People aged between 45 and 54 years made up the largest age group among the Italian population in 2024, counting around 9.14 million individuals, closely followed by those aged 55 to 64 years, who were 9.13 million people. Infants aged up to two years were 1.19 million, the less numerous age category. As these data show, Italy suffers from a deep demographic and natality crisis. The country's population is one of the oldest in the world. In recent years, the share of Italians aged 65 years and over constantly grew, whereas the percentage of younger people declined.

In the past years, the share of people aged over 65 years grew constantly in Italy. Estimates for 2024 report that 24.3 percent of the Italian inhabitants are aged 65 years and older. Moreover, 63.5 percent of the residents are predicted to be aged between 15 and 64 years and only 12.2 percent to be 14 years old and younger. In 2023, the Italian region with the highest proportion of kids up to 14 years old was Trentino-South Tyrol, with 14.4 percent. On the other hand, 28.9 percent of the people in Liguria were over 65 years, making it the region with the highest share of elderly among its residents. Causes of an aging population The growing proportion of old people in Italy is due to two main factors. First, the birth rate in the country decreased over the past years. In 2023, less than seven children were born per 1,000 inhabitants, two fewer infants than in 2002. Second, life expectancy increased over the same period. A 65-year-old Italian woman could expect to have almost 21 more years of life ahead in 2002, while by 2023 this number reached 22.4. The increase for men was even greater, with male life expectancy at 65 growing from around 17 years in 2002 to 19.5 years in 2023. Future demographic trends The aging trend in the Italian population is not expected to change in the upcoming years. Projections made in 2022 predicted that the country's population is going to sensibly decrease in numbers. Population forecasts for 2050 account for slightly more than 52 million citizens, around seven million fewer compared to 2020.

In 2019, people aged between 45 and 54 years (16.14 percent of the Italian population) made up the largest group of population in Italy. On the contrary, the youngest groups of population represented the lowest share. Italy's population is one of the oldest populations in the world. More specifically, Italy has the third oldest population, after Japan and Monaco.

About this dataset

Context

The dataset tabulates the Italy population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of Italy.

Content

The dataset constitues the following two datasets across these two themes

• Italy, TX Population Breakdown by Gender
• Italy, TX Population Breakdown by Gender and Age

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/.

About this dataset

Context

The dataset tabulates the population of Italy town by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Italy town across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 53.35% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Italy town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Italy town 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 Italy town Population by Race & Ethnicity. You can refer the same here

As of 2019, Italy's female population added up to 30.9 million people. According to data, women aged between 45 and 54 years made up the largest group of population (4.9 million individuals). Moreover, female newborns were 716 thousand.

Population: Italy: Age 25 to 29 data was reported at 3.249 Person mn in 2017. This records a decrease from the previous number of 3.250 Person mn for 2016. Population: Italy: Age 25 to 29 data is updated yearly, averaging 3.505 Person mn from Dec 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 4.230 Person mn in 2001 and a record low of 3.249 Person mn in 2017. Population: Italy: Age 25 to 29 data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Italy – Table IT.G002: Population: by Age.

About this dataset

Context

The dataset tabulates the Italy town population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Italy town. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 601 (62.87% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Italy town population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Italy town is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Italy town is shown in the following column.

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 Italy town Population by Age. You can refer the same here

In 2019, women aged between 45 and 54 years made up the largest share of population in Italy (15.91 percent). On the contrary, the youngest groups of females represented the smallest share of population.

About this dataset

Context

The dataset tabulates the data for the Italy, New York population pyramid, which represents the Italy town population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Italy, New York, is 17.4.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Italy, New York, is 35.1.
• Total dependency ratio for Italy, New York is 52.5.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Italy, New York is 2.9.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Italy town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Italy town for the selected age group is shown in the following column.
• Population (Female): The female population in the Italy town for the selected age group is shown in the following column.
• Total Population: The total population of the Italy town for the selected age group is shown in the following column.

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 Italy town Population by Age. You can refer the same here

There were 18 938 000 Messenger users in Italy in December 2019, which accounted for 31.8% of its entire population. The majority of them were women - 50.8%. People aged 25 to 34 were the largest user group (4 400 000). The highest difference between men and women occurs within people aged 55 to 64, where women lead by 1 200 000.

There were 43 600 000 Facebook users in Italy in December 2024, which accounted for 73.3% of its entire population. The majority of them were women - 51.4%. People aged 25 to 34 were the largest user group (9 000 000). The highest difference between men and women occurs within people aged 18 to 24, where women lead by 3 400 000.

As of January 2021, Italy's teenager population amounted to roughly four million. Young people aged 16 years old represented the largest group, with 295 thousand males and 278 thousand females. Overall, there were slightly more male teens were than female ones.

From January to December 2024, around 66,000 migrants arrived in Italy by sea. Between 2014 and 2024, the number of migrants setting foot in the country peaked in 2016 at 181,000 individuals, whereas in 2019 only 11,400 people were rescued from the sea. In fact, stricter immigration policies were enacted between 2018 and 2019 by the right-wing and populist government supported by the League and the Five-Star Movement. Among the most frequent countries of origin declared upon arrivals in 2024, Bangladesh and Syria ranked in the first places. About 13,800 were Bangladeshi citizens, while around 12,500 immigrants came from Syria. Asylum seekers and minors among the migrants In 2023, the largest number of asylum applicants in Italy were from Bangladesh. In fact, 23,450 requests were recorded as of December 2023, while 18,300 applicants were from Egypt, the second most common nationality among asylum seekers. In recent years, many unaccompanied minors reached the Italian coasts. In 2024, 8,043 children migrated without their parents into the country. Contrasting opinions and distorted perceptions  According to the data published by Ipsos, a part of Italians tend to overestimate the size of the immigrated population. The results of this survey uncovered the presence of distorted perceptions in 2018: people thought that about 28 percent of the Italian population was not born in Italy, whereas the actual percentage was around ten. Furthermore, the public opinion on migration was controversial. In the same year, roughly half of the population perceived migrants as a risk for the Italian economy. On the other hand, 18 percent of Italians believed that migration could be a resource for the country.

As of December 2022, prisoners aged between 50 and 59 years made up the largest group of inmates in Italy. By contrast, individuals between 18 and 20 years old added up to 595, the lowest amount among the different age groups.

Data on Italy's prison population broken down by marital status show that unmarried prisoners accounted for the largest group, whereas widowed people in custody represented the smallest group.

Throughout the early modern period, the largest city in Italy was Naples. The middle ages saw many metropolitan areas along the Mediterranean grow to become the largest in Europe, as they developed into meeting ports for merchants travelling between the three continents. Italy, throughout this time, was not a unified country, but rather a collection of smaller states that had many cultural similarities, and political control of these cities regularly shifted over the given period. Across this time, the population of each city generally grew between each century, but a series of plague outbreaks in the 1600s devastated the populations of Italy's metropolitan areas, which can be observed here. Naples At the beginning of the 1500s, the Kingdom of Naples was taken under the control of the Spanish crown, where its capital grew to become the largest city in the newly-expanding Spanish Empire. Prosperity then grew in the 16th and 17th centuries, before the city's international importance declined in the 18th century. There is also a noticeable dip in Naples' population size between 1600 and 1700, due to an outbreak of plague in 1656 that almost halved the population. Today, Naples is just the third largest city in Italy, behind Rome and Milan. Rome Over 2,000 years ago, Rome became the first city in the world to have a population of more than one million people, and in 2021, it was Italy's largest city with a population of 2.8 million; however it did go through a period of great decline in the middle ages. After the Fall of the Western Roman Empire in 476CE, Rome's population dropped rapidly, below 100,000 inhabitants in 500CE. 1,000 years later, Rome was an important city in Europe as it was the seat of the Catholic Church, and it had a powerful banking sector, but its population was just 55,000 people as it did not have the same appeal for merchants or migrants held by the other port cities. A series of reforms by the Papacy in the late-1500s then saw significant improvements to infrastructure, housing, and sanitation, and living standards rose greatly. Over the following centuries, the Papacy consolidated its power in the center of the Italian peninsula, which brought stability to the region, and the city of Rome became a cultural center. Across this period, Rome's population grew almost three times larger, which was the highest level of growth of these cities.

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The metropolitan, urban and rural population and all .administrative regional units. as defined in Official Europe Union Statistics (NUTS 2) covered proportionately the respective population aged 18 and above. The country was divided into an appropriate number of areas, grouping NUTS regions at whatever level appropriately. The NUTS covered in Italy were the following; Basilicata, Calabria, Campania, Emilia, Friuli, Venezia, Giulia, Lazio, Liguria, Lombardia, Marche, Milano, Molise e Abbruzzi, Puglie, Sardegna, Sicilia, Toscana, Trentino, Umbria, Valle d.Aosta/Piemonte, Veneto.

The basic sample design was a multi-stage, random probability sample. 100 sampling points were drawn with probability proportional to population size, for a total coverage of the country. The sampling points were drawn after stratification by NUTS 2 region and by degree of urbanisation. They represented the whole territory of the country surveyed and are selected proportionally to the distribution of the population in terms of metropolitan, urban and rural areas. In each of the selected sampling points, one address was drawn at random. This starting address forms the first address of a cluster of a maximum of 20 addresses. The remainder of the cluster was selected as every Nth address by standard random route procedure from the initial address. In theory, there is no maximum number of addresses issued per country. Procedures for random household selection and random respondent selection are independent of the interviewer.s decision and controlled by the institute responsible. They should be as identical as possible from to country, full functional equivalence being a must.

At every address up to 4 recalls were made to attempt to achieve an interview with the selected respondent. There was only one interview per household. The final sample size is 1,002 completed interviews.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

In January 2025 in Italy, Facebook was most popular amongst users aged 25 to 34 years, accounting for 20.6 percent of the country's audience. Overall, 18.6 percent of Facebook users in Italy were aged 45 to 54 years, and 17.4 percent belonged to the 35 to 44 year age group. In addition, users aged 65 years and older made up 11.7 percent of Facebook users, and 17.2 percent of users belonged to the 18 to 24 years age group.

The European Copernicus Coastal Flood Awareness System (ECFAS) project will contribute to the evolution of the Copernicus Emergency Monitoring Service by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS will provide a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.

The ECFAS Proof-of-Concept development will run from January 2021-December 2022. The ECFAS project is a collaboration between Istituto Universitario di Studi Superiori IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and is funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.

This project has received funding from the European Union’s Horizon 2020 programme

The deliverables will have restricted access at least until the end of ECFAS

Description of the containing files inside the Dataset.

The dataset was divided at European country level, except the Adriatic area which was extracted as a region and not on a country level due to the small size of the countries. The buffer zone of each data was 10km inland in order to be correlated with the new Copernicus product Coastal Zone LU/LC.

Specifically, the dataset includes the new Coastal LU/LC product which was implemented by the EEA and became available at the end of 2020. Additional information collected in relation to the location and characteristics of transport (road and railway) and utility networks (power plants), population density and time variability. Furthermore, some of the publicly available datasets that were used in CEMS related to the abovementioned assets were gathered such as OpenStreetMap (building footprints, road and railway network infrastructures), GeoNames (populated places but also names of administrative units, rivers and lakes, forests, hills and mountains, parks and recreational areas, etc.), the Global Human Settlement Layer (GHS) and Global Human Settlement Population Grid (GHS-POP) generated by JRC. Also, the dataset contains 2 layers with statistics information regarding the population of Europe per sex and age divided in administrative units at NUTS level 3. The first layers includes information fro the whole Europe and the second layer has only the information regaridng the population at the Coastal area. Finally, the dataset includes the global database of Floods protection standars. Below there are tables which present the dataset.

• Adriatic folder contains the countries: Slovenia, Croatia, Montenegro, Albania, Bosnia and Herzegovina

• Malta was added to the dataset

| Copernicus Land Monitoring Service | Resolution | Comment | | Coastal LU/LC | 1:10.000 | A Copernicus hotspot product to monitor landscape dynamics in coastal zones | | EU-Hydro - Coastline | 1:30.000 | EU-Hydro is a dataset for all European countries providing the coastline | | Natura 2000 | 1: 100000 | A Copernicus hotspot product to monitor important areas for nature conservation | | European Settlement Map | 10m | A spatial raster dataset that is mapping human settlements in Europe | | Imperviousness Density | 10m | The percentage of sealed area | | Impervious Built-up | 10m | The part of the sealed surfaces where buildings can be found | | Grassland 2018 | 10m | A binary grassland/non-grassland product | | Tree Cover Density 2018 | 10m | Level of tree cover density in a range from 0-100% |

| Joint Research Center | Resolution | Comment | | Global Human Settlement Population Grid GHS-POP) | 250m | Residential population estimates for target year 2015 | | GHS settlement model layer (GHS-SMOD) | 1km | The GHS Settlement Model grid delineates and classify settlement typologies via a logic of population size, population and built-up area densities | | GHS-BUILT | 10m | Built-up grid derived from Sentinel-2 global image composite for reference year 2018 | | ENACT 2011 Population Grid (ENACT-POP R2020A) | 1km | The ENACT is a population density for the European Union that take into account major daily and monthly population variations | | JRC Open Power Plants Database (JRC-PPDB-OPEN) | - | Europe’s open power plant database | | GHS functional urban areas (GHS-FUA R2019A) | 1km | City and its commuting zone (area of influence of the city in terms of labour market flows) | | GHS Urban Centre Database (GHS-UCDB R2019A) | 1km | Urban Centres defined by specific cut-off values on resident population and built-up surface |

| Additional Data | Resolution | Comment | | Open Street Map (OSM) | - | BF, Transportation Network, Utilities Network, Places of Interest | | CEMS | - | Data from Rapid Mapping activations in Europe | | GeoNames | - | Populated places, Adm. units, Hydrography, Forests, Hills/Mountains, Parks, etc. | | Global Administrative Areas | - | Administrative areas of all countries, at all levels of sub-division | | NUTS3 Population Age/Sex Group | - | Eurostat population by age ansd sex statistics interesected with the NUTS3 Units | | FLOPROS | | A global database of FLOod PROtection Standards, which comprises information in the form of the flood return period associated with protection measures, at different spatial scales |

| This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211 |