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Strengthening Multisectoral Community Event-Based Surveillance of Zoonotic Diseases in Senegal – Rapid Assessment of a Global Health Security Agenda Project

Strengthening Multisectoral Community Event-Based Surveillance of Zoonotic Diseases in Senegal – Rapid Assessment of a Global Health Security Agenda ProjectSenegal is committed to the Global Health Security Agenda (GHSA) and International Health Regulations 2005. The government has undertaken several initiatives in this direction, and promoting the One Health initiative is part of that approach. Recently, after approval by the Prime Minister, Senegal established a legal structure for the One Health platform. The national One Health platform is taking several steps to advance the One Health agenda in the country. Government ministries involved in advancing the agenda are Health, Livestock, Environment, Interior, Education, and Gender.

The first phase of MEASURE Evaluation’s GHSA One Health surveillance program is focused on strengthening multisectoral collaboration in community surveillance of six prioritized zoonotic diseases in two regions, Tambacounda (comprising Tambacounda and Koumpentoum districts) and Saint Louis (comprising Pété and Podor districts). The surveillance activity will be implemented in two additional regions during the next phase; these regions will be selected after consulting with the stakeholders. The activities in the One Health program extend the current work of MEASURE Evaluation in these four districts in the community surveillance of eight priority human diseases. This rapid assessment assessed the preparedness of the participating sectors, i.e., health, livestock, and environment in the four project districts. We also expect that a review of similar activities by other partners in the country could help MEASURE Evaluation find opportunities that complement the current project and avoid any duplication.

Our rapid assessment of the health sector revealed that it is well prepared for the implementation of the activity, in terms of the physical infrastructure, staffing distribution, and presence of organized community health volunteer groups in the four project districts. Community groups have recently undergone training in the surveillance of priority human diseases. Additionally, Senegal has the laboratory capacity for the diagnosis of the six prioritized zoonotic diseases.

The livestock sector is prepared in terms of their service delivery points and staff distribution. In addition to the government veterinarians and para-veterinarians, private veterinarians play a significant role in the delivery of services in certain regions, and therefore, must be included in the One Health activities. The private veterinarians work closely with auxiliary livestock agents from the community. These agents could potentially be included in the community health volunteer groups for detecting outbreaks of zoonotic diseases in animals. The central laboratory (LNERV) of Senegal is equipped to diagnose all the six zoonotic diseases; although, the laboratory capacity needs to be strengthened at the regional levels.

Part of the National Park Niokolo-Koba that is managed by the Ministry of Environment extends to one of the project districts, Tambacounda. The Eco-guides and Eco-guards working in these regions represent the community for surveillance activities, and therefore, could be potential members of the community health volunteer groups. The National Park personnel work closely with the Ministry of Livestock for the diagnosis of diseases. When an outbreak or an unusual health event is reported in the National Park, park personnel report the event to the nearest veterinarian, who then follows the routine surveillance pathway Livestock Sector.

Several partners working in community-based surveillance and One Health complement our project activities, including Food and Agriculture Organization (FAO), World Bank, Centers for Disease Control and Prevention (CDC), World Health Organization (WHO), Catholic Relief Services, PATH, EcoHealth Alliance, and One Health East and Central Africa (OHCEA). It would, therefore, be worthwhile to form a Partners’ Forum and schedule regular meetings to exchange information. FAO and OHCEA were identified as two active partners that complement specific activities in the One Health project.

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The Routine Health Information System in Punjab Province, Pakistan – Exploring the Potential for Integrating Health Information Systems for Family Planning Data

The Routine Health Information System in Punjab Province, Pakistan – Exploring the Potential for Integrating Health Information Systems for Family Planning DataBackground: Globally, a health management information system (HMIS) includes both routine and non-routine health data. A routine health information system (RHIS) generates data at regular intervals (no longer than a year) that have been collected at the public and private health facilities and institutions, as well as at community-level healthcare posts and clinics (MEASURE Evaluation, 2017). In developed countries, the RHIS exists in its true essence having both a facility-based and a community-based health information system (CHIS), yet the situation is different in developing countries, such as Pakistan. In Punjab, Pakistan, the HMIS is fragmented as there are more than 20 different HMISs, which use dedicated vertical channels. Among these, three systems gather and transmit information related to family planning (FP)/reproductive health: the District Health Information System (DHIS), the Lady Health Workers-Management Information System (LHW–MIS), and the Contraceptive Logistic Management Information System (cLMIS) which is combined with the Population Welfare Management Program-Management Information System (PWMP–MIS). In addition, nongovernmental organizations (NGOs) have their own HMIS, and there are separate HMISs for countless private hospitals and clinics. Gaps exist in the current RHIS, specifically about reproductive health data from different sources, whether public, private, community or facility-based. These data are not integrated and consolidated into the national HMIS and therefore are not used for decision making.

Objectives: The objective of the study was to review the RHIS in Punjab province of Pakistan and explore the potential for integrating community-level data into the national HMIS, particularly FP data, collected by public or private, for-profit, and not-for-profit organizations.

Methods: The study used both primary and secondary data. Primary data were collected through key informant interviews (KIIs), identified purposively and through snowball sampling technique. Secondary data were gathered through document review including reports, articles, and statistical data.

Findings: Community-based FP data are not fully integrated with RHIS. Some effort has been made to integrate FP data through Contraceptive Performance Report by the Pakistan Bureau of Statistics and the cLMIS, which is an integrated system where data from the DHIS, LHW–MIS, Population Welfare Department (PWD), and influential NGOs are presented and compiled in one form. There is potential for organizing CHIS with RHIS, yet structural barriers exist. For example, there is potential for integration between LHW–MIS and DHIS as they come under the province’s Department of Health (DoH), but it is difficult to integrate data between the DoH and PWD, as PWD has a separate administration and ministry. Nevertheless, though the cLMIS has provided a platform for including data from all public and private entities, several NGOs and public departments do not regularly report their data. In addition, there are several data quality issues in the RHIS which should be addressed before integration occurs, such as: fake entries; incomplete information; dissatisfaction about numbers and types of FP indicators; inaccurate data; duplication of data and services; overreporting; poor feedback mechanisms; and the way reports are consolidated. These issues must be tackled along with integration of CHIS into the RHIS.

Recommendations: To facilitate integration of CHIS with RHIS, the study suggests several recommendations. These include shifting the paradigm from an individual-level healthcare approach to a family-centered approach; promoting a culture and system of inter-organizational information sharing; sensitizing decision-makers about the benefits of interlinking the community-level data streams with RHIS; strengthening the computerized national identity card (CNIC)-based data entry; developing a single dashboard with core FP indicators; and expanding FP indicators beyond commodity-based indicators to psychosocial and behavioral indicators to understand the uptake, switching, and dropping of modern FP methods.

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Capacity Building Interventions in Health Information Systems: Action for Stronger Health Systems

Capacity Building Interventions in Health Information Systems: Action for Stronger Health SystemsMEASURE Evaluation works with global, national, and local partners to strengthen health information systems (HIS) in scores of countries. We have a set of prescribed results to achieve, many of which depend on successful capacity building—such as to strengthen the collection and use of routine health data, improve country-level capacity to manage HIS and conduct rigorous evaluations, and address health information gaps and challenges. We work to build country capacity to generate, manage, and use health information at national and subnational levels; foster country ownership and accountability for HIS; and promote the use of data for decision making.

This synthesis—one of a series produced by MEASURE Evaluation—explores the importance of individual capacity building for people working with HIS and, in turn, how capacity building may help to strengthen HIS and health outcomes, thereby strengthening the health system overall.

Monitoring, Evaluating, and Reporting PEPFAR’s Essential Survey Indicators for Orphans and Vulnerable Children Programs: Supervisor Manual Template

Monitoring, Evaluating, and Reporting PEPFAR's Essential Survey Indicators for Orphans and Vulnerable Children Programs: Supervisor Manual TemplateMEASURE Evaluation developed this supervisor manual template for organizations collecting Monitoring, Evaluation, and Reporting (MER) Orphans and Vulnerable Children (OVC) Essential Survey Indicators (ESI) of the United States President’s Emergency Plan for AIDS Relief (PEPFAR). This supervisor manual includes sections on the data collection team, organizing and supervising interviews, data management, and other procedures for data collection of the nine MER OVC ESI.

Supervisors overseeing enumerators who are implementing the PEPFAR OVC MER ESI questionnaire must follow the highest standards during oversite of data collection. This supervisor manual focuses on documenting the fieldwork procedures for data collection of the nine MER OVC ESI. Groups wanting to implement the OVC MER ESI questionnaire may need to adapt this manual and materials to reflect the aims and design of the specific study. However, the structure of the manual for supervisors should be similar, regardless of study objectives or design. This supervisor manual aims to provide as much guidance as possible for prospective supervisors to provide oversite to enumerators who are implementing the questionnaire.

Access this resource, a related protocal template, and the enumerator manual template.

Characterizing Male Sexual Partners of Adolescent Girls and Young Women in Mozambique: Findings from Focus Group Discussions in Xai-Xai, Beira, and Quelimane Districts

Characterizing Male Sexual Partners of Adolescent Girls and Young Women in Mozambique: Findings from Focus Group Discussions in Xai-Xai, Beira, and Quelimane DistrictsWhile a considerable amount of information is available on the factors that contribute to HIV risk for adolescent girls and young women (AGYW) in Mozambique, little is known about the characteristics of boys and men with whom AGYW engage in sexual activity and how AGYW form sexual partnerships. This knowledge is critical for targeting HIV services to this group of boys and men, and ultimately to reduce the spread of HIV and AIDS among AGYW. To address this knowledge gap, we undertook a study to answer the following research questions:

  • Who are the sexual partners of AGYW?
  • Is sexual risk-taking behavior (namely partner concurrency and unprotected sex) among AGYW and their male partners associated with certain sexual partner characteristics (such as age, education, employment, and income)?

We conducted a total of 15 focus group discussions (FGDs) with 102 AGWY ages 15–24 years in three Mozambique locations: Quelimane, Beira, and Xai-Xai Districts. Each FGD averages six to eight participants; one had only four.

We sampled AGYW with diverse demographic characteristics (such as in-school/out-of-school, married/single, and mother/childless). Our study team convened a committee in each district to devise a recruitment strategy in each location. Local PEPFAR implementing partner organizations recruited study participants from health clinics, schools, and other locations in the community.

This report shares findings from the FGDs.

 

Overviews: Spatial Quality Anomalies Diagnosis (SQUAD) Tool for ArcGIS and QGIS

Overview: Spatial Quality Anomalies Diagnosis (SQUAD) Tool for ArcGISKnowledge about health facility locations is important in addressing HIV, maternal and child mortality, and other issues. As a result, there has been a rapid growth in large geospatial data sets, such as master facility lists (MFLs). An MFL and other similar lists typically contain locations of health facilities as well as attributes of the facilities—including name, address, or which administrative unit the facility is located in.

Assessing the quality of these data sets can be challenging, because there are two types of possible errors: spatial errors and attribute errors. Assessing spatial errors involves looking at such things as the presence of a coordinate, whether it is properly recorded, and the accuracy of its location. Assessing attribute errors involves determining whether attributes such as site name or site ID are correct.

Overview: Spatial Quality Anomalies Diagnosis (SQUAD) Tool for QGISWhen you work with large spatial data sets, manually reviewing each record to validate both location and attribute information can be prohibitively time-consuming. A more effective approach would be to look for anomalies in the data that may indicate data quality issues. MEASURE Evaluation’s Spatial Quality Anomalies Diagnosis (SQUAD) tool identifies six types of anomalies in spatial data.

These fact sheet provide overviews of how to use the SQUAD tool with ArcGIS v10.5 with an advanced license and with QGIS2.18 or QGIS 3.0.

MEASURE Evaluation Modelo de Fortalecimiento del Sistema de Información en Salud: Un Resumen

MEASURE Evaluation Modelo de Fortalecimiento del Sistema de Información en Salud: Un ResumenMEASURE Evaluation ha elaborado un modelo para fortalecer los sistemas de información en salud (SIS) en países de ingresos bajos y medianos. El modelo de fortalecimiento de los sistemas de información en salud es un punto de partida para contextualizar lo que sabemos ahora y las oportunidades que tenemos para aprender más sobre el fortalecimiento del SIS. Este modelo explica la comprensión actual de MEASURE Evaluation y nos guía a medida que continuamos aprendiendo cómo los SIS en los países de ingresos bajos y medianos recursos se diseñan, desarrollan e implementan a lo largo del tiempo para apoyar los sistemas de salud y mejorar sus resultados. Un SIS se define ampliamente para abarcar todas las fuentes de datos de salud, incluidos los datos de las instalaciones de salud y de la comunidad recopilados como parte de los SIS de rutina o los sistemas de información de gestión de salud; registros electrónicos de salud para la atención del paciente; datos basados en la población; información de recursos humanos; información financiera; información de la cadena de suministro; e información de vigilancia. Nuestro modelo incluye todo tipo de información que puede usarse para la toma de decisiones en el sector de la salud. Este modelo fue elaborado en colaboración con expertos de todo el mundo, utilizando el Marco de la Red de Métricas de Salud (HMN—por sus siglas en inglés) como base (HMN, 2008) para abordar cuatro objetivos clave: (1) promover los SIS como una función esencial de un sistema de salud, (2) definir el fortalecimiento, (3) medir el desempeño, y (4) monitorear y evaluar las intervenciones del sistema de información en salud.

MEASURE Evaluation agradece los comentarios acerca del modelo de fortalecimiento de los sistemas de información en salud y compartirá actualizaciones a través de MEASURE Evaluation HIS Strengthening Resource Center: https://www.measureevaluation.org/his-strengthening-resource-center/.

MEASURE Evaluation Modelo de Fortalecimiento del Sistema de Información en Salud: Un Resumen