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Gynecology & Obstetrics
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Statistical Analysis of Socioeconomic Factors Correlating to Caesarean Section Rates 2015-2016

Nethra Parasuram* and Mark Martens

Jersey Shore University Medical Center, 1945 Route 33, Neptune, NJ 07753, USA

*Corresponding Author:
Nethra Parasuram
Jersey Shore University Medical Center
1945 Route 33, Neptune, NJ 07753, USA
Tel: (217) 402-4906
E-mail: [email protected]

Received November 30, 2016; Accepted date: January 05, 2017; Published date: January 10, 2017

Citation: Parasuram N, Martens M (2017) Statistical Analysis of Socioeconomic Factors Correlating to Caesarean Section Rates 2015-2016. Gynecol Obstet (Sunnyvale) 7:420. doi: 10.4172/2161-0932.1000420

Copyright: © 2017 Parasuram N. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Objective: Caesarean section rates have significantly increased in the past decade in the United States. To make an attempt at lowering these rates, it is important to first understand how states’ rates compare with each other and which factors correlate with these rates. Then, possible ways to impact these rates can be suggested. This investigation sought to determine whether or not there is a significant difference between the rates of caesarean sections in Utah, United States and New Jersey, United States using representative hospitals’ data. Also, we sought to evaluate selected socioeconomic factors and their possible correlation with higher or lower caesarean rates in each United States’ states. Methods: Information collected from various federal and private sources were utilized to collect caesarean section rate data. These data were correlated to selected variables including average birthing age, logarithm of the percent of females in the workforce, median household income, number of hospitals, logarithm of the percent of people who have graduated with a Bachelor’s degree or higher, average number of people in a household, and the standard of living. A Linear Multiple Regression Model and T-Test were utilized to determine significance of each variable. Results: There was a statistical difference between the caesarean section rates of Utah and the caesarean section rates of New Jersey. The p-value obtained from the T-Test was 1.805*10-5. Therefore, there is a significant difference between the two states’ caesarean data. The variables birthing age, logarithm of the percent of females working, and the number of hospitals significantly correlated with caesarean section rates. Median household income, logarithm of the percent of people who have graduated with a Bachelor’s degree or higher and average number of people in a household did not significantly correlate with caesarean section rate data. Conclusion: It appears that there are several economic factors that significantly correlate with caesarean section rates. Other economic factors which did not appear significant may have had several conflicting components, which under estimated any true significance. Understanding these factors may permit us to develop strategies to impact the caesarean section rate.

Introduction

It is reported that about one in every three deliveries in the United States is a caesarean section delivery. Nineteen percent has been proposed as an ideal rate of caesarean section delivery [1]. It is proposed that, as the rate increases, the rate of mortality decreases. Caesarean rates higher than 19 percent have shown that there is no significant decrease in infant mortality and no significant advantage to caesarean deliveries [1]. Caesarean sections can result in several adverse events including blood clots, embolisms, hemorrhages, infections, and injury to surrounding organs [2]. It is also estimated that 14 of every 100,000 live births end in maternal death in the United States [1]. Aside from immediate complications, there are also long term sequelae including possible infertility, placenta previa, placenta accreta, ectopic pregnancy, and uterine rupture [2].

The rate of caesarean delivery has increased tremendously, from 5 percent in 1970 and 20 percent in 1996 to about 33 percent currently [1]. A study by Keeler and Brodie [3] showed that women who have “private, fee-for-service insurance” have a higher rate of caesarean sections as opposed to women who have other types of insurances. Various socioeconomic factors have been cited as leading to higher caesarean section rates. It is important to recognize and understand these factors if there will be attempts to reduce caesarean section rates.

Caesarean section rates have increased due to many reasons. The objective of this study is to specifically investigate socioeconomic factors which correlate with caesarean section rates in the United States. International rates will also be discussed briefly in comparison.

Methodology

The current study investigated data collected by the Leapfrog Group, the Tax Foundation, the U.S. Census Database, mentalfloss.com, and caesareanrates.com. To retrieve the caesarean section rate data, the phrase, “C-Section Rates by Hospital,” was utilized in Google’s search engine. The average caesarean section rates from each state from 2015 were taken from the Leapfrog Group. To find the standard of living of each state, the phrase, “The Value of $100 by State in the United States,” was typed into the Google search engine. The site published by the Tax Foundation provided data about the value of $100 in each American state, which reflects the standard of living. The two states chosen were New Jersey and Utah because they had standards of living on the higher end of the spectrum and on the lower end, respectively. The caesarean section rates of these two states, whose data were provided by the Leapfrog Group, were analyzed using a T-Test. The U.S. Census Database provided data for the percent of females in the workforce, median household income, percent of people who have graduated with a Bachelor’s degree or higher and average number of people in a household. Average birthing age data was found on mentalfloss.com by searching up “Average birthing age by state mentalfloss.com” in the search engine and collecting corresponding data from the site. The number of hospitals by state was given in American Hospital Directory. “Number of Hospitals by State American Hospital Directory,” was typed into the search engine and corresponding data from the site was collected. To find specific data regarding hospital rates in a state, “C-section rates by state hospitals,” was typed into the search engine. The website caesareanrates.com provided the data. This data was used to analyze New Jersey and Utah.

T-Test was utilized to compare the two populations and a p-value less than 0.05 was considered a significant difference. The rates of caesarean sections from both cities were entered into a two tailed T-Test, which analyzed samples from populations with different variances. Percentages were calculated by dividing the sum of caesarean sections and natural deliveries for the trial by the number of caesarean sections for the trial. There were two arrays of data. The two arrays were 16 randomly chosen rates from 16 different hospitals for caesarean sections in Utah, USA and 16 randomly chosen rates from 16 different hospitals for caesarean sections in New Jersey, USA. The averages, medians, standard deviations, variations, and standard errors were calculated for these arrays, as well.

A Linear Multiple Regression Model was utilized to observe the variance of one variable when others are held constant. Also, it depicts the correlation among the variables. This was used to find which of the selected variables correlated with the states’ corresponding caesarean section rate. The critical value is 0.05. Data from the fifty states of the United States were used. Seven variables from each state were investigated: average birthing age, logarithm of the percent of females in the workforce, median household income, number of hospitals, logarithm of the percent of people who have graduated with a Bachelor’s degree or higher, average number of people in a household, and the standard of living. These factors were considered as a group.

Results

Rates of caesarean sections in Utah, U.S. and New Jersey, U.S. is shown in (Table 1). The two arrays of 16 data points from New Jersey and Utah were analyzed for a significant difference. The p-value obtained from the T-Test was 1.805*10-5, which is less than the alpha value of 0.05. The median caesarean section rate for Utah is 18.6% with a standard of deviation of 3.4. This caesarean section is near the aforementioned ideal rate of 19%. Similarly, the median caesarean section rate in New Jersey is 29.75% with a standard deviation of 7.7. This caesarean section rate is near the national average caesarean section rate of 32.2% [4].

Hospital Utah New Jersey
1 18.7 22.9
2 18.7 30
3 25.6 33.3
4 18.5 14.1
5 15.9 35
6 19.3 33.5
7 22 27.1
8 12.2 32.9
9 13.6 29.4
10 18.6 27.5
11 14 25.8
12 19.8 41.8
13 18.2 29.8
14 18.6 42.8
15 13.7 16.1
16 18.3 29.7

Table 1: Rates of cesareansectionsin Utah, U.S. and New Jersey, U.S.

Variables studied that possibly correlate with caesarean section rates in the United States are shown in (Table 2). Of the seven factors explored (average birthing age, percent of females in the workforce, median household income, number of hospitals, percent of people who have graduated with a Bachelor’s degree or higher, average number of people in a household, and the standard of living), birthing age, percent of females working, and the number of hospitals were significantly correlated with caesarean section rates with p-values of 1.79*10-2, 7.63*10-3 and 4.96*10-2, respectively. Three factors, median household income, logarithm of the percent of people who have graduated with a Bachelor’s degree or higher, and average number of people in a household produced p-values of 1.292*10-1, 6.535*10-1, and 7.616*10-1, respectively, which demonstrate an insignificant correlation. Standard of living produced a p-value of 8.949*10-2, which is considered to show a statistical trend. The Linear Multiple Regression Model requires the logarithm of the variables that are expressed as percentages, to be utilized for statistical evaluation. In Table 2, the closer the logarithmic value is to zero, the greater the frequency of the variable.

  Avg. birthingAge Log of % females 16+working Median householdincome Number of hospitals Log of % females holding grad. bachelorsorhigher Avg. number of people in a household Standard of living (value of $100) Log of cesareansectionrate
Lousiana 24.3 -0.24872 44991 109 -0.65561 2.6 109.65 -0.4023
New Jersey 28.3 -0.21681 72062 73 -0.4389 2.72 87.34 -0.41567
Florida 26.2 -0.25964 47212 215 -0.57187 2.62 101.21 -0.42251
Mississippi 23.6 -0.26761 39464 69 -0.69037 2.64 115.21 -0.4318
West Virginia 24.1 -0.30627 41576 35 -0.72816 2.43 112.87 -0.4437
Kentucky 24.4 -0.25885 43342 75 -0.66154 2.5 112.23 -0.451
Alabama 24.5 -0.27246 43511 92 -0.63639 2.55 114.03 -0.45223
Connecticut 27.4 -0.20135 69899 34 -0.4318 2.56 92.17 -0.45469
Texas 25 -0.23732 52576 381 -0.56703 2.83 103.41 -0.45469
South Carolina 24.7 -0.24795 45033 64 -0.59688 2.56 110.5 -0.45593
Arkansas 23.8 -0.26761 41264 49 -0.68613 2.53 114.16 -0.45842
Nevada 25.7 -0.22257 52205 27 -0.64782 2.71 101.83 -0.45842
Oklahoma 24.2 -0.25259 46235 101 -0.62342 2.56 111.23 -0.45967
New York 27.5 -0.23136 58687 198 -0.47237 2.62 86.66 -0.46218
Maryland 27.1 -0.18977 74149 52 -0.42829 2.67 89.85 -0.46218
Virginia 26.8 -0.21538 64792 94 -0.44612 2.61 96.9 -0.46471
Tennessee 24 -0.24872 22621 112 -0.61261 2.53 110.25 -0.46597
Delaware 26 -0.22257 60231 8 -0.53165 2.63 97.75 -0.4698
Georgia 25 -0.23582 49342 116 -0.54821 2.72 108.7 -0.47108
Rhode Island 27 -0.20971 56423 12 -0.50307 2.47 101.32 -0.48149
California 26.9 -0.24109 61489 349 -0.50864 2.95 88.57 -0.48149
Massachusetts 28.7 -0.1986 67846 80 -0.39794 2.53 93.28 -0.48149
Michigan 25.5 -0.24033 49087 107 -0.5784 2.52 105.93 -0.48678
Missouri 25.1 -0.22475 47764 86 -0.57349 2.48 113.51 -0.49621
Pennsylvania 26.1 -0.23433 53115 180 -0.55129 2.49 101.32 -0.50446
Illinois 26.5 -0.21467 57166 140 -0.49621 2.63 99.4 -0.50724
Nebraska 25.5 -0.1831 52400 30 -0.5376 2.47 110.99 -0.50724
North Carolina 25.6 -0.23657 46.693 108 -0.55596 2.54 109.17 -0.51145
Ohio 27.6 -0.22841 48.849 147 -0.59176 2.46 112.11 -0.51286
Kansas 25 -0.20691 51872 59 -0.51286 25.2 111.23 -0.5157
New Hampshire 27.2 -0.19314 65986 14 -0.46344 2.46 94.16 -0.51713
Iowa 25.5 -0.19723 52716 41 -0.5784 2.42 111.73 -0.51856
Indiana 24.7 -0.22768 48737 98 -0.62709 2.55 109.77 -0.51856
Montana 25.3 -0.22257 46766 16 -0.53611 2.4 106.16 -0.51856
Maine 26.3 -0.21968 48804 21 -0.54668 2.34 101.73 -0.52578
Washington 27 -0.22988 60294 63 -0.4908 2.55 96.9 -0.53018
Oregon 26.7 -0.23732 50521 38 -0.52143 2.5 101.21 -0.53165
Wyoming 27.1 -0.20273 58252 14 -0.60033 2.49 103.73 -0.5544
North Dakota 25.5 -0.18442 55579 10 -0.56384 2.32 110.62 -0.55752
Vermont 26.7 -0.1945 54447 7 -0.45346 2.34 99.11 -0.56067
Hawaii 26.7 -0.22768 68201 14 -0.5157 3 85.32 -0.56543
Minnesota 27 -0.17849 60828 56 -0.47886 2.48 102.56 -0.56703
Arizona 25.5 -0.26043 49928 76 -0.56703 2.69 101.94 -0.56864
South Dakota 25 -0.18842 50338 26 -0.57349 2.45 113.38 -0.57512
Wisconsin 26.1 -0.19518 52738 76 -0.56225 2.43 107.64 -0.58503
Colorado 26.8 -0.20135 59448 53 -0.42597 2.54 98.43 -0.5867
Idaho 24.6 -0.24336 47334 18 -0.59517 2.68 106.84 -0.60555
Utah 25 -0.22403 59846 36 -0.51428 3.14 103.31 -0.63639
New Mexico 23.9 -0.25727 44968 37 -0.58336 2.66 105.49 -0.64207
Alaska 25.5 -0.18642 71829 12 -0.55752 2.79 93.37 -0.66756

Table 2: Rates of cesarean sections in Utah, U.S. and New Jersey, U.S.

All variables had a positive coefficient except for the logarithm of the percent of females in the workforce and the logarithm of the percent of people who have graduated with a Bachelor’s degree or higher. A positive coefficient suggests that as the value of the variable increases, the rate of caesarean section increases. Conversely, a negative coefficient suggests that as the value of the variable decreases, the rate of caesarean section increases. The intercept of the linear regression model has a p-value of 5.16*10-4 indicating that the model is statistically significant. In conjunction to the p-value’s significance, the intercept is a number other than 0. This implies that there are factors that have not been considered in this model (Tables 1 and 2).

Conclusion

Caesarean section rates have increased dramatically in the past decade, and attempts to lower them have not been effective. It is therefore vital to understand that various factors that impact this rate.

In this investigation, increases in maternal age at birth demonstrated a significant correlation with increasing caesarean section rate. Advanced maternal age patients have been shown to have more complications, such as hypertension or diabetes. To avoid serious complications, a caesarean section may be required for the mother.

Infertility is also common in older patients resulting in the need for in-vitro procedures, which are shown to have a higher caesarean section rate [5]. Also, older gravidas have a higher incidence of leiomyomata and other anatomic alterations, therefore a risk for premature or breech deliveries. Placenta previa is also more likely to occur in older gravidas as is the likelihood of a previous delivery by caesarean section.

The first caesarean delivery often leads to a repeat caesarean section because of the risk of uterine rupture in patients. In a study done of potential Vaginal Birth after Caesarean (VBAC) patients in 2012, 98% of mothers who planned to deliver via caesarean section did so. However, only 43% of mothers who planned to deliver vaginally actually delivered vaginally [6].

The percent of females working during pregnancy also demonstrated to have a significant correlation. As the percent of working women increased, the caesarean section rate decreased. Women working are presumably more active possibly resulting in a lower caesarean section rate. Also, maternity leave is limited, so women needing to return to their jobs earlier may have a faster recovery for vaginal deliveries. Lastly, employed women more often have medical insurance coverage, which should provide better healthcare options, including earlier antepartum exams, testing, and greater choice in their obstetrician selection.

The number of hospitals in each state also significantly correlated with the caesarean section rate of each state. As the number of hospitals increased, the caesarean section rates also increased.

Hospitals are now being publicly rated on factors including patient mortality and safety. Presumably, a state with more hospitals is also more likely to have more malpractice attorneys. Obstetricians and gynecologists have one of the highest malpractices insurance rates of all the specialties as demonstrated by an 85% increased likelihood of being sued by patients than any other type of physician [7]. Because of this, obstetricians may be more likely to opt for caesarean sections to prevent as many complications as possible.

Median household income was shown to be insignificant in correlation with caesarean section rates in our review. We hypothesize that the income variability is less in America than it is in densely populated areas, such as Asian countries. Therefore, we hypothesize the disparity in income has a more apparent correlation with caesarean section rates. In our international review, there is a strong correlation between caesarean section rates and median household income. In India (personal communication - Parasuram), two cities were analyzed: Chennai and Madurai, which are urban and rural, respectively. Chennai is a more developed urban city with the cost of living in local areas reported to be 29% greater in Chennai (₹ ₹83,000 in Chennai versus ₹64,528 in Madurai) [8]. The caesarean section rate in a hospital in Chennai was 76.8%, while it was 33.2% in the less developed rural town of Madurai. One study done in China between 1988 and 2008,” said that the average increase in caesarean rates went from 3.4% in 1988 to 39.3% in 2008. The biggest increases were seen in urban cities, with 64.1% of women giving birth through caesarean section in 2008. In the least developed rural area, the rate went from less than 1% in 1988 to 11.3% in 2008. There are stark differences in the caesarean section rate even within the urban population and the rural population in China. Household income can be divided into four quartiles with the fourth quartile being the poorest. In 2008, Quartile I had 90.5% of births delivered via caesarean section, whereas Quartile IV, the poorest of the population, had 48.6% of births delivered through caesarean section. Similarly, in the rural areas, Quartile I, the wealthiest of the rural, had a caesarean section rate of 97.6%, while Quartile IV had 32.6% of births delivered via caesarean section [9]. These data show that household income does have a significant effect on caesarean section rates in highly populated Asian countries and as household income increases, so do caesarean section rates.

Thus, the controversy of attempting to correlate household income data with caesarean section rates appears to result in less variability in industrialized economies. When we attempt to add more economic variables to the median household income comparison in the United States, replacing it with a more comprehensive and inclusive category defined as “standard of living, there is a statistical trend between standard of living and caesarean section rates. Standard of living is comprised of various socioeconomic features such as, “Income, gross domestic product, national economic growth, economic and political stability, political and religious freedom, environmental quality, climate, and safety” [10]. Because of these additional variables, biases associated from a single number (median household income) may be controlled.

The percent of people who have graduated with a Bachelor’s degree or higher was not shown to have a significant correlation to caesarean section rates. This may be due again to the mixture of competing variables. The educated are more likely to have better jobs with more comprehensive health insurance, which has shown to decrease caesarean section rates [11]. However, in conjunction, states with a higher concentration of universities are likely to have more hospitals because of university hospitals. As shown, a higher number of hospitals in a state are more likely to increase the number of caesarean sections. Even though there are multiple factors involved, many are not reflected by education level alone. The statistical trend shown by standard of living may be more accurate because it considers more factors.

Our review also did not find that the average number of people in a household correlated significantly with caesarean section rates because there is a wide variability within subgroups of populations. For example, some cultures or religions may promote the idea of having smaller or larger families. Also, similarly, some faiths may not allow the use of birth control. Therefore, means of preventing pregnancy may not be a viable option.

In summary, there appear to be multiple factors impacting caesarean section rates. Understanding the factors and their interactions is the first step towards addressing the serious dramatic increase in caesarean section rates not just in the United States, but around the world.

In the past, solutions have narrowly focused on the hospital environment, such as availability of anesthesia, or practice patterns of the obstetric providers. Factors that were previously believed to be unrelated, such as employment and standard of living, are now shown to correlate with caesarean section rates. Interventions taking advantage of this new information will hopefully lead to governments, organizations, and societal initiatives to promote the employment of women who want to or need to work. Also, sufficient medical leave can encourage women to avoid delaying pregnancy and childbirth until they attain financial security. Having babies at an earlier age will then prevent health complications that arise in older gravidas. Lastly, consolidating the number of hospitals providing obstetrical services may lead to increased consistency in implementing strategies to reduce caesarean section rate. All in all, the rise in caesarean section rates is multifaceted and complicated, but efforts taken as a society may be able reduce these rates.

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