Reliability of the American Community Survey for unintentional drowning and submersion injury surveillance: a comprehensive assessment of 10 socioeconomic indicators derived from the 2006–2013 annual and multi-year data cycles

BACKGROUND: Our objective was to evaluate the reliability and predictability of ten socioeconomic indicators obtained from the 2006–2013 annual and multi-year ACS data cycles for unintentional drowning and submersion injury surveillance. METHODS: Each indicator was evaluated using its margin of error and coefficient of variation. For the multi-year data cycles we calculated the frequency that estimates for the same geographic areas from consecutive surveys were statistically significantly different. Relative risk estimates of drowning-related deaths were constructed using the National Center for Health Statistics compressed mortality file. All analyses were derived using census counties. RESULTS: Five of the ten socioeconomic indicators derived from the annual and multi-year data cycles produced high reliability CV estimates for at least 85 % of all US counties. On average, differences in socioeconomic characteristics for the same geographic areas for consecutive 3- and 5-year data cycles were unlikely to be caused by sampling error in only 17 % (5–89 %) and 21 % (5–93 %) of all counties. No indicator produced statistically significant relative risk estimates across all data cycles and survey years. CONCLUSIONS: The reliability of the annual and multi-year county-level ACS data cycles varies by census indicator. More than 75 % of the differences in estimates between consecutive multi-year surveys are likely to have occurred as a result of sampling error, suggesting that researchers should be judicious when interpreting overlapping survey data as reflective of real changes in socioeconomic conditions. Although no indicator predicted disparities in drowning-related injury mortality across all data cycles and years, further studies are needed to determine if these associations remain consistent at different geographic scales and for injury morbidity.