Introduction
The CDC’s “Adult Obesity Maps in 2022” suggests that the southern parts of the United States (US) have the highest prevalence rates of obesity(1). Similarly, the Health Resources and Services Administration identifies the Southern states as having the highest numbers of medically underserved areas(2).
The health implications of obesity are numerous, with some affecting reproductive ability in both genders. With infertility on the rise, studies have examined the numerous modalities that implicate obesity in female infertility. One such route associates the increased adipose tissue with reduced oocyte maturation by promoting functional hyperandrogenism and hyper-estrogenism, which triggers anovulation(3).
This study was devised to investigate the landscape across the different regions in the US and detect correlations between female infertility and obesity. The hypothesis was that the southern region would have the highest correlation between female infertility and obesity compared to other regions across the US.
Methods
This cross-sectional study used the NIH’s ‘All of Us’ Researcher Workbench to first create a workspace titled “Incidence of female infertility due to obesity in US regions.” Next two cohorts were devised containing all ages, ethnicities, and races. The first group, the control cohort, included “female infertility” participants and excluded “sex assigned at birth – male” participants. The second group, the variable cohort, consisted of participants with both “obesity” and “female infertility” while excluding “sex assigned at birth – male” participants.
The US was divided into four regions based on zip code. The first three digits of each participant’s zip code were logged and localized to the Midwest, Northeast, South, or West. The Midwest consisted of participants from Wisconsin, Michigan, Iowa, Illinois, Indiana, and Ohio. The Northeast region had participants from Pennsylvania, New York, and the District of Columbia. The South had participants from Louisiana, Texas, Mississippi, Alabama, Georgia, Tennessee, South Carolina, Virginia, Florida, and Arkansas. The West region had participants from California, Washington, Colorado, Arizona, Utah, and New Mexico.
Two datasets were then created using the control cohort and the variable cohort, respectively, and then the zip code socioeconomic status data was extracted. The participant’s data was coded into the Python analysis software. Various data points were also recorded and analyzed. For each dataset consisting of one cohort, the length of the entire code was run, and for the control cohort and for the variable cohort. Next, a code was run to extract all zip codes with the number of participants included in each zip code. After all the data was extracted, each participant’s zip code was assigned to their appropriate region. All the data was then put into Microsoft Excel. The number of participants with both female infertility and obesity was then compared to the number of females with infertility only and compared against each region to establish a correlation percentage.
Statistical analysis used Chi-Square and focused on identifying degrees of freedom.
Results
In the control group, the number of participants who qualified for infertile females was 2,754, with 1,182 participants from the Midwest, 1,062 from the Northeast, 279 from the South, and 231 from the West. For the variable cohort, 1,198 participants were concurrently infertile and obese females. This data was also sub-grouped based on region: the Midwest with 481 participants, the Northeast with 491 participants, the South had 133 participants, and the West had 93 participants. Each variable group number was subsequently divided by its respective control group’s number of infertile female participants. The percentages for each region were: Midwest (28.9%), Northeast (31.6%), South (32.3%), and West (28.7%).
The results suggest that the Southern US region has the highest percentage of concurrently infertile and obese females compared to the rest of the country, while the lowest is the West. Chi-squared results yielded 467.376 with 3 degrees of freedom. These findings were statistically significant (two-tailed P value < 0.0001).
Conclusion
Our study utilized the NIH ‘All of Us’ Researcher Workbench to demonstrate the variation among the different United States regions and their correlations between females who are both obese and infertile. Knowing that the southern region of the United States has (A) the highest rates of obesity, (B) the highest rates of medically unserved communities, in addition to (C) the highest rate of concurrent female infertility and obesity, this study serves as a platform to stress the need for increased awareness of the implications of obesity. Increasing access to health education and medical care across the country, especially in the southern states, focusing on managing obesity may help improve infertility.

References
1. “Adult Obesity Prevalence Maps.” Centers for Disease Control and Prevention, 17 Mar. 2023, www.cdc.gov/obesity/data/prevalence-maps.html.
2. Map Gallery, https://data.hrsa.gov/maps/map-gallery.
3. Gambineri, A., Laudisio, D., Marocco, C. et al. Female infertility: which role for obesity?. Int J Obes Supp 9, 65–72 (2019).
https://doi.org/10.1038/s41367-019-0009-1
4. The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1
OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551;
1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196;
Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and
Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2
OD025276. In addition, the All of Us Research Program would not be possible without the partnership of its participants.

Female Infertility And Obesity Across The United States – A Geographic Cross–Sectional Analysis 


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