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MEPS HC-207
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| Value | Definition |
|---|---|
| -1 INAPPLICABLE | Question was not asked due to skip pattern |
| -7 REFUSED | Question was asked and respondent refused to answer question |
| -8 DK | Question was asked and respondent did not know answer |
| -15 CANNOT BE COMPUTED | Value cannot be derived from data |
| Identifier | Description |
|---|---|
| Name | Variable name |
| Description | Variable descriptor |
| Format | Number of bytes |
| Type | Type of data: numeric (indicated by NUM) or character (indicated by CHAR) |
| Start | Beginning column position of variable in record |
| End | Ending column position of variable in record |
In general, variable names reflect the content of the variable, with an 8-character limitation. Edited variables end in an “X” and are so noted in the variable label. (CONDIDX, which is an encrypted identifier variable, also ends in an “X”.)
Variables contained in this delivery were derived either from the questionnaire itself or from the CAPI. The source of each variable is identified in Appendix 1, Variable-Source Crosswalk. Sources for each variable are indicated in one of three ways: (1) variables derived from CAPI or assigned in sampling are so indicated; (2) variables collected at one or more specific questions have those numbers and questionnaire sections indicated in the “SOURCE” column; and (3) variables constructed from multiple questions using complex algorithms are labeled “Constructed” in the “SOURCE” column.
The definitions of Dwelling Units (DUs) in the MEPS HC are generally consistent with the definitions employed for the National Health Interview Survey (NHIS). The dwelling unit ID (DUID) is a seven-digit random ID number assigned after the case was sampled for MEPS. A three-digit person number (PID) uniquely identifies each person within the DU.
The variable DUPERSID uniquely identifies each person represented on the file and is the combination of the variables DUID and PID. As part of the new CAPI design, the length of the ID variables has changed in the file. The additional 2 bytes in the IDs resulted from adding a 2-digit panel number to the beginning of all the IDs. Analysts wishing to pool data years 2017 and 2018 should add panel numbers to the beginning of Panel 22 Year 2017 ID variables, or remove the 2-digit panel number at the beginning of Panel 22 Year 2018 ID variables to ensure they identify the same person.
CONDN is the condition number and uniquely identifies each condition reported for an individual. The range on this file for CONDN is 1-906 and the range of total records for any one person on the file is 1-52.
The variable CONDIDX uniquely identifies each condition (i.e., each record on the file) and is the combination of DUPERSID and CONDN. CONDIDX has a length of 13 with DUPERSID (10) and CONDN (3) combined. Starting in FY18, CONDN is one less byte. Analysts wishing to pool data years 2017 and 2018 should add panel numbers to the beginning of Panel 22 Year 2017 ID variables and remove the padded leading zero from CONDN in the 2017 data.
PANEL is a constructed variable used to specify the panel number for the interview in which the condition was reported. PANEL will indicate either Panel 22 or Panel 23. Beginning in 2018, the panel number is included as the first two digits of the DUID and DUPERSID.
CONDRN indicates the round in which the condition was first reported. For a small number of cases, conditions that actually began in an earlier round were not reported by respondents until subsequent rounds of data collection. During file construction, editing was performed for these cases in order to reconcile the round in which a condition began and the round in which the condition was first reported.
This file contains variables describing medical conditions reported by respondents in several sections of the MEPS questionnaire, and all questionnaire sections collecting information about health provider visits and/or prescription medications (see Variable-Source Crosswalk in Appendix 1 for details).
Certain conditions were a priori designated as “priority conditions” due to their prevalence, expense, or relevance to policy. Some of these are long-term, life-threatening conditions, such as cancer, diabetes, emphysema, high cholesterol, hypertension, ischemic heart disease, and stroke. Others are chronic manageable conditions, including arthritis and asthma. The only mental health condition on the priority conditions list is attention deficit hyperactivity disorder/attention deficit disorder. See Appendix 3 for a full list of the priority conditions.
When a condition was first mentioned, respondents were asked whether it was due to an accident or injury (INJURY=1). Only non-priority conditions (i.e., conditions reported in a section other than PE) are eligible to be injuries. The interviewer is prevented from selecting priority conditions as injuries.
The age of diagnosis (AGEDIAG) was collected for all priority conditions, except joint pain.For confidentiality reasons, AGEDIAG is set to Inapplicable (-1) for cancer conditions.
To ensure confidentiality,age of diagnosis was top-coded to 85. This corresponds with the age top-coding in person-level PUFs.
When a respondent reported that a condition resulted from an accident or injury (INJURY=1), respondents were asked during the round in which the injury was first reported whether the accident/injury occurred at work (ACCDNWRK). This question was not asked about persons aged 15 and younger; the condition had ACCDNWRK coded to inapplicable (-1) for those persons.
The records on this file correspond with medical condition records collected by CAPI and stored on a person’s MEPS conditions roster. Conditions can be added to the MEPS conditions roster in several ways. A condition can be reported in the Priority Condition Enumeration (PE) section in which persons are asked if they have been diagnosed with specific conditions. The condition can be identified as the reason reported by the household respondent for a particular medical event (hospital stay, outpatient visit, emergency room visit, home health episode, prescribed medication purchase, or medical provider visit). Some condition information is collected in the Medical Provider Component of MEPS. However, since it is not available for everyone in the sample, it is not used to supplement, replace, or verify household-reported condition data. Conditions reported in the PE section that are not current are not included on this file.
Prior to the 2008 file, priority conditions reported during Rounds 1 and 2 of the second year panel were included on the file even if the conditions were not related to an event or reported as a serious condition occurring in the second year of the panel. Beginning in 2008, priority conditions are included on the file only if they are current conditions. From 2008–2017, a current condition was defined as a condition linked to an event or a condition the person was currently experiencing (i.e., a condition selected in the Condition Enumeration (CE) section). However, starting in Panel 21 Round 5 and Panel 22 Round 3, a current condition is defined only as a condition linked to a 2018 event. Conditions from Rounds 1 and 2 that are not included in the 2018 file may be available in the 2017 Medical Conditions File if the person had a positive person or family weight in 2017. For 2018, 68 conditions from Panel 22 Rounds 1 and 2 are included on the 2018 Medical Conditions File for persons who did not appear on the previous year’s file.
Note: Priority conditions are generally chronic conditions. Even though a person may not have reported an event in 2018 due to the condition, analysts should consider that the person is probably still experiencing the condition. If a Panel 22 person reported a priority condition in Round 1 or 2 and did not have an event for the condition in Round 3, 4, or 5, the condition will not be included on the 2018 Medical Conditions File.
A set of constructed variables indicates the round in which the condition was first reported (CONDRN), and the subsequent round(s) in which the condition was selected (CRND1 – CRND5). The condition may be reported or selected when the person reports an event that occurred due to the condition. For example, consider a condition for which CRND1 = 0, CRND2 = 1, and CRND3 = 1. For non-priority conditions (conditions not asked in the PE section), this sequence of indicators on a condition record implies that the condition was not present during Round 1 (CRND1 = 0), was first mentioned during Round 2 (CRND2 = 1, CONDRN = 2), and was selected again during Round 3 (CRND3 = 1). For priority conditions, this sequence of indicators implies that the condition was reported in the PE section in Round 1 (CONDRN = 1, CRND1 = 0) but was not connected with an event until Rounds 2 and 3 (CRND2 = 1, CRND3 = 1). Because priority conditions are asked in the context of “has person ever been told by a doctor or other health care professional that they have (condition)?” except joint pain and chronic bronchitis, which ask only about the last 12 months, a priority condition might not be selected in the round in which it was first reported. For Panel 22 records, a condition is current if there is an event linked to a condition in Rounds 3, 4, or 5. For Panel 23 records, a condition is current if there is an event linked to a condition in Rounds 1, 2, or 3.
The medical conditions reported by the Household Component respondent were recorded by the interviewer as verbatim text and then were coded to ICD-10-CM codes (ICD10CDX) by professional coders.
Coders followed specific guidelines in coding missing values to the ICD-10-CM diagnosis condition variable. ICD10CDX was coded -15 (Cannot be Computed) where the verbatim text fell into one of three categories: (1) the text indicated that the condition was unknown (e.g., DK); (2) the text indicated the condition could not be diagnosed by a doctor (e.g., doctor doesn’t know); or (3) the specified condition was not codeable. If the text indicated a procedure and the condition associated with the procedure could be discerned from the text, the condition itself is coded. For example, “cataract surgery” is coded as the condition “other cataract” (ICD10CDX is set to code “H26”). If the condition could not be discerned (e.g. “outpatient surgery”), ICD10CDX is set to -15.
In order to preserve confidentiality, all of the conditions provided on this file have been coded to 3-digit diagnosis code categories rather than the fully-specified ICD-10-CM code. For example, the ICD10CDX value of J02 “Acute pharyngitis” includes the fully-specified subclassifications J020 and J029; the value F31 “Bipolar disorder” includes the fully-specified subclassifications F3110 through F319. Table 1 in Appendix 2 provides unweighted and weighted frequencies for all ICD-10-CM condition code values reported on the file. Less than 1 percent of the ICD-10-CM codes on this file were edited further by collapsing two or more 3-digit codes into one 3-digit code. This includes clinically rare conditions that were recoded to broader codes by clinicians. A condition is determined to be clinically rare if it appears on the National Institutes of Health’s list of rare diseases.
For confidentiality purposes, approximately 6% of ICD-10-CM codes were recoded to -15 (Cannot be Computed) for conditions where the frequency was less than 20 for the total unweighted population in the file or less than 200,000 for the weighted population. Additional factors used to determine recoding include age and sex.
In a small number of cases, diagnosis and condition codes were recoded to -15 (Cannot be Computed) if they denoted a pregnancy for a person younger than 16 or older than 44. Less than one-tenth of 1 percent of records were recoded in this manner on the 2018 Medical Conditions File. The person’s age was determined by linking the 2018 Medical Conditions File to the 2017 and 2018 Person-Level Use PUFs. If the person’s age is under 16 or over 44 in the round in which the condition was reported, the appropriate condition code was recoded to -15 (Cannot be Computed).
Users should note that because of the design of the survey, most deliveries (i.e., births) are coded as pregnancies. For more accurate estimates for deliveries, analysts should use RSNINHOS “Reason Entered Hospital” found on the Hospital Inpatient Stays Public Use File (HC-206D).
Each year, a few conditions on the final file may fall below the confidentiality threshold. This is due to the multistage file development process. The confidentiality recoding is performed on the preliminary version of the Conditions file each year. This preliminary version is used in the development of other event PUFs and, in turn, these event PUFs are used in the development of the final conditions file. During this process, some records from the preliminary file are dropped because only records that are relevant to the current data year are reflected in the final Conditions PUF.
Conditions file data can be merged with the 2018 MEPS Event Files. Because the conditions have been coded to 3-digit diagnosis code categories rather than the fully-specified ICD-10-CM code, it is possible for there to be duplicate ICD-10-CM condition codes linked to a single medical event when different fully-specified conditions are coded to the same 3-digit code. For information on merging data on this file with the 2018 MEPS Event Files (HC-206A, and HC-206D through HC-206H) refer to the link files provided in HC-206I, and see HC-206I documentation for details.
Conditions were reported in several sections of the HC questionnaire (see Variable-Source Crosswalk in Appendix 1). Labels for all values of ICD10CDX, as shown in Table 1 of Appendix 2, are provided in the SAS programming statements included in this release (see the H207SU.TXT file).
Beginning in FY18, Clinical Classification Software Refined (CCSR) will be used alongside ICD-10-CM diagnosis codes to group medical conditions into clinically meaningful categories. One ICD-10-CM diagnosis code may map to up to five CCSR categories. However, for the 2018 Medical Conditions public use file (PUF), one ICD-10-CM diagnosis code may map to up to three CCSR categories (CCSR1X, CCSR2X, CCSR3X) using the v2019.1 release of the CCSR for ICD-10-CM diagnoses. The CCSR categories are listed in alphabetical order and do not indicate a primary and secondary diagnosis. For more information on CCSR, visit the user guide for CCSR.
For confidentiality purposes, less than 2% of the CCSR categories were collapsed into a broader code for the appropriate body system where the frequency was less than 20 for the total unweighted population in the file or less than 200,000 for the weighted population. For example, BLD001 (Nutritional Anemia), may be recoded to BLD000 (Disease of Blood and Disorders Involving Immune Mechanism), thus revealing only the body system. Less than 1% of CCSR codes were recoded to -15 (Cannot be Computed) based on frequencies of ICD10CDX and CCSR pairs.
Table 2 in Appendix 2 provides unweighted and weighted frequencies for CCSR combinations reported on the file.
The variables OBNUM, OPNUM, HHNUM, IPNUM, ERNUM, and RXNUM indicate the total number of 2018 events that can be linked to each condition record on the current file, i.e., office-based, outpatient, home health, inpatient hospital stays, emergency room visits, and prescribed medicines, respectively.
These counts of events were derived from Expenditure Event Public Use Files (HC-206G, HC-206F, HC-206H, HC-206D, HC-206E, and HC-206A). Events associated with conditions include all utilization that occurred between January 1, 2018 and December 31, 2018.
Because persons can be seen for more than one condition per visit, these frequencies will not match the person or event-level utilization counts. For example, if a person had one inpatient hospital stay and was treated for a fractured hip, a fractured shoulder, and a concussion, each of these conditions has a unique record in this file and IPNUM=1 for each record. By summing IPNUM for these records, the total inpatient hospital stays would be three when actually there was only one inpatient hospital stay for that person and three conditions were treated. These variables are useful for determining the number of inpatient hospital stays associated with a particular condition.
There is a single full year person-level weight (PERWT18F) assigned to each record for each key, in-scope person who responded to MEPS for the full period of time that he or she was in-scope during 2018. A key person was either a member of a responding NHIS household at the time of the interview or joined a family associated with such a household after being out-of-scope at the time of the NHIS (the latter circumstance includes newborns as well as those returning from military service, an institution, or residence in a foreign country). A person is in-scope whenever he or she is a member of the civilian noninstitutionalized portion of the U.S. population.
The person-level weight PERWT18F was developed in several stages. First, person-level weights for Panel 22 and Panel 23 were created separately. The weighting process for each panel included adjustments for nonresponse over time and calibration to independent population totals. The calibration was initially accomplished separately for each panel by raking the corresponding sample weights to Current Population Survey (CPS) population estimates based on six variables. The six variables used in the establishment of the initial person-level control figures were: educational attainment of the reference person (no degree, high school/GED no college, some college, bachelor degree or higher); census region (Northeast, Midwest, South, West); MSA status (MSA, non-MSA); race/ethnicity (Hispanic; Black, non-Hispanic; Asian, non-Hispanic; and other); sex; and age. A 2018 composite weight was then formed by multiplying each weight from Panel 22 by the factor .49 and each weight from Panel 23 by the factor .51. Using such factors to form composite weights serves to limit the variance of estimates obtained from pooling the two samples. The resulting composite weight was raked to the same set of CPS-based control totals. Then, when the poverty status information (derived from the MEPS income variables) became available, another raking was undertaken, establishing control figures reflecting poverty status rather than educational attainment. Thus control totals were established using poverty status (five categories: below poverty, from 100 to 125 percent of poverty, from 125 to 200 percent of poverty, from 200 to 400 percent of poverty, at least 400 percent of poverty) as well as the other five variables previously used in the weight calibration.
The person-level weight for an individual in MEPS Panel 22 was developed using the 2017 full year weight as a “base” weight for each survey participant present in 2017. For key, in-scope members who joined an RU some time in 2018 after being out-of-scope in 2017, the initially assigned person-level weight was the corresponding 2017 family weight. The weighting process included an adjustment for person-level nonresponse over Rounds 4 and 5 as well as raking to population control figures for December 2018 for key, responding persons in-scope on December 31, 2018. These control figures were derived by scaling back the population distribution obtained from the March 2019 CPS to reflect the December 31, 2018 estimated population total (estimated based on Census projections for January 1, 2019). Variables used for person-level raking included: educational attainment of the reference person (no degree, high school/GED no college, some college, bachelor degree or higher); census region (Northeast, Midwest, South, West); MSA status (MSA, non-MSA); race/ethnicity (Hispanic; Black, non-Hispanic; Asian, non-Hispanic; and other); sex; and age. The final weight for key, responding persons who were not in-scope on December 31, 2018 but were in-scope earlier in the year was the person weight after the nonresponse adjustment.
Note that the 2017 full-year weight that was used as the base weight for Panel 22 was derived using the MEPS Round 1 weight and adjusting it further for nonresponse over the remaining data collection rounds in 2017 and raking to the December 2017 population control figures.
The person-level weight for an individual in MEPS Panel 23 was developed using the 2018 MEPS Round 1 person-level weight as a “base” weight. For key, in-scope members who joined an RU after Round 1, the Round 1 family weight served as a “base” weight. The weighting process included an adjustment for nonresponse over the remaining data collection rounds in 2018 as well as raking to the same population control figures for December 2018 used for the MEPS Panel 22 weights for key, responding persons in-scope on December 31, 2018. The same six variables employed for Panel 22 raking (educational attainment of the reference person, census region, MSA status, race/ethnicity, sex, and age) were used for Panel 23 raking. Again, the final weight for key, responding persons who were not in-scope on December 31, 2018 but were in-scope earlier in the year was the person weight after the nonresponse adjustment.
Note that the MEPS Round 1 weights for Panel 23 incorporated the following components: the original household probability of selection for the NHIS and for the NHIS subsample reserved for MEPS and adjustment for NHIS nonresponse, the probability of selection for MEPS from NHIS, an adjustment for nonresponse at the dwelling unit level for Round 1, and poststratification to U.S. civilian noninstitutionalized population estimates at the family and person level obtained from the corresponding March CPS databases.
The final raking of those in-scope at the end of the year has been described above. In addition, the composite weights of two groups of persons who were out-of-scope on December 31, 2018 were adjusted for expected undercoverage. Specifically, the weights of those who were in-scope some time during the year, out-of-scope on December 31, and entered a nursing home during the year and still residing in a nursing home at the end of the year were adjusted to compensate for expected undercoverage for this subpopulation. The weights of persons who died while in-scope during 2018 were poststratified to corresponding estimates derived using data obtained from the Medicare Current Beneficiary Survey (MCBS) and Vital Statistics information provided by the National Center for Health Statistics (NCHS). Separate decedent control totals were developed for the “65 and older” and “under 65” civilian noninstitutionalized populations.
Overall, the weighted population estimate for the civilian noninstitutionalized population for December 31, 2018 is 322,920,490 (PERWT18F>0 and INSC1231=1). The sum of the person-level weights across all persons assigned a positive person-level weight is 326,327,888.
The target population for MEPS in this file is the 2018 U.S. civilian noninstitutionalized population. However, the MEPS sampled households are a subsample of the NHIS households interviewed in 2016 (Panel 22) and 2017 (Panel 23). New households created after the NHIS interviews for the respective panels and consisting exclusively of persons who entered the target population after 2016 (Panel 22) or after 2017 (Panel 23) are not covered by MEPS. Neither are previously out-of-scope persons who join an existing household but are unrelated to the current household residents. Persons not covered by a given MEPS panel thus include some members of the following groups: immigrants, persons leaving the military, U.S. citizens returning from residence in another country, and persons leaving institutions. The set of uncovered persons constitutes only a small segment of the MEPS target population.
MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data; however, there are a variety of methodological and statistical considerations when examining trends over time using MEPS. Examining changes over longer periods of time can provide a more complete picture of underlying trends. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution unless they are attributable to known factors such as changes in public policy, economic conditions, or survey methodology.
In 2013 MEPS survey operations introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in 2014. This effort resulted in improved data quality and a reduction in underreporting in the second half of 2013 and throughout 2014. Respondents tended to report more visits, especially non-physician visits, by sample members and the new approach appeared particularly effective among those subgroups with relatively large numbers of visits, such as the elderly, Medicare beneficiaries, and people with multiple chronic conditions, disabilities, or poor health. Reported spending on visits also tended to increase, especially for such subgroups.
The aforementioned change in the NHIS sample design in 2016 could also potentially affect trend analyses. The new NHIS sample design is based on more up-to-date information related to the distribution of housing units across the U.S. As a result, it can be expected to better cover the full U.S. civilian, noninstitutionalized population, the target population for MEPS, as well as many of its subpopulations. Better coverage of the target population helps to reduce the potential for bias in both NHIS and MEPS estimates.
A significant change to the Conditions file occurred in 2016 when ICD-10-CM condition codes replaced ICD-9-CM codes. See section 2.5.2.7 for details. In addition, prior to 2018, a single CCS code was assigned to each condition to group conditions into clinically meaningful categories. Beginning in 2018, MEPS transitioned to CCSR codes, and up to three CCSR codes were assigned to a single condition. See section 2.5.2.8 for details. Also in 2018, the inclusion criteria for conditions changed; therefore, fewer conditions are on the 2018 file compared to previous years. See section 2.0 for a discussion of conditions included on the file.
Another change with the potential to affect trend analysis involved modifications to the MEPS instrument design and data collection process. These were introduced in the Spring of 2018 and thus affected data beginning with Round 1 of Panel 23, Round 3 of Panel 22, and Round 5 of Panel 21. Since the Full Year 2017 PUFs were established from data collected in Rounds 1-3 of Panel 22 and Rounds 3-5 of Panel 21, they reflected two different instrument designs. In order to mitigate the effect of such differences within the same full year file, the Panel 22 Round 3 data and the Panel 21 Round 5 data were transformed to make them as consistent as possible with data collected under the previous design. The changes were designed to make the data collection effort more efficient and easy to administer with expectations that data on some items, such as those related to health care events, would be more complete with the potential for identifying more events. Increases in service use reported, since the implementation of these changes are consistent with these expectations.
As always, it is recommended that data users review relevant sections of the documentation for descriptions of these types of changes before undertaking trend analyses.
Analysts may also wish to consider using statistical techniques to smooth or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 2011-12), working with moving averages or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time.
Finally, statistical significance tests should be conducted to assess the likelihood that observed trends are not attributable to sampling variation. In addition, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.
Data from the current file can be used alone or in conjunction with other files. Merging characteristics of interest from person-level files expands the scope of potential estimates. See HC-206I for instructions on merging the Conditions File to the Medical Event Files. Person-level characteristics can be merged to this Conditions File using the following procedure (example given for the SAS programming language):
PROC SORT DATA=PERS(KEEP=DUPERSID AGE SEX EDUCYR HIDEG)
OUT=PERSX; BY DUPERSID;
RUN;
PROC SORT DATA=COND; BY DUPERSID;
RUN;
DATA COND;
MERGE COND (IN=A) PERSX(IN=B); BY DUPERSID;
IF A;
RUN;
Data from this file can be used alone or in conjunction with other files for different analytic purposes. Each MEPS panel can also be linked back to the previous years’ National Health Interview Survey public use data files. For information on MEPS/NHIS link files please see the AHRQ website.
Panel-specific longitudinal files are available for downloading in the data section of the MEPS website. For each panel, the longitudinal file comprises MEPS survey data obtained in Rounds 1 through 5 of the panel and can be used to analyze changes over a two-year period. Variables in the file pertaining to survey administration, demographics, employment, health status, disability days, quality of care, patient satisfaction, health insurance, and medical care use and expenditures were obtained from the MEPS full-year Consolidated files from the two years covered by that panel.
For more details or to download the data files, please see Longitudinal Data Files at the AHRQ website.
Chowdhury, S.R., Machlin, S.R., Gwet, K.L. Sample Designs of the Medical Expenditure Panel Survey Household Component, 1996–2006 and 2007–2016. Methodology Report #33. January 2019. Agency for Healthcare Research and Quality, Rockville, MD.
Cox, B. and Iachan, R. (1987). A Comparison of Household and Provider Reports of Medical Conditions. Journal of the American Statistical Association 82(400): 1013-18.
Edwards, W. S., Winn, D. M., Kurlantzick, V., et al. Evaluation of National Health Interview Survey Diagnostic Reporting. National Center for Health Statistics, Vital Health 2(120). 1994.
Health Care Financing Administration (1980). International Classification of Diseases, 9th Revision, Clinical Modification (ICD-CM). Vol. 1. (Department of Health and Human Services Pub. No (PHS) 80-1260). Department of Health and Human Services: U.S. Public Health Services.
Johnson, Ayah E., and Sanchez, Maria Elena. (1993), “Household and Medical Reports on Medical Conditions: National Medical Expenditure Survey.” Journal of Economic and Social Measurement, 19, 199-223.
| VARIABLE | LABEL | SOURCE1 |
|---|---|---|
| DUID | Panel # + Encrypted DU Identifier | Assigned In Sampling |
| PID | Person Number |
Assigned In Sampling |
| DUPERSID | Person ID (DUID + PID) | Assigned In Sampling |
| CONDN | Condition Number | CAPI Derived |
| CONDIDX | Condition ID | CAPI Derived |
| PANEL | Panel Number | Constructed |
| CONDRN | Condition Round Number | CAPI Derived |
| VARIABLE | LABEL | SOURCE1 |
|---|---|---|
| AGEDIAG | Age When Diagnosed | PE section |
| CRND1 | Has Condition Information In Round 1 | Constructed |
| CRND2 | Has Condition Information In Round 2 | Constructed |
| CRND3 |
Has Condition Information In Round 3 | Constructed |
| CRND4 | Has Condition Information In Round 4 | Constructed |
| CRND5 |
Has Condition Information In Round 5 |
Constructed |
| INJURY | Was Condition Due To Accident/Injury | AH80 |
| ACCDNWRK |
Did Accident Occur At Work |
AH90 |
| ICD10CDX | ICD-10-CM Code For Condition - Edited | HS40, ER30, OP60, MV70, HH80, PM120, PE Section (Edited) |
| CCSR1X | Clinical Classification Refined Code 1- Edited | HS40, ER30, OP60, MV70, HH80, PM120, PE section (Edited) |
| CCSR2X | Clinical Classification Refined Code 2- Edited | HS40, ER30, OP60, MV70, HH80, PM120, PE section (Edited) |
| CCSR3X | Clinical Classification Refined Code 3- Edited | HS40, ER30, OP60, MV70, HH80, PM120, PE section (Edited) |
| VARIABLE | LABEL | SOURCE1 |
|---|---|---|
| HHNUM | # Home Health Events Assoc. w/ Condition | Constructed |
| IPNUM |
# Inpatient Events Assoc. w/ Condition |
Constructed |
| OPNUM |
# Outpatient Events Assoc. w/ Condition |
Constructed |
| OBNUM | # Office-Based Events Assoc. w/ Condition | Constructed |
| ERNUM | # ER Events Assoc. w/ Condition | Constructed |
| RXNUM | # Prescribed Medicines Assoc. w/ Cond. | Constructed |
| VARIABLE | LABEL | SOURCE1 |
|---|---|---|
| PERWT18F | Expenditure File Person Weight, 2018 | Constructed |
| VARSTR | Variance Estimation Stratum, 2018 | Constructed |
| VARPSU | Variance Estimation PSU, 2018 | Constructed |
1See the Household Component section under Survey Questionnaires on the MEPS home page for information on the MEPS HC questionnaire sections shown in the Source column (e.g., PE).
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