Data Editing and Imputation Methodologies of Expenditure Variables Sample Clauses

Data Editing and Imputation Methodologies of Expenditure Variables. The general methodology used for editing and imputing expenditure data is described below. The MPC did not include either the dental events or other medical expenditures (such as glasses, contact lenses, and hearing devices). Therefore, although the general procedures remain the same for dental and other medical expenditures, editing and imputation methodologies were applied only to household-reported data. Please see below for details on the differences between these editing/imputation methodologies. Separate imputations were performed for flat fee and simple events, as well.

Data Editing and Imputation Methodologies of Expenditure Variables. The general methodology used for editing and imputing expenditure data is described below. However, please note, the MPC included home health events provided by an agency and did not include home health care provided by paid independent providers. Although the general procedures remain the same for all home health events, there were some differences in the editing and imputation methodologies applied to those events followed in the MPC and those events not followed in the MPC. Analysts should note that home health care provided by friends, family, or volunteers was assumed to be free and was not included in any imputation process. Please see below for details on the differences between these editing/imputation methodologies. Home health expenditure data for agency, hospital, and nursing home providers were collected exclusively from the MPC (i.e., household respondents were not asked to report home health expenditures from these types of providers). The MPC contacted 100 percent of the agency, hospital, and nursing home health providers identified by household respondents. Since paid independent home health providers were not included in the MPC, all expenditure data from these providers were collected from household respondents.

Data Editing and Imputation Methodologies of Expenditure Variables. The e xpenditure data included on this file were derived from both the MEPS Household (HC) and Medical Provider Components (MPC). The MPC contacted medical providers identified by household respondents. The charge and payment data from medical providers were used in the expenditure imputation process to supplement missing household data. For all hospital inpatient stays, MPC data were used if available; otherwise, HC data were used. Missing data for hospital inpatient stays where HC data were not complete and MPC data were not collected, or MPC data were not complete, were imputed during the imputation process.

Data Editing and Imputation Methodologies of Expenditure Variables. The expenditure data included on this file were derived from both the MEPS Household (HC) and Medical Provider Components (MPC). The MPC contacted medical providers identified by household respondents. The charge and payment data from medical providers were used in the expenditure imputation process to supplement missing household data. For all hospital inpatient stays, MPC data were used if available; otherwise, HC data were used. Missing data for hospital inpatient stays where HC data were not complete and MPC data were not collected, or MPC data were not complete, were imputed during the imputation process. General Data Editing Methodology Logical edits were used to resolve internal inconsistencies and other problems in the HC and MPC survey-reported data. The edits were designed to preserve partial payment data from households and providers, and to identify actual and potential sources of payment for each household-reported event. In general, these edits accounted for outliers, copayments or charges reported as total payments, and reimbursed amounts that were reported as out-of-pocket payments. In addition, edits were implemented to correct for misclassifications between Medicare and Medicaid and between Medicare HMOs and private HMOs as payment sources. These edits produced a complete vector of expenditures for some events and provided the starting point for imputing missing expenditures in the remaining events. Imputation Methodologies The predictive mean matching imputation method was used to impute missing expenditures. This procedure uses regression models (based on events with completely reported expenditure data) to predict total expenses for each event. Then, for each event with missing payment information, a donor event with the closest predicted payment with the same pattern of expected payment sources as the event with missing payment was used to impute the missing payment value. The imputations for the flat fee events were carried out separately from the simple events. The weighted sequential hot-deck procedure was used to impute the missing total charges. This procedure uses survey data from respondents to replace missing data while taking into account the persons’ weighted distribution in the imputation process. Hospital Inpatient Stay Data Editing and Imputation Facility expenditures for hospital inpatient stays were developed in a sequence of logical edits and imputations. “Household” edits were applied to sources and amounts of payment...