Improving the geographical precision of rural chronic disease surveillance by using emergency claims data: a cross-sectional comparison of survey versus claims data in Sullivan County, New York

Lee, David C; Feldman, Justin M.; Osorio, Marcela; Nguyen, Michael V.; Nagappan, Ashwini; Shim, Christopher J; Vinson, Andrew J.; Thorpe, Lorna E.; McGraw, Nancy

doi:10.1136/bmjopen-2019-033373

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…The goal of this study was to perform a comprehensive assessment of pre-pandemic and early pandemic food insecurity in Sullivan County, New York. As a rural county with the second-poorest health ranking among all counties in New York State, it may have been especially vulnerable to the stressors of the pandemic [ 13 ]. We used this approach as it would not be based on modelled estimates, but instead directly queried the same survey participants as to whether they experienced food insecurity in 2019 and 2020.…”

Section: Introductionmentioning

confidence: 99%

Food insecurity in high-risk rural communities before and during the COVID-19 pandemic

Quintero Arias,

Rony,

Jensen

et al. 2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Food insecurity in high-risk rural communities before and during the COVID-19 pandemic

Quintero Arias,

Rony,

Jensen

et al. 2024

Heliyon

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Demographic and geographic distribution of diabetes and pre-diabetes risk in rural settings: results from a cross-sectional, countywide rural health survey in Sullivan County, New York

Lee,

Ross,

Quintero Arias

et al. 2024

BMJ Open

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ObjectiveTo perform a detailed characterisation of diabetes burden and pre-diabetes risk in a rural county with previously documented poor health outcomes in order to understand the local within-county distribution of diabetes in rural areas of America.Design, setting, and participantsIn 2021, we prospectively mailed health surveys to all households in Sullivan County, a rural county with the second-worst health outcomes of all counties in New York State. Our survey included questions on demographics, medical history and the American Diabetes Association’s Pre-diabetes Risk Test.Primary outcome and methodsOur primary outcome was an assessment of diabetes burden within this rural county. To help mitigate non-response bias in our survey, raking adjustments were performed across strata of age, sex, race/ethnicity and health insurance. We analysed diabetes prevalence by demographic characteristics and used geospatial analysis to assess for clustering of diagnosed diabetes cases.ResultsAfter applying raking procedures for the 4725 survey responses, our adjusted diagnosed diabetes prevalence for Sullivan County was 12.9% compared with the 2019 Behavioural Risk Factor Surveillance System (BRFSS) estimate of 8.6%. In this rural area, diagnosed diabetes prevalence was notably higher among non-Hispanic Black (21%) and Hispanic (15%) residents compared with non-Hispanic White (12%) residents. 53% of respondents without a known history of pre-diabetes or diabetes scored as high risk for pre-diabetes. Nearest neighbour analyses revealed that hotspots of diagnosed diabetes were primarily located in the more densely populated areas of this rural county.ConclusionsOur mailed health survey to all residents in Sullivan County demonstrated higher diabetes prevalence compared with modelled BRFSS estimates that were based on small telephone samples. Our results suggest the need for better diabetes surveillance in rural communities, which may benefit from interventions specifically tailored for improving glycaemic control among rural residents.

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Discrepancies in ICD-9/ICD-10-based codes used to identify three common diseases in cancer patients in real-world settings and their implications for disease classification in breast cancer patients and patients without cancer: a literature review and descriptive study

Tu,

Henderson,

Sundararajan

et al. 2023

Front. Oncol.

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BackgroundInternational Classification of Diseases, Ninth/Tenth revisions, clinical modification (ICD-9-CM, ICD-10-CM) are frequently used in the U.S. by health insurers and disease registries, and are often recorded in electronic medical records. Due to their widespread use, ICD-based codes are a valuable source of data for epidemiology studies, but there are challenges related to their accuracy and reliability. This study aims to 1) identify ICD-9/ICD-10-based codes reported in literature/web sources to identify three common diseases in elderly patients with cancer (anemia, hypertension, arthritis), 2) compare codes identified in the literature/web search to SEER-Medicare’s 27 CCW Chronic Conditions Algorithm (“gold-standard”) to determine their discordance, and 3) determine sensitivity of the literature/web search codes compared to the gold standard.MethodsA literature search was performed (Embase, Medline) to find sources reporting ICD codes for at least one disease of interest. Articles were screened in two levels (title/abstract; full text). Analysis was performed in SAS Version 9.4.ResultsOf 106 references identified, 29 were included that reported 884 codes (155 anemia, 80 hypertension, 649 arthritis). Overall discordance between the gold standard and literature/web search code list was 32.9% (22.2% for ICD-9; 35.7% for ICD-10). The gold standard contained codes not found in literature/web sources, including codes for hypertensive retinopathy/encephalopathy, Page Kidney, spondylosis/spondylitis, juvenile arthritis, thalassemia, sickle cell disorder, autoimmune anemias, and erythroblastopenia. Among a cohort of non-cancer patients (N=684,376), the gold standard identified an additional 129 patients with anemia, 33,683 with arthritis, and 510 with hypertension compared to the literature/web search. Among a cohort of breast cancer patients (N=303,103), the gold standard identified an additional 59 patients with anemia, 10,993 with arthritis, and 163 with hypertension. Sensitivity of the literature/web search code list was 91.38-99.96% for non-cancer patients, and 93.01-99.96% for breast cancer patients.ConclusionDiscrepancies in codes used to identify three common diseases resulted in variable differences in disease classification. In all cases, the gold standard captured patients missed using the literature/web search codes. Researchers should use standardized, validated coding algorithms when available to increase consistency in research and reduce risk of misclassification, which can significantly alter the findings of a study.

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Improving the geographical precision of rural chronic disease surveillance by using emergency claims data: a cross-sectional comparison of survey versus claims data in Sullivan County, New York

Cited by 3 publications

References 21 publications

Food insecurity in high-risk rural communities before and during the COVID-19 pandemic

Food insecurity in high-risk rural communities before and during the COVID-19 pandemic

Demographic and geographic distribution of diabetes and pre-diabetes risk in rural settings: results from a cross-sectional, countywide rural health survey in Sullivan County, New York

Discrepancies in ICD-9/ICD-10-based codes used to identify three common diseases in cancer patients in real-world settings and their implications for disease classification in breast cancer patients and patients without cancer: a literature review and descriptive study

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