IntroductionHealth-facility data serves as a primary source for monitoring service provision and guiding the attainment of health targets. District Health Information Software (DHIS2) is a free open software predominantly used in low and middle-income countries to manage the facility-based data and monitor program wise service delivery. Evidence suggests the lack of quality in the routine maternal and child health information, however there is no robust analysis to evaluate the extent of its inaccuracy. We aim to bridge this gap by accessing the quality of DHIS2 data reported by health facilities to monitor priority maternal, newborn and child health indicators in Lumbini Province, Nepal.MethodsA facility-based descriptive study design involving desk review of Maternal, Neonatal and Child Health (MNCH) data was used. In 2021/22, DHIS2 contained a total of 12873 reports in safe motherhood, 12182 reports in immunization, 12673 reports in nutrition and 12568 reports in IMNCI program in Lumbini Province. Of those, monthly aggregated DHIS2 data were downloaded at one time and included 23 priority maternal and child health related data items. Of these 23 items, nine were chosen to assess consistency over time and identify outliers in reference years. Twelve items were selected to examine consistency between related data, while five items were chosen to assess the external consistency of coverage rates. We reviewed the completeness, timeliness and consistency of these data items and considered the prospects for improvement.ResultsThe overall completeness of facility reporting was found within 98% to 100% while timeliness of facility reporting ranged from 94% to 96% in each Maternal, Newborn and Child Health (MNCH) datasets. DHIS2 reported data for all 9 MNCH data items are consistent over time in 4 of 12 districts as all the selected data items are within ±33% difference from the provincial ratio. Of the eight MNCH data items assessed, four districts reported ≥5% monthly values that were moderate outliers in a reference year with no extreme outliers in any districts. Consistency between six-pairs of data items that are expected to show similar patterns are compared and found that three pairs are within ±10% of each other in all 12 districts. Comparison between the coverage rates of selected tracer indicators fall within ±33% of the DHS survey result.ConclusionGiven the WHO data quality guidance and national benchmark, facilities in the Lumbini province well maintained the completeness and timeliness of MNCH datasets. Nevertheless, there is room for improvement in maintaining consistency over time, plausibility and predicted relationship of reported data. Encouraging the promotion of data review through the data management committee, strengthening the system inbuilt data validation mechanism in DHIS2, and promoting routine data quality assessment systems should be greatly encouraged.

Managing data is hard. So many of our partner institutions are under-resourced when it comes to preparing, archiving, sharing and interpreting HIV-related datasets. Crucial datasets often sit on the laptops of local staff in Excel sheets and Word documents, or in large locked-down data warehouses where only a few have the understanding to access it. But data is useless if is not accessible by trusted parties for analysis. UNAIDS has identified the following challenges faced by our local partners: Administrative burden of data management Equipment failure Staff turnover Duplication of requests for data Secure sharing of data Keeping data up-to-date A new software project has been established to tackle these challenges and streamline the data management process... The AIDS Data Repository aims to improve the quality, accessibility and consistency of HIV data and HIV estimates by providing a centralised platform with tools to help countries manage and share their HIV data. The project includes the following features: Schema-based dataset management will help local staff with the process of preparing, validating and archiving key datasets according to the requirements from UNAIDS. Schemas that are designed or approved by UNAIDS determine the design of web forms and validation tools that guide users through the process of uploading essential data. Secure and licensed dataset sharing will give partners confidence that their data should only be used by the parties they trust for the purposes they have agreed. Data access management tools will help organisations understand who has access to use their datasets. Access can be requested, reviewed and granted through the site, but also revoked. This can be done for individual users or for entire organisations. Cloud based archiving and backup of all datasets means that data will not go missing when equipment fails or staff leave. All datasets can be tagged and searched according to their metadata and will be reliably accessible forever. DHIS2 interoperability will enable administrators to share DHIS2 data with all the features and tools provided by the AIDS data repository. Datasets comprising elements automatically pulled from a DHIS2 instance can be added to the site. Periodic pulling of data will ensure that these datasets do not fall out of date. Web-based tools will help administrators configure and monitor the DHIS2 configuration that will likely change over time. Spectrum/Naomi interoperability will streamline the process of preparing and running the Spectrum and HIVE statistical models that are supported by UNAIDS. Web forms and validation tools guide users through the process of preparing the source data sets. These source data sets can then be automatically pulled into the Spectrum and Naomi statistical modelling software tools, which will return the results to the AIDS Data Repository once finished.

The repository contains:

- Excel sheets for each round of indoor residual spraying from 2014 - 2018 for villages based on the Illovo Nchalo Estate (provided by public health officer)

- Weather data for 1999 - 2019 downloaded from Sasri Weather web for Malawi - Illovo Nchalo (Open access after signing up)

- Explanation of variables downloaded from Sasri Weather Web

- Expected population: number of residents living in Illovo clinic's catchment areas based on 2016 and 2019 census. Linear interpolation for the other years

- Malaria data per month per clinic from the public health officer's records at Illovo Nchalo for 7 clinics for 2014 - 2018

- Malaria data downloaded and selected from DHIS2 (access upon request and approval)

Description of IRS data:

- Village: Name of the villages based at Illovo being targeted for IRS

- Target_spray: Number of structures within the village targeted for spraying

- Sprayed: Number of structures actually sprayed

- Date_start: Start date of the IRS campaign in a village

- Date_end: End date of the IRS campaign in that village

- Coverage_p: Percentage of structures sprayed calculated from "target_spray" and "sprayed"

Notes on reconciling the different years of IRS:

1. Post office and D. compound have been added to Nkombedzi

2. B compound has been added to Riverside/Mess

3. The following villages attend the following clinics

The following villages attend the assigned clinics:
- Mess and Bonksville -> Factory
- Mlambe and Paxman -> Mangulenje
- Sande Ranch -> Lengwe
- Mechanical Pool -> Mwanza

Description of the malaria data:

- Date, month, year

- Time_dummy: 1 to 48, over the study period

- Village: The name of the village the clinic is based in. In further analyses, this is referred to as "clinic" instead to avoid confusion.

- Total_cases: total number of cases testing positive for malaria by RDT, or in a very small percentage of cases microscopy (only used when RDT gives inconclusive or conflicting results, or when symptoms persist with negative RDT). Cases_on + cases_off = total_cases

- Cases_on: Number of malaria cases from residents of villages located within the boundaries of the Illovo estate

- Cases_off: Number of malaria cases from residents of villages located (just) outside the boundaries of the Illovo estate

- Total_patients: Total number of patients attending the clinic that month

From the selected control clinics only "WHO NMCP P Confirmed malaria cases" was used to indicate the number of malaria cases and "CMED Total Population" to indicate the clinic catchment population. Further info on DHIS2 website.

For further information don't hesitate to contact Remy Hoek Spaans.

Data used for analysis of the research topic: A Framework for Integration of Electronic Medical Records Systems with Ministry of Health DHIS2 in Eastern Uganda

GH Pro conducted an endline evaluation of USAID’s Maternal Child Survival Program (MCSP-MNCH)1 to assess if it had achieved its objectives and planned outputs, as stated in its program description, in Nigeria’s Ebonyi and Kogi states. Five questions evaluated increases in access and utilization of reproductive, maternal, newborn, and child health interventions; gender-transformative strategies; sustainability; the program’s learning agenda vis-à-vis the Nigerian government’s learning needs; and use of program data. The evaluation team used a retrospective analytic and a cross-sectional design to address the five questions, and mixed methods were used for data collection, including reviews of the national District Health Information System (DHIS) 2, MCSP-MNCH datasets, and 51 program documents. Apparent improvements were noted in the utilization of six interventions: oxytocin, partograph, Chlorhexidine 4% gel, newborn resuscitation, essential newborn care, and integrated Community Case Management, particularly with referral of danger signs. MCSP-MNCH baseline data was not available nor calculable for magnesium sulphate or Kangaroo Mother Care. Data was also not available for postpartum family planning for first-time parents and Bubble Continuous Positive Airway Pressure for newborn resuscitation, as a study was undergoing analysis and data was not ready. Furthermore, the dataset MCSP-MNCH provided to the evaluation team was incomplete, imprecise, and contained errors, raising concerns about noted improvements. The program’s work in male engagement and Mothers Savings and Loans Clubs hold promise for transforming gender norms but reached too few people. Most of the program’s reproductive health and MNCH interventions are likely to be included in budgets in Ebonyi and Kogi through the World Bank’s Saving One Million Lives project, but without specific commitment from the states’ governors, funding release may be jeopardized. The learning agenda helped inform implementation, but the government did not help shape the research. Last, MCSP-MNCH project created a new DHIS database instance for its project data only, including new indicators that it introduced (like application of Chlorhexidine 4% gel for newborn cord care), as well as indicators that were already available in the national DHIS 2 database; it is housed within the same server as the national DHIS 2.

DHIS2 is a web-based platform primarily used in developing countries, ensuring reliable data and aiding decentralized decision-making. The Ministry of Health and Population has greatly emphasized using DHIS2 for data entry and reporting. However, studies regarding health workers’ experiences on DHIS2 and the utilization of data at the local level remain limited. Therefore, this study aims to investigate the usage and practical experience of DHIS2 at the local levels of Gandaki province, Nepal. An exploratory qualitative study was conducted in the Gandaki province from February to August 2023. We conducted twenty in-depth interviews among the DHIS2 users at local levels, health posts, and provincial health directorate using in-depth interview guidelines. The study participants were selected purposively. Thematic analysis was conducted to analyze the data, and NVivo was used to facilitate data analysis. Health professionals demonstrated dedication and commitment to use DHIS2 for reporting. DHIS2 has facilitated timely reporting, data storage, data analysis and visualization, feedback and communication mechanisms, and service delivery. Users’ self-motivation and support from the local and provincial levels and regular review and program-specific review meetings were major facilitators for DHIS2 use. Similarly, technical issues, poor internet connectivity, power outages, and inexperienced health professionals were the significant challenges to using DHIS2. The basic and refresher training needed improvement at all levels, and learning materials were unavailable in health facilities. In addition, the data utilization at the local level in various actions was unsatisfactory despite sufficient data. Health professionals have been facilitated by DHIS2 in various actions. Capacity building of health professionals on data analysis and interpretations, continued onsite coaching, reliable internet connectivity, availability of learning materials, and improved server capacity are needed to enhance the performance of DHIS2 at the local level.

The DHIS-2 data was used to assess the effect of NHIP on health service utilization, especially in terms of total visits and referral outs. The data was obtained from the DHIS-2 (Nepal’s national health information management system) through the Integrated Health Information Management Section under the Management Division, Department of Health Service (DHS) Nepal. We asked for the data on multiple variables. However, we only included few of them in our analysis based on our qualitative findings.

DHIS2 applications used to develop the Africa CDC EMS as of December 2023.

New family planning clients who used injectables, by district and injection type (Malawi DHIS2 data).

This dataset consists of about 80 laboratory tests that the Kenyan Ministry of Health requires laboratories to submit in DHIS2 every month. The dataset was therefore retrieved from DHIS2 after identifying the 204 hospitals based on set criteria. We used these laboratory reports submitted by 204 hospitals between January 2018 and Dec 2019. We complemented the dataset by adding the number of beds in each hospital. Data on the number of beds were obtained from the Kenya Master health facility List. We examined the reporting patterns of these tests and further compared them to the World Health Organization’s Essential Diagnostic List. Also, we investigated the testing scopes of these laboratories based on the inpatient bed capacity.

Routine clinical data uploaded by the tablet during the ALMANACH projectThis is the raw data transmitted through CommCare to DHIS2 and it represents all the clinical information checked at the moment of the consultationData AFG original Routine data V2.0.xlsxData collected during the surveyThis excel file resume the paper checklist used to assess the quality of care of the health workers during the consultation. They are aggregated dataData CRS.xlsx

Maternal and Newborn indicators used for comparison with PAC Data.

Background: Gaps still exist in reducing new HIV infections among adolescent girls and young women (AGYW) aged 10–24 years. High Internet coverage and mobile phone penetration rates present opportunities for the use of mobile health (mHealth) to support access to health services. We present results of an FHI 360 and Zimbabwe Health Interventions-implemented mHealth intervention for reproductive health (RH) and HIV testing service (HTS) referral among AGYW aged 10–19 years between October 2019 and September 2020.Methods: Adolescent girls and young women referred for RH and HTS under the Determined, Resilient, Empowered, AIDS-free, Mentored, and Safe (DREAMS) program had automatic reminders sent to their phones to facilitate access to services through short message service (SMS) and also using a paper-based system. These data were captured in a web-based District Health Information System (DHIS) database, which captured the referral completion status of the AGYW. Data for AGYW referred for RH and HTS for the period October 2018 to September 2019 for the paper-based system and October 2018 to September 2020 for the mHealth were extracted from District Health Information System version 2 (DHIS2) database and analyzed using SPSS to generate descriptive statistics. The Chi-square test was used to assess differences in referral completion rates by age-group; marital status, district, and type of service, as well as differences between mHealth and paper-based referral completion rates within each of the groups for the variables above.Results: A total of 8,800 AGYW referred for RH and HTS, where 4,355 and 4,445 were referred through the mHealth and paper-based systems, respectively. About 95.2% (4,148/4,355) and 87.8% (3,903/4,445) referred through mHealth and the paper-based system, respectively completed referrals. The median time for referral completion was 1 day (Range = 0–9 days) for mHealth and 11 days (Range = 0–28 days) for the paper-based system. AGYW referred through mHealth were 17.995 timesmore likely to complete the referral system than those referred through the paper-based system (OR =17.995; p

Overview of measures used to define each step of the coverage cascade for the different data sources: (1) NDHS and project data and (2) NDHS and DHIS2.

Wound trajectory and outcomes research has applications in different aspects of wound healing: forecasting wound healing time, access and utilization of wound care services, factors associated with disparities in wound care services, and its quality and outcomes. Wound care research benefits from a well-maintained record management system. In this article, we demonstrate the customization of the District Health Information Software (DHIS2) platform to integrate wound care clinical data with social determinants of health from several Comprehensive Wound Centers (CWC) in Indiana. We describe the modules and features of our platform, such as tracker capture, visualization, and maps. DHIS2 is used in more than 60 countries to monitor and evaluate health programs. However, to the best of our knowledge, this is the first attempt to use DHIS2 as a wound care data warehouse, a platform to perform wound care research for academic researchers and clinical practitioners. Clinicians can use the platform as one of the key tools to make an informed decision in determining the treatment for favorable healing trajectory and wound outcomes. We conducted a usability and acceptance survey among researchers at the Indiana Center for Regenerative Medicine and Engineering and found that DHIS2 can be a suitable infrastructure to manage metadata to import and analyze combined data from disparate sources, including Electronic Medical Records, WoundExpert, and clinical trials management software like REDCap.

The COVID-19 pandemic posed unprecedented challenges and threats to health systems, particularly affecting delivery of evidence-based interventions (EBIs) to reduce under-5 mortality (U5M) in resource-limited settings such as Bangladesh. We explored the level of disruption of these EBIs, strategies and contextual factors associated with preventing or mitigating service disruptions, and how previous efforts supported the work to maintain EBIs during the pandemic. We utilized a mixed methods implementation science approach, with data from: 1) desk review of available literature; 2) existing District Health Information System 2 (DHIS2) in Bangladesh; and 3) key informant interviews (KIIs), exploring evidence on changes in coverage, implementation strategies, and contextual factors influencing primary healthcare EBI coverage during March–December 2020. We used interrupted time series analysis (timeframe January 2019 to December 2020) using a Poisson regression model to estimate the impact of COVID-19 on DHIS2 indicators. We audio recorded, transcribed, and translated the qualitative data from KIIs. We used thematic analysis of coded interviews to identify emerging patterns and themes using the implementation research framework. Bangladesh had an initial drop in U5M-oriented EBIs during the early phase of the pandemic, which began recovering in June 2020. Barriers such as lockdown and movement restrictions, difficulties accessing medical care, and redirection of the health system’s focus to the COVID-19 pandemic, resulted in reduced health-seeking behavior and service utilization. Strategies to prevent and respond to disruptions included data use for decision-making, use of digital platforms, and leveraging community-based healthcare delivery. Transferable lessons included collaboration and coordination of activities and community and civil society engagement, and investing in health system quality. Countries working to increase EBI implementation can learn from the barriers, strategies, and transferable lessons identified in this work in an effort to reduce and respond to health system disruptions in anticipation of future health system shocks.