Many policies and programs aim to bring nutritious diets within reach of the poor. This paper uses retail prices and nutrient composition for 671 foods and beverages to compute the daily cost of essential nutrients required for an active and healthy life in 177 countries around the world. We compare this minimum cost of nutrient adequacy with the subsistence cost of dietary energy and per-capita spending on all goods and services, to identify stylized facts about how diet cost and affordability relate to economic development and nutrition outcomes. On average, the most affordable nutrient adequate diet exceeds the cost of adequate energy by a factor of 2.66, costing US$1.35 per day to meet median requirements of healthy adult women in 2011. Affordability is lowest in Sub-Saharan Africa. The sensitivity of diet costs to each requirement reveals the high cost of staying within acceptable macronutrient ranges, particularly the upper limit for carbohydrates. Among micronutrients, total diet costs are most sensitive to requirements for calcium as well as vitamins A, C, E, B12, folate and riboflavin. On average, about 5% of dietary energy in the least-cost nutrient adequate diets is derived from animal source foods, with small quantities of meat and fish. Over 70% of all animal products in least-cost diets is eggs and dairy, but only in upper-middle and high-income countries. In lower income countries where egg and dairy prices are significantly higher, they are replaced by larger volumes of vegetal foods. When controlling for national income, diet costs are most significantly correlated with rural travel times and rural electrification. These data suggest opportunities for targeted policies and programs that reduce market prices and the cost of nutritious diets, while improving affordability through nutrition assistance, safety nets and higher earnings among low-income households. MultiplThis study uses worldwide retail prices and nutrient composition data to identify the most affordable combination of foods and beverages needed to meet requirements in 2011, and thereby quantify whether and how national food systems bring nutrient adequate diets within reach of the poor. Previous analyses of food prices for policy analysis typically use farmgate or wholesale prices of a few bulk commodities to address farm income , or use First, we update existing methods for measuring the cost of nutritious diets, adding macronutrient balance and upper levels as well as minimum requirements for 21 essential nutrients needed for long-term health . PreviouSecond, we use the cost of nutrient adequacy to identify a series of stylized facts about global food systems, using data visualizations and regression results to examine similarities and differences in least-cost diets across countries. We map which food groups deliver which nutrients, and quantify the sensitivity of diet costs to each requirement. This whole-of-the-diet approach to nutrient adequacy is particularly important for policy interventions in food systems, providing a framework that links agricultural supply and commodity markets to the retail items that could meet each nutrient need at least cost. Our focus on individual nutrients complements the food group approach of previous global analyses e.g. , and ourThird, we use cross-country regressions to explore how structural factors relate to variation in the cost of nutrient adequacy, and how diet costs relate to nutrition outcomes. We hypothesize that retail costs depend on the efficiency of value chains and food markets, including factors such as rural travel times and rural electrification, urbanization and service sector development as well as trade restrictions and other interventions. We also test whether each country’s cost of nutrient adequacy is associated with their prevalence of undernutrition or diet-related obesity and non-communicable disease. Previous work along these lines has focused on individual foods e.g. , which mWe conclude with the implications of our results for food policies and programs, social protection and poverty alleviation. Food policies in developing countries have historically focused on farm income and lowering the cost of starchy staples needed for daily energy, rather than the diverse diets needed for lifelong health . Our wor2To compare the cost of a nutritious diet around the world, we use retail prices of the least expensive foods available in each location that meet estimated requirements for a median healthy woman of reproductive age. This builds on the concept of least-cost diets pioneered by The use of least-cost diets to measure a country’s food environment over time was pioneered by To address cross-country differences in access to nutritious foods, our principal measure is the Cost of Nutrient Adequacy (CoNA), defined as the minimum cost of foods that meet all known requirements for essential nutrients and dietary energy for a representative person. We compare this to the least-cost starchy staple providing just enough daily energy, which we call the Cost of Caloric Adequacy (CoCA). To measure CoNA, we use the price of each food and its nutrient content relative to lower bounds and upper limits needed for daily energy and long-term health:Subject to:thj nutrient in food i is denoted ija, which multiplied by its quantity consumed (iq) must meet estimated average requirements (EAR) for each nutrient j, while remaining below upper levels (UL) for micronutrients and within a range for macronutrients determined by acceptable macronutrient distribution ranges (lowerAMDR and upperAMDR), at lowest total cost given all prices (ip) within the further constraint of overall energy needs (E). Macronutrient ranges are defined as percentages of daily energy needs, given the energy density (je) of protein and carbohydrates which is 4 kcal per gram, and of lipids which is 9 kcal per gram. Solving this system of equations with all foods available at each time and place provides a lower bound on the cost of meeting all nutrient constraints, which we contrast with the cost of using only starchy staples to meet the daily energy constraint  in Eq. In this notation, the quantity of the For both CoNA and CoCA we report the foods needed in each country to meet nutritional needs at lowest cost. A key feature of our approach is to constrain nutritious diets to meet not only the EARs needed to avoid undernutrition, but also a balanced diet in terms of the three macronutrients through the AMDR, and upper bounds on micronutrients for which excess intake could be harmful. The resulting diets will differ from actual consumption patterns, which often fall below or above required levels of each nutrient as described for example in Focusing on nutrient adequacy is helpful in part to guide interventions, using information such as the sensitivity of least cost diets in each location to a change in requirements for each nutrient. That sensitivity is known as the shadow price of each constraint:jSP is the shadow price of each requirement for nutrient j or required total energy e, computed as SP’, defined as the increment of cost in dollars per day when each constraint is altered by 1%:here Solving for the least cost diet reduces shadow prices to zero for constraints that are not binding, and identifies the change in total cost if the binding requirements were to change by a small amount. If each food had only one nutrient, only lower-bound constraints would be binding, and all shadow prices would be the cost per unit of that nutrient from its most cost-effective source. Real foods have many nutrients, and reaching the lower bound for some may imply exceeding the upper bound for others. In certain settings the available foods may not be able to meet all constraints at once, for example at some times and places in rural Malawi , but theCalculations for all equations were completed in RStudio (version 1.2.5042) and resulting index values exported to Stata 15, RStudio or Excel for visualization purposes, with model code and data for replication posted online at the project website referenced in this paper’s acknowledgements.3Our food price data comes from the World Bank’s International Comparison Program (ICP), an initiative associated with the United Nations Statistical Commission to compare price levels and living standards across countries . The manOur final analytical dataset consists of 671 items matched to their nutrient composition using the Beyond the price and nutrient composition of available foods, a third kind of data needed to calculate CoNA and CoCA are nutrient requirements. For that we use updated DRI values from the U.S. After identifying the least-cost set of foods needed to reach nutrient adequacy in each country, this study then aims to establish stylized facts about how that cost of nutrient adequacy relates to national income and other characteristics of a country's development path. For this we draw on the World Development Indicators database compiled by the To test the specific hypotheses described in our motivation, the variables we use are gross national income (GNI) per capita, measured in US dollars at PPP prices in 2011, and four indicators for each of our principal hypotheses: urbanization, defined here as the share of the population living in urban areas as defined by national authorities, from 2 or higher, and for stunting rates we use the The final aim of this study is to examine associations between the least-cost diets of nutritious diets and actual food consumption, anthropometric outcomes and each country’s prevalence of micronutrient deficiencies. We contrast the composition of least-cost diets with each country’s national average food consumption from the FAO’s food balance sheets in the reference year , and als44.14.1.14.2To describe patterns in diet costs by level of national income, we use non-parametric locally weighted scatterplot smoothing (LOWESS) regressions to show local means of all countries at each income level. 4.3The composition of least-cost diets for nutrient adequacy in countries at each income level and geographic region are shown in The nutrients whose requirements most influence the affordability of nutritious diets are listed in 4.4Least-cost diets use available foods to meet nutritional criteria without reference to actual consumption, so the link between them and a population’s food choices reveals how tastes and preference relate to nutrient adequacy. At very low income levels people may be unable to afford nutrient adequacy even if they wanted it, while higher income people may not need to consume the least-cost sources of each nutrient. Furthermore, people at any income level might not know what nutrients are in each food, or what are their personal nutrient requirements. The contrast in calorie shares between least cost diets and food balance sheets is shown in 4.54.5.1The patterns shown in worldP), plus or minus any taxes, tariffs or transport margins denoted t, separating the quantity consumed (consQ) at each location from its quantity produced (prodQ). The bottom row shows the mechanisms that drive consumption, production and price of location-specific services and items that are highly perishable, bulky or fragile for long-distance trade. For those foods, the bottom row of Q) depends on the cost of transactions (t) between producers who receive prodP and local retail prices (Pretail) which may be high (Panel C) or low (Panel D).The central hypothesis motivating our work is that systemic factors in food production and distribution, including differences in post-harvest food systems, play an important role in the retail cost of a nutritious diet. The economic principles behind this hypothesis are illustrated in Nutritious diets involve a combination of items whose overall cost per day depends on different combinations of the market forces shown in 1.A larger service sector, offering more horizontal competition but also more vertical integration in post-harvest handling across markets;2.Greater urbanization, which concentrates consumers in space and allows for scale economies in farm-to-market supply chains;3.Easier rural transportation and access to electricity, thereby improving the efficiency of transport and storage from farm to market; and4.Easier access to international markets, including lower import tariffs, for tradable items that enter local food systems.These four hypotheses predict stylized facts about the retail prices shown in rreg command in STATA v15, which limits the influence of outliers) to examine associations between the cost and affordability of nutritious diets and key predictor variables that are summarized in Appendix Table A8. We present regression results for three outcome variables: log of CoNA, log of CoNA as a share of household food expenditure, and log of CoNA as a share of all household expenditure. Our regression models control for national income, population and region fixed effects to absorb the differences in agroecology, culture and data-collection systems associated with ICP regions, while are main transformation indicators reflect urbanization, travel times to cities, electrificationWith this foundation, we run robust regressions , we extend these results to address the potential effects of agricultural trade policies. Nominal rates of protection were available for 54 of the 136 countries included in 4.5.2The last aim of this study is to describe the relationship of diet costs with nutrition outcomes and dietary intake at the national level. Since we have a large number of variables, regression results are provided in the annex of supplemental information, describing links with anthropometric outcomes , symptoms of malnutrition , and estimated intake of eight specific dietary risk factors .To visualize the relationship of diet costs with nutrition outcomes allowing for variation in functional forms, we used semi-parametric regressions reported in Figs. A5-A7. These compare the association of each outcome with our two metrics that do not require currency conversion, namely affordability of CoNA as a share of all household expenditure (in log form) and the CoNA premium as a multiple of CoCA. In countries where nutritious diets are least affordable, we observe more prevalence of stunting and a smaller prevalence of obesity, as well as more prevalence of anemia, vitamin A deficiency, and zinc deficiency. This relationship holds only for affordability as a share of household expenditure, revealing that cross-country variation in diet costs relative to income is much larger and more significant than variation in the nutrient premium relative to starchy staples. Parametric tests of the link between affordability and nutrition outcomes is reported in Table A13, showing significance only for adult women’s anemia prevalence and not for the other outcomes, after controlling for a cubic function of GNI per capita, urbanization and sanitation as well as indicator variables for geographic region. We then used similar regressions to describe the relationship between affordability, again measured as CoNA’s share of average household expenditure (in log form) with a variety of controls such as a cubic function of GNI per capita, urbanization, rural travel times and rural electrification. Those show significance for 3 of the 8 dietary factors , whereas the others are significantly correlated only with the control variables.5This study uses nutrient composition and retail prices of available items to describe food systems in nutritional terms, identifying stylized facts about food prices, the cost of meeting all nutrient requirements, the sensitivity of diet costs to variation in each individual nutrient, relationships between least-cost diets and food consumption patterns, and links between diet costs and other aspects of national food systems such as rural electrification as well as nutrition outcomes.5.1We use a single, nationally representative average set of prices to obtain a single diet cost for each country, whose relevance to any particular question is limited by our data and methods.First, the standardization imposed by the IPC provides a transparent method with which to compare countries, but international lists may omit the lowest-cost foods use by specific populations, and national average prices omit a country’s lowest-cost marketplaces or other ways of acquiring food such as donations or self-provisioning. The timing of observation also matters, as 2011 was an unusually high-priced year for many internationally traded commodities, and using a single price omits seasonality and fluctuations that allow people to substitute between foods over time . Future Second, our focus on international comparisons also leads us to select a single set of nutrient requirements, notably EARs for a representative adult woman of reproductive age which aims to meet median requirements in a healthy population. In related work we explore variation in needs around that benchmark , and addA final limitation of our study concerns the focus on affordability. Counting only the most affordable items to meet requirements helps identify substitutions that improve cost-effectiveness, but selecting on extreme values makes least-cost diets more vulnerable to measurement error than methods that use a weighted average of all foods. Ongoing research aims to overcome these limitations with improved data and measurement methods, in partnership with national statistical services and international development agencies.5.2Our primary finding is that nutrient adequacy remains out of reach for the world’s poorest people. It costs an average of $1.35 per day in 2011 purchasing power parity terms, more than twice the cost of daily subsistence from a starchy staple which averages $0.57 per day . In the Our second finding is substantial variation among countries at each level of national income and within geographic regions. Most differences among income groups and regions are not statistically significant , with thA third finding concerns sensitivity of dietary costs to energy and nutrient constraints. For example, each one percent increase in daily energy needs would cost an average of 0.6 cents per day , which aFinally, we show that differences in least-cost diets could potentially be explained with the standard economic models used to address price formation and food choice . These m5.3Our findings have four important implications for nutrition-sensitive food policies.First, we demonstrate the value of using retail prices, nutrient composition data and least-cost diets to quantify food systems in nutritional terms, identifying how human requirements can be met in the most affordable way. Previous use of food prices to guide policy has long focused on individual foods especially agricultural commodities to address farm income and food choice, or analysis of all retail prices in proportion to expenditure shares to measure overall inflation. Calculating the cost of diets chosen to meet nutritional targets helps guide intervention to the most important populations, foods and nutrients, for example the need to lower the cost of low-carbohydrate foods to stay within average macronutrient distribution ranges, and opportunities for lower-priced eggs and dairy to improve affordability as suggested by Second, we confirm earlier findings that nutrient-adequate diets are currently out of reach for the poorest, reinforcing results such as Third, while targeted nutrition-sensitive interventions and safety nets are important now, large and sustained improvements in the long run depends on higher earnings among low-income households. Systemic linkages between international trade, migration and urbanization, agricultural production and rural demography provide a variety of mechanisms to promote pro-poor economic growth beyond what can be discerned from household surveys, calling for modelling evidence on the poverty impacts of specific agri-food investments and policies such as Finally, to inform food system policies and programs, our findings reveal the usefulness of tracking the nutritional value and prices of available foods at each time and place. The cost of nutritious diets has already been integrated into policy dashboards  and offiYan Bai: Data curation, Investigation, Visualization, Writing - original draft. Robel Alemu: Data curation, Investigation, Visualization, Writing - original draft. Steven A. Block: Conceptualization, Methodology, Investigation. Derek Headey: Resources, Conceptualization, Methodology, Investigation, Funding acquisition, Writing - review & editing. William A. Masters: Resources, Conceptualization, Methodology, Investigation, Funding acquisition, Writing - review & editing.


This presentation will describe the legislative charge of two advisory councils established in 2018 through the Recognize, Assist, Include, Support and Engage Family Caregivers Act and the Supporting Grandparents Raising Grandchildren Act to support working family caregivers. The advisory councils are charged with 1) providing recommendations on effective models of family caregiving 2) improving coordination across federal government programs; and 3) identifying, coordinating, and promoting information, resources, and 4) best practices for working grandparents raising grandchildren, while maintaining their own physical, mental, and emotional well-being. The experiences of working family caregivers will be the foundation for these recommendations that are being developed over the next two years to present to Congress. These recommendations will support the development and execution of a national family caregiving strategy.


The digitalization of health and medicine and the growing availability of electronic health records (EHRs) has encouraged healthcare professionals and clinical researchers to adopt cutting-edge methodologies in the realms of artificial intelligence (AI) and big data analytics to exploit existing large medical databases. In Hospital and Health System pharmacies, the application of natural language processing (NLP) and machine learning to access and analyze the unstructured, free-text information captured in millions of EHRs  may become an essential tool to improve patient care and perform real-time evaluations of the efficacy, safety, and comparative effectiveness of available drugs. This approach has an enormous potential to support share-risk agreements and guide decision-making in pharmacy and therapeutics (P&T) Committees. Healthcare settings gather and store large digital sets of patient data resulting from routine medical examinations, prescriptions, genome sequencing, laboratory testing, and administrative claims , 2; mostDue to the vast and heterogeneous nature of the digital data sets currently generated in health care settings, classical computing and research methods are no longer suited to handle and analyze this information. In response to these limitations, recent advancements in the realms of artificial intelligence (AI), most notably machine learning and natural language processing (NLP) have markedly improved the extraction, organization, and analysis of large amounts of clinical data regardless of their structure and linguistic complexity. However, despite these contributions, extra efforts are needed towards implementing these methods in the context of Hospital Pharmacy.The analysis of massive amounts of data generated in Hospital Pharmacy settings  has enorThe term ‘big data’ is commonly used to refer to datasets that are too large and heterogeneous to be stored and analyzed using traditional research methods , 7. In HEHRs are widely and growingly available. By 2015, more than 94% of hospitals in the US already had a certified EHR system [R system ; this trThe information captured in EHRs is rich and heterogeneous. EHRs contain information regarding prescriptions, treatment outcomes, sociodemographic characteristics, previous comorbidities, test results, differential diagnosis, procedures, genetic background, signs and symptoms, family medical history, and lifestyle habits [e habits .EHRs contain longitudinal data at the single-patient level. As records are updated over time, they are suitable to address clinical questions that require regular patient follow-up and to predict outcomes at different stages of the patient’s journey [ journey .The information captured in EHRs is scalable. Regardless of the original structure and format, data contained in EHRs can be aggregated across patients and healthcare sites, and can be integrated with other valuable sources of health-related information such as genetic databanks, nutrition or health apps, census, and social media [al media .To understand the full potential of the data captured in EHRs in assisting clinical research and practice, we must consider the following:Routinely acquired medical data can be classified into structured and unstructured data. Whereas structured data refer to information that is stored in a consistent, organized manner and is typically reported using standard units and ranges , unstructured data are devoid of a clear organization and precision  . CrucialThe free-text narratives jotted down by health professionals  in EHRsThe extraction of written text from EHRs is achieved through a combination of NLP and machine learning techniques. NLP is a field that borrows concepts and techniques from linguistics, computer science, and engineering to process naturally occurring language , whereas machine learning models enable computers to extract patterns in datasets and draw conclusions on their own. Deep learning classification methods, which feed and learn from large amounts of data in EHRs, are used to teach the system to describe medical entities in terms of negative, speculative, or affirmative clinical statements. The extracted and processed information is then structured with artificial neural networks. Finally, analytical tools such as random forests, decision trees, and logistic regression enable the construction and visualization of predictive models derived from EHR data.Extracting clinical information from free text is certainly challenging . The maiThe effective exploitation of big data is thought to advance healthcare delivery by promoting the following actions , 11.The costs and time associated with manual data collection largely surpass those associated with the use of automatized tools. The combination of machine learning and NLP to explore EHRs has offered novel descriptive and predictive insights into clinical populations , patientBy integrating patients’ ‘-omics’ data  with the information captured in EHRs, the Electronic Medical Records and Genomics (eMERGE) Network  has alreClinical information in EHRs can be exploited to perform real-time predictive analyses to optimize resource use and management in terms of cost–benefit analysis. Relevant predictive outcomes achieved via analysis of EHR data include identification of risk factors associated to high-cost patients, readmissions, triage, and decompensation .® technology now allows researchers to rapidly answer clinical questions in real time using patient-centered data [To move the field forward, we believe that the following three aspects should be considered in NLP research using EHRs. First, these studies always benefit from a multicentric, multilanguage methodology; unlike single-center studies, this approach enables access to even larger datasets (in turn generating more accurate predictive models), inclusion of more diverse study populations, and the possibility of comparing results across centers and regions. Second, the output of a clinical NLP system should always validated against a corpus of expert-reviewed clinical notes in terms of sensitivity and recall of extracted medical concepts . Finallyred data , 18, 19.Although system pharmacies may have lagged behind in their use of AI, the application of NLP and machine learning to extract and analyze unstructured information in EHRs has already provided valuable insights into pharmacovigilance, including identification of drug-related adverse events that were previously unknown, identification of discrepancies in patients’ medications and errors in prescriptions based on comorbidities and risk factors, and adherence to treatment monitoring .EHRs also contain relevant drug cost data that can be used to evaluate treatment options and palliate the financial burden of patients . The reaAltogether, the application of NLP and machine learning to analyze patients’ EHRs may become an essential tool to perform real-time evaluations of the efficacy, safety, and comparative effectiveness of available drugs (including those currently in post-marketing surveillance). This may in turn facilitate share-risk agreements and assist decision-making in pharmacy and therapeutics (P&T) Committees  Fig. 2)2).As integral elements of the Health System, hospital pharmacists are ethically obliged to document the care they provide in patients’ health records. However, there are some discrepancies and controversies around (a) standard guidelines for the recording and documentation of hospital pharmacists’ activities, (b) reporting ‘near miss’ and other interventions regarding potential risks for adverse events that have been successfully intercepted, and (c) lack of standardization of EHR data across hospital sites .A recurrent concern with the secondary use of EHRs and is data privacy and how it affects data sharing. To this end, tools and procedures have been created to de-identify  EHRs. Once EHRs are de-identified, they can be used for research and clinical purposes since they are no longer subject to country- and region-specific privacy regulations for identifiable patient information. However, as the potential for data aggregation and linkage across data sources grows exponentially, doubts loom large for de-identification procedures and their actual efficiency.Though often based on misconceptions around data privacy and security, existing concerns by policy makers, hospital managers, and local regulatory agencies have limited data availability and sharing among professionals and clinical researchers. The application of big data analytics in healthcare is an unescapable reality in need of solid regulations that facilitate data availability and sharing. Precisely, big data relies on the principle of interoperability, which translates into the ability to exchange data across organizational boundaries in a timely fashion.The clinical data captured in EHRs provide RWE and can be aggregated on a large scale to describe patient populations and offer predictive insights into disease prognosis, treatment responses, and resource use in healthcare settings. Using available tools in the fields of machine learning and NLP, clinical pharmacists can exploit the vast amounts of clinical data collected in their daily practice.Realizing that these large datasets are important resources to improve healthcare rather than mere byproducts of its delivery is the first step to unlock the full potential of the data generated in hospital pharmacies, improve the needed visibility of services provided and recognition of Hospital Pharmacy professionals , and abo


Grandparents raising grandchildren build strong foundations for their grandchildren. Despite grandparents’ significant contributions to their grandchildren’s future and society in general, there is a limited understanding of the unique needs and service utilization of grandparents raising grandchildren in various contexts. This symposium is focused on the needs of and services for the grandparent population at the regional, national, and international levels. Stucki will present findings from an examination of types and locally available services for grandparents raising grandchildren in Appalachia by sub-region. Musil and colleagues will discuss the service need utilization and unmet service needs of a nationwide sample of 284 grandmothers living with/ raising grandchildren and the relationships between service use/need and resilience, resourcefulness, perceived stress, reward, and appraisals of their current living environment for themselves and their grandchildren. Lastly, Lee will describe research findings from her comparative transnational research on needs and experiences of grandparents raising grandchildren in Malawi (n=29), South Korea (n=23), and the U.S. (n=23). Unique needs and cultural interpretation of intergenerational caregiving in each country will be presented. The symposium discussion will address diverse needs of grandparents raising grandchildren and strategies to meet those needs at regional, national, and international levels. Grandparents as Caregivers Interest Group Sponsored Symposium.


Function and Behavior Focused Care (FBFC) is a two tiered intervention based on the Social Ecological Model and Social Cognitive Theory that focuses on training long term care staff to cue, model, and assist residents with dementia to engage in physical activity and perform functional tasks. FBFC was implemented by a Research Nurse and a facility based Champion for 12 months and involved a four step approach: (1) Environment and Policy Assessments; (2) Staff Education; (3) Establishing Resident Care Plans; and (4) Mentoring and Motivating of Staff and Residents. Facilitators of implementation included administrative support, an identified champion, and supportive environments and policies. Challenges and barriers included the fear of falls, fear of behavioral symptom exacerbation, competing priorities, and lack of facility specific goals. Innovative approaches to overcoming these barriers will be reviewed.


Cognitive, behavioral and social dimensions also demonstrate increasing heterogeneity with aging. For example, a longitudinal study of over 1,000 clergy revealed increasing heterogeneity in cognitive function and rate of decline with aging. Moreover, studies of individuals with probable Alzheimer’s disease have shown heterogeneity in terms of clinical manifestations and rates of cognitive decline. Older adults also demonstrate greater heterogeneity in mood, anxiety, and the nature and patterns of symptoms over time. Heterogeneity of overall health status increases with aging, as does reported quality of life. Health and Retirement Study (HRS) data have shown that low socioeconomic status or being an underrepresented minority are both associated with greater intra-individual variability in health status in old age, with greatest differences seen in Hispanics. Finally, early life adversity can contribute to heterogeneity of multidimensional health trajectories even in late life.


Environmental exposures have profound effects on health and disease. While public repositories exist for a variety of exposures data, these are generally difficult to access, navigate, and interpret. We describe the research, development, and application of three open application programming interfaces (APIs) that support access to usable, nationwide, exposures data from three public repositories: airborne pollutant estimates from the US Environmental Protection Agency; roadway data from the US Department of Transportation; and socio-environmental exposures from the US Census Bureau’s American Community Survey. Three open APIs were successfully developed, deployed, and tested using random latitude/longitude values and time periods as input parameters. After confirming the accuracy of the data, we used the APIs to extract exposures data on 2550 participants from a cohort within the Environmental Polymorphisms Registry (EPR) at the National Institute of Environmental Health Sciences, and we successfully linked the exposure estimates with participant-level data derived from the EPR. We then conducted an exploratory, proof-of-concept analysis of the integrated data for a subset of participants with self-reported asthma and largely replicated our prior findings on the impact of select exposures and demographic factors on asthma exacerbations. Together, the three open exposures APIs provide a valuable resource, with application across environmental and public health fields. Environmental exposures profoundly impact health and disease, both independently and by virtue of established  interactions with the genome . A varieWhile the challenges presented by open environmental data have been well considered, less considered has been the need to move beyond epidemiological studies that demonstrate correlations in patterns of environmental exposures, such as airborne pollutant levels and population-based health outcomes. For example, many studies have demonstrated seasonal patterns in regional levels of pollen and correlation with emergency department visits for respiratory issues ,19. OtheThe Biomedical Data Translator (“Translator”) program, funded by the National Center for Advancing Translational Sciences, aims to develop an open informatics platform to support the integration and semantic harmonization of disparate data sources and types and to enable interrogation of those data to inform a mechanistic characterization of health and disease ,22,23. AHerein, we describe the research and development of three open application programming interfaces (APIs) that were motivated by our work on ICEES and a need to provide open access to individual-level environmental exposures data independent of ICEES, for application in environmental health research and epidemiological studies. The open availability of usable environmental exposures data will allow our team and others to identify mechanisms based on individual-level exposures and quantitatively assess their contribution to health outcomes. Indeed, the Translator architecture is based on distributed, open APIs to support nimble translational science, promote a culture of open team science, and facilitate translational science and collaboration beyond the Translator program ,22,23. S2.5). The data set sources fused by the downscaler originate from air quality monitoring data from the National Air Monitoring Stations and State or Local Air Monitoring Stations (NAMS/SLAMS) and numerical outputs from the Models-3/Community Multiscale Air Quality (CMAQ) modeling system [n = 72,282) encompassed by the 12-km CMAQ modeling domain throughout the contiguous United States. Additional information on the downscaler method and its inputs can be found at the US EPA’s Remote Sensing Information Gateway [Airborne pollutant estimates were derived from the US EPA’s data fusion method, namely, the Bayesian space-time “downscaler model” . The datg system . The dowg system . UltimatRoadway data were collected from GIS shapefiles provided by the United States Federal Highway Administration (FHWA)’s Highway Performance Monitoring System (HPMS). The HPMS Field Manual contains a detailed explanation of methods and data fields . ResultsThe HPMS data included values for road type, AADT, and speed limit for most, but not all, road segments. For HPMS road segments that did not contain road type, that information was obtained using TIGER  data from the US Census Bureau . For HPMn = 217,739). Block Groups, the smallest geographic area for which ACS data are released, generally contain data on 600 to 3000 people.  US Census Bureau geography, even Block Groups, may cross municipal lines and may include both urbanized and rural areas. Because population size is a large factor in determining US Census Bureau geography, some Block Groups may cover an extensive rural or wilderness area, while others are quite compact. Because the US Census Bureau is mandated to preserve the confidentiality of individual data, and because the variables of interest may not apply to all Block Groups, missing data can occur in a small number of situations [Socio-environmental data were extracted from two releases of ACS data for the US (50 states plus the District of Columbia), for the 2007–2011 and 2012–2016 survey periods. The ACS is the US Census Bureau’s largest household survey . Approxituations . In partFor the Translator Socio-environmental Exposures API, a range of variables relevant to environmental health research were extracted. Standard errors for the estimates were calculated whenever replicates were available. The variables that were extracted included: (1) population count; (2) residential density; (3) median household income; (4) proportion of the population who are not non-Hispanic white; (5) proportion of adults ≥ 25 years of age with a high school degree as highest maximum level of education; (6) proportion of households without an automobile; (7) proportion of the population without health insurance; (8) proportion of the population ≥ 5 years of age speaking a language other than English at home; (9) two alternative measures of the proportion of teenagers who are high school dropouts; (10) two alternative measures of the proportion of households receiving public assistance; (11) proportion of adult males aged 16–64 years who worked less than 26 weeks during the previous year; (12) proportion of households headed by a female (no spouse); (13) proportion of households headed by a female with a biological child; (14) proportion of households headed by a female with any child; (15) proportion of households receiving social security income; (16) proportion of households receiving at least partial governmental assistance; (17) proportion of adult males with little work; and (18) proportion of adults who are not employed. In addition, each Block Group was classified as being in a rural or urban area.The Translator Exposures APIs were created using the Swagger OpenAPI platform. The Swagger toolset facilitates the automatic generation of an OpenAPI document (YAML format). The Swagger Codegen tool is used to create boilerplate source code for implementation of the API. The APIs described herein were created using the Python/Flask code generator. The Python/Flask API template was then fleshed out with code logic to process API input parameters and form appropriate SQL queries to return requested exposures data.A PostgreSQL database with PostGIS extension was used for each API. The sqlacodegen tool was used to generate database schema models and class definitions that are used by the SQLAlchemy and GeoAlchemy Python packages. SQLAlchemy and GeoAlchemy were used in the API logic to perform database queries for calculating distances and other spatial relationships.SQL, Python, and bash scripts were created to load CSV and shapefile data into the database. Most of the exposures data sets were quite large and required the development of custom Python applications that implemented high-performance data manipulation libraries in order to load the data in a timely manner.The SQL databases for each API also required tuning to handle queries against large amounts of exposures data and calculate spatial relationships between data sets. Indexes were created for common query data relationships and geospatial proximity comparisons.The APIs were tested initially using random input latitude and longitude values and default settings for other input parameters. After verifying the output, we applied the APIs to the use case described below.Our application use case for research and development of the Translator Exposures APIs was a cohort of participants in the NIEHS EPR. The registry, which originated ~20 years ago and is comprised of ~20,000 study participants, is a resource dedicated to the advancement of the study of interactions between environmental and genetic determinants of health and disease. Study participants contributed survey-derived data on exposures and disease, in addition to biological samples . We aimed to use the Translator Exposures APIs to extract participant-level data on airborne pollutant exposures, roadway exposures, and socio-environmental exposures and then integrate the data with EPR data on a cohort created to foster understanding of the role of inflammatory processes in asthma and other conditions . For resAn exploratory analysis of the integrated data was conducted using summary statistics and Chi Square tests to examine the impact of select demographic and exposures variables. The variables that were selected were representative of each of the data sources . Importantly, they were also chosen to replicate and extend our prior work ,26,35,36The functionalities of the three Translator Exposures APIs are complementary. For each API, a latitude and longitude value are required input parameters. For the Airborne Pollutant Exposures API, users also must enter a start date and an end date, as well as a UTC time zone offset. For the Roadway Exposures API, a maximum distance (in meters) is required . For the Socio-environmental Exposures API, a survey period must be selected. In response to the input parameters, the services return the estimated environmental exposures for the indicated geospatial location and, when applicable, the time period of interest. The input file format is JSON, as is the output file format, and batch service requests are possible. Users may also enter input and receive output directly from the Swagger UIs.For all three APIs, results are provided for all available data types for a given API. We successfully used the three Translator Exposures APIs to extract data on 2550 study participants who were successfully geocoded from a total of 4130 total participants in the overall EPR cohort on inflammation and respiratory conditions . The inpn = 932 participants with a self-reported diagnosis of asthma, of which n = 923 participants had data on the primary endpoint measure of self-reported ED or urgent care visits for asthma (one or more in prior 12 months), and n = 879 of those had geocodes and therefore had exposures data available from the Translator Exposures APIs. We explored the impact of select demographic features and environmental exposures on asthma exacerbations, chosen again to facilitate comparison with our prior work: sex (from EPR); race (from EPR); history of smoking (from EPR); obesity (from EPR); exposure to PM2.5 (from Airborne Pollutant Exposures API) exposure to a major roadway or highway (from Roadway Exposures API); and estimated median household income exposure . Self-reported ED or urgent care visits for asthma (one or more in prior 12 months) (from EPR) were used as a proxy for asthma exacerbations and served as the primary endpoint measure.To facilitate comparison with prior EPR findings on participants with self-reported asthma  and resup < 0.10), African Americans versus non–African Americans (p < 0.0001), participants with a history of smoking versus those without a history of smoking (p < 0.10), obese participants versus non-obese participants (p < 0.001), and participants with low median household income versus those with higher household income (p < 0.001) . No relationship was found between participant self-report of ED or urgent care visits for asthma and exposure to major roadways or highways.We found that the proportion of participants reporting ED or urgent care visits for asthma was higher among females versus males (< 0.001) . A similWe developed three open Translator Exposures APIs in response to a need to openly expose usable, individual-level data on environmental exposures that are known to impact health and disease, and in an effort to facilitate research by our team and others in environmental health, epidemiology, and related fields. We successfully used the open APIs to extract exposures data on 100% of geocoded participants within an EPR cohort, and we integrated the exposures data with EPR data at the participant level. Importantly, we applied the data to a proof-of-concept asthma use case and demonstrated an association between asthma exacerbations, as measured by participant self-report of ED or urgent care visit for asthma, and sex, race, smoking history, obesity, median household income, and exposure to airborne particulate matter, thus largely supporting our hypothesis that the Translator Exposures APIs could be used to replicate our prior findings ,26,35,36While we were able to largely replicate our prior findings, certain key differences in both the methodological approach and the findings should be noted. First, we adopted a primary endpoint of self-reported ED or urgent care visits for asthma in the present study, whereas in our prior EPR study , we wereAnother difference between the present work and our prior work is that the association between asthma exacerbations and exposure to airborne particulate matter was not significant in the use case reported here, in contrast to our findings with ICEES ,25,26,36We also did not identify an association between exposure to major roadways or highways and asthma exacerbations in participants with self-reported asthma, in contrast to our prior EPR study , in whicThe present study, our prior studies ,26,35,362, NOx, SO2, and benzene), and we plan to make those data available via the Airborne Pollutant Exposures API. In addition, we plan to offer an option to conduct statistical calculations on the data  in order to better model exposure history. We also are exploring the possibility of creating an integrated Translator Exposures API that would expose the underlying integrated data via an interactive, spatiotemporal, nationwide visual map, in order to allow users to explore exposure clusters across the continental United States. In addition, we are expanding the demonstration use case on asthma to include additional exposure variables and more rigorous statistical analyses . Finally, we are developing new use cases and identifying new public exposures data to further demonstrate the utility of the open Translator Exposures APIs and increase our user base. In conclusion, we anticipate that the Translator Exposures APIs will have broad applicability across many fields within environmental research and public health.The findings reported here demonstrate the utility of the open Translator Exposures APIs and their application in a proof-of-concept use case, which largely supported our hypothesis. We plan to continue research and development of these open services and expand the available data and functionalities. For instance, we recently acquired and processed data on additional airborne pollutants (i.e., acetaldehyde, formaldehyde, CO, NO, NO


The goal of this review is to provide a broad overview of the biomechanical maturation and regulation of vertebrate cardiovascular (CV) morphogenesis and the evidence for mechanistic relationships between function and form relevant to the origins of congenital heart disease (CHD). The embryonic heart has been investigated for over a century, initially focusing on the chick embryo due to the opportunity to isolate and investigate myocardial electromechanical maturation, the ability to directly instrument and measure normal cardiac function, intervene to alter ventricular loading conditions, and then investigate changes in functional and structural maturation to deduce mechanism. The paradigm of “Develop and validate quantitative techniques, describe normal, perturb the system, describe abnormal, then deduce mechanisms” was taught to many young investigators by Dr. Edward B. Clark and then validated by a rapidly expanding number of teams dedicated to investigate CV morphogenesis, structure–function relationships, and pathogenic mechanisms of CHD. Pioneering studies using the chick embryo model rapidly expanded into a broad range of model systems, particularly the mouse and zebrafish, to investigate the interdependent genetic and biomechanical regulation of CV morphogenesis. Several central morphogenic themes have emerged. First, CV morphogenesis is inherently dependent upon the biomechanical forces that influence cell and tissue growth and remodeling. Second, embryonic CV systems dynamically adapt to changes in biomechanical loading conditions similar to mature systems. Third, biomechanical loading conditions dynamically impact and are regulated by genetic morphogenic systems. Fourth, advanced imaging techniques coupled with computational modeling provide novel insights to validate regulatory mechanisms. Finally, insights regarding the genetic and biomechanical regulation of CV morphogenesis and adaptation are relevant to current regenerative strategies for patients with CHD. The embryonic heart has been the subject of intense investigation for over a century. Initially, the chick embryo was the focus for academic investigation due to the opportunity to characterize morphogenesis via careful serial sections and histology ,5,6,7,8 Due to both the high morbidity and mortality associated with congenital heart disease (CHD) , determiIn the early 1980s, Dr. Edward Clark and Norman Hu launched a series of pioneering studies to quantify the normal functional maturation of the embryonic chick CV system using higher fidelity pressure-recording systems and the application of piezoelectric crystal recorded pulsed Doppler velocimetry followed by an expanded investigation of the consequences of altered metabolic and biochemical stressors as well as altered biomechanical loading on developing cardiac structure and function. These pioneering studies were then coupled to computational approaches to investigate cardiac biomechanics and the central paradigm that biomechanical forces regulate embryonic CV morphogenesis and are fundamental in the etiology of CHD has now been validated in multiple vertebrate species using the broadest range of investigative techniques.The development and validation of reproducible, quantitative experimental techniques was fundamental to the Clark and Hu approach to exploring embryonic structure–function relationships in the chick embryo. Chick embryo stages based on external morphology , which rThese normative data set the stage for a rapid expansion of studies that investigated the ability of the developing embryonic CV system to adapt to acute and chronic challenges ,62,63,64Quantifying adult myocardial performance using load-dependent and load independent paradigms was a central theme of the adult cardiology and CV bioengineering programs at the Johns Hopkins Hospital at the same time the Clark and Hu lab was exploring the regulation of embryonic CV structure–function relations. Pivotal papers defined CV function using pressure–volume (PV) relations ,84,85,86Applying a PV paradigm to the embryonic chick heart required the adaptation of available methods to quantify dynamic changes in cardiac dimensions during the simultaneous recording of ventricular pressure. Video microscopy of the beating embryonic heart ,22,91 waUsing a simple ellipsoid equation to convert area to volume, embryonic PV loops displayed an end-systolic pressure volume relationship and ventricular–vascular coupling typical of the mature circulation  and alsoA similar approach allowed for the description of passive and active atrioventricular (AV) coupling using simultaneous recordings of atrial and ventricular pressure , atrial Similar to the mature circulation, embryonic ventricular–vascular coupling is an essential attribute of developmental CV physiology and is evident from the onset of cardiac contraction in the chick embryo. Video microscopy of the embryonic conotruncal cushions during the cardiac cycle reveals that the cushions are separated at the end of ventricular ejection, yet there is no retrograde flow back into the ventricle during diastole at the intrinsic heart rate and blood volume . Volume th of the mature circulation  ,98 22,98Experiments utilizing VVL, CTB, LAL, or unilateral vitelline arterial ligation (VAL) to alter intracardiac and aortic arch mechanical loading also highlight the adaptive responses of the developing embryonic vasculature ,220,221.A transformative advance in our understanding of cardiac morphogenesis and the origins of CHD occurred following the observation that the ablation of small segments of neural crest at critical developmental windows resulted in a denervated avian heart  and, morExploration of CV functional maturation and the relationships between function, biomechanics, and morphogenesis in mammalian embryos began with the adaptation of hemodynamic measurement techniques developed for the exposed chick embryo with novel methods to ensure normal maternal oxygenation during sedation, normal embryo temperature, maternal oxygenation, and undisturbed maternal–embryo coupling  232,233233,234. The rapidly developing and transparent zebrafish embryo provides another unique and robust experimental model to test genetics–structure–function CV morphogenesis and adaptation paradigms ,260. SimUltrasound studies of the developing embryonic heart revealed a progressive increase in intracardiac and aortic velocities at morphologic stages similar to avian and murine embryos ,286,287.After more than a century of investigation of the relationships between structure and function during CV morphogenesis, much has been revealed from the macro-scale level of chamber dimensions and developed pressures and flows to the micro-scale level molecular regulation of cell lineage expansion, morphogenesis, and adaptation during normal heart morphogenesis and in response to altered trajectories ,59,288.


Nutrition-sensitive interventions such as homestead production of diverse, nutrient-rich foods, coupled with behavior change communication, may have positive effects on the nutritional status and health of rural households engaged in agriculture, particularly among women and young children. Engagement of agriculture and health extension workers in these communities may be an effective way of delivering nutrition-sensitive interventions given the dearth of trained health care providers in many developing countries. This study aims to assess the effects of integrated homestead food production, food consumption and women’s empowerment interventions using a multi-sectoral approach on women’s and child’s health and nutrition.This is a cluster-randomized community-based prospective study set in Rufiji district, a rural area in Eastern Tanzania. Ten randomly selected villages within the Rufiji Health and Demographic Surveillance Site (HDSS) in Eastern-Tanzania were paired and randomly assigned to the intervention or control arm. The Rufiji HDSS dataset was used to randomly sample households with women of reproductive age and children 6–36 months. The intervention includes provision of small agricultural inputs, garden training support, and nutrition and health counseling. This is delivered by community health workers and agriculture extension workers through home visits and farmer field schools. There are three time points for data collection: baseline, midline, and endline. Primary outcomes are women’s and children’s dietary diversity, maternal and child anemia status and growth .This integrated agriculture and nutrition intervention among rural farming households is a simple and innovative solution that may contribute to the reduction of undernutrition and disease burden among women and children in developing countries. Engaging already existing workforce in the community may facilitate sustainability of the intervention package.NCT03311698, Retrospectively registered on October 17, 2017.ClinicalTrials.gov  Maternal and child undernutrition remains one of the most challenging health problems in low and middle-income countries (LMICs) . TogetheInadequate dietary intake and poor weaning foods in young children, both of which are important causes of undernutrition, are still major problems in many LMICs. Daily meals often lack the diversity needed for supplying essential nutrients for normal growth and prevention of diseases , 5. OthePregnant and lactating women are among the most vulnerable groups to suffer from undernutrition and micronutrient deficiencies because they have physiologically higher nutrient requirements that are often not met . Women sSimilarly, infants and young children have high nutrient requirements due to rapid growth and development . During Investment in the agriculture sector in various countries in Africa has resulted in large increases in staple crops that are typically rich in carbohydrates but poor in micronutrient content . PromotiIn rural settings, where most households are engaged in smallholder farming, homestead food production (HFP) interventions offer one of the most promising pathways towards improving livelihoods, the diversity of agricultural outputs, food security, and maternal and child nutrition. Small-scale, nutrition-sensitive agricultural interventions equip smallholder farmers with the tools and skills to cultivate home gardens and raise livestock. Home gardens promote production of nutrient-rich fruits and vegetables that can grow well in local conditions. Household members, particularly women, are trained in cultivating the crops and raising livestock . HFP intFor maximum health and nutrition impact, HFP interventions should be coupled with behavior change communication campaigns that include nutrition and public health messages, especially among women. This will effectively improve knowledge on the importance of a healthy diet, dietary diversity, and best practices for food preparation, child feeding, and health seeking behavior . HoweverThis study evaluates the effects of integrated nutrition-sensitive interventions on the health and nutrition of children and women in rural Tanzania while exploring the incorporation of existing cadres of agricultural extension workers together with community health workers as a sustainable workforce for providing basic health and nutrition education in rural communities.The study is implemented in Rufiji district, Tanzania. Rufiji is one of six districts in the Coast Region of Tanzania, and has a population size of 217,274. It has 94 registered villages and 385 hamlets. The study is operated within the Rufiji Health and Demographic Surveillance Site (RHDSS) where about 20% of households have at least one child under the age of 3 years . The preThis study engages existing Agriculture Extension Workers (AEWs), Livestock Extension Workers (LEWs) and Community Health Workers (CHWs) to deliver the interventions. AEWs and LEWs are employed by the government to train and support farmers in agriculture and animal husbandry and best practices for increased productivity. CHWs are an existing cadre in many rural settings of Tanzania, but have no formal wages. The project therefore takes advantage of existing cadres of workers who are familiar with their communities.Typically, each AEW, LEW and CHW works in one village with approximately 400 households. They conduct regular home visits among all households to which they have been assigned providing basic health education, promoting preventive health care services, and supporting best practices in agriculture and livestock production. All three forms of home-based services; health, agriculture and livestock exist in all Rufiji villages..This is a 36-month long community-based prospective study carried out in 10 villages. A cluster-randomized design is used to evaluate the effects of the intervention. Data collection take place at three time points, at baseline, midline, and endline. Study activities are summarized in Fig. 2Eligibility criteria for households participating in the study are: (i) having a woman of reproductive age (18–49 years) and at least one child 6–36 months of age, (ii) having access to a plot of land or containers where vegetables could be grown, and (iii) provided informed consent.The unit of randomization is a village. Ten villages within the Rufiji HDSS were randomly selected and matched based on location, proximity to water, and population size. One village in each matched-pair was randomly assigned to receive the intervention (five villages) and the other to the control group (five villages).The Rufiji HDSS dataset was used in random sampling of households with at least one woman of reproductive age and at least one child 6–36 months of age in all study villages. We considered a proportional-based representation of households based village size in the random selection of households. Thus, a total of 1006 households were enrolled with a ratio of 1:1 in intervention and control villages  to be sufficient for pilot implementation of integrated nutrition-sensitive interventions but no formal sample size calculation was performed.Participating households in each study arm were screened, consented, and enrolled. Research assistants (RA) visited and screened sampled households for each study village. RAs introduced the study to the household member and verified participant’s inclusion criteria. For each eligible household, one woman and one child were enrolled in the study after providing informed consent. In case the household has more than one eligible member, simple random sampling technique (lottery method) within each household was used to select one woman. The same technique was used to select one child 6–36 months of age. RAs then proceeded with administration of electronic baseline data collection.Households in the intervention arm receive: (1) interventions to promote homestead food production and increase agricultural production and food diversity, (2) nutritional counselling, including prevention and management of child malnutrition with a focus on the first 1000 days, locally adapted instructions on the mix and quantity of food suitable for children ages 6–36 months, (3) a health-focused intervention, including information on micronutrient supplementation, integrated management of child illnesses, and safe water, sanitation and hygiene practices (WASH).Households enrolled in the intervention also receive agricultural inputs such as seeds, fertilizer, and watering cans. Local Agricultural Officers identified six varieties of crops  for the study and recommended specific varieties for each intervention village based on growing conditions and village preference. Each participating household in intervention villages receive three varieties of seeds at least three times during the study period.The behavior change component of the intervention includes information on cultivation of nutrient-rich crop varieties, best practices for home gardening, safe water, sanitation, and hygiene practices (WASH), breastfeeding and complementary feeding, dietary intake and nutrition, and other basic public health messages. AEWs, LEWs and CHWs are trained on topics related to cultivation of nutrient-rich crop varieties and provision of basic public health services to households. This includes encouraging and monitoring the consumption of iron and folate supplements during pregnancy; use of zinc oral rehydration solution for management of diarrhea; promotion of hygienic and sanitary practices at the household level, promotion of preventive health behaviors such as immunization for children; and referral of sick and malnourished children to local health centers for appropriate care.There are two delivery mechanisms for the intervention: farmer field schools and households visits. Household visits are conducted approximately every 2 weeks. Households receive separate visits from an AEW/LEW or CHW on a rotating schedule. Field schools are also held approximately every 2 weeks and attended by all three types of extension workers.In collaboration with village and hamlet leaders, AEWs identified one participating household farm in each of four to five hamlets per village to be used as Farmer Field Schools (FFS). Participants within a given hamlet were invited to partake in FFSs. Farmer field schools are open to the public as they also existed in these communities before the intervention took place. Households participating in the intervention are highly encouraged to attend by extension workers. The number of participants attending a FFS typically ranges from 10 to 15, determined by the number of enrolled households per hamlet. Collaboration with village and hamlet leaders ensure that the selected individuals are valued and trusted community members interested in leading fellow farmers. The FFS is designed to (i) illustrate the benefits of innovative agricultural practices to the community, (ii) improve community knowledge about nutritious crops available in the district, (iii) reinforce messages provided by during household visits, (iv) offer a forum for discussion and collaboration between farmers, and (v) empower women with knowledge and skills for increased decision-making at the household level. Model farmers use the demonstration farm as a classroom and hold organized “classes” with other women farmers in their hamlet to illustrate how to become a more productive farmer. The design of FFS enables AEWs to maintain close connections with model farmers, providing them with information about new agricultural technologies and practices as they become available in the district.AEWs, LEWs and CHWs implement a system of household visits with all enrolled smallholder farming households in the community. All cadres receive basic nutritional, agricultural and maternal and child health training to ensure messages act to reinforce and complement each other. The transmission of the same messages through multiple channels aims to increase their credibility and the probability of longer term adoption of recommended behavioral change. One-on-one meetings with AEWs, LEWs and CHWs through home visit are held biweekly for the purposes of providing maximum amount of information in a feasible manner. Household visits and FFSs are staggered so that farmers receiving the intervention are continually exposed to information through one of these channels.Extension workers are continuously monitored by the study field manager to sure that the above two main approaches are delivered routinely to participants as outlined in an intervention manual developed by the research team in collaboration with local officials. A list of randomly selected participating households in each intervention village is generated every week and visited by the field manager to conduct monitoring and evaluation of the intervention.Data are collected by trained interviewers using surveys administered with electronic tablets at three time points, once at baseline, once 12 months after intervention implementation, and once approximately 36 months after intervention implementation. Agricultural practices and primary outcomes are also measured in a random sample of non-participant households in the intervention villages which may assist to inform spillover effect or spread of the intervention. Anthropometric measurements of women and young children take place at three time points. Hemoglobin for women and young children is measured among a subset of the study population at these time points.Questionnaires developed by the research team include socio-demographic and economic data, self-reported recent history of illness, infant and young child feeding, dietary intake, physical activity, agriculture production, food security, and women’s empowerment. Agriculture production data include the area of specified crop production, production quantities, sales of produce and income realized, purchase and use of agriculture inputs, and other operational expenses. Dietary intake is assessed using a food frequency questionnaire.Qualitative methods are used to assess the reach of the intervention, opportunities and challenges. Focus group discussions will be conducted with study participants and non-study participants in intervention villages. Key informant interviews will also be conducted with health care providers, extension workers, and village and district officials. Interviews and focus groups aim to ascertain participants’ experiences with the intervention and suggestions for improvement in the delivery and sustainability of the intervention. In addition, the interviews and discussion will also identify bottlenecks and sources of improvement or adaption of homestead agriculture and nutrition interventions in the community.The primary outcome for the study is women’s and children’s dietary diversity (number of food groups consumed out of 10) , 21. SecData will be analyzed based on an intent-to-treat strategy. Data will be classified into three main categories; (i) the primary outcome variables including women and children dietary diversity, (ii) secondary outcomes including women’s and children’s growth and anemia status; and (iii) other covariates including socio-demographic characteristics, village, age, gender, health status, WASH practices, and crop production and practices, and livestock ownership. In addition, standard tools to assess food security (food reserve availability index), dietary diversity in women of reproductive age and children, women empowerment in agriculture index, water and sanitation (WASH) practices as well as household asset index will be incorporated, and aggregate data used to predict vulnerable households.Height (or length) and weight will be standardized based on 2006 WHO child growth standards to create HAZ, WAZ and WHZ scores. Biological implausible values based on WHO recommended cutoffs at 6 SD will be eliminated.Generalized linear mixed models with random intercepts and robust standard errors will be used to assess for continuous repeated primary outcomes. Generalized linear mixed models with random intercepts and robust standard errors will be used to assess repeated primary outcomes.Enrolment into the study began in July 2016 and completed in November 2017. The intervention period is August 2016 through July 2019. The study is ongoing.Multi-sectoral cadres of extension workers may be effective in addressing malnutrition among rural women and children in Tanzania. Most LMICs face severe health workforce shortages, hampering the implementation of nutrition and health initiatives and attainment of health goals . Many ruThis study will contribute to a growing body of evidence on a knowledge gap that is central to maternal and child health implementation research: how agriculture programs can be designed and harnessed to improve nutrition. This study will test the effectiveness of an integrated nutrition-sensitive intervention with a focus on homestead food production for enhancing women’s empowerment, increasing income, improving crop and dietary diversity, and ultimately improving nutritional and health outcomes. This evaluation will demonstrate the feasibility and potential of nutrition-sensitive approaches for improving nutrition and health among vulnerable populations in rural Tanzania.There is a possibility of bias whereby study villages may have different nutrition indicators as well as varied climatic conditions for crop production, including potential difference in access to water. We have minimized this bias by pairing control and intervention villages based on proximity to water sources. Issues of sustainability include the feasibility of this workload on extension workers after the study period has concluded. Currently, this study is providing logistical and financial support to participating workers to supplement the extra workload. Agriculture extension workers are paid by the district and therefore could continue these visits as part of their normal workload, but CHWs are typically volunteers. Our study will examine the effectiveness of the proposed intervention as a proof-of-principal and we will use the experience and findings generated from this study to engage with policy makers in Tanzania about next steps, including the possibility of refining the intervention and further scale up with additional evaluation of effectiveness and cost-effectiveness in the next phase of this effort.


High-quality early childhood education and care (ECEC) can strengthen the social and emotional skills that are crucial for children’s ongoing development. With research highlighting an increasing prevalence of emotional and behavioural challenges in young children, there is emphasis on embedding teaching practices and pedagogies to support social and emotional skills within early learning programs. A growing body of research has examined the impact of social and emotional learning programs in ECEC; however, few studies describe the intervention development process, or how educators and other professionals were engaged to increase the relevance and feasibility of the program. The current paper describes the development of the Cheshire Social-Emotional Engagement and Development (SEED) Educational Program, an online learning tool to support early childhood educators to foster children’s positive mental health. Cheshire SEED was designed using five steps of the Intervention Mapping methodology: (i) comprehensive needs assessment to create a logic model of the problem; (ii) creation of program outcomes and change objectives mapped against determinants of educator behaviour; (iii) co-design of theory-based methods and practical strategies; (iv) program development; and (v) adoption and implementation planning. The process and decisions at each step of the IM protocol are presented, and the strengths and limitations of the approach to develop a mental health intervention for ECEC settings are discussed. Social and emotional competence in early childhood is an important predictor of ongoing health and wellbeing . The cogIn addition to individual variables such as genetics, temperament, physical health and cognitive functioning, familial factors including financial disadvantage, parental mental health, low self-efficacy and stress, certain parenting styles, exposure to family violence, and insecure caregiver–child attachment histories ,19,20,21The potential to foster children’s social and emotional development through early childhood education and care (ECEC) programs has experienced a surge of attention from educators, policy-makers and researchers over recent decades ,27. A suOne way in which early learning programs can respond to children’s social and emotional needs is through social and emotional learning (SEL) intervention. SEL describes the active process whereby children attain and apply knowledge and skills relating to self-awareness, social awareness, self-management, relationships, and responsible decision-making . ProgramWhile a growing body of research has examined the impact of SEL programs in ECEC, few studies describe the intervention development process  or how eIn the current study, we sought to adapt and translate practices from an existing evidence-based educational program, called The Cheshire School, into the early years environment. The Cheshire School is an 18-month intervention program for children aged 4–11 years who experienced significant social, emotional, and behavioural challenges in mainstream school . The resThe IM framework is a six-step iterative process, where each step builds on the decisions and products produced in the preceding steps . The secThis research project was conducted in Victoria, Australia. Regulation, assessment and quality improvement for Australian ECEC services is guided by the National Quality Standard , with eaStep 1 considered the epidemiologic, behavioural, and social perspectives of the community at risk for health-related problems (preschool-aged children), the intervention target population (early childhood educators), and the program setting (ECEC services). A detailed assessment of the needs and capacity of early childhood educators with regards to supporting children’s social and emotional development was undertaken to inform the program logic and program goals.An intervention design group was convened to provide input, guidance, and oversight of the development process. This group included eight participants: two educators, one with experience in early childhood settings, and the other with experience in both early childhood and primary programs, including The Cheshire School; three paediatric psychologists working within The Cheshire School, one of whom was also a play therapist; one ECEC pedagogical leader; and two researchers with expertise in developmental psychology. The group met regularly through the 18-month design period to discuss the enablers and barriers for educators in supporting children’s social-emotional development, findings from a series of literature reviews and qualitative research, intervention co-design, trialling and refining the program, program implementation, and evaluation. In addition, regular input was sought from individuals who could inform intervention design and delivery, including senior ECEC managers and a speech therapist.Pertinent literature was reviewed to understand the determinants of educator behaviour with regards to children’s social and emotional skills, and the availability and benefits of existing SEL programs for preschool-aged children. SEL intervention in educational settings have been described within a response-to-intervention tiered model, with intervention intensity aligned to child need ,55. TierThe response-to-intervention model was used as a framework to review the availability and outcomes associated with SEL programs, and to ensure that Cheshire SEED did not replicate, but added to, existing approaches. The following reviews were conducted: (i) systematic literature review and meta-analysis examining the social, emotional, and early learning outcomes associated with universal (Tier 1) curriculum-based SEL programs delivered to children aged 2–6 years in ECEC settings ; (ii) syn = 13 participants) and three focus groups (n = 17 participants) were conducted with professionals working within the ECEC sector. The key informant interviews were carried out by one author (Claire Blewitt). The three focus groups discussions were facilitated by two authors (Heidi Bergmeier and Helen Skouteris) and one researcher with a Doctorate in Psychology. Twenty educators (working in both kindergarten and full day childcare rooms) from four Melbourne-based ECEC centres took part, along with five staff who held a leadership or executive management position with oversight of ECEC service provision; three researchers with expertise in early child development within ECEC settings; and two staff from non-government agencies with knowledge or involvement in efforts to increase early social and emotional development. Questions were consistent across interviews and focus groups, and aimed to ascertain participant knowledge of children’s social and emotional development in early childhood, approaches or strategies to support children’s social and emotional development, enablers that support knowledge and skills, perceived barriers to SEL, and potential pathways to overcome these barriers. Interviews were audio-recorded and transcribed by one author (Claire Blewitt). Two authors (Claire Blewitt and Amanda O’Connor) coded 20% of transcripts to ensure the identification of consistent themes. Any discrepancies were resolved by discussion. One author (C.B.) then coded the remaining transcripts. Research themes were cross-checked with the research team to ensure accurate coding of participant perspectives. Thematic analysis . Th. Th73]. Performance objectives for each level of intervention  are presented in Informed by the outcomes of the systematic reviews and qualitative research, conceptual model and design workshops, the intervention design group proposed a multi-faceted learning tool for early childhood educators who want to build expertise in fostering children’s social and emotional skills. The following program components were discussed and prioritised during the design group workshops: a phone or tablet app of strategies, visual guides and factsheets, instructional bite size videos, professional learning community, educator workshops, and coaching at the point of practice. Following the workshops, the lead researcher reviewed behaviour change theories and methods suitable for the determinants and change objectives at each intervention level. Practical applications (specific activities) aligned with the behaviour change methods were identified  , and setThe Cheshire SEED platform presents tailored content based on educator input. Each strategy includes an explanation of the technique and how it supports children’s development, a video explanation from the Cheshire School, step-by-step visual guide, examples of language and phrases, and an information sheet for caregivers. Several Module 3 strategies also include downloadable resources such as visual timetables and choice boards , visual cards, and a feelings thermometer . Finally, Module 5 offers tools to assess whether the SEED strategies have benefited the children in the service. It also includes options to extend learning and share experiences with other educators using webinars and discussion boards.The Program Implementation Plan focused on mechanisms to deliver Cheshire SEED through ECEC providers and the ongoing support needed to ensure sustainability. It was decided the Lead Educator/Centre Director would facilitate implementation within their service, with support and guidance from the program provider. Each educator within a participating service creates an individualised profile to access Cheshire SEED. The next stage of this project will focus on child, educator, and process outcome measures for the intervention, and evaluating the feasibility and benefits of implementation across diverse early years settings.The aim of this paper is to describe the application of IM methodology to design, implement, and evaluate a pedagogical intervention to support positive mental health in preschoolers. A challenge for educational researchers is designing initiatives that are usable, sustainable and scalable . While tA challenge encountered during the design process was clearly articulating the opportunities for behaviour change in the Australian early childhood sector. ECEC services across Australia are diverse, with educators from varied educational backgrounds, with a range of qualifications, professional learning, and experiences. Early learning programs contrast in terms of their overall quality  and educThe qualitative component of this work corroborated the need for practical and explicit strategies that built on educators’ current knowledge and expertise, could be embedded into their daily practice, and tailored to the social, emotional, and behavioural needs of the child. The intervention design group similarly suggested focusing on educators’ capability to promote SEL through their everyday interactions, by utilising the language, conversational strategies, and responsive practices that can support preschooler’s social-emotional competencies and learning outcomes. This finding was critical for the subsequent design of the program and underlines the importance of combining qualitative and quantitative data in this step.The design group established both individual behavioural outcomes for educators, and environmental outcomes at the interpersonal and organisational levels . During why that technique is valuable for children’s development by drawing on attachment theory, positive behaviour and support, play therapy, and positive psychology perspectives, thereby strengthening explicit knowledge.Following the IM process ensured Cheshire SEED was a theory and evidence-based professional learning approach. Facilitating explicit knowledge is critical for educator learning , howeverThe Cheshire SEED intervention was also strengthened by the co-design approach. While participatory design methods are commonly reported for health-related behaviour change interventions , this isThere are also several limitations to the intervention. While a rationale for focusing on educator behaviour was provided, the caregiver and family environment are the first and foremost influence on children’s social and emotional skills. Cheshire SEED incorporates information that educators can provide to caregivers, however the intervention did not include influencing caregiver behaviour as an outcome ; this woThis paper describes the development of the Cheshire SEED Educational Program using the IM methodology. IM was successfully utilised to translate an evidence-based educational approach from an early primary school to early years setting. This was a comprehensive process that enabled a multi-disciplinary team to develop an intervention based on theory and evidence, with potential to be delivered at scale to early childhood educators. The findings suggest the IM protocol may offer a valuable roadmap for educators, educational researchers, and early childhood professionals to design interventions that target educator behaviour and practice.