Breast cancer incidence and mortality trends in an affluent population: Marin County, California, USA, 1990–1999

BACKGROUND: Elevated rates of breast cancer in affluent Marin County, California, were first reported in the early 1990s. These rates have since been related to higher regional prevalence of known breast cancer risk factors, including low parity, education, and income. Close surveillance of Marin County breast cancer trends has nevertheless continued, in part because distinctive breast cancer patterns in well-defined populations may inform understanding of breast cancer etiology. METHODS: Using the most recent incidence and mortality data available from the California Cancer Registry, we examined rates and trends for 1990–1999 for invasive breast cancer among non-Hispanic, white women in Marin County, in other San Francisco Bay Area counties, and in other urban California counties. Rates were age adjusted to the 2000 US standard, and temporal changes were evaluated with weighted linear regression. RESULTS: Marin County breast cancer incidence rates between 1990 and 1999 increased 3.6% per year (95% confidence interval, 1.8–5.5), six times more rapidly than in comparison areas. The increase was limited to women aged 45–64 years, in whom rates increased at 6.7% per year (95% confidence interval, 3.8–9.6). Mortality rates did not change significantly in Marin County despite 3–5% yearly declines elsewhere. CONCLUSION: Patterns of breast cancer incidence and mortality in Marin County are unlike those in other California counties, and they are probably explained by Marin County's unique sociodemographic characteristics. Similar trends may have occurred in other affluent populations for which available data do not permit annual monitoring of cancer occurrence

Impact of intercensal population projections and error of closure on breast cancer surveillance: examples from 10 California counties

INTRODUCTION: In 2001, data from the California Cancer Registry suggested that breast cancer incidence rates among non-Hispanic white (nHW) women in Marin County, California, had increased almost 60% between 1991 and 1999. This analysis examines the extent to which these and other breast cancer incidence trends could have been impacted by bias in intercensal population projections. METHOD: We obtained population projections for the year 2000 projected from the 1990 census from the California Department of Finance (DOF) and population counts from the 2000 US Census for nHW women living in 10 California counties and quantified age-specific differences in counts. We also computed age-adjusted incidence rates of invasive breast cancer in order to examine and quantify the impact of differences between the population data sources. RESULTS: Differences between year 2000 DOF projections and year 2000 census counts varied by county and age and ranged from underestimates of 60% to overestimates of 64%. For Marin County, the DOF underestimated the number of nHW women aged 45 to 64 years by 32% compared to the 2000 US census. This difference produced a significant 22% discrepancy between breast cancer incidence rates calculated using the two population data sources. In Los Angeles and Santa Clara counties, DOF-based incidence rates were significantly lower than rates based on census data. Rates did not differ significantly by population data source in the remaining seven counties examined. CONCLUSION: Although year 2000 population estimates from the DOF did not differ markedly from census counts at the state or county levels, greater discrepancies were observed for race-stratified, age-specific groups within counties. Because breast cancer incidence rates must be calculated with age-specific data, differences between population data sources at the age-race level may lead to mis-estimation of breast cancer incidence rates in county populations affected by these differences, as was observed in Marin County. Although intercensal rates based on population projections are important for timely breast cancer surveillance, these rates are prone to bias due to the error of closure between population projections and decennial census population counts. Intercensal rates should be interpreted with this potential bias in mind

Recent trends in hormone therapy utilization and breast cancer incidence rates in the high incidence population of Marin County, California

<p>Abstract</p> <p>Background</p> <p>Recent declines in invasive breast cancer have been reported in the US, with many studies linking these declines to reductions in the use of combination estrogen/progestin hormone therapy (EPHT). We evaluated the changing use of postmenopausal hormone therapy, mammography screening rates, and the decline in breast cancer incidence specifically for Marin County, California, a population with historically elevated breast cancer incidence rates.</p> <p>Methods</p> <p>The Marin Women's Study (MWS) is a community-based, prospective cohort study launched in 2006 to monitor changes in breast cancer, breast density, and personal and biologic risk factors among women living in Marin County. The MWS enrolled 1,833 women following routine screening mammography between October 2006 and July 2007. Participants completed a self-administered questionnaire that included items regarding historical hormone therapy regimen (estrogen only, progesterone only, EPHT), age of first and last use, total years of use, and reason(s) for stopping, as well as information regarding complementary hormone use. Questionnaire items were analyzed for 1,083 non-Hispanic white participants ages 50 and over. Breast cancer incidence rates were assessed overall and by tumor histology and estrogen receptor (ER) status for the years 1990-2007 using data from the Northern California Surveillance, Epidemiology and End Results (SEER) cancer registry.</p> <p>Results</p> <p>Prevalence of EPHT use among non-Hispanic white women ages 50 and over declined sharply from 21.2% in 1998 to 6.7% by 2006-07. Estrogen only use declined from 26.9% in 1998 to 22.4% by 2006-07. Invasive breast cancer incidence rates declined 33.4% between 2001 and 2004, with drops most pronounced for ER+ cancers. These rate reductions corresponded to declines of about 50 cases per year, consistent with population attributable fraction estimates for EPHT-related breast cancer. Self-reported screening mammography rates did not change during this period. Use of alternative or complementary agents did not differ significantly between ever and never hormone users. Of women who reported stopping EPHT in the past 5 years, 60% cited "health risks" or "news reports" as their primary reasons for quitting.</p> <p>Conclusion</p> <p>A dramatic reduction in EPHT use was followed temporally by a significant reduction in invasive and ER+ breast cancer rates among women living in Marin County, California.</p

Differences in reproductive risk factors for breast cancer in middle-aged women in Marin County, California and a sociodemographically similar area of Northern California

<p>Abstract</p> <p>Background</p> <p>The Northern California county of Marin (MC) has historically had high breast cancer incidence rates. Because of MC's high socioeconomic status (SES) and racial homogeneity (non-Hispanic White), it has been difficult to assess whether these elevated rates result from a combination of established risk factors or other behavioral or environmental factors. This survey was designed to compare potential breast cancer risks and incidence rates for a sample of middle-aged MC women with those of a demographically similar population.</p> <p>Methods</p> <p>A random sample of 1500 middle-aged female members of a large Northern California health plan, half from Marin County (MC) and half from a comparison area in East/Central Contra Costa County (ECCC), were mailed a survey covering family history, reproductive history, use of oral contraceptives (OC) and hormone replacement therapy (HRT), behavioral health risks, recency of breast screening, and demographic characteristics. Weighted data were used to compare prevalence of individual breast cancer risk factors and Gail scores. Age-adjusted cumulative breast cancer incidence rates (2000–2004) were also calculated for female health plan members aged 40–64 residing in the two geographic areas.</p> <p>Results</p> <p>Survey response was 57.1% (n = 427) and 47.9% (n = 359) for MC and ECCC samples, respectively. Women in the two areas were similar in SES, race, obesity, exercise frequency, current smoking, ever use of OCs and HRT, age at onset of menarche, high mammography rates, family history of breast cancer, and Gail scores. However, MC women were significantly more likely than ECCC women to be former smokers (43.6% vs. 31.2%), have Ashkenazi Jewish heritage (12.8% vs. 7.1%), have no live births before age 30 (52.7% vs. 40.8%), and be nulliparous (29.2% vs. 15.4%), and less likely to never or rarely consume alcohol (34.4% vs. 41.9%). MC and ECCC women had comparable 2000–2004 invasive breast cancer incidence rates.</p> <p>Conclusion</p> <p>The effects of reproductive risks factors, Ashkenazi Jewish heritage, smoking history, and alcohol consumption with regard to breast cancer risk in Marin County should be further evaluated. When possible, future comparisons of breast cancer incidence rates between regions should adjust for differences in income and education in addition to age and race/ethnicity, preferably by using a sociodemographically similar comparison group.</p

Dietary reference values for sodium

Following a request from the European Commission, the EFSA Panel&nbsp;on Nutrition, Novel Foods and Food Allergens (NDA) derived dietary reference values (DRVs) for sodium. Evidence from balance studies on sodium and on the relationship between sodium intake and health outcomes, in particular cardiovascular disease (CVD)-related endpoints and bone health, was reviewed. The data were not sufficient to enable an average requirement (AR) or population reference intake (PRI) to be derived. However, by integrating the available evidence and associated uncertainties, the Panel&nbsp;considers that a sodium intake of 2.0&nbsp;g/day represents a level of sodium for which there is sufficient confidence in a reduced risk of CVD in the general adult population. In addition, a sodium intake of 2.0&nbsp;g/day is likely to allow most of the general adult population to maintain sodium balance. Therefore, the Panel&nbsp;considers that 2.0&nbsp;g sodium/day is a safe and adequate intake for the general EU population of adults. The same value applies to pregnant and lactating women. Sodium intakes that are considered safe and adequate for children are extrapolated from the value for adults, adjusting for their respective energy requirement and including a growth factor, and are as follows: 1.1&nbsp;g/day for children aged 1\u20133&nbsp;years, 1.3&nbsp;g/day for children aged 4\u20136&nbsp;years, 1.7&nbsp;g/day for children aged 7\u201310&nbsp;years and 2.0&nbsp;g/day for children aged 11\u201317&nbsp;years, respectively. For infants aged 7\u201311&nbsp;months, an Adequate Intake (AI) of 0.2&nbsp;g/day is proposed based on upwards extrapolation of the estimated sodium intake in exclusively breast-fed infants aged 0\u20136&nbsp;months

Development of Text-Based Algorithm for Opioid Overdose Identification in EMS Data

ObjectiveTo develop and implement a classifcation algorithm to identify likely acute opioid overdoses from text fields in emergency medical services (EMS) records.IntroductionOpioid overdoses have emerged within the last five to ten years to be a major public health concern. The high potential for fatal events, disease transmission, and addiction all contribute to negative outcomes. However, what is currently known about opioid use and overdose is generally gathered from emergency room data, public surveys, and mortality data. In addition, opioid overdoses are a non-reportable condition. As a result, state/national standardized procedures for surveillance or reporting have not been developed, and local government monitoring is frequently not specific enough to capture and track all opioid overdoses. Lastly, traditional means of data collection for conditions such as heart disease through hospital networks or insurance companies are not necessarily applicable to opioid overdoses, due to the often short disease course of addiction and lack of consistent health care visits. Overdose patients are also reluctant to follow-up or provide contact information due to law enforcement or personal reasons. Furthermore, collected data related to overdoses several months or years after the fact are useless in terms of short-term outreach. Therefore, given the potentially brief timeline of addiction or use to negative outcome, the current project set to create a near real-time surveillance and treatment/outreach system for opioid overdoses using an already existing EMS data collection framework.MethodsMarin County Department of Health and Human Services EMS data (2015-2017) was used for development of the system. The pool of data for model development and evaluation consisted of 15,000 EMS records randomly selected from 2015, 2016, and 2017. Each record was manually classified in a binary manner with the criteria of “more likely than not opioid related”, using only selected text fields. The event did not need to be exclusively opioid related, nor did opioids have to be the primary cause for the EMS call. 2,000 records were selected for review by the medical director for Marin County EMS, with a Cohen’s kappa coefficient of approximately 0.94. Overall, the proportion of opioid overdoses was less than 0.01 amongst the 15,000 records. An enriched data set of 80 randomly selected overdoses and 320 randomly selected non-overdoses was created for the purposes of feature engineering. These 400 records were excluded for further use in model training and testing. Within the enriched set, the descriptive text fields were tokenized based on the hypothesis that opioid overdoses and non-overdoses are separable based on the content of the descriptive fields. Each field was tokenized as words, bigrams (pairs of consecutive words), and trigrams (triplets of consecutive words). The frequencies of each token as a percentage of overall words were calculated separately for opioid overdoses and non-overdoses. Structured fields used in the analysis were not tokenized prior to frequency calculations. The frequencies for each token/phrase were then compared across opioid overdoses status with a proportion test for equality at an alpha of 0.05 with a Bonferroni correction for multiple comparisons. The tokens/phrases that were statistically significantly more likely to be present in opioid overdoses were assigned to a quintile based on their p-value, with smallest p-values assigned five, and largest p-values assigned one. Tokens/phrases statistically significantly more likely to be present in non-overdoses were scored in the same manner, with the smallest p-value assigned negative five, and the largest p-value negative one. The tokens/phrases that were statistically different across opioid overdose status were stored along with their quintile scores in dictionaries that were kept for future modeling use. From the initial 15,000 classified records, excluding the 400 used for the enriched data set, 10,000 records were randomly selected for model training and development. Each record had their text fields tokenized into words, bigrams, and trigrams, and each was compared with the corresponding dictionary. If a token was present in the entry and also in the dictionary, that token’s quintile score was assigned to the record, with multiple tokens being summed to produce a score for each field-token option. The final created feature was the count of opioid specific terms such as “heroin”, “fentanyl”, “narcan”, etc. within the main narrative field. The intent was to create a variety of numerical features that were indicative of presence of tokens/phrases that were positively associated with opioid overdoses such that higher scores were more associated. Several models including support vector machines, neural nets, gradient boosted machines, and logistic regression were tested via 10-fold cross validation, with logistic regression yielding the best error rates and lowest computational costs. Although all models resulted in a sensitivity greater than 85 percent, logistic regression was by far the best in terms of false positive rate. The coefficients for the logistic regression model were selected from the eight created features along with patient sex and patient age by best subsets selection via Akaike information criterion (AIC), and the probability threshold for classification was selected via optimizing the receiver operating curve (ROC).ResultsFollowing the variable selection and threshold optimization for logistic regression, the sensitivity and specificity of the model were between 90 percent and 95 percent. However, given the large number of records fed through the algorithm either each week for 'real-time' surveillance and treatment/outreach, or for larger retrospective data sets, improving specificity is crucial to reduce the number of false positives. Additionally, given that a public health treatment/outreach staff has a finite amount of time and resources, limiting false positives will allow them to focus on the true cases. Further model improvements were made with a series of binary filters that allowed for overall sensitivity/specificity improvements as well as ensuring that the records sent for outreach are appropriate for outreach. The application of the filters pushed the classification sensitivity and specificity to greater than 99 percent. Further, the filters removed cases inappropriate for outreach at greater than 90 percent efficiency.ConclusionsThe algorithm was able to classify opioid overdoses in EMS data with a sensitivity and specificity greater than 99 percent. It was implemented into a viable public health treatment/outreach system through the Marin County Department of Health and Human Services in May 2018, and has identified approximately 50 overdoses for outreach as of September, 2018. It is possible, using minimal computational power and infrastructure to develop a fully realized surveillance system through EMS data for nearly any size public health entity. Additionally, the framework allows for flexibility such that the system can be tailored for specific clinical or surveillance needs - there is no 'black box' component. Lastly, the application of this methodology to other diseases/conditions is possible and has already been done using the same data for both sepsis and falls in older adults.References1) R Core Team. 2018. R: A Language and Environment for Statistical Computing. Available: https://www.r-project.org/.2) RStudio Team. 2018. RStudio: Integrated Development Environment for R. Available: http://www.rstudio.com/.

Using Local Toxicology Data for Drug Overdose Mortality Surveillance

ObjectiveTo describe the potential impact of using toxicology data to supportdrug overdose mortality surveillance.IntroductionAlthough Marin County ranks as the healthiest county in California,it ranks poorly in substance abuse indicators, including drug overdosemortality.1Death certificates do not always include specific detail onthe substances involved in a drug overdose.2This lack of specificitymakes it difficult to identify public health issues related to specificprescription drugs in our community. We analyzed 2013 drugoverdose death toxicology reports to determine if they could improvethe description of drug overdose deaths in our community and todescribe associated data characteristics.MethodsToxicology reports were requested from the Office of the Sheriff-Coroner for 37 drug overdose deaths among Marin County residents,comprising 95% of the 39 total drug overdose deaths in 2013.The remaining two deaths were excluded as they were associated withinhalation of therapeutic gases. Select information from toxicologyreports was entered into a database for aggregate analyses. Drugoverdose deaths were considered “fully detailed” if they included thespecific types of drugs involved in the death and did not use any broadlanguage to describe the death (i.e. narcotic, multiple drugs). Student’sT-tests (α=0.05) were used to identify significant differences betweengroups of interest.ResultsOf the 37 drug poisoning deaths analyzed, 34 (92%) had availabletoxicology information. The remaining three (8%) deaths occurredoutside of Marin County and were thus investigated by anotherjurisdiction. A basic toxicology panel was ordered on 17 (50%) ofthe 34 drug overdose deaths, while an expanded toxicology panelwas ordered on the remaining 17 (50%). Alcohol was identified inthe toxicology screen of 15 (44%); Amphetamines were identifiedin 8 (24%); and opiates were identified in 25 (74%) drug overdosedeaths. Among the 25 deaths with at least one opiate identified on thetoxicology screen, the majority (52%, n=13) also had alcohol present.The majority of drug overdose deaths, 18 (53%), did not have fullinformation about the type of drug involved. The average numberof drugs identified on the toxicology screen of all 34 drug overdosedeaths was 6 (SD: 3). The average number of drugs identified in thetoxicology screen significantly differed (p=0.0001) between causes ofdeath that were fully detailed (Mean: 4; 95% CI: 3-5) and those thatwere not fully detailed (Mean: 8; 95% CI: 7-10).ConclusionsData from the Sheriff-Coroner’s office provided detail on thetypes of drugs involved in overdose deaths; however, it is difficultfor local public health practitioners to make decisions about causalityor contributions of these drugs to the death. These data may beuseful in understanding the difference between fully detailed andnon-detailed drug overdose deaths, and a broader context of drugcombinations associated with these deaths. Less drugs were identifiedin the toxicology screen of deaths that were fully detailed, suggestingthat overdose deaths that are not fully detailed may be exceedinglycomplex, making it difficult for medical examiners and coroners toassess causality. Approximately three-quarters of 2013 drug overdosedeaths contained opiates on the toxicology screen, indicating thatopiates may be a significant contributor to overdose deaths in ourcommunity. Our results are descriptive in nature; therefore, eventhough alcohol or opiates were identified on the toxicology screen,they may not be responsible for the overdose death. Given that overhalf of our 2013 overdose deaths were not fully detailed with drugtype, local jurisdictions should work closely with their corner and/ormedical examiner to fully detail death certificates with drugs involvedin overdose deaths