{"id":366,"date":"2024-04-29T12:14:14","date_gmt":"2024-04-29T19:14:14","guid":{"rendered":"https:\/\/labs.wsu.edu\/chase\/?page_id=366"},"modified":"2024-05-07T08:01:01","modified_gmt":"2024-05-07T15:01:01","slug":"health-equity","status":"publish","type":"page","link":"https:\/\/labs.wsu.edu\/chase\/health-equity\/","title":{"rendered":"Health Equity"},"content":{"rendered":"
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\n\t\n\t\t\t\n\t\tAddiction\t\t\t<\/a>\n<\/div>\n\n<\/div>\r\n\n\n
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\n\t\n\t\t\t\n\t\tEnvironmental Health\t\t\t<\/a>\n<\/div>\n\n<\/div>\r\n\n\n
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\n\t\n\t\t\t\n\t\tHealth Equity\t\t\t<\/a>\n<\/div>\n\n<\/div>\r\n\n\n
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\n\t\n\t\t\t\n\t\tCOVID-19\t\t\t<\/a>\n<\/div>\n\n<\/div>\r\n\n<\/div>\n <\/div><\/section>\n\n
\r\n\t\nCollege of Medicine<\/span><\/a><\/span>Community Health and Spatial Epidemiology Lab<\/span><\/a><\/span><\/div>
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\r\n\t\tHealth Equity Research\t<\/h1>\r\n\t\t<\/header>\r\n\n\n\n

Identification of a Hot-Spot of Premature Death from Colorectal Cancer among Non-Whites in Southeast Washington State, by Distance to Ambulatory Endoscopy Services<\/h2>\n\n\n\n

Background<\/h3>\n\n\n\n

This study evaluates relationships among race, access to endoscopy services, and colorectal cancer (CRC) mortality in Washington state (WA).<\/p>\n\n\n\n

Methods<\/h3>\n\n\n\n

We overlayed the locations of ambulatory endoscopy services with place of residence at time of death, using Department of Health data (2011-2018). We compared CRC mortality data within and outside a 10km buffer from services. We used linear regression to assess the impact of distance and race on age at death while adjusting for gender and education level.<\/p>\n\n\n\n

Results<\/h3>\n\n\n\n

Race impacted age at death: 72.9y vs. 68.2y for white vs. non-white (p below 0.001). The adjusted model showed that non-whites residing outside the buffer died 6.9y younger on average (p below 0.001). Non-whites residing inside the buffer died 5.2y younger on average (pbelow 0.001), and whites residing outside the buffer died 1.6y younger (p below 0.001). We used heatmaps to geolocate death density.<\/p>\n\n\n\n

Conclusion<\/h3>\n\n\n\n

Results suggest that geographic access to endoscopy services disproportionately impacts non-whites in Washington. These data help identify communities which may benefit from improved access to alternative colorectal cancer screening methods.<\/p>\n\n\n\n

\"map<\/figure>\n\n\n\n
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Socioeconomic and Racial Inequities in Breast Cancer Screening During the COVID-19 Pandemic in Washington State<\/h2>\n\n\n\n

The COVID-19 pandemic has disrupted preventive care, including cancer screening. In this study, we used clinical data to examine differences in breast cancer screenings before and during the COVID-19 pandemic overall and among sociodemographic population groups. Data included completed screening mammograms within a large statewide nonprofit community health care system in Washington State between April 1, 2018, and December 31, 2020.<\/p>\n\n\n\n

Among the 55\u202f678 screenings in April to December 2019, 45\u202f572 patients were non-Hispanic White (81.8%), 54\u202f620 patients lived in urban areas (98.1%), and 22\u202f761 patients were commercially insured (40.9%); the mean (SD) age was 62.0 (11.3) years.<\/p>\n\n\n\n

We observed greater and significant reductions in the number of screenings from 2019 to 2020 for women who were Hispanic (1727 vs 619; \u221264.2%), American Indian\/Alaska Native (215 vs 84; \u221260.9%), mixed race (1892 vs 828; \u221256.2%), Native Hawaiian or Pacific Islander (365 vs 166; \u221254.5%), Asian (2779 vs 1265; \u221254.5%), and Black (2320 vs 1069; \u221253.9%) compared with women who were White (45\u202f572 vs 23\u202f163; \u221249.2%). Women living in rural areas experienced greater reduction in screenings compared with their urban counterparts. In terms of insurance, women who self-paid for treatment and who were insured by Medicaid experienced the largest reduction in screening, whereas those with commercial insurance or Medicare showed smaller reductions.<\/p>\n\n\n\n

This study found a substantial overall decline in breast cancer screening in women living in Washington State during the COVID-19 pandemic, as well as inequities in this decline.<\/p>\n\n\n

\n\t\n\t\t\t\n\t\tView Paper\t\t\t<\/a>\n<\/div>\n\n\n\n
\"Socioeconomic<\/figure>\n\n\n\n
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Area Deprivation Amplifies Racial Inequities in Premature Mortality in Washington State<\/h2>\n\n\n
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In the United States, place of residence and racial identity are closely tied to health and wellbeing. A large body of evidence has confirmed that whites living in more-affluent areas have the best chances of a long, healthy life. However, less is known about if and how race and neighborhood deprivation interact in relation to health. In this epidemiological study, we analyzed 242,667 deaths in WA state, for which we could obtain information about the last residential address for each individual at the time of their death. Addresses were used to determine decedents\u2019 exposure to deprivation based on the Area Deprivation Index. We also classified decedents\u2019 race using federal racial categories, as well as their education, gender, and other socioeconomic and demographic characteristics. Our resutls show that deaths among non-whites from deprived neighborhoods were between three and eight times more likely to be premature compared to more-affluent whites.<\/p>\n\n\n

\n\t\n\t\t\t\n\t\tView Report and Interactive Map\t\t\t<\/a>\n<\/div>\n\n\n
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\"premature<\/figure>\n\n<\/div>\r\n\n<\/div>\n\n\n
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Eastern Washington Health Profile<\/h2>\n\n\n
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The Eastern Washington Health Report aims evaluated the community health status and health issues known to affect individuals and communities in eastern Washington. We compared this region of the state and its distinctly different set of health and social issues to those in western Washington.<\/p>\n\n\n

\n\t\n\t\t\t\n\t\tView Report and Interactive Map\t\t\t<\/a>\n<\/div>\n\n\n
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\"Health<\/figure>\n\n<\/div>\r\n\n<\/div>\n\n\n
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The Association Between Obesity, Socio-Economic Status, and Neighborhood Environment: A Multi-Level Analysis of Spokane Public Schools<\/h2>\n\n\n\n

Socio economic inequities in obesity have been attributed to individuals\u2019 psychosocial and behavioral characteristics. School environment, where children spend a large part of their day, may play an important role in shaping their health. This study aims to assess whether prevalence of overweight and obesity among elementary school students was associated with the school\u2019s social and built environments. Analyses were based on 28 public elementary schools serving a total of 10,327 children in the city of Spokane, Washington. Schools were classified by percentage of students eligible for free and reduced meals (FRM). Crime rates, density of arterial roads, healthy food access, and walkability were computed in a one-mile walking catchment around schools to characterize their surrounding neighborhood. In the unadjusted multilevel logistic regression analyses, age, sex, percentage of students eligible for FRM, crime, walkability, and arterial road exposure were individually associated with the odds of being overweight or obese. In the adjusted model, the odds of being overweight or obese were higher with age, being male, and percentage of students eligible for FRM. The results call for policies and programs to improve the school environment, students\u2019 health, and safety conditions near schools.<\/p>\n\n\n

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Characteristics of Students in Public Schools in Spokane, Washington, Overall and Stratified by Percent Free and Reduced Meal Students<\/h3>\n\n\n\n
School-Level Characteristics* 2016\u20132017<\/th>Low (0-50%)<\/th>Middle (51-74%)<\/th>High (75-100%)<\/th>Total<\/th><\/tr><\/thead>
Number of Schools<\/td>8<\/td>8<\/td>12<\/td>28<\/td><\/tr>
Total Enrollment (no.)<\/td>3971<\/td>3620<\/td>5955<\/td>13,546<\/td><\/tr>
White (no.(%))<\/td>3121 (78.59%)<\/td>2450 (67.68%)<\/td>3498 (58.74%)<\/td>9069 (66.95%)<\/td><\/tr>
Hispanic (no.(%))<\/td>301 (7.58%)<\/td>395 (10.91%)<\/td>767 (12.88%)<\/td>1463 (10.8%)<\/td><\/tr>
Asian (no. (%))<\/td>64 (1.61%)<\/td>70 (1.93%)<\/td>141 (2.37%)<\/td>275 (2.03%)<\/td><\/tr>
Black (no.(%))<\/td>63 (1.59%)<\/td>107 (2.96%)<\/td>115 (1.93%)<\/td>164 (1.21%)<\/td><\/tr>
Indian (no. (%))<\/td>22 (0.55%)<\/td>27 (0.75%)<\/td>115 (1.93%)<\/td>164 (1.21%)<\/td><\/tr>
Special Education (no. (%))<\/td>612 (15.41%)<\/td>634 (17.51%)<\/td>1138 (19.11%)<\/td>2384 (17.6%)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Catchment Characteristics<\/th>Low (0-50%)<\/th>Middle (51-74%)<\/th>High (75-100%)<\/th>Total<\/th><\/tr><\/thead>
Median Residential Property Value ($)<\/td>$212,184<\/td>$147,265<\/td>$106,517<\/td>$148,35()<\/td><\/tr>
Walkability<\/td>0.20<\/td>0.48<\/td>0.47<\/td>0.39<\/td><\/tr>
Crime Rate Per 100k people<\/td>94<\/td>349<\/td>404<\/td>300<\/td><\/tr>
Arterial Road Exposure (%)<\/td>41%<\/td>56%<\/td>52%<\/td>50%<\/td><\/tr>
Access to Green Space (%)<\/td>4.18%<\/td>3.31%<\/td>4.15%<\/td>3.92%<\/td><\/tr>
mRFEI** (%)<\/td>35.42%<\/td>15.69%<\/td>19.96%<\/td>23.16%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Weight Status (2017\u20132018)<\/th>Low (0-50%)<\/th>Middle (51-74%)<\/th>High (75-100%)<\/th>Total<\/th><\/tr><\/thead>
Students with BMl (no.)<\/td>3053<\/td>2875<\/td>4399<\/td>10,327<\/td><\/tr>
Classified as Overweight (BMl> 85th percentile)<\/td>408 (13.36%)<\/td>431 (14.99%)<\/td>786 (17.87%)<\/td>1625 (15.74%)<\/td><\/tr>
Classified as Obese (BMl > 95th percentile)<\/td>277 (9.07%)<\/td>460 (16.00%)<\/td>853 (19.39%)<\/td>1590 (15.40%)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

*School-level characteristics obtained from the Washington State Office of the superintendent of public instruction
*mRFEI modi fed retail food environment index<\/p>\n\n\n\n

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Bring Your Own Location Data: Use of Google Smartphone Location History Data for Environmental Health Research<\/h3>\n\n\n\n

Example of GTL data coverage for one individual.<\/p>\n\n\n\n

\"Google<\/figure>\n\n\n\n
\"Google<\/figure>\n\n\n\n

Temporal coverage of GTL data provided by 61 individuals. Each colored box represents the percentage of individuals who had data for the given day. In 2019 and 2020, almost 60% of the individuals had data for everyday of the year.<\/p>\n\n\n\n

\"chart<\/figure>\n\n\n\n

Differences in GTL coverage, measured using median number of days and places per day of data, by key individual, geographic and smart phone device characteristics.<\/p>\n\n\n\n

Number of People<\/th>Number of People<\/th>Median # of Days of GTL<\/th>Median # of Places per Day<\/th><\/tr><\/thead>
All Individuals<\/td>61<\/td>752<\/td>3.76<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Ages<\/th>Number of People<\/th>Median # of Days of GTL<\/th>Median # of Places per Day<\/th><\/tr><\/thead>
Under 50<\/td>43<\/td>748<\/td>3.87<\/td><\/tr>
50 and Over<\/td>18<\/td>1,345<\/td>3.67<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Sex<\/th>Number of People<\/th>Median # of Days of GTL<\/th>Median # of Places per Day<\/th><\/tr><\/thead>
Male<\/td>22<\/td>1,353<\/td>3.87<\/td><\/tr>
Female<\/td>39<\/td>748<\/td>3.80<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Race<\/th>Number of People<\/th>Median # of Days of GTL<\/th>Median # of Places per Day<\/th><\/tr><\/thead>
White<\/td>52<\/td>768<\/td>3.80<\/td><\/tr>
Non-White<\/td>9<\/td>732<\/td>3.67<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Marital Status<\/th>Number of People<\/th>Median # of Days of GTL<\/th>Median # of Places per Day<\/th><\/tr><\/thead>
Single<\/td>15<\/td>698<\/td>3.74<\/td><\/tr>
Married<\/td>33<\/td>732<\/td>3.68<\/td><\/tr>
Other<\/td>13<\/td>1,610<\/td>4.01<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Education<\/th>Number of People<\/th>Median # of Days of GTL<\/th>Median # of Places per Day<\/th><\/tr><\/thead>
High School or Less<\/td>4<\/td>970<\/td>3.30<\/td><\/tr>
Some College,
Associate, Vocational<\/td>		10<\/td>1,606<\/td>4.36<\/td><\/tr>
Bachelors or higher<\/td>47<\/td>748<\/td>3.67<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Household Income<\/th>Number of People<\/th>Median # of Days of GTL<\/th>Median # of Places per Day<\/th><\/tr><\/thead>
< $50k<\/td>7<\/td>1,130<\/td>3.83<\/td><\/tr>
50\u201380k<\/td>17<\/td>783<\/td>4.00<\/td><\/tr>
80\u2013100k<\/td>7<\/td>752<\/td>4.00<\/td><\/tr>
> 100k<\/td>28<\/td>678<\/td>3.67<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Smart Phone Device<\/th>Number of People<\/th>Median # of Days of GTL<\/th>Median # of Places per Day<\/th><\/tr><\/thead>
Apple<\/td>6<\/td>227<\/td>2.89<\/td><\/tr>
Android<\/td>10<\/td>1,700<\/td>4.01<\/td><\/tr>
Multiple<\/td>1<\/td>577<\/td>4.48<\/td><\/tr>
Missing<\/td>44<\/td>768<\/td>3.80<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
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Deep Learning of Street View Imagery to assess the built enviroment<\/h2>\n\n\n\n

Segmentation of GSV image (left) and segmented features with trees in green (right). For major built env. features the prediction accuracy is >95%<\/p>\n\n\n\n

\"map<\/figure>\n\n\n\n
Exposure Measure<\/th>Segmentation Classes Included<\/th><\/tr><\/thead>
Physical Features<\/td>
Wall, building, road, windowpane, sidewalk, house, fence, railing, signboard, skyscraper, path, stairs, runway, screen, door, screen door, stairway, bridge, bench, booth, awning, streetlight, pole, bannister, escalator, fountain, swimming pool, step, sculpture, traffic light, pier<\/td><\/tr>
Accessibility Features<\/td>Sidewalk, escalator, path, stairs, stairway, streetlight, bench, step<\/td><\/tr>
Natural Features<\/td>Tree, grass, plant, field, land, flower, water, sea, waterfall, lake, earth, rock, sky, sand, hill, dirt track<\/td><\/tr>
Green Space<\/td>Tree, grass, plant, field, flower<\/td><\/tr>
Trees<\/td>Tree<\/td><\/tr>
Blue Space<\/td>Water, sea, waterfall, lake<\/td><\/tr><\/tbody><\/table><\/figure>\n","protected":false},"excerpt":{"rendered":"

Identification of a Hot-Spot of Premature Death from Colorectal Cancer among Non-Whites in Southeast Washington State, by Distance to Ambulatory Endoscopy Services Background This study evaluates relationships among race, access to endoscopy services, and colorectal cancer (CRC) mortality in Washington state (WA). Methods We overlayed the locations of ambulatory endoscopy services with place of residence […]<\/p>\n","protected":false},"author":34189,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"categories":[],"tags":[],"wsuwp_university_location":[],"wsuwp_university_org":[],"_links":{"self":[{"href":"https:\/\/labs.wsu.edu\/chase\/wp-json\/wp\/v2\/pages\/366"}],"collection":[{"href":"https:\/\/labs.wsu.edu\/chase\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/labs.wsu.edu\/chase\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/labs.wsu.edu\/chase\/wp-json\/wp\/v2\/users\/34189"}],"replies":[{"embeddable":true,"href":"https:\/\/labs.wsu.edu\/chase\/wp-json\/wp\/v2\/comments?post=366"}],"version-history":[{"count":16,"href":"https:\/\/labs.wsu.edu\/chase\/wp-json\/wp\/v2\/pages\/366\/revisions"}],"predecessor-version":[{"id":497,"href":"https:\/\/labs.wsu.edu\/chase\/wp-json\/wp\/v2\/pages\/366\/revisions\/497"}],"wp:attachment":[{"href":"https:\/\/labs.wsu.edu\/chase\/wp-json\/wp\/v2\/media?parent=366"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/labs.wsu.edu\/chase\/wp-json\/wp\/v2\/categories?post=366"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/labs.wsu.edu\/chase\/wp-json\/wp\/v2\/tags?post=366"},{"taxonomy":"wsuwp_university_location","embeddable":true,"href":"https:\/\/labs.wsu.edu\/chase\/wp-json\/wp\/v2\/wsuwp_university_location?post=366"},{"taxonomy":"wsuwp_university_org","embeddable":true,"href":"https:\/\/labs.wsu.edu\/chase\/wp-json\/wp\/v2\/wsuwp_university_org?post=366"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}