Data contains 2022 world population data publised by the UN DESA for six most populous countries of the world. File also contains the analysis of decomposition of demographic indicators on population growth.

Pocket Calculators Market Outlook

The global pocket calculators market size is projected to grow significantly, with an estimated CAGR of 6.2% from 2024 to 2032. In 2023, the market was valued at approximately $3.5 billion, and by 2032, it is expected to reach around $6.4 billion. This robust growth can be attributed to the continuous demand for calculators in education and business sectors, technological advancements in calculator functions, and the increasing prevalence of digital learning environments. The integration of advanced features and connectivity options in calculators is further expected to bolster market expansion during the forecast period.

One of the primary growth factors driving the pocket calculators market is the enduring necessity for dedicated calculation devices in educational settings. Despite the proliferation of multifunctional devices like smartphones and tablets, pocket calculators remain indispensable in schools and universities due to their specialized functionalities and ease of use in examination settings. Calculators designed specifically for educational use, such as scientific and graphing calculators, are integral to curricula in mathematics and science courses worldwide. The emphasis on STEM education globally continues to create a substantial demand for calculators, as they provide students with essential tools for learning complex mathematical concepts.

Another significant factor contributing to market growth is the advancements in calculator technology. Modern calculators now come with enhanced features such as programmable functions, connectivity with other devices, and cloud storage capabilities. These innovations have expanded the applications of calculators beyond traditional arithmetic functions to complex problem-solving tools used in various professional fields such as finance and engineering. The integration of graphing capabilities and data analysis functions has widened the appeal of calculators among professionals who require robust computational power on-the-go, further driving the market growth.

The rise of digital learning and remote education platforms has also positively impacted the demand for pocket calculators. As educational institutions adopt digital tools to facilitate learning, the need for reliable, non-disruptive devices like calculators has increased. This trend is prominent in regions with advanced technological infrastructures, where digital literacy is a priority. In addition, the convenience and affordability of pocket calculators make them a preferred choice for students and educators alike. The market is expected to benefit from ongoing investments in educational technology, which frequently incorporate calculators as integral tools for effective learning experiences.

Regionally, Asia Pacific is anticipated to witness the highest growth in the pocket calculators market. The region's rapid economic development, coupled with an expanding middle-class population, supports the growing demand for educational tools. Countries like China, India, and Japan are significant contributors to this growth, driven by large student populations and increasing investments in education. North America and Europe are also substantial markets due to their established education systems and consistent demand for educational aids. In contrast, the markets in Latin America and the Middle East & Africa are expected to experience moderate growth, influenced by their evolving educational landscapes and economic conditions.

Product Type Analysis

When dissecting the pocket calculators market by product type, it becomes evident that scientific calculators hold a significant share due to their extensive application in educational environments. Scientific calculators are indispensable tools for students and professionals dealing with subjects that require complex calculations such as physics, chemistry, and engineering. Their ability to perform a wide range of functions, including trigonometric, logarithmic, and exponential operations, makes them crucial in academic settings. The demand for scientific calculators is particularly high in regions with a strong emphasis on STEM education, and this segment is anticipated to continue its dominance over the forecast period.

Graphing calculators represent another vital segment within the pocket calculators market, catering to advanced educational and professional needs. These calculators are equipped with the capability to plot graphs and solve simultaneous equations, making them essential in higher-level mathematics and engineering courses. Their extensive functionality is p

India population growth rate for 2023 was 0.88%, a 0.09% increase from 2022.

<ul style='margin-top:20px;'>

<li>India population growth rate for 2022 was <strong>0.79%</strong>, a <strong>0.03% decline</strong> from 2021.</li>
<li>India population growth rate for 2021 was <strong>0.82%</strong>, a <strong>0.15% decline</strong> from 2020.</li>
<li>India population growth rate for 2020 was <strong>0.97%</strong>, a <strong>0.07% decline</strong> from 2019.</li>
</ul>Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.

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

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and ZIP Codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential ZIP Code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality.

For the ZIP Code-level life expectancy calculation, it is assumed that postal ZIP Codes share the same boundaries as ZIP Code Census Tabulation Areas (ZCTAs). More information on the relationship between ZIP Codes and ZCTAs can be found at http://www.census.gov/geo/reference/zctas.html. ZIP Code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 ZIP Code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for ZIP Codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest ZIP Code with population. ZIP Code population for 2000 estimates comes from the Decennial Census. ZIP Code population for 2013 estimates are from the American Community Survey (5-Year Average). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly important for ZCTAs with high homeless population (not living in group quarters) where the ACS may underestimate the ZCTA population and therefore underestimate the life expectancy. The ACS provides ZIP Code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to ZIP Codes based on majority land-area.

ZIP Codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, ZIP Codes with populations of less than 5,000 were aggregated with neighboring ZIP Codes until the merged areas had a population of more than 5,000. ZIP Code 94103, representing Treasure Island, was dropped from the dataset due to its small population and having no bordering ZIP Codes. In this way, the original 305 Bay Area ZIP Codes were reduced to 217 ZIP Code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

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.

This table contains 2394 series, with data for years 1991 - 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 2;Income adequacy quintile 3 ...), Age (14 items: At 25 years; At 30 years; At 40 years; At 35 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Life expectancy; High 95% confidence interval; life expectancy; Low 95% confidence interval; life expectancy ...).

Stroke Risk Calculator App Market Outlook

According to our latest research, the global Stroke Risk Calculator App market size reached USD 412 million in 2024, and is expected to grow at a robust CAGR of 14.2% from 2025 to 2033. By the end of 2033, the market is forecasted to attain a value of USD 1,285 million, driven by the increasing integration of artificial intelligence into digital health tools and the growing prevalence of stroke risk factors worldwide. This growth is primarily attributed to the rising adoption of mobile health applications, expanding awareness about preventive healthcare, and the ongoing digital transformation of the healthcare industry.

One of the most significant growth factors for the Stroke Risk Calculator App market is the global surge in stroke incidence and the corresponding need for early risk identification and intervention. With the aging population and lifestyle changes leading to increased prevalence of hypertension, diabetes, obesity, and other risk factors, healthcare systems are prioritizing preventive strategies. Stroke risk calculator apps empower both patients and healthcare professionals to assess individual risk profiles using validated algorithms, thereby facilitating timely medical advice and intervention. The proliferation of smartphones and the widespread availability of internet connectivity have further amplified the reach and utility of these apps, making them accessible to diverse demographic groups across urban and rural settings.

Another pivotal driver is the rapid technological advancement in mobile health (mHealth) and artificial intelligence (AI). Modern stroke risk calculator apps now incorporate advanced features such as machine learning-based risk prediction, real-time health data integration from wearable devices, and personalized health recommendations. These innovations not only enhance the accuracy and reliability of risk assessments but also improve user engagement and adherence to preventive measures. The integration of these apps with electronic health records (EHRs) and telemedicine platforms is also enabling seamless communication between patients and healthcare providers, fostering a holistic approach to stroke prevention and management.

Regulatory support and increasing healthcare expenditure on digital health infrastructure are also fueling the expansion of the Stroke Risk Calculator App market. Government initiatives aimed at promoting digital health solutions, particularly in developed regions like North America and Europe, are encouraging hospitals, clinics, and research institutes to adopt these tools as part of their standard care protocols. Additionally, the COVID-19 pandemic has accelerated the adoption of remote health monitoring and telehealth solutions, further highlighting the importance of digital tools in chronic disease management. This trend is expected to persist, with healthcare providers and payers recognizing the long-term cost savings and improved patient outcomes associated with early stroke risk detection.

From a regional perspective, North America currently dominates the Stroke Risk Calculator App market with the largest market share, owing to high smartphone penetration, advanced healthcare infrastructure, and proactive government initiatives supporting digital health adoption. Europe follows closely, benefiting from robust regulatory frameworks and strong collaboration between healthcare providers and technology developers. The Asia Pacific region is poised for the fastest growth, driven by increasing healthcare digitization, rising awareness of stroke prevention, and a burgeoning middle-class population demanding accessible health solutions. Latin America and the Middle East & Africa are also witnessing gradual growth, supported by improving healthcare access and mobile connectivity, although challenges such as limited digital literacy and infrastructure gaps persist in some areas.

Component Analysis

The Stroke Risk Calculator App market is segmented by component into Soft

As of 2024, South Africa's population increased, counting approximately 63 million inhabitants. Of these, roughly 27.5 million were aged 0-24, while 654,000 people were 80 years or older. Gauteng and Cape Town are the most populated South Africa’s yearly population growth has been fluctuating since 2013, with the growth rate dropping below the world average in 2024. The majority of people lived in the borders of Gauteng, the smallest of the nine provinces in terms of land area. The number of people residing there amounted to 16.6 million in 2023. Although the Western Cape was the third-largest province, the city of Cape Town had the highest number of inhabitants in the country, at 3.4 million. An underemployed younger population South Africa has a large population under 14, who will be looking for job opportunities in the future. However, the country's labor market has had difficulty integrating these youngsters. Specifically, as of the fourth quarter of 2024, the unemployment rate reached close to 60 percent and 384 percent among people aged 15-24 and 25–34 years, respectively. In the same period, some 27 percent of the individuals between 15 and 24 years were economically active, while the labor force participation rate was higher among people aged 25 to 34, at 74.3 percent.

Performance of developed risk calculators compared with general CVD risk calculators.

Reclassification for centers with high and low CVD risk for Model A compared with Framingham Risk Score (FRS).

Characteristics of included cohorts.

Baseline characteristics of the patient population and of those who survived and died.

Predictors from the multivariable model and points indicating an increased risk of death.

Abstract

This study sought to develop a user-friendly decision-making tool to explore country-specific estimates for costs and economic consequences of different options for scaling screening and psychosocial interventions for women with common perinatal mental health problems in Malawi. We developed a simple simulation model using a structure and parameter estimates that were established iteratively with experts, based on published trials, international databases and resources, statistical data, best practice guidance and intervention manuals. The model projects annual costs and returns to investment from 2022 to 2026. The study perspective is societal, including health expenditure and productivity losses. Outcomes in the form of health-related quality of life are measured in Disability Adjusted Life Years, which were converted into monetary values. Economic consequences include those that occur in the year in which the intervention takes place. Results suggest that the net benefit is relatively small at the beginning but increases over time as learning effects lead to a higher number of women being identified and receiving (cost‑)effective treatment. For a scenario in which screening is first provided by health professionals (such as midwives) and a second screening and the intervention are provided by trained and supervised volunteers to equal proportions in group and individual sessions, as well as in clinic versus community setting, total costs in 2022 amount to US$ 0.66 million and health benefits to US$ 0.36 million. Costs increase to US$ 1.03 million and health benefits to US$ 0.93 million in 2026. Net benefits increase from US$ 35,000 in 2022 to US$ 0.52 million in 2026, and return-on-investment ratios from 1.05 to 1.45. Results from sensitivity analysis suggest that positive net benefit results are highly sensitive to an increase in staff salaries. This study demonstrates the feasibility of developing an economic decision-making tool that can be used by local policy makers and influencers to inform investments in maternal mental health

Description of data set

Iteratively, information was gathered from desk-based searches and from talking to and exchanging emails with experts in the maternal health field to establish a model structure and the parameter values. This included the development of an information request form that presents a list of parameters, parameter values and details about how the values were estimated and the data sources. We collected information on: Intervention’s effectiveness; prevalence rates; population and birth estimates; proportion of women attending services; salaries and reimbursement rates for staff and volunteers; details about training, supervision, intervention delivery (e.g., frequency, duration); unit costs, and data needed to derive economic consequences (e.g. women’s income, health weights). Data were searched from the following sources: published randomised controlled trials and meta-analyses; WHO published guidance and intervention manual; international databases and resources (WHO-CHOICE, Global Burden of Disease Database; International Monetary Fund; United Nations Treasury, World Bank, Global Investment Framework for Women’s and Children’s Health). We consulted two groups of experts: one group included individuals with clinical, research or managerial expertise in funding, managing, delivering, or evaluating screening of common mental health problems and PSIs; the second group included individuals from the Malawi Government, Ministry of Health Reproductive Health Unit and Non-Communicable Disease Committee and Mental Health Unit. The first group of experts provided information from research and administrative data systems concerned with implementing and evaluating screening for maternal mental health and the delivery of interventions. The second group of experts from the Malawi Government provided information on unit costs for hospital use and workforce data, as well as information on how training and supervision might be delivered at scale. Individuals were identified by colleagues of this team based or part-time based in Malawi, which included a psychiatrist specialising in perinatal mental health (co-author RS) and the coordinator of the African Maternal Mental Health Alliance (co-author DN), an organisation concerned with disseminating information and evidence on perinatal mental health to policy makers and influencers, and the wider public.

In 2023, about 26.9 percent of Asian private households in the U.S. had an annual income of 200,000 U.S. dollars and more. Comparatively, around 13.9 percent of Black households had an annual income under 15,000 U.S. dollars.

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

Low income cut-offs (LICOs) before and after tax by community size and family size, in current dollars, annual.