This graph depicts the overall rates for poor infant outcomes in Butler County (BC), Ohio, and the United States (USA). You can use the drop down to select to view 'Infant Mortality Rate' (deaths per 1,000 live births), 'Percent of Babies Born Premature' (number of babies per 100), or 'Percent of Low Birth Weight Babies' (number of babies per 100). Note that Ohio and USA include Butler County in their calculations. The * signifies that data is only available for USA up to 2016.

Overall, Butler County's IMR has been decreasing since 2013. In 2017, the IMR was lower than the Healthy People 2020 goal of 6.0 deaths per 1000 live births. The rate in 2013 was almost 10 deaths per 1000 live births, while in 2017 the rate had dropped to 5.2 deaths per 1000 live births. Despite the 2016 spike in the number of babies born at a low weight, there has been an overall decrease in the percent of babies born in Butler County at a low birthweight. Over the five years of data included, the percentage of babies born prematurely has remained within a 1% range in Butler County.

This graph focuses on how the economic and social standing of a mother influences the outcome of their child's birth. You have the option to use the drop down to categorize the percentage of poor birth outcomes ('Percent of Babies Born Premature' or 'Percent of Low Birth Weight Babies') by various socioeconomic factors selected by the socioeconomic drop down ('Payer Type', 'WIC Recipient', 'Education Level' or 'Marital Status'). The size of the dot represent the population size. Remember, larger populations tend to be less susceptible to change over time, meaning small changes have larger effects on smaller populations.

This graph focuses on how the health of a mother impacts the outcome of their child's birth. You have the option to use the drop down to categorize the percentage of poor birth outcomes ('Percent of Babies Born Premature' or 'Percent of Low Birth Weight Babies') by various health factors selected by the health drop down ('Mother's Age', 'Smoking Status', or 'Sexual Transmitted Infections'). Included in the count of STIs are Gonorrhea, Syphilis, Chlamydia, and Hepatitis B. The size of the dot represent the population size. Larger populations tend to be less susceptible to change over time, meaning small changes have larger effects on smaller populations.

This graph depicts the infant mortality rate in Butler County over the years 2013-2017. A darker shaded region indicates higher infant mortality rates. You can look at the infant mortality rate in Butler County by zipcode or census tract by clicking the button nearest to the name.

This graph depicts the percentage of low birth weight babies in Butler County over the years 2013-2017. A darker shaded region indicates higher percentages of low birth weight babies. You can look at the precent of low birth weight babies in Butler County by zipcode or census tract by clicking the button nearest to the name.

This graph depicts the percentage of babies born prematurely in Butler County over the years 2013-2017. A darker shaded region indicates higher percentages of premature births. You can look at the precent of babies born prematurely in Butler County by zipcode or census tract by clicking the button nearest to the name.

Start by selecting a variable to explore from the drop down (infant mortality rate, percent of babies born premature, or percent of low birth weight babies). Next select a year to focus on. Finally select a hypothetical number of additional poor birth outcomes for that year using the additional poor outcome drop down. For instance, choosing infant mortality in 2013 and 1 additional poor birth outcome would be saying what would have happened to the infant mortality rate in 2013 if one more baby had died. The rates are broken down by race so that the volatility of the rates (due to the difference in population size) can be observed.

These tables show how significant of an impact population size has on the volitility of the rates. This means that one additional poor birth outcome could have a drastic effect on the rates. Smaller groups (NH Black and Hispanic) are more susceptible to this. This is important to keep in mind while examining the trends of such rates over time because sharp spikes and dips may not be as drastic as they appear. One additional poor birth outcome will effect NH Black and Hispanic groups differently than the NH White group.

This graph allows you to further explore how the overall poor birth outcome rates would change in various hypothical situations. You can select the rate from the drop down ('Infant Mortality Rate', 'Percent of Babies Born Premature', or 'Percent of Low Birth Weight Babies') and the hypothetical numeric change in poor birth outcomes using the poor outcomes drop down (from 1-5). The sensitivity bands show the volitility of the rates, which can be turned on or off by checking the box labeled 'Sensitivity Bands'. For instance, if infant mortality rate and '1' additional poor birth outcome were selected, the bands would represent how the rate would change with 1 more and 1 less poor birth outcome. The rates are again broken down by race so that the volatility of the rates (due to the difference in population size) can be observed.

This plot reinforces how significant of an impact population size has on the volitility of the rates. The wider sensitivity bands for NH Black and Hispanic compared to NH White show how much more the rates would change with additional poor birth outcomes for these groups.