Strengths and Limitations of Secondary Data Sources involving Pregnancy

SECONDARY DATA SOURCES 1

Healthcare Research: Strengths and Weaknesses of Secondary Data

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 for causes related to the pregnancy’s management (Hoyert, 2022). Maternal mortality rates are measured by the number of maternal deaths in every 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 Diseases 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 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 analyse the relationship between these causes and maternal mortality, three data sets were obtained.

Evaluation of Dataset One

The first data set was obtained from the WHO and focuses on antenatal care coverage. The link to the data set is included in the references section of this text. The data provides antenatal care coverage rates across all countries between 2002 and 2019 (WHO, 2021b). The data measures antenatal care coverage by the proportion of pregnant women who made at least four antenatal visits in the course of their pregnancy. It 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 low antenatal care access could 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 set does not indicate how many times other researcher have used the above data for their studies. However, Tripathy (2013) points out, that researchers have alternative ways of assessing the integrity of secondary data. One of these is looking at the accuracy of the methodology used in collecting the data. The data in this case was collected by the WHO Centers for Health Equity Monitoring. Further, the data 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 Dataset, 2021). This triangulation of data sources enhances the accuracy of the data collected.

A second limitation of the data in this set is that it does not provide all the information that the researcher would need. This forces users to search for other relevant complementary data from other sources to draw conclusions. For instance, the only data available is that of the percentage of pregnant women who make at least four antenatal visits by country. Data on the annual maternal mortality rates for each of the years is, however, not available in the data set. Thus, a researcher wishing to make comparisons at a glance between antenatal care access and maternal mortality rates is unable to do so. According to Tripathy (2013), this is because the data was not collected to address the present research questions, but to meet the aims of the researcher then.

Evaluation of Dataset Two

The second data set is obtained from the CDC and looks at influenza vaccination coverage among pregnant women by state. The link to the data set is provided in the references section (CDC, 2022). The variable of interest in the data set is influenza vaccination coverage rates among pregnant women. When making their decision, the researcher will compare the state data on vaccination rates (independent variable) with the state maternal mortality rates (dependent variable). This would provide a means to determine whether influenza vaccination influences maternal mortality rates. However, as is the case with the first data set, this data set does not provide information on states’ maternal mortality rates and the researcher is forced to obtain the same from a different source. This would not be the case if the researcher was using primary data as they would design their data collection instrument to provide answers on all variables of interest to the study.

All the same, the data set could be regarded as valid and credible. First, since its creation in 2021, the data has attracted over 1,800 views and approximately 299 download from researchers. The data owner, the National Center for Immunization and Respiratory Diseases, issues a disclaimer that the data estimates may not be accurate due to the small sample size (CDC, 2022). However, users have the option of contacting the data set owner to obtain more clarity and the methodology, which helps to build confidence in the data.

Analysis of Dataset Three

The third data set is obtained from the WHO and looks at the percentage of pregnant mothers by country who sleep under insecticide-treated nets to minimize their risk of exposure to Malaria. Malaria increases the risk of neonatal death, low birth-weight, premature delivery, intrauterine demise, miscarriage, and maternal death (Schantz-Dunn & Nour, 2009). The variable of interest from the data set is the percentage of women sleeping under insecticide-treated mosquito nets by country (WHO, 2021a). The researcher will then obtain data on maternal mortality rates for all countries and compare whether there are significant differences in maternal mortality between countries with high rates of insecticide-treated nets usage and those with low usage.

The provided data could be considered credible despite the fact that the owners do not provide information on its use in research studies. The first indicator of validity is the accuracy of the data collection processes and overall methodology. As is the case with the first data set, the owners of this data set bring together data from a variety of credible surveys that are publicly available, which helps to build confidence in the data (WHO, 2021a). Further, the data was collected recently, in 2021, implying that it is relevant for addressing the current research questions.

Conclusion

In summary, secondary data has its share of advantages such as being readily available, cheap to collect, convenient, and time-saving. However, researchers using secondary data often face several challenges as identified in the analysis of the three data sets above. First, it may be difficult to ascertain the validity and integrity of the data, and the user often has to rely on certain 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 forced to obtain prior authorization to access the data, which may cause delays, and in cases where the owner does not grant access, the researcher may have to forego the data altogether. The best practice when using secondary data, therefore, would be to ensure that one begins their study early enough so that they can obtain all the necessary authorizations for the date they may need in time.