When analyzing the ratio of homelessness to state population, New York, Vermont, and Oregon had the highest rates in 2023. However, Washington, D.C. had an estimated 73 homeless individuals per 10,000 people, which was significantly higher than any of the 50 states. Homeless people by race The U.S. Department of Housing and Urban Development performs homeless counts at the end of January each year, which includes people in both sheltered and unsheltered locations. The estimated number of homeless people increased to 653,104 in 2023 – the highest level since 2007. However, the true figure is likely to be much higher, as some individuals prefer to stay with family or friends - making it challenging to count the actual number of homeless people living in the country. In 2023, nearly half of the people experiencing homelessness were white, while the number of Black homeless people exceeded 243,000. How many veterans are homeless in America? The  number of homeless veterans in the United States has halved since 2010. The state of California, which is currently suffering a homeless crisis, accounted for the highest number of homeless veterans in 2022. There are many causes of homelessness among veterans of the U.S. military, including post-traumatic stress disorder (PTSD), substance abuse problems, and a lack of affordable housing.

In 2023, there were about 653,104 homeless people estimated to be living in the United States, the highest number of homeless people recorded within the provided time period. In comparison, the second-highest number of homeless people living in the U.S. within this time period was in 2007, at 647,258. How is homelessness calculated? Calculating homelessness is complicated for several different reasons. For one, it is challenging to determine how many people are homeless as there is no direct definition for homelessness. Additionally, it is difficult to try and find every single homeless person that exists. Sometimes they cannot be reached, leaving people unaccounted for. In the United States, the Department of Housing and Urban Development calculates the homeless population by counting the number of people on the streets and the number of people in homeless shelters on one night each year. According to this count, Los Angeles City and New York City are the cities with the most homeless people in the United States. Homelessness in the United States Between 2022 and 2023, New Hampshire saw the highest increase in the number of homeless people. However, California was the state with the highest number of homeless people, followed by New York and Florida. The vast amount of homelessness in California is a result of multiple factors, one of them being the extreme high cost of living, as well as opposition to mandatory mental health counseling and drug addiction. However, the District of Columbia had the highest estimated rate of homelessness per 10,000 people in 2023. This was followed by New York, Vermont, and Oregon.

"Ratio of Homeless Population to General Population in major US Cities in 2012. *This represents a list of large U.S. cities for which DHS was able to confirm a recent estimate of the unsheltered population. Unsheltered estimates are from 2011 except for Seattle and New York City (2012) and Chicago (2009). All General Population figures are from the 2010 U.S. Census enumeration."

Yearly statewide and by-Continuum of Care total counts of individuals receiving homeless response services by age group, race, and gender.

This data comes from the Homelessness Data Integration System (HDIS), a statewide data warehouse which compiles and processes data from all 44 California Continuums of Care (CoC)—regional homelessness service coordination and planning bodies. Each CoC collects data about the people it serves through its programs, such as homelessness prevention services, street outreach services, permanent housing interventions and a range of other strategies aligned with California’s Housing First objectives.

The dataset uploaded reflects the 2024 HUD Data Standard Changes. Previously, Race and Ethnicity are separate files but are now combined.

Information updated as of 2/06/2025.

Homelessness Report January 2025. Published by Department of Housing, Local Government, and Heritage. Available under the license Creative Commons Attribution Share-Alike 4.0 (CC-BY-SA-4.0).Homelessness data Official homelessness data is produced by local authorities through the Pathway Accommodation and Support System (PASS). PASS was rolled-out nationally during the course of 2013. The Department’s official homelessness statistics are published on a monthly basis and refer to the number of homeless persons accommodated in emergency accommodation funded and overseen by housing authorities during a specific count week, typically the last full week of the month. The reports are produced through the Pathway Accommodation & Support System (PASS), collated on a regional basis and compiled and published by the Department. Homelessness reporting commenced in this format in 2014. The format of the data may change or vary over time due to administrative and/or technology changes and improvements. The administration of homeless services is organised across nine administrative regions, with one local authority in each of the regions, “the lead authority”, having overall responsibility for the disbursement of Exchequer funding. In each region a Joint Homelessness Consultative Forum exists which includes representation from the relevant State and non-governmental organisations involved in the delivery of homeless services in a particular region. Delegated arrangements are governed by an annually agreed protocol between the Department and the lead authority in each region. These protocols set out the arrangements, responsibilities and financial/performance data reporting requirements for the delegation of funding from the Department. Under Sections 38 and 39 of the Housing (Miscellaneous Provisions) Act 2009 a statutory Management Group exists for each regional forum. This is comprised of representatives from the relevant housing authorities and the Health Service Executive, and it is the responsibility of the Management Group to consider issues around the need for homeless services and to plan for the implementation, funding and co-ordination of such services. In relation to the terms used in the report for the accommodation types see explanation below: PEA - Private Emergency Accommodation: this may include hotels, B&Bs and other residential facilities that are used on an emergency basis. Supports are provided to services users on a visiting supports basis. STA - Supported Temporary Accommodation: accommodation, including family hubs, hostels, with onsite professional support. TEA - Temporary Emergency Accommodation: emergency accommodation with no (or minimal) support....

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and ZIP Codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential ZIP Code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality.

For the ZIP Code-level life expectancy calculation, it is assumed that postal ZIP Codes share the same boundaries as ZIP Code Census Tabulation Areas (ZCTAs). More information on the relationship between ZIP Codes and ZCTAs can be found at http://www.census.gov/geo/reference/zctas.html. ZIP Code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 ZIP Code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for ZIP Codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest ZIP Code with population. ZIP Code population for 2000 estimates comes from the Decennial Census. ZIP Code population for 2013 estimates are from the American Community Survey (5-Year Average). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly important for ZCTAs with high homeless population (not living in group quarters) where the ACS may underestimate the ZCTA population and therefore underestimate the life expectancy. The ACS provides ZIP Code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to ZIP Codes based on majority land-area.

ZIP Codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, ZIP Codes with populations of less than 5,000 were aggregated with neighboring ZIP Codes until the merged areas had a population of more than 5,000. ZIP Code 94103, representing Treasure Island, was dropped from the dataset due to its small population and having no bordering ZIP Codes. In this way, the original 305 Bay Area ZIP Codes were reduced to 217 ZIP Code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

There are several forms, regulations and data associated with the Emergency Assistance (EA) Family Shelter Program for our business partners and constituents.

Abstract copyright UK Data Service and data collection copyright owner.

The Homelessness Case Level Information Collection (H-CLIC) project aims to create a linked dataset of information about homelessness in England, to improve understanding of its causes and impacts. It is the result of a collaboration between the Office for National Statistics (ONS) and the Department for Levelling Up, Housing and Communities (DLUHC). The project will allow decision-makers to develop more effective policies to reduce homelessness and improve the lives of people across the country. Specifically, the H-CLIC project will involve linking data from across local authorities in England, and to other administrative datasets. The H-CLIC data records local authorities' actions under the 2017 Homelessness Reduction Act, which significantly reformed England's homelessness legislation by placing duties on local authorities to intervene at earlier stages to prevent and reduce homelessness. The H-CLIC project has three elements, some of which will be phased:

• The first aims to use the linked H-CLIC data from across local authorities to develop a better understanding of whether homelessness is resolved in the long term, particularly across local authority boundaries. This will help to establish what works to prevent homelessness, and which elements of the Homelessness Reduction Act are most effective.
• The second aims to match together H-CLIC data and the Rough Sleeping Evaluation Questionnaire to identify how effectively interventions have prevented homelessness and improved other outcomes in the longer term. DLUHC already have rough sleeping evaluations in progress that would benefit from this work.
• Finally, the third element aims to match the H-CLIC data to data gathered from other government departments/health agencies to determine the wider circumstances and outcomes of people who have experienced homelessness, such as educational outcomes, employment, benefits and health. This will enable us to identify the wider impacts and longer-term outcomes, and estimate the costs of homelessness.

The DLUHC intends to use the data to assess the implementation of the Homelessness Reduction Act, for example by identifying the factors associated with better or worse outcomes for households at risk of homelessness and to understand more about the factors that drive homelessness and how best to address them.

Ultimately, the project will provide central government departments, local public services and delivery partners with valuable information about the cycle of homelessness and its impact on the lives of those it affects, as well as the impact and cost-benefit of interventions and services targeted at reducing homelessness. The information should be useful to inform future service design and reform and investment decisions.

Further information, including reports and tables, may be found on the Gov.uk Homelessness Statistics Collection webpage.

For the second edition (August 2024), data files for 2020-2021 and 2021-2022 have been added, and the data file for 2019-2020 has been updated to include additional cases. The documentation has also been expanded and updated.

Main Topics:

The variables include:

• Local authority code, name and region;
• Assessment of the household’s homelessness circumstances; year and the quarter of the original assessment of circumstances and needs;
• Household's accommodation arrangements at the time of their homeless application; household's last settled accommodation; main reason for loss of last settled home;
• Household type; ethnic group, age, gender, nationality and employment status of the main applicant; employment status of the main applicant's partner if in same household;
• Main prevention activity undertaken by the local authority as part of the Prevention Duty and reason for that; household’s accommodation when the Prevention Duty ended;
• Main relief activity that was undertaken by the local authority as part of the Relief Duty; reason the Relief Duty was ended; whether household was homeless at the end of Relief;
• Coded outcome of the decision on what duty (if any) is owed; household’s accommodation following that decision;
• Year and quarter that the household entered and left local authority temporary accommodation;
• Whether the household is in receipt of housing benefits or means-tested benefits;
• Whether under support needs the household includes:
  • Anyone in specific categories covering young persons, young parents, care leavers, older people, anyone with physical/mental ill health and/or a physical or learning disability;
  • anyone who has experience of, or is at risk of, specific types of exploitation or abuse;
  • anyone with drug or alcohol dependency needs, or with an...

In 2023, the around 11.1 percent of the population was living below the national poverty line in the United States. Poverty in the United StatesAs shown in the statistic above, the poverty rate among all people living in the United States has shifted within the last 15 years. The United Nations Educational, Scientific and Cultural Organization (UNESCO) defines poverty as follows: “Absolute poverty measures poverty in relation to the amount of money necessary to meet basic needs such as food, clothing, and shelter. The concept of absolute poverty is not concerned with broader quality of life issues or with the overall level of inequality in society.” The poverty rate in the United States varies widely across different ethnic groups. American Indians and Alaska Natives are the ethnic group with the most people living in poverty in 2022, with about 25 percent of the population earning an income below the poverty line. In comparison to that, only 8.6 percent of the White (non-Hispanic) population and the Asian population were living below the poverty line in 2022. Children are one of the most poverty endangered population groups in the U.S. between 1990 and 2022. Child poverty peaked in 1993 with 22.7 percent of children living in poverty in that year in the United States. Between 2000 and 2010, the child poverty rate in the United States was increasing every year; however,this rate was down to 15 percent in 2022. The number of people living in poverty in the U.S. varies from state to state. Compared to California, where about 4.44 million people were living in poverty in 2022, the state of Minnesota had about 429,000 people living in poverty.

Abstract

The 2011 Population and Housing Census is the third national Census to be conducted in Namibia after independence. The first was conducted 1991 followed by the 2001 Census. Namibia is therefore one of the countries in sub-Saharan Africa that has participated in the 2010 Round of Censuses and followed the international best practice of conducting decennial Censuses, each of which attempts to count and enumerate every person and household in a country every ten years. Surveys, by contrast, collect data from samples of people and/or households.

Censuses provide reliable and critical data on the socio-economic and demographic status of any country. In Namibia, Census data has provided crucial information for development planning and programme implementation. Specifically, the information has assisted in setting benchmarks, formulating policy and the evaluation and monitoring of national development programmes including NDP4, Vision 2030 and several sector programmes. The information has also been used to update the national sampling frame which is used to select samples for household-based surveys, including labour force surveys, demographic and health surveys, household income and expenditure surveys. In addition, Census information will be used to guide the demarcation of Namibia's administrative boundaries where necessary.

At the international level, Census information has been used extensively in monitoring progress towards Namibia's achievement of international targets, particularly the Millennium Development Goals (MDGs).

The latest and most comprehensive Census was conducted in August 2011. Preparations for the Census started in the 2007/2008 financial year under the auspices of the then Central Bureau of Statistics (CBS) which was later transformed into the Namibia Statistics Agency (NSA). The NSA was established under the Statistics Act No. 9 of 2011, with the legal mandate and authority to conduct population Censuses every 10 years. The Census was implemented in three broad phases; pre-enumeration, enumeration and post enumeration.

During the first pre-enumeration phase, activities accomplished including the preparation of a project document, establishing Census management and technical committees, and establishing the Census cartography unit which demarcated the Enumeration Areas (EAs). Other activities included the development of Census instruments and tools, such as the questionnaires, manuals and field control forms.

Field staff were recruited, trained and deployed during the initial stages of the enumeration phase. The actual enumeration exercise was undertaken over a period of about three weeks from 28 August to 15 September 2011, while 28 August 2011 was marked as the reference period or 'Census Day'.

Great efforts were made to check and ensure that the Census data was of high quality to enhance its credibility and increase its usage. Various quality controls were implemented to ensure relevance, timeliness, accuracy, coherence and proper data interpretation. Other activities undertaken to enhance quality included the demarcation of the country into small enumeration areas to ensure comprehensive coverage; the development of structured Census questionnaires after consultat.The post-enumeration phase started with the sending of completed questionnaires to Head Office and the preparation of summaries for the preliminary report, which was published in April 2012. Processing of the Census data began with manual editing and coding, which focused on the household identification section and un-coded parts of the questionnaire. This was followed by the capturing of data through scanning. Finally, the data were verified and errors corrected where necessary. This took longer than planned due to inadequate technical skills.

Geographic coverage

National coverage

Analysis unit

Household and person/individual

Universe

The sampling universe is defined as all households (private and institutions) from 2011 Census dataset.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Sample Design The stratified random sample was applied on the constituency and urban/rural variables of households list from Namibia 2011 Population and Housing Census for the Public Use Microdata Sample (PUMS) file. The sampling universe is defined as all households (private and institutions) from 2011 Census dataset. Since urban and rural are very important factor in the Namibia situation, it was then decided to take the stratum at the constituency and urban/rural levels. Some constituencies have very lower households in the urban or rural, the office therefore decided for a threshold (low boundary) for sampling within stratum. Based on data analysis, the threshold for stratum of PUMS file is 250 households. Thus, constituency and urban/rural areas with less than 250 households in total were included in the PUMS file. Otherwise, a simple random sampling (SRS) at a 20% sample rate was applied for each stratum. The sampled households include 93,674 housing units and 418,362 people.

Sample Selection The PUMS sample is selected from households. The PUMS sample of persons in households is selected by keeping all persons in PUMS households. Sample selection process is performed using Census and Survey Processing System (CSPro).

The sample selection program first identifies the 7 census strata with less than 250 households and the households (private and institutions) with more than 50 people. The households in these areas and with this large size are all included in the sample. For the other households, the program randomly generates a number n from 0 to 4. Out of every 5 households, the program selects the nth household to export to the PUMS data file, creating a 20 percent sample of households. Private households and institutions are equally sampled in the PUMS data file.

Note: The 7 census strata with less than 250 households are: Arandis Constituency Rural, Rehoboth East Urban Constituency Rural, Walvis Bay Rural Constituency Rural, Mpungu Constituency Urban, Etayi Constituency Urban, Kalahari Constituency Urban, and Ondobe Constituency Urban.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following questionnaire instruments were used for the Namibia 2011 Population and and Housing Census: - Form A (Long Form): For conventional households and residential institutions - Form B1 (Short Form): For special population groups such as persons in transit (travellers), police cells, homeless and off-shore populations - Form B2 (Short Form): For hotels/guesthouses - Form B3 (Short Form): For foreign missions/diplomatic corps - Form C: For recording Emigrant characteristics

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) During data collection in the field b) Manual editing and coding in the office c) During data entry (Primary validation/editing) Structure checking and completeness using Structured Query Language (SQL) program d) Secondary editing: i. Imputations of variables ii. Structural checking in Census and Survey Processing System (CSPro) program

Sampling error estimates

Sampling Error The standard errors of survey estimates are needed to evaluate the precision of the survey estimation. The statistical software package such as SPSS or SAS can accurately estimate the mean and variance of estimates from the survey. SPSS or SAS software package makes use of the Taylor series approach in computing the variance.

Data appraisal

Data Quality Great efforts were made to check and ensure that the Census data was of high quality to enhance its credibility and increase its usage. Various quality controls were implemented to ensure relevance, timeliness, accuracy, coherence and proper data interpretation. Other activities undertaken to enhance quality included the demarcation of the country into small enumeration areas to ensure comprehensive coverage; the development of structured Census questionnaires after consultation with government ministries, university expertise and international partners; the preparation of detailed supervisors' and enumerators' instruction manuals to guide field staff during enumeration; the undertaking of comprehensive publicity and advocacy programmes to ensure full Government support and cooperation from the general public; the testing of questionnaires and other procedures; the provision of adequate training and undertaking of intensive supervision using four supervisory layers; the editing of questionnaires at field level; establishing proper mechanisms which ensured that all completed questionnaires were properly accounted for; ensuring intensive verification, validating all information and error corrections; and developing capacity in data processing with support from the international community.

Study Objective: Facemask use is associated with reduced transmission of SARS-CoV-2. Most surveys assessing perceptions and practices of mask use miss the most vulnerable racial, ethnic, and socio-economic populations. These same populations have suffered disproportionate impacts from the pandemic. The purpose of this study was to assess beliefs, access, and practices of mask wearing across 15 urban emergency department (ED) populations. Methods: This was a secondary analysis of a cross-sectional study of ED patients from December 2020 to March 2021 at 15 geographically diverse, safety net EDs across the US. The primary outcome was frequency of mask use outside the home and around others. Other outcome measures included having enough masks and difficulty obtaining them. Results: Of 2,575 patients approached, 2,301 (89%) agreed to participate; nine had missing data pertaining to the primary outcome, leaving 2,292 included in the final analysis. A total of 79% of respondents reported wearing masks “all of the time” and 96% reported wearing masks over half the time. Subjects with PCPs were more likely to report wearing masks over half the time compared to those without PCPs (97% vs 92%). Individuals experiencing homelessness were less likely to wear a mask over half the time compared to those who were housed (81% vs 96%). Conclusions: Study participants reported high rates of facemask use. Respondents who did not have PCPs and those who were homeless were less likely to report wearing a mask over half the time and more likely to report barriers in obtaining masks. The ED may serve a critical role in education regarding, and provision of, masks for vulnerable populations. Methods Study Design and Setting We conducted this secondary analysis of a previously published study regarding ED patients perceptions’ of COVID-19 vaccination.[13] The parent study was a prospective, cross-sectional survey of ED patients at 15 safety net EDs in 14 US cities. The University of California Institutional Review Board approved this study. Verbal consent was obtained. Data Processing Participant ethnicity (Latinx/non-Latinx) and race were self-reported. We categorized those who self-identified as any race other than Latinx as ‘reported race’, non-Latinx (i.e. Black, non-Latinx and White, non-Latinx). If the patient identified themselves as Latinx, they were placed in that category and not in that of any other race. If an individual identified as more than one non-Latinx race, they were categorized as multiracial. Individuals who reported that they were currently applying for health insurance, were unsure if they were insured, or if their response to the question was missing (18 respondents) were categorized as uninsured in a binary variable, and separate analysis was done based on type of insurance reported. The survey submitted in our supplement (S1) is the version used at the lead site. Each of the remaining sites revised their survey to include wording applicable to their community (i.e., the site in Los Angeles changed Healthy San Francisco to Healthy Los Angeles), and these local community health plans were coded together. We identified individuals who reported English and Spanish as their primary language, and grouped those who reported Arabic, Bengali, Cantonese, Tagalog, or Other as “Other” primary language. With regards to gender, we categorized those who identified as gender queer, nonbinary, trans man and trans woman as “other”. Study Outcomes and Key Variables Our primary outcome was subjects’ response to the question, “Do you wear a mask when you are outside of your home when you are around other people?” with answer choices a) always, b) most of the time (more than 50%), c) sometimes, but less than half of the time (less than 50%), and d) I never wear a mask. Respondents were provided with these percentages to help quantify their responses. We stratified respondents into two groups: those who responded always or most of the time as “wears masks over half the time” and those who responded sometimes or never as “wears masks less than half the time. We sorted each of the 15 sites into four geographic regions within the United States. There were 3 sites located in New Jersey, Massachusetts, and Pennsylvania which we categorized in the Northeast region. We categorized 3 sites in Michigan and Iowa as Midwest, and 3 sites in North Carolina, Louisiana, and Maryland as the South. There were 6 sites located on the West Coast from California and Washington State.

The index is constructed using socioeconomic and demographic, exposure, health, and housing indicators and is intended to serve as a planning tool for health and climate adaptation. Steps for calculating the index can be found in in the "An Assessment of San Francisco’s Vulnerability to Flooding & Extreme Storms" located at https://sfclimatehealth.org/wp-content/uploads/2018/12/FloodVulnerabilityReport_v5.pdf.pdfData Dictionary: (see attachment here also: https://data.sfgov.org/Health-and-Social-Services/San-Francisco-Flood-Health-Vulnerability/cne3-h93g)

Field Name Data Type Definition Notes (optional)

Census Blockgroup Text San Francisco Census Block Groups

Children Numeric Percentage of residents under 18 years old. American Community Survey 2009 - 2014.

Chidlren_wNULLvalues Numeric Percentage of residents under 18 years old. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Elderly Numeric Percentage of residents aged 65 and older. American Community Survey 2009 - 2014.

Elderly_wNULLvalues Numeric Percentage of residents aged 65 and older. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

NonWhite Numeric Percentage of residents that do not identify as white (not Hispanic or Latino). American Community Survey 2009 - 2014.

NonWhite_wNULLvalues Numeric Percentage of residents that do not identify as white (not Hispanic or Latino). American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Poverty Numeric Percentage of all individuals below 200% of the poverty level. American Community Survey 2009 - 2014.

Poverty_wNULLvalues Numeric Percentage of all individuals below 200% of the poverty level. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Education Numeric Percent of individuals over 25 with at least a high school degree. American Community Survey 2009 - 2014.

Education_wNULLvalues Numeric Percent of individuals over 25 with at least a high school degree. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

English Numeric Percentage of households with no one age 14 and over who speaks English only or speaks English "very well". American Community Survey 2009 - 2014.

English_wNULLvalues Numeric Percentage of households with no one age 14 and over who speaks English only or speaks English "very well". American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Elevation Numeric Minimum elevation in feet. United States Geologic Survey 2011.

SeaLevelRise Numeric Percent of land area in the 100-year flood plain with 36-inches of sea level rise. San Francisco Sea Level Rise Committee, AECOM 77inch flood inundation layer, 2014.

Precipitation Numeric Percent of land area with over 6-inches of projected precipitation-related flood inundation during an 100-year storm. San Francisco Public Utilities Commission, AECOM, 2015.

Diabetes Numeric Age-adjusted hospitalization rate due to diabetes; adults 18+. California Office of Statewide Health Planning and Development, 2004-2015.

MentalHealth Numeric Age-adjusted hospitalization rate due to schizophrenia and other psychotic disorders. California Office of Statewide Health Planning and Development, 2004-2015.

Asthma Numeric Age-adjusted hospitalization rate due to asthma; adults 18+. California Office of Statewide Health Planning and Development, 2004 - 2015.

Disability Numeric Percentage of total civilian noninstitutionalized population with a disability. American Community Survey 2009 - 2014.

Disability_wNULLvalues

Percentage of total civilian noninstitutionalized population with a disability. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

HousingQuality Numeric Annual housing violations, per 1000 residents. San Francisco Department of Public Health, San Francisco Department of Building Inspections, San Francisco Fire Department, 2010 - 2012.

Homeless Numeric Homeless population, per 1000 residents. San Francisco Homeless Count 2015.

LivAlone Numeric Households with a householder living alone. American Community Surevey 2009 - 2014.

LivAlone_wNULLvalues Numeric Households with a householder living alone. American Community Surevey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

FloodHealthIndex Numeric Comparative ranking of flood health vulnerability, by block group. The Flood Health Index weights the six socioeconomic and demographic indicators (Children, Elderly, NonWhite, Poverty, Education, English) as 20% of the final score, the three exposure indicators (Sea Level Rise, Precipitation, Elevation) as 40% of the final score, the four health indicators (Diabetes, MentalHealth, Asthma, Disability) as 20% of the final score, and the three housing indicators (HousingQuality, Homeless, LivAlone) as 20% of the final score. For methodology used to develop the final Flood Health Index, please read the San Francisco Flood Vulnerability Assessment Methodology Section.

FloodHealthIndex_Quintiles Numeric Comparative ranking of flood health vulnerability, by block group. The Flood Health Index weights the six socioeconomic and demographic indicators (Children, Elderly, NonWhite, Poverty, Education, English) as 20% of the final score, the three exposure indicators (Sea Level Rise, Precipitation, Elevation) as 40% of the final score, the four health indicators (Diabetes, MentalHealth, Asthma, Disability) as 20% of the final score, and the three housing indicators (HousingQuality, Homeless, LivAlone) as 20% of the final score. For methodology used to develop the final Flood Health Index, please read the San Francisco Flood

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

Abstract

The MPLCS 2015 is a comprehensive study of how people in Myanmar live. It is a joint analysis conducted by a technical team from the Ministry of Planning and Finance, Government of Myanmar, and the Poverty and Equity Global Practice of the World Bank. It collects data on the occupations of people, how much income they earn, and how they use this to meet the food, housing, health, education, and other needs of their families.

The Myanmar Poverty and Living Conditions Survey has the following objectives: - Put forward trends in poverty between 2004/05, 2009/10 and 2015 - Present a measure of poverty that reflects the situation of poverty in Myanmar in 2015 at the national, urban/rural and agro-zone - Conduct analysis about the situation and nature of poverty in Myanmar that informs policy choices and strategies.

Geographic coverage

National coverage. The survey is a representative of the Union Territory, four agro-zones, and urban/rural areas.

Analysis unit

• Households
• Individuals
• Agricultural parcel and crops
• Consumption items

Universe

The survey covered only the usual household residents, excluding people living in hotels/motels/guesthouses, military camps, police camps, orphanages/homes for the aged, religious centers, boarding schools/colleges/universities, correctional facilities/prisons, hospitals, camps/hostels for workers, and homeless/other collective quarters.

Kind of data

Sample survey data [ssd]

Sampling procedure

The MPLCS sample design was developed based on the sampling frame from the April 2014 Census pre-enumeration listing data. In addition to providing statistically representative estimates at the national level, the sample was designed so that representative estimates were derived for each of four agro-ecological zones (Hills and Mountains, Dry Zone, Coastal and Delta), for the urban/rural levels overall, and specifically Yangon and surrounding area. The data are not representative at the state or region level.

The sample primary sampling units (PSUs) for this sample are the enumeration areas (EAs) defined for the 2014 Myanmar Population and Housing Census. There are 304 EAs and 3648 sample households.

A stratified multi-stage sample design is used for the MLPCS 2015. The stratum are agro--ecological zone and rural/urban. The classification of the EAs in the 2014 Myanmar Census of Population and Housing frame by urban and rural stratum was based on the administrative structure of the hierarchical geographic areas in Myanmar; all EAs in administrative areas defined as wards are considered urban, and all EAs in village tracks are classified as rural. The distribution of the households in the 2014 Myanmar Census of Population and Housing frame by region, urban and rural stratum, based on the preliminary Census data.

Sampling deviation

A total of 14 sample EAs selected for the MPLCS could not be enumerated, mostly because of security problems.

Refer to MPLCS 2014/15 Survey Conduct and Quality Control Report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The MPLCS questionnaire builds from earlier household expenditure and living conditions surveys conducted in Myanmar, in particular, the Integrated Household Living Conditions Assessment (IHCLA-I, 2005 and IHLCA-II, 2010) and the Household Income and Expenditure Survey (between 1989 and 2012) and WORLD BANK's LIVING STANDARD surveys. The MPLCS brings all these previous household surveys together into a single survey and provides one comprehensive source of living conditions information.

The MPLCS 2014/2015 household questionnaire consists of 13 modules. 1. Roster 2. Education and literacy 3a. Health status 3b. Health care 4. Labor and employment 5a. International migration (current household members) 5b. Remittances (former household members and others) 6. Housing 7. Household assets/durables 8a. Household consumption in the last 7 days 8b. Non-food consumption expenditure in the last 30 days 8c. Non-food consumption expenditure in 6 and 12 months 9. Non-farm enterprises 10a. Parcel roster 10b. Inputs 10c. Labor 10d. Harvest and crop disposition 10e. Livestock 10f. Agricultural machinery and equipment 10g. Aquaculture and fisheries 11a. Loans/credit 11b. Financial inclusion 12. Food security/subjective assessment of well-being 13. Shocks and coping strategies

Sampling error estimates

Tables with calculated sampling errors and confidence intervals for the most important survey estimates, the different sources of non-sampling error presented in MPLCS 2015 Survey Conduct and Quality Control Report section 5.

Data appraisal

For detail of data quality control and measurement, see in MPLCS 2015 Survey Conduct and Quality Control Report section 3.5.

A comparison of the race and ethnicity of highly capable students compared to the same demographic groups in the general student population. Comparisons are also included for the Low-Income, English Language Learners, Students with Disabilities, Section 504, Homeless, and Highly Mobile student groups. Data is aggregated by school, district, and state level.

Out of all OECD countries, Cost Rica had the highest poverty rate as of 2022, at over 20 percent. The country with the second highest poverty rate was the United States, with 18 percent. On the other end of the scale, Czechia had the lowest poverty rate at 6.4 percent, followed by Denmark.

The significance of the OECD

The OECD, or the Organisation for Economic Co-operation and Development, was founded in 1948 and is made up of 38 member countries. It seeks to improve the economic and social well-being of countries and their populations. The OECD looks at issues that impact people’s everyday lives and proposes policies that can help to improve the quality of life.

Poverty in the United States

In 2022, there were nearly 38 million people living below the poverty line in the U.S.. About one fourth of the Native American population lived in poverty in 2022, the most out of any ethnicity. In addition, the rate was higher among young women than young men. It is clear that poverty in the United States is a complex, multi-faceted issue that affects millions of people and is even more complex to solve.

Abstract

The main objective of the Namibia 2011 Census was to provide socioeconomic information necessary for decision making at all levels. The census provides up to date information on the population size and growth, composition and structure, as well as the geographic distribution – by constituencies and regions. Specifically, the census will be expected to: • provide an objective and adequate statistical basis for overall social and economic planning, monitoring and evaluation; • provide an adequate statistical basis for measuring the size and growth of the population; • determine the structure and composition of the population by age, sex, region and other socio-economic characteristics; • provide a basis for estimating basic demographic characteristics, which include, among others, the levels of fertility and mortality, not only at national and regional levels, but also for specific population sub-groups; • make it possible to estimate future population trends through population projections; • provide information for updating the electoral boundaries and register; • provide information for the delineation of regional as well as constituency boundaries; • serve as a database for up-dating the Frame for the National Master Sample; and, • provide statistical basis for small area estimation of key social, economic and other population-based indicators.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Housing units

Universe

De facto census enumerates persons according to where they are found on the reference night. De jure census, on the other hand, enumerates persons according to where they usually live, and potentially increases chances of double counting. The de facto approach to enumeration is, therefore, preferred as it reduces coverage errors. The Namibia 2011 Census used the de facto enumeration approach. However, information on the de jure population can also be obtained.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The following questionnaires were used to collect census: • Form A (Long Form): For conventional households and residential institutions • Form B1 (Short Form): For special population groups such as persons in transit (travellers), police cells, homeless and off-shore populations • Form B2 (Short Form): For hotels/guesthouses • Form B3 (Short Form): For foreign missions/diplomatic corps; • Form C: For recording Emigrant characteristics

Cleaning operations

Data processing activities started pre scanning activities. The following are the activities which were carried out in preparation for the release of the preliminary results and Data processing operation. - Once the Questionnaire were received from field, the office staff had to sort them out according to their enumeration areas (EAs), constituencies and regions and create a shelve system where they are safely stored and will be retrieved for data processing.

• Questionnaires editing: The questionnaire was edited to ensure that all persons are correctly placed in their respective EAs, constituencies and regions where they were enumerated. The Geocode list was used to cross check the EA number on the questionnaire book cover to ensure that the number is correct. In addition, the editing looked at the identification section, thus, the region, constituency, EA code, Rural/urban, dwelling unit, household numbering. It is important to ensure that information on this section is correct to avoid transferring data from one region to another or constituency to another.

• Coding of the questionnaire: The coding looked at questions which the coders were not able to provide codes for, due to limited descriptions. These were only migration and labour force questions. Staff in the office did a detailed study to find codes for the occupation and industry by consulting other documentations such as international classifications.

Data appraisal

Census Coverage Errors

There are two main types of coverage errors. These relate respectively to under-coverage and over-coverage. Under-coverage errors occur when persons who should have been enumerated in the census are missed or the completed questionnaires relating to them are misplaced or lost. On the other hand, over-coverage errors are caused by mistaken inclusions, such as multiple enumerations of the same persons and the enumeration of persons who were not in the country during the Census Reference Night.

Under-coverage errors may be an outcome of one of the following situations: - localities that are completely omitted from the census count because they were not covered by the interviewer - houses or dwelling units not enumerated in localities that were covered by the interviewer - households omitted in houses or dwelling units that were covered - persons not enumerated in households that were covered - persons not belonging to private households and were not counted

Over-coverage is likely to occur when: - persons are enumerated more than once thereby inflating the population figure for an area - either respondents or the interviewers are not careful to ensure that only persons who spent the census reference night in the household are counted

The latter case may occur when the concept of the census reference night is not clearly understood by the respondents, or the interviewer fails to pose this question properly.

For this census, the PES was limited to the household population. Under-coverage for the special population groups like institutions and the homeless was not included. It is assumed that these are relatively small and with high mobility. The cost of including them in the PES is not commensurate with their likely contribution to the coverage error.

Abstract

The Namibia 2001 Population and Housing Census is the second post-independence census, the first one having been undertaken in 1991. The census was undertaken in accordance with the Statistics Act of 1976. Cabinet authorised the National Planning Commission Secretariat to undertake the Population and Housing Census in 2001.

The main objectives of the census are to: - Provide an objective and adequate statistical basis for overall social and economic planning - Provide an adequate statistical basis for measuring the size and growth of the population - Determine the structure and composition of our population by age, sex, region and other socio-economic characteristics - Provide a basis for estimating basic demographic characteristics, which include, among others, the levels of fertility and mortality, not only at national and regional levels, but also for specific population sub-groups - Make it possible to estimate future population trends through population projections - Provide information for updating the Electoral Register - Provide information for the delineation of Regional as well as Constituency boundaries - Serves as a database for up-dating the Frame for the National Master Sample

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Housing units

Universe

The de facto approach was used during this census. The night of 27th to the morning of 28th August 2001 was designated as the Census Reference Night. All persons who were in Namibia during this night, irrespective of their citizenship, nationality or place of usual residence were enumerated at the places where they spent this census reference night. It should be noted that Namibian citizens who were out of the country on this reference night were not eligible for enumeration.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The census information was collected through a questionnaire, which was administered by trained interviewers. Three types of questionnaires were used. The main one, known as Form A was used for the household. The second one, Form B, was applied to institutional population, while the third one, Form C, was used for the homeless and the overnight travellers.

Form A, the household questionnaire, was made up of the following sections: - Section A: Identification particulars of the household - Section B: Basic information on all members of the household - Section C: Early childhood development for those aged 3-6 years - Section D: Literacy and education particulars for those aged 6 years and above - Section E: Labour force questions for those aged 8 years and above - Section F: Fertility information for females aged 12 - 49 years - Section G: Housing conditions and other household characteristics - Section H: Information on mortality, and - Control Section for administrative and logistical purposes.

Form B, the institutional questionnaire, is the same as Form A except that Sections G and H on housing conditions and household characteristics and mortality, are not included.

Form C, the questionnaire for the homeless, overnight travellers and persons who were in hotels and lodges, was a relatively short form, which collected information on age, sex marital status, citizenship and place of usual residence.

Data appraisal

Census Coverage Errors

There are two main types of coverage errors. These relate respectively to under-coverage and over-coverage. Under-coverage errors occur when persons who should have been enumerated in the census are missed or the completed questionnaires relating to them are misplaced or lost. On the other hand, over-coverage errors are caused by mistaken inclusions, such as multiple enumerations of the same persons and the enumeration of persons who were not in the country during the Census Reference Night.

Under-coverage errors may be an outcome of one of the following situations: - localities that are completely omitted from the census count because they were not covered by the interviewer - houses or dwelling units not enumerated in localities that were covered by the interviewer - households omitted in houses or dwelling units that were covered - persons not enumerated in households that were covered - persons not belonging to private households and were not counted

Over-coverage is likely to occur when: - persons are enumerated more than once thereby inflating the population figure for an area - either respondents or the interviewers are not careful to ensure that only persons who spent the census reference night in the household are counted

The latter case may occur when the concept of the census reference night is not clearly understood by the respondents, or the interviewer fails to pose this question properly.

For this census, the PES was limited to the household population. Under-coverage for the special population groups like institutions and the homeless was not included. It is assumed that these are relatively small and with high mobility. The cost of including them in the PES is not commensurate with their likely contribution to the coverage error.