Surveillance as Our Sextant

NYC FITNESSGRAM: Population-Level Physical Fitness Surveillance for New York City Youth

NYC FITNESSGRAM, monitored by the New York City (NYC) Department of Education and the NYC Department of Health and Mental Hygiene, functions as the NYC Department of Education's citywide youth fitness surveillance program. Here we present the methods, characteristics, and data used in this surveillance system to monitor physical fitness in public school students (grades kindergarten through 12; initiated in 2006; n = 6,748,265 observations; mean sample of 519,097 observations per year to date) in New York, New York. Youth physical fitness prevalence estimates, longitudinal trends, and spatial analyses may be investigated using continuous fitness composite percentile scores and Cooper Institute for Aerobic Research-defined sex- and age-specific Healthy Fitness Zones. Healthy Fitness Zones are based on individual-child fitness test performance, with standard errors clustered at the school and student levels and adjusted for sociodemographic characteristics. Results may be used to show trends in youth fitness attainment over time and highlight disparities in the fitness prevalence of NYC students. In sum, continuous fitness composite percentile scores offer the opportunity for prospective tracking of shifts in youth physical fitness on a population scale and across subpopulations. NYC FITNESSGRAM can accompany a growing body of surveillance tools demonstrating the potential for population-level surveillance tools to promote global public health.

The use of electronic health records to inform cancer surveillance efforts: a scoping review and test of indicators for public health surveillance of cancer prevention and control

INTRODUCTION

State cancer prevention and control programs rely on public health surveillance data to set objectives to improve cancer prevention and control, plan interventions, and evaluate state-level progress towards achieving those objectives. The goal of this project was to evaluate the validity of using electronic health records (EHRs) based on common data model variables to generate indicators for surveillance of cancer prevention and control for these public health programs.

METHODS

Following the methodological guidance from the PRISMA Extension for Scoping Reviews, we conducted a literature scoping review to assess how EHRs are used to inform cancer surveillance. We then developed 26 indicators along the continuum of the cascade of care, including cancer risk factors, immunizations to prevent cancer, cancer screenings, quality of initial care after abnormal screening results, and cancer burden. Indicators were calculated within a sample of patients from the New York City (NYC) INSIGHT Clinical Research Network using common data model EHR data and were weighted to the NYC population using post-stratification. We used prevalence ratios to compare these estimates to estimates from the raw EHR of NYU Langone Health to assess quality of information within INSIGHT, and we compared estimates to results from existing surveillance sources to assess validity.

RESULTS

Of the 401 identified articles, 15% had a study purpose related to surveillance. Our indicator comparisons found that INSIGHT EHR-based measures for risk factor indicators were similar to estimates from external sources. In contrast, cancer screening and vaccination indicators were substantially underestimated as compared to estimates from external sources. Cancer screenings and vaccinations were often recorded in sections of the EHR that were not captured by the common data model. INSIGHT estimates for many quality-of-care indicators were higher than those calculated using a raw EHR.

CONCLUSION

Common data model EHR data can provide rich information for certain indicators related to the cascade of care but may have substantial biases for others that limit their use in informing surveillance efforts for cancer prevention and control programs.

Context, importance, and process for creating a body mass index surveillance system to monitor childhood obesity within the New York City public school setting

The Office of School Health, a joint program of the Departments of Health and Education, administers New York City’s (NYC) body mass index (BMI) surveillance system to monitor childhood obesity. We describe the context, importance, and process for creating a multi-agency, school-based BMI surveillance system using BMI collected from annual FITNESSGRAM® physical fitness assessments conducted as part of a larger physical activity and wellness curriculum in NYC public schools. We also summarize our current system and methodology, highlighting the types of data and data sources that comprise the system and partnership between the Departments of Health and Education that enable data sharing. Strategies for addressing threats to data quality, including missing data, biologically implausible values, and imprecise/subjective weight or height equipment are discussed. We also review current and future surveillance data products, and provide recommendations for collecting, analyzing, interpreting, and reporting BMI data for childhood obesity surveillance. Collaboration between Departments of Health and Education as well as attention to safeguards of BMI reporting and data quality threats have enabled NYC to collect high quality BMI data to accurately monitor childhood obesity trends. These findings have implications for youth BMI surveillance systems in the United States and globally.

Neighborhood-Level Chronic Disease Surveillance: Utility of Primary Care Electronic Health Records and Emergency Department Claims Data

CONTEXT

Disease burden may vary substantively across neighborhoods in an urban setting. Yet, data available for monitoring chronic conditions at the neighborhood level are scarce. Large health care data sets have potential to complement population health surveillance. Few studies have examined the utility of health care data for neighborhood-level surveillance.

OBJECTIVE

We examined the use of primary care electronic health records (EHRs) and emergency department (ED) claims for identifying neighborhoods with higher chronic disease burden and neighborhood-level prevalence estimation.

DESIGN

Comparison of hypertension and diabetes estimates from EHRs and ED claims with survey-based estimates.

SETTING

Forty-two United Hospital Fund neighborhoods in New York City.

PARTICIPANTS

The EHR sample comprised 708 452 patients from the Hub Population Health System (the Hub) in 2015, and the ED claim sample comprised 1 567 870 patients from the Statewide Planning and Research Cooperative System in 2015. We derived survey-based estimates from 2012 to 2016 Community Health Survey (n = 44 189).

MAIN OUTCOME MEASURE

We calculated hypertension and diabetes prevalence estimates by neighborhood from each data source. We obtained Pearson correlation and absolute difference between EHR-based or claims-based estimates and survey-based estimates.

RESULTS

Both EHR-based and claims-based estimates correlated strongly with survey-based estimates for hypertension (0.91 and 0.72, respectively) and diabetes (0.83 and 0.82, respectively) and identified similar neighborhoods of higher burden. For hypertension, 10 and 17 neighborhoods from the EHRs and ED claims, respectively, had an absolute difference of more than 5 percentage points from the survey-based estimate. For diabetes, 15 and 4 neighborhoods from the EHRs and ED claims, respectively, differed from the survey-based estimate by more than 5 percentage points.

CONCLUSIONS

Both EHRs and ED claims data are useful for identifying neighborhoods with greater disease burden and have potential for monitoring chronic conditions at the neighborhood level.

The Use of Small Area Estimates in Place-Based Health Research

Interest in the impact of the built environment on health behaviors, outcomes, and disparities is increasing, and the growing development of statistical modeling techniques has allowed researchers to better investigate these relationships. However, without enough data that are identifiable at smaller geographic levels (e.g., census tract), place-based health researchers are unable to reliably estimate the prevalence of a health outcome at these more granular and potentially more salient neighborhood levels.When reliable direct survey estimates cannot be produced because of small samples or a lack of samples, estimates based on small area estimation techniques are often used. As place-based health research and the production and secondary use of small area estimates increase, it is critical that researchers understand both the underlying methods used to create these estimates and their limitations. Without this foundation, researchers may fit inappropriate models, or interpret findings inaccurately.As a demonstrative example, we focus this discussion on the small area health indicator estimates recently produced through the 500 Cities Project by the Robert Wood Johnson Foundation, the Centers for Disease Control and Prevention (CDC), and the CDC Foundation.