DATIM Analytics Improvements. Webinar Series 1 Thursday, March 2, 2017

Transcription

1 DATIM Analytics Improvements Webinar Series 1 Thursday, March 2,

2 Agenda Overview Data Entry Improvements DATIM Analytics Improvements Overview of Enhanced DATIM Favorites Wrap-Up Q&A and Feedback 2

3 Overview MER 2.0 indicators have been streamlined and have new disaggregates by population to implement and monitor interventions at more granular levels. The DATIM analytic environment continues to improve in order to support analyses needed to reach 90/90/90 by geography and populations. 3

4 Data entry auto-calculations DATA ENTRY IMPROVEMENTS

5 List of auto-calculations in DATIM data entry for MER 2.0 Indicator Auto-Calculation Field Disaggregate Calculation PMTCT_ART Numerator New on ART + Already on ART at the beginning of current pregnancy PMTCT_EID Numerator Total of Infant Test Results PMTCT_STAT (Denominator) KP_PREV (Numerator) Denominator Numerator Age Total PWID Female + PWID Male + FSW + MSM + TG + NSW MSM + NSW TG + People in prison and other enclosed settings GEND_GBV Sexual Violence Sexual Violence disaggregated by Age and Sex GEND_GBV Physical and / or Emotional Violence Physical and / or Emotional Violence disaggregated by Age and Sex OVC_SERV Numerator Active + Graduated + Transferred + Exited without Graduation OVC_HIVSTAT Numerator KP_MAT Numerator Female + Male + Unknown Sex Reported HIV positive to IP (includes tested in the reporting period and known positive) + Reported HIV Negative to IP + No HIV status reported to the implementing partner TX_PVLS (Numerator) Numerator Indication total: Routine + Targeted + Not Documented

6 List of auto-calculations in DATIM data entry for MER 2.0 (cont d) Indicator Auto-Calculation Field Disaggregate Calculation PMTCT_FO INVS_COMD Numerator Number of commodities Purchased: HIV ARVs HIV-infected + HIV-uninfected + HIV-final status unknown + Died without status known 1st Line ARVs + 2nd Line ARVs INVS_COMD US Dollars Spent: HIV ARVs 1st Line ARVs + 2nd Line ARVs HRH_STAFF Numerator Total of Cadre HRH_CURR Numerator Clinical Salary + Clinical Stipend + Clinical Non-Monetary + Management Salary + Management Stipend + Management Non- Monetary + Clinical Support Salary + Clinical Support Stipend + Clinical Support Non-Monetary + Social Services + Social Services Stipend + Social Services Non- Monetary + Lay Salary + Lay Stipend + Lay Non-Monetary + Other Salary + Other Stipend + Other Non-Monetary HRH_PRE Numerator Doctors + Nurses + Midwives + Social Service Workers + Laboratory Professionals + Other PMTCT_ARV_NAT Numerator New on ART + Already on ART + Other PMTCT_ARV_SUBNAT Numerator New on ART + Already on ART + Other

7 Example of PMTCT_ART autocalculation PMTCT_ART Numerator New on ART + Already on ART + Other PMTCT_ART (N, DSD, NewExistingArt): ARVs Life-long ART, New PMTCT_ART (N, DSD, NewExistingArt): ARVs Life-long ART, Already

8 PMTCT_STAT Denominator PMTCT_STAT (Denominator) Denominator Age Total PMTCT_STAT (D, DSD, Age) v2: New ANC clients Unknown Age PMTCT_STAT (D, DSD, Age) v2: New ANC clients <10 PMTCT_STAT (D, DSD, Age) v2: New ANC clients PMTCT_STAT (D, DSD, Age) v2: New ANC clients PMTCT_STAT (D, DSD, Age) v2: New ANC clients PMTCT_STAT (D, DSD, Age) v2: New ANC clients PMTCT_STAT (D, DSD, Age) v2: New ANC clients 50+

9 GEND_GBV GEND_GBV Physical and / or Emotional Violence Physical and / or Emotional Violence disaggregated by Age and Sex GEND_GBV (N, DSD, Age/Sex/ViolenceType): GBV Care Unknown Age, Female, Physical and/or Emotional Violence GEND_GBV (N, DSD, Age/Sex/ViolenceType): GBV Care <10, Female, Physical and/or Emotional Violence GEND_GBV (N, DSD, Age/Sex/ViolenceType): GBV Care 10-14, Female, Physical and/or Emotional Violence GEND_GBV (N, DSD, Age/Sex/ViolenceType): GBV Care 15-19, Female, Physical and/or Emotional Violence GEND_GBV (N, DSD, Age/Sex/ViolenceType): GBV Care 20-24, Female, Physical and/or Emotional Violence GEND_GBV (N, DSD, Age/Sex/ViolenceType): GBV Care 25-49, Female, Physical and/or Emotional Violence GEND_GBV (N, DSD, Age/Sex/ViolenceType): GBV Care 50+, Female, Physical and/or Emotional Violence GEND_GBV (N, DSD, Age/Sex/ViolenceType): GBV Care Unknown Age, Male, Physical and/or Emotional Violence GEND_GBV (N, DSD, Age/Sex/ViolenceType): GBV Care <10, Male, Physical and/or Emotional Violence GEND_GBV (N, DSD, Age/Sex/ViolenceType): GBV Care 10-14, Male, Physical and/or Emotional Violence GEND_GBV (N, DSD, Age/Sex/ViolenceType): GBV Care 15-19, Male, Physical and/or Emotional Violence GEND_GBV (N, DSD, Age/Sex/ViolenceType): GBV Care 20-24, Male, Physical and/or Emotional Violence GEND_GBV (N, DSD, Age/Sex/ViolenceType): GBV Care 25-49, Male, Physical and/or Emotional Violence GEND_GBV (N, DSD, Age/Sex/ViolenceType): GBV Care 50+, Male, Physical and/or Emotional Violence

10 HRH_CURR HRH_CURR Numerator Clinical Salary + Clinical Stipend + Clinical Non- Monetary + Management Salary + Management Stipend + Management Non-Monetary + Clinical Support Salary + Clinical Support Stipend + Clinical Support Non-Monetary + Social Services + Social Services Stipend + Social Services Non-Monetary + Lay Salary + Lay Stipend + Lay Non-Monetary + Other Salary + Other Stipend + Other Non-Monetary HRH_CURR (N, DSD, CadreCategory/FinancialSupport): Health Workers Clinical, Salary HRH_CURR (N, DSD, CadreCategory/FinancialSupport): Health Workers Clinical, Stipend HRH_CURR (N, DSD, CadreCategory/FinancialSupport): Health Workers Clinical, Non-Monetary HRH_CURR (N, DSD, CadreCategory/FinancialSupport): Health Workers Management, Salary HRH_CURR (N, DSD, CadreCategory/FinancialSupport): Health Workers Management, Stipend HRH_CURR (N, DSD, CadreCategory/FinancialSupport): Health Workers Management, Non- Monetary HRH_CURR (N, DSD, CadreCategory/FinancialSupport): Health Workers Clinical Support, Salary HRH_CURR (N, DSD, CadreCategory/FinancialSupport): Health Workers Clinical Support, Stipend HRH_CURR (N, DSD, CadreCategory/FinancialSupport): Health Workers Clinical Support, Non- Monetary HRH_CURR (N, DSD, CadreCategory/FinancialSupport): Health Workers Social Service, Salary HRH_CURR (N, DSD, CadreCategory/FinancialSupport): Health Workers Social Service, Stipend HRH_CURR (N, DSD, CadreCategory/FinancialSupport): Health Workers Social Service, Non- Monetary HRH_CURR (N, DSD, CadreCategory/FinancialSupport): Health Workers Lay, Salary HRH_CURR (N, DSD, CadreCategory/FinancialSupport): Health Workers Lay, Stipend HRH_CURR (N, DSD, CadreCategory/FinancialSupport): Health Workers Lay, Non-Monetary HRH_CURR (N, DSD, CadreCategory/FinancialSupport): Health Workers Other, Salary HRH_CURR (N, DSD, CadreCategory/FinancialSupport): Health Workers Other, Stipend HRH_CURR (N, DSD, CadreCategory/FinancialSupport): Health Workers Other, Non-Monetary

11 Auto-calculates Advantages Less data entry needed, reduces repetition of the same data elements being entered Improved data quality Less room for differences between numerators and the sums of their disaggregates Reduces need for completeness checks for disaggregates Easier to analyze, as the disaggregate totals always align with the numerator or denominator

12 Age/sex disaggregates changed DATA ENTRY IMPROVEMENTS

13 Age/sex disaggregates changed Aligning the age/sex disaggregates in the clinical cascade indicators HTS, TX_NEW, TX_CURR, TB indicators and PMTCT indicators Not OVC indicators Mutual exclusive data entry for age / sex disaggregates Country teams have completed the finer disaggregates

14 MER 2.0 Changes: Mutually Exclusive Disaggregations Mutually exclusive age/sex disaggregations 14

15 DATIM Analytics Improvements: Advantages Kenya, TX_NEW (DSD) Age-Sex disaggregate entry: Fine & Coarse duplicate entry, Coarse only, and Fine only disaggregate entry for FY16 FY16Q3 91.1% 8.9% FY16Q4 0.0% 100.0% Fine & Coarse Fine only Fine & Coarse Fine only Kenya, TX_NEW (DSD): Volume (% volume); # of entries FY16Q1 FY16Q2 FY16Q3 FY16Q4 Fine & Coarse 3,613 (11.35%); (0.03%); 2 3,726 (8.94%); 21 - Coarse only Fine only 28,208 (88.65%); 2,008 35,831 (99.97%); 2,209 37,938 (91.06%); 2,265 69,939 (100%); 2,361 15

16 DATIM ANALYTICS IMPROVEMENTS

17 DATIM Analytics Improvements: Process Overview User Driven Requirements Gathering and Development Approach Feedback from DCMMs and HQ Staff analyzing data have informed first round of analytics improvements This webinar series is an opportunity to get reactions to the initial round and to get feedback on what other improvements we want to prioritize. Your participation in this webinar series is critical. 17

18 DATIM Analytics Improvements Below illustrates the expansion of MER data analytic capabilities, which have been implemented iteratively with ongoing feedback from end users. Actual analytic dimensions are now available and will continue to develop in DATIM to reflect end user feedback. Now Before 18

19 Background on Group Set Analysis in DATIM Components & Structure MER indicators in DATIM are a series of data elements and disaggregations (DHIS2 category combinations). Group sets dimensions (or analytic dimensions) can be used as a means to generate metadata to support particular analyses. Types of group sets in DATIM: Data element group set (e.g. an analytic dimension) Organization unit group sets (e.g. Facility, Community, Medical Store) Category option group sets (e.g. specific disaggregation types) Using the group set feature of DATIM, we can quickly generate pivot tables that drill down into the dimensions of the MER 2.0 indicators. This relies on grouping data elements into appropriate group sets. The following slide demonstrates common components of group sets to be aware of. 19

20 Group set principles: Exclusivity and compulsory Technical area: Every data element belongs to one technical area Results / targets: Every data element belongs to either RESULT or TARGET Support type: Every data element belongs to either DSD or TA Disaggregation type: Every data element belongs to exactly one disaggregation group 20

21 Technical Approach Data Element Group Sets: 5 Common Components: Example: HTC_TST (N, DSD, Age/Sex/Result) TARGET: HTC received results 1. Technical area: HTC_TST 2. Numerator /Denominator: N 3. Results / targets: TARGET 4. Support type: DSD 5. Disaggregation type: Age/Sex/Result 21

22 STEP-BY-STEP OVERVIEW OF ENHANCED DATIM FAVORITES FOR AGE BY SEX ANALYSES 22

23 Step 1: Calculate HTS_TST Yield by Age/Sex/Modalities Name of Favorite: PEPFAR FY17 Q1 HTS_TST Yield Age by Sex by Modality (Updated)* Link to Favorite: Please make sure you are signed into DATIM prior to clicking the URL. Description: This favorite will allow DATIM users to pull all of the data dimensions required to calculate HTS yield by age, sex, and modality. *Note that this favorite will not include pediatrics, malnutrition, PMTCT ANC, and VMMC 23

24 Screenshot of Favorite: PEPFAR FY17 Q1 HTS_TST Yield Age by Sex by Modality 24

25 Important Considerations when using HTS_TST Yield by Age/Sex/Modality Favorite The total number in this pivot table does not equal the total number of positives tested, because it is important to note that this favorite excludes the following HTS modalities: PMTCT_ANC and VMMC are excluded because they have a single implicit sex associated with the data. Pediatrics and Malnutrition are excluded because they have a single age association, and sex is unspecified To obtain the total number of positives tested, follow the instructions on slides below, or refer to the attached guide, Getting Started with Group Set Analysis. 25

26 Step 2: Calculate HTS_TST Yield by Age/Modality for PMTCT and VMMC To create this pivot table, use the HTS_Yield by Age/Sex/Modality, then take the below steps: 1. Select the Cascade Gender menu option and remove all gender options 2. Select the HTS Modality menu option and de-select all modalities. Add in the modalities PMTCT_ANC and VMMC. 3. Select Refresh. 26

27 Screenshot of Favorite: PEPFAR FY17 Q1 HTS_TST Yield Age by Modality: PMTCT_ANC and VMMC 27

28 Important Considerations when using HTS_TST Yield by Age/Modality for PMTCT_ANC and VMMC It is important to note that this favorite should not be used on its own, as it does not equal the total number of positives tested. It should be used along with the favorite outlined in slides 9-11 and The results for PMTCT_ANC may be assumed to be all Females, and VMMC may be assumed to be all Males. These results may be combined with the results of the previous pivot table using those assumptions. Do not attempt to filter this pivot table by Cascade Gender, as the results will not populate. 28

29 Step 3: Calculate HTS_TST Yield by Modality for Pediatrics and Malnutrition To create this pivot table, use the HTS_Yield by Age/Sex/Modality, then take the below steps: 1. Select the Cascade Gender menu option and remove all gender options 2. Select the Cascade Age Bands menu option and remove all age options 3. Select the HTS Modality menu option and de-select all modalities. Add in the modalities Pediatrics and Malnutrition. 4. Select Refresh. 29

30 Screenshot of Favorite: PEPFAR FY17 Q1 HTS_TST Yield Age by Modality: Pediatrics and Malnutrition 30

31 Important Considerations when using Calculate HTS_TST Yield by Modality for Pediatrics and Malnutrition Reminder: It is important to note that this favorite should not be used on its own, as it does not equal the total number of positives tested. It should be used along with the favorite outlined in slides The results for both Pediatrics and Malnutrition may be assumed to all be Age <5. Additionally, they may all be assumed to be Sex Unassigned, as MER 2.0 does not record sex distinctions for children under the age of 10. These results may be combined with the results of the previous pivot tables using those assumptions. Do not attempt to filter this pivot table by Cascade Gender, or Cascade Age as the results will not populate. 31

32 Calculate TX_CURR and TX_NEW by Age and Sex Name of Favorite: PEPFAR FY17 Q1 TX_CURR TX_NEW by Age By Sex Link to Favorite: Please make sure you are signed into DATIM prior to clicking the URL. Description: This favorite will allow DATIM users to examine the Q1 clinical cascade indicators by Age and Sex. 32

33 Screenshot of Favorite: PEPFAR FY17 Q1 TX_CURR TX_NEW by Age By Sex 33

34 We want your feedback! This is just the first step in developing and deploying enhanced data analysis capabilities in DATIM. We ll be using feedback from end users to gather and prioritize requirements for further improvements. The webinar series will continue bi-weekly (3/16, 3/30, 4/13, 4/27, and 5/11) for 5 more episodes. Agendas will be driven by user feedback. Here s how to provide feedback: Watch your inboxes next week for an invitation to a bi-weekly DATIM Data Analysis Improvement webinar series. The series will kick off on Thursday, March 2at 8-9am Eastern. These webinars will be an opportunity for to demonstrate new functionality and to get feedback from end users on what is working and what could be improved. Inform your SI Advisor who can pass along the feedback Send a ticket to DATIM support. 34