary Diabetes Diagnosis

Week                           Number of ERVs     Absolute    Percent
                                                 Difference   Change
                                2011     2012

October 1-7                      34       26         -8        -23.5
October 8-14                     29       24         -5        -17.2
October 15-21                    42       33         -9        -21.4
October 22-28                    38       30         -8        -21.0
October 29-November 4#          22#      53#        31#       140.9#
November 5-11                    35       33         -2        -5.7
November 12-18                   29       28         -1        -3.4
November 19-25                   37       39         2          5.4
November 26-December 2           23       30         7         30.4
December 3-9                     32       29         -3        -9.4
December 10-16                   39       37         -2        -5.1
December 17-23                   39       25        -14        -35.9
December 24-30                   23       22         -1        -4.4
Total of the remaining weeks   1,308    1,339
Annual total                   1,730    1,748

Secondary Diabetes Diagnosis

Week                           Number of ERVs     Absolute    Percent
                                                 Difference   Change
                                2011     2012

October 1-7                     467      473         6          1.3
October 8-14                    483      510         27         5.6
October 15-21                   481      481         0          0.0
October 22-28                   497      517         20         4.0
October 29-November 4#          426#     527#       101#       23.7#
November 5-11                   484      484         0          0.0
November 12-18                  472      452        -20        -4.2
November 19-25                  442      448         6          1.4
November 26-December 2          467      479         12         2.6
December 3-9                    494      442        -52        -10.5
December 10-16                  499      463        -36        -7.2
December 17-23                  457      439        -18        -3.9
December 24-30                  507      466        -41        -8.1
Total of the remaining weeks   18,162   19,778
Annual total                   24,338   25,959

Note: The data from the week Hurricane Sandy occurred are in bold.

Note: The data from the week Hurricane Sandy that occurred in bold
are indicated with #.

TABLE 2
Type II Diabetes Emergency Room Visit (ERV) Demographics by Year:
Three New Jersey Counties, Week of Hurricane Sandy, 2011-2012

Demographics                                   2011

                                      Primary      Secondary
                                     Diabetes      Diabetes
                                     Diagnoses     Diagnoses
Hospital ERV characteristics
Age, mean (SD)                      55.1 (16.6)   60.4 (15.4)
Gender, %
  Male                                 77.3          50.9
  Female                               22.7          49.1
Race/ethnicity, % (n *)
  Hispanic/Latino                       9.5           4.5
  Non-Hispanic Black                   38.1          14.7
  Asian, Non-Hispanic                    0            2.3
  Multiracial/Other, Non-Hispanic       4.8           1.8
  Non-Hispanic White                   47.6          95.7
Neighborhood characteristics
Municipal poverty, %
  0-10                                 45.45         71.6
  11-20                                9.10           6.3
  21-40                                45.45         22.1

Demographics                                   2012

                                      Primary      Secondary
                                     Diabetes      Diabetes
                                     Diagnoses     Diagnoses
Hospital ERV characteristics
Age, mean (SD)                      60.3 (14.0)   64.0 (15.2)
Gender, %
  Male                                 66.0          50.7
  Female                               34.0          49.3
Race/ethnicity, % (n *)
  Hispanic/Latino                       3.6           2.2
  Non-Hispanic Black                    3.6           7.4
  Asian, Non-Hispanic                    0            1.1
  Multiracial/Other, Non-Hispanic        0            2.5
  Non-Hispanic White                   92.9          87.0
Neighborhood characteristics
Municipal poverty, %
  0-10                                 52.8          70.2
  11-20                                13.2           5.7
  21-40                                34.0          24.1

* Note: Numbers <5 not presented to preserve patient
confidentiality.

TABLE 3
Number of Municipalities With Diabetes Emergency Room Visits
Before and After Flooding (October 29-November 4)

Municipalities in               2011                    2012
Flood Zone Area
                        Primary    Secondary    Primary    Secondary
                       Diabetes    Diabetes    Diabetes    Diabetes
                       Diagnoses   Diagnoses   Diagnoses   Diagnoses
Atlantic County
  Inside                   1           4           3           4
  Partially inside         1          10           6           9
  Completely outside       1           2           0           3
Cape May County
  Inside                   0           1           2           2
  Partially inside         1           4           1           5
  Completely outside       0           1           1           1
Ocean County
  Inside                   1           7           2           8
  Partially inside         4          13           6          12
  Completely outside       3           5           2           6
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Article Details
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Title Annotation:ADVANCEMENT OF THE SCIENCE
Author:Velez-Valle, Enid M.; Shendell, Derek; Echeverria, Sandra; Santorelli, Melissa
Publication:Journal of Environmental Health
Geographic Code:1U2NJ
Date:Sep 1, 2016
Words:5179
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