Nonresponse Bias in Estimates From the 2012 National Ambulatory Medical Care Survey.
BACKGROUND The National Ambulatory Medical Care Survey (NAMCS) is an annual, nationally representative sample survey of physicians and of visits to physicians. Two major changes were made to the 2012 NAMCS to support reliable state estimates. The sampling design changed from an area sample to a fivefold larger list sample of physicians stratified by the nine U.S. Census Bureau divisions and 34 states. At the same time, the data collection mode changed from paper forms to laptop assisted data collection and from physician or office staff abstraction of medical records to predominantly Census interviewer abstraction using automated Patient Record Forms (PRFs) OBJECTIVES This report presents an analysis of potential nonresponse bias in 2012 NAMCS estimates of physicians and visits to physicians. This analysis used two sets of physician based estimates: one measuring the completion of the physician induction interview and another based on completing any PRF. Evaluation of visit response was measured by the percentage of expected PRFs completed. For each type of physician estimate, response was evaluated by (a) comparing percent distributions of respondents and nonrespondents by physician characteristics available for all in scope sample physicians, (b) comparing response rates by physician characteristics with the national response rate, and (c) analyzing nonresponse bias after adjustments for nonresponse were applied in survey weights. For visit estimates, response was evaluated by (a) comparing the percent distributions of expected visits and completed visits, (b) comparing visit response rates by physician characteristics with the national visit response rate, and (c) analyzing visit level nonresponse bias after adjustments for nonresponse were applied in visit survey weights. Finally, potential bias in the two physician level estimates was computed by comparing them with those from an external survey.
Vital and health statistics. Series 2, Data evaluation and methods research