Collateral Impacts of the COVID-19 Pandemic: The New York City Experience.

OBJECTIVE: To adapt an existing surveillance system to monitor the collateral impacts of the COVID-19 pandemic on health outcomes in New York City across 6 domains: access to care, chronic disease, sexual/reproductive health, food/economic insecurity, mental/behavioral health, and environmental health. DESIGN: Epidemiologic assessment. Public health surveillance system. SETTING: New York City. PARTICIPANTS: New York City residents. MAIN OUTCOME MEASURES: We monitored approximately 30 indicators, compiling data from 2006 to 2022. Sources of data include clinic visits, surveillance surveys, vital statistics, emergency department visits, lead and diabetes registries, Medicaid claims, and public benefit enrollment. RESULTS: We observed disruptions across most indicators including more than 50% decrease in emergency department usage early in the pandemic, which rebounded to prepandemic levels by late 2021, changes in reporting levels of probable anxiety and depression, and worsening birth outcomes for mothers who identified as Asian/Pacific Islander or Black. Data are processed in SAS and analyzed using the R Surveillance package to detect possible inflections. Data are updated monthly to an internal Tableau Dashboard and shared with agency leadership. CONCLUSIONS: As the COVID-19 pandemic continues into its third year, public health priorities are returning to addressing non-COVID-19-related diseases and conditions, their collateral impacts, and postpandemic recovery needs. Substantial work is needed to return even to a suboptimal baseline across multiple health topic areas. Our surveillance framework offers a valuable starting place to effectively allocate resources, develop interventions, and issue public communications.

Comparative hospitalization risk for SARS-CoV-2 Omicron and Delta variant infections, by variant predominance periods and patient-level sequencing results, New York City, August 2021-January 2022.

BACKGROUND: Comparing disease severity between SARS-CoV-2 variants among populations with varied vaccination and infection histories can help characterize emerging variants and support healthcare system preparedness. METHODS: We compared COVID-19 hospitalization risk among New York City residents with positive laboratory-based SARS-CoV-2 tests when ≥98% of sequencing results were Delta (August-November 2021) or Omicron (BA.1 and sublineages, January 2022). A secondary analysis defined variant exposure using patient-level sequencing results during July 2021-January 2022, comprising 1-18% of weekly confirmed cases. RESULTS: Hospitalization risk was lower among patients testing positive when Omicron (16,025/488,053, 3.3%) than when Delta predominated (8268/158,799, 5.2%). In multivariable analysis adjusting for demographic characteristics and prior diagnosis and vaccination status, patients testing positive when Omicron predominated, compared with Delta, had 0.72 (95% CI: 0.63, 0.82) times the hospitalization risk. In a secondary analysis of patients with sequencing results, hospitalization risk was similar among patients infected with Omicron (2042/29,866, 6.8%), compared with Delta (1780/25,272, 7.0%), and higher among the subset who received two mRNA vaccine doses (adjusted relative risk 1.64; 95% CI: 1.44, 1.87). CONCLUSIONS: Hospitalization risk was lower among patients testing positive when Omicron predominated, compared with Delta. This finding persisted after adjusting for prior diagnosis and vaccination status, suggesting intrinsic virologic properties, not population-based immunity, explained the lower severity. Secondary analyses demonstrated collider bias from the sequencing sampling frame changing over time in ways associated with disease severity. Representative data collection is necessary to avoid bias when comparing disease severity between previously dominant and newly emerging variants.

Presyndromic surveillance for improved detection of emerging public health threats.

Existing public health surveillance systems that rely on predefined symptom categories, or syndromes, are effective at monitoring known illnesses, but there is a critical need for innovation in "presyndromic" surveillance that detects biothreats with rare or previously unseen symptomology. We introduce a data-driven, automated machine learning approach for presyndromic surveillance that learns newly emerging syndromes from free-text emergency department chief complaints, identifies localized case clusters among subpopulations, and incorporates practitioner feedback to automatically distinguish between relevant and irrelevant clusters, thus providing personalized, actionable decision support. Blinded evaluations by New York City's Department of Health and Mental Hygiene demonstrate that our approach identifies more events of public health interest and achieves a lower false-positive rate compared to a state-of-the-art baseline.

Built by epidemiologists for epidemiologists: an internal COVID-19 dashboard for real-time situational awareness in New York City.

Objective: New York City (NYC) experienced a large first wave of coronavirus disease 2019 (COVID-19) in the spring of 2020, but the Health Department lacked tools to easily visualize and analyze incoming surveillance data to inform response activities. To streamline ongoing surveillance, a group of infectious disease epidemiologists built an interactive dashboard using open-source software to monitor demographic, spatial, and temporal trends in COVID-19 epidemiology in NYC in near real-time for internal use by other surveillance and epidemiology experts. Materials and methods: Existing surveillance databases and systems were leveraged to create daily analytic datasets of COVID-19 case and testing information, aggregated by week and key demographics. The dashboard was developed iteratively using R, and includes interactive graphs, tables, and maps summarizing recent COVID-19 epidemiologic trends. Additional data and interactive features were incorporated to provide further information on the spread of COVID-19 in NYC. Results: The dashboard allows key staff to quickly review situational data, identify concerning trends, and easily maintain granular situational awareness of COVID-19 epidemiology in NYC. Discussion: The dashboard is used to inform weekly surveillance summaries and alleviated the burden of manual report production on infectious disease epidemiologists. The system was built by and for epidemiologists, which is critical to its utility and functionality. Interactivity allows users to understand broad and granular data, and flexibility in dashboard development means new metrics and visualizations can be developed as needed. Conclusions: Additional investment and development of public health informatics tools, along with standardized frameworks for local health jurisdictions to analyze and visualize data in emergencies, are warranted.

Epidemiological characteristics of the B.1.526 SARS-CoV-2 variant.

To characterize the epidemiological properties of the B.1.526 SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) variant of interest, here we used nine epidemiological and population datasets and model-inference methods to reconstruct SARS-CoV-2 transmission dynamics in New York City, where B.1.526 emerged. We estimated that B.1.526 had a moderate increase (15 to 25%) in transmissibility, could escape immunity in 0 to 10% of previously infected individuals, and substantially increased the infection fatality risk (IFR) among adults 65 or older by >60% during November 2020 to April 2021, compared to estimates for preexisting variants. Overall, findings suggest that new variants like B.1.526 likely spread in the population weeks before detection and that partial immune escape (e.g., resistance to therapeutic antibodies) could offset prior medical advances and increase IFR. Early preparedness for and close monitoring of SARS-CoV-2 variants, their epidemiological characteristics, and disease severity are thus crucial to COVID-19 (coronavirus disease 2019) response.

COVID-19 Outbreak - New York City, February 29-June 1, 2020.

New York City (NYC) was an epicenter of the coronavirus disease 2019 (COVID-19) outbreak in the United States during spring 2020 (1). During March-May 2020, approximately 203,000 laboratory-confirmed COVID-19 cases were reported to the NYC Department of Health and Mental Hygiene (DOHMH). To obtain more complete data, DOHMH used supplementary information sources and relied on direct data importation and matching of patient identifiers for data on hospitalization status, the occurrence of death, race/ethnicity, and presence of underlying medical conditions. The highest rates of cases, hospitalizations, and deaths were concentrated in communities of color, high-poverty areas, and among persons aged ≥75 years or with underlying conditions. The crude fatality rate was 9.2% overall and 32.1% among hospitalized patients. Using these data to prevent additional infections among NYC residents during subsequent waves of the pandemic, particularly among those at highest risk for hospitalization and death, is critical. Mitigating COVID-19 transmission among vulnerable groups at high risk for hospitalization and death is an urgent priority. Similar to NYC, other jurisdictions might find the use of supplementary information sources valuable in their efforts to prevent COVID-19 infections.

Evidence for Limited Early Spread of COVID-19 Within the United States, January-February 2020.

From January 21 through February 23, 2020, public health agencies detected 14 U.S. cases of coronavirus disease 2019 (COVID-19), all related to travel from China (1,2). The first nontravel-related U.S. case was confirmed on February 26 in a California resident who had become ill on February 13 (3). Two days later, on February 28, a second nontravel-related case was confirmed in the state of Washington (4,5). Examination of four lines of evidence provides insight into the timing of introduction and early transmission of SARS-CoV-2, the virus that causes COVID-19, into the United States before the detection of these two cases. First, syndromic surveillance based on emergency department records from counties affected early by the pandemic did not show an increase in visits for COVID-19-like illness before February 28. Second, retrospective SARS-CoV-2 testing of approximately 11,000 respiratory specimens from several U.S. locations beginning January 1 identified no positive results before February 20. Third, analysis of viral RNA sequences from early cases suggested that a single lineage of virus imported directly or indirectly from China began circulating in the United States between January 18 and February 9, followed by several SARS-CoV-2 importations from Europe. Finally, the occurrence of three cases, one in a California resident who died on February 6, a second in another resident of the same county who died February 17, and a third in an unidentified passenger or crew member aboard a Pacific cruise ship that left San Francisco on February 11, confirms cryptic circulation of the virus by early February. These data indicate that sustained, community transmission had begun before detection of the first two nontravel-related U.S. cases, likely resulting from the importation of a single lineage of virus from China in late January or early February, followed by several importations from Europe. The widespread emergence of COVID-19 throughout the United States after February highlights the importance of robust public health systems to respond rapidly to emerging infectious threats.

Surveillance data confirm multiyear predictions of rotavirus dynamics in New York City.

Prediction skill is a key test of models for epidemic dynamics. However, future validation of models against out-of-sample data is rare, partly because of a lack of timely surveillance data. We address this gap by analyzing the response of rotavirus dynamics to infant vaccination. Syndromic surveillance of emergency department visits for diarrhea in New York City reveals a marked decline in diarrheal incidence among infants and young children, in line with data on rotavirus-coded hospitalizations and laboratory-confirmed cases, and a shift from annual to biennial epidemics increasingly affecting older children and adults. A published mechanistic model qualitatively predicted these patterns more than 2 years in advance. Future efforts to increase vaccination coverage may disrupt these patterns and lead to further declines in the incidence of rotavirus-attributable gastroenteritis.

Surveillance for Mosquitoborne Transmission of Zika Virus, New York City, NY, USA, 2016.

A large number of imported cases of Zika virus infection and the potential for transmission by Aedes albopictus mosquitoes prompted the New York City Department of Health and Mental Hygiene to conduct sentinel, enhanced passive, and syndromic surveillance for locally acquired mosquitoborne Zika virus infections in New York City, NY, USA, during June-October 2016. Suspected case-patients were those >5 years of age without a travel history or sexual exposure who had >3 compatible signs/symptoms (arthralgia, fever, conjunctivitis, or rash). We identified 15 suspected cases and tested urine samples for Zika virus by using real-time reverse transcription PCR; all results were negative. We identified 308 emergency department visits for Zika-like illness, 40,073 visits for fever, and 17 unique spatiotemporal clusters of visits for fever. We identified no evidence of local transmission. Our experience offers possible surveillance tools for jurisdictions concerned about local mosquitoborne Zika virus or other arboviral transmission.

Evaluating and implementing temporal, spatial, and spatio-temporal methods for outbreak detection in a local syndromic surveillance system.

The New York City Department of Health and Mental Hygiene has operated an emergency department syndromic surveillance system since 2001, using temporal and spatial scan statistics run on a daily basis for cluster detection. Since the system was originally implemented, a number of new methods have been proposed for use in cluster detection. We evaluated six temporal and four spatial/spatio-temporal detection methods using syndromic surveillance data spiked with simulated injections. The algorithms were compared on several metrics, including sensitivity, specificity, positive predictive value, coherence, and timeliness. We also evaluated each method's implementation, programming time, run time, and the ease of use. Among the temporal methods, at a set specificity of 95%, a Holt-Winters exponential smoother performed the best, detecting 19% of the simulated injects across all shapes and sizes, followed by an autoregressive moving average model (16%), a generalized linear model (15%), a modified version of the Early Aberration Reporting System's C2 algorithm (13%), a temporal scan statistic (11%), and a cumulative sum control chart (<2%). Of the spatial/spatio-temporal methods we tested, a spatial scan statistic detected 3% of all injects, a Bayes regression found 2%, and a generalized linear mixed model and a space-time permutation scan statistic detected none at a specificity of 95%. Positive predictive value was low (<7%) for all methods. Overall, the detection methods we tested did not perform well in identifying the temporal and spatial clusters of cases in the inject dataset. The spatial scan statistic, our current method for spatial cluster detection, performed slightly better than the other tested methods across different inject magnitudes and types. Furthermore, we found the scan statistics, as applied in the SaTScan software package, to be the easiest to program and implement for daily data analysis.

Real-time surveillance of heat-related morbidity: Relation to excess mortality associated with extreme heat.

The impact of heat on mortality is well documented but deaths tend to occur after (or lag) extreme heat events, and mortality data is generally not available for timely surveillance during extreme heat events. Recently, systems for near-real time surveillance of heat illness have been reported but have not been validated as predictors of non-external cause of deaths associated with extreme heat events. We analyzed associations between daily weather conditions, emergency medical system (EMS) calls flagged as heat-related by EMS dispatchers, emergency department (ED) visits classified as heat-related based on chief complaint text, and excess non-external cause mortality in New York City. EMS and ED data were obtained from data reported daily to the city health department for syndromic surveillance. We fit generalized linear models to assess the relationships of daily counts of heat related EMS and ED visits to non-external cause deaths after adjustment for weather conditions during the months of May-September between 1999 and 2013. Controlling for temporal trends, a 7% (95% confidence interval (CI): 2-12) and 6% (95% CI: 3-10) increase in non-external cause mortality was associated with an increase from the 50th percentile to 99th percentile of same-day and one-day lagged heat-related EMS calls and ED visits, respectively. After controlling for both temporal trends and weather, we observed a 7% (95% CI: 3-12) increase in non-external cause mortality associated with one-day lagged heat-related EMS calls and a 5% mortality increase with one-day lagged ED visits (95% CI: 2-8). Heat-related illness can be tracked during extreme heat events using EMS and ED data which are indicators of heat associated excess non-external cause mortality during the warm weather season.

Advancing the Use of Emergency Department Syndromic Surveillance Data, New York City, 2012-2016.

INTRODUCTION: The use of syndromic surveillance has expanded from its initial purpose of bioterrorism detection. We present 6 use cases from New York City that demonstrate the value of syndromic surveillance for public health response and decision making across a broad range of health outcomes: synthetic cannabinoid drug use, heat-related illness, suspected meningococcal disease, medical needs after severe weather, asthma exacerbation after a building collapse, and Ebola-like illness in travelers returning from West Africa. MATERIALS AND METHODS: The New York City syndromic surveillance system receives data on patient visits from all emergency departments (EDs) in the city. The data are used to assign syndrome categories based on the chief complaint and discharge diagnosis, and analytic methods are used to monitor geographic and temporal trends and detect clusters. RESULTS: For all 6 use cases, syndromic surveillance using ED data provided actionable information. Syndromic surveillance helped detect a rise in synthetic cannabinoid-related ED visits, prompting a public health investigation and action. Surveillance of heat-related illness indicated increasing health effects of severe weather and led to more urgent public health messaging. Surveillance of meningitis-related ED visits helped identify unreported cases of culture-negative meningococcal disease. Syndromic surveillance also proved useful for assessing a surge of methadone-related ED visits after Superstorm Sandy, provided reassurance of no localized increases in asthma after a building collapse, and augmented traditional disease reporting during the West African Ebola outbreak. PRACTICE IMPLICATIONS: Sharing syndromic surveillance use cases can foster new ideas and build capacity for public health preparedness and response.

The associations between daily spring pollen counts, over-the-counter allergy medication sales, and asthma syndrome emergency department visits in New York City, 2002-2012.

BACKGROUND: Many types of tree pollen trigger seasonal allergic illness, but their population-level impacts on allergy and asthma morbidity are not well established, likely due to the paucity of long records of daily pollen data that allow analysis of multi-day effects. Our objective in this study was therefore to determine the impacts of individual spring tree pollen types on over-the-counter allergy medication sales and asthma emergency department (ED) visits. METHODS: Nine clinically-relevant spring tree pollen genera (elm, poplar, maple, birch, beech, ash, sycamore/London planetree, oak, and hickory) measured in Armonk, NY, were analyzed for their associations with over-the-counter allergy medication sales and daily asthma syndrome ED visits from patients' chief complaints or diagnosis codes in New York City during March 1st through June 10th, 2002-2012. Multi-day impacts of pollen on the outcomes (0-3 days and 0-7 days for the medication sales and ED visits, respectively) were estimated using a distributed lag Poisson time-series model adjusting for temporal trends, day-of-week, weather, and air pollution. For asthma syndrome ED visits, age groups were also analyzed. Year-to-year variation in the average peak dates and the 10th-to-90th percentile duration between pollen and the outcomes were also examined with Spearman's rank correlation. RESULTS: Mid-spring pollen types (maple, birch, beech, ash, oak, and sycamore/London planetree) showed the strongest significant associations with both outcomes, with cumulative rate ratios up to 2.0 per 0-to-98th percentile pollen increase (e.g., 1.9 [95% CI: 1.7, 2.1] and 1.7 [95% CI: 1.5, 1.9] for the medication sales and ED visits, respectively, for ash). Lagged associations were longer for asthma syndrome ED visits than for the medication sales. Associations were strongest in children (ages 5-17; e.g., a cumulative rate ratio of 2.6 [95% CI: 2.1, 3.1] per 0-to-98th percentile increase in ash). The average peak dates and durations of some of these mid-spring pollen types were also associated with those of the outcomes. CONCLUSIONS: Tree pollen peaking in mid-spring exhibit substantive impacts on allergy, and asthma exacerbations, particularly in children. Given the narrow time window of these pollen peak occurrences, public health and clinical approaches to anticipate and reduce allergy/asthma exacerbation should be developed.

Using Syndromic Surveillance to Investigate Tattoo-Related Skin Infections in New York City.

In response to two isolated cases of Mycobacterium chelonae infections in tattoo recipients where tap water was used to dilute ink, the New York City (NYC) Department of Health and Mental Hygiene conducted an investigation using Emergency Department (ED) syndromic surveillance to assess whether an outbreak was occuring. ED visits with chief complaints containing the key word "tattoo" from November 1, 2012 to March 18, 2013 were selected for study. NYC laboratories were also contacted and asked to report skin or soft tissue cultures in tattoo recipients that were positive for non-tuberculosis mycobacterial infection (NTM). Thirty-one TREDV were identified and 14 (45%) were interviewed to determine if a NTM was the cause for the visit. One ED visit met the case definition and was referred to a dermatologist. This individual was negative for NTM. No tattoo-associated NTM cases were reported by NYC laboratories. ED syndromic surveillance was utilized to investigate a non-reportable condition for which no other data source existed. The results were reassuring that an outbreak of NTM in tattoo recipients was not occurring. In response to concerns about potential NTM infections, the department sent a letter to all licensed tattoo artists advising them not to dilute tattoo ink with tap water.

Relationship between anthropometric factors, radiation exposure, and colon cancer incidence in the Life Span Study cohort of atomic bomb survivors.

PURPOSE: We examined colon cancer risk in atomic bomb survivors to investigate whether excess body weight after the bombings alters sensitivity to radiation effects. METHODS: Of the 56,064 Japanese atomic bomb survivors with follow-up through 2002 with self-reported anthropometric data obtained from periodic mail surveys, 1,142 were diagnosed with colon cancer. We evaluated the influence of body mass index (BMI) and height on radiation-associated colon cancer risk using Poisson regression. RESULTS: We observed a similar linear dose-response relationship for the 56,064 subjects included in our analysis and the entire cohort of Japanese atomic bomb survivors [excess relative risk (ERR) per Gray (Gy) = 0.53, 95 % confidence interval (CI) 0.25-0.86]. Elevation in earliest reported BMI, BMI reported closest to colon cancer diagnosis, and time-varying BMI were associated with an elevated risk of colon cancer [relative risk (RR) per 5 kg/m(2) increase in BMI = 1.14, 95 % CI 1.03-1.26; RR = 1.16, 95 % CI 1.05-1.27; and RR = 1.15, 95 % CI 1.04-1.27, respectively]. Height was not significantly related to colon cancer risk. Inclusion of anthropometric variables in models had little impact on radiation risk estimates, and there was no evidence that sensitivity to the effect of radiation on colon cancer risk depended on BMI. CONCLUSIONS: Radiation exposure and BMI are both risk factors for colon cancer. BMI at various times after exposure to the atomic bombings does not significantly influence the relationship between radiation dose and colon cancer risk, suggesting that BMI and radiation impact colon cancer risk independently of each other.

Fine particulate matter constituents associated with cardiovascular hospitalizations and mortality in New York City.

BACKGROUND: Recent time-series studies have indicated that both cardiovascular disease (CVD)mortality and hospitalizations are associated with particulate matter (PM). However, seasonal patterns of PM associations with these outcomes are not consistent, and PM components responsible for these associations have not been determined. We investigated this issue in New York City (NYC), where PM originates from regional and local combustion sources. OBJECTIVE: In this study, we examined the role of particulate matter with aerodynamic diameter ≤ 2.5 µm (PM(2.5)) and its key chemical components on both CVD hospitalizations and on mortality in NYC. METHODS: We analyzed daily deaths and emergency hospitalizations for CVDs among persons ≥ 40 years of age for associations with PM(2.5), its chemical components, nitrogen dioxide (NO(2)), carbon monoxide, and sulfur dioxide for the years 2000-2006 using a Poisson time-series model adjusting for temporal and seasonal trends, temperature effects, and day of the week. We estimated excess risks per interquartile-range increases at lags 0 through 3 days for warm (April through September) and cold (October through March) seasons. RESULTS: The CVD mortality series exhibit strong seasonal trends, whereas the CVD hospitalization series show a strong day-of-week pattern. These outcome series were not correlated with each other but were individually associated with a number of PM(2.5) chemical components from regional and local sources, each with different seasonal patterns and lags. Coal-combustion-related components (e.g., selenium) were associated with CVD mortality in summer and CVD hospitalizations in winter, whereas elemental carbon and NO(2) showed associations with these outcomes in both seasons. CONCLUSION: Local combustion sources, including traffic and residual oil burning, may play a year-round role in the associations between air pollution and CVD outcomes, but transported aerosols may explain the seasonal variation in associations shown by PM(2.5) mass.

Assessing syndromic surveillance of cardiovascular outcomes from emergency department chief complaint data in New York City.

BACKGROUND: Prospective syndromic surveillance of emergency department visits has been used for near-real time tracking of communicable diseases to detect outbreaks or other unexpected disease clusters. The utility of syndromic surveillance for tracking cardiovascular events, which may be influenced by environmental factors and influenza, has not been evaluated. We developed and evaluated a method for tracking cardiovascular events using emergency department free-text chief complaints. METHODOLOGY/PRINCIPAL FINDINGS: There were three phases to our analysis. First we applied text processing algorithms based on sensitivity, specificity, and positive predictive value to chief complaint data reported by 11 New York City emergency departments for which ICD-9 discharge diagnosis codes were available. Second, the same algorithms were applied to data reported by a larger sample of 50 New York City emergency departments for which discharge diagnosis was unavailable. From this more complete data, we evaluated the consistency of temporal variation of cardiovascular syndromic events and hospitalizations from 76 New York City hospitals. Finally, we examined associations between particulate matter ≤2.5 µm (PM(2.5)), syndromic events, and hospitalizations. Sensitivity and positive predictive value were low for syndromic events, while specificity was high. Utilizing the larger sample of emergency departments, a strong day of week pattern and weak seasonal trend were observed for syndromic events and hospitalizations. These time-series were highly correlated after removing the day-of-week, holiday, and seasonal trends. The estimated percent excess risks in the cold season (October to March) were 1.9% (95% confidence interval (CI): 0.6, 3.2), 2.1% (95% CI: 0.9, 3.3), and 1.8% (95%CI: 0.5, 3.0) per same-day 10 µg/m(3) increase in PM(2.5) for cardiac-only syndromic data, cardiovascular syndromic data, and hospitalizations, respectively. CONCLUSIONS/SIGNIFICANCE: Near real-time emergency department chief complaint data may be useful for timely surveillance of cardiovascular morbidity related to ambient air pollution and other environmental events.

The association of tree pollen concentration peaks and allergy medication sales in new york city: 2003-2008.

The impact of pollen exposure on population allergic illness is poorly characterized. We explore the association of tree pollen and over-the-counter daily allergy medication sales in the New York City metropolitan area. Dates of peak tree pollen (maple, oak, and birch) concentrations were identified from 2003 to 2008. Daily allergy medication sales reported to the city health department were analyzed as a function of the same-day and lagged tree pollen peak indicators, adjusting for season, year, temperature, and day of week. Significant associations were found between tree pollen peaks and allergy medication sales, with the strongest association at 2-day lag (excess sales of 28.7% (95% CI: 17.4-41.2) over the average sales during the study period). The cumulative effect over the 7-day period on and after the tree pollen peak dates was estimated to be 141.1% (95% CI: 79.4-224.1). In conclusion, tree pollen concentration peaks were followed by large increases in over-the-counter allergy medication sales.

Migraine history and breast cancer risk among postmenopausal women.

PURPOSE Both migraine and breast cancer are hormonally mediated. Two recent reports indicate that women with a migraine history may have a lower risk of postmenopausal breast cancer than those who never suffered migraines. This finding requires confirmation; in particular, an assessment of the influence of use of nonsteroidal anti-inflammatory drugs (NSAID) is needed, because many studies indicate that NSAID use also may confer a reduction in breast cancer risk. METHODS We assessed the relationship between self-reported history of migraine and incidence of postmenopausal breast cancer in 91,116 women enrolled on the Women's Health Initiative Observational Study prospective cohort from 1993 to 1998 at ages 50 to 79 years. Through September 15, 2005, there were 4,006 eligible patients with breast cancer diagnosed. Results Women with a history of migraine had a lower risk of breast cancer (hazard ratio [HR], 0.89; 95% CI, 0.80 to 98) than women without a migraine history. This risk did not vary by recent NSAID use. The lower risk was somewhat more pronounced for invasive estrogen-receptor-positive and progesterone-receptor-positive tumors (HR, 0.83; 95% CI, 0.71 to 0.97), as no reduction in risk was observed for invasive ER-negative/PR-negative tumors (HR, 1.16; 95% CI, 0.86 to 1.57), and this difference in risk estimates was borderline statistically significant (P = .06). CONCLUSION This study supports the hypothesis that a history of migraine is associated with a lower risk of breast cancer and that this relationship is independent of recent NSAID use.

Relationship between migraine history and breast cancer risk among premenopausal and postmenopausal women.

Both migraine and breast cancer are hormonally mediated diseases, and it is biologically plausible that women with a history of migraine may have a reduced breast cancer risk. However, this relationship has only been assessed in a single relatively small study that was unable to assess the effect of migraine triggers, which are also well-established breast cancer risk factors (e.g., use of alcohol and exogenous hormones), on the inverse association observed. Utilizing data on 4,568 breast cancer cases and 4,678 controls who participated in a multicenter population-based case-control study in the United States, we evaluated the association between migraine history and breast cancer risk using unconditional logistic regression. Migraine history data were obtained from structured in-person interviews. Women with a history of migraine had a reduced risk of breast cancer [odds ratio, 0.74; 95% confidence interval (CI), 0.66-0.82]. This risk did not differ by menopausal status, age at migraine diagnosis, use of prescription migraine medications, or when analyses were restricted to women who avoided various migraine triggers (including alcohol, exogenous hormones, and smoking). These data support a previous finding that a history of migraine may be associated with a reduced risk of breast cancer. It extends the prior report in observing that this relationship holds for both premenopausal and postmenopausal women and is independent of exposure to common migraine triggers.