Data cleaning process for HIV-indicator data extracted from DHIS2 national reporting system: a case study of Kenya

Abstract Background The District Health Information Software-2 (DHIS2) is widely used by countries for national-level aggregate reporting of health-data. To best leverage DHIS2 data for decision-making, countries need to ensure that data within their systems are of the highest quality. Comprehensive, systematic, and transparent data cleaning approaches form a core component of preparing DHIS2 data for analyses. Unfortunately, there is paucity of exhaustive and systematic descriptions of data cleaning processes employed on DHIS2-based data. The aim of this study was to report on methods and results of a systematic and replicable data cleaning approach applied on HIV-data gathered within DHIS2 from 2011 to 2018 in Kenya, for secondary analyses. Methods Six programmatic area reports containing HIV-indicators were extracted from DHIS2 for all care facilities in all counties in Kenya from 2011 to 2018. Data variables extracted included reporting rate, reporting timeliness, and HIV-indicator data elements per facility per year. 93,179 facility-records from 11,446 health facilities were extracted from year 2011 to 2018. Van den Broeck et al.’s framework, involving repeated cycles of a three-phase process (data screening, data diagnosis and data treatment), was employed semi-automatically within a generic five-step data-cleaning sequence, which was developed and applied in cleaning the extracted data. Various quality issues were identified, and Friedman analysis of variance conducted to examine differences in distribution of records with selected issues across eight years. Results Facility-records with no data accounted for 50.23% and were removed. Of the remaining, 0.03% had over 100% in reporting rates. Of facility-records with reporting data, 0.66% and 0.46% were retained for voluntary medical male circumcision and blood safety programmatic area reports respectively, given that few facilities submitted data or offered these services. Distribution of facility-records with selected quality issues varied significantly by programmatic area (p < 0.001). The final clean dataset obtained was suitable to be used for subsequent secondary analyses. Conclusions Comprehensive, systematic, and transparent reporting of cleaning-process is important for validity of the research studies as well as data utilization. The semi-automatic procedures used resulted in improved data quality for use in secondary analyses, which could not be secured by automated procedures solemnly.

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Data Cleaning Process for HIV- Indicator Data Extracted from DHIS2 National Reporting System: A Case study of Kenya

10.21203/rs.3.rs-21675/v2 ◽

2020 ◽

Author(s):

Milka Gesicho ◽

Martin Were ◽

Ankica Babic

Keyword(s):

Data Cleaning ◽

National Level ◽

Voluntary Medical Male Circumcision ◽

Cleaning Process ◽

Process Data ◽

District Health ◽

Medical Male Circumcision ◽

Secondary Analyses ◽

Indicator Data ◽

Quality Issues

Abstract Background: The District Health Information Software-2 (DHIS2) is widely used by countries for national-level aggregate reporting of health-data. To best leverage DHIS2 data for decision-making, countries need to ensure that data within their systems are of the highest quality. Comprehensive, systematic, and transparent data cleaning approaches form a core component of preparing DHIS2 data for analyses. Unfortunately, there is paucity of exhaustive and systematic descriptions of data cleaning processes employed on DHIS2-based data. The aim of this study was to report on methods and results of a systematic and replicable data cleaning approach applied on HIV-data gathered within DHIS2 from 2011 to 2018 in Kenya, for secondary analyses. Methods: Six programmatic area reports containing HIV-indicators were extracted from DHIS2 for all care facilities in all counties in Kenya from 2011 to 2018. Data variables extracted included reporting rate, reporting timeliness, and HIV-indicator data elements per facility per year. 93,179 facility-records from 11,446 health facilities were extracted from year 2011 to 2018. Van den Broeck et al’s framework, involving repeated cycles of a three-phase process (data screening, data diagnosis and data treatment), was employed semi-automatically within a generic five-step data-cleaning sequence, which was developed and applied in cleaning the extracted data. Various quality issues were identified, and Friedman analysis of variance conducted to examine differences in distribution of records with selected issues across eight years. Results: Facility-records with no data accounted for 50.23% and were removed. Of the remaining, 0.03% had over 100% in reporting rates. Of facility-records with reporting data, 0.66% and 0.46% were retained for voluntary medical male circumcision and blood safety programmatic area reports respectively, given that few facilities submitted data or offered these services. Distribution of facility-records with selected quality issues varied significantly by programmatic area (p<0.001). The final clean dataset obtained was suitable to be used for subsequent secondary analyses. Conclusions: Comprehensive, systematic, and transparent reporting of cleaning-process is important for validity of the research studies as well as data utilization. The semi-automatic procedures used resulted in improved data quality for use in secondary analyses, which could not be secured by automated procedures solemnly.

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Data Cleaning Process for HIV Indicator Data Extracted from DHIS2 National Reporting System: Case Example of Kenya

10.21203/rs.3.rs-21675/v1 ◽

2020 ◽

Author(s):

Milka Gesicho ◽

Ankica Babic ◽

Martin Were

Keyword(s):

Error Detection ◽

Data Cleaning ◽

National Level ◽

Quality Data ◽

Process Data ◽

Systematic Data ◽

Report Quality ◽

Indicator Data ◽

Quality Issues ◽

Error Types

Abstract Background The District Health Information Software 2 (DHIS2) is widely used by countries for national-level aggregate reporting of health data. To best leverage DHIS2 data for decision-making, countries need to ensure that data within their systems are of the highest quality. Comprehensive, systematic and transparent data cleaning approaches form a core component of preparing DHIS2 data for use. Unfortunately, there is paucity of exhaustive and systematic descriptions of data cleaning processes employed on DHIS2-based data. In this paper, we describe results of systematic data cleaning approach applied on a national-level DHIS2 instance, using Kenya as the case example. Methods Broeck et al’s framework, involving repeated cycles of a three-phase process (data screening, data diagnosis and data treatment), was employed on six HIV indicator reports collected monthly from all care facilities in Kenya from 2011 to 2018. This resulted to repeated facility reporting instances. Quality dimensions evaluated included reporting rate, reporting timeliness, and indicator completeness of submitted reports each done per facility per year. The various error types were categorized, and Friedman analyses of variance conducted to examine differences in distribution of facilities by error types. Data cleaning was done during the treatment phases. Results A generic five-step data cleaning sequence was developed and applied in cleaning HIV indicator data reports extracted from DHIS2. Initially, 93,179 facility reporting instances were extracted from year 2011 to 2018. 50.23% of these instances submitted no reports and were removed. Of the remaining reporting instances, there was over reporting in 0.03%. Quality issues related to timeliness included scenarios where reports were empty or had data but were never on time. Percentage of reporting instances in these scenarios varied by reporting type. Of submitted reports empty reports also varied by report type and ranged from 1.32–18.04%. Report quality varied significantly by facility distribution (p = 0.00) and report type. Conclusions The case instance of Kenya reveals significant data quality issues for HIV reported data that were not detected by the inbuilt error detection procedures within DHIS2. More robust and systematic data cleaning processes should be integrated to current DHIS2 implementations to ensure highest quality data.

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Automating indicator data reporting from health facility EMR to a national aggregate data system in Kenya: An Interoperability field-test using OpenMRS and DHIS2

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v8i2.6722 ◽

2016 ◽

Vol 8 (2) ◽

Cited By ~ 7

Author(s):

James M Kariuki ◽

Eric-Jan Manders ◽

Janise Richards ◽

Tom Oluoch ◽

Davies Kimanga ◽

...

Keyword(s):

Health Information ◽

Health Facility ◽

Field Test ◽

Health Information System ◽

Hiv Care ◽

Process Data ◽

District Health ◽

Data Completeness ◽

Reporting Process ◽

Indicator Data

Introduction: Developing countries are increasingly strengthening national health information systems (HIS) for evidence-based decision-making. However, the inability to report indicator data automatically from electronic medical record systems (EMR) hinders this process. Data are often printed and manually re-entered into aggregate reporting systems. This affects data completeness, accuracy, reporting timeliness, and burdens staff who support routine indicator reporting from patient-level data. Method: After conducting a feasibility test to exchange indicator data from Open Medical Records System (OpenMRS) to District Health Information System version 2 (DHIS2), we conducted a field test at a health facility in Kenya. We configured a field-test DHIS2 instance, similar to the Kenya Ministry of Health (MOH) DHIS2, to receive HIV care and treatment indicator data and the KenyaEMR, a customized version of OpenMRS, to generate and transmit the data from a health facility. After training facility staff how to send data using the module, we compared completeness, accuracy and timeliness of automated indicator reporting with facility monthly reports manually entered into MOH DHIS2.Results: All 45 data values in the automated reporting process were 100% complete and accurate while in manual entry process, data completeness ranged from 66.7% to 100% and accuracy ranged from 33.3% to 95.5% for seven months (July 2013-January 2014). Manual tally and entry process required at least one person to perform each of the five reporting activities, generating data from EMR and manual entry required at least one person to perform each of the three reporting activities, while automated reporting process had one activity performed by one person. Manual tally and entry observed in October 2013 took 375 minutes. Average time to generate data and manually enter into DHIS2 was over half an hour (M=32.35 mins, SD=0.29) compared to less than a minute for automated submission (M=0.19 mins, SD=0.15).Discussion and Conclusion: The results indicate that indicator data sent electronically from OpenMRS-based EMR at a health facility to DHIS2 improves data completeness, eliminates transcription errors and delays in reporting, and reduces the reporting burden on human resources. This increases availability of quality indicator data using available resources to facilitate monitoring service delivery and measuring progress towards set goals.

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Reducing provider workload while preserving patient safety via a 2-way texting intervention in Zimbabwe’s voluntary medical male circumcision program: study protocol for an un-blinded, prospective, non-inferiority, randomized controlled trial

10.21203/rs.2.258/v2 ◽

2019 ◽

Author(s):

Caryl Feldacker ◽

Vernon Murenje ◽

Scott Barnhart ◽

Sinokuthemba Xaba ◽

Batsirai Makunike-Chikwinya ◽

...

Keyword(s):

Healthcare Worker ◽

Male Circumcision ◽

National Level ◽

Scale Up ◽

Routine Care ◽

Adverse Event Rate ◽

Control Group ◽

Voluntary Medical Male Circumcision ◽

Medical Male Circumcision

Abstract Background: Surgical male circumcision (MC) safely reduces risk of female-to-male HIV-1 transmission by up to 60%. The average rate of global moderate and severe adverse events (AEs) is 0.8%: 99% of men heal from MC without incident. To reach the 2016 global MC target of 20 million, productivity must double in countries plagued by severe healthcare worker shortages like Zimbabwe. The ZAZIC consortium partners with the Zimbabwe Ministry of Health and Child Care and performed over 120,000 MCs. MC care in Zimbabwe requires in-person, follow-up visits at post-operative days 2,7, and 42. ZAZIC program adverse event rate is 0.4%; therefore, overstretched clinic staff conducted more than 200,000 unnecessary reviews for MC clients without complications. Methods: Through an un-blinded, prospective, randomized, control trial in two high-volume MC facilities, we will compare two groups of adult MC clients with cell phones randomized 1:1 into two groups: 1) routine care (control group N=361) and 2) clients who receive and respond to a daily text with in-person follow-up only if desired or if a complication is suspected (intervention N=361). If an intervention client responds affirmatively to any automated daily text with a suspected AE, an MC nurse will exchange manual, modifiable, scripted texts with the client to determine symptoms and severity, requesting an in-person visit if desired or warranted. Both arms will complete a study-specific, Day 14, in-person, follow-up review for verification of self-reports (intervention) and comparison (control). Data collection includes extraction of routine client MC records, study-specific database reports, and participant usability surveys. Intent-to-treat (ITT) analysis will explore differences between groups to determine if two-way texting (2wT) can safely reduce MC follow-up visits; estimate the cost savings associated with 2wT over routine MC follow-up; and assess the acceptability and feasibility of 2wT for scale-up. Discussion: It is expected that this mobile health intervention will be as safe as routine care while providing distinct advantages in efficiency, costs, and reduced healthcare worker burden. The success of this intervention could lead to adaptation and adoption of this intervention at the national level, increasing efficiency of MC scale up, reducing burdens on providers and patients. Clinical Trial Registration Number NCT03119337

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