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Module 2 Scholar Practitioner Project:
Reduction of Infant Mortality Rate in the United States
Shelby Ballard
PhD in Public Health, Walden University
PHLT 8270-1
Health Informatics and Surveillance
Dr. Gonzalez
February 5
th
, 2023
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Reduction of Infant Mortality Rate
Reduction of Infant Mortality Rate in the United States
In 2019, over 20,000 infants died prior to their first birthday in the United States (Mortality and Morbidity, 2020). While this is a remarkable decrease in infant mortality since the
1980’s, mainly due to medical advancements, there is still significant work to be done outside of technological intervention (Shapiro-Mendoza et al., 2016). Unfortunately, significant racial and socioeconomic disparities in healthcare still exist (Shapiro-Mendoza et al., 2016). Because of this, Healthy People 2030 initiatives have set a goal to continue to reduce the infant mortality rate (Reduce the rate of infant deaths, n.d.). The infant mortality rate is one of the life expectancy
measurement tools used in the United States (Pabayo et al., 2019). The rate is defined as the number of infant deaths that occur prior to their first birthday per 1000 live births (Pabayo et al., 2019). The most recent data collected in 2019 shows that the nation’s infant mortality rate is currently 5.6 deaths per 1000 live births, which is targeted to be reduced to 5.0 by 2030 (Reduce the rate of infant deaths, n.d.).
Importance and Public Health Priorities of Infant Mortality Rate
Although the nation’s infant mortality rate has declined over the last several decades within the United States, survival rate for babies under the age of one year old is significantly low when compared to other high-income countries (Bairoilya and Fink, 2018). In a recent study done to compare the United States with six European countries, data showed that infants born in the United States are up to 200 times more likely to experience mortality (Bairoilya and Fink, 2018). The findings of this study were that there is a major gap in effective state, regional, and national policies in the United States and major improvements are needed in order to see significant decreases to the nation’s overall infant mortality rate (Bairoilya and Fink, 2018).
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While the previous study did not specify which US policies would benefit from higher priority, it did present the importance of focusing on infant mortality rate, especially when compared to other countries that do not struggle with infant survival (Bairoilya and Fink, 2018). Another study recognized the significant gap in social intervention and public health policies within the state of California (Ratnasiri et al., 2020). Researchers analyzed birth and death records through the state’s public health department in order to identify predictors that could be the focus for future initiatives to decrease infant mortality rates (Ratnasiri et al., 2020). While the
results of the study showed an overall decrease in infant mortality between 2007 and 2015, they also revealed substantial inequalities among various population groups (Ratnasiri et al., 2020). For example, data from this study showed that African American infants had twice the risk of mortality when compared with those of Caucasian origin (Ratnasiri et al., 2020). In addition, children of mothers without college degrees or of lower socioeconomic status were also at higher
risk for mortality (Ratnasiri et al., 2020). Overall, the study concluded that major interventions are needed to support current sociodemographic, economic and behavioral disparities among pregnant mothers within California (Ratnasiri et al., 2020). Specifically, the focus should be on inspiring and supporting women into higher education degrees, employment opportunities, and increasing their overall quality of life through their socioeconomic status (Ratnasiri et al., 2020). At the time of this study, California did have public health initiatives in place to support pregnant
women, however, many were medical education programs focused on risk of obesity and smoking on pregnancy and overall infant health (Ratnasiri et al., 2020). However, other states are
already making improvements to support women of racial and sociodemographic minority. The Healthy People 2030 initiative noted addition of programs in Georgia to improve birth outcomes (Reduce the rate of infant deaths, n.d.). These programs concentrate on unbiased and high-
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quality healthcare for all moms and babies, regardless of race/ethnicity, income, and geographic location (Reduce the rate of infant deaths, n.d.).
Reducing preterm birth and the overall infant mortality rate has been and currently is a public health priority within the United States for several decades (Shapiro-Mendoza et al., 2016). While medical developments have improved infant survival, high-risk population groups continue to suffer healthcare disparities (Shapiro-Mendoza et al., 2016). The Centers for Disease Control and Prevention recommends that state and regional government agencies work together to collect data through surveillance systems in order to implement and monitor strategies that pertain to their demographic population (Shapiro-Mendoza et al., 2016). Many of these initiatives include improving access to and quality of maternal healthcare from preconception to delivery (Shapiro-Mendoza et al., 2016). However, it is imperative that these monitoring systems
and prevention strategies to reduce infant mortality rate are consistently monitored by public health departments and government agencies to locate high-risk population groups and support these women through the entirety of pregnancy in order to succeed (Shapiro-Mendoza et al., 2016).
Methods of Reduction of Infant Mortality Rate in the United States
One of the most standard, basic measurements used for public health examination in various countries around the world is infant mortality (Mathews and Driscoll, 2017). In the United States, the Centers for Disease Control and Prevention’s Division of Reproductive Health
specifically monitors infant mortality in order to expand the nation’s understanding of the issue (Data and Statistics, 2022). The infant mortality rate is calculated yearly in the United States and is used in a variety of public health surveillance systems as it reflects both maternal and infant health during pregnancy and a year thereafter (Mathews and Driscoll, 2017). The overall goal of
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these public health surveillance systems is to identify significant issues that could be the reflection of impact from various economic, socio-cultural, and environmental influences, as well as other external factors (Health United States Appendixes, 2019). Without proper surveillance and evaluation of this data, infant mortality rate in the United States will continue to
be an issue. This project uses datasets from linked birth and death records, provided by the National Vital Statistics System (Health United States Appendixes, 2019). The datasets include all infant deaths under the age of one as reported on death certificates, as well as all live births reported from birth certificates within the chosen time intervals (Health United States Appendixes, 2019). The datasets produced by infant birth and death records are essential information as they are the primary dataset for analyzing infant mortality trends (Health United States Appendixes, 2019). In
addition, it is the only source of surveillanced data within the United States that examines the effect of race and ethnicity, as well as other contributing factors, on infant mortality (Health United States Appendixes, 2019). Surveillance systems for infant mortality became increasingly prevalent in 2005, which is marked as the most recent year that the infant mortality rate significantly peaked (Health United States Appendixes, 2019). Since then, national, regional, and
state agencies have examined and researched a widespread of contributing factors in order to determine trends in increasing rate and develop further support in areas with increased needs (Health United States Appendixes, 2019). One example of this is the Pregnancy Risk Monitoring
System, or PRAMS (PRAMS, 2022). This surveillance program is financially supported by the Centers for Disease Control and Prevention (CDC), as well as state and local departments around
the United States in order to gather data on maternal, fetal, and infant health (PRAMS, 2022). This project is unique in that it provides risk-assessment indicators by state to allow for more
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population-based monitoring and intervention in hopes to decrease the nation’s maternal and infant mortality rates (PRAMS, 2022). From information provided from the CDC’s Division of Reproductive Health and PRAMS surveillance systems, demographic location and a biological mother’s nationality tend to be the most prominent socioeconomic factors that are investigated. However, it is unclear from either system if the two predictor variables are codependent. Therefore, it is hypothesized that maternal ethnicity and race, as well as state of residence during infant birth influences infant mortality rate. Defining Infant Mortality with Surveillance Systems
No matter which surveillance system is analyzed, the definition and statistical analysis of infant mortality rate remains the same (Data and Statistics, 2022). The rate calculates the number
of deaths among children under one year old per every 1,000 live births during a given time interval (Data and Statistics, 2022). Two surveillance systems from 2016 and 2020 will be evaluated for this project (Linked Birth Infant Death Records, n.d.). The datasets will originate from the National Center for Health Statistics’ National Vital Statistics System located on the CDC’s website (Linked Birth Infant Death Records, n.d.). While the calculations of infant mortality rate have remained the same, expanded data on several external factors were introduced in 2020 (Infant Death Data Summary, 2022). Therefore, the focus of this paper will be to compare methods of data collection from infant mortality rate in 2016 to the infant mortality rate including the extended data in 2020 (Linked Birth Infant Death Records, n.d.).
Ethnicity
The 2016 dataset for linked infant birth and death records provides four Hispanic origin categories (Infant Deaths, 2022). However, those of non-Hispanic origin were given the option
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Reduction of Infant Mortality Rate
of white, black, other races, or unknown (Infant Deaths, 2022). As of 2016, all states implemented the 2003 revised birth certificate standards, which separates Hispanic origin and race, which provides more accurate data in terms of nationality within infant mortality (Infant Deaths Data Summary, 2022). There are two main Hispanic origin categories on the 2020 dataset, including Hispanic or latino or not Hispanic or latino (Infant Deaths Data Summary, 2022). For those who are of Hispanic origin can then choose from expanded categories as the ones used in the 2016 records (Infant Deaths Data Summary, 2022). Additionally, the 2020 surveillance system also included an additional Hispanic origin category (Infant Deaths Data Summary, 2022). In the 2016 dataset, persons of Dominican origin were included in the “other Hispanic” category (Infant Deaths Data Summary, 2022).
Race
Most surveillance systems for any type of data including race use the 1997 OMB Standards, which limit mothers to select one of six races on a newborn’s birth certificate (Infant Deaths, 2022). These standards are what is used on the 2016 linked infant birth and death records
for infant mortality rate (Infant Deaths, 2022). However, by 2020, states had converted to the 2003 revision of the standards, which provides 15 single-race categories and 31 single/multi-race
categories, including expanded Asian and Pacific Islander options (Infant Death Data Summary, 2022). Demographic Location by State
Both 2016 and 2020 dataset provide national, regional, and divisional summary counts of
infant deaths, live births, and infant death rates (Stats of the States, 2022). State and county records are also available to be selected separately if desired (Stats of the States, 2022).
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However, starting in 2020, data is available by urbanization category for counties based on the 2006 and 2013 urbanization classification schema (Infant Death Data Summary, 2022).
Considerations within Surveillance Systems in the United States
Ethnicity and Race
While the overall infant mortality rate in the United States is 5.8 as of 2020, there are significant discrepancies in race and ethnicity (Jang and Lee, 2022). However, specific racial and
ethnic populations have been examined since the 1990’s, and the infant mortality rate has decreased since then (Jang and Lee, 2022). Surveillance system data has continued to improve in
terms of accurately evaluating ethnic and racial groups, as seen with the addition of several categories between the 2016 and 2020 surveillance system datasets (Infant Death Data Summary,
2022).
Demographic Location by State
The location used in these surveillance systems is based on mothers’ legal residence recorded on the birth certificate at the time of the infant’s birth (Infant Death Data Summary, 2022). Demographic characteristics like mother’s residing state at time of birth are used to aid in identifying trends in infant death rate (Infant Mortality, 2022). Location is a nominal, categorical
variable because it cannot be changed, and there are more than a couple of categories that does not require a specific order. While no central tendency measurement tool is used for this variable
within the specified datasets, mean is used to calculate the national infant mortality rate from all rates from the 50 states.
Surveillance System Weaknesses
Recent reporting modifications for race and ethnicity has caused issues when analyzing these variables with infant mortality rate (Infant Death Data Summary, 2022). For example, in
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Reduction of Infant Mortality Rate
the 2020 dataset, many corrections were needed to Hispanic origin in order to provide more accurate data (Infant Death Data Summary, 2022). In addition, any variations to the instructions for these variables causes the data to go into “unknown” category, which can also cause reporting trends (Infant Death Data Summary, 2022).
For events to be included in the linked file for these datasets, both the birth and the death must occur within the 50 states and/or District of Columbia (Infant Death Data Summary, 2022). Additionally, births occurring within the United States to a mother who is a resident of a foreign country are excluded from this surveillance system (Infant Death Data Summary, 2022). In addition, any state or county that reports fewer than 20 deaths within the specified time interval will not have a calculated rate due to lack of reliability and precision (Infant Death Data Summary, 2022). Results of Reduction of Infant Mortality Rate in the United States
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Figure 4
HHS Regions in the United States
Note.
The HHS regions of the United States include all 50 states, as well as Puerto Rico, the Virgin Islands, American Samoa, Commonwealth of the Northern Mariana Islands, Federated States of Micronesia, Guam, Marshall Islands, and Republic of Palau. HHS Region Offices, by Office of Intergovernmental and External Affairs (IEA), 2021 (https://www.hhs.gov/about/agencies/iea/regional-offices/index.html) In the public domain.
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Analysis and Interpretation of Results from Infant Mortality Rate Data
The goal of the digital dashboards is to provide end users with visual presentations of the data collected (Cheng et al., 2011). By doing this, end users can make quick interpretations of the
presented data in the forms of various figures, tables, and charts (Cheng et al., 2011). The main visual representation chosen for this digital dashboard was clustered bar charts. These graphs were chosen to represent this dataset for various reasons. First, the hypothesis includes multiple variables, including infant mortality rate, maternal race and ethnicity, and demographic location. Other visual options for this dashboard would solely focus on one variable rather than multiple variables and the associations between them. Another reason clustered bar charts were chosen is the visual appeal. While it was an option to provide this data in a table format, this was abandoned because of its lack of visual attractiveness to the end user. Clustered bar charts allow the same data to be presented in a way that is not overwhelmed by numerical values, which would not give the end users the same experience to quickly examine the data and create an informed interpretation.
The data collected was presented in clustered bar charts in two different methods to give end users multiple visual aids to create an interpretation. Figure 1 offers visualization into the comparison of infant mortality rate by maternal bridged race and Hispanic origin for both 2017 and 2020. The significance of this figure is to visualize the association of maternal race and ethnicity and infant mortality rate with datasets collected from two different years. Figure 2 provides infant mortality rates from 2017 by maternal race and Hispanic origin, organized by demographic location. Figure 3 provides the same data chart as Figure 2, but for 2020 data rather
than 2017. Figure 4 is a map of the HHS regions provided by the Office of Intergovernmental and External Affair.
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For this project, it is hypothesized that a biological mother’s ethnicity and race, as well as
the regional location of residence within the United States, influences infant mortality rate. When
looking at the digital dashboards, non-Hispanic black/African American mothers show double the rate of infant mortality when compared to the other presented races in both 2017 and 2020. Overall, Figure 1 shows the consistency of infant mortality rate between 2017 and 2020 when shown in terms of maternal race and Hispanic origin. When looking at regional data for infant mortality rate, regions 1, 2, and 10 have the lowest overall infant mortality rate when analyzing data based on maternal race and Hispanic origin. Regions 3, 5, and 7 have higher over infant mortality rates when comparing maternal race
and Hispanic origin, and when looking at the digital dashboard, it is clear that infants of non-
Hispanic black/African American mothers have the highest mortality rate, causing a significant increase in these regions. However, this data analysis does not come without limitations. When looking at Figure 1,
maternal non-Hispanic other races has the second highest mortality rate of the bridged race and ethnic origins analyzed for this project. Using other races in this dataset, and therefore the analysis, threatens validity of the overall association of maternal race and Hispanic origin tio infant mortality rate because it is not clear where the infants of this category belong.
Recommended Strategies to Improve Infant Mortality Rate Surveillance
Advanced Machine Learning
Recent studies regarding infant mortality rate have found that using artificial intelligence methods rather than conventional methods provide more accurate data in regards to risk factor determination (Mfateneza et al., 2022). More specifically, Advanced Machine Learning has been
found to provide more accurate predictions of infant mortality in many areas across the world
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(Mfateneza et al., 2022). This branch of artificial intelligence is superior to traditional methods in
that it is more flexible when producing predictions and can uncover patterns within the data using algorithms that can assist in the analysis of risk factor determination (Mfateneza et al., 2022). Many studies have used machine learning to not only to determine who is at risk for mortality, but when certain populations are at higher risk (Mfateneza et al., 2022).
Machine learning has also been used to analyze predictions of infant mortality using data on usage of social service programs and health services (Peet et al., 2021). The significance of this analysis is to understand the disconnect between the social and medical services within communities, and which populations are at risk because of it (Peet et al., 2021). Inputting data into the machine learning method for this type of analysis can provide researchers with predictions of infant mortality rate and social service program involvement, as well as outcomes regarding both health and social services on infant mortality rate (Peet et al., 2021).
Machine learning methods are fairly user-friendly with the availability of online software like Python, which can apply machine learning to various models, including Random Forest, Decision Tree, Support Vector Machine, Logistic Regression, and many more (Mfateneza et al., 2022). The significance of applying this method into multiple models is to see which has the best
prediction ability based on the data and factors provided (Mfateneza et al., 2022). Data analysis for machine learning can also be performed using IBM SPSS statistics programs as well as STATA-16.0 (Saroj et al., 2022). Using machine learning techniques allows faster data analysis in multipole models within the risk of human error, as seen with conventional methods (Saroj et al., 2022). Geospatial Analysis
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Another increasingly popular technological tool for surveillance and other studies is geospatial analysis. Geospatial analysis allows identification of geographical inequalities and focus areas for various diseases and health conditions and can even help determine possible reasons for such inequalities (Alemu et al., 2022). Specifically, geospatial analyses have been used in previous studies around the world to develop and initiate policies based on determinants of infant death, especially in high-risk areas (Canuto et al., 2018). Results from geospatial analyses can pinpoint areas in need of significant intervention and potential gaps in healthcare availability and accessibility (Alemu et al., 2022). Geospatial analysis requires data engineering and many other specific tasks in order to prepare data for visual analysis (Forrest, 2022). Spatial data and analysis is a fairly complex technological tool, and therefore, has generally been the responsibility of trained geospatial teams, comprised of data engineers, data scientists, analysts, developers, and many other essential individuals (Forrest, 2022). The traditional way of requiring a skilled team to create geospatial analysis is not realistic for the increasing demands, so some companies are creating software that are user friendly for anyone with any skillset (Forrest, 2022). For example, CARTO created a workflow that is operational for any individual, which allows more analyses (Forrest, 2022). This specific workflow gives access to data warehouses as well as other high-
quality analysis tools like Builder Maps (Forrest, 2022). Future Research and Surveillance for Infant Mortality in the United States
Machine Learning
A recent study done in Rwanda used machine learning methods rather than standard conventional regression analyses in order to achieve a more accurate prediction of infant mortality throughout the country (Mfateneza et al., 2022). Using machine learning methods in
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the United States with data already being collected by the Centers for Disease Control and Prevention could allow better prediction of infant mortality using various factors including environmental, biodemographic, and socioeconomic (Mfateneza et al., 2022). In addition, machine learning methods could be used to determine the efficacy of interventions and current policies for maternal and infant health, when compared to different populations based on various socioeconomic and environmental factors (Peet et al., 2021). Geospatial Analysis Geospatial analysis can be used to create a complete epidemiological profile, including various determining factors of infant mortality rate in order to create and initiate policies, especially in high-risk areas (Canuto et al., 2018). This can be useful for policymakers from the national to the local level (Canuto et al., 2018). When analyzing data at a national level, it is clear that the national infant mortality rate is greatly diverse throughout the country (Canuto et al., 2018). Therefore, geospatial analysis can be used to determine what social, demographic, and
environmental factors at state and local areas are impacting mortality (Canuto et al., 2018).
Positive Social Change Implementations from Infant Mortality Rate Surveillance
Recent attention has been put on infant mortality rate in the United States by researchers and policymakers, primarily because of the lack of progress in the last several years when compared to other countries (Shapiro-Mendoza et al., 2016). While the last several decades have been dedicated to overall medical care for infants, recent interventions are addressing social determinants of health in order to reduce disparities (Infants, n.d.). For example, previous interventions focused on breastfeeding rates and promoting vaccines (Infants, n.d.). However, the
national infant mortality rate has yet to be significantly impacted (Shapiro-Mandoza et al., 2016).
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Through collaboration efforts at national, regional, state, and local levels, social policies and programs have begun to be implemented throughout the country (Shapiro-Mandoza et al., 2016). While there is a lot of work yet to be done, there are several programs and service initiatives that have been established. For example, Mamatoto village, originally based in Washington D.C., is a non-profit organization that focuses on supporting pregnant women of color and their families both medically and socially from the first ultrasound through postpartum appointments (Taylor et al., 2019). The organization is based around community support in hopes to empower other women of color to become professionals in the field (Taylor et al., 2019). Across the country, programs like Head Start and Early Head Start have been developed in order to offer support and education for families (Taylor et al., 2019). These programs offer parenting classes, crisis intervention, services for postpartum depression and other mental health conditions, and can also assist with housing options in high-risk communities (Taylor et al., 2019). Universal home visit programs have also been established in various areas of the United States that offer free home visits by a registered nurse and/or a parenting coach to families of newborns following their stay at a local hospital (Taylor et al., 2019). These programs have significantly improved family support and the use of community resources as well as increased maternal and infant health and wellbeing to set them up for a better future (Taylor et al., 2019). Using surveillance techniques to analyze infant mortality rate in the United States has led to significant indications of social inequalities and exposes the weaknesses present within the current healthcare and social services throughout the country (Canuto et al., 2018). Although important programs have been established, these services need to be expanded over the entire country in substantial numbers in order to see significant changes to infant mortality rate in the United States (Taylor et al., 2019).
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Reduction of Infant Mortality Rate
Taylor, J., Novoa, C., Hamm, K., & Phadke, shilpa. (2019, May 2). Eliminating racial disparities in maternal and infant mortality
. Eliminating Racial Disparities in Maternal and
Infant Mortality. Retrieved from https://www.americanprogress.org/article/eliminating-
racial-disparities-maternal-infant-mortality/
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