22 Data Collection Methods
Accurate data collection is essential for understanding malaria trends and responding effectively. Different data collection methods are used in malaria programs, each serving unique roles in gathering comprehensive information across diverse environments. This section explores the primary methods used to collect malaria data and highlights their strengths, limitations, and applications in malaria control and elimination efforts.
22.1 Routine Health Facility Reporting
Routine health facility reporting is a cornerstone of malaria surveillance, providing continuous data on malaria cases and treatments from healthcare facilities. In this system, health providers record cases as patients seek care, testing for malaria when symptoms are present and reporting confirmed cases.
Strengths:
Cost-Effective: Routine reporting leverages existing healthcare infrastructure, making it one of the most affordable surveillance options.
Large Coverage: Health facilities serve large populations, providing a significant volume of data in high-burden areas.
Limitations:
Underreporting in Low-Access Areas: Populations in remote or underserved areas may not access healthcare, resulting in underreported malaria cases.
Data Quality Issues: In resource-limited settings, errors in data recording and reporting are common due to staff shortages, limited training, or lack of standardized reporting tools.
Applications:
Routine data is essential for tracking trends in malaria cases and evaluating the effectiveness of interventions. It provides a broad understanding of malaria burden in areas where healthcare access is available, though it may miss cases in harder-to-reach populations.
Routine health reporting data is the cornerstone of understanding malaria case trends.
Every year the World Malaria Report brings together all the data reported by countries to WHO, including their routine surveillance data. For countries that do not have adequate surveillance systems, WHO produces estimates of cases and deaths.
22.2 Community-Based Surveillance
Community-based surveillance (CBS) involves engaging community health workers (CHWs) to monitor malaria cases within their communities. CHWs often work in areas with limited healthcare access, extending the reach of malaria programs by identifying cases that might not otherwise be reported.
Strengths:
Enhanced Reach in Remote Areas: CHWs can access remote and underserved populations, improving surveillance coverage.
Early Detection of Localized Outbreaks: CHWs live within communities and are often the first to notice changes in malaria incidence, enabling faster response to outbreaks.
Limitations:
Resource-Intensive: CBS requires training, supervision, and supplies for CHWs.
Data Consistency: Ensuring consistent data collection can be challenging, especially if CHWs lack standardized reporting tools.
Applications:
- CBS is particularly valuable in rural and low-transmission settings, where it improves malaria detection and complements facility-based reporting.
22.3 Cross-Sectional and Sentinel Surveys
Surveys are used to collect malaria data at specific points in time, providing snapshots of malaria prevalence, treatment coverage, and vector presence. Surveys are typically conducted at regular intervals and can target specific populations, such as children under five.
Cross-Sectional Surveys:
Demographic Health Surveys (DHS) and Malaria Indicator Surveys (MIS) collect data on malaria indicators such as prevalence, treatment coverage, and bed net ownership.
Advantages: These surveys provide detailed data across large populations and are valuable for evaluating program impacts over time.
Limitations: Cross-sectional surveys are expensive and labor-intensive, so they are usually conducted every few years rather than continuously.
Sentinel Surveillance:
Sentinel surveillance uses select health facilities (sentinel sites) to monitor malaria trends more frequently. These sites are often strategically located to represent various malaria transmission settings.
Advantages: Sentinel sites provide high-quality data at regular intervals, which is useful for tracking changes in transmission patterns or drug resistance.
Limitations: Sentinel data is limited to specific locations and may not represent the entire country or region.
Applications:
- Surveys are key for evaluating malaria program impacts, tracking changes in prevalence, and assessing intervention coverage. Sentinel surveillance is useful for monitoring drug and insecticide resistance.
The Demographic and Health Survey (DHS) and the Malaria Indicator Survey (MIS) are both household-based surveys that collect data on malaria. They can be used together to provide a more comprehensive picture of malaria behaviors and the factors that influence them. Both the DHS and MIS are designed to be nationally representative, with sample sizes typically ranging from 5,000 to 30,000 households.
These are complex surveys but their data is freely available to use for analysis.
You can explore the results of the surveys through:
The written reports produced from each survey at a country level - the reports will provide a comprehensive overview of the results and the methods of that years specific survey.
StatCompiler- a tool that allows users to create custom tables, charts, and maps using demographic and health data from over 90 countries. The tool is designed to help users compare data across countries and over time. For tutorials on how to use StatCompiler see the DHS Website.
The rdhs package is a free R package that helps users analyze data from the Demographic and Health Survey (DHS). This includes functionality to:
Access standard indicator data (i.e. DHS STATcompiler) in R via the DHS API.
Identify surveys and datasets relevant to a particular analysis.
Download survey datasets from the DHS website.
Load datasets and associated metadata into R.
Extract variables and combining datasets for pooled multi-survey analyses.
The Guide to DHS Statistics - this guide has two main purposes: To explain how statistics produced in Demographic and Health Surveys (DHS) reports are defined and calculated and serve as a reference document for researchers. To provide an overview of the structure and use of DHS datasets. It is a good place to start for understanding indicators.
In addition the DHS YouTube Channel has a tonne of helpful videos that help explain the complexities of using and working with survey data and methodological approaches for analysis - a great starting point for understanding the surveys.
22.4 Other Data Sources
In addition to the primary methods, other data sources contribute to malaria surveillance by capturing information that impacts malaria transmission and control.
Laboratory Data: Laboratories provide data on confirmed malaria cases and drug resistance, often used to monitor trends in diagnostic testing and resistance.
Travel Histories: Data on travel histories help identify cases of imported malaria, which is important in low-transmission areas nearing elimination.
Weather and Environmental Data: Weather conditions, such as rainfall and temperature, impact mosquito breeding and malaria transmission. Integrating meteorological data can improve outbreak prediction and intervention planning.
Entomological Data: Monitoring mosquito populations, species distribution, and insecticide resistance is essential for understanding transmission dynamics and adjusting vector control strategies.
22.4.0.1 Data Collection Challenges
Each data collection method faces unique challenges that can impact the quality and utility of malaria surveillance data.
Limited Access and Coverage: In many regions, particularly rural and conflict-affected areas, data collection is hindered by limited healthcare access, security issues, or geographical barriers.
Resource Constraints: Surveillance systems require resources for training, supervision, transportation, and supplies. Resource constraints can limit data collection efforts.
Data Standardization and Consistency: Different data sources and reporting systems may use varied formats, creating challenges in merging and analyzing data consistently. In addition variables and indicators can change in their definition or reporting structure over time making consistent analysis difficult.
Timeliness of Data Collection and Reporting: Delays in data collection and reporting can impact response times and reduce the effectiveness of surveillance activities.
22.5 Innovations in Malaria Data Collection
Advances in technology are helping address some of these challenges, making data collection more efficient and accurate.
Mobile Health (mHealth) Technologies: mHealth applications enable CHWs to report cases in real-time, improving the timeliness and accuracy of community-based surveillance.
Digital Health Records: Electronic health records improve data quality and accessibility, making it easier to track patient histories and treatment outcomes.
Remote Sensing and GIS: Remote sensing tools, such as satellite imagery, provide environmental data that support malaria mapping and prediction models.