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.

Finland was ranked the happiest country in the world, according to the World Happiness Report from 2025. The Nordic country scored 7.74 on a scale from 0 to 10. Two other Nordic countries, Denmark and Iceland, followed in second and third place, respectively. The World Happiness Report is a landmark survey of the state of global happiness that ranks countries by how happy their citizens perceive themselves to be. Criticism The index has received criticism from different perspectives. Some argue that it is impossible to measure general happiness in a country. Others argue that the index places too much emphasis on material well-being as well as freedom from oppression. As a result, the Happy Planet Index was introduced, which takes life expectancy, experienced well-being, inequality of outcomes, and ecological footprint into account. Here, Costa Rica was ranked as the happiest country in the world. Afghanistan is the least happy country Nevertheless, most people agree that high levels of poverty, lack of access to food and water, as well as a prevalence of conflict are factors hindering public happiness. Hence, it comes as no surprise that Afghanistan was ranked as the least happy country in the world in 2024. The South Asian country is ridden by poverty and undernourishment, and topped the Global Terrorism Index in 2024.

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

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 IFLS4 drew its sample from IFLS1, IFLS2, IFLS2+ and IFLS3. The IFLS1 sampling scheme stratified on provinces and urban/rural location, then randomly sampled within these strata (see Frankenberg and Karoly, 1995, for a detailed description). 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 costeffectiveness reasons, 14 of the then existing 27 provinces were excluded.3 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.4 The IFLS randomly selected 321 enumeration areas in the 13 provinces, over-sampling urban EAs and EAs in smaller provinces to facilitate urban-rural and Javanese-non-Javanese comparisons.

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.

IFLS4 Re-Contact Protocols The target households for IFLS4 were the original IFLS1 households, minus those all of whose members had died by 2000, plus all of the splitoff households from 1997, 1998 and 2000 (minus those whose members had died). Main fieldwork went on from late November 2008 through May 2009. In total, 13,995 households were contacted, including those that died between waves, those that relocated into other IFLS households and new splitoff households. Of these, 13,535 households were actually interviewed. Of the 10,994 target households, we re-contacted 90.6%: 6,596 original IFLS1 households and 3,366 old splitoff households. An additional 4,033 new splitoff households were contacted in IFLS4. Of IFLS1 dynastic households, we contacted 6,761, or 93.6%. Lower dynasty re-contact rates were achieved in Jakarta (80.3%), south Sumatra (88%) and north Sumatra (88.6%). Jakarta is of course the major urban center in Indonesia, and Medan,

A list of some key resources for comparing London with other world cities.

European Union/Eurostat, Urban Audit

Arcadis, Sustainable cities index

AT Kearney, Global Cities Index

McKinsey, Urban world: Mapping the economic power of cities

Knight Frank, Wealth report

OECD, Better Life Index

UNODC, Statistics on drugs, crime and criminal justice at the international level

Economist, Hot Spots

Economist, Global Liveability Ranking and Report August 2014

Mercer, Quality of Living Reports

PWC, Cities of opportunity

BCG, Decoding Global Talent

Forbes, World's most influential cities

Mastercard, Global Destination Cities Index

Numbeo, Database of user contributed data

Anti Anxiety Drugs Market Outlook

The global anti-anxiety drugs market size was valued at approximately USD 14 billion in 2023 and is expected to reach around USD 22 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 5.1%. One of the primary growth factors driving this market is the increasing prevalence of anxiety disorders globally, coupled with rising awareness and diagnosis of mental health conditions.

The growing awareness and acceptance of mental health issues have significantly contributed to the demand for anti-anxiety medications. As societies become more open about discussing mental health, more individuals are seeking medical help for anxiety disorders. Governments and non-profit organizations are also playing a crucial role by advocating mental health awareness, which is pivotal in reducing the stigma around these conditions. This heightened awareness is leading to early diagnosis and treatment, thereby driving the market for anti-anxiety drugs.

Technological advancements and innovations in drug delivery systems are also augmenting the growth of the anti-anxiety drugs market. Innovations such as extended-release formulations and novel drug delivery mechanisms are enhancing the effectiveness and patient compliance of these medications. Pharmaceutical companies are investing heavily in research and development to introduce drugs with fewer side effects, which can provide a better quality of life for patients. These advancements are making treatments more accessible and efficient, thereby expanding the market size.

The increasing burden of work-related stress and urbanization is another key factor contributing to the growth of the anti-anxiety drugs market. As urban areas continue to expand, the fast-paced lifestyle associated with city living often leads to higher levels of stress and anxiety. The pressure to perform in highly competitive environments, alongside financial uncertainties, further exacerbates anxiety conditions. Hence, the demand for anti-anxiety medications is rising among the urban population, contributing significantly to the market growth.

Regionally, North America holds the largest share in the anti-anxiety drugs market due to the high prevalence of anxiety disorders and the well-established healthcare infrastructure. Europe follows closely, driven by increasing healthcare expenditure and growing awareness of mental health. The Asia-Pacific region is expected to witness the highest growth rate during the forecast period, attributed to increasing urbanization, economic growth, and rising awareness about mental health. Latin America and the Middle East & Africa, though currently holding smaller market shares, are also anticipated to show gradual growth, primarily due to improving healthcare infrastructure and rising awareness about mental health issues.

Drug Class Analysis

The anti-anxiety drugs market is segmented into several drug classes, including Benzodiazepines, Antidepressants, Beta-Blockers, and Others. Benzodiazepines have historically been one of the most commonly prescribed classes of drugs for anxiety disorders due to their rapid onset of action and efficacy in reducing acute anxiety symptoms. Drugs like diazepam, lorazepam, and alprazolam fall under this category. However, their potential for dependence and side effects have led to a cautious approach in their prescription. Despite these challenges, benzodiazepines continue to hold a significant market share due to their effectiveness in treating severe and acute anxiety episodes.

Antidepressants, particularly SSRIs (Selective Serotonin Reuptake Inhibitors) and SNRIs (Serotonin-Norepinephrine Reuptake Inhibitors), are increasingly being used to manage anxiety disorders. These medications are preferred for long-term treatment as they address the underlying neurochemical imbalances associated with anxiety. Drugs like fluoxetine, sertraline, and venlafaxine are commonly prescribed SSRIs and SNRIs. The shift towards antidepressants is driven by their lower potential for abuse and better side-effect profile compared to benzodiazepines. This trend is likely to continue, contributing to the growth of the antidepressant segment in the anti-anxiety drugs market.

Beta-blockers, though traditionally used for cardiovascular conditions, are also prescribed for anxiety, particularly performance anxiety. Drugs such as propranolol help manage the physical symptoms of anxiety, such as tachycardia and tremors. The use of beta-blockers is more situational, often prescribed for short-term or specific anxiety-inducing events rather t

BackgroundSodium consumption is a modifiable risk factor for higher blood pressure (BP) and cardiovascular disease (CVD). The US Food and Drug Administration (FDA) has proposed voluntary sodium reduction goals targeting processed and commercially prepared foods. We aimed to quantify the potential health and economic impact of this policy.Methods and findingsWe used a microsimulation approach of a close-to-reality synthetic population (US IMPACT Food Policy Model) to estimate CVD deaths and cases prevented or postponed, quality-adjusted life years (QALYs), and cost-effectiveness from 2017 to 2036 of 3 scenarios: (1) optimal, 100% compliance with 10-year reformulation targets; (2) modest, 50% compliance with 10-year reformulation targets; and (3) pessimistic, 100% compliance with 2-year reformulation targets, but with no further progress. We used the National Health and Nutrition Examination Survey and high-quality meta-analyses to inform model inputs. Costs included government costs to administer and monitor the policy, industry reformulation costs, and CVD-related healthcare, productivity, and informal care costs. Between 2017 and 2036, the optimal reformulation scenario achieving the FDA sodium reduction targets could prevent approximately 450,000 CVD cases (95% uncertainty interval: 240,000 to 740,000), gain approximately 2.1 million discounted QALYs (1.7 million to 2.4 million), and produce discounted cost savings (health savings minus policy costs) of approximately $41 billion ($14 billion to $81 billion). In the modest and pessimistic scenarios, health gains would be 1.1 million and 0.7 million QALYS, with savings of $19 billion and $12 billion, respectively. All the scenarios were estimated with more than 80% probability to be cost-effective (incremental cost/QALY < $100,000) by 2021 and to become cost-saving by 2031. Limitations include evaluating only diseases mediated through BP, while decreasing sodium consumption could have beneficial effects upon other health burdens such as gastric cancer. Further, the effect estimates in the model are based on interventional and prospective observational studies. They are therefore subject to biases and confounding that may have influenced also our model estimates.ConclusionsImplementing and achieving the FDA sodium reformulation targets could generate substantial health gains and net cost savings.

The main results of costs and effectiveness in both China and the US.

The average for 2024 based on 138 countries was 5.56 points. The highest value was in Finland: 7.74 points and the lowest value was in Afghanistan: 1.72 points. The indicator is available from 2013 to 2024. Below is a chart for all countries where data are available.

The statistic shows the gross domestic product (GDP) per capita in Japan from 1987 to 2024, with projections up until 2030. In 2024, the estimated gross domestic product per capita in Japan was around 32,498.15 U.S. dollars. For further information, see Japan's GDP. Japan's economy Japan is the world’s second largest developed economy and a member of the Group of Eight, also known as G8, which is comprised of the eight leading industrialized countries. Due to a weak financial sector, overregulation and a lack of demand, Japan suffered substantially from the early 1990s until 2000, a time referred to as ‘’The Lost Decade’’. Japan’s economy is still slowly recovering from the country’s asset price bubble collapse; however it continues to struggle to retain economic milestones achieved in the 1980s. Japan’s response to the crash was to stimulate the economy, which in turn resulted in extensive amounts of debt that further increased into the 21st century, most notably after the 2008 financial crisis. Despite maintaining a surprisingly low unemployment rate, demand within the country remains inadequate, primarily because Japanese residents spend a rather small fraction of the money they earned from the workplace. Lower demand often has a direct effect on production, with companies seeing not enough profits to continue production at such a high rate. Based on the consumer confidence index, Japanese households found that their quality of life, income growth, employment and propensity to durable goods was below satisfactory standards, perhaps due to these households still experiencing the effects of the 1990s bubble crash.

This report provides the findings from the Adult Social Care Outcomes Framework (ASCOF) in England for the period 1 April 2014 to 31 March 2015. The ASCOF draws on data from a number of collections; details of these data sources and which measures they are used for can be found in the Data Sources chapter within this report. Further details of the measures, including the purpose of the framework, can be found in the ASCOF Handbook of Definitions, which is published by the Department of Health. Please note: As part of the 2015-16 validation round, councils were invited to resubmit SALT 2014-15 data. 50 councils submitted restated data to NHS Digital and the revised data are now available as part of the 2015-16 SALT publication. Additionally, this restated data has been used to refresh the 2014-15 SALT-based indicators contained within the 2015-16 ASCOF publication. Links to these outputs are available in the Related Links, below. The ASCOF is part of a range of outcomes frameworks (alongside those of Public Health and the NHS) which collectively reflect the joint contribution of health and social care to improving outcomes. The ASCOF is used both locally and nationally to set priorities for care and support, measure progress and to strengthen transparency and accountability. Its purpose is three-fold: Locally, the ASCOF supports councils to improve the quality of care and support. The ASCOF fosters greater transparency in the delivery of adult social care, supporting local people to hold their council to account for the quality of the services they provide. Nationally, the ASCOF measures the performance of the adult social care system as a whole and its success in delivering high-quality, personalised care and support. The ASCOF measures how well care and support services achieve the outcomes that matter most to people. The measures are grouped into four domains which are typically reviewed in terms of movement over time. A number of these measures however have seen changes to their source data or definition which have resulted in year-on-year comparisons not being appropriate. Time-based comparisons are therefore not always provided and further explanation can be found in Chapter 3 (Comparability). In summary however, the new Short and Long Term Support (SALT) data collection has replaced the previous activity (RAP and ASC-CAR) collections. This impacts on the following measures: 1C, 1E, 1G, 2A, 2B and 2D. Furthermore, the introduction of SALT has also affected the eligible population used in determining the Adult Social Care Survey (ASCS) samples. The following measures are therefore also impacted 1A, 1B, 1I(1), 3A, 3D(1), 4A and 4B. As mentioned above, some of the measures included use survey data (the Adult Social Care Survey and the Survey of Adult Carers in England) and are therefore based on a sample of possible respondents. It is not possible to know the true value for the overall population in these cases however the variation present in the sampled data can be used to assess whether a change or difference is statistically significant. Where this is the case, statistical significance will be stated in the report. The non-survey-based measures use transactional data drawn from operational systems and so use all available data points. Any changes or differences presented, on the assumption of robust data quality, can therefore be taken as conclusive. ASCOF Indicator 1J The existing ASCOF Indicator 1A (Social Care related Quality of Life) tells us about the current (care-related) quality of life of people using social care. Following discussions in 2011 at the Outcomes and Information Development Board (OIDB), it was agreed that the Department of Health would commission a research project from the Quality and Outcomes of Person Centred Care Policy Research Unit (QORU) to develop a 'value added' measure of social care-related quality of life. This indicator, to be known as ASCOF Indicator 1J, will form part of the 2016-17 framework. The summary paper below (IIASC Report Summary 2014-15) describes the background, methods and results of the QORU study; the application of this calculation to existing data flows to derive aggregate local authority-level data; and the interpretation of these individual and aggregate measures, again drawing on the QORU study. A section covering the impact of changing the source of the eligible population for the survey from RAP in 2013-14 to SALT in 2014-15, as well as the change from Primary Client Group to Primary Support Reason as part of the inclusion criteria, is also included. A dataset of local authority data (based on 2014-15 Adult Social Care Survey Submissions) is provided (IIASC Dataset 2014-15) along with a calculator (IIASC 2014-15 Calculator) to enable councils to calculate and analyse their individual-level scores using their own 2014-15 ASCS data return. A similar dataset will be made available for 2015-16 in due course before this measure is included as part of the standard ASCOF reporting outputs for 2016-17. For further details, QORU's papers detailing the conclusions of the research and development phase of their work can be found via the 'Related links' section below. Any queries or comments should be directed to ascof@dh.gsi.gov.uk in the first instance.