Since 1950 there has been a relatively large difference in the number of males and females in Latvia, particularly when put in context with the total overall population. The number of women exceeds the number of men by over 260 thousand in 1950, which is one of the long-term effects of the Second World War. During the war, Latvia lost approximately 12.5 percent of its overall population, an the number of women was already higher than men before this, however the war caused this gap in population to widen much further. From 1950 onwards both male and female populations grow, and by 1990 the gap has shrunk down to 180,000 people. In 1990 Latvia gained it's independence from the Soviet Union, and from this point both populations begin to decline, falling to 870 thousand men in 2020, and just over one million women, with a difference of 150 thousand people.

Latvia LV: Population: Female: Ages 35-39: % of Female Population data was reported at 6.067 % in 2017. This records an increase from the previous number of 6.038 % for 2016. Latvia LV: Population: Female: Ages 35-39: % of Female Population data is updated yearly, averaging 6.809 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 8.295 % in 1963 and a record low of 6.037 % in 2015. Latvia LV: Population: Female: Ages 35-39: % of Female Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Latvia – Table LV.World Bank.WDI: Population and Urbanization Statistics. Female population between the ages 35 to 39 as a percentage of the total female population.; ; World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; ;

Actual value and historical data chart for Latvia Population Female Percent Of Total

This graph shows the total population of Estonia, Latvia and Lithuania in the years between 1922 and 1935, as well as the total number of males and females. After the First World War the Baltic states began claiming their independence from tsarist Russia, as the events of the Russian Revolution took place. Inter-war Estonia The Estonian War of Independence from 1918 to 1920 led to the country's first period of independence, until it became occupied by the Soviet Union again in 1940 during the Second World War. After Estonia gained independence the country experienced a period of political turmoil, including a failed coup d'etat in 1924, and was hit hard by the Great Depression in 1929 before things became more stable in the mid 1930s. Between 1939 and 1945 Estonia's population was devastated by the Second World War, with some estimates claiming that as many as 7.3 percent of all civilians perished as a result of the conflict. From the graph we can see the population grew by 119 thousand people during the 12 years shown, growing from 1.107 million to 1.126 million. The number of women was also higher than the number of men during this time, by 67 thousand in 1922 and 68 thousand in 1934. Inter-war Latvia For Latvia, Independence was a hard-won struggle that had devastated the population in the late 1910s. Similarly to Estonia, the advent of independence brought many challenges to Latvia, and a period of political and economic turmoil followed, which was exacerbated by the Great Depression in 1929. After economic recovery began in 1933, and a coup d'etat established stricter control in 1934, the Latvian economy and political landscape became more stable and the quality of life improved. This lasted until the Second World War, where Latvia became one of the staging grounds of Germany's war against Soviet Russia, and approximately 12.5 percent of all civilians died. From the data we can see that Latvia's population between 1925 and 1935 grew steadily by 95,000 in this decade, with the number of men and women growing at a similar rate. Inter-war Lithuania Lithuania's experience in the interwar period was slightly different to that of Latvia and Estonia. The end of the First World War led to a growing movement for independence from German, Russian or Polish influence, however these countries were reluctant to cede control to one another, and independence was finally achieved in 1922. A right wing dictatorship was established in 1926, which maintained political and civil control until the outbreak of the Second World War, however interference from other nations, particularly Germany, was ever-present in Lithuanian economic activity. From the graph we have only one set of figures, showing that the Lithuanian population was just over 2 million in 1929, with approximately 5 percent more women than men. World War II again devastated Lithuania's population, with almost 14.4 percent of the entire population falling during the conflict.

This dataset contains data on population movement (population, marriages, births, deaths, infant deaths (under 1 year), natural increase of population) in Latvia in 1919-1939. Data on the number of population (male, female, total) were used from the dataset “Number of Population in Counties in Latvia, 1919-1939”. For sources of the data see metadata field Origin of Sources below. Dataset "Population Movement in Latvia, 1919-1939" was published implementing project "Historical Sociology of Modern Restorations: a Cross-Time Comparative Study of Post-Communist Transformation in the Baltic States" from 2018 to 2022. Project leader is prof. Zenonas Norkus. Project is funded by the European Social Fund according to the activity "Improvement of researchers' qualification by implementing world-class R&D projects' of Measure No. 09.3.3-LMT-K-712".

This scatter chart displays proportion of seats held by women in national parliaments (%) against male population (people) in Latvia. The data is filtered where the date is 2021. The data is about countries per year.

This dataset contains two data tables on number of population in counties in Latvia in 1919-1939. In the data table LiDA_HistatData_0245_Data_0001_v2 data of number of male, female and total in the cells (1920-1924, 1931, 1936-1939 by administrative region) is computed by interpolating data published in Latvian statistical publications (a more detailed description of is provided in the document LiDA_HistatData_0245_Interpolation_0001_v1.pdf). The data table LiDA_HistatData_0245_Data_0002_v1 of the mid-year population data is computed by interpolated population data. Mid-year population data is arithmetic mean of the population of two consecutive years (data of beginning or end of the year) (a more detailed description of is provided in the document LiDA_HistatData_0245_Interpolation_0002_v1.pdf). The aim of such an computation was to have standardized data for the mid-year population data. Dataset "Number of Population in Counties in Latvia, 1919-1939" was published implementing project "Historical Sociology of Modern Restorations: a Cross-Time Comparative Study of Post-Communist Transformation in the Baltic States" from 2018 to 2022. Project leader is prof. Zenonas Norkus. Project is funded by the European Social Fund according to the activity "Improvement of researchers' qualification by implementing world-class R&D projects' of Measure No. 09.3.3-LMT-K-712".

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

Globally, about 25 percent of the population is under 15 years of age and 10 percent is over 65 years of age. Africa has the youngest population worldwide. In Sub-Saharan Africa, more than 40 percent of the population is below 15 years, and only three percent are above 65, indicating the low life expectancy in several of the countries. In Europe, on the other hand, a higher share of the population is above 65 years than the population under 15 years. Fertility rates The high share of children and youth in Africa is connected to the high fertility rates on the continent. For instance, South Sudan and Niger have the highest population growth rates globally. However, about 50 percent of the world’s population live in countries with low fertility, where women have less than 2.1 children. Some countries in Europe, like Latvia and Lithuania, have experienced a population decline of one percent, and in the Cook Islands, it is even above two percent. In Europe, the majority of the population was previously working-aged adults with few dependents, but this trend is expected to reverse soon, and it is predicted that by 2050, the older population will outnumber the young in many developed countries. Growing global population As of 2025, there are 8.1 billion people living on the planet, and this is expected to reach more than nine billion before 2040. Moreover, the global population is expected to reach 10 billions around 2060, before slowing and then even falling slightly by 2100. As the population growth rates indicate, a significant share of the population increase will happen in Africa.

Latvia LV: Life Expectancy at Birth: Total data was reported at 74.529 Year in 2016. This records an increase from the previous number of 74.480 Year for 2015. Latvia LV: Life Expectancy at Birth: Total data is updated yearly, averaging 69.879 Year from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 74.529 Year in 2016 and a record low of 65.664 Year in 1994. Latvia LV: Life Expectancy at Birth: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Latvia – Table LV.World Bank: Health Statistics. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision, or derived from male and female life expectancy at birth from sources such as: (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;