United States

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 &lt; 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.

AMA Research Challenge 2023

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


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.

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Background
The Pennsylvania Rural Health Model (PARHM) is a novel alternative payment model (APM) that began in 2019 and is slated to run through 2024. It is designed to test whether hospital global budgets and transformation plans can improve the financial viability of rural hospitals and the health of their patients. The expectation is that, when hospitals operate on a prospective budget, they have an incentive to reduce potentially avoidable hospitalizations (PAH) to control costs. Therefore, our objective was to assess the early impacts of PARHM on PAH for ambulatory care sensitive conditions.

Methods
We conducted a retrospective cohort study using a difference-in-differences (DID) with multiple time periods approach. Hospitals joined PARHM in three cohorts at different points in time (January 2019, 2020, and 2021), creating a natural policy experiment to study this program’s effects. The treated group included HSAs of 18 treated hospitals and the control group included HSAs of 43 eligible but not participating hospitals. We used visit-level inpatient discharge data from the Pennsylvania Health Care Cost Containment Council between 1/1/2016 and 5/31/2022. We measured PAH using the Agency for Healthcare Research and Quality Prevention Quality Indicators algorithm. Analyses were conducted at the hospital service area (HSA)-quarter level. We fit models using a doubly robust DID estimator that accounted for staggered treatment timing. All models were weighted using propensity scores and population size in each HSA.

Results
The introduction of PARHM was associated with 9.71 fewer PAHs per 100k residents per quarter (95% CI: 0.21 to 19.21) overall. This represents a 2.25% relative reduction in treated vs. control HSAs. However, when cohorts were analyzed separately, only PARHM Cohort 1 HSAs consistently a showed statistically significant (p<0.05) relative decreases in PAHs across overall, acute, chronic, and diabetes composite measures for PAH compared to changes in the control group. Results were similar across sensitivity and subgroup analyses.

Conclusion
Our findings on the early impacts of PARHM suggest that it is associated with fewer PAHs. Although, heterogeneous treatment effects by cohort were observed. This study addresses an important gap in the current literature regarding the impact of PARHM on health care utilization. Findings can be used to evaluate the success of PARHM and inform the development and implementation of future APMs across rural settings.

Pennsylvania Rural Health Model: Early Impacts on Potentially Avoidable Hospitalizations

**Scoring is only possible in the semifinals hall**

**Abstract:**



Assessing the healthcare needs of migrants and improving continuity of care through portable health documents

This study aims to explore the relationship between bullying either in school or electronically and substance use including tobacco, alcohol, drugs, marijuana, and electronic vapor product use post-pandemic. Data was taken from the 2021 Youth Risk Behavior Surveillance System (YRBSS), which surveys a representative sample of high school students in grades 9-12 across the United States. Questions related to tobacco use, alcohol use, and drug use were combined into their respective categories. A weighted frequency was obtained to see the frequency of students who used certain substances. An adjusted odds ratio was run to explore the relationship between bullying and substance use (tobacco, alcohol drugs, marijuana, and electronic vapor products) adjusted for age, sex, and race/ethnicity. There were 17232 survey respondents with a representative sample based on age, grade, sex, and race/ethnicity. It was found that bullying was associated with an increased odds of using substances with a statistically significant result. Alcohol was the most frequently used substance. This study showed that the association between bullying and substance use is still a problem post-pandemic. This provides a useful indication for teachers, school administrators, and clinicians to be mindful of when dealing with a child who is a victim of bullying or is using substances.

Association between bullying and substance use: A look at the 2021 YRBSS Data

Background
Differing health outcomes in maternal and child health among communities are the result of a multitude of factors. Despite considerable advancements in women’s reproductive health, social disparities continue to affect the health and well-being of mothers and infants. These racial and socioeconomic disparities become evident especially in medically underserved regions where health providers and resources are few. This study investigated the relationship between race and maternal and infant health outcomes in San Bernardino County compared to Los Angeles and Riverside Counties.

Methods
Public datasets were retrieved from the California Health and Human Services and California Department of Public Health, containing data on the state’s birth and death records and infant health indicators for 2010 to 2021. Only data from San Bernardino (SB), Los Angeles (LA), and Riverside (RS) Counties were included. Within each county, maternal and infant deaths and infant health measures such as birthweight and gestational age were further examined and stratified by race. To compare maternal mortality, a rate of pregnancy-related deaths per 1000 live births was calculated. ANOVA was performed to analyze for differences in maternal and infant mortality and infant health measure rates relative to race among the three counties, with significance level set at 0.05.

Results
Average pregnancy-related deaths in SB, LA, and RS Counties were 124, 300, and 55 per year, respectively, with Hispanics accounting for approximately 50% of maternal deaths across each county. After analyzing pregnancy-related mortality rates, there were significant differences between SB and LA (4.3 vs 2.4, p=0.009) and SB and RS (4.3 vs 2.0, p=0.003) among Hispanics and between SB and LA (8.5 vs 5.1, p=0.04) only among Blacks. Also, SB County had a significantly greater pregnancy-related mortality rate than LA and RS Counties (both p<0.001). Furthermore, SB had higher rates compared to RS for low birthweight (p=0.02), preterm status (p<0.001), and infant mortality (p<0.001). SB also had a higher infant mortality rate compared to LA (p<0.001).

Conclusion
SB County experienced higher rates of pregnancy-related mortality compared with its neighboring counties, with infant mortality and birth complications following a similar trend. Our findings suggest that racial disparities can contribute to health inequity, as predominantly Hispanic and Black communities were adversely affected. Further research is needed to understand the potential social barriers confronting underserved communities in San Bernardino. Identifying the root causes for racial disparities in health outcomes can help guide community health programming and policy-making to address gaps in maternal and infant health.

Examining Disparities in Maternal and Child Health: A Comparative Study of San Bernardino County and Its Neighbors

Background:
Anemia remains a major public health concern, especially in developing countries, despite various public health policies targeted for correction. With this study, the authors aim to underline the need for region-specific policies contrasting the correlates of anemia in non-pregnant females of East and West Uttar Pradesh (UP), India.

Methods:
A detailed secondary data analysis was performed on NFHS-5 data covering the socio-demographic correlates of East and West UP non-pregnant females. Various explanatory variables were tested against the outcome variable of anemia using binary and ordinal logistic regression. For variables whose proportional odds assumptions were not satisfied, binary categorization of the ordinal variables were done.

Results:
The study found a significant difference in correlates of anemia among non-pregnant females of East and West UP on a detailed analysis. The odds of being anemic were higher in women living in rural settings, especially in West UP. Further, in West UP, the odds of being anemic were higher in women who were alcoholics (OR=1.153; p=0.000), having heart disease (OR=1.155; p=0.000), younger, economically weak, illiterate (OR=1.335; p=0.000), or underweight (OR=1.523; p=0.012). While, in East UP, the odds of having anemia were found to be higher among women who were illiterate (OR=1.095, p=0.010), had age of last child ≤48 months (OR=1.296; p=0.000), were currently breastfeeding (OR=1.067, p=0.000), were alcoholics (OR=1.155, p=0.000), were hypertensive (OR=1.502, p=0.000) or were underweight (OR=1.523; p=0.012).

Conclusion:
In line with the demographic differences between East and West UP, a stark difference exists in socio-epidemiological correlates of anemia between both regions, and a blanket public health policy is not enough. In the age of precision medicine, we must evolve to a “Precision Public Policy” tailored specifically towards the regions allowing better implementation and outcomes.

Epidemiological correlates of Anemia among non-pregnant females of East and West
Uttar Pradesh, India

Background: The impact of the COVID-19 pandemic on mental health has been substantial. Existing evidence indicates that religious coping behaviors could serve as a protective factor against the onset of mental illness. To delve deeper into this relationship, our study focused on exploring the religious coping behaviors and mental health status of the Muslim diaspora during the pandemic.

Methods: Two surveys were administered to a total of 1,723 Muslims residing in predominantly non-Muslim countries during the initial phases of the COVID-19 pandemic. The first stage of the survey centered on coping behaviors and anxiety levels, while the second stage emphasized coping behaviors in relation to depression. Regression analysis methods were employed to explore the variables associated with anxiety and depression.

Results: In Stage 1, both negative coping behaviors (B = 0.11, p < 0.001) and intolerance of uncertainty (B = 0.47, p < 0.001) showed a positive association with anxiety. In contrast, daily prayer (B = -0.09, p < 0.01) was negatively associated with anxiety. In Stage 2, intolerance of uncertainty (OR = 1.60, p < 0.01) and negative coping behaviors (OR = 1.69, p < 0.001) were linked with higher odds of reporting depression, while prayer and reading scripture did not exhibit a significant correlation with depression.

Conclusion: Our results indicate that during the early stages of the pandemic, Muslims predominantly relied on religious coping mechanisms like prayer and reading scripture, which were linked with lower levels of self-reported anxiety. However, these coping behaviors showed no correlation with depression.

Exploring the Intersection of Faith, Distress, and Resilience: An Investigation into the Experiences of Muslim Communities in Western Countries during COVID-19

Grow502: a Pop-up Clinic to Tackle Health Disparities in the Houseless Population

Background
Over 100 bills have been introduced in the United States 2023 legislative session that restrict access to care for transgender and gender diverse (TGD) individuals. Passage of these bills by individual states may prompt medical students to seek residency training in other states, exacerbating the healthcare disparities and barriers to care that already exist for TGD individuals. This study assesses medical students’ perspectives on legislation prohibiting gender transition procedures (GTP) and how this affects where medical students apply to residency programs.

Methods
We analyzed responses from medical students at a Midwest institution. Data examined includes demographics, self-reported knowledge of GTP legislation, impact on residency applications and future practice, and agreement with proposed legislation. Respondents were asked to share factors influencing their feelings about applying for residency in states that restrict GTP, as well as considerations informing their position about regulations. Responses were coded for recurring themes and analyzed.

Results
80 medical students participated, and 39 responded to both free response questions. 36.8% report GTP legislation is important when considering where to apply for residency. 60.5% think this will impact how they practice medicine in the future, regardless of specialty. 57.3% are against restricting GTP for minors, and 82.7% for adults. Factors influencing residency applications fell into five themes: LGBTQ+ safety (38.5%), government interference (33.3%), education/training (33.3%), neutral (20.5%), and against GTP (15.4%). Four themes emerged regarding factors informing position on GTP: best medical practices (71.8%), minors’ capacity to consent (30.8%), government interference (30.8%), and a need for more data (7.7%).

Conclusion
The majority of medical students were against restriction of GTP, and over one-third felt it was an important consideration when applying to residency programs, suggesting that legislation prohibiting GTP may drive training physicians to other states. Responses also highlighted misconceptions and knowledge gaps about GTP, including rates and reasons for detransitioning, minors’ capacity to consent, and long-term risks and benefits of gender affirming care, which may improve with additional training during undergraduate medical education.

Medical students’ perspectives on transgender care legislation and its impact on medical residency applications.

Background
Despite the availability of effective preventative measures, women in rural communities of Louisiana face health disparities, specifically regarding human papillomavirus (HPV) infections. Along with suboptimal vaccination rates and cervical cancer screening, these disparities are further exacerbated by a lack of awareness and access to healthcare services. To effectively promote equity for women in rural Louisiana about HPV, understanding their needs is essential for developing appropriate strategies to eliminate the stigma surrounding reproductive health and decrease these health disparities. Therefore, this study aims to identify the risk factors contributing to health disparities related to HPV among women residing in rural areas of Louisiana.
Methods
This cross-sectional study is conducted in a Mobile Unit (MU) that travels to remote North Louisiana areas. Two hundred total participants are estimated for this study, with 123 responses providing preliminary results. The inclusion criteria are English-speaking women aged 25 – 64 without a total hysterectomy and cervical cancer history. With written informed consent and HIPAA authorization, participants responded to a questionnaire. HPV screening was offered and collected by the MU Nurse (NP).
Results
Based on preliminary results, the average age of participants was 54.31 years old. Most participants were African Americans (55.1%). Fifty-two percent had completed high school, 32.8% were married, 37.1% worked full-time, and 80.8% had low income. Regarding cancer history, 64.8% had a family history of cancer. Among them, 52% had a parent with cancer, while 35.8% had a grandparent with cancer. Eight percent had cancer, and 11% had been diagnosed with HPV. Regarding knowledge about HPV, 30% had never heard of it before our study. 46% did not know that HPV is sexually transmitted, 68.8% were unaware HPV causes genital warts, 33.6% did not know HPV can cause cervical cancer, and 47.6% were unaware that an HPV vaccine is available. Additionally, almost 91% of participants believed they were not at risk of HPV infection.
Conclusion
To achieve health equity for women in rural areas of Louisiana about HPV, we must focus on targeted interventions that address the specific challenges they encounter. With preliminary results showing most participants not knowing about HPV or its risk factors, further efforts should be prioritized to improve access to healthcare services, improve education and awareness, and foster community engagement. Understanding these disparities will allow us to create a future where women have equal access to healthcare regardless of where they live.

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

Next from AMA Research Challenge 2023

Pennsylvania Rural Health Model: Early Impacts on Potentially Avoidable Hospitalizations