NOTE: This dataset has been retired and marked as historical-only. The recommended dataset to use in its place is https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccination-Coverage-Region-HCEZ-/5sc6-ey97.

COVID-19 vaccinations administered to Chicago residents by Healthy Chicago Equity Zones (HCEZ) based on the reported address, race-ethnicity, and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE).

Healthy Chicago Equity Zones is an initiative of the Chicago Department of Public Health to organize and support hyperlocal, community-led efforts that promote health and racial equity. Chicago is divided into six HCEZs. Combinations of Chicago’s 77 community areas make up each HCEZ, based on geography. For more information about HCEZs including which community areas are in each zone see: https://data.cityofchicago.org/Health-Human-Services/Healthy-Chicago-Equity-Zones/nk2j-663f

Vaccination Status Definitions:

·People with at least one vaccine dose: Number of people who have received at least one dose of any COVID-19 vaccine, including the single-dose Johnson & Johnson COVID-19 vaccine.

·People with a completed vaccine series: Number of people who have completed a primary COVID-19 vaccine series. Requirements vary depending on age and type of primary vaccine series received.

·People with a bivalent dose: Number of people who received a bivalent (updated) dose of vaccine. Updated, bivalent doses became available in Fall 2022 and were created with the original strain of COVID-19 and newer Omicron variant strains.

Weekly cumulative totals by vaccination status are shown for each combination of race-ethnicity and age group within an HCEZ. Note that each HCEZ has a row where HCEZ is “Citywide” and each HCEZ has a row where age is "All" so care should be taken when summing rows.

Vaccinations are counted based on the date on which they were administered. Weekly cumulative totals are reported from the week ending Saturday, December 19, 2020 onward (after December 15, when vaccines were first administered in Chicago) through the Saturday prior to the dataset being updated.

Population counts are from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-year estimates.

Coverage percentages are calculated based on the cumulative number of people in each population subgroup (age group by race-ethnicity within an HCEZ) who have each vaccination status as of the date, divided by the estimated number of people in that subgroup.

Actual counts may exceed population estimates and lead to >100% coverage, especially in small race-ethnicity subgroups of each age group within an HCEZ. All coverage percentages are capped at 99%.

All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH.

Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined.

CDPH uses the most complete data available to estimate COVID-19 vaccination coverage among Chicagoans, but there are several limitations that impact its estimates. Data reported in I-CARE only includes doses administered in Illinois and some doses administered outside of Illinois reported historically by Illinois providers. Doses administered by the federal Bureau of Prisons and Department of Defense are also not currently reported in I-CARE. The Veterans Health Administration began reporting doses in I-CARE beginning September 2022. Due to people receiving vaccinations that are not recorded in I-CARE that can be linked to their record, such as someone receiving a vaccine dose in another state, the number of people with a completed series or a booster dose is underesti

Retirement Home Rentals and Sales Market Outlook

The global market size for retirement home rentals and sales was valued at approximately $250 billion in 2023 and is projected to reach around $420 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 6.2%. The growing aging population and increasing demand for specialized senior living facilities are major factors driving the growth of this market.

A significant growth factor for the retirement home rentals and sales market is the aging global population. As life expectancy continues to rise, a larger portion of the population is entering retirement age, necessitating a greater number of senior living options. Countries with advanced healthcare systems and higher standards of living are particularly witnessing a surge in the senior population, which drives the demand for retirement communities that can provide not only accommodation but also necessary healthcare and social support services. Furthermore, the preference for specialized care communities such as assisted living and memory care facilities is becoming more pronounced, catering to the distinct needs of elderly individuals with different levels of independence and health conditions.

Another significant growth driver is the increasing wealth and disposable income among seniors. Many individuals reaching retirement age today have amassed considerable savings and assets over their working lives, allowing them to afford higher-quality living arrangements during their retirement years. This demographic shift is creating a robust market for premium retirement home options, which offer a range of amenities including healthcare services, recreational activities, and social engagement opportunities. Moreover, the trend of retirees looking to downsize from larger family homes to more manageable living spaces is contributing to the marketÂ’s growth. This transition often leads to increased demand for both rental and sales properties within retirement communities.

The advent of advanced healthcare technologies and improved healthcare services in retirement homes is also a major growth catalyst. Modern retirement communities are increasingly integrating state-of-the-art medical facilities and services to cater to the health needs of their residents. The availability of such comprehensive healthcare solutions within retirement homes makes them an attractive option for seniors who require regular medical attention but prefer to maintain a degree of independence. This integration of healthcare within living spaces not only enhances the quality of life for residents but also positions retirement homes as a viable alternative to traditional nursing homes and hospitals.

Regionally, North America dominates the retirement home rentals and sales market, driven by a significant aging population and well-developed healthcare infrastructure. Europe follows closely, with countries like Germany and the UK experiencing substantial growth in demand for senior living facilities. The Asia Pacific region is poised for rapid growth, with developing countries such as China and India witnessing increasing investments in retirement housing due to their large aging populations and improving economic conditions. The regional market dynamics are influenced by varying factors such as cultural attitudes towards aging, economic development, and the availability of healthcare services.

Assisted Living Facilities have become an integral part of the senior living landscape, offering a unique blend of independence and support for older adults. These facilities are designed to provide personalized care tailored to the needs of each resident, ensuring that they receive the right level of assistance with daily activities such as bathing, dressing, and medication management. The growing popularity of Assisted Living Facilities can be attributed to their ability to offer a home-like environment where seniors can maintain their dignity and autonomy while having access to necessary healthcare services. Families often choose these facilities for their loved ones because they provide a safe and nurturing environment that bridges the gap between independent living and more intensive nursing care. As the demand for specialized senior care continues to rise, Assisted Living Facilities are evolving to include more amenities and services, making them an attractive option for seniors seeking a balanced lifestyle.

Type Analysis

The retirement home rentals and sales mar

Abstract

By the middle of the 1990s, Indonesia had enjoyed over three decades of remarkable social, economic, and demographic change and was on the cusp of joining the middle-income countries. Per capita income had risen more than fifteenfold since the early 1960s, from around US$50 to more than US$800. Increases in educational attainment and decreases in fertility and infant mortality over the same period reflected impressive investments in infrastructure.

In the late 1990s the economic outlook began to change as Indonesia was gripped by the economic crisis that affected much of Asia. In 1998 the rupiah collapsed, the economy went into a tailspin, and gross domestic product contracted by an estimated 12-15%-a decline rivaling the magnitude of the Great Depression.

The general trend of several decades of economic progress followed by a few years of economic downturn masks considerable variation across the archipelago in the degree both of economic development and of economic setbacks related to the crisis. In part this heterogeneity reflects the great cultural and ethnic diversity of Indonesia, which in turn makes it a rich laboratory for research on a number of individual- and household-level behaviors and outcomes that interest social scientists.

The Indonesia Family Life Survey is designed to provide data for studying behaviors and outcomes. The survey contains a wealth of information collected at the individual and household levels, including multiple indicators of economic and non-economic well-being: consumption, income, assets, education, migration, labor market outcomes, marriage, fertility, contraceptive use, health status, use of health care and health insurance, relationships among co-resident and non- resident family members, processes underlying household decision-making, transfers among family members and participation in community activities. In addition to individual- and household-level information, the IFLS provides detailed information from the communities in which IFLS households are located and from the facilities that serve residents of those communities. These data cover aspects of the physical and social environment, infrastructure, employment opportunities, food prices, access to health and educational facilities, and the quality and prices of services available at those facilities. By linking data from IFLS households to data from their communities, users can address many important questions regarding the impact of policies on the lives of the respondents, as well as document the effects of social, economic, and environmental change on the population.

The Indonesia Family Life Survey complements and extends the existing survey data available for Indonesia, and for developing countries in general, in a number of ways.

First, relatively few large-scale longitudinal surveys are available for developing countries. IFLS is the only large-scale longitudinal survey available for Indonesia. Because data are available for the same individuals from multiple points in time, IFLS affords an opportunity to understand the dynamics of behavior, at the individual, household and family and community levels. In IFLS1 7,224 households were interviewed, and detailed individual-level data were collected from over 22,000 individuals. In IFLS2, 94.4% of IFLS1 households were re-contacted (interviewed or died). In IFLS3 the re-contact rate was 95.3% of IFLS1 households. Indeed nearly 91% of IFLS1 households are complete panel households in that they were interviewed in all three waves, IFLS1, 2 and 3. These re-contact rates are as high as or higher than most longitudinal surveys in the United States and Europe. High re-interview rates were obtained in part because we were committed to tracking and interviewing individuals who had moved or split off from the origin IFLS1 households. High re-interview rates contribute significantly to data quality in a longitudinal survey because they lessen the risk of bias due to nonrandom attrition in studies using the data.

Second, the multipurpose nature of IFLS instruments means that the data support analyses of interrelated issues not possible with single-purpose surveys. For example, the availability of data on household consumption together with detailed individual data on labor market outcomes, health outcomes and on health program availability and quality at the community level means that one can examine the impact of income on health outcomes, but also whether health in turn affects incomes.

Third, IFLS collected both current and retrospective information on most topics. With data from multiple points of time on current status and an extensive array of retrospective information about the lives of respondents, analysts can relate dynamics to events that occurred in the past. For example, changes in labor outcomes in recent years can be explored as a function of earlier decisions about schooling and work.

Fourth, IFLS collected extensive measures of health status, including self-reported measures of general health status, morbidity experience, and physical assessments conducted by a nurse (height, weight, head circumference, blood pressure, pulse, waist and hip circumference, hemoglobin level, lung capacity, and time required to repeatedly rise from a sitting position). These data provide a much richer picture of health status than is typically available in household surveys. For example, the data can be used to explore relationships between socioeconomic status and an array of health outcomes.

Fifth, in all waves of the survey, detailed data were collected about respondents¹ communities and public and private facilities available for their health care and schooling. The facility data can be combined with household and individual data to examine the relationship between, for example, access to health services (or changes in access) and various aspects of health care use and health status.

Sixth, because the waves of IFLS span the period from several years before the economic crisis hit Indonesia, to just prior to it hitting, to one year and then three years after, extensive research can be carried out regarding the living conditions of Indonesian households during this very tumultuous period. In sum, the breadth and depth of the longitudinal information on individuals, households, communities, and facilities make IFLS data a unique resource for scholars and policymakers interested in the processes of economic development.

Geographic coverage

National coverage

Analysis unit

• Communities
• Facilities
• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Because it is a longitudinal survey, the IFLS2 drew its sample from IFLS1. The IFLS1 sampling scheme stratified on provinces and urban/rural location, then randomly sampled within these strata. Provinces were selected to maximize representation of the population, capture the cultural and socioeconomic diversity of Indonesia, and be cost-effective to survey given the size and terrain of the country. For mainly cost-effectiveness reasons, 14 provinces were excluded. The resulting sample included 13 of Indonesia's 27 provinces containing 83% of the population: four provinces on Sumatra (North Sumatra, West Sumatra, South Sumatra, and Lampung), all five of the Javanese provinces (DKI Jakarta, West Java, Central Java, DI Yogyakarta, and East Java), and four provinces covering the remaining major island groups (Bali, West Nusa Tenggara, South Kalimantan, and South Sulawesi). Within each of the 13 provinces, enumeration areas (EAs) were randomly chosen from a nationally representative sample frame used in the 1993 SUSENAS, a socioeconomic survey of about 60,000 households. The IFLS randomly selected 321 enumeration areas in the 13 provinces, oversampling urban EAs and EAs in smaller provinces to facilitate urban-rural and Javanese-non-Javanese comparisons.

Household Survey Within a selected EA, households were randomly selected based upon 1993 SUSENAS listings obtained from regional BPS office. A household was defined as a group of people whose members reside in the same dwelling and share food from the same cooking pot (the standard BPS definition). Twenty households were selected from each urban EA, and 30 households were selected from each rural EA. This strategy minimized expensive travel between rural EAs while balancing the costs of correlations among households. For IFLS1 a total of 7,730 households were sampled to obtain a final sample size goal of 7,000 completed households. This strategy was based on BPS experience of about 90% completion rates. In fact, IFLS1 exceeded that target and interviews were conducted with 7,224 households in late 1993 and early 1994.

In IFLS1 it was determined to be too costly to interview all household members, so a sampling scheme was used to randomly select several members within a household to provide detailed individual information. IFLS1 conducted detailed interviews with the following household members: • the household head and his/her spouse • two randomly selected children of the head and spouse age 0 to 14 • an individual age 50 or older and his/her spouse, randomly selected from remaining members • for a randomly selected 25% of the households, an individual age 15 to 49 and his/her spouse, randomly selected from remaining members.

IFLS2 Recontact Protocols In IFLS2 our goal was to relocate and reinterview the 7,224 households interviewed in 1993. If no members of the household were found in the 1993 interview location, we asked local residents (including an informant identified by the household in 1993) where the household had gone. If the household was thought to be within any of the 13 IFLS provinces, the household was tracked to the new location and if

The leading causes of death among Black residents in the United States in 2022 included diseases of the heart, cancer, unintentional injuries, and stroke. The leading causes of death for African Americans generally reflects the leading causes of death for the entire United States population. However, a major exception is that death from assault or homicide is the seventh leading cause of death among African Americans, but is not among the ten leading causes for the general population. Homicide among African Americans The homicide rate among African Americans has been higher than that of other races and ethnicities for many years. In 2023, around 9,284 Black people were murdered in the United States, compared to 7,289 white people. A majority of these homicides are committed with firearms, which are easily accessible in the United States. In 2022, around 14,189 Black people died by firearms. However, suicide deaths account for over half of all deaths from firearms in the United States. Cancer disparities There are also major disparities in access to health care and the impact of various diseases. For example, the incidence rate of cancer among African American males is the greatest among all ethnicities and races. Furthermore, although the incidence rate of cancer is lower among African American women than it is among white women, cancer death rates are still higher among African American women.

Increases in wages and purchase costs have challenged group homes' expenses to erode revenue. However, the industry's weak performance is mainly attributed to declines in demand, the impact of professional advocates campaigning for smaller, community-based settings and government policies supporting transitions to family settings and foster homes. Various socioeconomic conditions had also negatively impacted service providers' earnings. And contributing to softening in demand are decreases in crime, divorce and incarceration rates, which have reduced stressors on families and individuals and the need for group home services. Despite shifts in policy towards family settings, group homes and congregate care facilities serving newborns to young adults continue to provide services, but with industry revenue dropping at a CAGR of 1.0% through 2025 and gaining 0.2% to $9.9 billion in 2025 alone. Unfortunately, under-capacity does provide specific opportunities for group homes by encouraging them to diversify their services and engage more with their local community, including providing support for families. Providers can improve their financial performance by spurring demand by adopting new care and technology strategies, including trauma-informed care and technology-based, efficient health monitoring. The industry will see a higher growth in the number of enterprises than establishments, signaling a trend towards consolidation. However, the placement of enterprises may be influenced more by historical profitability rather than current market needs, potentially leading to a rise in closures of lower-performing providers. California, for instance, has a surplus of service providers for its population size, while some states have a shortage. Despite governmental policies favoring family-centered environments, a lack of foster homes across numerous states could thwart the shift away from group homes and residential treatment facilities will still be required for children who struggle to adapt or fit into a foster setting. Facing these challenges and remaining uncertain over the federal budget impacts for FY 2026, industry revenue is estimated to edge upward at a CAGR of 0.3%, to reach $10.1 billion through 2030, with declining profit.

Organisations in the Community Services subdivision play an integral role in Australia's wider care and support economy. Subdivision operators help to arrange paid and unpaid care and counsel for individuals in need, including the economically disadvantaged and other vulnerable members of society like children and the elderly. Favourable demographic trends and ongoing government funding - especially funding associated with the staged aged care reforms, the 2023 Cheaper Child Care policy and the National Disability Insurance Scheme - have supported the subdivision's performance in recent years. With a growing number of Australians experiencing persistent social and economic disadvantages, housing insecurity and associated mental health challenges, the Community Services subdivision has become increasingly overstretched and underfunded, especially as government indexation continues to lag cost inflation. At the same time, recent national inquiries have exposed several failings in Australia's aged care, disability and mental health systems, with the subdivision being the subject of several Royal Commission reviews, including the Royal Commission into Aged Care Quality and Safety and the Royal Commission into Violence, Abuse, Neglect and Exploitation of People with Disability. Sweeping regulatory reforms stemming from these Royal Commissions are now underway, shaking up the subdivision's operating environment. The subdivision's not-for-profit organisations and private enterprises are expected to receive $115.1 billion from government funding, donations and private income in 2025-26, following annualised growth of 7.3% over the past five years. This includes expected revenue growth of 2.0% in 2025-26, with legislative uncertainty and funding shortfall concerns constraining the ability of community service providers to help disadvantaged Australians grappling with the ongoing housing and cost-of-living crisis. Cost pressures, combined with increased regulatory burdens, will impact already slim profit margins during the year. Rising demand from Australia's ageing population and ongoing demand for child care services will support future industry growth, as will continued disadvantages for Australia's most vulnerable members of society. At the same time, labour shortages and profit margin pressures arising from long-term chronic underfunding will hamper the ability of the subdivision to meet demand. Further changes to the industry's operating backdrop in view of rising concerns over former government policies favouring the privatisation of aged care and child care may be imminent. Despite these challenges, revenue for the Community Services subdivision is forecast to climb at an annualised 4.0% through the end of 2030-31 to total $145.2 billion.

Abstract

The main objectives of the 2018/19 NLSS are: i) to provide critical information for production of a wide range of socio-economic and demographic indicators, including for benchmarking and monitoring of SDGs; ii) to monitor progress in population’s welfare; iii) to provide statistical evidence and measure the impact on households of current and anticipated government policies. In addition, the 2018/19 NLSS could be utilized to improve other non-survey statistical information, e.g. to determine and calibrate the contribution of final consumption expenditures of households to GDP; to update the weights and determine the basket for the national Consumer Price Index (CPI); to improve the methodology and dissemination of micro-economic and welfare statistics in Nigeria.

The 2018/19 NLSS collected a comprehensive and diverse set of socio-economic and demographic data pertaining to the basic needs and conditions under which households live on a day to day basis. The 2018/19 NLSS questionnaire includes wide-ranging modules, covering demographic indicators, education, health, labour, expenditures on food and non-food goods, non-farm enterprises, household assets and durables, access to safety nets, housing conditions, economic shocks, exposure to crime and farm production indicators.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2018/19 NLSS sample is designed to provide representative estimates for the 36 states and the Federal Capital Territory (FCT), Abuja. By extension. The sample is also representative at the national and zonal levels. Although the sample is not explicitly stratified by urban and rural areas, it is possible to obtain urban and rural estimates from the NLSS data at the national level. At all stages, the relative proportion of urban and rural EAs as has been maintained.

Before designing the sample for the 2018/19 NLSS, the results from the 2009/10 HNLSS were analysed to extract the sampling properties (variance, design effect, etc.) and estimate the required sample size to reach a desired precision for poverty estimates in the 2018/19 NLSS.

EA SELECTION: The sampling frame for the 2018/19 NLSS was based on the national master sample developed by the NBS, referred to as the NISH2 (Nigeria Integrated Survey of Households 2). This master sample was based on the enumeration areas (EAs) defined for the 2006 Nigeria Census Housing and Population conducted by National Population Commission (NPopC). The NISH2 was developed by the NBS to use as a frame for surveys with state-level domains. NISH2 EAs were drawn from another master sample that NBS developed for surveys with LGA-level domains (referred to as the “LGA master sample”). The NISH2 contains 200 EAs per state composed of 20 replicates of 10 sample EAs for each state, selected systematically from the full LGA master sample. Since the 2018/19 NLSS required domains at the state-level, the NISH2 served as the sampling frame for the survey.

Since the NISH2 is composed of state-level replicates of 10 sample EAs, a total of 6 replicates were selected from the NISH2 for each state to provide a total sample of 60 EAs per state. The 6 replicates selected for the 2018/19 NLSS in each state were selected using random systematic sampling. This sampling procedure provides a similar distribution of the sample EAs within each state as if one systematic sample of 60 EAs had been selected directly from the census frame of EAs.

A fresh listing of households was conducted in the EAs selected for the 2018/19 NLSS. Throughout the course of the listing, 139 of the selected EAs (or about 6%) were not able to be listed by the field teams. The primary reason the teams were not able to conduct the listing in these EAs was due to security issues in the country. The fieldwork period of the 2018/19 NLSS saw events related to the insurgency in the north east of the country, clashes between farmers and herdsman, and roving groups of bandits. These events made it impossible for the interviewers to visit the EAs in the villages and areas affected by these conflict events. In addition to security issues, some EAs had been demolished or abandoned since the 2006 census was conducted. In order to not compromise the sample size and thus the statistical power of the estimates, it was decided to replace these 139 EAs. Additional EAs from the same state and sector were randomly selected from the remaining NISH2 EAs to replace each EA that could not be listed by the field teams. This necessary exclusion of conflict affected areas implies that the sample is representative of areas of Nigeria that were accessible during the 2018/19 NLSS fieldwork period. The sample will not reflect conditions in areas that were undergoing conflict at that time. This compromise was necessary to ensure the safety of interviewers.

HOUSEHOLD SELECTION: Following the listing, the 10 households to be interviewed were selected from the listed households. These households were selected systemically after sorting by the order in which the households were listed. This systematic sampling helped to ensure that the selected households were well dispersed across the EA and thereby limit the potential for clustering of the selected households within an EA.

Occasionally, interviewers would encounter selected households that were not able to be interviewed (e.g. due to migration, refusal, etc.). In order to preserve the sample size and statistical power, households that could not be interviewed were replaced with an additional randomly selected household from the EA. Replacement households had to be requested by the field teams on a case-by-case basis and the replacement household was sent by the CAPI managers from NBS headquarters. Interviewers were required to submit a record for each household that was replaced, and justification given for their replacement. These replaced households are included in the disseminated data. However, replacements were relatively rare with only 2% of sampled households not able to be interviewed and replaced.

Sampling deviation

Although a sample was initially drawn for Borno state, the ongoing insurgency in the state presented severe challenges in conducting the survey there. The situation in the state made it impossible for the field teams to reach large areas of the state without compromising their safety. Given this limitation it was clear that a representative sample for Borno was not possible. However, it was decided to proceed with conducting the survey in areas that the teams could access in order to collect some information on the parts of the state that were accessible.

The limited area that field staff could safely operate in in Borno necessitated an alternative sample selection process from the other states. The EA selection occurred in several stages. Initially, an attempt was made to limit the frame to selected LGAs that were considered accessible. However, after selection of the EAs from the identified LGAs, it was reported by the NBS listing teams that a large share of the selected EAs were not safe for them to visit. Therefore, an alternative approach was adopted that would better ensure the safety of the field team but compromise further the representativeness of the sample. First, the list of 788 EAs in the LGA master sample for Borno were reviewed by NBS staff in Borno and the EAs they deemed accessible were identified. The team identified 359 EAs (46%) that were accessible. These 359 EAs served as the frame for the Borno sample and 60 EAs were randomly selected from this frame. However, throughout the course of the NLSS fieldwork, additional insurgency related events occurred which resulted in 7 of the 60 EAs being inaccessible when they were to be visited. Unlike for the main sample, these EAs were not replaced. Therefore, 53 EAs were ultimately covered from the Borno sample. The listing and household selection process that followed was the same as for the rest of the states.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two sets of questionnaires – household and community – were used to collect information in the NLSS2018/19. The Household Questionnaire was administered to all households in the sample. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

Household Questionnaire: The Household Questionnaire provides information on demographics; education; health; labour; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; agriculture and land tenure; and other sources of household income.

Community Questionnaire: The Community Questionnaire solicits information on access to transported and infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Cleaning operations

CAPI: The 2018/19 NLSS was conducted using the Survey Solutions Computer Assisted Person Interview (CAPI) platform. The Survey Solutions software was developed and maintained by the Development Economics Data Group (DECDG) at the World Bank. Each interviewer and supervisor was given a tablet

Community resilience describes the capacity of individuals and households within a community to absorb a disaster’s external stressors. The standard Community Resilience Estimates (CRE) measures a community’s social vulnerability to natural disasters. However, the social vulnerabilities to extreme heat exposure differ from other natural disasters. As a result, the CRE Team created a new set of estimates called the Community Resilience Estimates for Heat (CRE for Heat).The CRE for Heat is an experimental data product from the U.S. Census Bureau. Experimental data products are innovative statistical products created using new data sources or methodologies that benefit data users in the absence of other relevant products. The Census Bureau is seeking feedback from data users and stakeholders on the quality and usefulness of these new products.In collaboration with Arizona State University’s Knowledge Exchange for Resilience (KER), the CRE Team produced the 2022 CRE for Heat using data on individuals and households. The data sources include the 2022 American Community Survey (ACS), the Census Bureau’s Population Estimates Program (PEP), and the 2020 Census. Based on feedback from data users, the CRE for Heat contains a new component of social vulnerability, “Households that potentially lack air conditioning”. This component of social vulnerability was created using data from the 2021 American Housing Survey, machine learning techniques, and auxiliary data. More information about this is found in the CRE for Heat Quick Guide.Local planners, policymakers, public health officials, and community stakeholders can use the CRE for Heat to assess their community’s vulnerability to extreme heat and plan cooling and intervention strategies. WHAT’S NEWComponents of Social Vulnerability (SV)The CRE adjusted terminology from “risk factors” to “components of social vulnerability” after discussions with stakeholders such as emergency managers and urban planners. In these fields, “risk” refers to the likelihood a disaster or event will occur. “Vulnerabilities” refer to the conditions people experience which may compound the impact of a disaster.The CRE Program is committed to providing a data product that is understandable and meets the needs of its users. To better explain the purpose of the estimates and how they were developed, the language was adjusted.“Components” highlights the combination of factors that define social vulnerability. “Social vulnerability” refers to the characteristics that could impede a community’s ability to deal with disasters and external stressors. The results of this assessment form the basis of a community’s Community Resilience Estimate.Extreme Heat ExposureThe CRE for Heat 2022 estimates contain an additional measure of exposure to extreme heat (PRED3EXP). Not all socially vulnerable communities are equally exposed to extreme heat. Pairing the CRE for Heat estimates with heat exposure data provides a more comprehensive look at social vulnerability to heat. In the 2022 CRE for Heat dataset, an area is considered exposed to extreme heat if it meets one of two criteria. The two heat exposure criteria are:Areas where the maximum air temperature has reached or exceeded 90 degrees Fahrenheit for two or more days in a row during 2022.Areas where estimated wet bulb temperature has reached or exceeded 80 degrees at any time during 2022.On the county and tract level files, these exposure variables are available as LONG_90_DAY and MAX_WBT.On the state and national file, the exposure variable, PRED3EXP_E, measures the estimated number of individuals with three plus components of social vulnerability who also live in a county exposed to an extreme heat event in 2022. Similarly, PREDEXP_PE, measures the rate of individuals with three plus components of social vulnerability who also live in a county exposed to an extreme heat event in 2022. These variables, and their accompanying margins of error, are available on the national and state files.Components of Social VulnerabilityComponents of Social Vulnerability (SV) for Households (HH) and Individuals (I)SV 1: Financial hardship defined as: Income-to-Poverty Ratio (IPR) < 130 percent (HH) or50% < for housing/rental costs (HH). SV 2: Single or zero caregiver household - only one or no individuals living in the household who are 18-64 (HH).SV 3: Housing quality described as:Unit-level crowding with > 0.75 persons per room (HH) orLive in mobile home, boat, RV, Van, or other (HH). SV 4: Communication barrier defined as either:Limited English-speaking households (HH) or No one in the household has a high school diploma (HH). SV 5: No one in the household is employed full-time, year-round. The flag is not applied if all residents of the household are aged 65 years or older (HH).SV 6: Disability posing constraint to significant life activity. Persons who report having any one of the six disability types (I): hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. SV 7: No health insurance coverage (I). SV 8: Being aged 65 years or older (I). SV 9: Transportation exposure described as:No vehicle access (HH) orWork commuting methods with increased exposure to heat (e.g., public transportation, bicycle, walking) (I). SV 10: Households without broadband Internet access (HH). SV 11: Households that potentially lack air conditioning (HH).

In 2024, residents aged 65 years and above made up 18 percent of the total resident population in Singapore. Singapore is currently one of the most rapidly aging societies in Asia, along with Japan. The elderly in Singapore Improvements in healthcare and the standard of living over the years have contributed to an increase in life expectancy in Singapore. This was reflected in the decreasing death rate of elderly residents over the decades. The increase in the share of the elderly population was further compounded by a decreasing total fertility rate, which was well below the 2.1 needed for a balanced population. By 2050, the elderly population in Singapore was forecasted to be a third of its total population. Economic burden of an aging society Singapore thus faces significant economic challenges due to an increasingly elderly population. The number of elderly dependents to the working age population had been steadily increasing. As Singaporeans face the prospect of living longer, more and more elderly had chosen to return to work after retirement. Singapore society still places the responsibility of caring for the elderly on younger family members. However, the burden of care is expected to increase with the years, and whether this model is sustainable remains to be seen.

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US Behavioral Health Market Size 2025-2029

The US behavioral health market size is forecast to increase by USD 9.17 billion at a CAGR of 4.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing prevalence of behavioral disorders and the advent of digital health solutions. Telehealth and telemedicine, including video conferencing, have become catalysts for delivering mental health services, particularly in areas with a shortage of skilled professionals. The use of digital software and tools is transforming the way mental health services are delivered, making them more accessible and convenient for patients. Furthermore, the legalization of marijuana for medicinal purposes in some US states is also impacting the market, as it provides an alternative treatment option for certain behavioral disorders.


These trends are expected to continue, as insurers increasingly cover telehealth services and technology continues to advance. However, challenges such as data security and privacy concerns, as well as the need for standardized telehealth regulations, must be addressed to ensure the effective and safe delivery of behavioral health services.

What will be the Size of the market During the Forecast Period?

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The market encompasses a range of mental and emotional disorders, including forensic psychiatry, drug abuse, family therapy, perinatal mental health, interpersonal therapy, peer support, eating disorders, post-traumatic stress disorder, biopsychosocial assessment, stress management, public health, geriatric psychiatry, mindfulness-based stress reduction, autism spectrum disorder, attention-deficit/hyperactivity disorder, crisis hotlines, group therapy, healthcare access, holistic health, suicide prevention, support groups, psychotropic medications, opioid use disorder, community resources, developmental disabilities, health disparities, harm reduction, health equity, motivational interviewing, mood stabilizers, alcohol use disorder, and obsessive-compulsive disorder. 


This vast market is driven by increasing awareness and acceptance of mental health issues, growing prevalence of mental disorders, and advancements in treatment methods. The market is expected to grow significantly due to the rising burden of mental health conditions, increasing healthcare spending, and the availability of new technologies and therapies. The market is also influenced by public health initiatives, policy changes, and societal trends towards holistic health and wellness.

How is this market segmented, and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Inpatient hospital treatment services
  Outpatient counselling
  Home-based treatment services
  Emergency mental health services


Type

  Substance abuse disorders
  Alcohol use disorders
  Eating disorders
  ADHD
  Others


Age Group

  Adult
  Geriatric
  Pediatric


Geography

  US

By End-user Insights

The inpatient hospital treatment services segment is estimated to witness significant growth during the forecast period. Behavioral health services encompass a range of treatments for mental health conditions and substance use disorders. Inpatient hospital treatment, which includes medication management and regular check-ups, involves shorter stays compared to residential or home-based services. The high cost of inpatient hospital treatment is a significant factor, making it an essential component of the market. The prevalence of behavioral health conditions, such as anxiety, depression, substance use disorder, attention-deficit/hyperactivity disorder (ADHD), bipolar disorder, and more, is substantial in the US. The high number of hospital admissions due to substance abuse is expected to drive the growth of the inpatient hospital treatment segment during the forecast period.
Care coordination, a critical aspect of behavioral health services, is facilitated through electronic health records and health information technology. Crisis intervention, trauma-sensitive care, and trauma-informed care are essential components of mental wellness and recovery support. Value-based care models, such as partial hospitalization and intensive outpatient programs, are increasingly being adopted to improve quality and reduce healthcare costs. Mental health policy, clinical trials, and behavioral health research are essential for advancing evidence-based practices, such as dialectical behavior therapy and cognitive behavioral therapy. Virtual care, employee assistance programs, patient education, and school-based services are also crucial components of the market. Machine learning, data analytics, and artificial intellige

Measure and Map Access to Grocery StoresFrom the perspective of the people living in each neighborhoodHow do people in your city get to the grocery store? The answer to that question depends on the person and where they live. This web map helps answer the question in this app.Some live in cities and stop by a grocery store within a short walk or bike ride of home or work. Others live in areas where car ownership is more prevalent, and so they drive to a store. Some do not own a vehicle, and rely on a friend or public transit. Others rely on grocery delivery for their needs. And, many live in rural areas far from town, so a trip to a grocery store is an infrequent event involving a long drive.This map from Esri shows which areas are within a ten minute walk or ten minute drive of a grocery store in the United States and Puerto Rico. Darker color indicates access to more stores. The chart shows how many people can walk to a grocery store if they wanted to or needed to.It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store.Look up your city to see how the numbers change as you move around the map. Or, draw a neighborhood boundary on the map to get numbers for that area.Every census block is scored with a count of walkable and drivable stores nearby, making this a map suitable for a dashboard for any city, or any of the 50 states, DC and Puerto Rico. Two colorful layers visualize this definition of access, one for walkable access (suitable for looking at a city neighborhood by neighborhood) and one for drivable access (suitable for looking across a city, county, region or state).On the walkable layer, shades of green define areas within a ten minute walk of one or more grocery stores. The colors become more intense and trend to a blue-green color for the busiest neighborhoods, such as downtown San Francisco. As you zoom in, a layer of Census block points visualizes the local population with or without walkable access.As you zoom out to see the entire city, the map adds a light blue - to dark blue layer, showing which parts of the region fall within ten minutes' drive of one or more grocery stores. As a result, the map is useful at all scales, from national to regional, state and local levels. It becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car?How to Use This MapUse this map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying.The map was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access.There is data behind the map, which can be summarized to show how many people have walkable access to local grocery stores. The map includes a feature layer of population in Census block points, which are visible when you zoom in far enough. This feature layer can be plugged into an app like this one that summarizes the population with/without walkable or drivable access.Lastly, this map can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples).The map is a useful visual and analytic resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved.Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer.Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters.The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Alternative versions of these layers are available. These pairs use different colors but are otherwise identical in content.Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis.The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels.The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer.MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway.A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in.The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle).The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step.Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect.Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a

Abstract

This survey covering 252 primary health facilities and 30 local governments was carried out in the states of Kogi and Lagos in Nigeria in the latter part of 2002. Nigeria is one of the few countries in the developing world to systematically decentralize the delivery of basic health and education services to locally elected governments. Its health policy has also been guided by the Bamako Initiative to encourage and sustain community participation in primary health care services. The survey data provide systematic evidence on how these institutions of decentralization are functioning at the level local—governments and community based organizations—to deliver primary health service.

The evidence shows that locally elected governments indeed do assume responsibility for services provided in primary health care facilities. However, the service delivery environments between the two states are strikingly different. In largely urban Lagos, public delivery by local governments is influenced by the availability of private facilities and proximity to referral centers in the state. In largely rural Kogi, primary health services are predominantly provided in public facilities, but with extensive community participation in the maintenance of service delivery. The survey identified an issue which is highly relevant for decentralization policies—the non-payment of health staff salaries in Kogi—which is suggestive of problems with local accountability when local governments are heavily dependent on fiscal transfers from higher tiers of government.

Geographic coverage

Data were collected in 30 local governments, 252 health facilities, and from over 700 health workers, in Lagos and Kogi states.

Kind of data

Sample survey data [ssd]

Sampling procedure

A multi-stage sampling process was employed where first 15 local governments were randomly selected from each state; second, 100 facilities from Lagos and 152 facilities from Kogi were selected using a combination of random and purposive sampling from the list of all public primary health care facilities in the 30 selected LGAs that was provided by the state governments; third, the field data collectors were instructed to interview all staff present at the health facility at the time of the visit, if the total number of staff in a facility were less than or equal to 10. In cases where the total number of staff were greater than 10, the field staff were instructed to randomly select 10 staff, but making sure that one staff in each of the major ten categories of primary health care workers was included in the sample.

Health facilities were selected through a combination of random and purposive sampling. First, all facilities were randomly selected from the available list for 30 LGAs. This process resulted in no facility being selected from a few LGAs. Between 1-3 facilities were then randomly selected from these LGAs, and an equal number of facilities were randomly dropped from overrepresented LGAs, defined as those where the proportion of selected facility per LGA is higher than the average proportion of selected facilities for all sampled LGAs.

A list of replacement facilities was also randomly selected in the event of closure or non-functioning of any facility in the original sample. An inordinate amount of facilities were replaced in Kogi (27 in total), some due to inaccessibility given remote locations and hostile terrain, and some due to non-availability of any health staff. The local community volunteered in these cases that the reason there was no staff available was because of non-payment of salaries by the LGA. This characteristic of the functioning of health facilities in Kogi is a striking result that will be discussed in this report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The approach adopted to addressing these issues revolves around extensive and rigorous survey work, at the level of the primary health care facilities and the local governments. Two basic survey instruments of primary data collection were agreed upon, based on collecting information from government officials and public service delivery facilities: 1. Survey of primary health care facilities—including interviews of facility managers and workers, as well as direct collection of data on inputs and outputs from facility records. 2. Survey of local governments (under whose jurisdiction the health facilities reside)—including interviewers of local government treasurers for information on budgeted resources and investment activity, and interviews of primary health care coordinators for roles, responsibilities, and outcomes at the local government level.

Survey instruments at the health facility level

The facility level survey instruments were designed to collect data along the following lines: 1. Basic characteristics of the health facility: who built it; when was it built; what other facilities exist in the neighborhood; access to the facility; hours of service etc. 2. Type of services provided: focusing on ante-natal care; deliveries; outpatient services, with special emphasis on malaria and routine immunization 3. Availability of essential equipment to provide the above services 4. Availability of essential drugs to provide the above services 5. Utilization of the above services, referral practices 6. Tracking and use of epidemiological and public health data 7. Characteristics of health facility staff: professional qualifications; training; salary structure, and whether payments are received in a timely fashion; informal payments received; fringe benefits received; do they have their own private practice; time allocation across different services; residence; place of origin 8. Sources of financing-who finances the building infrastructure and its maintenance; who finances the purchase of basic equipment; who finances the purchase of drugs; what is the user fee policy; revenues from user fees; retention rate of these revenues; financing available from the community 9. Management structure and institutions of accountability: activities of and interaction with the local government and with the community development committees

Survey instrument at the local government level

The local government survey instruments were designed to collect data along the following lines: 1. Basic characteristics: when was the local government created, population, proportion urban and rural, presence of an urban center, presence of NGOs and international donors 2. Number of primary health care facilities by type (types 1 and 2) and ownership (public-local government, state, and federal government; private-for-profit; private-not-for-profit) 3. Supervisory responsibilities over the general functioning of the primary health care centers 4. Health staff: number of staff by type of professional training and civil service cadre; salary; 5. Monitoring the performance of health staff: how is staff performance monitored and by whom; are staff rewarded for good performance or sanctioned for poor performance, and how; instances when local government has received complaints; what disciplinary action was taken 6. Budget and financing: data on actual LGA revenues and expenditure from available budget documents; 7. Management structures: functioning of the Primary Health Care Management Committee (PHCMC), the Primary Health Care Technical Committee (PHCTC), and the community based organizations-the Village Development Committee (VDC) and the District Development Committee (DDC) 8. Health services outputs at the local government level: records of immunization, and environmental health activities

The focus of the study is thus public service delivery outcomes as measured at the level of frontline delivery agencies—the public primary health care facilities. We also originally planned to include interviews of patients present at the health facilities, to get the user’s perspective on public service delivery, but found that difficult to follow-through given local capacity constraints in implementing a survey of this kind.

The survey instruments were developed through an iterative process of discussions between the World Bank team, NPHCDA, and local consultants at the University of Ibadan, over the months of March-May 2002. During May 2002, four questionnaires were finalized through repeated field-testing—1) Health Facility Questionnaire: to be administered to the health facility manager, and to collect recorded data on inputs and outputs at the facility level; 2) Staff Questionnaire: to be administered to individual health workers; 3) Local Government Treasurer Questionnaire: to collect local government budgetary information; and 4) Primary Health Care Coordinator Questionnaire: to collect information on local government activities and policies in primary health care service service delivery.

Cleaning operations

Random Data Checking Procedure

Following the dual data entry of all records by Nigerian consultants and the merging and cleaning of the data files(as outlined below) by World Bank staff, the hard copies of the questionnaires were randomly checked against the entries in the data files (*) for errors by World Bank staff. Five LGAs were selected at random in both the Kogi and Lagos states. In each of these ten LGAs, the hard copy of the PHC Coordinator Questionnaire, the hard copy of the LGA Treasurer Questionnaire, and up to five hard copies of both the Staff Questionnaires and the Health Facility Questionnaires were randomly selected and checked against the entries in the data files. While in several instances parts of the alphanumeric entries were abbreviated or omitted, no substantive differences between the hard copies of the

This publication contains the official statistics about uses of the Mental Health Act(1) ('the Act') in England during 2020-21. Under the Act, people with a mental disorder may be formally detained in hospital (or 'sectioned') in the interests of their own health or safety, or for the protection of other people. They can also be treated in the community but subject to recall to hospital for assessment and/or treatment under a Community Treatment Order (CTO). In 2016-17, the way we source and produce these statistics changed. Previously these statistics were produced from the KP90 aggregate data collection. They are now primarily produced from the Mental Health Services Data Set (MHSDS). The MHSDS provides a much richer data source for these statistics, allowing for new insights into uses of the Act. However, some providers that make use of the Act are not yet submitting data to the MHSDS, or submitting incomplete data. Improvements in data quality have been made over the past year. NHS Digital is working with partners to ensure that all providers are submitting complete data and this publication includes guidance on interpreting these statistics. Please note: This publication covers the 2020-21 reporting year and, as such, it is likely the impact of COVID-19 may be evident as the national lockdown began on 23 March 2020. The time series data for people subject to detention does show a decrease in people subject to detention in March 2021 so the context of COVID-19 should be kept in mind when using and interpreting these statistics. Footnotes (1) The Mental Health Act 1983 as amended by the Mental Health Act 2007 and other legislation.

In 2011, 87.2 percent of the total population of the United Kingdom were white British. A positive net migration in recent years combined with the resultant international relationships following the wide-reaching former British Empire has contributed to an increasingly diverse population.

Varied ethnic backgrounds

Black British citizens, with African and/or African-Caribbean ancestry, are the largest ethnic minority population, at three percent of the total population. Indian Britons are one of the largest overseas communities of the Indian diaspora and make up 2.3 percent of the total UK population. Pakistani British citizens, who make up almost two percent of the UK population, have one of the highest levels of home ownership in Britain.

Racism in the United Kingdom

Though it has decreased in comparison to the previous century, the UK has seen an increase in racial prejudice during the first decade and a half of this century. Racism and discrimination continues to be part of daily life for Britain’s ethnic minorities, especially in terms of work, housing, and health issues. Moreover, the number of hate crimes motivated by race reported since 2012 has increased, and in 2017/18, there were 3,368 recorded offenses of racially or religiously aggravated assault with injury, almost a thousand more than in 2013/14.