This statistic shows the percentage of people engaging in specific coping behaviors when they feel lonely or socially isolated in the U.S. as of 2018. Among those reporting loneliness and social isolation, 78 percent indicated that they almost always or sometimes distract themselves with TV, computer, or video games when they are feeling lonely.

According to a global survey, about 33 percent of adults experienced feelings of loneliness worldwide. Brazil had the highest percentage of people experiencing this, with 50 percent of respondents declaring that they felt lonely either often, always, or sometimes. Turkey, India, and Saudi Arabia followed, with 43 percent to 46 percent of respondents having experienced loneliness at least sometimes. On the contrary, the Netherlands, Japan, Germany, and Russia registered the largest share of interviewees which did not feel lonely.

Coping with loneliness during the pandemic The COVID-19 pandemic has suddenly cut off people from all over the world from their social life, and the lack of companionship has been a difficult situation for many to cope with. In the United States, people who experienced lack of company were, unsurprisingly, individuals living alone, and unemployed, disabled, or unemployed people. In relation to mental health, Americans who reported more symptoms of depression were by far more likely to feel lonely.

Impact of mental health According to a survey conducted in 2021 among G7 countries, about seven in 10 people experienced a worsening of their psychological health during the pandemic. A study on clinician-reported changes in selected health behaviors in the United States showed that during the pandemic patients have suffered more from feelings of loneliness, depression or anxiety, and burnout. Also nutrition and other habits have been impacted. The study reported an increase in alcohol consumption, smoking cigarettes, poor nutrition, and use of other substances.

Around 36 percent of Brazilian respondents who participated in an IPSOS survey said it was likely they would feel lonely in 2021. From the Latin American countries included in this list, the people interviewed in Argentina and Mexico were the least likely to think they'd be lonely in 2021.

This statistic shows the specific reasons given for feelings of loneliness and social isolation in the United States in 2018. In this year, 18 percent of those who reported experiencing loneliness or social isolation attributed their feelings to the death of a loved one and 12 percent gave the reason of physical/health problems.

In June 2020, more than half of U.S. adults aged 50-80 years felt isolated from others due to the COVID-19 pandemic. In comparison, 27 percent of older adults felt so before the pandemic in October 2018. This statistic portrays the percentage of older adults in the U.S. who reported feeling lonely or isolated before and during the COVID-19 pandemic as of June 2020.

In 2018, 58 percent of people who reported feeling lonely or socially isolated stated that feeling lonely has had a negative impact on their mental health. This statistic shows the negative impacts of feeling lonely and socially isolated in the U.S. in 2018.

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows English ability and linguistic isolation by age group. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Linguistically isolated households are households in which no one 14 and over speak English only or speaks a language other than English at home and speaks English very well. This layer is symbolized to show the percent of adult (18+) population who have limited English ability. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B16003, B16004 (Not all lines of ACS table B16004 are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 28, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

A survey of U.S. adults from December 2021 found that 57 percent of men and 59 percent of women felt lonely. This statistic shows the percentage of adults in the United States who reported feeling lonely as of December 2021, by gender.

In the United States, 40 percent of people who say that they have a debilitating disability or chronic disease report experiencing loneliness and social isolation. This statistic shows the proportion of people experiencing loneliness and social isolation in the U.S. in 2018, by their physical or mental health condition.

Replication data and R code for "Class Isolation and Affluent Americans’ Perception of Social Conditions" by Adam Thal.

A survey from 2022 found that around 52 percent of adults in the United States aged 18 to 29 years felt anxious always or often in the past 12 months. This statistic shows the percentage of adults in the United States who stated they always or often felt anxious, depressed, or lonely in the past 12 months as of 2022, by age.

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During a March 2020 survey of consumers in the United States, 74 percent of respondents stated that if confined to their homes during the coronavirus, they were likely to purchase grocery store items online. Women seemed slightly more likely to do so, with (*.7 percent saying they would shop online for groceries, while the same was true for 94.7 percent of responding men. Online delivery orders of groceries have surged in the wake of the coronavirus pandemic and pasta delivery orders increased almost 700 percent in the first quarter of 2020 compared to the previous year.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data American Community Survey 5-year estimates for 2011-2015 to show population with less than full English proficiency, by zip code in the Atlanta region. The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number. ACS data presented here represent combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2011-2015). Therefore, these data do not represent any one specific point in time or even one specific year. For further explanation of ACS estimates and methodology, click here. Attributes: ZIP = Zip code (text) ZIP_dbl = Zip code (numeric) Total_Population_2010 = Total Population, 2010 Census Total_Population_2011_2015_ACS = Total Population, 2011-2015 American Community Survey (ACS)- - - - - -Pop_5Years_andOlder = #, Population 5 years and Over SpeakEngl_lessThan_vWell = #, Speak English Less than "very well" Pct_SpeakEng_lessThan_vWell = %, Speak English Less than "very well"- - - - - -last_edited_date = Last date the feature was edited by ARC Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2011-2015

Of the U.S. adults surveyed, most agreed to some extent that social media usage is related to feelings of loneliness or social isolation. This statistic shows the percentage of U.S. adults who completely or somewhat agree or disagree with the statement "social media usage is related to feelings of loneliness or social isolation" as of 2019.

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A survey conducted in March 2021 among U.S. adults found that around 22 percent reported feeling lonely a few times a month before the COVID-19 pandemic. Women were more likely than men to report feeling lonely. This statistic shows the percentage of adults in the United States who felt lonely with select frequency before the COVID-19 pandemic as of March 2021, by gender.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show number and percentage of U.S. population 5 years and older that speaks English less than "very well" and don’t speak English at home by Georgia House in the Atlanta region. The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website. Naming conventions: Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)Suffixes:NoneChange over two periods_eEstimate from most recent ACS_mMargin of Error from most recent ACS_00Decennial 2000 Attributes:SumLevelSummary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)GEOIDCensus tract Federal Information Processing Series (FIPS) code NAMEName of geographic unitPlanning_RegionPlanning region designation for ARC purposesAcresTotal area within the tract (in acres)SqMiTotal area within the tract (in square miles)CountyCounty identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)CountyNameCounty NamePop5P_e# Population 5 years and over, 2017Pop5P_m# Population 5 years and over, 2017 (MOE)EnglishOnly_e# Speaks English only, 2017EnglishOnly_m# Speaks English only, 2017 (MOE)pEnglishOnly_e% Speaks English only, 2017pEnglishOnly_m% Speaks English only, 2017 (MOE)NotEnglish_e# Speaks language other than English at home, 2017NotEnglish_m# Speaks language other than English at home, 2017 (MOE)pNotEnglish_e% Speaks language other than English at home, 2017pNotEnglish_m% Speaks language other than English at home, 2017 (MOE)EngLtVeryWell_e# English not spoken at home, speaks English less than 'very well', 2017EngLtVeryWell_m# English not spoken at home, speaks English less than 'very well', 2017 (MOE)pEngLtVeryWell_e% English not spoken at home, speaks English less than 'very well', 2017pEngLtVeryWell_m% English not spoken at home, speaks English less than 'very well', 2017 (MOE)Spanish_e# Speaks Spanish at home, 2017Spanish_m# Speaks Spanish at home, 2017 (MOE)pSpanish_e% Speaks Spanish at home, 2017pSpanish_m% Speaks Spanish at home, 2017 (MOE)SpanishEngLtVeryWell_e# Speaks Spanish at home, speaks English less than 'very well', 2017SpanishEngLtVeryWell_m# Speaks Spanish at home, speaks English less than 'very well', 2017 (MOE)pSpanishEngLtVeryWell_e% Speaks Spanish at home, speaks English less than 'very well', 2017pSpanishEngLtVeryWell_m% Speaks Spanish at home, speaks English less than 'very well', 2017 (MOE)IndoEurNotEnglish_e# Speaks other Indo-European language at home, 2017IndoEurNotEnglish_m# Speaks other Indo-European language at home, 2017 (MOE)pIndoEurNotEnglish_e% Speaks other Indo-European language at home, 2017pIndoEurNotEnglish_m% Speaks other Indo-European language at home, 2017 (MOE)IndoEurEngLtVeryWell_e# Speaks other Indo-European language at home, speaks English less than 'very well', 2017IndoEurEngLtVeryWell_m# Speaks other Indo-European language at home, speaks English less than 'very well', 2017 (MOE)pIndoEurEngLtVeryWell_e% Speaks other Indo-European language at home, speaks English less than 'very well', 2017pIndoEurEngLtVeryWell_m% Speaks other Indo-European language at home, speaks English less than 'very well', 2017 (MOE)AsianNotEnglish_e# Speaks Asian language at home, 2017AsianNotEnglish_m# Speaks Asian language at home, 2017 (MOE)pAsianNotEnglish_e% Speaks Asian language at home, 2017pAsianNotEnglish_m% Speaks Asian language at home, 2017 (MOE)AsianEngLtVeryWell_e# Speaks Asian language at home, speaks English less than 'very well', 2017AsianEngLtVeryWell_m# Speaks Asian language at home, speaks English less than 'very well', 2017 (MOE)pAsianEngLtVeryWell_e% Speaks Asian language at home, speaks English less than 'very well', 2017pAsianEngLtVeryWell_m% Speaks Asian language at home, speaks English less than 'very well', 2017 (MOE)OthLangNotEnglish_e# Speaks other language at home, 2017OthLangNotEnglish_m# Speaks other language at home, 2017 (MOE)pOthLangNotEnglish_e% Speaks other language at home, 2017pOthLangNotEnglish_m% Speaks other language at home, 2017 (MOE)OthLangEngLtVeryWell_e# Speaks other language at home, speaks English less than 'very well', 2017OthLangEngLtVeryWell_m# Speaks other language at home, speaks English less than 'very well', 2017 (MOE)pOthLangEngLtVeryWell_e% Speaks other language at home, speaks English less than 'very well', 2017pOthLangEngLtVeryWell_m% Speaks other language at home, speaks English less than 'very well', 2017 (MOE)last_edited_dateLast date the feature was edited by ARC Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2013-2017 For additional information, please visit the Census ACS website.

Demand in the galvanic isolation market is projected to grow at a moderate CAGR of around 5.2% during the forecast period from 2022 to 2032. Growth registered can be attributable to the rising demand from various verticals such as telecom, healthcare and industrial sectors.

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