DATIM Analytics Improvements · •DATIM Analytics Improvements •Overview of Enhanced DATIM...

DATIM Analytics Improvements

Webinar Series 1 Thursday, March 2, 2017

1

Agenda

• Overview

• Data Entry Improvements

• DATIM Analytics Improvements

• Overview of Enhanced DATIM Favorites

• Wrap-Up

• Q&A and Feedback

2

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

DATA ENTRY IMPROVEMENTS Data entry auto-calculations

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) Denominator Age Total

KP_PREV (Numerator) Numerator 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

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

KP_MAT Numerator Female + Male + Unknown Sex

TX_PVLS (Numerator) Numerator Indication total: Routine + Targeted + Not Documented

List of auto-calculations in DATIM data entry for MER 2.0 (cont’d)

Indicator Auto-Calculation Field Disaggregate Calculation

PMTCT_FO Numerator HIV-infected + HIV-uninfected + HIV-final status unknown + Died

without status known

INVS_COMD Number of commodities

Purchased: HIV ARVs 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

Example of PMTCT_ART auto-calculation

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

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 10-14




PMTCT_STAT (D, DSD, Age) v2: New ANC clients 50+

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,








GEND_GBV (N, DSD, Age/Sex/ViolenceType): GBV Care 50+,


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


Male, Physical and/or Emotional Violence







GEND_GBV (N, DSD, Age/Sex/ViolenceType): GBV Care 50+, Male,

Physical and/or Emotional Violence

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

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

DATA ENTRY IMPROVEMENTS Age/sex disaggregates changed

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

MER 2.0 Changes: Mutually Exclusive Disaggregations

• Mutually exclusive age/sex disaggregations

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DATIM Analytics Improvements: Advantages

15

FY16Q1 FY16Q2 FY16Q3 FY16Q4

Fine & Coarse 3,613 (11.35%); 103 10 (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

8.9%

91.1%

FY16Q3

Fine & Coarse Fine only

0.0%

100.0%

FY16Q4

Fine & Coarse Fine only

Kenya, TX_NEW (DSD) Age-Sex disaggregate entry: Fine & Coarse duplicate entry, Coarse only, and Fine only disaggregate entry for FY16

Kenya, TX_NEW (DSD): Volume (% volume); # of entries

DATIM ANALYTICS IMPROVEMENTS

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.

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DATIM Analytics Improvements

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Before

Now

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.

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.

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Group set principles: Exclusivity and compulsory

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• 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

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

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

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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: https://www.datim.org/dhis-web-pivot/?id=MHX6dECMmO2

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

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https://www.datim.org/dhis-web-pivot/?id=MHX6dECMmO2





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

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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 12-17 below, or refer to the attached guide, “Getting Started with Group Set Analysis.”

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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.”

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

27

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 15-17.

• 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.

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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

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

Pediatrics and Malnutrition

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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 9-16.

• 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.

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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: https://www.datim.org/dhis-web-pivot/index.html?id=INDPVWFBYQB 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.

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https://www.datim.org/dhis-web-pivot/index.html?id=INDPVWFBYQB






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

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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.

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