Based on: www.github.com/hrecht

Sample Abstract

Geographic Disparities in Potential Accessibility to Gynecologic Oncologists in the United States from 2013 to 2022.

Objective: To use a spatial modeling approach to capture potential disparities of gynecologic oncologist accessibility in the United States at the block group level between 2013 and 2022.

Methods: Physician registries identified the 2013 to 2022 gynecologic oncology workforce and were aggregated to each county. The at-risk cohort (women aged 18 years or older) was stratified by race and rurality demographics. We computed the distance from at-risk women to physicians. We set drive time to 30, 60, 180, and 240 minutes.

Results: Between 2013 and 2022, the gynecologic oncology workforce increased. By 2022, there were x active physicians, and x% practiced in urban block groups. Geographic disparities were identified, with x physicians per 100,000 women in urban areas compared with 0.1 physicians per 100,000 women in rural areas. In total, x block groups (x million at-risk women) lacked a gynecologic oncologist. Additionally, there was no increase in rural physicians, with only x% practicing in rural areas in 2013-2015 relative to ??% in 2016-2022 (p=?). Women in racial minority populations exhibited the lowest level of access to physicians across all periods. For example, xx% of American Indian or Alaska Native women did not have access to a physician with a 3-hour drive from 2013-2015, which did not improve over time. Black women experience an increase in relative accessibility, with a ??% increase by 2016-2022. However, Asian or Pacific Islander women exhibited significantly better access than ??? women across all periods.

Conclusion: Although the US gynecologic oncologist workforce has increased steadily over 20 years, this has not translated into evidence of improved access for many women from rural and underrepresented areas. However, distance and availability may not influence healthcare utilization and cancer outcomes only.

Methods: There are subspecialists in obstetrics and gynecology across the United States in four categories: Gynecologic Oncology, Female Pelvic Medicine and Reconstructive Surgery, Maternal-Fetal Medicine, and Reproductive Endocrinology and Infertility. We gathered data from the National Plan and Provider Enumeration System (NPPES) to extract each obstetrician-gynecologist and their address of practice (available at https://www.nber.org/research/data/national-plan-and-provider-enumeration-system-nppes). The NPPES file contains a list of every physician practicing in the United States and is closest to a physician data clearinghouse. This data also includes their graduation and license year. Therefore, we identified the approximate year that each subspecialist entered the workforce as a physician. We had data for NPPES files from 2013 to 2023.

Utilization data for each physician were unavailable, so our analysis focused on at-risk women aged 18 years or older in the United States at the block group level (Appendix 0). Analysis was also done at the American College of Obstetricians and Gynecologists District level (Appendix 1). We collected American Community Survey 5-year estimates and decennial census data. Along with the total adult female population in each cross-section, we also collected population on race to identify potential disparities among women in minority populations (Appendix 2). The race categories included White, Black, Asian or Pacific Islander, and American Indian or Alaska Native.

To measure the distance between the patient location and the point of care (hospital or emergency department), the shortest or "straight-line" distance (i.e., the geodetic or great circle distance) is commonly used because it can be readily calculated (e.g., through statistical software programs such as SAS®). An alternative distance metric is the driving distance or driving times obtained from various mapping software such as HERE, Google Maps, MapQuest, OpenStreetMaps, and ArcGIS Network Analyst. Multiple organizations, such as the Veteran’s Administration and the Department of Transportation, prefer drive time for measuring access. We use the HERE API to calculate optimal routes and directions for driving with traffic on the third Friday in October at 0900 (Appendix 3).

Additionally, our analysis examined potential access to obstetrician-gynecologist subspecialists across the United States. We used drive time isochrones.

Appendix 0: Choosing block groups over counties for data analysis could be driven by several factors, depending on the nature of the analysis you are conducting:

• Resolution of Data: Block groups provide a finer data resolution than counties. • Local Trends and Patterns: Block groups are small enough to reveal local trends and patterns that might be obscured when data is aggregated at the county level. • Socioeconomic Analysis: Block groups can provide more precise information about demographic and economic conditions for studies involving socioeconomic factors since they represent smaller communities. • Policy Impact Assessment: Block groups may be more appropriate when assessing the impact of local policies or interventions because policies might vary significantly within a county. • Spatial Analysis: For spatial analyses that require precision, such as identifying hotspots or conducting proximity analysis, the smaller geographic units of block groups are more valuable.

We utilized the tigris package to get the block group geometries for different years. This required downloading each state’s data and joining them into one national file for years 2018 and earlier.

Appendix 1: The American College of Obstetricians and Gynecologists (ACOG) is a professional organization representing obstetricians and gynecologists in the United States. ACOG divides its membership into various geographical regions known as "ACOG Districts."

State ACOG_District State_Abbreviations Alabama District VII AL Alaska District VIII AK Arizona District VIII AZ Arkansas District VII AR California District IX CA Colorado District VIII CO Connecticut District I CT District of Columbia District IV DC Florida District XII FL Georgia District IV GA Hawaii District VIII HI Illinois District VI IL Indiana District V IN Iowa District VI IA Kansas District VII KS Kentucky District V KY Louisiana District VII LA Maryland District IV MD Massachusetts District I MA Michigan District V MI Minnesota District VI MN Mississippi District VII MS Missouri District VII MO Nebraska District VI NE Nevada District VIII NV New Hampshire District I NH New Jersey District III NJ New Mexico District VIII NM New York District II NY North Carolina District IV NC North Dakota District VI ND Ohio District V OH Oklahoma District VII OK Oregon District VIII OR Pennsylvania District III PA Puerto Rico District IV PR Rhode Island District I RI South Carolina District IV SC South Dakota District VI SD Tennessee District VII TN Texas District XI TX Utah District VIII UT Vermont District I VT Virginia District IV VA Washington District VIII WA West Virginia District IV WV Wisconsin District VI WI

Appendix 2: US Census Bureau Codes Decennial Census and the American Community Survey. Decennial Census – Demographic and Housing Characteristics File (API variables: https://api.census.gov/data/2020/dec/dhc/variables.html)

(https://www2.census.gov/programs-surveys/decennial/2020/technical-documentation/complete-tech-docs/demographic-and-housing-characteristics-file-and-demographic-profile/2020census-demographic-and-housing-characteristics-file-and-demographic-profile-techdoc.pdf )

American Community Survey (API variables: https://api.census.gov/data/2022/acs/acs1/variables.html )

B01001_026E Estimate _Total _Female female_10_to_14 = "B01001_029", female_15_to_17 = "B01001_030", female_18_to_19 = "B01001_031", female_20years = "B01001_032", female_21years = "B01001_033", female_22_to_24 = "B01001_034", female_25_to_29 = "B01001_035", female_30_to_34 = "B01001_036", female_35_to_39 = "B01001_037", female_40_to_44 = "B01001_038", female_45_to_49 = "B01001_039", female_50_to_54 = "B01001_040", female_55_to_59 = "B01001_041", female_60_to_61 = "B01001_042", female_62_to_64 = "B01001_043", female_65_to_66 = "B01001_044", female_67_to_69 = "B01001_045", female_70_to_74 = "B01001_046", female_75_to_79 = "B01001_047", female_80_to_84 = "B01001_048", female_85_over = "B01001_049"

Appendix 3: Characteristics of Isochrones The bespoke R code generates individual maps for each drive time, visually representing the accessible areas on a map. The function shapefiles are geospatial data files used for storing geographic information, including the boundaries of the reachable areas. The HERE API (here.com) was utilized because traffic and time could be standardized yearly. Each year, the isochrones are built on the third Friday in October at 0900, defined as “posix_time”. We imagined that patients would see their primary care provider at this time of year for an influenza vaccination or other issues. The hereR package (https://github.com/munterfi/hereR/ ) is a wrapper around the R code that calls the HERE REST API for isoline routing (platform.here.com) and returns it as an sf object. There is a cost of $5.50 for every 1,000 isolines created (https://www.here.com/get-started/pricing#here---platform---pricing---page-title ).
• October 18, 2013 • October 17, 2014 • October 16, 2015 • October 21, 2016 • October 20, 2017 • October 19, 2018 • October 18, 2019 • October 16, 2020 • October 15, 2021 • October 21, 2022

R code utilizing the hereR package with the isoline library. The range of isochrones was 30 minutes, 60 minutes, 120 minutes, and 180 minutes.
hereR::isoline( poi = row_data, range = c(1), datetime = posix_time, routing_mode = "fast", range_type = "time", transport_mode = "car", url_only = FALSE, optimize = "balanced", traffic = TRUE, aggregate = FALSE)

We used github LFS (large file storage) mainly for storing shapefiles.

git lfs install
git config http.postBuffer 524288000
git lfs track
git lfs track "*.dbf" "*.shp"
git ls-tree -r -t -l HEAD | sort -k 4 -n -r | head -n 10
git add .gitattributes
git commit -m "Track large files with Git LFS"
git push origin main