Many different sectors have programs that address anemia; they rely on a variety of delivery mechanisms and platforms to reach their intended recipients. This guidance includes information on interventions to address both the immediate and underlying causes of anemia. Given the specifics of the anemia prevalence in your country and the burden of the various causes, you may identify certain interventions as a higher priority than others. It is important to remember that implementing effective interventions depends partly on the policy context in your country (see Step 3: Review Anemia Policies).
The interventions listed in Table 1 are described in more detail on the following pages; including information on collecting and analyzing data related to the intervention. We recommend that you explore the data that are available to you on each intervention, including its delivery platform. The information will help you understand the current strength of your country’s anemia prevention and control programs. Knowing the poorly performing interventions can help you identify actions to improve your country’s anemia outcomes. Data on interventions are often available through surveys (to understand coverage or use), routine data collection (to understand provision or supply), or other ad hoc data collection mechanisms.
Table 1: Anemia interventions, organized by sector
Water, Sanitation, and Hygiene
How to include this information in your landscape analysis
Your landscape analysis should include information on the interventions that have the potential to reduce anemia. Document which interventions are being planned or already exist. For the existing interventions, include information on the coverage of the intervention. Coverage—the measure of an intervention’s success—calculates the percentage of eligible individuals who received the service. To illustrate the variations in your country, you can also include graphs of the coverage for each intervention by target group, or by various characteristics. Looking at the coverage of these interventions, over time, may help you determine if any trends are related to reductions in the prevalence of anemia.
In the sections that follow, we include a number of methodological issues that are important for each of the interventions. However, there are a number of considerations for reviewing anemia intervention data, which we discuss here.
- Carefully note the dates of your data sources. Routine population-based surveys like Demographic and Health Surveys or Multiple Indicator Cluster Surveys are widely available data sources, but they are only collected every five or more years and may not accurately reflect the current coverage rates. Similarly, reporting rates for administrative data may be slow and may only be available during multiple reporting periods, after the data were collected.
- Coverage is difficult to estimate if you rely on administrative data for the numerator (number of people reached) or denominator (number of eligible people in the population).
- Administrative data used to estimate coverage are often based on tally sheets or other handwritten records, frequently relying on the summation of totals by hand. These data sources are, therefore, subject to human error and may also have incomplete or delayed reporting.
- Accurate denominator estimates (i.e., number of infants 6–11 months of age or pregnant women) can be difficult to access, which will influence coverage estimates. Inaccurate population estimates will lead to inaccurate coverage estimates. Therefore, if you are calculating coverage based on estimated denominators, make sure the basis for the denominator is documented and reported.
- Coverage estimates based on the health monitoring information system, or other routine data, will only capture individuals who sought or received treatment through the public health care system. As a result, these data may underestimate coverage. You may consider highlighting populations that you think may have been left out of the estimates. While intervention coverage can also be collected through administrative data, representative, population-based surveys are probably the most reliable source of data.
- Information available through the country’s health monitoring information system can be useful, although its quality will vary, depending on the robustness of the design of the health monitoring information system and the in-country capacity for monitoring.
- Interventions that rely on campaign-based distribution, such as high-dose vitamin A supplementation or antihelminth treatment, may use administrative data or post-event coverage surveys to estimate coverage, often relying on comparisons between the two to identify any over- or underestimation.
- To estimate coverage, you can compare administrative data collected during the distribution campaign against the total target population. Human error in the collection and summation of administrative data, and reliance on population estimates, mean that administrative data can result in significant overestimation of program coverage and it should be validated, when possible, with population-based survey data (Nyhus Dhillon et al. 2013).
- Post-event coverage surveys take place within a month of the last distribution round and they collect recall data on receipt of supplements or medications during a specific period—timed to coincide with the latest distribution campaign. These surveys can be expensive, but they can be useful to assess coverage estimates from administrative data. Although the surveys face the challenge of recall bias when compared with administrative data, changes to the questions asked—such as showing pictures or asking about other services provided in mass campaigns—can improve the respondents recall (Ouédraogo et al. 2016). Post-event coverage survey data may also be available for parts of the country; Lot Quality Assurance Sampling methodology can reduce costs and highlight performance against a target (The Global Alliance for Vitamin A 2016).
- Supply chain issues can have a dramatic effect on intervention performance. Including data on supply system performance will be just as important for many of these interventions as information on coverage. Health management information systems, Service Provision Assessments, or other facility-based assessments may collect data on anemia-related supplies (e.g., deworming medication, micronutrient supplements, bed nets), including stockouts or other supply-related issues that may affect their distribution. Reviewing logistics management information systems may also be useful in gauging the availability of anemia-related supplies at distribution points, forecasting necessary supplies, and identifying formulations or specifications of each product provided in your country.
- A number of anemia-related interventions use common delivery platforms to reach eligible populations, such as antenatal care for iron-folic acid supplementation and intermittent preventive treatment during pregnancy, or child health days for deworming and high-dose vitamin A supplementation. Understanding how well these platforms work will help you better understand the performance of each intervention. Therefore, your data collection should include indicators like attendance at antenatal care clinics or number of children reached by child health days, when relevant. Sometimes, improving the performance of an intervention may require changes to the delivery platform, in addition to modifying the services.
- Implementing anemia-related interventions is often the endpoint of a long chain of events. Improvement of an intervention may rely on reviewing supply processes, strengthening a delivery platform, or improving provider training. While indicators of these integral issues are not included in the discussion below, you should keep these issues in mind when conducting your landscape analysis.
Nyhus Dhillon, Christina, Hamsa Subramaniam, Generose Mulokozi, Zo Rambeloson, and Rolf Klemm. 2013. “Overestimation of Vitamin a Supplementation Coverage from District Tally Sheets Demonstrates Importance of Population-Based Surveys for Program Improvement: Lessons from Tanzania.” PloS One 8 (3): e58629. doi:10.1371/journal.pone.0058629.
Ouédraogo, Césaire T., Elodie Becquey, Shelby E. Wilson, Lea Prince, Amadou Ouédraogo, Noël Rouamba, Jean-Bosco Ouédraogo, Stephen A. Vosti, Kenneth H. Brown, and Sonja Y. Hess. 2016. “Factors Affecting the Validity of Coverage Survey Reports of Receipt of Vitamin A Supplements During Child Health Days in Southwestern Burkina Faso.” Food and Nutrition Bulletin 37 (4): 529–43. doi:10.1177/0379572116666167.
The Global Alliance for Vitamin A. 2016. “Vitamin A Supplementation Regional Symposium Report.” Dakar, Senegal. http://www.vas2016symposium.org/index.php.