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Rapport de l’analyse des données de routine de la cohorte des femmes enceintes séropositives vues en consultation prénatale jusqu’à l’accouchement

Rapport de l’analyse des données de routine de la cohorte des femmes enceintes séropositives vues en consultation prénatale jusqu’à l’accouchementDepuis la dernière enquête de sérosurveillance sentinelle du VIH chez la femme enceinte qui a eu lieu en 2008, la Côte d’Ivoire utilise, pour ses estimations dans l’outil SPECTRUM, des données de littérature pour le taux d’abandon du traitement VIH chez la femme enceinte séropositive (VIH+). Ces données d’estimation sont ensuite utilisées par le pays pour la planification stratégique en matière de lutte contre le VIH/SIDA. Dans le souci d’améliorer ces estimations cette année, le MSHP a souhaité utiliser les données de routine de la PTME. C’est dans ce cadre que le projet MEASURE Evaluation a apporté une assistance technique au MSHP pour conduite cette étude.

L’objectif de cette étude est d’améliorer l’utilisation des informations relatives au VIH/SIDA pour la prise de décision basée sur l’évidence.

Méthodologie de l’étude : 134 structures de santé réparties dans 62 districts de 19 régions sanitaires ont été sélectionnées sur une base d’échantillonnage raisonné selon un critère. Les données sur une cohorte des femmes enceintes VIH+ sous traitement du dernier trimestre 2016 jusqu’à leur accouchement dans les registres PTME de suivi mère-enfant des sites sélectionnés ont été collectées du 23 avril au 15 mai 2018, puis analysées.

Résultats : Sur un total de 2654 femmes enceintes VIH+ notifiées dans le rapport VIH obtenu à l’issue de la validation des données du dernier trimestre 2016 :

  • 68% des dossiers de femmes enceintes VIH+ ont été retrouvés
  • 96% des femmes enceintes VIH+ ont été mises sous traitement ARV au cours de cette grossesse
  • 94% des femmes enceintes VIH+ sous ARV ont suivi régulièrement le traitement ARV
  • 6% des femmes enceintes VIH+ sous ARV ont pris de façon irrégulière leurs ARV
  • 0.2% des femmes enceintes VIH+ sous ARV ont abandonné le traitement
  • Des informations sur l’accouchement sont disponibles pour 75% des 1799 femmes enceintes VIH+
  • 81,8% des examens par PCR ont été réalisés dans les 6 premières semaines suivant les naissances
  • 4,2% de ces examens par PCR réalisés chez ces enfants exposés étaient positifs.

Utilisation des données : Ces données ont été utilisées dans SPECTRUM et ont permis la validation des estimations 2018 par le Pays et l’ONUSIDA à Genève, les estimations décentralisées par région et par district pour le VIH.

Rapport de l’analyse des données de routine de la cohorte des femmes enceintes séropositives vues en consultation prénatale jusqu’à l’accouchement

Pilot Testing a Gender-Integrated Routine Data Quality Assessment Tool in Kenya

Pilot Testing a Gender-Integrated Routine Data Quality Assessment Tool in KenyaReducing the incidence and impact of HIV in Kenya is a significant priority for the Kenyan government. In addition to increasing access to HIV testing and treatment, addressing the needs of orphans and vulnerable children and reducing the burden of gender-based violence are critical pathways in HIV-prevention efforts. Collecting age- and sex-disaggregated data and gender-sensitive indicators provides fundamental knowledge to assess the needs of diverse populations, their access to services, and the country’s progress toward controlling the HIV epidemic.

MEASURE Evaluation, in collaboration with the United States Agency for International Development and implementing partners, pilot-tested a new tool to collect and analyze information from a gender perspective: Routine Data Quality Assessment, Plus Gender (RDQA+G).

This brief summarizes the results of the RDQA+G pilot test, conducted as part of a larger initiative to assess gender and HIV data quality, build capacity, and identify best  practices for improving data quality in Kenya. Gender-specific results are emphasized here to illustrate the capacity and utility of the modified assessment tool.

Comparative Analysis of Data Quality Assessment Tools

Comparative Analysis of Data Quality Assessment ToolsThe advent of the United States President’s Emergency Plan for AIDS Relief (PEPFAR) and the Global Fund to Fight AIDS, Tuberculosis and Malaria (the Global Fund) 15 years ago brought significantly increased investments in disease control and prevention in developing countries. As more funds became available, so did the need to show returns on investment in the form of public health gains. Monitoring and evaluation (M&E) of interventions is critical for demonstrating the effectiveness of health programs but is dependent on data reported from health facilities that are often of poor quality. Resources have been devoted to improve data quality in health and disease programs, but problems persist as countries struggle to maintain capacity for data management, analysis, and use.

The number of patients on treatment is a very high-profile and useful indicator for monitoring the effectiveness of HIV programs. Treating patients over their lifetime and accurately recording these results is a challenge, however. Longitudinal treatment records (registers) for patients who return repeatedly for treatment and evaluation need to be summarized periodically in static reports. Counting accurately becomes more challenging as patients come and go from active treatment cohorts, move from one site to another, stop treatment as a result of side effects, or become lost to follow-up.

With the advent of “test and start”—an effort to expand the rolls of those on treatment and reduce the “waiting list” (those enrolled in care but not yet on treatment)—more scrutiny has been applied to treatment results, and the findings have not always been up to standard.

Several new tools have been developed to try to meet the need for data quality assurance, particularly for HIV and AIDS. The tools all use similar methods for gauging the accuracy of reporting, though many differences exist between them regarding the objectives and scope of their methodologies. This comparative analysis of data quality tools seeks to aid in the understanding of their similarities and differences as well as the selection of the appropriate tools and methods for assessing and improving data quality within a particular context.

Country-Led, Holistic Data Quality Assurance: Institutionalizing Data Quality through a National Technical Working Group and the Data Quality Review

Country-Led, Holistic Data Quality Assurance: Institutionalizing Data Quality through a National Technical Working Group and the Data Quality ReviewData quality review (DQR) is a method to rapidly evaluate the quality and adequacy of health data used for planning. The DQR aims to institutionalize data quality assessment as a systematic and routine aspect of health sector and program planning and provide a minimum standard of quality for health data. It is intended to be applied across program areas to provide a holistic picture of a country’s data quality from health facility-based information systems and identify areas in need of strengthening. The method and indicators for the DQR have been developed in consultation with international health program experts from leading donor and technical assistance agencies, such as the World Health Organization (WHO), the United States Agency for International Development (USAID), Gavi Vaccine Alliance, and the Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund), with consensus on a minimum standard for data quality.

The DQR is a suite of tools and guidelines. The DQR electronic tools facilitate data collection and analysis. The guidelines provide instructions for collecting the data, preparing the data for analysis, conducting the data verifications, analyzing and interpreting results, and indicating how and when to apply the methods. The electronic analysis tools facilitate data analysis and presentation, as well as the identification of problematic data points and subnational reporting units.

The DQR contributes to the vision of the United States Agency for International Development (USAID) of improving the evidence base for public health monitoring, evaluation, and planning, by improving the quality of routine health data. The USAID- and PEPFAR-funded MEASURE Evaluation assisted in the development of the DQR and tested approaches to improve country ownership and leadership of data quality assurance. A routine, holistic, and country-led system of data quality assurance can help institutionalize data quality in countries. This document provides guidance for establishing a technical working group (TWG) for holistic data quality centered around the DQR. It includes best practices for the TWG as well as implementation steps for the DQR. The TWG is modeled after the successful example of the interagency coordinating committees (ICCs) established for immunization in many countries.

Access the resource.

Promoting Good Data Through a Data Competition in Mali

Promoting Good Data Through a Data Competition in MaliThe quality of health data is fundamental to a health information system (HIS). In 2013, the HIS in Mali was assessed using the Performance of Routine Information System Management (PRISM) tool and it was determined that Mali should deploy an integrated platform to improve the HIS. A plan to strengthen the HIS then became part of the country’s Ten-Year Health and Social Development Program (PRODESS II).

Soon after, in August 2015, the country chose the DHIS 2 platform to house its health data—a big step forward toward a stronger HIS. The platform first was deployed nationwide at the district, regional, and national levels and in the health facility level in 2016. The implementation of DHIS 2 has resulted in improvements in data collection, data transmission, processing, analysis, security, availability, and data quality.

To take advantage of the enthusiasm among decision makers and others in the health sector, and to reinforce the value of good-quality data, Mali initiated a competition in December 2017 among all data producing units at all levels of the health system. The goal was to foster the production of high-quality data, promote excellence in skills, foster a culture of data use, and provide consistency in data management. Other goals were to create friendly competition among health units and to motivate health units to be timely in reporting data.

MEASURE Evaluation, funded by the United States Agency for International Development (USAID), had assisted with the deployment of DHIS 2 and helped plan the competition, assisting representatives from the central level of the health system to establish rules for the competition, judging, and awarding of prizes to the winners.

This brief shares more details.

Monitoring Outcomes of PEPFAR Orphans and Vulnerable Children Programs in Haiti: Zanmi Lasante/Partners in Health 2018 Survey Findings

Monitoring Outcomes of PEPFAR Orphans and Vulnerable Children Programs in Haiti: Zanmi Lasante/Partners in Health 2018 Survey FindingsThe AIDS epidemic in Haiti has left many children in the country vulnerable to HIV, often without parents to care for them. Recognizing the enormous need for programs and services for orphans and vulnerable children (OVC), the United States President’s Emergency Plan for AIDS Relief (PEPFAR) has partnered with the government of Haiti to strengthen services for OVC and their households. Since 2010, PEPFAR OVC support has included services such as HIV testing and linkages to care and treatment, potable water, immunizations, access to healthcare and psychosocial support, provision of school fees and supplies, dietary assessment and nutritional support, HIV prevention and life skills programs, and assistance with income generating activities for foster families and caregivers.

To better understand the effects of its programs on the well-being of OVC, PEPFAR launched a global reporting requirement in 2014 to monitor the outcomes of selected projects in Haiti and other countries where it provides support for OVC. The requirement involves the collection of data for nine outcome indicators, referred to as the PEPFAR monitoring, evaluation, and reporting (MER) OVC essential survey indicators (ESIs). In 2016, the United States Agency for International Development (USAID)/Haiti requested assistance from the USAID- and PEPFAR-funded MEASURE Evaluation project to conduct surveys to collect the required data for two of its ongoing OVC programs: the Zanmi Lasante/Partners in Health (PIH) project, funded through the United States Centers for Disease Control and Prevention, and the USAID Bien Et ak Sante Timoun (BEST) project.

This report presents the findings from the survey that MEASURE Evaluation with its local research partner, Société d’Etudes et de Formation en Information Stratégique (SEFIS), conducted for the PIH project in 2018. Survey results for the BEST project are reported here.

What are the stages of progression to a strong HIS and how are they measured?

What are the stages of progression to a strong HIS and how are they measured?Strong health information systems (HIS) are essential for a country to meet its health goals. Health information is critical for monitoring, tracking, and solving some of the world’s most important health threats. We need to know if we are making progress in eradicating and preventing disease, to plan for and allocate needed resources, and to evaluate the effectiveness of health interventions. A national HIS encompasses all sources of health data to answer these questions and to help a country plan and implement its national health strategy.

Examples of HIS data sources are records on patient care, health facility data, surveillance data, census data, population surveys, vital event records, human resource records, financial data, infrastructure data, and logistics and supply data (MEASURE Evaluation, 2017a). A strong HIS should be well-defined, comprehensive, functional, adaptable and scalable, and resilient. The system should be able to collect, manage, analyze, and disseminate health data in a timely manner so that managers can make decisions, track progress, and provide feedback on HIS performance to improve data quality and use.

To accomplish these tasks, it is essential for HIS stakeholders to know the state of their system on the continuum to a strong HIS and to understand what is needed to achieve an optimized HIS. This document defines five stages of progression to a strong HIS, as described in our HIS Stages of Continuous Improvement (SOCI) tool kit. The five stages are: (1) emerging/ad hoc, (2) repeatable, (3) defined, (4) managed, and (5) optimized.