Secondary Data Sources for Maternal Mortality Research
This paper evaluates three secondary datasets used to investigate contributors to maternal mortality in the United States and globally. Using the World Health Organization's definition of maternal mortality, the paper examines datasets covering antenatal care coverage, influenza vaccination rates among pregnant women, and insecticide-treated net usage. For each dataset, the paper assesses credibility, validity, methodological accuracy, and key limitations — particularly the challenge of missing complementary variables. The analysis concludes with a broader reflection on the advantages and disadvantages of using secondary data in public health research, offering practical guidance for researchers navigating these challenges.
- Introduction: Maternal Mortality as a Public Health Issue: Defines maternal mortality and presents key US statistics
- Evaluation of Dataset One: Antenatal Care Coverage: Assesses WHO antenatal care dataset credibility and gaps
- Evaluation of Dataset Two: Influenza Vaccination Among Pregnant Women: Evaluates CDC vaccination coverage dataset strengths and limits
- Evaluation of Dataset Three: Insecticide-Treated Net Usage: Reviews WHO insecticide-treated net usage dataset validity
- Conclusion: Advantages and Limitations of Secondary Data: Summarizes secondary data trade-offs and best practices
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What makes this paper effective
- Applies a consistent evaluation framework — methodology accuracy, data completeness, and credibility indicators — across all three datasets, giving the analysis a coherent structure.
- Balances acknowledgment of each dataset's strengths with an honest discussion of its limitations, demonstrating critical thinking rather than uncritical acceptance of official sources.
- Grounds abstract claims about secondary data in concrete examples, such as the missing maternal mortality rate variable in two of the three datasets.
Key academic technique demonstrated
The paper demonstrates source triangulation as an evaluative strategy: rather than simply accepting datasets at face value, it examines how the original data collectors themselves combined multiple credible surveys (e.g., Demographic and Health Surveys, Multiple Indicator Cluster Surveys) to strengthen validity — and then applies the same logic to assess whether the resulting secondary data is trustworthy for a new research purpose.
Structure breakdown
The paper opens with a focused introduction defining the public health problem and presenting key statistics. Three parallel body sections each evaluate one dataset using the same criteria. A brief conclusion synthesizes lessons learned about secondary data use in research. The structure is straightforward and replicable, making it a useful model for comparative dataset evaluation assignments at the undergraduate level.
Introduction: Maternal Mortality as a Public Health Issue
The public health issue selected for analysis is maternal mortality. The World Health Organization (WHO) defines maternal mortality as death during pregnancy or within 42 days of a pregnancy's termination due to causes related to the pregnancy's management (Hoyert, 2022). Maternal mortality rates are measured by the number of maternal deaths per 100,000 live births (Hoyert, 2022). In 2020, the maternal mortality rate in the US was 23.8 per 100,000 live births, up from 20.1 per 100,000 live births in 2019 (Hoyert, 2022). According to the Centers for Disease Control and Prevention (CDC), 861 women died of maternal causes in the US in 2020, up from 754 in 2019 (Hoyert, 2022). There are many contributing causes of maternal mortality, including lack of proper antenatal care, failure to obtain influenza vaccination during pregnancy, and failure to sleep under insecticide-treated nets. To analyze the relationship between these causes and maternal mortality, three datasets were obtained and evaluated.
Evaluation of Dataset One: Antenatal Care Coverage
The first dataset was obtained from the WHO and focuses on antenatal care coverage. The data provides antenatal care coverage rates across all countries between 2002 and 2019 (WHO, 2021b). Antenatal care coverage is measured by the proportion of pregnant women who made at least four antenatal visits during their pregnancy. The data provides a means to compare antenatal coverage rates in the US and other developed nations. If antenatal care coverage in the US is lower than in other developed countries, one could conclude that limited antenatal care access may be a contributor to maternal mortality. According to the WHO (2021b), antenatal care increases access to effective maternal health interventions that reduce the risk of maternal mortality.
The metadata does not indicate how many times other researchers have used this data in their studies. However, Tripathy (2013) points out that researchers have alternative ways of assessing the integrity of secondary data. One such method is examining the accuracy of the methodology used in data collection. In this case, the data was collected by the WHO Centers for Health Equity Monitoring and was obtained through a re-analysis of multiple credible surveys, including the Reproductive Health Survey, Multiple Indicator Cluster Surveys, and the Demographic and Health Surveys (WHO, 2021b). This triangulation of data sources enhances the accuracy of the collected data.
A notable limitation of this dataset is that it does not provide all the information a researcher would need. This forces users to seek complementary data from other sources in order to draw conclusions. For instance, the only available data is the percentage of pregnant women who made at least four antenatal visits, organized by country. Data on annual maternal mortality rates for each corresponding year is not included in the dataset. As a result, a researcher wishing to make direct comparisons between antenatal care access and maternal mortality rates cannot do so at a glance. According to Tripathy (2013), this occurs because the data was not collected to address the present research questions, but to meet the aims of the original researcher.
Evaluation of Dataset Two: Influenza Vaccination Among Pregnant Women
The second dataset is obtained from the CDC and examines influenza vaccination coverage among pregnant women by state (CDC, 2022). The variable of interest is influenza vaccination coverage rates among pregnant women. A researcher would compare state-level vaccination rates (the independent variable) with state maternal mortality rates (the dependent variable) to determine whether influenza vaccination influences maternal mortality rates. However, as with the first dataset, this dataset does not include information on states' maternal mortality rates, requiring the researcher to obtain that data from a separate source. This limitation would not arise if the researcher were using primary data, as they would design their own data collection instrument to capture all variables of interest.
Despite this limitation, the dataset can be regarded as valid and credible. Since its creation in 2021, the data has attracted over 1,800 views and approximately 299 downloads by researchers. The data owner, the National Center for Immunization and Respiratory Diseases, issues a disclaimer that the data estimates may not be fully accurate due to small sample sizes in some states (CDC, 2022). However, users have the option of contacting the dataset owner to obtain further clarification on the methodology, which helps to build confidence in the data's integrity.
Conclusion: Advantages and Limitations of Secondary Data
In summary, secondary data offers several advantages, including being readily available, inexpensive to collect, convenient, and time-saving. However, researchers using secondary data often face significant challenges, as identified in the analysis of the three datasets above. First, it may be difficult to ascertain the validity and integrity of the data; the user often has to rely on indirect indicators, such as the number of times the data has been downloaded for use in research studies. In other cases, the researcher may have limited access to the data they need, particularly when the data owner is a private organization. In such cases, a researcher may be required to obtain prior authorization to access the data, which can cause delays. If access is denied, the researcher may have to forego that data source entirely.
The best practice when using secondary data, therefore, is to begin the study early enough to secure all necessary authorizations well in advance of the research timeline. Careful planning helps ensure that data availability issues do not impede the research process.
References
CDC. (2022). Vaccination coverage among pregnant women. Centers for Disease Control and Prevention. Retrieved from https://data.cdc.gov/Pregnancy-Vaccination/Vaccination-Coverage-among-Pregnant-Women/h7pm-wmjc/data
Hoyert, D. (2022). Maternal mortality rates in the United States, 2020. Centers for Disease Control and Prevention. Retrieved from https://www.cdc.gov/nchs/data/hestat/maternal-mortality/2020/E-stat-Maternal-Mortality-Rates-2022.pdf
Schantz-Dunn, J., & Nour, N. (2009). Malaria and pregnancy: A global health perspective. Reviews in Obstetrics and Gynaecology, 2(3), 186–192.
Tripathy, J. (2013). Secondary data analysis: Ethical issues and challenges. Iranian Journal of Public Health, 42(12), 1478–1479.
World Health Organization. (2021a). Pregnant women sleeping under insecticide-treated nets. Retrieved from )
World Health Organization. (2021b). Antenatal care coverage: At least four visits. Retrieved from https://www.who.int/data/gho/data/indicators/indicator-details/GHO/antenatal-care-coverage-at-least-four-visits
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