description: This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census 1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey. 2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons. 3. description: Provides a concise description of the variable. 4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS. 5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (CountSE). DEMOGRAPHIC CATEGORIES 1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable. 2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314 columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use). 3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest. 4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data. 5. education: Educational attainment is divided into "No Diploma," "High School Grad," "Some College," and "College Grad." High school graduates are considered to include GED completers, and those with some college include community college attendees (and graduates) and those who have attended certain postsecondary vocational or technical schools--in other words, it signifies additional education beyond high school, but short of attaining a bachelor's degree or equivilent. Note that educational attainment is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by education, even if they are otherwise considered part of the universe for the variable of interest. 6. sex: "Male" and "Female" are the two groups in this category. The CPS does not currently provide response options for intersex individuals. 7. race: This category includes "White," "Black," "Hispanic," "Asian," "Am Indian," and "Other" groups. The CPS asks about Hispanic origin separately from racial identification; as a result, all persons identifying as Hispanic are in the Hispanic group, regardless of how else they identify. Furthermore, all non-Hispanic persons identifying with two or more races are tallied in the "Other" group (along with other less-prevelant responses). The Am Indian group includes both American Indians and Alaska Natives. 8. disability: Disability status is divided into "No" and "Yes" groups, indicating whether the person was identified as having a disability. Disabilities screened for in the CPS include hearing impairment, vision impairment (not sufficiently correctable by glasses), cognitive difficulties arising from physical, mental, or emotional conditions, serious difficulty walking or climbing stairs, difficulty dressing or bathing, and difficulties performing errands due to physical, mental, or emotional conditions. The Census Bureau began collecting data on disability status in June 2008; accordingly, this category is unavailable in Supplements prior to that date. Note that disability status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by disability status, even if they are otherwise considered part of the universe for the variable of interest. 9. metro: Metropolitan status is divided into "No," "Yes," and "Unkown," reflecting information in the dataset about the household's location. A household located within a metropolitan statistical area is assigned to the Yes group, and those outside such areas are assigned to No. However, due to the risk of de-anonymization, the metropolitan area status of certain households is unidentified in public use datasets. In those cases, the Census Bureau has determined that revealing this geographic information poses a disclosure risk. Such households are tallied in the Unknown group. 10. scChldHome: 11.

The Priority Neighborhoods dataset is a part of the City of Oakland Department of Transportation's (OakDOT's) Geographic Equity Toolbox. The Priority Neighborhoods GIS dataset relies upon demographic data from the American Community Survey (ACS). This dataset assigns each census tract in Oakland a numerical priority value and a quantile from lowest and highest, as determined by the following seven weighted demographic factors (with weights in brackets "[XX%]"): • People of Color [25%] • Low-income Households (<50% of Area Median Income for a 4-person household) [25%] • People with Disability [10%] • Seniors 65 Years and Over [10%] • Single Parent Families [10%] • Severely Rent-Burdened Households [10%] • Low Educational Attainment (less than a bachelor's degree) [10%]

This dataset was last updated in October 2024 with data from the 2022 5-year (i.e., averaged from 2018 through 2022) American Community Survey (ACS). The ACS is managed by the United States Census Bureau; learn more about the ACS at: https://www.census.gov/programs-surveys/acs.

See the online map and read the methodology at: https://www.oaklandca.gov/resources/oakdot-geographic-equity-toolbox. This dataset is maintained by the OakDOT Race and Equity Team; learn more about the team at: https://www.oaklandca.gov/topics/oakdot-race-and-equity-team.

Field Descriptions: • TRACT: Census Tract Number • QUINTILE: Priority Quintile (calculated) • PLAN_AREA: OakDOT Planning Area • POPULATION: Population (average from 2018 through 2022) • PCT_POC: Percent People of Color • PCT_INC: Percent Low Income • PCT_SRB: Percent Severely Rent-Burdened • PCT_PWD: People with a Disability • PCT_SENIOR: Percent Seniors • PCT_SPH: Percent Single Parent Households • PCT_EDU: Percent Low Educational Attainment • RAT_POC: Ratio of People of Color (compared to Citywide average) • RAT_INC: Ratio of Low Income (compared to Citywide average) • RAT_SRB: Ratio of Severely Rent-Burdened (compared to Citywide average) • RAT_PWD: Ratio of People with a Disability (compared to Citywide average) • RAT_SENIOR: Ratio of Seniors (compared to Citywide average) • RAT_SPH: Ratio of Single Parent Households (compared to Citywide average) • RAT_EDU: Ratio of Low Educational Attainment (compared to Citywide average) • RAT_SCORE: Priority Ratio (compared to Citywide average) • ALAND: Land Area in square feet

City of Oakland, Department of Transportation (OakDOT) 250 Frank H. Ogawa Plaza, Suite 4314 | Oakland, CA 94612

BackgroundPoststroke depression (PSD) is a highly prevalent and serious mental health condition affecting a significant proportion of stroke survivors worldwide. While its exact causes remain under investigation, managing PSD presents a significant challenge.AimThis study aimed to evaluate the prevalence and predictors of depression among Bangladeshi stroke victims.MethodsA cross-sectional study was carried out with 725 stroke victims who were receiving medical care at three designated tertiary care hospitals in Sylhet from January to December 2023. Depression and disability were measured using the Patient Health Questionnaire-9 and the Modified Rankin Scale. Logistic regression analysis was employed to examine the predictors linked to depression.ResultsAccording to the study, 80.8% of individuals had moderate to severe disability, and 58.1% of them experienced a moderate to severe level of depression. Individuals who had hemorrhagic stroke (AOR 1.31, 95% CI: 0.77–2.25), repeated episodes (AOR 3.41, 95% CI: 1.89–6.14), tobacco use (AOR 1.76, 95% CI: 1.16–2.67), or coexisting health conditions (AOR 1.68, 95% CI: 1.00–2.82) exhibited elevated levels of depression. Participants whose medical expenses covered by relatives or others were six times more likely to experience depressive symptoms (AOR 6.32, 95% CI: 1.61–24.76). Individuals who did not receive rehabilitation services had two times greater odds of being depressed (OR 1.85, 95% CI: 1.23–2.77, p = 0.003). Consequently, individuals with low functional status had eleven times greater levels of depression (AOR 11.03, 95% CI: 7.14–17.04).ConclusionMore than half of the participants in this present study reported moderate to extreme levels of depression which is a serious health issue among Bangladeshi stroke survivors. Understanding the predictors of depression linked to stroke could enhance the effectiveness of therapeutic interventions for this condition. In addition, multidisciplinary teams should work collaboratively to address this serious issue.

Sub-district level socio-economic and environmental data.