Meta Platforms had ****** full-time employees as of December 2024, down from ****** people in 2023. As of December 2023, more than ******* employees at tech companies worldwide were laid off throughout the year across more than 1,000 companies. Facebook: how it all beganIn 2003, a sophomore at named Mark Zuckerberg hacked into protected areas of the university's computer network in order to find photos of other students. He then would pair two of them next to each other on a program called “Facemash” and ask users to choose the more attractive person. At the beginning of 2004, Zuckerberg launched “The Facebook,” a social network dedicated to Harvard students, which later grew to encompass Columbia, Yale and Stanford. The popularity of this new service sky-rocketed and in mid-2004, Zuckerberg interrupted his studies and moved his operation to Palo Alto, California, in the heart of Silicon Valley. By 2006, Facebook was open to the general public. In 2020, the company reported almost ** billion U.S. dollars in revenue and a net income of ***** billion US dollars. It is also the most popular social network in the world, with *** billion monthly active users as of December 2020. Facebook employee diversity criticismLike many other tech companies, Facebook has been criticized for having a diversity problem. As of June 2020, tech positions, as well as management roles in U.S. offices were overwhelmingly occupied by men. Furthermore, almost ** percent of Facebook employees in the U.S. are White and only *** percent are African-American, which has sparked concern regarding representation and equal opportunities. Around **** percent of senior level positions are occupied by White employees and only *** percent by Hispanic-Americans.

In fiscal year 2024, the total number of employees at Meta Platforms was 74,067. The employee count increasedby 6,750 from 67,317 (in 2023) to 74,067 (in 2024). It represents a 10.03% year-over-year growth in employee count.

Meta reported 67.32K in Employees for its fiscal year ending in December of 2023. Data for Meta | FB - Employees Total Number including historical, tables and charts were last updated by Trading Economics this last September in 2025.

Meta’s workforce has shifted dramatically. In key industries like tech and advertising, employee numbers affect agility, innovation, and cost-efficiency. For instance, advertising platforms must balance headcount with dynamic campaign demands. Likewise, AI teams rely on small, highly skilled clusters to drive rapid breakthroughs. See how Meta’s staffing shapes its performance,...

In 2023, Amazon.com was the top-ranked internet company based on number of employees. The e-commerce giant reported a workforce of more than **** million employees. Amazon has consistently topped the ranking as the online company with the biggest workforce, but the global COVID-19 pandemic has widened the gap as e-commerce has boomed since. During the same period, Meta (formerly Facebook Inc.) had a total of ****** full-time employees. Additionally, Google's parent company Alphabet had ******* full-time workers in 2024.

In 2022, 6.5 percent of Meta employees in the United States identified as Hispanic and 4.9 percent identified as Black. Asian employees accounted for over 46.5 percent of the overall workforce, whilst white employees made up 37.6 percent of Meta's workforce.

As of June 2022, 57.6 percent of employees in leadership roles at Meta were white, whilst 28.6 percent were Asian. Overall, 11.7 percent of employees in non-technical roles were Hispanic, and 11.2 percent were Black. Moreover, Asian employees accounted for the majority of employees in technical roles, making up 55.8 percent of employees in these positions.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Meta. The dataset can be utilized to gain insights into gender-based income distribution within the Meta population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Meta, among individuals aged 15 years and older with income, there were 56 men and 49 women in the workforce. Among them, 28 men were engaged in full-time, year-round employment, while 19 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 17.86% fell within the income range of under $24,999, while 5.26% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: none of men in full-time roles earned incomes exceeding $100,000, while none of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Meta median household income by race. You can refer the same here

Yearly citation counts for the publication titled "Perceived Control by Employees: A Meta-Analysis of Studies Concerning Autonomy and Participation at Work".

About this dataset

Context

The dataset tabulates the population of Meta by race. It includes the population of Meta across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Meta across relevant racial categories.

Key observations

The percent distribution of Meta population by race (across all racial categories recognized by the U.S. Census Bureau): 90.63% are white and 9.38% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Meta
• Population: The population of the racial category (excluding ethnicity) in the Meta is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Meta total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Meta Population by Race & Ethnicity. You can refer the same here

Facebook probably needs no introduction; nonetheless, here is a quick history of the company. The world’s biggest and most-famous social network was launched by Mark Zuckerberg while he was a...

PLEASE, CITE AS Kalabikhina IE, Kuznetsova PO, Zhuravleva SA (2024) Size and factors of the motherhood penalty in the labour market: A meta-analysis. Population and Economics 8(2): 178-205. https://doi.org/10.3897/popecon.8.e121438

Explanatory note 1: List of papers used in the meta-analysis - see the file "Meta_regression_analysis_papers".

The data is presented in WORD format.

Explanatory note 2: Set of data used in the meta-analysis - see the file "Meta_regression_analysis_table".

The data is presented in EXCEL format.

Description of table headers:

estimate_number - Number of the estimate

paper_number - Number of the paper

paper_name - Paper (year and first author)

paper_excluded - Paper was excluded from the final sample

survey - Data source

table_in_paper - Number of the table with the regression results in the paper

coeff - Regression coefficient for parenthood variable (estimate)

se - SE of the estimate

t - t-value of the estimate

ols - Estimate is obtained using the OLS method

fixed_effects - Estimate is obtained using the fixed effects method

panel - Model considers panel data (for several years)

quintile - Estimate is obtained using the quintile regression method

other - Estimate is obtained using other methods

selection_into_motherhood - Estimate is obtained allowing for selection into motherhood

hackman - Estimate is obtained allowing for selection into employment (Heckman procedure)

annual_earnings - Annual earnings are considered in the model

monthly_wage - Monthly wage is considered in the model

daily_wage - Daily wage is considered in the model

hourly_wage - Hourly wage is considered in the model

min_age_kid - Child's age (minimum)

max_age_kid - Child's age (maximum)

motherhood - Model uses a dummy variable of the presence of children

num_kids - Model uses a variable of the number of children

kid1 - Model uses a variable of the presence of one child

kid2p - Model uses a variable of the presence of two or more children

kid2 - Model uses a variable of the presence of two children

kid3p - Model uses a variable of the presence of three or more children

kid3 - Model uses a variable of the presence of three children

kid4p - Model uses a variable of the presence of three or more children

race/nationality - Model includes a race/ethnicity variable

age - Model includes the age variable

marstat - Model includes the marital status variable

oth_char_hh - Model includes any other variables of other household characteristics

settl_type - Model includes a variable of the type of settlement (urban, rural)

region - Model includes a variable of the region of the country

education - Model includes information on the level of education

experience - Model includes a variable of work experience

pot_experience - Model includes a variable of potential work experience, to be calculated from the data on age and number of years of education

tenure - Model includes a variable of the duration of employment at the current job

interruptions - Model includes a variable of employment interruptions (related to motherhood)

occupation - Model includes an occupation variable

industry - Model includes a variable of the industry of employment

union - Model includes a variable of trade union membership

friendly_conditions - Model includes a variable of the favourable working conditions for mothers (flexible schedule, possibility to work from home, etc.).

hours - Model includes a variable of the number of hours worked

sector - Model includes a variable of the type of employer ownership (public or private)

informal - Model includes a variable of informal employment

size_ent - Model includes a variable of the employer size

min_age_woman - Woman's age (minimum)

max_age_woman - Woman's age (maximum)

mean_age_woman - Woman's age (mean)

restricted - Sample is limited

private - Model considers only private sector employees

state - Model considers only public sector employees

full_time - Model considers only full-time workers

part_time - Model considers only part-time workers

better_educated - Model considers only women with a high level of education

lower_educated - Model considers only women with a low level of education

married - Model includes only married women

single - Model includes only single women

natives - Model includes only native women (born in the country)

immigrants - Model includes only immigrant women (born abroad)

race - Model includes only women of a particular race

min_year - Time period (minimum year)

max_year - Time period (maximum year)

journal - Type of publication

usa - Sample includes women from the USA

western_europe - Sample includes women from Western Europe (Belgium, France, Germany, Luxembourg, the Netherlands, Switzerland)

north_europe - Sample includes women from Northern Europe (Denmark, Finland, Norway, Sweden)

south_europe - Sample includes women from Southern Europe (Greece, Italy, Portugal, Spain)

east_centre_europe - Sample includes women from Central or Eastern Europe (Czechia, Hungary, Poland, Russia, Serbia, Ukraine)

china - Sample includes women from China

Russia - Sample includes women from Russia

others - Sample includes women from other countries

country - Country name

Meta's family of apps continues to dominate the global social media landscape, with **** billion daily active users across its core products in the second quarter of 2025. This represents a slight increase from the previous quarter and underscores the company's enduring influence in connecting people worldwide. Instagram's global reach and demographics Among Meta's portfolio, Instagram stands out with its impressive user base in key markets. India leads with *** million users, followed by the United States with *** million and Brazil with *** million. Messenger and WhatsApp usage trends Meta's messaging apps continue to see robust engagement. Facebook Messenger averaged 394 million daily active users on Android devices between October 2023 and October 2024, with iOS users adding another *** million on average. Meanwhile, WhatsApp has solidified its position as the leading communication channel for global smartphone users, reaching an estimated ***** billion unique active users worldwide in June 2024.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Meta: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 0 households where the householder is under 25 years old, 15(20%) households with a householder aged between 25 and 44 years, 33(44%) households with a householder aged between 45 and 64 years, and 27(36%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the city of Meta, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Meta median household income by age. You can refer the same here

Table_S1: Datasets for meta-analysis of public RNA-sequencing data in eusocial insects

Table_S2: Number of transcripts translated into proteins in each species.

Table_S3: Gene expression ratios when comparing queens and workers (QW-ratio)

Table_S4: QW-score calculated based on the QW-ratio (212 genes were evaluated)

Table_S5: QW-score by species

Table_S6: QW-score calculated based on the QW-ratio (2595 genes were evaluated)

Table_S7: The reference genome and transcriptome used for quantifying the gene expression level

Figure_S1: Number of transcripts presented commonly across species. The blue bars indicate the number of transcripts that could be converted to Apis mellifera IDs for each species. The orange line indicates the number of transcripts that were commonly present when the expression ratio tables for each species were merged. Merging was done from left to right for the species listed. In the sequence similarity search, two protein sequence sets of Temnothorax longispinosus were used as listed in Table S6. One is the de novo assembled reference (Temnothorax longispinosus1) transcriptome, and the second was extracted from the genome information (Temnothorax longispinosus2).

Figure_S2: Scatter plots of QW-score (2595 genes were evaluated). This gene set was created using expression data from a subset of Temnothorax, Reticulitermes, and Macrotermes. Red plots indicate genes with high or low QW-score from the 212 gene set.

Supplemental_Discussion: Additional discussion of the genes identified in this study was described.

peptide.tar.gz: The files of amino acid sequences newly created for the present study

transcript.tar.gz: The files of transcript sequences newly created for the present study

About this dataset

Context

The dataset presents a breakdown of households across various income brackets in Meta, MO, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for Meta, MO reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of Meta households based on income levels.

Key observations

• For Family Households: In Meta, the majority of family households, representing 27.59%, earn $100,000 to $124,999, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising 0.0%, have incomes falling $60,000 to $74,999, representing a smaller but still significant segment of the community.
• For Non-Family Households: In Meta, the majority of non-family households, accounting for 13.04%, have income $15,000 to $19,999, indicating that a substantial portion of non-family households falls within this income bracket. On the other hand, the minority of non-family households, comprising 0.0%, earn $60,000 to $74,999, representing a smaller, yet notable, portion of non-family households in the community.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Income Level: The income level represents the income brackets ranging from Less than $10,000 to $200,000 or more in Meta, MO (As mentioned above).
• All Households: Count of households for the specified income level
• % All Households: Percentage of households at the specified income level relative to the total households in Meta, MO
• Family Households: Count of family households for the specified income level
• % Family Households: Percentage of family households at the specified income level relative to the total family households in Meta, MO
• Non-Family Households: Count of non-family households for the specified income level
• % Non-Family Households: Percentage of non-family households at the specified income level relative to the total non-family households in Meta, MO

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Meta median household income. You can refer the same here

As of January 2024, the tech startup with the most layoffs was Amazon, with over 27 thousand layoffs, across five separate rounds of layoffs. It was followed by Meta and Google with around 21 thousand and 12 thousand job cuts announced respectively.

Layoffs in in the technology industry

Overall, layoffs across all industries began in 2020 due to the outbreak of the coronavirus (COVID-19) pandemic, with tech layoffs increasing in 2022. In the first quarter of 2023 alone, more than 167 thousand employees had been fired worldwide, a record number of job cuts in a single quarter and more than all of the layoffs announced in 2022 combined, marking a harsh start to of 2023 for the tech sector. From retail to finance and education, all sectors are suffering from this widespread downsizing. However, retail tech startups were hit the most, with almost 29 thousand layoffs announced as of September 2023. Most job losses happened in the United States, where tech giants like Amazon, Meta, and Google are based.

Reasons behind increasing tech layoffs

Layoffs in the technology sector started with the COVID-19 pandemic in 2020 when entire cities were in lockdown and mobility was restricted. Although restrictions loosened up in 2021, events such as the Russia-Ukraine war, the downturn in Chinese production, and rising inflation had a significant impact on the tech industry and continue to represent major concerns for tech companies. As a consequence, companies across the world have yet to overcome all economic challenges, examples of which are rising material and labor costs, as well as decreasing profit margins. To address such difficulties, tech companies have appointed business plans. For instance, in the United States, tech firms planned to focus more on consumer retention, automating software, and cutting operating expenses.

Yearly citation counts for the publication titled "Risk of cataract among interventional cardiologists and catheterization lab staff: A systematic review and meta‐analysis".

BackgroundJob satisfaction is a key factor influencing pharmacists’ performance and the level of trust patients place in them. Recognizing the relationship between these elements is essential for enhancing pharmacy services, improving patient outcomes, and creating a supportive work environment in healthcare settings.ObjectiveThe purpose of this study is to assess how pharmacists’ job satisfaction relates to their professional performance and the level of trust patients place in them. This review aims to provide evidence-based insights into how these factors collectively influence healthcare outcomes.MethodsWe conducted a comprehensive systematic literature search across several electronic databases—PubMed, Scopus, Web of Science, Embase, and Cochrane Library—covering publications up to December 2024. Our search strategy effectively identified relevant studies exploring job satisfaction among pharmacists and its impact on work performance and patient trust. We utilized Medical Subject Headings (MeSH) terms along with targeted free-text keywords such as “pharmacist job satisfaction,” “work performance,” “patient trust,” “pharmacy practice,” and “professional commitment.” By employing Boolean operators (AND, OR), we refined and enhanced our results. To assess the risk of bias, we used the Cochrane Risk of Bias Tool for randomized controlled trials and the ROBINS-I tool for non-randomized studies. This review was registered on PROSPERO under registration number CRD42024627893.ResultsA total of 65 studies were included in the final analysis, representing a variety of geographic regions and pharmacist populations. The meta-analysis revealed that job satisfaction among pharmacists was significantly linked to improved professional performance and increased patient trust. Monthly income and workplace environment emerged as the most prominent predictors, followed by years of experience and professional role. A fixed-effects model was utilized due to low to moderate heterogeneity (I2

BackgroundAs some countries announced to remove Coronavirus Disease 2019 (COVID-19) border, it indicates that the COVID-19 may have entered its terminal stage. In this COVID-19 pandemic, the mental health of frontline healthcare workers (HCWs) experienced unprecedented challenges. However, the impact of the COVID-19 pandemic on mental health among frontline HCWs lacks a high-quality and long-term systematic review and meta-analysis.MethodsWe conducted a systematic review and meta-analysis according to PRISMA guidelines. The system searches EMBASE, MEDLINE, PsycINFO, Cochrane Library, ScienceNet, and ERIC. Analyze the mental health problems of frontline HCWs in different regions and periods, including insomnia, stress, anxiety and depression. This study was registered in PROSPERO under the number CRD42021253821.ResultsA total of 19 studies on the effects of COVID-19 pandemic on mental health among frontline HCWs were included in this study. The overall prevalence of insomnia was 42.9% (95% CI, 33.9–51.9%, I2 = 99.0%) extracted from data from 14 cross-sectional studies (n = 10 127), 1 cohort study (n = 4,804), and 1 randomized controlled trial (RCT; n = 482) in 10 countries. The overall prevalence of stress was 53.0% (95% CI, 41.1–64.9%, I2 = 78.3%) extracted from data from nine cross-sectional studies (n = 5,494) and 1 RCT study (n = 482) from eight countries. The overall prevalence of anxiety and depression was 43.0% (95% CI, 33.8–52.3%, I2 = 99.0%) and 44.6% (95% CI, 36.1–53.1%, I2 = 99.0%) extracted from data from 17 cross-sectional studies (n = 11,727), one cohort study (n = 4,804), and one RCT study (n = 482) from 12 countries. The prevalence of stress and depression was higher in 2020, while the prevalence of insomnia and anxiety was higher in 2021. The prevalence of mental health problems among physicians was higher than that of other frontline HCWs. The prevalence of mental health problems among frontline HCWs is higher in South America and lower in North America.ConclusionsThis systematic review and meta-analysis showed that the COVID-19 pandemic have significant effects on mental health among frontline HCWs. The overall prevalence of insomnia, stress, anxiety and depression among frontline HCWs is high. Therefore, the health policy-makers should pay attention to and respond to the mental health problems of frontline HCWs in the context of public health emergencies.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/.