1. Database contents

The Russian Short-Term Mortality Fluctuations database (RusSTMF) contains a series of standardized and crude death rates for men, women and both sexes for Russia as a whole and its regions for the period from 2000 to 2021.

All the output indicators presented in the database are calculated based on data of deaths registered by the Vital Registry Office. The weekly death counts are calculated based on depersonalized individual data provided by the Russian Federal State Statistics Service (Rosstat) at the request of the HSE. Time coverage: 03.01.2000 (Week 1) – 31.12.2021 (Week 1148)

2. A brief description of the input data on deaths

Date of death: date of occurrence

Unit of time: week

First and last days of the week: Monday – Sunday

First and last week of the year: The weeks are organized according to ISO 8601:2004 guidelines. Each week of the year, including the first and last, contains 7 days. In order to get 7-day weeks, the days of previous years are included in this first week (if January 1 fell on Tuesday, Wednesday or Thursday) or in the last calendar week (if December 31 fell on Thursday, Friday or Saturday).

Age groups: the entire population

Sex: men, women, both sexes (men and women combined)

Restrictions and data changes: data on deaths in the Pskov region were excluded for weeks 9-13 of 2012

Note: Deaths with an unknown date of occurrence (unknown year, month, or day) account for about 0.3% of all deaths and are excluded from the calculation of week-age-specific and standardized death rates.

3. Description of the week-specific mortality rates data file

Week-specific standardized death rates for Russia as a whole and its regions are contained in a single data file presented in .csv format. The format of data allows its uploading into any system for statistical analysis. Each record (row) in the data file contains data for one calendar year, one week, one territory, one sex.

The decimal point is dot (.)

The first element of the row is the territory code ("PopCode" column), the second element is the year ("Year" column), the third element ("Week" column) is the week of the year, the fourth element ("Sex" column) is sex (F – female, M – male, B – both sexes combined). This is followed by a column "CDR" with the value of the crude death rate and "SDR" with the value of the standardized death rate. If the indicator cannot be calculated for some combination of year, sex, and territory, then the corresponding meaningful data elements in the data file are replaced with ".".

Death rate of Heilongjiang shot up by 29.08% from 6.74 per thousand population in 2019 to 8.70 per thousand population in 2021. Since the 7.20% drop in 2010, death rate soared by 72.96% in 2021. Death Rate (or Crude Death Rate) refers to the ratio of the number of deaths to the average population (or mid-period population) during a certain period of time (usually a year), expressed in ‰. Death rate refers to annual death rate. The following formula is used: (Number of deaths)/(Annual average population)*1000‰.

5.59 (per thousand population) in 2021. Death Rate (or Crude Death Rate) refers to the ratio of the number of deaths to the average population (or mid-period population) during a certain period of time (usually a year), expressed in ‰. Death rate refers to annual death rate. The following formula is used: (Number of deaths)/(Annual average population)*1000‰.

Objective: To examine if the rankings of state HIV age-standardized death rates (ASDRs) changed if different standard population (SP) was used. Design: A cross-sectional population-based observational study. Setting 36 states in the United States. Participants: People died from 2015 to 2019. Main outcome measures: State HIV ASDR using 4 SPs, namely WHO2000, US2000, US2mor020, and Eur2011–2030. Results: The rankings of 19 states did not change when ASDRs were calculated using US2000 and US2020. Of the 17 states whose rankings changed, the rankings of 9 states calculated using US2000 were higher than those calculated using US2020; in 8 states, the rankings were lower. The states with the greatest changes in rankings between US2000 and US2020 were Kentucky (12th and 9th, respectively) and Massachusetts (8th and 11th, respectively). Conclusions: State ASDRs calculated using the current official SP (US2000) weigh middle-age HIV death rates more heavily than older-age HIV death rates, resulting in lower ASDRs among states with higher older-age HIV death rates. Methods The data were extracted from CDC WONDER.

Crude birth rates, age-specific fertility rates and total fertility rates (live births), 2000 to most recent year.

8,28 (per thousand population) in 2021. Death Rate (or Crude Death Rate) refers to the ratio of the number of deaths to the average population (or mid-period population) during a certain period of time (usually a year), expressed in ‰. Death rate refers to annual death rate. The following formula is used: (Number of deaths)/(Annual average population)*1000‰.

This data shows premature deaths (Age under 75), numbers and rates by gender, as 3-year moving-averages. All-Cause Mortality rates are a summary indicator of population health status. All-cause mortality is related to Life Expectancy, and both may be influenced by health inequalities. Directly Age-Standardised Rates (DASR) are shown in the data (where numbers are sufficient) so that death rates can be directly compared between areas. The DASR calculation applies Age-specific rates to a Standard (European) population to cancel out possible effects on crude rates due to different age structures among populations, thus enabling direct comparisons of rates. A limitation on using mortalities as a proxy for prevalence of health conditions is that mortalities may give an incomplete view of health conditions in an area, as ill-health might not lead to premature death. Data source: Office for Health Improvement and Disparities (OHID), Public Health Outcomes Framework (PHOF) indicator ID 108. This data is updated annually.

COVID-19 rate of death, or the known deaths divided by confirmed cases, was over ten percent in Yemen, the only country that has 1,000 or more cases. This according to a calculation that combines coronavirus stats on both deaths and registered cases for 221 different countries. Note that death rates are not the same as the chance of dying from an infection or the number of deaths based on an at-risk population. By April 26, 2022, the virus had infected over 510.2 million people worldwide, and led to a loss of 6.2 million. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. Note that Statista aims to also provide domestic source material for a more complete picture, and not to just look at one particular source. Examples are these statistics on the confirmed coronavirus cases in Russia or the COVID-19 cases in Italy, both of which are from domestic sources. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

A word on the flaws of numbers like this

People are right to ask whether these numbers are at all representative or not for several reasons. First, countries worldwide decide differently on who gets tested for the virus, meaning that comparing case numbers or death rates could to some extent be misleading. Germany, for example, started testing relatively early once the country’s first case was confirmed in Bavaria in January 2020, whereas Italy tests for the coronavirus postmortem. Second, not all people go to see (or can see, due to testing capacity) a doctor when they have mild symptoms. Countries like Norway and the Netherlands, for example, recommend people with non-severe symptoms to just stay at home. This means not all cases are known all the time, which could significantly alter the death rate as it is presented here. Third and finally, numbers like this change very frequently depending on how the pandemic spreads or the national healthcare capacity. It is therefore recommended to look at other (freely accessible) content that dives more into specifics, such as the coronavirus testing capacity in India or the number of hospital beds in the UK. Only with additional pieces of information can you get the full picture, something that this statistic in its current state simply cannot provide.

8,70 (per thousand population) in 2021. Death Rate (or Crude Death Rate) refers to the ratio of the number of deaths to the average population (or mid-period population) during a certain period of time (usually a year), expressed in ‰. Death rate refers to annual death rate. The following formula is used: (Number of deaths)/(Annual average population)*1000‰.

7,58 (per thousand population) in 2021. Death Rate (or Crude Death Rate) refers to the ratio of the number of deaths to the average population (or mid-period population) during a certain period of time (usually a year), expressed in ‰. Death rate refers to annual death rate. The following formula is used: (Number of deaths)/(Annual average population)*1000‰.

7,86 (per thousand population) in 2021. Death Rate (or Crude Death Rate) refers to the ratio of the number of deaths to the average population (or mid-period population) during a certain period of time (usually a year), expressed in ‰. Death rate refers to annual death rate. The following formula is used: (Number of deaths)/(Annual average population)*1000‰.

Abstract

The 1995 Uganda Demographic and Health Survey (UDHS-II) is a nationally-representative survey of 7,070 women age 15-49 and 1,996 men age 15-54. The UDHS was designed to provide information on levels and trends of fertility, family planning knowledge and use, infant and child mortality, and maternal and child health. Fieldwork for the UDHS took place from late-March to mid-August 1995. The survey was similar in scope and design to the 1988-89 UDHS. Survey data show that fertility levels may be declining, contraceptive use is increasing, and childhood mortality is declining; however, data also point to several remaining areas of challenge.

The 1995 UDHS was a follow-up to a similar survey conducted in 1988-89. In addition to including most of the same questions included in the 1988-89 UDHS, the 1995 UDHS added more detailed questions on AIDS and maternal mortality, as well as incorporating a survey of men. The general objectives of the 1995 UDHS are to: - provide national level data which will allow the calculation of demographic rates, particularly fertility and childhood mortality rates; - analyse the direct and indirect factors which determine the level and trends of fertility; - measure the level of contraceptive knowledge and practice (of both women and men) by method, by urban-rural residence, and by region; - collect reliable data on maternal and child health indicators; immunisation, prevalence, and treatment of diarrhoea and other diseases among children under age four; antenatal visits; assistance at delivery; and breastfeeding; - assess the nutritional status of children under age four and their mothers by means of anthropometric measurements (weight and height), and also child feeding practices; and - assess among women and men the prevailing level of specific knowledge and attitudes regarding AIDS and to evaluate patterns of recent behaviour regarding condom use.

MAIN RESULTS

• Fertility:

Fertility Trends. UDHS data indicate that fertility in Uganda may be starting to decline. The total fertility rate has declined from the level of 7.1 births per woman that prevailed over the last 2 decades to 6.9 births for the period 1992-94. The crude birth rate for the period 1992-94 was 48 live births per I000 population, slightly lower than the level of 52 observed from the 1991 Population and Housing Census. For the roughly 80 percent of the country that was covered in the 1988-89 UDHS, fertility has declined from 7.3 to 6.8 births per woman, a drop of 7 percent over a six and a half year period.

Birth Intervals. The majority of Ugandan children (72 percent) are born after a "safe" birth interval (24 or more months apart), with 30 percent born at least 36 months after a prior birth. Nevertheless, 28 percent of non-first births occur less than 24 months after the preceding birth, with 10 percent occurring less than 18 months since the previous birth. The overall median birth interval is 29 months. Fertility Preferences. Survey data indicate that there is a strong desire for children and a preference for large families in Ugandan society. Among those with six or more children, 18 percent of married women want to have more children compared to 48 percent of married men. Both men and women desire large families.

• Family planning:

Knowledge of Contraceptive Methods. Knowledge of contraceptive methods is nearly universal with 92 percent of all women age 15-49 and 96 percent of all men age 15-54 knowing at least one method of family planning. Increasing Use of Contraception. The contraceptive prevalence rate in Uganda has tripled over a six-year period, rising from about 5 percent in approximately 80 percent of the country surveyed in 1988-89 to 15 percent in 1995.

Source of Contraception. Half of current users (47 percent) obtain their methods from public sources, while 42 percent use non-governmental medical sources, and other private sources account for the remaining 11 percent.

• Maternal and child health:

High Childhood Mortality. Although childhood mortality in Uganda is still quite high in absolute terms, there is evidence of a significant decline in recent years. Currently, the direct estimate of the infant mortality rate is 81 deaths per 1,000 births and under five mortality is 147 per 1,000 births, a considerable decline from the rates of 101 and 180, respectively, that were derived for the roughly 80 percent of the country that was covered by the 1988-89 UDHS.

Childhood Vaccination Coverage. One possible reason for the declining mortality is improvement in childhood vaccination coverage. The UDHS results show that 47 percent of children age 12-23 months are fully vaccinated, and only 14 percent have not received any vaccinations.

Childhood Nutritional Status. Overall, 38 percent of Ugandan children under age four are classified as stunted (low height-for-age) and 15 percent as severely stunted. About 5 percent of children under four in Uganda are wasted (low weight-for-height); 1 percent are severely wasted. Comparison with other data sources shows little change in these measures over time.

• AIDS:

Virtually all women and men in Uganda are aware of AIDS. About 60 percent of respondents say that limiting the number of sexual partners or having only one partner can prevent the spread of disease. However, knowledge of ways to avoid AIDS is related to respondents' education. Safe patterns of sexual behaviour are less commonly reported by respondents who have little or no education than those with more education. Results show that 65 percent of women and 84 percent of men believe that they have little or no chance of being infected.

Availability of Health Services. Roughly half of women in Uganda live within 5 km of a facility providing antenatal care, delivery care, and immunisation services. However, the data show that children whose mothers receive both antenatal and delivery care are more likely to live within 5 km of a facility providing maternal and child health (MCH) services (70 percent) than either those whose mothers received only one of these services (46 percent) or those whose mothers received neither antenatal nor delivery care (39 percent).

Geographic coverage

The 1995 Uganda Demographic and Health Survey (UDHS-II) is a nationally-representative survey. For the purpose of the 1995 UDHS, the following domains were utilised: Uganda as a whole; urban and rural areas separately; each of the four regions: Central, Eastern, Northern, and Western; areas in the USAID-funded DISH project to permit calculation of contraceptive prevalence rates.

Analysis unit

• Household
• Women age 15-49
• Men age 15-54
• Children under four

Universe

The population covered by the 1995 UDHS is defined as the universe of all women age 15-49 in Uganda. But because of insecurity, eight EAs could not be surveyed (six in Kitgum District, one in Apac District, and one in Moyo District). An additional two EAs (one in Arua and one in Moroto) could not be surveyed, but substitute EAs were selected in their place.

Kind of data

Sample survey data

Sampling procedure

A sample of 303 primary sampling units (PSU) consisting of enumeration areas (EAs) was selected from a sampling frame of the 1991 Population and Housing Census. For the purpose of the 1995 UDHS, the following domains were utilised: Uganda as a whole; urban and rural areas separately; each of the four regions: Central, Eastern, Northern, and Western; areas in the USAID-funded DISH project to permit calculation of contraceptive prevalence rates.

Districts in the DISH project area were grouped by proximity into the following five reporting domains: - Kasese and Mbarara Districts - Masaka and Rakai Districts - Luwero and Masindi Districts - Jinja and Kamuli Districts - Kampala District

The sample for the 1995 UDHS was selected in two stages. In the first stage, 303 EAs were selected with probability proportional to size. Then, within each selected EA, a complete household listing and mapping exercise was conducted in December 1994 forming the basis for the second-stage sampling. For the listing exercise, 11 listers from the Statistics Department were trained. Institutional populations (army barracks, hospitals, police camps, etc.) were not listed.

From these household lists, households to be included in the UDHS were selected with probability inversely proportional to size based on the household listing results. All women age 15-49 years in these households were eligible to be interviewed in the UDHS. In one-third of these selected households, all men age 15-54 years were eligible for individual interview as well. The overall target sample was 6,000 women and 2,000 men. Because of insecurity, eight EAs could not be surveyed (six in Kitgum District, one in Apac District, and one in Moyo District). An additional two EAs (one in Arua and one in Moroto) could not be surveyed, but substitute EAs were selected in their place.

Since one objective of the survey was to produce estimates of specific demographic and health indicators for the areas included in the DISH project, the sample design allowed for oversampling of households in these districts relative to their actual proportion in the population. Thus, the 1995 UDHS sample is not self-weighting at the national level; weights are required to estimate national-level indicators. Due to the weighting factor and rounding of estimates, figures may not add to totals. In addition, the percent total may not add to 100.0 due to rounding.

Mode of data collection

Face-to-face

Research instrument

Four questionnaires were used in the 1995 UDHS.

a) A Household Schedule was used to list the names and certain

6,01 (per thousand population) in 2021. Death Rate (or Crude Death Rate) refers to the ratio of the number of deaths to the average population (or mid-period population) during a certain period of time (usually a year), expressed in ‰. Death rate refers to annual death rate. The following formula is used: (Number of deaths)/(Annual average population)*1000‰.

8,08 (per thousand population) in 2021. Death Rate (or Crude Death Rate) refers to the ratio of the number of deaths to the average population (or mid-period population) during a certain period of time (usually a year), expressed in ‰. Death rate refers to annual death rate. The following formula is used: (Number of deaths)/(Annual average population)*1000‰.

This data shows deaths (of people age 10 and over) from Suicide and Undetermined Injury, numbers and rates by gender, as 3-year moving-averages.

Suicide is a significant cause of premature deaths occurring generally at younger ages than other common causes of premature mortality. It may also be seen as an indicator of underlying rates of mental ill-health.

Directly Age-Standardised Rates (DASR) are shown in the data, where numbers are sufficient, so that death rates can be directly compared between areas. The DASR calculation applies Age-specific rates to a Standard (European) population to cancel out possible effects on crude rates due to different age structures among populations, thus enabling direct comparisons of rates.

The figures in this dataset include deaths recorded as suicide (people age 10 and over) and undetermined injury (age 15 and over) as those are mostly likely also to have been caused by self-harm rather than unverifiable accident, neglect or abuse. The population denominators for rates are age 10 and over. Low numbers may result in zero values or missing data.

Data source: Office for Health Improvement and Disparities (OHID), Public Health Outcomes Framework (PHOF) indicator 41001 (E10). This data is updated annually.

6,91 (per thousand population) in 2021. Death Rate (or Crude Death Rate) refers to the ratio of the number of deaths to the average population (or mid-period population) during a certain period of time (usually a year), expressed in ‰. Death rate refers to annual death rate. The following formula is used: (Number of deaths)/(Annual average population)*1000‰.

This data shows premature deaths (Age under 75) from Respiratory Disease, numbers and rates by gender, as 3-year range. Smoking is the major cause of chronic obstructive pulmonary disease (COPD), one of the major Respiratory diseases. COPD (which includes chronic bronchitis and emphysema) results in many hospital admissions. Respiratory diseases can also be caused by environmental factors (such as pollution, or housing conditions) and influenza. Respiratory disease mortality rates show a socio-economic gradient. Directly Age-Standardised Rates (DASR) are shown in the data, where numbers are sufficient, so that death rates can be directly compared between areas. The DASR calculation applies Age-specific rates to a Standard (European) population to cancel out possible effects on crude rates due to different age structures among populations, thus enabling direct comparisons of rates. A limitation on using mortalities as a proxy for prevalence of health conditions is that mortalities may give an incomplete view of health conditions in an area, as ill-health might not lead to premature death. Data source: Office for Health Improvement and Disparities (OHID) Public Health Outcomes Framework (PHOF) indicator 4.07i. This data is updated annually.

This data shows premature deaths (Age under 75) from all Cancers, numbers and rates by gender, as 3-year moving-averages.

Cancers are a major cause of premature deaths. Inequalities exist in cancer rates between the most deprived areas and the most affluent areas.

Directly Age-Standardised Rates (DASR) are shown in the data (where numbers are sufficient) so that death rates can be directly compared between areas. The DASR calculation applies Age-specific rates to a Standard (European) population to cancel out possible effects on crude rates due to different age structures among populations, thus enabling direct comparisons of rates.

A limitation on using mortalities as a proxy for prevalence of health conditions is that mortalities may give an incomplete view of health conditions in an area, as ill-health might not lead to premature death.

Data source: NHS Health and Social Care Information Centre (NHS-HSCIC) (Dataset unique identifier P00399). This data is updated annually.

Abstract

The JPFHS is part of the worldwide Demographic and Health Surveys (DHS) program, which is designed to collect data on fertility, family planning, and maternal and child health.

The 1990 Jordan Population and Family Health Survey (JPFHS) was carried out as part of the Demographic and Health Survey (DHS) program. The Demographic and Health Surveys is assisting governments and private agencies in the implementation of household surveys in developing countries.

The JPFIS was designed to provide information on levels and trends of fertility, infant and child mortality, and family planning. The survey also gathered information on breastfeeding, matemal and child health cam, the nutritional status of children under five, as well as the characteristics of households and household members.

The main objectives of the project include: a) Providing decision makers with a data base and analyses useful for informed policy choices, b) Expanding the international population and health data base, c) Advancing survey methodology, and d) Developing skills and resources necessary to conduct high quality demographic and health surveys in the participating countries.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men

Kind of data

Sample survey data

Sampling procedure

The sample for the JPFHS survey was selected to be representative of the major geographical regions, as well as the nation as a whole. The survey adopted a stratified, multi-stage sampling design. In each governorate, localities were classified into 9 strata according to the estimated population size in 1989. The sampling design also allowed for the survey results to be presented according to major cities (Amman, Irbid and Zarqa), other urban localities, and the rural areas. Localities with fewer than 5,000 people were considered rural.

For this survey, 349 sample units were drawn, containing 10,708 housing units for the individual interview. Since the survey used a separate household questionnaire, the Department of Statistics doubled the household sample size and added a few questions on labor force, while keeping the original individual sample intact. This yielded 21,172 housing units. During fieldwork for the household interview, it was found that 4,359 household units were ineligible either because the dwelling was vacant or destroyed, the household was absent during the team visit, or some other reason. There were 16,296 completed household interviews out of 16,813 eligible households, producing a response rate of 96.9 percent.

The completed household interviews yielded 7,246 women eligible for the individual interview, of which 6,461 were successfully interviewed, producing a response rate of 89.2 percent.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face

Research instrument

The 1990 JPFIS utilized two questionnaires, one for the household interview and the other for individual women. Both questionnaires were developed first in English and then translated into Arabic. The household questionnaire was used to list all members of the sample households, including usual residents as well as visitors. For each member of the household, basic demographic and socioeconomic characteristics were recorded and women eligible for the individual interview were identified. To be eligible for individual interview, a woman had to be a usual member of the household (part of the de jure population), ever-married, and between 15 and 49 years of age. The household questionnaire was expanded from the standard DHS-II model questionnaire to facilitate the estimation of adult mortality using the orphanhood and widowhood techniques. In addition, the questionnaire obtained information on polygamy, economic activity of persons 15 years of age and over, family type, type of insurance covering the household members, country of work in the summer of 1990 which coincided with the Gulf crisis, and basic data for the calculation of the crude birth rate and the crude death rate. Additional questions were asked about deceased women if they were ever-married and age 15-49, in order to obtain information for the calculation of materoal mortality indices.

The individual questionnaire is a modified version of the standard DHS-II model "A" questionnaire. Experience gained from previous surveys, in particular the 1983 Jordan Fertility and Family Health Survey, and the questionnaire developed by the Pan Arab Project for Child Development (PAPCHILD), were useful in the discussions on the content of the JPFHS questionnaire. A major change from the DHS-II model questionnaire was the rearrangement of the sections so that the marriage section came before reproduction; this allowed the interview to flow more smoothly. Questions on children's cause of death based on verbal autopsy were added to the section on health, which, due to its size, was split into two parts. The first part focused on antenatal care and breastfeeding; the second part examined measures for prevention of childhood diseases and information on the morbidity and mortality of children loom since January 1985. As questions on sexual relations were considered too sensitive, they were replaced by questions about the husband's presence in the household during the specified time period; this served as a proxy for recent sexual activity.

The JPFHS individual questionnaire consists of nine sections: - Respondent's background and household characteristics - Marriage - Reproduction - Contraception - Breastfeeding and health - Immunization, morbidity, and child mortality - Fertility preferences - Husband's background, residence, and woman's work - Height and weight of children

Response rate

For the individual interview, the number of eligible women found in the selected households and the number of women successfully interviewed are presented. The data indicate a high response rate for the household interview (96.9 percent), and a lower rate for the individual interview (89.2 percent). Women in large cities have a slightly lower response rate (88.6 percent) than those in other areas. Most of the non-response for the individual interview was due to the absence of respondents and the postponement of interviews which were incomplete.

Note: See summarized response rates by place of residence in Table 1.1 of the survey report.

Sampling error estimates

The results from sample surveys are affected by two types of errors, non-sampling error and sampling error. Nonsampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way the questions are asked, misunderstanding on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and implementation of the JPFHS to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically

Sampling errors, on the other hand, can be measured statistically. The sample of women selected in the JPFHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each one would have yielded results that differed somewhat from the actual sample selected. The sampling error is a measure of the variability between all possible samples; although it is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of standard error of a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which one can reasonably assured that, apart from nonsampling errors, the true value of the variable for the whole population falls. For example, for any given statistic calculated from a sample survey, the value of that same statistic as measured in 95 percent of all possible samples with the same design (and expected size) will fall within a range of plus or minus two times the standard error of that statistic.

If the sample of women had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the JPFI-IS sample design depended on stratification, stages and clusters. Consequently, it was necessary to utilize more complex formulas. The computer package CLUSTERS, developed by the International Statistical Institute for the World Fertility Survey, was used to assist in computing the sampling errors with the proper statistical methodology.

Note: See detailed estimate of sampling error calculation in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar year since birth - Reporting of age at death in days - Reporting of age at death in months

Note: See detailed tables in APPENDIX C of the report which is presented in this documentation.

8,89 (per thousand population) in 2021. Death Rate (or Crude Death Rate) refers to the ratio of the number of deaths to the average population (or mid-period population) during a certain period of time (usually a year), expressed in ‰. Death rate refers to annual death rate. The following formula is used: (Number of deaths)/(Annual average population)*1000‰.