According to the data, nearly 90 percent of people with disabilities had insurance coverage in 2021, an increase from under 82 percent in 2008. This statistic presents the percentage of people with and without disabilities who had insurance coverage from 2008 to 2021.

In 2023, the U.S. states with the highest share of the population that had a disability were West Virginia, Arkansas, and Kentucky. At that time, around 19.7 percent of the population of West Virginia had some form of disability. The states with the lowest rates of disability were New Jersey, Utah, and Minnesota. Disability in the United States A disability is any condition, either physical or mental, that impairs one’s ability to do certain activities. Some examples of disabilities are those that affect one’s vision, hearing, movement, or learning. It is estimated that around 14 percent of the population in the United States suffers from some form of disability. The prevalence of disability increases with age, with 46 percent of those aged 75 years and older with a disability, compared to just six percent of those aged 5 to 15 years. Vision impairment One common form of disability comes from vision impairment. In 2023, around 3.6 percent of the population of West Virginia had a vision disability, meaning they were blind or had serious difficulty seeing even when wearing glasses. The leading causes of visual disability are age-related and include diseases such as cataracts, glaucoma, and age-related macular degeneration. This is clear when viewing the prevalence of vision disability by age. It is estimated that 8.3 percent of those aged 75 years and older in the United States have a vision disability, compared to 4.3 percent of those aged 65 to 74 and only 0.9 percent of those aged 5 to 15 years.

The prevalence of disabilities in the United States shows a clear correlation with age, with nearly half of Americans aged 75 and older experiencing some form of disability. This stark contrast to younger age groups highlights the increasing challenges faced by the elderly population in maintaining their independence and quality of life. Disability rates across age groups According to 2023 data, only 0.7 percent of children under 5 years old have a disability, compared to 6.3 percent of those aged 5 to 15. The percentage rises steadily with age, reaching 11.2 percent for adults between 21 and 64 years old. A significant jump occurs in the 65 to 74 age group, where 23.9 percent have a disability. The most dramatic increase is seen in those 75 and older, with 45.3 percent experiencing some form of disability. These figures underscore the importance of accessible services and support systems for older Americans. The Individuals with Disabilities Education Act (IDEA) The prevalence of disabilities among younger Americans has significant implications for the education system. The Individuals with Disabilities Education Act (IDEA) is a law in the United States that guarantees the right to a free appropriate education for children with disabilities. In the 2021/22 academic year, 7.26 million disabled individuals aged 3 to 21 were covered by the Individuals with Disabilities Education Act (IDEA). This number includes approximately 25,000 children with traumatic brain injuries and 434,000 with intellectual disabilities.

ContextDespite the high prevalence of work disability due to common mental disorders (CMD), no information exists on the rates and predictors of recurrence in a United States population.ObjectiveTo estimate recurrent work disability statistics and evaluate factors associated with recurrence due to CMDs including adjustment, anxiety, bipolar, and depressive disorders.MethodsRecurrent work disability statistics were calculated using a nationwide database of disability claims. For the CMDs, univariate and multiple variable analyses were used to examine demographic factors and comorbidities associated with the time to recurrence.ResultsOf the CMDs, cases with bipolar (n = 3,017) and depressive disorders (n = 20,058) had the highest recurrence densities, 98.7 and 70.9 per 1000 person-years, respectively. These rates were more than three times higher than recurrence rates for other chronic disorders (e.g., diabetes, asthma; n = 105,558) and non-chronic disorders (e.g., injury, acute illnesses; n = 153,786). Individuals with CMD were also more likely to have a subsequent disability distinct from their mental health condition. Risk factors for recurrent CMD disability included being younger, being an hourly employee, living in a geographic area with more college graduates, having more previous psychiatric visits, having a previous work leave, and the type of work industry.ConclusionsResults indicate that CMD patients may benefit from additional care and disability management both during and after their work absence to help prevent subsequent CMD and non-CMD related leaves.

IntroductionBased on questions about impairments and activity limitations, the American Community Survey shows that roughly 13% of the U.S. population is experiencing disability. As most people live in households with other persons, this study explores disability at the household level. Considering the literature on household decision-making, solidarity, and capabilities in disability, this analysis of the household context of disability takes into account residential settings, household composition, and urban–rural differences.MethodThe 2015–2019 ACS Public Use Microdata Sample (PUMS), which shows persons with disability (PwD) and persons without disability (PwoD), also indicates household membership, used here to separately identify PwoD as those living in households with persons with disability (PwoD_HHwD) and those in households without any household member with disability (PwoD_HHwoD). Relationship variables reveal the composition of households with and without disabilities. An adaption of Beale's rural–urban continuum code for counties is used to approximate rural–urban differences with ACS PUMS data.ResultsSolo living is two times as common among persons with disability than among persons without disability, and higher in rural than urban areas. In addition to 43 million PwD, there are another 42 million PwoD_HHwD. Two times as many persons are impacted by disability, either of their own or that of a household member, than shown by an analysis of individual-level disability. For family households, differences in the composition of households with and without disabilities are considerable with much greater complexities in the makeup of families with disability. The presence of multiple generations stands out. Adult sons or daughters without disability play an important role. Modest urban–rural differences exist in the composition of family households with disability, with a greater presence of multigenerational households in large cities.DiscussionThis research reveals the much wider scope of household-level disability than indicated by disability of individuals alone. The greater complexity and multigenerational makeup of households with disability imply intergenerational solidarity, reciprocity, and resource sharing. Household members without disability may add to the capabilities of persons with disabilities. For the sizeable share of PwD living solo, there is concern about their needs being met.

Despite important advances in the rights of disabled people over the past decades, stigma and prejudice remain widespread. Meanwhile, disabled political representatives are few and far between. This raises the question: do voters discriminate against disabled candidates? Using data from conjoint experiments in the US and the UK, this study shows that candidates with physical or sensory impairments are preferred by voters on the left while receiving less support from voters on the right. However, these effects are almost entirely due to voters’ perceptions of disabled candidates as more left wing. Disability has little direct effect on the vote choice – except among left-wing voters, who reward left-wing disabled candidates for representing under-represented groups. The findings expand our understanding of the role of disability in electoral politics and yield important insights for candidates and parties who are concerned about discrimination at the ballot box and their campaign strategies.

Contains the count of new initial applications for the Social Security Administration’s SSDI and SSI disability benefit programs and the total number of initial applications that are still pending nationwide for each month.

The average elapsed time (in days) between the submission of an initial SSDI or SSI application and the decision for each month. Applications denied prior to a medical determination (i.e., a “technical denial”) are not included.

Contains the total size of the Social Security Administration’s backlog of unprocessed initial applications for its disability benefit programs (SSDI and SSI) for 2019 and 2022 in each state. Calculations for percent change in backlog size between the two years by state and the average difference in backlog size per state on a monthly basis are also included.

There is scarce literature that comprehensively examines the production of disability-related publications in Latin America and the Caribbean (LAC). Thus, the present study sought to quantify and analyze trends over time and their relationship with the description of programs for people with disabilities (PWD) in LAC. Studies were identified in the Scopus/PubMed/LILACS databases for the period 2003–2023. To compare country-specific production, specific rates per million inhabitants were calculated. In the last two decades, the region had a sustained growth of scientific production on disabilities (n=2,886), with Brazil, Colombia, and Chile having the highest production. However, Chile has the highest standardized production with 12.4 publications per million inhabitants. 2.1% (61/2,886) of these publications describe programs to address PWD; most of these studies were related to health (36.1%) and education (19.7%), and 62.3% reported some type of evaluation of the impact of the program implemented. Further, those with a focus on multiple disabilities and publications with data from the intergenerational age group were the most prevalent, 52.2% (n=1,506) and 48.6% (n=1,403), respectively. Significant gaps are evident in the area of disabilities in the region that local and regional decision-makers should address to improve the quality of life of PWD in LAC.

Data SourcesAmerican Community Survey (ACS):Conducted by: U.S. Census BureauDescription: The ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States.Content: The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions.Frequency: The ACS offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households.Purpose: ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.CDC/ATSDR Social Vulnerability Index (SVI):Created by: ATSDR’s Geospatial Research, Analysis & Services Program (GRASP)Utilized by: CDCDescription: The SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events.Content: SVI ranks U.S. Census tracts based on 15 social factors, including unemployment, minority status, and disability, and groups them into four related themes. Each tract receives rankings for each Census variable and for each theme, as well as an overall ranking, indicating its relative vulnerability.Purpose: SVI data provides insights into the social vulnerability of communities at the census tract level, helping public health officials and emergency response planners allocate resources effectively.Utilization and IntegrationBy integrating data from both the ACS and the SVI, this dataset enables an in-depth analysis and understanding of various socio-economic and demographic indicators at the census tract level. This integrated data is valuable for research, policymaking, and community planning purposes, allowing for a comprehensive understanding of social and economic dynamics across different geographical areas in the United States.ApplicationsLocalized Interventions: Facilitates the development of localized interventions to address the needs of vulnerable populations within specific census tracts.Resource Allocation: Assists emergency response planners in allocating resources more effectively based on community vulnerability at the census tract level.Research: Provides a detailed dataset for academic and applied research in socio-economic and demographic studies at a granular census tract level.Community Planning: Supports the planning and development of community programs and initiatives aimed at improving living conditions and reducing vulnerabilities within specific census tract areas.Note: Due to limitations in the data environment, variable names may be truncated. Refer to the provided table for a clear understanding of the variables.CSV Variable NameShapefile Variable NameDescriptionStateNameStateNameName of the stateStateFipsStateFipsState-level FIPS codeState nameStateNameName of the stateCountyNameCountyNameName of the countyCensusFipsCensusFipsCounty-level FIPS codeState abbreviationStateFipsState abbreviationCountyFipsCountyFipsCounty-level FIPS codeCensusFipsCensusFipsCounty-level FIPS codeCounty nameCountyNameName of the countyAREA_SQMIAREA_SQMITract area in square milesE_TOTPOPE_TOTPOPPopulation estimates, 2014-2018 ACSEP_POVEP_POVPercentage of persons below poverty estimateEP_UNEMPEP_UNEMPUnemployment Rate estimateEP_HBURDEP_HBURDHousing cost burdened occupied housing units with annual income less than $75,000EP_UNINSUREP_UNINSURUninsured in the total civilian noninstitutionalized population estimate, 2014-2018 ACSEP_PCIEP_PCIPer capita income estimate, 2014-2018 ACSEP_DISABLEP_DISABLPercentage of civilian noninstitutionalized population with a disability estimate, 2014-2018 ACSEP_SNGPNTEP_SNGPNTPercentage of single parent households with children under 18 estimate, 2014-2018 ACSEP_MINRTYEP_MINRTYPercentage minority (all persons except white, non-Hispanic) estimate, 2014-2018 ACSEP_LIMENGEP_LIMENGPercentage of persons (age 5+) who speak English "less than well" estimate, 2014-2018 ACSEP_MUNITEP_MUNITPercentage of housing in structures with 10 or more units estimateEP_MOBILEEP_MOBILEPercentage of mobile homes estimateEP_CROWDEP_CROWDPercentage of occupied housing units with more people than rooms estimateEP_NOVEHEP_NOVEHPercentage of households with no vehicle available estimateEP_GROUPQEP_GROUPQPercentage of persons in group quarters estimate, 2014-2018 ACSBelow_5_yrBelow_5_yrUnder 5 years: Percentage of Total populationBelow_18_yrBelow_18_yrUnder 18 years: Percentage of Total population18-39_yr18_39_yr18-39 years: Percentage of Total population40-64_yr40_64_yr40-64 years: Percentage of Total populationAbove_65_yrAbove_65_yrAbove 65 years: Percentage of Total populationPop_malePop_malePercentage of total population malePop_femalePop_femalePercentage of total population femaleWhitewhitePercentage population of white aloneBlackblackPercentage population of black or African American aloneAmerican_indianamerican_iPercentage population of American Indian and Alaska native aloneAsianasianPercentage population of Asian aloneHawaiian_pacific_islanderhawaiian_pPercentage population of Native Hawaiian and Other Pacific Islander aloneSome_othersome_otherPercentage population of some other race aloneMedian_tot_householdsmedian_totMedian household income in the past 12 months (in 2019 inflation-adjusted dollars) by household size – total householdsLess_than_high_schoolLess_than_Percentage of Educational attainment for the population less than 9th grades and 9th to 12th grade, no diploma estimateHigh_schoolHigh_schooPercentage of Educational attainment for the population of High school graduate (includes equivalency)Some_collegeSome_collePercentage of Educational attainment for the population of Some college, no degreeAssociates_degreeAssociatesPercentage of Educational attainment for the population of associate degreeBachelor’s_degreeBachelor_sPercentage of Educational attainment for the population of Bachelor’s degreeMaster’s_degreeMaster_s_dPercentage of Educational attainment for the population of Graduate or professional degreecomp_devicescomp_devicPercentage of Household having one or more types of computing devicesInternetInternetPercentage of Household with an Internet subscriptionBroadbandBroadbandPercentage of Household having Broadband of any typeSatelite_internetSatelite_iPercentage of Household having Satellite Internet serviceNo_internetNo_internePercentage of Household having No Internet accessNo_computerNo_computePercentage of Household having No computer

BackgroundReading disability (RD) is a common neurodevelopmental disorder with genetic basis established in families segregating “pure” dyslexia. RD commonly occurs in neurodevelopmental disorders including Rolandic Epilepsy (RE), a complex genetic disorder. We performed genomewide linkage analysis of RD in RE families, testing the hypotheses that RD in RE families is genetically heterogenenous to pure dyslexia, and shares genetic influences with other sub-phenotypes of RE. MethodsWe initially performed genome-wide linkage analysis using 1000 STR markers in 38 US families ascertained through a RE proband; most of these families were multiplex for RD. We analyzed the data by two-point and multipoint parametric LOD score methods. We then confirmed the linkage evidence in a second US dataset of 20 RE families. We also resequenced the SEMA3C gene at the 7q21 linkage locus in members of one multiplex RE/RD pedigree and the DISC1 gene in affected pedigrees at the 1q42 locus. ResultsIn the discovery dataset there was suggestive evidence of linkage for RD to chromosome 7q21 (two-point LOD score 3.05, multipoint LOD 3.08) and at 1q42 (two-point LOD 2.87, multipoint LOD 3.03). Much of the linkage evidence at 7q21 derived from families of French-Canadian origin, whereas the linkage evidence at 1q42 was well distributed across all the families. There was little evidence for linkage at known dyslexia loci. Combining the discovery and confirmation datasets increased the evidence at 1q42 (two-point LOD = 3.49, multipoint HLOD = 4.70), but decreased evidence at 7q21 (two-point LOD = 2.28, multipoint HLOD  = 1.81), possibly because the replication sample did not have French Canadian representation. DiscussionReading disability in rolandic epilepsy has a genetic basis and may be influenced by loci at 1q42 and, in some populations, at 7q21; there is little evidence of a role for known DYX loci discovered in “pure” dyslexia pedigrees. 1q42 and 7q21 are candidate novel dyslexia loci.

ObjectivesNational health insurance is now common in most developed countries. This study reviews the evidence and synthesizes the cost-effectiveness information for national health insurance or disability insurance programs across high-income countries.Data sourcesA literature search using health, economics and systematic review electronic databases (PubMed, Embase, Medline, Econlit, RepEc, Cochrane library and Campbell library), was conducted from April to October 2015.Study selectionTwo reviewers independently selected relevant studies by applying screening criteria to the title and keywords fields, followed by a detailed examination of abstracts.Data extractionStudies were selected for data extraction using a quality assessment form consisting of five questions. Only studies with positive answers to all five screening questions were selected for data extraction. Data were entered into a data extraction form by one reviewer and verified by another.Evidence synthesisData on costs and quality of life in control and treatment groups were used to draw distributions for synthesis. We chose the log-normal distribution for both cost and quality-of-life data to reflect non-negative value and high skew. The results were synthesized using a Monte Carlo simulation, with 10,000 repetitions, to estimate the overall cost-effectiveness of national health insurance programs.ResultsFour studies from the United States that examined the cost-effectiveness of national health insurance were included in the review. One study examined the effects of medical expenditure, and the remaining studies examined the cost-effectiveness of health insurance reforms. The incremental cost-effectiveness ratio (ICER) ranged from US$23,000 to US$64,000 per QALY. The combined results showed that national health insurance is associated with an average incremental cost-effectiveness ratio of US$51,300 per quality-adjusted life year (QALY). Based on the standard threshold for cost-effectiveness, national insurance programs are cost-effective interventions.ConclusionsAlthough national health insurance programs have been introduced in most developed countries, only a few studies have examined their cost-effectiveness. All the selected studies revealed strong evidence to support health insurance programs or health reforms in the United States. The average ICER in this study is below the standard threshold for cost-effectiveness used in the US. The small number of relevant studies is the main limitation of this study.

we utilized data from two main sources: the United States Census Bureau's American Community Survey (ACS) and the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry (CDC/ATSDR) Social Vulnerability Index (SVI).American Community Survey (ACS):Conducted by the U.S. Census Bureau, the ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States.The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions.It offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households.The ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.CDC/ATSDR Social Vulnerability Index (SVI):Created by ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) and utilized by the CDC, the SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events.

This application provides an interactive maps for model-based chronic disease related estimates of the CDC PLACES (Population Level Analysis and Community Estimates). PLACES is an expansion of the original 500 Cities project and is funded by the Robert Wood Johnson Foundation through the CDC Foundation. PLACES includes 49 measures (12 health outcomes, 7 prevention measures, 4 health risk behaviors, 7 disabilities, 3 health status, 7 health-related social needs, and 9 social determinants of health) at county, place (incorporated and census designated places), census tract, and ZIP Code Tabulation Area (ZCTA) levels.The health outcomes measures include arthritis, current asthma, high blood pressure, cancer (non-skin) or melanoma, high cholesterol, chronic obstructive pulmonary disease (COPD), coronary heart disease, diagnosed diabetes, depression, obesity, all teeth lost, and stroke.The prevention measures include lack of health insurance, routine checkup within the past year, visited dentist or dental clinic in the past, taking medicine to control high blood pressure, cholesterol screening, mammography use for women, cervical cancer screening for women, and colorectal cancer screening.The health risk behaviors include binge drinking, current cigarette smoking, physical inactivity, and short sleep duration.The disability measures are six disability types (hearing, vision, cognitive, mobility, self-care, and independent living) and any disability.The health status measures include frequent mental distress, frequent physical distress, and poor or fair health.The health-related social needs measures include social isolation, food stamps, food insecurity, housing insecurity, utility services threat, transportation barriers, and lack of social and emotional support. The non-medical factor measures include population 65 years or older, no broadband, crowding, housing cost burden, no high school diploma, poverty, racial or ethnic minority status, single-parent households, and unemployment from U.S. Census Bureau’s American Community Health Survey.For more information, please visit https://www.cdc.gov/places or to contact places@cdc.gov.

To ensure respondent confidentiality, estimates below a certain threshold are suppressed. For Canada, Quebec, Ontario, Alberta and British Columbia suppression is applied to all data below 1,500. The threshold level for Newfoundland and Labrador, Nova Scotia, New Brunswick, Manitoba and Saskatchewan is 500, while in Prince Edward Island, estimates under 200 are suppressed. For census metropolitan areas (CMAs) and economic regions (ERs), use their respective provincial suppression levels mentioned above. Estimates are based on smaller sample sizes the more detailed the table becomes, which could result in lower data quality. Fluctuations in economic time series are caused by seasonal, cyclical and irregular movements. A seasonally adjusted series is one from which seasonal movements have been eliminated. Seasonal movements are defined as those which are caused by regular annual events such as climate, holidays, vacation periods and cycles related to crops, production and retail sales associated with Christmas and Easter. It should be noted that the seasonally adjusted series contain irregular as well as longer-term cyclical fluctuations. The seasonal adjustment program is a complicated computer program which differentiates between these seasonal, cyclical and irregular movements in a series over a number of years and, on the basis of past movements, estimates appropriate seasonal factors for current data. On an annual basis, the historic series of seasonally adjusted data are revised in light of the most recent information on changes in seasonality. Number of civilian, non-institutionalized persons 15 years of age and over who, during the reference week, were employed or unemployed. Estimates in thousands, rounded to the nearest hundred. Number of persons who, during the reference week, worked for pay or profit, or performed unpaid family work or had a job but were not at work due to own illness or disability, personal or family responsibilities, labour dispute, vacation, or other reason. Those persons on layoff and persons without work but who had a job to start at a definite date in the future are not considered employed. Estimates in thousands, rounded to the nearest hundred. Number of persons who, during the reference week, were without work, had looked for work in the past four weeks, and were available for work. Those persons on layoff or who had a new job to start in four weeks or less are considered unemployed. Estimates in thousands, rounded to the nearest hundred. The unemployment rate is the number of unemployed persons expressed as a percentage of the labour force. The unemployment rate for a particular group (age, gender, marital status, etc.) is the number unemployed in that group expressed as a percentage of the labour force for that group. Estimates are percentages, rounded to the nearest tenth. Industry refers to the general nature of the business carried out by the employer for whom the respondent works (main job only). Industry estimates in this table are based on the 2022 North American Industry Classification System (NAICS). Formerly Management of companies and administrative and other support services"." This combines the North American Industry Classification System (NAICS) codes 11 to 91. This combines the North American Industry Classification System (NAICS) codes 11 to 33. This combines the North American Industry Classification System (NAICS) codes 41 to 91. Unemployed persons who have never worked before, and those unemployed persons who last worked more than 1 year ago. For more information on seasonal adjustment see Seasonally adjusted data - Frequently asked questions." Labour Force Survey (LFS) North American Industry Classification System (NAICS) code exception: add group 1100 - Farming - not elsewhere classified (nec). When the type of farm activity cannot be distinguished between crop and livestock, (for example: mixed farming). Labour Force Survey (LFS) North American Industry Classification System (NAICS) code exception: add group 2100 - Mining - not elsewhere classified (nec). Whenever the type of mining activity cannot be distinguished. Also referred to as Natural resources. The standard error (SE) of an estimate is an indicator of the variability associated with this estimate, as the estimate is based on a sample rather than the entire population. The SE can be used to construct confidence intervals and calculate coefficients of variation (CVs). The confidence interval can be built by adding the SE to an estimate in order to determine the upper limit of this interval, and by subtracting the same amount from the estimate to determine the lower limit. The CV can be calculated by dividing the SE by the estimate. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of the standard errors for 12 previous months The standard error (SE) for the month-to-month change is an indicator of the variability associated with the estimate of the change between two consecutive months, because each monthly estimate is based on a sample rather than the entire population. To construct confidence intervals, the SE is added to an estimate in order to determine the upper limit of this interval, and then subtracted from the estimate to determine the lower limit. Using this method, the true value will fall within one SE of the estimate approximately 68% of the time, and within two standard errors approximately 95% of the time. For example, if the estimated employment level increases by 20,000 from one month to another and the associated SE is 29,000, the true value of the employment change has a 68% chance of falling between -9,000 and +49,000. Because such a confidence interval includes zero, the 20,000 change would not be considered statistically significant. However, if the increase is 30,000, the confidence interval would be +1,000 to +59,000, and the 30,000 increase would be considered statistically significant. (Note that 30,000 is above the SE of 29,000, and that the confidence interval does not include zero.) Similarly, if the estimated employment declines by 30,000, then the true value of the decline would fall between -59,000 and -1,000. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of standard errors for 12 previous months. They are updated twice a year The standard error (SE) for the year-over-year change is an indicator of the variability associated with the estimate of the change between a given month in a given year and the same month of the previous year, because each month's estimate is based on a sample rather than the entire population. The SE can be used to construct confidence intervals: it can be added to an estimate in order to determine the upper limit of this interval, and then subtracted from the same estimate to determine the lower limit. Using this method, the true value will fall within one SE of the estimate, approximately 68% of the time, and within two standard errors, approximately 95% of the time. For example, if the estimated employment level increases by 160,000 over 12 months and the associated SE is 55,000, the true value of the change in employment has approximately a 68% chance of falling between +105,000 and +215,000. This change would be considered statistically significant at the 68% level as the confidence interval excludes zero. However, if the increase is 40,000, the interval would be -15,000 to +95,000, and this increase would not be considered statistically significant since the interval includes zero. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of standard errors for 12 previous months and are updated twice a year Excluding the territories. Starting in 2006, enhancements to the Labour Force Survey data processing system may have introduced a level shift in some estimates, particularly for less common labour force characteristics. Use caution when comparing estimates before and after 2006. For more information, contact statcan.labour-travail.statcan@statcan.gc.ca