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Brief on the Routine Health Information System Curriculum

fs-17-204coverThe USAID-funded MEASURE Evaluation project developed a new online curriculum on routine health information systems (RHIS), working with other leaders in the field of RHIS—the World Health Organization; the Free University of Brussels/European Agency for Development and Health (AEDES); the University of Oslo, in Norway; the National Institute of Public Health (INSP), in Mexico; the University of Queensland, in Australia; and the Public Health Foundation of India.

RHIS (also called health facility and community information systems) regularly generate data that have been collected at public and private health facilities and institutions, as well as at community-level healthcare posts and clinics. The purpose of this curriculum is to enhance participants’ capacity to conceptualize, design, develop, govern, and manage an RHIS, and use the information the system generates to improve public health practice and service delivery.

One one-page flier describes the curriculum and provides links to its various components.

Effects of individual, household and community characteristics on child nutritional status in the slums of urban Bangladesh

Effects of individual, household and community characteristics on child nutritional status in the slums of urban BangladeshBackground
Bangladesh urban population is expected to overtake rural population by 2040, and a significant part of the increase will be in slums. Wide disparities between urban slums and the rest of the country can potentially push country indicators off track unless the specific health and nutrition needs of the expanding slum communities are addressed. The study aims at describing the individual, household and community determinants of undernutrition status among children living in major urban strata, viz. City Corporation slums and non-slums, in order to understand the major drivers of childhood undernutrition in urban slum settings.

Data are derived from Bangladesh Urban Health Survey conducted in 2013. This survey is a large-scale, nationally representative of urban areas, household survey designed specifically to provide health and nutrition status of women and children in urban Bangladesh.

Data showed that 50% of under-5 children in slums are stunted and 43% are underweight, whereas for non-slums these rates are 33 and 26% respectively. In terms of severity, proportion of under-5 children living in slums severely underweight or stunted are nearly double than the children living in non-slums. Logistic analyses indicate that mother’s education, child’s age, and household’s socio-economic status significantly affects stunting and underweight levels among children living in the urban slums. Logistic models also indicate that all individual-level characteristics, except exposure to mass media and mother’s working outside home, significantly affect undernutrition levels among children living on non-slums. Among the household- and community-level characteristics, only household’s socioeconomic status remains significant for the non-slums.

Poor nutritional status is a major concern in slum areas, particularly as this group is expected to grow rapidly in the next few years. The situation calls for specially designed and well targeted interventions that take into account that many of the mothers are poorer and less educated, which affects their ability to provide care to their children.

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Availability and Use of Sex-Disaggregated Data in Tanzania: An Assessment

tr-16-132-coverMEASURE Evaluation has been working to support the Government of Tanzania at national and subnational levels to ensure data quality for sex-disaggregated and gender-sensitive data, and to better use data from routine health information systems (RHIS) for health and social service program and policy decision making.

In support of these efforts, MEASURE Evaluation-Tanzania conducted a data and gender assessment of the national and subnational RHIS to understand the current availability and use of sex-disaggregated and gender-sensitive indicators.

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Evaluation Research on Results-Based Financing

An Annotated Bibliography of Health Science Literature on RBF Indicators for Reproductive, Maternal, Newborn, Child, and Adolescent Health

Evaluation Research on Results-Based Financing: An Annotated Bibliography of Health Science Literature on RBF Indicators for Reproductive, Maternal, Newborn, Child, and Adolescent HealthThis annotated bibliography offers a critical review of peer-reviewed and gray literature, published between 2002 and 2016, and relevant to indicators for the monitoring and evaluation of results-based financing (RBF) initiatives for reproductive, maternal, neonatal, child, and adolescent health (RMNCAH). Unlike a systematic review, this annotated bibliography does not aim to be a comprehensive assessment of the research on RBF for health. Rather, it seeks to describe the conceptual contribution and practical experiences of experts in using indicators to assess performance and quality throughout RMNCAH-focused RBF schemes.

The review includes peer-reviewed articles, toolkits, technical briefs, case studies, and evaluation reports. Microsoft PowerPoint presentations, posters, and books are not included. Articles were identified via key informant interviews, online database searches, and website reviews. For Google Scholar and the PubMed databases, search terms were indicators, results based financing, performance based financing, performance based funding, quality, reproductive, maternal, neonatal, child, and adolescent health. Gray literature was identified through searches of the following regional, multilateral, and donor websites: RBFHealth, World Bank, World Health Organization, BlueSquare, United States Agency for International Development (USAID), USAID TRAction Project, Salud Mesoamerica Initiative, Pan American Health Organization, the United Nations Children’s Fund, and the USAID-funded MEASURE Evaluation. Review of the listed references of pertinent articles yielded additional resources.

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Understanding Data Demand and Use in Kenya – Successes and Challenges in Kakamega, Kilifi, and Kisumu Counties

ddu-kenyaEvidence-based decision making is essential for the success of health systems, programs, and services. Global commitments to improving health systems and outcomes have led to improved monitoring and evaluation (M&E) and better health information systems, thus providing an opportunity to use data for decision making and not simply for reporting. MEASURE Evaluation has developed a conceptual approach and logic model that guides the health sector in adopting best practices in data-informed decision making and data use.

Overall, the relationship between improved information, demand for data and continued data use creates a cycle that leads to improved health programs and policies. Improving data demand and use is necessary to make a health system more effective and sustainable.

Data demand and use (DDU) is a core component of MEASURE Evaluation PIMA’s objectives to strengthen M&E at the national and subnational levels of Kenya’s health care system. The DDU strategic approach is the foundation of the overall goal of the PIMA project to build sustainable M&E capacity to use quality health data for evidence-based decisions and program planning in the following six areas: malaria; civil registration and vital statistics; reproductive health; referral systems strengthening; disease surveillance; and orphans and vulnerable children.

At the beginning of the PIMA project, the M&E Capacity Assessment Tool (MECAT) was used to determine the M&E capacity of PIMA beneficiaries at the national and county levels. Findings from the MECAT showed that across all counties where PIMA was going to provide support over the project lifetime, no data use strategies existed and some counties only had a data use approach mentioned in a strategic plan or draft M&E work plans. At the national level, data use infrastructure was weak since most national programs did not have guidelines or plans on data use.

Following the mid-term review of the project in Year 3, PIMA set out to conduct a DDU learning exercise in Year 4 to provide data on the extent to which select counties have integrated data for decision making into routine programming and planning processes.

PIMA provided extensive technical assistance and support to each of the three counties selected for this exercise. This support included the formation of M&E technical working groups (TWGs), assistance with data review meetings, assistance with program planning and budgeting, and training in data demand and use tools and approaches.

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Tidy Data and How to Get It

tidy data slider-min

By John Spencer, MA, Senior GIS Technical Specialist, MEASURE Evaluation

You’re ready to sit down with a newly-obtained dataset, excited about how it will open a world of insight and understanding, and then find you can’t use it. You’ll first have to spend a significant amount of time to restructure the data to even begin to produce a set of basic descriptive statistics or link it to other data you’ve been using.

If you’ve had this experience, you’ve run into an untidy dataset.

Untidy data is a mess. The variable names are weird; observations are stored in columns when they should be in rows; time series data is recorded so that it’s difficult to calculate elapsed time. Bottom line:  When data is untidy, a separate effort will likely be required before you can use it for any analysis.

In global health, untidy data is a problem that can undermine the effectiveness of even the strongest health information system and introduce a barrier to effective data use. As health systems grow, it is as important that data coming out of the system is as easy to work with as the data going into the system. This is because more and more people rely on the data for programmatic decisions, specialized analysis, and linking with other systems. Tidy data is essential to a strong health system.

“Tidy data” is a term meant to provide a framework for producing data that conform to standards that make data easier to use. Tidy data may still require some cleaning for analysis, but the job will be much easier. The concepts behind tidy data were described in a 2014 paper: “Tidy Data” by Hadley Wickham.[1] He describes three fundamental attributes of tidy data:

  1. Each variable forms a column
  2. Each observation forms a row
  3. Each type of observational unit forms a table

Untidy data most often look like this:

  • Column headers are values, not variable names
  • Multiple variables are stored in one column
  • Variables are stored in both rows and columns
  • Multiple types of observational units are stored in the same table
  • A single observational unit is stored in multiple tables

I’ve run across all of these issues over the years. It’s such a common problem that people reference the 90/10 rule—90 percent of your effort will be getting the data ready and only 10 percent will be analysis and mapping. There are those who refer to it as the 80/20 rule, but they’re the optimists among us.

Where does untidy data come from? I’ve usually found it originates in two ways:

  1. Data in independent systems such as national health information systems, donor reporting systems or other government ministries are designed to serve that organization’s needs but little consideration is given to how the data can be integrated with data from other systems.
  2. Data are originally tidy but become untidy when they are exported. For instance, data stored in a routine health information system is well structured but becomes untidy when it is exported to an Excel file or CSV file.

Resolving both of these issues requires an increased awareness of tidy data among data providers and data users. Data providers can make their data available in one well-defined export format that conforms to tidy data standards. Data users might still need to make some modifications to the data but it’s much easier to do that when the data is tidy to begin with.

Tidy data are an important component of fully realizing the potential that exists as data proliferate. Until the world is rid of untidy data, here are some tools that can help with the tidying:

Jean-Nicholas Hould provides an overview of tools in Python programming language. Find it at (

The statistical programming language R is a great tool for data analysis and for tidying data. There are several packages available in R that can help with tidy data. Tidyr by Hadley Wickham is probably the best one to start with. Find it at

More information about the R programming language in general can be found at and there are many good R tutorials on the web.

Excel is not necessarily the best tool to tidy data but it can do some things. Microsoft describes how to clean data and includes some plug-ins that could be helpful at

And, you’ll also find a good overview of some useful functions at

For more information

MEASURE Evaluation is funded by USAID to strengthen capacity in developing countries to gather, interpret, and use data to improve health. For more information on the project’s work in data science, visit:

And for useful information on tidy data and the importance of privacy and confidentiality regarding geospatial data, see these two FAQs from MEASURE Evaluation.

Frequently Asked Questions about Geographic Information Systems – Tidy Data: The Key to Success with Spatial Data – Tips on Data Structure

Frequently Asked Questions about Geographic Information Systems – Using Spatial Data Wisely and Ethically: Privacy and Confidentiality 

Master of Public Health (MPH) program at IIPH-Delhi

The public health system in India can be strengthened through the presence of well-trained and competent public health professionals. Qualified public health professionals are a necessity to achieve the Sustainable Development Goals. Such an inter-disciplinary public health workforce can also advance the vision of Universal Health Care. The
aim of the MPH program at the Indian Institute of Public Health – Delhi (IIPH-Delhi) is to make public health education and research activities relevant to India in content and context, while attaining standards which are qualitatively comparable with the best in the world.

IIPH-Delhi is pleased to announce its call for applications for the second batch of Master of Public Health being offered in the academic years July 2017 through June 2019, affiliated with Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST) Trivandrum, an Institute of National Importance under the government of India.