David A. Marker

Comparison of Wipe Materials and Wetting Agents for Pesticide Residue Collection from Hard Surfaces

Different wipe materials and wetting agents have been used to collect pesticide residues from surfaces, but little is known about their comparability. To inform the selection of a wipe for the National Childrens Study, the analytical feasibility, collection efficiency, and precision of Twillwipes wetted with isopropanol (TI), Ghost Wipes (GW), and Twillwipes wetted with water (TW), were evaluated. Wipe samples were collected from stainless steel surfaces spiked with high and low concentrations of 27 insecticides, including organochlorines, organophosphates, and pyrethroids. Samples were analyzed by GC/MS/SIM. No analytical interferences were observed for any of the wipes. The mean percent collection efficiencies across all pesticides for the TI, GW, and TW were 69.3%, 31.1%, and 10.3% at the high concentration, respectively, and 55.6%, 22.5%, and 6.9% at the low concentration, respectively. The collection efficiencies of the TI were significantly greater than that of GW or TW (p<0.0001). Collection efficiency also differed significantly by pesticide (p<0.0001) and spike concentration (p<0.0001). The pooled coefficients of variation (CVs) of the collection efficiencies for the TI, GW, and TW at high concentration were 0.08, 0.17, and 0.24, respectively. The pooled CV of the collection efficiencies for the TI, GW, and TW at low concentration were 0.15, 0.19, and 0.36, respectively. The TI had significantly lower CVs than either of the other two wipes (p=0.0008). Though the TI was superior in terms of both accuracy and precision, it requires multiple preparation steps, which could lead to operational challenges in a large-scale study.

Novel Methods and Data Sources for Surveillance of State-Level Diabetes and Prediabetes Prevalence

States bear substantial responsibility for addressing the rising rates of diabetes and prediabetes in the United States. However, accurate state-level estimates of diabetes and prediabetes prevalence that include undiagnosed cases have been impossible to produce with traditional sources of state-level data. Various new and nontraditional sources for estimating state-level prevalence are now available. These include surveys with expanded samples that can support state-level estimation in some states and administrative and clinical data from insurance claims and electronic health records. These sources pose methodologic challenges because they typically cover partial, sometimes nonrandom subpopulations; they do not always use the same measurements for all individuals; and they use different and limited sets of variables for case finding and adjustment. We present an approach for adjusting new and nontraditional data sources for diabetes surveillance that addresses these limitations, and we present the results of our proposed approach for 2 states (Alabama and California) as a proof of concept. The method reweights surveys and other data sources with population undercoverage to make them more representative of state populations, and it adjusts for nonrandom use of laboratory testing in clinically generated data sets. These enhanced diabetes and prediabetes prevalence estimates can be used to better understand the total burden of diabetes and prediabetes at the state level and to guide policies and programs designed to prevent and control these chronic diseases.

State-level estimation of diabetes and prediabetes prevalence: Combining national and local survey data and clinical data.

Many statisticians and policy researchers are interested in using data generated through the normal delivery of health care services, rather than carefully designed and implemented population-representative surveys, to estimate disease prevalence. These larger databases allow for the estimation of smaller geographies, for example, states, at potentially lower expense. However, these health care records frequently do not cover all of the population of interest and may not collect some covariates that are important for accurate estimation. In a recent paper, the authors have described how to adjust for the incomplete coverage of administrative claims data and electronic health records at the state or local level. This article illustrates how to adjust and combine multiple data sets, namely, national surveys, state-level surveys, claims data, and electronic health record data, to improve estimates of diabetes and prediabetes prevalence, along with the estimates of the methods accuracy. We demonstrate and validate the method using data from three jurisdictions (Alabama, California, and New York City). This method can be applied more generally to other areas and other data sources.