About this dataset

Context

The dataset tabulates the data for the Italy, TX population pyramid, which represents the Italy population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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, TX, is 44.3.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Italy, TX, is 28.5.
• Total dependency ratio for Italy, TX is 72.9.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Italy, TX is 3.5.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Italy for the selected age group is shown in the following column.
• Population (Female): The female population in the Italy for the selected age group is shown in the following column.
• Total Population: The total population of the Italy 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 Population by Age. You can refer the same here

People aged between ********* years made up the largest age group among the Italian population in 2025, counting around *** million individuals, closely followed by those aged ******** years, who were *** million people. Infants aged up to two years were **** 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.

About this dataset

Context

The dataset tabulates the population of Italy town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Italy town. The dataset can be utilized to understand the population distribution of Italy town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Italy town. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Italy town.

Key observations

Largest age group (population): Male # 65-69 years (80) | Female # 55-59 years (66). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

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.

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 is shown in the following column.
• Population (Female): The female population in the Italy town is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Italy town for each age group.

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 Gender. You can refer the same here

In 2025, 24.7 percent of the total population in Italy is estimated to be 65 years and older. According to data, the share of elderly people in the Italian society has been growing constantly since 2009. Consequently, the share of young population experienced a decrease in the last years. As a result, the average age of Italians has risen. In 2011, it was 43.6 years, whereas in 2024 it was estimated to be 46.8 years. The oldest country in Europe Italy and Portugal are the European countries with the largest percentage of elderly citizens. In 2024, 24 percent of the total population was aged 65 years and older. Bulgaria and Finland followed in the ranking, while Azerbaijan had the lowest share of elder population, less than ten percent. An increasingly longer lifespan might provide an explanation for such a high share of citizens over 65 years in Italy. The Republic ranks among the countries with the highest life expectancy worldwide. In Europe, only people in Switzerland and Spain can expect to live longer. Fewer babies than ever The share of young people is getting slimmer, not only because the elderly are living longer than ever before. In fact, Italians are having fewer children compared to previous years. The birth rate in the country has been constantly decreasing: in 2024, only 6.3 babies were born per 1,000 inhabitants, three children less than in 2010. In the south of Italy, in 2023 the birth rate stood at 6.7 infants per 1,000 inhabitants, whereas in central Italy this figure reached only 5.8, the highest and lowest rates in the country, respectively.

Male population aged 45 to 54 years represented the largest group of male population in Italy, amounting to around 4.8 million people. The number of male newborns was roghly 758 thousand, while infants aged six to 11 years were 1.7 million.

About this dataset

Context

The dataset tabulates the Italy 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. 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 1,141 (46.38% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Italy is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Italy 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 Population by Age. You can refer the same here

Italy is struggling with an ageing population and a decreasing birthrate. According to a recent study, this will bring the labor force age distribution to lean towards older segments of the population in 2030. The study, in fact, foresees the most numerous age group in the labor force to be the ***** years one by that date. This group is projected to count over *** million female workers and almost *** million male workers.

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.

A description of the modelling methods used for age and gender structures can be found in "https://pophealthmetrics.biomedcentral.com/articles/10.1186/1478-7954-11-11" target="_blank"> Tatem et al and Pezzulo et al. Details of the input population count datasets used can be found here, and age/gender structure proportion datasets here.
Both top-down 'unconstrained' and 'constrained' versions of the datasets are available, and the differences between the two methods are outlined here. The datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. The unconstrained datasets are available for each year from 2000 to 2020.
The constrained datasets are only available for 2020 at present, given the time periods represented by the building footprint and built settlement datasets used in the mapping.
Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00646

Analysis unit

Persons, households, and dwellings Low ages grouped into categories

UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: yes

UNIT DESCRIPTIONS: - Dwellings: Dwelling refers to a room (or set of rooms) that is: regularly dedicated to residential use; separate (i.e. surrounded by walls and covered by a roof); independent (with at least one external access that is either independent or through shared entry areas - road, courtyard, stairs, landings, common balconies, terraces, etc. - access, in other words, does not require passing through other dwellings); incorporated in a building (or constitutes a building). - Households: Group of persons who are cohabiting as usual residents of a dwelling - Group quarters: Collective residential structure refers to facilities used to house large groups of people and/or one or more families. This category includes hotels, hospitals, rest homes for senior citizens and reception centers and institutes of various kinds (religious, healthcare-related, welfare support, educational, etc.).

Universe

All citizens or foreigners registered to stay in Italy

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: National Institute of Statistics

SAMPLE SIZE (person records): 2968065.

SAMPLE DESIGN: 5% sample drawn by national statistics office

Mode of data collection

Face-to-face [f2f]

Research instrument

Two forms for dwellings and persons: a short form (CP.1B) and a long form (CP.1). One third of families in population centers from municipalities with at least 20,000 inhabitants or provincial capitals received the long form. Persons living in cohabitation received a separate form (CP.2).

Our analysis focuses on seven metropolitan cities across Italy. Here, we report the number of spatial cells of the mobile phone network and the population (in thousands) of each of these cities split across 6 age groups. Population data is retrieved from the 2011 Italian census and comprises all the census sections within the phone cells considered for each city. It is important to highlight that in each cell of the network there can be several mobile phone users, thus we cannot estimate the fraction of the census population included in our data set. Note that the age groups provided by the Italian census do not perfectly match those of the Telecom Italia dataset.

Analysis unit

Persons, households, and dwellings Age is grouped into categories

UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: yes

UNIT DESCRIPTIONS: - Dwellings: Not specified - Households: A group of people, bound by marriage, kinship, affinity, adoption, guardianship or emotional ties, who are partners and live in the same Municipality. - Group quarters: Not specified

Universe

All population who reside in Italy and all persons who do not live in Italy but are present at the time of the census

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: National Institute of Statistics

SAMPLE SIZE (person records): 2990739.

SAMPLE DESIGN: Systematic sample of every 20th dwelling.

Mode of data collection

Face-to-face [f2f]

Research instrument

A single booklet of Household Form with five sections: 1) List A: household members, 2) List B: individuals who do not usually reside in the accommodation, 3) Section I, information on the dwelling (for all persons in List A and List B), 4) Section II, information on the individuals who usually reside in the accomodation (persons on List A), and 5) Section III, information on individuals who do not usually reside in the dwelling (persons on List A). Section I to III are de-identified with names removed.

We stratified the Italian population according to age and gender in order to evaluate mortality trends over more than one century. Data covering the 1901–2008 period were used to study the yearly variations in mortality. Fluctuations in age-adjusted mortality curves were analyzed by Join Point Regression Models, identifying Join Points and Annual Percent Changes. A consistent decline in all-cause mortality occurred across the whole period, the most striking variations being observed in the 0–49 years population. In 1901, other and undefined diseases were the main causes of death, followed by infectious, digestive, and respiratory diseases in the 0–49 years population and by respiratory, cardiovascular, and cerebrovascular diseases in the ≥50 years population groups. In 2008 the main causes of death were accidents (males) and tumors (females) in the 0–49 age class, tumors in the 50–69 age class (both genders), and tumors (males) and cardiovascular diseases (females) in the elderly. The results highlight the interplay between age and gender in affecting mortality trends and reflect the dramatic progress in nutritional, lifestyle, socioeconomic, medical, and hygienic conditions.

In 2025, Italy’s resident population is estimated to be almost 59 million inhabitants. About one-sixth of them lived in Lombardy, the most populous region in the country. Lazio and Campania followed, with roughly 5.7 million and 5.6 million inhabitants, respectively. These figures are mainly driven by Rome and Naples, the administrative capitals of these regions, and two of the largest metropolitan areas in the country. Which region has the oldest population? The population in Italy has become older and older over the last years. The average age in the country is equal to 46.8 years, but in some regions this figure is even higher. Liguria records an average age of 49.6 years and has one of the lowest birth rates in the country. Demographic trends for the future Liguria’s case, however, is not an outlier. Italy is already the country with the highest share of old people in Europe. At the same time, the very low number of new births means that, despite an always-increasing life expectancy, the Italian population is declining. Indeed, projections estimate that the country will have five million fewer inhabitants by 2050.

On 26 September 2013, Istat released the inter-census reconstruction of the resident population by age, gender and citizenship (Italians vs. foreigners) for all municipalities in Italy. As communicated by Istat, it is based on the evidence provided by the last census, together with the comparative examination with the demographic flows (births, deaths, migrations) that occurred in the same period. It therefore has the objective of improving both the statistics on the population itself - in terms of consistency, structural composition and demographic events - as well as those statistical indicators which cannot do without their use. The reconstructed population is, in fact, a final product resulting from estimates, although it is largely based on data subject to survey, and therefore it is not possible to attribute to it a meaning other than an exclusively statistical one. The dataset contains data relating to the Municipality of Milan between 1 January 2002 and 1 January 2011.

A data set of cross-nationally comparable microdata samples for 15 Economic Commission for Europe (ECE) countries (Bulgaria, Canada, Czech Republic, Estonia, Finland, Hungary, Italy, Latvia, Lithuania, Romania, Russia, Switzerland, Turkey, UK, USA) based on the 1990 national population and housing censuses in countries of Europe and North America to study the social and economic conditions of older persons. These samples have been designed to allow research on a wide range of issues related to aging, as well as on other social phenomena. A common set of nomenclatures and classifications, derived on the basis of a study of census data comparability in Europe and North America, was adopted as a standard for recoding. This series was formerly called Dynamics of Population Aging in ECE Countries. The recommendations regarding the design and size of the samples drawn from the 1990 round of censuses envisaged: (1) drawing individual-based samples of about one million persons; (2) progressive oversampling with age in order to ensure sufficient representation of various categories of older people; and (3) retaining information on all persons co-residing in the sampled individual''''s dwelling unit. Estonia, Latvia and Lithuania provided the entire population over age 50, while Finland sampled it with progressive over-sampling. Canada, Italy, Russia, Turkey, UK, and the US provided samples that had not been drawn specially for this project, and cover the entire population without over-sampling. Given its wide user base, the US 1990 PUMS was not recoded. Instead, PAU offers mapping modules, which recode the PUMS variables into the project''''s classifications, nomenclatures, and coding schemes. Because of the high sampling density, these data cover various small groups of older people; contain as much geographic detail as possible under each country''''s confidentiality requirements; include more extensive information on housing conditions than many other data sources; and provide information for a number of countries whose data were not accessible until recently. Data Availability: Eight of the fifteen participating countries have signed the standard data release agreement making their data available through NACDA/ICPSR (see links below). Hungary and Switzerland require a clearance to be obtained from their national statistical offices for the use of microdata, however the documents signed between the PAU and these countries include clauses stipulating that, in general, all scholars interested in social research will be granted access. Russia requested that certain provisions for archiving the microdata samples be removed from its data release arrangement. The PAU has an agreement with several British scholars to facilitate access to the 1991 UK data through collaborative arrangements. Statistics Canada and the Italian Institute of statistics (ISTAT) provide access to data from Canada and Italy, respectively. * Dates of Study: 1989-1992 * Study Features: International, Minority Oversamples * Sample Size: Approx. 1 million/country Links: * Bulgaria (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02200 * Czech Republic (1991), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06857 * Estonia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06780 * Finland (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06797 * Romania (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06900 * Latvia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02572 * Lithuania (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03952 * Turkey (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03292 * U.S. (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06219

As mobility within the European Economic Area (EEA) is on the rise, it is important to understand migrants' health-related behaviors (such as physical activity [PA]) within this context. This study investigated i) the extent to which Italian immigrants in Norway perceive that moving had a negative or positive impact on their PA; ii) possible differences between the PA of the Italian immigrants compared with the Norwegian population; and iii) possible associations of the Italian immigrants' PA with key sociodemographic characteristics (gender, age, region of residence, and educational level). The data were retrieved from the Mens Sana in Corpore Sano study. In order to enhance the sample's representativeness, the original dataset (n = 321) was oversampled in accordance with the proportion of key sociodemographic characteristics of the reference population using the ADASYN method (resampled n = 531). The results indicate that a large majority of Italian immigrants perceived that they were as or even more physically active in Norway than they would have been if they continued living in Italy, while 20% of the Italians perceived instead a negative impact. No significant differences were found in the PA levels of the Italians in comparison with the Norwegian population, though some differences were found in relation to specific modes of PA. After controlling for multiple sociodemographic characteristics, men, those with lower educational levels and, to a certain extent, older adults tended to perceive a more negative impact and be less physically active than their respective counterparts. Compared with those living in the most urbanized regions, a larger proportion of those living in less urbanized regions perceived a negative impact, though no differences were observed in terms of PA levels. The findings are discussed in light of acculturation, gender, and social gradient. The knowledge generated by this study sheds light on an important health-related behavior among Italians in Norway, which can inform initiatives that aim at promoting PA in this specific group as well as other similar contexts of intra-EEA migration.

Analysis unit

Persons

UNITS IDENTIFIED: - Dwellings: no - Vacant Units: No - Households: no - Individuals: yes - Group quarters: no

UNIT DESCRIPTIONS: - Dwellings: no - Households: The family is understood as a de facto family, that is as a group of people linked by bonds of marriage, kinship, affinity, adoption, protection or by emotional bonds, cohabiting and having habitual residence in the same municipality; if the selected family cohabits with other families, only the extracted family is interviewed. - Group quarters: no

Universe

All members of families residing in Italy, even if temporarily emigrated abroad, excluding permanent members of institutional quarters (hospices, religious institutes, barracks, etc.).

Kind of data

Sample survey data [ssd]

Sampling procedure

MICRODATA SOURCE: National Institute of Statistics (ISTAT)

SAMPLE SIZE (person records): ~100,000.

SAMPLE DESIGN: Two-stage sample with stratification of the first-stage units. The first-stage units are municipalities; second-stage units are families. Within each province municipalities are stratified according to population size. Municipalities larger than a predetermined population threshold are included in the sample; remaining municipalities are stratified by population size and extracted with probability proportional to the population size. The sample municipalities remain the same over time. In each quarterly survey, around 1,400 municipalities and 70,000 families are selected. The families included in the sample are interviewed 4 times within 15 months. Each family is interviewed for two consecutive quarters; an interruption follows for the next two quarters, after which the family is interviewed again for another two quarters. Microdata samples distributed by IPUMS are from Quarter 1 surveys.

Mode of data collection

Face-to-face [f2f]

Research instrument

A single booklet with a general record of respondents and 12 sections: 1) Classification of individuals; 2) Labor status in the reference week (for people aged 15 or more); 3) Main job for employed people; 4) Second job for employed people; 5) Previous work experience (for unemployed people); 6) Search for employment (for people aged 15 or more); 7) Employment services and agencies (for people aged 15-74); 8) Education and training (for people aged 15 or more); 9) Self-perceived condition and residency (for people aged 15 or more); 10) Information on the household (for the last family member interviewed); 11) Closing questions for the interviewer; 12) Pending coding.

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

BackgroundMost studies on immigrant health focus on immigrant groups coming from extra-European and/or low-income countries. Little attention is given to self-rated health (SRH) in the context EU/EEA migration. To know more about health among European immigrants can provide new insights related to social determinants of health in the migration context. Using the case of Italian immigrants in Norway, the aim of this study was to (i) examine the levels of SRH among Italian immigrants in Norway as compared with the Norwegian and the Italian population, (ii) examine the extent to which the Italian immigrant perceived that moving to Norway had a positive or negative impact on their SRH; and (iii) identify the most important factors predicting SRH among Italian immigrants in Norway.MethodsA cross-sectional survey was conducted among adult Italian immigrants in Norway (n = 321). To enhance the sample's representativeness, the original dataset was oversampled to match the proportion of key sociodemographic characteristics of the reference population using the ADASYN method (oversampled n = 531). A one-sample Chi-squared was performed to compare the Italian immigrants' SRH with figures on the Norwegian and Italian populations according to Eurostat statistics. A machine-learning approach was used to identify the most important predictors of SRH among Italian immigrants.ResultsMost of the respondents (69%) rated their SRH as “good” or “very good”. This figure was not significantly different with the Norwegian population, nor to the Italians living in Italy. A slight majority (55%) perceived that their health would have been the same if they continued living in Italy, while 23% perceived a negative impact. The machine-learning model selected 17 variables as relevant in predicting SRH. Among these, Age, Food habits, and Years of permanence in Norway were the variables with the highest level of importance, followed by Trust in people, Educational level, and Health literacy.ConclusionsItalian immigrants in Norway can be considered as part of a “new mobility” of high educated people. SHR is shaped by several interconnected factors. Although this study relates specifically to Italian immigrants, the findings may be extended to other immigrant populations in similar contexts.

After entering Italy, coronavirus (COVID-19) has been spreading fast. An analysis of the individuals who died after contracting the virus revealed that the vast majority of deaths occurred among the elderly. As of May, 2023, roughly 85 percent were patients aged 70 years and older.

Italy's death toll was one of the most tragic in the world. In the last months, however, the country saw the end to this terrible situation: as of May 2023, roughly 84.7 percent of the total Italian population was fully vaccinated.

As of May, 2023, the total number of cases reported in the country were over 25.8 million. The North of the country was the mostly hit area, and the region with the highest number of cases was Lombardy.

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