Comparing methods of performing geographically targeted rural health surveillance

Background Worsening socioeconomic conditions in rural America have been fueling increases in chronic disease and poor health. The goal of this study was to identify cost-effective methods of deploying geographically targeted health surveys in rural areas, which often have limited resources. These health surveys were administered in New York’s rural Sullivan County, which has some of the poorest health outcomes in the entire state. Methods Comparisons were made for response rates, estimated costs, respondent demographics, and prevalence estimates of a brief health survey delivered by mail and phone using address-based sampling, and in-person using convenience sampling at a sub-county level in New York’s rural Sullivan County during 2017. Results Overall response rates were 27.0% by mail, 8.2% by phone, and 71.4% for convenience in-person surveys. Costs to perform phone surveys were substantially higher than mailed or convenience in-person surveys. All modalities had lower proportions of Hispanic respondents compared to Census estimates. Unadjusted and age-adjusted prevalence estimates were similar between mailed and in-person surveys, but not for phone surveys. Conclusions These findings are consistent with declining response rates of phone surveys, which obtained an inadequate sample of rural residents. Though in-person surveys had higher response rates, convenience sampling failed to obtain a geographically distributed sample of rural residents. Of modalities tested, mailed surveys provided the best opportunity to perform geographically targeted rural health surveillance.

Why Policies Fail: The Illusion of Services in the Opioid Epidemic

Context: Although New York State is a generous provider of substance-use treatment, people who ask for help have difficulty accessing services. If the laws are on the books, the agency is there to act, and the options are available, why is treatment so hard to get? Methods: The authors conducted 87 open-ended interviews and observed local task force meetings in Sullivan County, New York. They open coded data, identifying key topics and themes. Findings: Even though New York is a best-case scenario for treatment, people who seek help cannot always access it. The state sees the problem as a lack of beds or information about beds, but people on the ground face real barriers that make it difficult to get treatment, including the medical model of detoxification, admissions criteria, staff shortages, and other life complications. Conclusions: Contrary to the policy literature, this article shows that policies may fail not because they are poorly designed or implemented but because the policy itself does not address the actual underlying problem. Furthermore, in the case of opioids, it shows how misplaced solutions can hide evidence of the underlying problem, exacerbating the very issue that policy makers want to fix.

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

ObjectivesSome of the most pressing health problems are found in rural America. However, the surveillance needed to track and prevent disease in these regions is lacking. Our objective was to perform a comprehensive health survey of a single rural county to assess the validity of using emergency claims data to estimate rural disease prevalence at a sub-county level.DesignWe performed a cross-sectional study of chronic disease prevalence estimates using emergency department (ED) claims data versus mailed health surveys designed to capture a substantial proportion of residents in New York’s rural Sullivan County.SettingSullivan County, a rural county ranked second-to-last for health outcomes in New York State.ParticipantsAdult residents of Sullivan County aged 25 years and older who responded to the health survey in 2017–2018 or had at least one ED visit in 2011–2015.Outcome measuresWe compared age and gender-adjusted prevalence of hypertension, hyperlipidaemia, diabetes, cancer, asthma and chronic obstructive pulmonary disease/emphysema among nine sub-county areas.ResultsOur county-wide mailed survey obtained 6675 completed responses for a response rate of 30.4%. This sample represented more than 12% of the estimated 53 020 adults in Sullivan County. Using emergency claims data, we identified 34 576 adults from Sullivan County who visited an ED at least once during 2011–2015. At a sub-county level, prevalence estimates from mailed surveys and emergency claims data correlated especially well for diabetes (r=0.90) and asthma (r=0.85). Other conditions were not well correlated (range: 0.23–0.46). Using emergency claims data, we created more geographically detailed maps of disease prevalence using geocoded addresses.ConclusionsFor select conditions, emergency claims data may be useful for tracking disease prevalence in rural areas and providing more geographically detailed estimates. For rural regions lacking robust health surveillance, emergency claims data can inform how to geographically target efforts to prevent chronic disease.