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

Collective data of Japan's birth-related statistics from 1899 to 2022. Some data are missing between the years 1944 and 1946 due to records lost during World War II.

For use case and analysis reference, please take a look at this notebook Japan Birth Demographics Analysis

Feature Descriptions

• year: The year.
• birth_total: The total number of births.
• birth_male: The total number of male births.
• birth_female: The total number of female births.
• birth_rate: The birth rate. Equation is birth_total / population_total * 1,000
• birth_gender_ratio: The birth gender ratio. Equation is birth_male / birth_female * 1,000
• total_fertility_rate: The average number of children that are born to a woman over her lifetime.
• population_total: The total population.
• population_male: The total male population.
• population_female: The total female population.
• infant_death_total: The total infant deaths.
• infant_death_male: The total male infant deaths.
• infant_death_female: The total female infant deaths.
• infant_death_unknown_gender: The total unknown gender infant deaths.
• infant_death_rate: The infant death rate. Equation is infant_death_total / birth_total * 1,000
• infant_death_gender_ratio: The infant death gender ratio. Equation is infant_death_male / infant_death_female * 1,000
• infant_deaths_in_total_deaths: The infant death ratio among other deaths.
• stillbirth_total: The total number of stillbirths (dead born).
• stillbirth_male: The total number of male stillbirths.
• stillbirth_female: The total number of female stillbirths.
• stillbirth_unknown_gender: The total number of unknown gender stillbirths.
• stillbirth_rate: The stillbirth rate. Equation is stillbirth_total / (birth_total + stillbirth_total) * 1,000
• stillbirth_gender_ratio: The stillbirth gender ratio. Equation is stillbirth_male / stillbirth_female * 1,000
• firstborn: The number of firstborns.
• secondborn: The number of secondborns.
• thirdborn: The number of thirdborns.
• forthborn: The number of forthborns.
• fifthborn_and_above: The number of fifthborns and above.
• weeks_under_28: The number of births occurred under week 28. Early terms.
• weeks_28-31: The number of births occurred between weeks 28 and 31. Early terms.
• weeks_32-36: The number of births occurred between weeks 32 and 36. Early terms.
• weeks_37-41: The number of births occurred between weeks 37 and 41. Full terms.
• weeks_over_42: The number of births occurred over week 42. Late terms.
• mother_age_avg: The mother's average age.
• mother_age_firstborn: The mother's average age of the firstborn.
• mother_age_secondborn: The mother's average age of the secondborn.
• mother_age_thirdborn: The mother's average age of the thirdborn.
• mother_age_under_19: The number of births by mothers under age 19.
• mother_age_20-24: The number of births by mothers between age 20 and 24.
• mother_age_25-29: The number of births by mothers between age 25 and 29.
• mother_age_30-34: The number of births by mothers between age 30 and 34.
• mother_age_35-39: The number of births by mothers between age 35 and 39.
• mother_age_40-44: The number of births by mothers between age 40 and 44.
• mother_age_over_45: The number of births by mothers over 45.
• father_age_avg: The father's average age.
• father_age_firstborn: The father's average age of the firstborn.
• father_age_secondborn: The father's average age of the secondborn.
• father_age_thirdborn: The father's average age of the thirdborn.
• legitimate_child: The Number of births under married parents.
• illegitimate_child: The number of births under non-married parents.

Acknowledgement

E-Stat Demographic Survey

In 2023, the crude birth rate in live births per 1,000 inhabitants in the Philippines stood at 16.02. Between 1960 and 2023, the figure dropped by 31.14, though the decline followed an uneven course rather than a steady trajectory.

The Demographic and Health Surveys (DHS) are a major source for many demographic and health indicators in developing countries. Although these indicators are well defined in the literature, using survey data to calculate some of these indicators has never been an easy task for data users. This paper presents the DHS.rates software, a user-friendly R package developed to calculate fertility indicators, such as the total fertility rate, general fertility rate, and age-specific fertility rates, and childhood mortality indicators, such as the neonatal mortality rate, post-neonatal mortality rate, infant mortality rate, child mortality rate, and under-5 mortality rate, from the DHS data. The package allows for national and subnational indicators. In addition, the package calculates sampling error indicators such as standard error, design effect, relative standard error, and confidence interval for each demographic indicator. The package can also be used to calculate the same indicators from other population surveys such as the Multiple Indicator Cluster Survey (MICS).

Abstract

The 1997 Jordan Population and Family Health Survey (JPFHS) is a national sample survey carried out by the Department of Statistics (DOS) as part of its National Household Surveys Program (NHSP). The JPFHS was specifically aimed at providing information on fertility, family planning, and infant and child mortality. Information was also gathered on breastfeeding, on maternal and child health care and nutritional status, and on the characteristics of households and household members. The survey will provide policymakers and planners with important information for use in formulating informed programs and policies on reproductive behavior and health.

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data

Sampling procedure

SAMPLE DESIGN AND IMPLEMENTATION

The 1997 JPFHS sample was designed to produce reliable estimates of major survey variables for the country as a whole, for urban and rural areas, for the three regions (each composed of a group of governorates), and for the three major governorates, Amman, Irbid, and Zarqa.

The 1997 JPFHS sample is a subsample of the master sample that was designed using the frame obtained from the 1994 Population and Housing Census. A two-stage sampling procedure was employed. First, primary sampling units (PSUs) were selected with probability proportional to the number of housing units in the PSU. A total of 300 PSUs were selected at this stage. In the second stage, in each selected PSU, occupied housing units were selected with probability inversely proportional to the number of housing units in the PSU. This design maintains a self-weighted sampling fraction within each governorate.

UPDATING OF SAMPLING FRAME

Prior to the main fieldwork, mapping operations were carried out and the sample units/blocks were selected and then identified and located in the field. The selected blocks were delineated and the outer boundaries were demarcated with special signs. During this process, the numbers on buildings and housing units were updated, listed and documented, along with the name of the owner/tenant of the unit or household and the name of the household head. These activities took place between January 7 and February 28, 1997.

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

Mode of data collection

Face-to-face

Research instrument

The 1997 JPFHS used two questionnaires, one for the household interview and the other for eligible women. Both questionnaires were developed in English and then translated into Arabic. The household questionnaire was used to list all members of the sampled households, including usual residents as well as visitors. For each member of the household, basic demographic and social characteristics were recorded and women eligible for the individual interview were identified. The individual questionnaire was developed utilizing the experience gained from previous surveys, in particular the 1983 and 1990 Jordan Fertility and Family Health Surveys (JFFHS).

The 1997 JPFHS individual questionnaire consists of 10 sections: - Respondent’s background - Marriage - Reproduction (birth history) - Contraception - Pregnancy, breastfeeding, health and immunization - Fertility preferences - Husband’s background, woman’s work and residence - Knowledge of AIDS - Maternal mortality - Height and weight of children and mothers.

Cleaning operations

Fieldwork and data processing activities overlapped. After a week of data collection, and after field editing of questionnaires for completeness and consistency, the questionnaires for each cluster were packaged together and sent to the central office in Amman where they were registered and stored. Special teams were formed to carry out office editing and coding.

Data entry started after a week of office data processing. The process of data entry, editing, and cleaning was done by means of the ISSA (Integrated System for Survey Analysis) program DHS has developed especially for such surveys. The ISSA program allows data to be edited while being entered. Data entry was completed on November 14, 1997. A data processing specialist from Macro made a trip to Jordan in November and December 1997 to identify problems in data entry, editing, and cleaning, and to work on tabulations for both the preliminary and final report.

Response rate

A total of 7,924 occupied housing units were selected for the survey; from among those, 7,592 households were found. Of the occupied households, 7,335 (97 percent) were successfully interviewed. In those households, 5,765 eligible women were identified, and complete interviews were obtained with 5,548 of them (96 percent of all eligible women). Thus, the overall response rate of the 1997 JPFHS was 93 percent. The principal reason for nonresponse among the women was the failure of interviewers to find them at home despite repeated callbacks.

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

Sampling error estimates

The estimates from a sample survey are subject to two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the result of mistakes made in implementing data collection and data processing (such as failure to locate and interview the correct household, misunderstanding questions either by the interviewer or the respondent, and data entry errors). Although during the implementation of the 1997 JPFHS numerous efforts were made to minimize this type of error, nonsampling errors are not only impossible to avoid but also difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The respondents selected in the 1997 JPFHS constitute only one of many samples that could have been selected from the same population, given the same design and expected size. Each of those samples would have yielded results differing somewhat from the results of the sample actually selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for 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 the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, since the 1997 JDHS-II sample resulted from a multistage stratified design, formulae of higher complexity had to be used. The computer software used to calculate sampling errors for the 1997 JDHS-II was the ISSA Sampling Error Module, which uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics, such as fertility and mortality rates.

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 years - Reporting of age at death in days - Reporting of age at death in months

Note: See detailed tables in APPENDIX C of the survey report.

Abstract

The 1993 Turkish Demographic and Health Survey (TDHS) is a nationally representative survey of ever-married women less than 50 years old. The survey was designed to provide information on fertility levels and trends, infant and child mortality, family planning, and maternal and child health. The TDHS was conducted by the Hacettepe University Institute of Population Studies under a subcontract through an agreement between the General Directorate of Mother and Child Health and Family Planning, Ministry of Health and Macro International Inc. of Calverton, Maryland. Fieldwork was conducted from August to October 1993. Interviews were carried out in 8,619 households and with 6,519 women.

The Turkish Demographic and Health Survey (TDHS) is a national sample survey of ever-married women of reproductive ages, designed to collect data on fertility, marriage patterns, family planning, early age mortality, socioeconomic characteristics, breastfeeding, immunisation of children, treatment of children during episodes of illness, and nutritional status of women and children. The TDHS, as part of the international DHS project, is also the latest survey in a series of national-level population and health surveys in Turkey, which have been conducted by the Institute of Population Studies, Haeettepe University (HIPS).

More specifically, the objectives of the TDHS are to:

Collect data at the national level that will allow the calculation of demographic rates, particularly fertility and childhood mortality rates; Analyse the direct and indirect factors that determine levels and trends in fertility and childhood mortality; Measure the level of contraceptive knowledge and practice by method, region, and urban- rural residence; Collect data on mother and child health, including immunisations, prevalence and treatment of diarrhoea, acute respiratory infections among children under five, antenatal care, assistance at delivery, and breastfeeding; Measure the nutritional status of children under five and of their mothers using anthropometric measurements.

The TDHS information is intended to assist policy makers and administrators in evaluating existing programs and in designing new strategies for improving family planning and health services in Turkey.

MAIN RESULTS

Fertility in Turkey is continuing to decline. If Turkish women maintain current fertility rates during their reproductive years, they can expect to have all average of 2.7 children by the end of their reproductive years. The highest fertility rate is observed for the age group 20-24. There are marked regional differences in fertility rates, ranging from 4.4 children per woman in the East to 2.0 children per woman in the West. Fertility also varies widely by urban-rural residence and by education level. A woman living in rural areas will have almost one child more than a woman living in an urban area. Women who have no education have almost one child more than women who have a primary-level education and 2.5 children more than women with secondary-level education.

The first requirement of success ill family planning is the knowledge of family planning methods. Knowledge of any method is almost universal among Turkish women and almost all those who know a method also know the source of the method. Eighty percent of currently married women have used a method sometime in their life. One third of currently married women report ever using the IUD. Overall, 63 percent of currently married women are currently using a method. The majority of these women are modern method users (35 percent), but a very substantial proportion use traditional methods (28 percent). the IUD is the most commonly used modern method (I 9 percent), allowed by the condom (7 percent) and the pill (5 percent). Regional differences are substantial. The level of current use is 42 percent in tile East, 72 percent in tile West and more than 60 percent in tile other three regions. "File common complaints about tile methods are side effects and health concerns; these are especially prevalent for the pill and IUD.

One of the major child health indicators is immunisation coverage. Among children age 12-23 months, the coverage rates for BCG and the first two doses of DPT and polio were about 90 percent, with most of the children receiving those vaccines before age one. The results indicate that 65 percent of the children had received all vaccinations at some time before the survey. On a regional basis, coverage is significantly lower in the Eastern region (41 percent), followed by the Northern and Central regions (61 percent and 65 percent, respectively). Acute respiratory infections (ARI) and diarrhea are the two most prevalent diseases of children under age five in Turkey. In the two weeks preceding the survey, the prevalence of ARI was 12 percent and the prevalence of diarrhea was 25 percent for children under age five. Among children with diarrhea 56 percent were given more fluids than usual.

Breastfeeding in Turkey is widespread. Almost all Turkish children (95 percent) are breastfed for some period of time. The median duration of breastfeeding is 12 months, but supplementary foods and liquids are introduced at an early age. One-third of children are being given supplementary food as early as one month of age and by the age of 2-3 months, half of the children are already being given supplementary foods or liquids.

By age five, almost one-filth of children arc stunted (short for their age), compared to an international reference population. Stunting is more prevalent in rural areas, in the East, among children of mothers with little or no education, among children who are of higher birth order, and among those born less than 24 months after a prior birth. Overall, wasting is not a problem. Two percent of children are wasted (thin for their height), and I I percent of children under five are underweight for their age. The survey results show that obesity is d problem among mothers. According to Body Mass Index (BMI) calculations, 51 percent of mothers are overweight, of which 19 percent are obese.

Geographic coverage

The Turkish Demographic and Health Survey (TDHS) is a national sample survey.

Analysis unit

• Household
• Women age 12-49
• Children under five

Universe

The population covered by the 1993 DHS is defined as the universe of all ever-married women age 12-49 who were present in the household on the night before the interview were eligible for the survey.

Kind of data

Sample survey data

Sampling procedure

The sample for the TDHS was designed to provide estimates of population and health indicators, including fertility and mortality rates for the nation as a whole, fOr urban and rural areas, and for the five major regions of the country. A weighted, multistage, stratified cluster sampling approach was used in the selection of the TDHS sample.

Sample selection was undertaken in three stages. The sampling units at the first stage were settlements that differed in population size. The frame for the selection of the primary sampling units (PSUs) was prepared using the results of the 1990 Population Census. The urban frame included provinces and district centres and settlements with populations of more than 10,000; the rural frame included subdistricts and villages with populations of less than 10,000. Adjustments were made to consider the growth in some areas right up to survey time. In addition to the rural-urban and regional stratifications, settlements were classified in seven groups according to population size.

The second stage of selection involved the list of quarters (administrative divisions of varying size) for each urban settlement, provided by the State Institute of Statistics (SIS). Every selected quarter was subdivided according tothe number of divisions(approximately 100 households)assigned to it. In rural areas, a selected village was taken as a single quarter, and wherever necessary, it was divided into subdivisions of approximately 100 households. In cases where the number of households in a selected village was less than 100 households, the nearest village was selected to complete the 100 households during the listing activity, which is described below.

After the selection of the secondary sampling units (SSUs), a household listing was obtained for each by the TDHS listing teams. The listing activity was carried out in May and June. From the household lists, a systematic random sample of households was chosen for the TDHS. All ever-married women age 12-49 who were present in the household on the night before the interview were eligible for the survey.

Mode of data collection

Face-to-face

Research instrument

Two questionnaires were used in the main fieldwork for the TDHS: the Household Questionnaire and the Individual Questionnaire for ever-married women of reproductive age. The questionnaires were based on the model survey instruments developed in the DHS program and on the questionnaires that had been employed in previous Turkish population and health surveys. The questionnaires were adapted to obtain data needed for program planning in Turkey during consultations with population and health agencies. Both questionnaires were developed in English and translated into Turkish.

a) The Household Questionnaire was used to enumerate all usual members of and visitors to the selected households and to collect information relating to the socioeconomic position of the households. In the first part of the Household Questionnaire, basic information was collected on the age, sex, educational attainment, marital status and relationship to the head of household for each person listed as a household member

The global palm oil in baby formula market presents a compelling investment opportunity, driven by increasing demand for affordable and nutritionally-rich infant formulas. While precise market sizing requires proprietary data, analyzing industry trends and competitor activities suggests a substantial market value. Considering the prevalent use of vegetable oils in baby formula and the cost-effectiveness of palm oil, a conservative estimate places the 2025 market value at approximately $500 million. A Compound Annual Growth Rate (CAGR) of 5-7% is plausible, fueled by factors such as rising birth rates in developing economies, increased awareness of nutritional requirements in infants, and the ongoing expansion of the baby food industry. Key market drivers include the palm oil's high saturated fat content (contributing to caloric intake), its affordability compared to other vegetable oils, and its readily available supply. However, restraints include growing consumer concerns regarding the environmental impact of palm oil production and the presence of potentially harmful contaminants like aflatoxins. This necessitates strict regulatory compliance and sustainable sourcing practices for companies to maintain consumer trust and mitigate risks. Market segmentation is crucial, focusing on product type (powdered vs. liquid), distribution channel (online vs. retail), and geographic region. Major players like Felda Global Ventures, Wilmar, and others are likely vying for market share through product innovation, strategic partnerships, and efficient supply chains. The competitive landscape is intensely dynamic, with companies focusing on enhancing their product formulations, improving supply chain transparency, and marketing their products to address consumer concerns about sustainability and health. The forecast period (2025-2033) anticipates consistent growth, particularly in emerging markets with high birth rates. However, successful companies will need to prioritize sustainable practices and invest in robust quality control to maintain market share and consumer trust in the face of increasing scrutiny on the environmental and health impacts of palm oil. Continued innovation in sustainable palm oil production techniques and transparent supply chain management will be key factors influencing market trajectory and shaping consumer perception in the coming years.

In this project I will concentrate on some important factors that will affect humanity's potential to survive on planet Earth: - Global Demographic Shifts. - Inequality. - Climate change. - Resource depletion.

I chose the following countries from the data (HNP_StatsData.csv), for the bulk of the project. However, the data contains many more countries.

Australia, Belgium, Canada, China, Denmark, France, Germany, India, Italy, Japan, Mexico, Morocco, Russian Federation, South Africa, Spain, Switzerland, United Kingdom, United States.

1. Global Demographic Shifts:

First I decided to look at the Crude Birth Rate (CBR) for the above countries, for the years 1961-2021.

The crude birth rate is the number of live births occurring among the population of a given geographical area during a given year, per 1,000 of the population estimated at midyear. It is called "crude" because it does not take into account age or gender differences within the population.

Formula: CBR = Midyear population / Number of births in a year × 1000

Visualisation of CBR: For the chosen countries - using Google sheets. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2F011e9ff704f24c74ab2393cdd66e8ee2%2FScreenshot%202023-09-23%2014.50.36.png.jpg?generation=1695508413958340&alt=media" alt="">

Next I looked at CDR for the above countries, for the years 1961-2021. The crude death rate is the number of deaths occurring among the population of a given geographical area during a given year, per 1,000 of the population estimated at midyear. Like the CBR, it is called "crude" because it doesn't consider the age or gender differences within the population.

Formula: CDR = Midyear population / Number of deaths in a year × 1000

Visualisation of CDR: For the chosen countries - using Google sheets. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2Fe79ff544bff129ec2193471f9e6b4480%2FScreenshot%202023-09-23%2014.55.38.png.jpg?generation=1695510070529372&alt=media" alt="">

Both these rates (CBR & CDR) are basic demographic indicators that give a general overview of the demographic situation in a country or region. They do offer a broad understanding of birth and death patterns.

A high CDR in a particular year or time period can be influenced by various factors, including epidemics, famines, natural disasters, wars, and social and economic changes.

Let's look at China and Morocco during the 1960s:

• The Great Chinese Famine (1959-1961): This is arguably the main reason for the high CDR in China around 1960. This devastating famine was the result of a combination of social, political, and natural factors. The "Great Leap Forward" campaign (1958–1962), initiated by Mao Zedong, aimed to transform China from an agrarian society into an industrial socialist society. One of the policy implementations was the formation of people's communes, which resulted in the collectivisation of agriculture. The government's over-reporting of grain production and subsequent excessive grain requisition, coupled with poor weather conditions and pestilence, led to widespread food shortages. The resultant famine caused the deaths of millions of Chinese people.
• Public health issues: Prior to the significant public health reforms that would later be implemented in the 1960s and 1970s, China grappled with various diseases that could have contributed to a high CDR.
• Tuberculosis (TB): Throughout the early to mid-20th century, TB was a major public health concern in China. Malnutrition, poor living conditions, and lack of access to medical treatment contributed to the spread of this bacterial disease.
• Schistosomiasis: This parasitic disease, caused by flatworms and transmitted by freshwater snails, was and still is endemic in certain regions of China. Chronic infection can lead to liver damage, kidney failure, and other complications.
• Malaria: While major strides have been made in controlling malaria in China in recent decades, in the 1960s, the disease was still prevalent in many regions of the country.
• Other diseases: Cholera, typhoid, and dysentery were also concerns in certain areas, especially given the combination of poor sanitation, water contamination, and food shortages.

Morocco in 1960: - Colonial legacy and independence: Morocco achieved independence from France and Spain in 1956. The post-independence years were marked by political instability, which can indirectly impact public health, food security, and other factors related to the death rate. - Economic conditions: Morocco, being a primarily agrarian society during that period, was vulnerable to fluctuations in agricultural output. Poor harvests due to droughts, pests, or other factors could impact food availability and lead to higher death rates. - Public health: Like many developing nations ...

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

Abstract

The 2009 Kiribati Demographic and Health Survey was the first survey in phase two of Pacific DHS Project with funding support from ADB. The primary objective of this survey was to provide up-to-date information for policy-makers, planners, researchers and programme managers, for use in planning, implementing, monitoring and evaluating population and health programmes within the country. The survey was intended to provide key estimates of Kiribati’s demographic and health situation.

The main objective of the 2009 Kiribati Demographic and Health Survey (2009 KDHS) is to provide current and reliable data on fertility and family planning behaviour, child mortality, adult and maternal mortality, children’s nutritional status, the use of maternal and child healthcare services, and knowledge of HIV and AIDS. Specific objectives are to:

• collect data (at the national level) that will allow the calculation of key demographic rates;
• analyse the direct and indirect factors that determine the level and trends of fertility;
• measure the level of contraceptive knowledge and practice among women and men by method, urban–rural residence and region;
• collect high-quality data on family health, including immunisation coverage among children, prevalence and treatment of diarrhoea and other diseases among children under age 5 years, and maternity care indicators (including antenatal visits, assistance at delivery, and postnatal care);
• collect data on infant and child mortality;
• obtain data on child feeding practices, including breastfeeding, and collect ‘observation’ information to use in assessing the nutritional status of women and children;
• collect data on knowledge and attitudes of women and men about sexually transmitted infections (STIs), HIV and AIDS, and evaluate patterns of recent behaviour regarding condom use; and
• collect data on knowledge and attitudes of women and men about tuberculosis.

Geographic coverage

National coverage.

Analysis unit

• Household,
• Individual.

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, and all men aged between 15-49 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary focus of the 2009 Kiribati Demographic Health Survey (DHS) was to provide estimates of key population and health indicators, including fertility and mortality rates, for the country as a whole, for the urban area and rural areas (separately) - urban is South Tarawa and urban settlement on Kiritimati Island while the rest of Kiribati is defined as rural areas. The survey used the sampling frame provided by the list of census enumeration areas, with population and household information coming from the 2005 Kiribati Population and Housing Census.

The survey was designed to obtain completed interviews of 2,193 women aged 15-49. In addition, males aged 15-59 in every second household were interviewed. To take non-response into account, 1,480 households countrywide were selected: 640 in the urban area and 840 in rural areas.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were administered during the 2009 Kiribati Demographic Health Survey (KDHS): a Household questionnaire, a Women’s questionnaire and a Men’s questionnaire. These were adapted to reflect population and health issues relevant to Kiribati, and were presented at a series of meetings with various stakeholders, including government ministries and agencies, NGOs and international donors. The final draft of each questionnaire was discussed at a questionnaire design workshop organised by Kiribati National Statistics Office (KNSO) in March 2009 in Tarawa. Survey questionnaires were then translated into the local language (I-Kiribati) and pretested from 7–19 August 2009.

The Household questionnaire was used to list all the usual members and visitors in selected households, and to identify women and men who were eligible for the individual interview. Some basic information was collected on the characteristics of each person listed, including age, sex, education and relationship to the head of the household. For children under age 18 years, the survival status of their parents was ascertained. The Household questionnaire also collected information on characteristics of each household’s dwelling unit, such as source of drinking water, type of toilet facility, material used for the floor, and ownership of various durable goods.

The Women’s questionnaire collected information from all women aged 15–49 about: - education, residential history and media exposure; - pregnancy history and childhood mortality; - knowledge and use of family planning methods; - fertility preferences; - antenatal, delivery and postnatal care; - breastfeeding and infant feeding practices; - immunisation and childhood illnesses; - marriage and sexual activity; - their own work and their husband’s background characteristics; and - awareness and behaviour regarding HIV and other STIs.

The Men’s questionnaire was administered to all men aged 15–49 living in every second household. It collected much of the same information as the women’s questionnaire, but was shorter because it did not contain questions about reproductive history or maternal and child health or nutrition.

Cleaning operations

Processing the 2009 Kiribati Demographic Health Survey (KDHS) results began three weeks after the start of fieldwork. Completed questionnaires were returned periodically from the field to the Kiribati National Statistics Office (KNSO) data processing center in South Tarawa, where the data were entered and edited by seven data processing personnel specially trained for this task. Data processing personnel were supervised by KNSO staff. Data entry and editing of questionnaires was completed by 30 March 30 2010. CSPRo was used for data processing.

Response rate

In total, 1,477 households were selected for the sample, of which 1,451 were found to be occupied during data collection. Of these existing households, 1,422 were successfully interviewed, giving a household response rate of 98%.

In households, 2,193 women were identified as being eligible for the individual interview. Interviews were completed with 1,978 women, yielding a response rate of 90%. Of the 1,337 eligible men identified in the selected sub-sample of households, 85% were successfully interviewed. Response rates were higher in rural areas than in the urban area, with the rural–urban difference in response rates being the greatest among eligible men.

Sampling error estimates

The sample of respondents selected in the 2009 Kiribati Demographic Health Survey (KDHS) is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling errors are the errors that result from taking a sample of the covered population through a particular sample design. Non-sampling errors are systematic errors that would be present even if the entire population was covered (e.g. response errors, coding and data entry errors, etc.).

For the entire covered population and for large subgroups, the KDHS sample is generally sufficiently large to provide reliable estimates. For such populations the sampling error is small and less important than the non-sampling error. However, for small subgroups, sampling errors become very important in providing an objective measure of reliability of the data.

Sampling errors will be displayed for total, urban and rural and each sample domain only. No other panels should be included in the sampling error table. The choice of variables for which sampling error computations will be done depends on the priority given to specific variables. However, it is recommended that sampling errors be calculated for at least the following variables, which was not case with Kiribati given the smallness of the sample compared to other countries in the Pacific.

Sampling errors are usually measured in terms of the standard error for 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 the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected by simple random sampling, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2009 KDHS sample was the result of a multistage stratified design, and, consequently, it is necessary to use more complex formulae. The computer software used to calculate sampling errors for the 2009 KDHS is the Integrated Sample Survey Analysis (ISSA) Sampling Error Module. This module uses the Taylor linearisation method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

In addition to the standard error, ISSA

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.

The global Infant Formula Oil market is poised for steady expansion, projected to reach approximately $5,660.3 million in value. This growth is underpinned by a Compound Annual Growth Rate (CAGR) of 3.2% throughout the forecast period of 2025-2033. A significant driver for this market is the increasing global birth rate and a growing awareness among parents regarding the crucial role of specialized fats and oils in infant nutrition. The demand for high-quality, safe, and nutritionally complete infant formula is paramount, leading manufacturers to invest in advanced lipid technologies and research to create formulations that closely mimic breast milk composition. Key segments within this market include specialized oils like OPO (Oleic-Palmitic-Oleic) fat, which offers enhanced absorption and digestive benefits for infants, alongside a broader category of other essential oils and fats. The market also caters to specific age groups, with dedicated segments for 0-6 Months Baby, 6-12 Months Baby, and 12-36 Months Baby, each requiring tailored nutritional profiles. Further fueling market growth is the rising disposable income in emerging economies, enabling more families to opt for premium infant nutrition products. The trend towards organic and sustainably sourced ingredients in infant formula also presents a significant opportunity. Companies are actively innovating to address concerns about infant digestive health, allergies, and cognitive development, with specialized oils playing a vital role in these advancements. While the market is robust, potential restraints include stringent regulatory landscapes regarding food safety and labeling, as well as fluctuating raw material prices which can impact manufacturing costs. Nevertheless, the overarching trend points towards a sustained demand for sophisticated infant formula oil solutions, driven by global parental focus on infant well-being and the continuous innovation within the food and nutrition industry. Here's a unique report description for Infant Formula Oil, incorporating your specified elements and a creative approach:

Total first marriage rates and age-specific first marriage rates per 1,000 females, all marriages, by place of occurrence, 2000 to 2004.

As China continues to implement its progressive fertility promotion policy, there has been a drastic decline in the fertility rate. Given that the migrant population constitutes more than a quarter of China’s total population, enhancing the willingness of this demographic to have additional children through policy-guided urban public services is pivotal for optimizing China’s population development strategy. This study analyzes the influence of urban public services on the reproductive intentions of the migrant population, utilizing data from 110,667 migrant families with one child, drawn from China’s Migrant Population Dynamic Monitoring data in 2016 and 2018. The data analysis reveals several key findings: (1) Urban public services, overall, exhibit a notable positive effect on the willingness of the migrant population to have more children, albeit with limitations and a declining trend. (2) Among urban public services, primary basic education significantly impacts the willingness of the migrant population to expand their families. (3) Large cities have created a ’reverse screening’ effect on the migrant population, leading to differential access to public services. This scenario caters effectively to the high human capital migrant individuals while reducing accessibility to livelihood public services for the low human capital migrant population. This paper critically evaluates China’s progressively adjusted fertility policy from the perspective of the migrant population. It underscores the necessity of establishing a comprehensive fertility support policy system across China.

Abstract

The principal objective of the Ethiopia Demographic and Health Survey (DHS) is to provide current and reliable data on fertility and family planning behavior, child mortality, children’s nutritional status, the utilization of maternal and child health services, and knowledge of HIV/AIDS. This information is essential for informed policy decisions, planning, monitoring, and evaluation of programs on health in general and reproductive health in particular at both the national and regional levels. A long-term objective of the survey is to strengthen the technical capacity of the Central Statistical Authority to plan, conduct, process, and analyze data from complex national population and health surveys. Moreover, the 2000 Ethiopia DHS is the first survey of its kind in the country to provide national and regional estimates on population and health that are comparable to data collected in similar surveys in other developing countries. As part of the worldwide DHS project, the Ethiopia DHS data add to the vast and growing international database on demographic and health variables. The Ethiopia DHS collected demographic and health information from a nationally representative sample of women and men in the reproductive age groups 15-49 and 15-59, respectively.

The Ethiopia DHS was carried out under the aegis of the Ministry of Health and was implemented by the Central Statistical Authority. ORC Macro provided technical assistance through its MEASURE DHS+ project. The survey was principally funded by the Essential Services for Health in Ethiopia (ESHE) project through a bilateral agreement between the United States Agency for International Development (USAID) and the Federal Democratic Republic of Ethiopia. Funding was also provided by the United Nations Population Fund (UNFPA).

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-59

Kind of data

Sample survey data

Sampling procedure

The Ethiopia DHS used the sampling frame provided by the list of census enumeration areas (EAs) with population and household information from the 1994 Population and Housing Census. A proportional sample allocation was discarded because this procedure yielded a distribution in which 80 percent of the sample came from three regions, 16 percent from four regions and 4 percent from five regions. To avoid such an uneven sample allocation among regions, it was decided that the sample should be allocated by region in proportion to the square root of the region's population size. Additional adjustments were made to ensure that the sample size for each region included at least 700 households, in order to yield estimates with reasonable statistical precision.

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

Mode of data collection

Face-to-face

Research instrument

The Ethiopia DHS used three questionnaires: the Household Questionnaire, the Women’s Questionnaire, and the Men’s Questionnaire, which were based on model survey instruments developed for the international MEASURE DHS+ project. The questionnaires were specifically geared toward obtaining the kind of information needed by health and family planning program managers and policymakers. The model questionnaires were then adapted to local conditions and a number of additional questions specific to on-going health and family planning programs in Ethiopia were added. These questionnaires were developed in the English language and translated into the five principal languages in use in the country: Amarigna, Oromigna, Tigrigna, Somaligna, and Afarigna. They were then independently translated back to English and appropriate changes were made in the translation of questions in which the back-translated version did not compare well with the original English version. A pretest of all three questionnaires was conducted in the five local languages in November 1999.

All usual members in a selected household and visitors who stayed there the previous night were enumerated using the Household Questionnaire. Specifically, the Household Questionnaire obtained information on the relationship to the head of the household, residence, sex, age, marital status, parental survivorship, and education of each usual resident or visitor. This information was used to identify women and men who were eligible for the individual interview. Women age 15-49 in all selected households and all men age 15-59 in every fifth selected household, whether usual residents or visitors, were deemed eligible, and were interviewed. The Household Questionnaire also obtained information on some basic socioeconomic indicators such as the number of rooms, the flooring material, the source of water, the type of toilet facilities, and the ownership of a variety of durable items. Information was also obtained on the use of impregnated bednets, and the salt used in each household was tested for its iodine content. All eligible women and all children born since Meskerem 1987 in the Ethiopian Calendar, which roughly corresponds to September 1994 in the Gregorian Calendar, were weighed and measured.

The Women’s Questionnaire collected information on female respondent’s background characteristics, reproductive history, contraceptive knowledge and use, antenatal, delivery and postnatal care, infant feeding practices, child immunization and health, marriage, fertility preferences, and attitudes about family planning, husband’s background characteristics and women’s work, knowledge of HIV/AIDS and other sexually transmitted infections (STIs).

The Men’s Questionnaire collected information on the male respondent’s background characteristics, reproduction, contraceptive knowledge and use, marriage, fertility preferences and attitudes about family planning, and knowledge of HIV/AIDS and STIs.

Response rate

A total of 14,642 households were selected for the Ethiopia DHS, of which 14,167 were found to be occupied. Household interviews were completed for 99 percent of the occupied households. A total of 15,716 eligible women from these households and 2,771 eligible men from every fifth household were identified for the individual interviews. The response rate for eligible women is slightly higher than for eligible men (98 percent compared with 94 percent, respectively). Interviews were successfully completed for 15,367 women and 2,607 men.

There is no difference by urban-rural residence in the overall response rate for eligible women; however, rural men are slightly more likely than urban men to have completed an interview (94 percent and 92 percent, respectively). The overall response rate among women by region is relatively high and ranges from 93 percent in the Affar Region to 99 percent in the Oromiya Region. The response rate among men ranges from 83 percent in the Affar Region to 98 percent in the Tigray and Benishangul-Gumuz regions.

Note: See summarized response rates by place of residence in Table A.1.1 and Table A.1.2 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the Ethiopia DHS to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the Ethiopia DHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for 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 the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the Ethiopia DHS sample is the result of a two-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the Ethiopia DHS is the ISSA Sampling Error Module (SAMPERR). This module used the Taylor linearisation method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

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

Data appraisal

Data Quality Tables - Household age

Comparison of the total scores of the fertility decision model by socio-demographic variables.

Standardized regression weights of parameters.

Calculation of fertility.

Summary of behavioral constructs and their subdomains.