Skip to content

Reproductive Health Cost Reporting System

Reproductive Health Cost Reporting SystemProgram managers, finance directors, and funders often want to know the cost of delivering services. To answer this, programs often undertake or commission cost studies. Such studies take time, can be expensive, and provide snapshot information of a point in time. Organizations frequently collect service delivery data and track expenditures on human resources and labor and medical supplies, including pharmaceuticals and other regularly incurred office and equipment expenses. Yet these data are rarely assessed together. A better way may be to harness the basic information already available to an organization in real time—housed in its tracking systems and service delivery records. That is where the Reproductive Health Cost Reporting System (RHCRS) can assist.

This resource provides an overview of the RHCRS. A user guide is also available.

Engagement de la communauté dans la revue et l’amélioration des services de santé en utilisant les données du système national d’information sanitaire (SNIS)

Engagement de la communauté dans la revue et l’amélioration des services de santé en utilisant les données du système national d’information sanitaire (SNIS)Les activités de revue des données du SNIS pour les niveaux communautaires ont pour but de mettre en place un cadre de concertation et une synergie d’action entre les acteurs sanitaires au niveau local et communautaire. Il s’agit d’un projet pilote centré sur le renforcement des capacités et sur la promotion de l’utilisation des données qui vise l’implication et l’engagement des agents de santé communautaire, des comités de santé et d’hygiène (COSAH), et des centres de santé dans la gestion des programmes et services de santé. Ce projet pilote se déroule au travers des réunions de revue de données pour la prise de décision basée sur des données de qualité.

National health information system data review activities at the community level aim to put in place a framework for consultation and coordinated action between health actors at the local and community level in Guinea. This pilot project focuses on capacity building and promoting data use and aims for the implication and the commitment of community health workers, health and hygiene committees (COSAH), and health centers in the management of health programs and services. This pilot project takes place through data review meetings for data-driven decision-making.

Access the resource in French.

Integrating Family Planning Data from Public and Private Health Facilities in Malawi: How Current Approaches Align with FP2020 Goals

Integrating Family Planning Data from Public and Private Health Facilities in Malawi: How Current Approaches Align with FP2020 GoalsIntroduction: Family planning (FP) data from public and private health providers in Malawi is not integrated. The country’s 2016 costed implementation plan  review of progress indicated a modern methods contraceptive prevalence rate of 45 percent, far below the 60 percent goal. However, this figure excludes data from private facilities, which provide up to 40 percent of the health care in Malawi.

Objectives: The objective of this study was to find approaches to improve the national health information system by integrating FP data from private-sector service delivery points and government facilities. This research aligns with MEASURE Evaluation’s approach of addressing health information systems holistically

Methods: A qualitative approach brought both primary and secondary data sources into the analysis. Primary data were collected through key informant interviews and field observations. The study targeted three main actors from the private sector: Christian Health Association of Malawi (CHAM) facilities; Banja La Mtsogolo (BLM) clinics, a Marie Stopes International (MSI) franchise; and Population Services International (PSI) and its franchising clinics and pharmacies.

Findings: Both private and public institutions make a significant contribution toward provision of FP services, even though they do not always provide the same FP methods. A system is in place for dataflow from private facilities to the nearest government facility for consolidation in monthly reports to be included in DHIS 2. However, this system faces multiple challenges.

Recommendations: To integrate FP data generated by private facilities in the government system, we recommend conducting periodic meetings between the DHOs and private hospitals to share data, instituting proper systems for consolidating shared data, and harmonizing the private health facilities’ data management systems with the government system. Furthermore, the DHOs must take responsibility for encouraging private service providers to share their data for a minimum set of indicators.

Conclusion: Because both public and private facilities provide FP services, FP data integration is an important step toward improving site-level health services, a goal shared by the Government of Malawi and MEASURE Evaluation.

Access the resource.

Measuring the Pathway to Better Outcomes for Children Affected by HIV

TMeasuring the Pathway to Better Outcomes for Children Affected by HIVhe United States President’s Emergency Plan for AIDS Relief (PEPFAR) engaged the United States Agency for International Development (USAID)- and PEPFAR-funded MEASURE Evaluation project to develop and support the rollout of an overarching framework that outlines the pathway toward better outcomes for children affected by HIV. The logic model, designed by MEASURE Evaluation, complements and incorporates a series of benchmarks for assessing achievement of household case plans and determining the readiness of orphans and vulnerable children (OVC) households to exit from OVC programs through graduation. The logic model serves several purposes:

  • Graphically illustrates the relationship among PEPFAR reporting requirements (e.g., SIMS, MER 2.0, Graduation Benchmarks) and where they “fit” in the pathway
  • Demonstrates OVC programming contributions to the 95-95-95 cascade and how to help OVC programs and USAID missions tell their OVC stories more compellingly
  • Provides supplementary process and output indicators that can be monitored to ensure that implementation occurs as intended and beneficiaries are getting sufficient exposure to program interventions. These indicators will provide actionable data so that course corrections can be made in a timely manner.

This logic model is a resource for countries wishing to develop custom indicators to support program monitoring across an OVC country portfolio.

Renforcement organisationnel pour un Système d’Information Sanitaire durable

ARenforcement organisationnel pour un Système d’Information Sanitaire durableu regard des insuffisances identifiées à la suite de l’évaluation du Système National d’Information Sanitaire (SNIS) en 2014, il est apparu primordial d’organiser le SNIS autour d’une vision stratégique. Cette vision stratégique est partagée dans une perspective systémique pour offrir aux usagers et intervenants du secteur de la santé une base de travail et un référentiel unifié. Le Bureau de Stratégie et de Développement (BSD) du Ministère de la Santé de Guinée, en collaboration avec ses partenaires techniques et financiers, a produit un plan stratégique 2016–2020 pour renforcer le SNIS. A la fin de l’année 2017, le BSD, en collaboration avec les partenaires et les représentants de tous les niveaux du système sanitaire, a passé en revue le progrès réalisé à la mi-parcours du plan stratégique. Les groupes ont produit un rapport des solutions, interventions et recommandations selon les axes stratégiques pour le plan opérationnel de 2018.

In view of the shortcomings identified following the evaluation of Guinea’s National Health Information System (NHIS) in 2014, it seemed essential to organize the NHIS around a strategic vision. This strategic vision is shared in a systemic perspective to provide users and stakeholders in the health sector with a unified frame of reference. The Bureau of Strategy and Development (BSD) of the Ministry of Health of Guinea, in collaboration with its technical and financial partners, has produced a strategic plan for 2016-2020 to strengthen the NHIS. At the end of 2017, the BSD, in collaboration with partners and representatives from all levels of the health system, reviewed the progress made at the mid-point of the strategic plan. The groups produced a report of the solutions, interventions, and recommendations along the strategic lines for the 2018 operational plan.

Access the resource in French.

Spatial Quality and Anomalies Diagnosis (SQUAD) Tool for QGIS Quick Start Guide

Spatial Quality and Anomalies Diagnosis (SQUAD) Tool for QGIS Quick Start GuideQGIS is a free and open-source geographic information system (GIS) software. The Spatial Quality and Anomalies Diagnosis (SQUAD) tool is a QGIS plug-in that will assess data quality of large spatial data sets. Because it can be difficult to perform data quality checks on large data sets, the SQUAD tool rapidly and automatically looks for anomalies in the data that may indicate data quality issues.

The tool requires two data sets (in the form of shapefiles): one consisting of point locations and the other consisting of polygons that represent administrative units. The tool will review the point locations and identify one of six anomalies:

  1. Missing coordinates
  2. Truncated coordinates (lack of adequate precision)
  3. Duplicate coordinates for distinct records
  4. Duplicate key attributes (two identical names, but plotting in different locations)
  5. Coordinate not located exactly where it would be expected (but falling within two kilometers of a border)
  6. Coordinate not located anywhere near where expected

This quick start guide provides a general overview of the use of the tool with the sample data available for the tool. A more detailed overview is available on the MEASURE Evaluation web site, here:

A Stronger Health Information System Means a Healthier Country

A Stronger Health Information System Means a Healthier CountryThis poster illustrates how the quality and availability of health data can improve health outcomes. The illustration depicts a health system with only limited data function and what kinds of capabilities that health system would possess. It then shows what additional functions might be possible for a health system that had additional data at hand; and, finally, what kinds of health questions and trends could be identified with still more available and robust data.