Nutritionists' and dietitians' services growth and resilience amid the COVID-19 pandemic disruptions result from the industry's diversity of services and addressable markets. Even before the pandemic, there was a strong demand for these services driven by factors like rising obesity and the aging population. Adding to this increase in demand, consumers are increasingly interested in adopting healthy and sustainable eating habits. The rise in telemedicine has been an effective tool for meeting new demands and delivering services conveniently. Despite the pandemic's effect, structural features and demographic factors enabled services to grow and industry-wide revenue is expected to climb at a CAGR of 4.9% through 2024 to total $782.7 million in 2024. In the same year, revenue is expected to climb by an estimated 1.8%. The industry market is fragmented, with small-scale enterprises dominating it. Low entry barriers have facilitated new players' entry and intensified competition. Companies within and outside the sector leverage advanced technologies to enhance their product offerings and service quality. The emergence of AI-powered data from medical wearables, remote monitoring and interactive platforms can potentially replace services, so companies and independent contractors must enhance their technological capabilities to remain competitive, potentially leading to mergers and acquisitions that confer economies of scale. From now on, with trends toward healthier lifestyles, rising obesity rates and physician visits, the industry is well-positioned to thrive. A more robust economy and reduced insurance costs resulting from promoting healthy lifestyles will incentivize individuals and communities to prioritize health-promoting services. With unemployment rates decreasing and disposable income rising, discretionary spending is expected to swell, further boosting industry revenue. With these positive economic headwinds, industry revenue is forecast to strengthen at a CAGR of 3.5% through the end of 2029 to total $929.0 million as profit inches up.

Abstract copyright UK Data Service and data collection copyright owner.

The National Diet and Nutrition Survey (NDNS) Rolling Programme (RP) began in 2008 and is designed to assess the diet, nutrient intake and nutritional status of the general population aged 1.5 years and over living in private households in the UK. (For details of the previous NDNS series, which began in 1992, see the documentation for studies 3481, 4036, 4243 and 5140.)

The programme is funded by Public Health England (PHE), an executive agency of the Department of Health, and the UK Food Standards Agency (FSA).

The NDNS RP is currently carried out by a consortium comprising NatCen Social Research (NatCen) (NatCen, contract lead) and the MRC Epidemiology Unit, University of Cambridge (scientific lead). The MRC Epidemiology Unit joined the consortium in November 2017. Until December 2018, the consortium included the MRC Elsie Widdowson Laboratory, Cambridge (former scientific lead). In Years 1 to 5 (2008/09 – 2012/13) the consortium also included the University College London Medical School (UCL).

Survey activities at the MRC Epidemiology Unit are delivered with the support of the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (IS-BRC-1215- 20014), comprising the NIHR BRC Nutritional Biomarker Laboratory and NIHR BRC Dietary Assessment and Physical Activity Group. The NIHR Cambridge Biomedical Research Centre is a partnership between Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge, funded by the NIHR.

The NDNS RP provides the only source of high quality, nationally representative UK data on the types and quantities of foods consumed by individuals, from which estimates of nutrient intake for the population are derived. Results are used by Government to develop policy and monitor progress toward diet and nutrition objectives of UK Health Departments, for example work to tackle obesity and monitor progress towards a healthy, balanced diet as visually depicted in the Eatwell Guide. The NDNS RP provides an important source of evidence underpinning the Scientific Advisory Committee on Nutrition (SACN) work relating to national nutrition policy. The food consumption data are also used by the FSA to assess exposure to chemicals in food, as part of the risk assessment and communication process in response to a food emergency or to inform negotiations on setting regulatory limits for contaminants.

Further information is available from the gov.uk National Diet and Nutrition Survey webpage.

The survey of young people aged 4 to 18 years aimed to collect data on diet through a questionnaire and a 7-day weighed intake record for a nationally representative sample living in private households in Great Britain. The study also included a 7-day bowel movement record, a 7-day physical activity diary, anthropometric and blood pressure measurem ents, a spot urine sample and a blood sample. The survey consisted of two parts: Part 1, the Diet and Nutrition Survey, and Part 2, the Oral Health Survey.

In addition to the aims of the survey series as a whole, the survey of young people was designed to:

• provide data to assist in the development of dietary guidelines for young people, including dietary guidelines for food provided by schools
• determine the frequency of bowel movement in this age group
• provide baseline and comparative data for blood pressure and some anthropometric measurements in this age group
• provide baseline and comparative data for some haematological and biochemical indices in blood and urine in this age group

Main Topics:

There are two groups of datasets, primary datasets containing data in the format originally collected and derived datasets.

The primary data includes dietary data from the food diary at four levels: person level, day level (seven days were collected per diary), plate level (this is a group of foods consumed together at the same serving) and food item level. It includes data from the physical activity diary for seven days and the keyed anthropometric data. The dataset also contains a file of blood and urine analyte data.

The derived datasets provided include average nutrient intake for each person, intake of food sub-groups, nutrient intakes at the food item level and average daily intake of each of 55 nutrients from different food types. In addition, SPSS data files are provided containing the key derived variables for the initial dietary interview, the smoking and drinking self-completion, blood and urine analytes, physical measurements and variables derived from the physical activity diary data.

This study also contains the MAFF nutrient databank, used to convert food weights into nutrient intake.

Full details of the datasets are provided in Section 4 of the...

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Herbalife Nutrition (HLF): Poised for Growth or Facing a Shake-Up?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Abstract

Unemployment is strikingly high in Djibouti: only 30% of the adult population is classified as employed, according to the most recent household survey data (EDAM 2012). Since the time of the 2008 food and fuel crisis and in the lack of social safety nets that could be scaled up to respond to the crisis, the Government aimed at promoting temporary access to employment through workfare. Moreover, in spite of progress made towards meeting some health and education-related MDGs, Djibouti's health indicators remain among the poorest in the world. The national prevalence of moderate and severe stunting in the most recent MICS survey is of 33% among children aged 0-5 years old, a prevalence rate comparable to Sub-Saharan countries of a much lower income per capita.

In order to address both issues, the Government of Djibouti is piloting an innovative integrated public works and nutrition intervention. The intervention (i) actively involves the main caregiver in a number of ways (nutrition, workfare) to strengthen her role in the household and (ii) makes access to income (workfare) conditional on the caregiver attending regular nutrition promotion activities. The program targets households with pregnant women and children 0-2 years of age in poor areas (urban and rural) in Djibouti.

The first objective of this evaluation is to test the value added of combining a public works program targeted to women over and above the provision of information and promotion of behavioral change in child care practices. That is, the evaluation will specifically measure the impact of making an integrated nutrition and workfare intervention available compared to a nutrition program by itself. As access to the public works is given only to households where the women has registered to the community nutrition program, the program's goal is to leverage the additional cash income (net of the opportunity cost of participation) to enhance the adoption of improved nutrition practices. The planned evaluation will provide a test for the interaction effect between income and the information and promotion of behavioral change.

The second objective is to test whether these effects are only short term, or whether they extend beyond the fifty days of participation in the public works program. The safety net, by design, provides only short term income support during the first 1,000 days. This evaluation is interested in measuring the extent that the impact extends beyond the contemporaneous duration of the safety net program, after the women have stopped participating. Participants and the control group will be interviewed at endline five months after having exited the workfare program. The medium term effects might in part persist through short term savings, but also through labor supply activation and improved psychological wellbeing of the participants.

Geographic coverage

The public works component of the intervention has been rolled out in two out of three eligible neighborhoods in the capital Djibouti ville shortly after launching the community growth promotion. In contrast, the public work component was only launched in May 2014 in the neighborhood called "Hayableh".

Analysis unit

The units of observations are urban households eligible to a community-based nutrition intervention (i.e. households with pregnant women and/or children aged 0-2 years old at the time they joined the nutrition meetings).

Kind of data

Sample survey data [ssd]

Sampling procedure

• Evaluation design: To estimate the impact of offering the public works program to households that are participating in the community growth promotion activities, a randomized experiment was used. From a list containing all eligible households surveyed at baseline (1,011 in total), households were randomly assigned to two groups: i) a "treatment" group where households are given priority to participate in the public works and services offered by the program, and ii) a "control" group where households will get the offer later. The evaluation design was reviewed and cleared by the J-PAL Europe Institutional Review Board on February 2014.

The time lag in the exposure to the intervention between the two groups takes advantage of the phase-in design of the intervention itself: 250 public works positions are being set up every 5 months between May 2014 and December 2015. Thus, the program will make available 250 positions between May and September 2014, 250 between November 2014 and March 2015, 250 between April and July 2015 and 250 between August and December 2015.

Households interviewed at baseline, 1,011 in total, were randomly assigned to 4 groups: • Group A: public works and services offered between May and September 2014 • Group B: public works and services will be offered between November 2014 and March 2015 • Group C: public works and services will be offered between April and July 2015 • Group D: public works and services will be offered between August and December 2015

The evaluation exploits the gradual rollout of the public works within the neighborhood with a randomized assignment of the timing of offer in the program. The 500 hundred households that will be given the opportunity to work between May 2014 and March 2015 will constitute the treatment group. The remaining 500 households will constitute the control group, randomized to receive. They will receive the intervention on average nine months later than in the treatment group (or, equivalently, seven months after the intervention in the corresponding treatment group ends).

Each group will receive a baseline survey immediately before the start of the program, a first follow-up survey, collected during the public works intervention, and an endline survey, after the program had ended. While all groups were administered a baseline survey between January and March 2014, the different groups are interviewed in a staggered fashion, so that 'each treatment' group will be interviewed with its corresponding randomized control group, both during the intervention as well as at endline. Group A will be interviewed with group C, and group B will be interviewed with group D. The endline surveys for the treatment groups and their corresponding randomized 'control' groups will take place before the latter get offered the intervention.

• Overview of Sampling for the Baseline: the sample consists of all eligible households resident in Hayableh District of Djibouti City. Eligibility is defined as follows: a household with a pregnant or lactating woman (i.e. with children younger than 2 years old) that registered in the program and had been assigned to a nutrition session group. A total of 1,055 eligible households were identified based on program administrative data. Out of it, 1,011 households were interviewed (96% response rate). One third of the non-responses are due to the absence of the household and another third to the refusal of the beneficiary and her husband to be interviewed.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

A baseline household survey was administered to beneficiary women, and another shorter survey to the husbands of these women.

Cleaning operations

Data was downloaded from CAPI server on regular basis and systematic daily checks on the quality of collected data were ensured by another consultant hired by the Bank. This allowed giving frequent feedback to surveyors on data quality. The field coordinator was in charge to share this feedback with the whole group and with specific surveyors if necessary.

BackgroundMotivating proper nutrition during childhood is the basis for optimal health, learning, productivity, and social wellbeing throughout life. Stunting is among the major public health problems. According to the Ethiopian mini demographic and health survey, the prevalence of stunting among under five children was 37%. In addition, stunting has a trans-generational effect on a mother’s nutritional status. However, evidence on the causal contribution of maternal employment to stunting among under five children is not well understood in Ethiopia. This study aimed to compare the stunting status and associated factors among under five children of employed and unemployed mothers in the Gurage Zone, Southern Ethiopia, in 2021. A community-based comparative cross-sectional study was conducted among 671 (330 employed and 341 unemployed) randomly selected mother–child pairs in the Gurage Zone, Southern Ethiopia. A pretested semi-structured tool and validated anthropometric measurements were used to collect the data. The data were entered into Epi Data version 3.1 and exported to Statistical Package for Social Science (SPSS) version 23.0 for analysis. Frequency, percent, mean, median, and SD were computed and presented by using tables and figures. A bivariable and multivariable binary logistic regression analysis was conducted to assess the association between factors and outcome variables.ResultsIn this study, a total of 671 mother–child pairs (330 (94.60%) employed and 341 (97.70%) unemployed) participated, with a total response rate of 96%. Among the total participants, about 70 (21.2%) [95% CI: (17.0, 25.5)] and 98 (28.8%) [95% CI: (23.0, 33.4)] of children of employed and unemployed mothers, respectively, were stunted. Mothers’ level of education, primary and secondary [AOR = 1.79, 95% CI: (0.8, 3.7), age between 25 and 29 years [AOR = 0.08, 95% CI: (0.006, 0.904)], monthly family income > 5,000 birr [AOR = 0.42, 95% CI: (0.00, 0.64)], and children aged between 6 and 23 months [AOR = 2.9; 95% CI: (1.48, 5.80)] were predictors of stunting among the children of employed mothers. Compared to the mothers who did not receive nutritional education [AOR = 2.5; 95% CI: (1.10, 5.60)], monthly family income of 2,000 ETB [AOR = 2.64; 95% CI: (1.34, 5.19)], sex of child (girl) [AOR = 2.3; 95% CI: (1.30, 3.80), and mothers educational status of read-and-write only [AOR = 2.9, 95% CI: (1.40, 5.80)] were predictors of stunting among the children of unemployed mothers. The nutrition intervention should focus on encouraging women’s education as it increases the probability of being employed, improving the income of families by using different income-generating strategies, and strengthening the existing essential nutrition counseling strategy. Likewise, further research work on the difference between employed and unemployed mothers on stunting status is also recommended to researchers.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Herbalife (HLF): Nutrition or Hype?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Balchem's (BCPC) Stock: A Health and Nutrition Conundrum?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Direct-selling companies retail a range of products from one person to another away from a fixed retail location. The COVID-19 outbreak caused a substantial shift in the industry, as mass layoffs propelled industry participation levels, resulting in heightened performance. However, intense competition from big-box retailers and e-commerce has pressured the industry, as competitors can offer a wider selection of substitute products at lower prices and in a convenient one-stop location. Direct sellers have embraced innovative sales strategies and digital platforms to maintain growth. Direct selling revenue is expected to climb at a CAGR of 5.0% to $75.2 billion through the end of 2025, including growth of 2.3% in 2025 alone. Profit will also improve as rising per capita disposable income levels improve spending on high-priced goods. Direct-selling companies have relatively low start-up costs and some unemployed or underemployed Americans establish direct-selling businesses as a means of income. As the unemployment rate fluctuated but ultimately climbed in recent years, more enterprises entered the industry. As demand and direct sellers' revenue rose, more businesses entered the industry to use it as a flexible, low-commitment way to earn supplemental income. The health and wellness segment has boomed, with consumers seeking natural and sustainable products. This shift has fueled sales of nutritional supplements and skincare products. Direct sellers have harnessed social media to reach wider audiences, creating personal connections that resonate with consumers. Positive economic trends, like rising consumer confidence and spending, will contribute to rising revenue for direct-selling companies in the coming years. However, rising incomes and consumer spending will also lead many consumers to shop at substitute industries, like mass retailers and online competitors. As e-commerce continues to expand, direct sellers will further integrate digital tools and platforms to enhance customer engagement and streamline sales processes. Artificial intelligence and data analytics will enable companies to fine-tune marketing strategies, personalize shopping experiences and optimize inventory management. Sustainability will continue to be a critical focus, with consumers demanding greater transparency and environmentally friendly practices. Regulatory scrutiny remains a wildcard, as the industry must navigate potential challenges to ensure ethical practices and the protection of both consumers and sellers. Revenue is expected to expand at a CAGR of 3.0% to $87.0 billion through the end of 2030.

Child morbidity in the past seven days and dietary diversity status of children.