Hurricane Sandy Evacuation Among World Trade Center Health Registry Enrollees in New York City

Objective Timely evacuation is vital for reducing adverse outcomes during disasters. This study examined factors associated with evacuation and evacuation timing during Hurricane Sandy among World Trade Center Health Registry (Registry) enrollees. Methods The study sample included 1162 adults who resided in New York City’s evacuation zone A during Hurricane Sandy who completed the Registry’s Hurricane Sandy substudy in 2013. Factors assessed included zone awareness, prior evacuation experience, community cohesion, emergency preparedness, and poor physical health. Prevalence estimates and multiple logistic regression models of evacuation at any time and evacuation before Hurricane Sandy were created. Results Among respondents who evacuated for Hurricane Sandy (51%), 24% had evacuated before the storm. In adjusted analyses, those more likely to evacuate knew they resided in an evacuation zone, had evacuated during Hurricane Irene, or reported pre-Sandy community cohesion. Evacuation was less likely among those who reported being prepared for an emergency. For evacuation timing, evacuation before Hurricane Sandy was less likely among those with pets and those who reported 14 or more poor physical health days. Conclusions Higher evacuation rates were observed for respondents seemingly more informed and who lived in neighborhoods with greater social capital. Improved disaster messaging that amplifies these factors may increase adherence with evacuation warnings

Multiple Chronic Conditions and Limitations in Activities of Daily Living in a Community-Based Sample of Older Adults in New York City, 2009

Introduction Nationally, 60% to 75% of older adults have multiple (2 or more) chronic conditions (MCCs), and the burden is even higher among low-income, racial/ethnic minority populations. MCCs limit activities of daily living (ADLs), yet this association is not well characterized outside of clinical populations. We examined the association of MCCs with ADLs in a racially/ethnically diverse population of low-income older adults living in New York City public housing. Methods A representative sample of 1,036 New York City Housing Authority residents aged 65 or older completed a telephone survey in June 2009. We examined the association of up to 5 chronic conditions with basic ADL (BADL) limitations, adjusting for potential confounders by using logistic regression. Results Of respondents, 28.7% had at least 1 BADL limitation; 92.9% had at least 1 chronic condition, and 79.0% had MCCs. We observed a graded association between at least 1 BADL limitation and number of chronic conditions (using 0 or 1 condition as the reference group): adjusted odds ratio (AOR) for 3 conditions was 2.2 (95% confidence interval [CI], 1.3–3.9); AOR for 4 conditions, 4.3 (95% CI, 2.5–7.6); and AOR for 5 conditions, 9.2 (95% CI, 4.3–19.5). Conclusion Prevalence of BADL limitations is high among low-income older adults and increases with number of chronic conditions. Initiating prevention of additional conditions and treating disease constellations earlier to decrease BADL limitations may improve aging outcomes in this population

Prevalence of concomitant rheumatologic diseases and autoantibody specificities among racial and ethnic groups in SLE patients

Objective: Leveraging the Manhattan Lupus Surveillance Program (MLSP), a population-based registry of cases of systemic lupus erythematosus (SLE) and related diseases, we investigated the proportion of SLE with concomitant rheumatic diseases, including Sjögren’s disease (SjD), antiphospholipid syndrome (APLS), and fibromyalgia (FM), as well as the prevalence of autoantibodies in SLE by sex and race/ethnicity. Methods: Prevalent SLE cases fulfilled one of three sets of classification criteria. Additional rheumatic diseases were defined using modified criteria based on data available in the MLSP: SjD (anti-SSA/Ro positive and evidence of keratoconjunctivitis sicca and/or xerostomia), APLS (antiphospholipid antibody positive and evidence of a blood clot), and FM (diagnosis in the chart). Results: 1,342 patients fulfilled SLE classification criteria. Of these, SjD was identified in 147 (11.0%, 95% CI 9.2–12.7%) patients with women and non-Latino Asian patients being the most highly represented. APLS was diagnosed in 119 (8.9%, 95% CI 7.3–10.5%) patients with the highest frequency in Latino patients. FM was present in 120 (8.9%, 95% CI 7.3–10.5) patients with non-Latino White and Latino patients having the highest frequency. Anti-dsDNA antibodies were most prevalent in non-Latino Asian, Black, and Latino patients while anti-Sm antibodies showed the highest proportion in non-Latino Black and Asian patients. Anti-SSA/Ro and anti-SSB/La antibodies were most prevalent in non-Latino Asian patients and least prevalent in non-Latino White patients. Men were more likely to be anti-Sm positive. Conclusion: Data from the MLSP revealed differences among patients classified as SLE in the prevalence of concomitant rheumatic diseases and autoantibody profiles by sex and race/ethnicity underscoring comorbidities associated with SLE

Evaluation of Syndrome Algorithms for Detecting Pneumonia Emergency Department Visits

ObjectiveTo validate and improve the syndromic algorithm used to describe pneumonia emergency department (ED) visit trends in New York City (NYC).IntroductionThe NYC Department of Health and Mental Hygiene (DOHMH) uses ED syndromic surveillance to monitor near real-time trends in pneumonia visits. The original pneumonia algorithm was developed based on ED chief complaints, and more recently was modified following a legionella outbreak in NYC. In 2016, syndromic data was matched to New York State all payer database (SPARCS) for 2010 through 2015. We leveraged this matched dataset to validate ED visits identified by our pneumonia algorithm and suggest improvements. An effective algorithm for tracking trends in pneumonia could provide critical information to inform and facilitate public health decision-making.MethodsThe DOHMH syndromic surveillance system includes daily ED data from 53 NYC hospitals. Most syndrome algorithms rely solely on chief complaint, which has historically been reported more consistently than discharge diagnosis. For this analysis, the validation dataset was restricted to matched visits with consistent age (plus or minus two years) and sex between the syndromic and SPARCS datasets.The original pneumonia algorithm used a basic text search function to identify any mention of ICD-9-CM and ICD-10-CM diagnosis codes indicating pneumonia or key words “PNEUMON” or “MONIA” within the chief complaint only. The updated algorithm additionally searches the chief complaint for any mention of key words specific to legionella (“LEGIONA”, “LEGIONN”, “LEGIONE”) and also searches for pneumonia ICD codes in the discharge diagnosis field. Syndrome sensitivity and positive predictive value (PPV) were evaluated by comparing visits identified by each algorithm to visits identified by billing diagnosis codes. A true SPARCS pneumonia ED visit was defined to contain an admitting or principal diagnosis billing code indicating pneumonia.Alternate algorithms were created using regular expressions, which allowed for more flexible and accurate pattern matching. The algorithms were further revised based on additional inclusion and exclusion key words identified using the validation dataset.ResultsBetween 2010 and 2015, there were a total of 204,101 true pneumonia visits based on the SPARCS billing records. Evaluation of the original algorithm found a sensitivity of 15.6% (31,771/204,101) and a PPV of 55.6% (31,771/57,180). Over the same time period, syndromic surveillance identified a total of 127,560 pneumonia visits using the updated algorithm; 86,590 of the 127,560 syndromic cases identified were determined to be a true visit based on the billing diagnosis codes, resulting in an algorithm sensitivity of 42.4% and PPV of 67.9%. Of the 127,560 cases identified by the updated algorithm, 19 cases were classified as a pneumonia visit solely due to the presence of legionella key words in the chief complaint. Regular expression usage as opposed to the basic text search on the updated algorithm found similar sensitivity (42.4%, 86,561/204,101) and PPV (68.0%, 86,561/127,238).Among all true pneumonia visits with a non-blank discharge diagnosis field, 65.3% (68,001/104,223) had mention of a pneumonia diagnosis code. Use of the discharge diagnosis code field in addition to the chief complaint found the algorithm to be almost three times more sensitive and 1.2 times greater in PPV than an algorithm without use of discharge diagnosis. Seasonal trends captured with and without use of discharge diagnosis were both similar to the true pneumonia trend indicated by SPARCS.Among the 117,540 pneumonia cases missed by the updated algorithm, 58.6% had fewer than three words in the chief complaint. With the most popular key words among the missed cases being highly non-specific (i.e., “fever”, “cough”, “pain”), inclusion of these key words in addition to regular expression and discharge diagnosis field usage elevated algorithm sensitivity at a severe cost to the PPV. Including “fever” in the list of pneumonia key words resulted in a sensitivity of 56.5% (115,280/204,101) and a PPV of 9.0% (115,280/1,282,342), while addition of the key word combination “fever” and “cough” led to a sensitivity of 46.7% (95,264/204,101) and a PPV of 29.8% (95,264/319,876).As we were unable to identify novel key word indicators that were good markers for pneumonia events, regular expression search functionality was the best improvement to the pneumonia syndrome algorithm. This revised, new algorithm maintained sensitivity (42.4%, 86,561/204,101) and provided slight improvements to PPV (68.0%, 86,561/127,219).However, performance of the updated algorithm varied across age groups. The algorithm was most effective in identifying younger cases (43.9% sensitivity, 80.4% PPV for those 17 years and younger), while it performed the worst among those 65 years and older (39.6% sensitivity, 58.7% PPV).ConclusionsBased on our evaluation of the pneumonia syndromic surveillance algorithm, we found that search of the discharge diagnosis field greatly improved algorithm sensitivity and PPV and usage of regular expressions increased PPV slightly. Including additional words possibly indicating pneumonia did not substantially improve sensitivity or PPV. However, integration of the ED chief complaint triage notes which are not currently utilized could further enhance the effectiveness of the pneumonia syndrome algorithm and better characterize daily pneumonia trends in NYC.

COVID-19-Specific Mortality among World Trade Center Health Registry Enrollees Who Resided in New York City

We examined the all-cause and COVID-19-specific mortality among World Trade Center Health Registry (WTCHR) enrollees. We also examined the socioeconomic factors associated with COVID-19-specific death. Mortality data from the NYC Bureau of Vital Statistics between 2015–2020 were linked to the WTCHR. COVID-19-specific death was defined as having positive COVID-19 tests that match to a death certificate or COVID-19 mentioned on the death certificate via text searching. We conducted step change and pulse regression to assess excess deaths. Limiting to those who died in 2019 (n = 210) and 2020 (n = 286), we examined factors associated with COVID-19-specific deaths using multinomial logistic regression. Death rate among WTCHR enrollees increased during the pandemic (RR: 1.70, 95% CL: 1.25–2.32), driven by the pulse in March–April 2020 (RR: 3.38, 95% CL: 2.62–4.30). No significantly increased death rate was observed during May–December 2020. Being non-Hispanic Black and having at least one co-morbidity had a higher likelihood of COVID-19-associated mortality than being non-Hispanic White and not having any co-morbidity (AOR: 2.43, 95% CL: 1.23–4.77; AOR: 2.86, 95% CL: 1.19–6.88, respectively). The racial disparity in COVID-19-specific deaths attenuated after including neighborhood proportion of essential workers in the model (AOR:1.98, 95% CL: 0.98–4.01). Racial disparities continue to impact mortality by differential occupational exposure and structural inequality in neighborhood representation. The WTC-exposed population are no exception. Continued efforts to reduce transmission risk in communities of color is crucial for addressing health inequities

Investigating a Syndromic Surveillance Signal with Complimentary Data Systems

In early June, the New York City syndromic surveillance system detected five signals in sales of over-the-counter antidiarrheal medications. To determine if this increase reflected a concerning cluster of diarrheal illness, we examined multiple communicable disease surveillance data systems. After further investigation of syndromic and other systems, we determined that findings possibly reflected sales promotions but did not suggest increased diarrheal illness in NYC

Incidence rates of systemic lupus erythematosus in the USA:estimates from a meta-analysis of the Centers for Disease Control and Prevention national lupus registries

OBJECTIVE: To estimate the annual incidence rate of SLE in the USA. METHODS: A meta-analysis used sex/race/ethnicity-specific data spanning 2002–2009 from the Centers for Disease Control and Prevention network of four population-based state registries to estimate the incidence rates. SLE was defined as fulfilling the 1997 revised American College of Rheumatology classification criteria. Given heterogeneity across sites, a random effects model was employed. Applying sex/race/ethnicity-stratified rates, including data from the Indian Health Service registry, to the 2018 US Census population generated estimates of newly diagnosed SLE cases. RESULTS: The pooled incidence rate per 100 000 person-years was 5.1 (95% CI 4.6 to 5.6), higher in females than in males (8.7 vs 1.2), and highest among black females (15.9), followed by Asian/Pacific Islander (7.6), Hispanic (6.8) and white (5.7) females. Male incidence was highest in black males (2.4), followed by Hispanic (0.9), white (0.8) and Asian/Pacific Islander (0.4) males. The American Indian/Alaska Native population had the second highest race-specific SLE estimates for females (10.4 per 100 000) and highest for males (3.8 per 100 000). In 2018, an estimated 14 263 persons (95% CI 11 563 to 17 735) were newly diagnosed with SLE in the USA. CONCLUSIONS: A network of population-based SLE registries provided estimates of SLE incidence rates and numbers diagnosed in the USA

Prevalence of systemic lupus erythematosus in the united states:estimates from a meta‐analysis of the centers for disease control and prevention national lupus registries

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167791/1/art41632_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167791/2/art41632.pd