Writing the Specifications for NHIRD Research. Tzeng-Ji Chen

Transcription

1 Writing the Specifications for NHIRD Research Tzeng-Ji Chen

2 From Internet * including 答應來演講?!

3 My Sweet Home

4 3 Months after Furnishing Lesson: Specs do matter.

5 From Internet

6 Key Concepts

7 Paper Production Flow Atmosphere Idea Method Writing Journal Materials Computing Statistics English Teams Tools Infrastructure

8 1. 資料擷取 2. 資料處理 3. 統計分析 Courtesy of Yu-Chun Chen

9 PR time ( )

10 PR time ( )

11 GDP (Good Data Processing) Protocol Diary Script comments Avoid fancy or illegible writing Programming style

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14 File Structure of NHIRD Datasets to Process Single file Multiple files: Of the same format Of similar formats in different years Of different formats, but connected through Primary key / foreign key Loop-up table

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18 Main Frame of Processing

19 Setting the Goal Quantity One is enough More papers, more better Quality trade-off Fulfilling the minimum requirement High-impact Willingness to pay (for NHIRD datasets) Willingness to devote (time and effort)

20 Writing the Specs for NHIRD Research Decide on the selection of datasets Complete datasets Datasets sampled by visit (OPD + ER: 1/500; Adm.: 1/20) Datasets sampled by person (1 million cohort: old vs. new) Time frame Connected and ancillary datasets Decide on the selection of fields Decide on the processing of fields Define the major events Elaborate the processing workflow interactive, reiterative, collaborative process Decide on the structure and format of output table(s)

21 Real-Life Examples

22 1. Trazodone as poor men's Viagra: a prescription database analysis among urological practice in Taiwan

23 Source of Idea:

24 J Urol 2003;169:

25 J Urol 2003;169:

26 J Urol 2003;169:

27 Datasets (1/3) Ambulatory sector Datasets sampled by visit (=> utilization amount)? Datasets sampled by person (=> prevalence)? Complete datasets (=> $$$) X

28 Datasets (2/3) CD: ambulatory care expenditures by visits (visit file): , OO: details of ambulatory care orders (order file): ,

29 DRUG 藥品主檔 29

30 Datasets (3/3) HOSB: registry for contracted medical facilities? PER: registry for medical personnel? DOC: registry for board-certified specialists? DRUG: registry for drug prescriptions? ID: registry for beneficiaries => + Complete!

31 Fields OO DRUG_NO CD ID, ID_BIRTHDAY, ID_SEX ACODE_ICD9_1, ACODE_ICD9_2, ACODE_ICD9_3 FUNC_DATE FUNC_TYPE, PRSN_ID, HOSP_ID HOSB HOSP_CONT_TYPE, AREA_NO_H ID ID, INS_ID, INS_ID_TYPE, INS_AMT, ID_IN_DATE, ID_OUT_DATE

32 Field Processing Patient s age and age group Age = (FUNC_DATE - ID_BIRTHDAY) in Y? Age = FUNC_DATE in Y - ID_BIRTHDAY in Y? <=39, 40-59, >=60? Diagnosis group Major Dx field? All 3 Dx fields? All Dx groups? ICD-9-CM s first 3 digits CCS single level Target Dx group(s)? (y/n) Visit-based? Person-based? Exclusivity? Non-exclusivity? Hierarchy?

33 Major Event (DRUG_NO) Trazodone (ATC: N06AX05) A A A A A A A A A A A B B B A

34 Data Processing (1/5) Extract records from OO according to DRUG_NO (=> OO_Trazodone_?.txt) Extract records from CD according to PK (FEE_YM + APPL_TYPE + HOSP_ID + APPL_DATE + CASE_TYPE + SEQ_NO) in OO (=> CD_Trazodone_?.txt) Extract records from CD_Trazodone_?.txt according to FUNC_TYPE = 08 (=> CD_Trazodone_?_GU.txt) Extract records from OO_Trazodone_?.txt according to PK in CD_Trazodone_?_GU.txt (=> OO_Trazodone_?_GU.txt) For both S_{CD,OO}{ } and LHID2000 s R{01..20}_{CD,OO}{ } Further processing

35 Trazodone Rx in S_{CD,OO} (1:500) Year No. of Visits with Trazodone No. of GU Visits with Trazodone F + M + U F M F + M + U F M , , , , , , ,164 1,095 1, ,087 1, ,181 1,160 1, ,305 1,265 1, ,358 1,338 1,

36 Data Processing (2/5) For LHID2000 : limited to male patients User in each year Based on FUNC_DATE New user in each year No GU visit with Trazodone Rx in any of previous years Denominator in each year Based on R{01..20}_ID{ } Judged according to ID_IN_DATE and ID_OUT_DATE : ( ID_IN_DATE <= target year ) AND ( ( ID_OUT_DATE = blank ) OR ( ID_OUT_DATE >= target year ) )

37 Trazodone Rx at GU Visits in LHID2000 Year No. of Pat. / No. of New Pat. No. of Male Beneficiaries 0-39 y y 60+ y 0-39 y y 60+ y / / / ,737 99,731 55, / / / , ,946 57, / / / , ,035 58, / / / , ,633 59, / / / , ,791 60, / / / , ,365 61, / / / , ,445 62, / / / , ,940 62, / / / , ,354 63, / / / , ,346 64, / / / , ,060 65, / / / , ,526 66,935

38 Data Processing (3/5) No. of GU visits with Trazodone Rx by a patient between 1997 and 2008 Frequency distribution stratified by patient s age Age : age at the first visit

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40 Data Processing (4/5) Distribution of diagnoses among GU visits with Trazodone by visit by patient Diagnosis groups (after observing the preliminary Dx distrib.) Impotence : Impotence of organic origin : Psychosexual dysfunction : Unspecified psychosexual disorder Depression 296 : Affective psychoses 300 : Neurotic disorders 311 : Depressive disorder, not elsewhere classified Impotence + Depression

41 impotence depression Visit-based (n = 18,052) 9,002 (49.9%) 1,141 (6.3%) 986 (5.5%) 6,923 (38.4%) impotence depression Pat.-based (n = 4,676) 2,596 (55.5%) 407 (8.7%) 242 (5.2%) 1,431 (30.6%)

42 Data Processing (5/5) Distribution of highest monthly insured income ( insured payroll-related amount ) in patients having GU visits for Trazodone in general population (cohort) excluding patients with Trazodone at GU Highest monthly insured income of a patient between 1997 and 2008 INS_AMT : monthly insured income of type 1-3 of the insured Cave : INS_ID_TYPE = 4 OR 5 after 2006 In an ID record of those insured dependently Income = INS_IDs highest monthly insured income * One can have several INS_IDs. Minimal monthly insured income : arbitrarily set to 14,400

43 Median (IQR) : 33,300 (27,200) (n = 4,676) Median (IQR) : 31,800 (24,800) (n = 499,798) * Wilcoxon rank sum test with continuity correction : p =

44 2. Venous thromboembolism (VTE) among cancer patients in Taiwan

45 Datasets Datasets on special request All claims belonging to cancer patients according to HV registry datasets, > 100 GB ID registry Whole population 43 datasets, 22.6 GB

46 Cancer Patients Patients hospitalized with cancer diagnosis ( ) from (DD) (follow up until 2006) Excluding those who had cancer diagnosis in 1996, either at OPD or at hospitalization

47 VTE Events Hospitalization with VTE diagnosis Pulmonary embolism: DVT: Use of anticoagulants during the hospitalization with VTE diagnosis B01AA: Vitamin K antagonists B01AB: Heparin group (total dose >= 5 KIU / Adm.) VTE Dx AND ( (B01AA [Y]) OR (B01AB [dosage]) )

48 Output Table (1/6) One cancer patient one record 1. ID 2. Date of first cancer diagnosis The IN_DATE of the first hospitalization with cancer diagnosis 3. Birthday 4. Sex

49 Output Table (2/6) 5. Type of first cancer (2 fields) Use original ICD9CM code for cancer Concomitant cancer ICD code => Do not record Type of other cancers than first cancer (judged according to first 3 digits) (5 pairs [dx + IN_DATE]) (in hospitalization records) (only from the records with discharge) (after the time of first cancer diagnosis : IN_DATE of the record > OUT_DATE of first cancer diagnosis)

50 Output Table (3/6) 7. Co-morbidities (in both ambulatory and hospitalization records) (before or at the time of first cancer diagnosis) (consider all existing diagnoses except CM10) CM1: Hypertension ( ): 1 or 0 CM2: DM (250): 1 or 0 CM3: Congestive heart failure (428): 1 or 0 CM4: Ischemic heart disease ( ): 1 or 0 CM5: Renal diseases ( ): 1 or 0 CM6: Liver diseases ( ): 1 or 0

51 Output Table (4/6) 7. Co-morbidities (in both ambulatory and hospitalization records) (before or at the time of first cancer diagnosis) (consider all appearing diagnoses except CM10) CM7: Cerebrovascular disease ( ): 1 or 0 CM8: COPD ( ): 1 or 0 CM9: Hyperlipidemia (272): 1 or 0 CM10: History of VTE (415.1, , before the index hospitalization with cancer diagnosis): 1 or 0 (in hospitalization records) (before the time of first cancer diagnosis)

52 Output Table (5/6) 8. Type of the first VTE event (in hospitalization records) (after or at the time of first cancer diagnosis) No VTE: 0 PE (415.1): 1 DVT ( ): 2 PE + DVT ( ): 3 9. Date of the first VTE event The IN_DATE of the index hospitalization with VTE diagnosis and treatment No VTE: blank

53 Output Table (6/6) 10. Type of death No death / unknown: 0 In-hospital death: 1 Assumed death: 2 (drop-out from NHI within 30 days after the last hospitalization with AAD) 11. Date of last follow-up (OPD or hospitalization or drop-out from NHI) (in living patients), in-hospital death or assumed death

54 Data Processing (1/5) Cancer.plx => Cancer.txt [ PatID, InDate, OutDate, Birthday, Sex, Dx1, Dx2 ] the first admission with cancer diagnosis admission date between 1997 and 2005 because of cancer as one of diagnoses ( ) no cancer diagnosis in 1996, either at OPD or at hospitalizations

55 Data Processing (2/5) VTE_All.plx => VTE_All_DD_ txt, VTE_All_DD_ txt, VTE_All_DO_ txt all admissions with VTE diagnosis not limited to defined cancer patients

56 Data Processing (3/5) VTE.plx => VTE.txt [ PatID, VTEInDate, VTEType ] all admissions with VTE and drug treatment non-heparin (NON_HEPARIN.txt) => VTE heparin (HEPARIN_KIU.txt [ heparin, dosage in KIU ]) dosage >= 5 KIU => VTE not limited to defined cancer patients VTE type : 0 (no), 1 (PE, 415.1), 2 (DVT, ), 3 (PE + DVT)

57 Data Processing (4/5) Death.plx => Death.txt [ PatID, DeathType, LastFUDate ] not limited to defined cancer patients death type : 0 (no), 1 (in-hospital death), 2 (assumed death)

58 Data Processing (5/5) Final.plx (combine Cancer.txt, VTE.txt, Death.txt) => Final.txt [ PatID, InDate, Birthday, Sex, Dx1, Dx2, ExtraCa1, ExtraDate1, ExtraCa2, ExtraDate2, ExtraCa3, ExtraDate3, ExtraCa4, ExtraDate4, ExtraCa5, ExtraDate5, CM1, CM2, CM3, CM4, CM5, CM6, CM7, CM8, CM9, CM10, VTEType, VTEInDate, DeathType, LastFUDate ]

59 Tips in Data Processing (1/2) VTE.txt [ PatID, VTEInDate, VTEType ] B01AA + B01AB : 48 available drug items + 28 old items Heparin s total dosage in a combined full admission (fragmented claims) : based on ID + IN_DATE In all admissions with VTE Dx (not limited to defined cancer patients) with non-heparin : 15,299 admission records with heparin >= 5 KIU / admission record : 8,191 admission records with heparin >= 10 KIU / admission record : 6,988 admission records with heparin >= 25 KIU / admission record : 6,394 admission records with non-heparin or heparin : 18,532 admission records with non-heparin or heparin >= 5 KIU / admission record : 18,120 admission records with non-heparin or heparin >= 10 KIU / admission record : 17,576 admission records with non-heparin or heparin >= 25 KIU / admission record : 17,364 admission records

60 Tips in Data Processing (2/2) Death.txt [ PatID, DeathType, LastFUDate ] Last date of medical care use Last date of hospitalizations (OUT_DATE of the last hospitalization) Last date of ambulatory visits (FUNC_DATE of the last visit) Last date of hospitalization with AAD (discharge against [medical] advice) Last date of entry into insurance Last date of cancellation of insurance ID (?) (=> => ) Patients with in-hospital death : Last date of hospitalization AAD with assumed death : cancellation of insurance within 30 days after the last date of hospitalizations with AAD, and no more entry into insurance Others (living or death unknown) : last date of medical care use as LastFUDate

61 Output 497,185 records into Microsoft Access

62 I have a dream

63 The NHIRD research will enable the current generation of medical professionals in Taiwan to know the Amis better than the Amish. * Amis : 阿美族

64 NHIRD (GPRD) (NAMCS)

65 Cum. no. of papers Courtesy of Yu-Chun Chen Year

66 Thanks for Your Attention!