Ecological Landscape Design and Evaluation in Public Charging Facilities for Electric Vehicles.

The deterioration of the environment in the 21st century has made environmental issues one of the most severe tests for modern society. With this comes a change in energy structure from high-carbon to low-carbon direction, and electric vehicles are gradually developing into the darling of a city with low-carbon transportation and safe travel. This paper carries out a systematic analysis of landscape design and environmental protection in the development of new energy electric vehicle charging facilities in urban habitat. By categorizing the content and provisions of published domestic and international standards, new requirements for standardization are obtained, including barrier-free design, electromagnetic radiation, child safety protection, and urban landscape integration. Among them, ecological landscape public charging facilities can enhance the overall quality of urban environment. This paper analyzes the necessity of landscape design in charging facilities, explores the ecological concepts extended by macroscopic landscape design principles and the problems of public charging facilities, and proposes a design and evaluation method of ecologically landscaped public charging facilities based on hierarchical analysis and neural networks. The hierarchical analysis method is introduced to establish a landscape design assessment index system, and then a neural network is introduced to describe the characteristics of electric vehicle charging, and the landscape design assessment learning samples are trained to establish a landscape design assessment model. Finally, a comparison experiment is conducted with other landscape design assessment methods using specific examples, and the results show that the proposed method has more obvious advantages in ecological landscape public charging facility design assessment with high accuracy, faster landscape design assessment, charging efficiency, and environmental protection.

Which Definition of Rurality Should I Use?: The Relative Performance of 8 Federal Rural Definitions in Identifying Rural-Urban Disparities.

BACKGROUND: The federal government uses multiple definitions for identifying rural communities based on various geographies and different elements of rurality. OBJECTIVES: The objectives of this study were to: (1) assess the degree to which rural definitions identify the same areas as rural; and (2) assess rural-urban disparities identified by each definition across socioeconomic, demographic, and health access and outcome measures. RESEARCH DESIGN: We determined the rural status of each census tract and calculated the rural-urban disparity resulting from each definition, as well as across the number of definitions in which tracts were designated as rural (rurality agreement). SUBJECTS: The population in 72,506 census tracts. MEASURES: We used 8 federal rural definitions. Population characteristics included percent with a bachelor's degree, income below 200% poverty, population density, percent with health insurance and whether various health care services were within 30 minutes driving time of the tract centroid. RESULTS: The rural population varied from slightly < 6.9 million people to >75.5 million across definitions. The largest rural-urban disparities were found using Urban Influence Codes. Urbanized Area and Urbanized Cluster tended to generate smaller disparities. Population characteristics such as population density and percent White had notable discontinuities across levels of rurality, while others such as percent with a bachelor's degree and income below 200% poverty varied continuously. CONCLUSIONS: Rural-urban populations and disparities were sensitive to the specific definition and the relative strength of definitions varied across population characteristics. Researchers and policymakers should carefully consider the choice of outcome and region when deciding the most appropriate rural definition.

County Reclassifications and Rural-Urban Mortality Disparities in the United States (1970-2018).

Objectives. To demonstrate how inferences about rural-urban disparities in age-adjusted mortality are affected by the reclassification of rural and urban counties in the United States from 1970 to 2018.Methods. We compared estimates of rural-urban mortality disparities over time, produced through a time-varying classification of rural and urban counties, with counterfactual estimates of rural-urban disparities, assuming no changes in rural-urban classification since 1970. We evaluated mortality rates by decade of reclassification to assess selectivity in reclassification.Results. We found that reclassification amplified rural-urban mortality disparities and accounted for more than 25% of the rural disadvantage observed from 1970 to 2018. Mortality rates were lower in counties that reclassified from rural to urban than in counties that remained rural.Conclusions. Estimates of changing rural-urban mortality differentials are significantly influenced by rural-urban reclassification. On average, counties that have remained classified as rural over time have elevated mortality. Longitudinal research on rural-urban health disparities must consider the methodological and substantive implications of reclassification.Public Health Implications. Attention to rural-urban reclassification is necessary when evaluating or justifying policy interventions focusing on geographic health disparities.

Nota informativa n° 2/2020 - GST/DVAST/SVS/SES: nota informativa recomendações para trabalhadores e empregadores do setor de entrega de produtos delivery ­ prevenção ao Covid-19 / Informative note No. 2/2020 - GST / DVAST / SVS / SES: Informative note recommendations for workers and employers in the delivery of products sector - prevention to Covid-19

Orienta quanto a higiene das mãos e disponibilização a funcionários e entregadores o acesso fácil a pias providas com água corrente, sabonete líquido, toalhas de papel descartáveis, lixeiras com tampa de acionamento por pedal, além de frascos com álcool gel 70%.

As for hand hygiene and providing employees and delivery personnel with easy access to sinks provided with running water, liquid soap, disposable paper towels, trash cans with a pedal-operated lid, in addition to bottles with 70% alcohol gel.

En cuanto a la higiene de manos y al personal y repartidor de fácil acceso a lavabos provistos de agua corriente, jabón líquido, toallas de papel desechables, basureros con tapa a pedal, además de botellas con gel de alcohol al 70%.

Understanding urbanization: A study of census and satellite-derived urban classes in the United States, 1990-2010.

Most of future population growth will take place in the world's cities and towns. Yet, there is no well-established, consistent way to measure either urban land or people. Even census-based urban concepts and measures undergo frequent revision, impeding rigorous comparisons over time and place. This study presents a new spatial approach to derive consistent urban proxies for the US. It compares census-designated urban blocks with proxies for land-based classifications of built-up areas derived from time-series of the Global Human Settlement Layer (GHSL) for 1990-2010. This comparison provides a new way to understand urban structure and its changes: Most land that is more than 50% built-up, and people living on such land, are officially classified as urban. However, 30% of the census-designated urban population and land is located in less built-up areas that can be characterized as mainly suburban and peri-urban in nature. Such insights are important starting points for a new urban research program: creating globally and temporally consistent proxies to guide modelling of urban change.

Measuring socio-economic status of an urban squatter settlement in Pakistan using WAMI Index.

OBJECTIVE: To determine the socio-economic status of a peri-urban community. METHODS: The cross-sectional study was conducted at Deh Chuhar village, Gaddap Town, Karachi from December 2015 to February 2016. The Water/Sanitation, Assets, Maternal Education and Income Index was used. All variables were given a score on the scale of 0-8. The resulting index was illustrated in the form of quintiles. RESULTS: : A total of 254 households were surveyed. Total population was 2117 with mean number of household members being 8±4. Mean index score was 0.39±0.14 and the median score was 0.375. Percentile distribution of the score indicated that 152(60%) households scored below 0.40 whereas, 51 (20%) were in the highest quintile with a score above 0.50. CONCLUSIONS: Water/Sanitation, Assets, Maternal Education and Income index suggested poor socio-economic status of the community studied.

Revised Kuppuswamy's Socioeconomic Status Scale: Explained and Updated.

Some of the facets of the Kuppuswamy's socioeconomic status scale sometimes create confusion and require explanation on how to classify, and need some minor updates to bring the scale up-to-date. This article provides a revised scale that allows for the real-time update of the scale.

Managing distance and covariate information with point-based clustering.

BACKGROUND: Geographic perspectives of disease and the human condition often involve point-based observations and questions of clustering or dispersion within a spatial context. These problems involve a finite set of point observations and are constrained by a larger, but finite, set of locations where the observations could occur. Developing a rigorous method for pattern analysis in this context requires handling spatial covariates, a method for constrained finite spatial clustering, and addressing bias in geographic distance measures. An approach, based on Ripley's K and applied to the problem of clustering with deliberate self-harm (DSH), is presented. METHODS: Point-based Monte-Carlo simulation of Ripley's K, accounting for socio-economic deprivation and sources of distance measurement bias, was developed to estimate clustering of DSH at a range of spatial scales. A rotated Minkowski L1 distance metric allowed variation in physical distance and clustering to be assessed. Self-harm data was derived from an audit of 2 years' emergency hospital presentations (n = 136) in a New Zealand town (population ~50,000). Study area was defined by residential (housing) land parcels representing a finite set of possible point addresses. RESULTS: Area-based deprivation was spatially correlated. Accounting for deprivation and distance bias showed evidence for clustering of DSH for spatial scales up to 500 m with a one-sided 95 % CI, suggesting that social contagion may be present for this urban cohort. CONCLUSIONS: Many problems involve finite locations in geographic space that require estimates of distance-based clustering at many scales. A Monte-Carlo approach to Ripley's K, incorporating covariates and models for distance bias, are crucial when assessing health-related clustering. The case study showed that social network structure defined at the neighbourhood level may account for aspects of neighbourhood clustering of DSH. Accounting for covariate measures that exhibit spatial clustering, such as deprivation, are crucial when assessing point-based clustering.

Do the classification of areas and distance matter to the assessment results of achieving the treatment targets among type 2 diabetes patients?

BACKGROUND: Type 2 diabetes is a major health concern all over the world. The prevention of diabetes is important but so is well-balanced diabetes care. Diabetes care can be influenced by individual and neighborhood socio-economic factors and geographical accessibility to health care services. The aim of the study is to find out whether two different area classifications of urban and rural areas give different area-level results of achieving the targets of control and treatment among type 2 diabetes patients exemplified by a Finnish region. The study exploits geo-referenced patient data from a regional primary health care patient database combined with postal code area-level socio-economic variables, digital road data and two grid based classifications of areas: an urban-rural dichotomy and a classification with seven area types. METHODS: The achievement of control and treatment targets were assessed using the patient's individual laboratory data among 9606 type 2 diabetes patients. It was assessed whether hemoglobin A1c (HbA1c) was controlled and whether the recommended level of HbA1c was achieved in patients by different area classes and as a function of distance. Chi square test and logistic regression analysis were used for testing. RESULTS: The study reveals that area-level inequalities exist in the care of type 2 diabetes in a detailed 7-class area classification but if the simple dichotomy of urban and rural is applied differences vanish. The patient's gender and age, area-level education and the area class they belonged to were associated with achievements of control and treatment targets. Longer distance to health care services was not a barrier to good achievements of control or treatment targets. CONCLUSIONS: A more detailed grid-based area classification is better for showing spatial differences in the care of type 2 diabetes patients. Inequalities exist but it would be misleading to state that the differences are simply due to urban or rural location or due to distance. From a planning point of view findings suggest that detailed geo-coded patient information could be utilized more in resourcing and targeting the health care services to find the area-level needs of care and to improve the cost-efficient allocation of resources.

Knowledge and associated factors towards type 2 diabetes among a rural population in the Red River Delta region, Vietnam.

INTRODUCTION: Knowledge about type 2 diabetes (T2D) and attitude towards the condition are known to affect compliance and play an important role in diabetes management. T2D knowledge is a prerequisite for individuals and communities to take action on control of the disease. METHODS: A cross-sectional study was designed to identify knowledge and related factors towards T2D, risk factors, complications, prevention and treatment of the disease. A total of 2580 subjects representative of the general population aged 40-64 years was recruited from a typical province of Red River Delta region, Vietnam. The trained surveyors interviewed subjects directly to collect data, using a structured questionnaire. To evaluate the overall knowledge of T2D, 14 questions were used to calculate the 100 points. Total knowledge score was classified into the following four categories: highly insufficient (≤25 points), insufficient (26-50 points), satisfactory (51-75 points), and highly satisfactory (>75 points). Association between inadequate knowledge (<50 points) and variables was evaluated using multivariate logistic regression. RESULTS: The highly insufficient, insufficient, satisfactory, and highly satisfactory levels of the overall knowledge were 75, 17.9, 6.8, and 0.3%, respectively. Of the total population, more than 65% thought that there is no cure for diabetes, and more than 90% did not know the essential combination of drugs, diet, and physical activity in T2D treatment. Less than 10% of the population understood the concept of T2D, its risk factors, complications, approaches to prevention and treatment. The rural-urban difference of T2D knowledge was found in rates of understanding at least one risk factor (34.8% vs 63%), all the three methods for T2D prevention (1.7% vs 10.3%), and three combined approaches for T2D treatment (8.9% vs 16.4%). Age, residence, educational level, and occupation were the most significant factors associated with inadequate knowledge. CONCLUSIONS: The study shows a low level of diabetes knowledge among the general population aged 40-64 years in the Red River Delta, and significantly lower awareness in rural areas compared with urban areas. The limited awareness has indicated the urgent need for communication and education to improve the T2D knowledge of the Vietnamese population on risk factors, serious level, complications, prevention and treatment, taking into account the age, residence, educational level, and occupation of the subjects.

Availability, Price, and Quality of Fruits and Vegetables in 12 Rural Montana Counties, 2014.

We assessed the consumer food environment in rural areas by using the Nutrition Environment Measures Survey for Stores (NEMS-S) to measure the availability, price, and quality of fruits and vegetables. We randomly selected 20 grocery stores (17 rural, 3 urban) in 12 Montana counties using the 2013 US Department of Agriculture's rural-urban continuum codes. We found significant differences in NEMS-S scores for quality of fruits and vegetables; of 6 possible points, the mean quality score was 4.5; of rural stores, the least rural stores had the highest mean quality scores (6.0). Intervention strategies should aim to increase fruit and vegetable quality in rural areas.

2013 NCHS Urban-Rural Classification Scheme for Counties.

OBJECTIVES: This report details development of the 2013 National Center for Health Statistics' (NCHS) Urban-Rural Classification Scheme for Counties (update of the 2006 NCHS scheme) and applies it to health measures to demonstrate urban-rural health differences. METHODS: The methodology used to construct the 2013 NCHS scheme was the same as that used for the 2006 NCHS scheme, but 2010 census-based data were used rather than 2000 census-based data. All U.S. counties and county-equivalent entities are assigned to one of six levels (four metropolitan and two nonmetropolitan) based on: 1) their February 2013 Office of Management and Budget designation as metropolitan, micropolitan, or noncore; 2) for metropolitan counties, the population size of the metropolitan statistical area (MSA) to which they belong; and 3) for counties in MSAs of 1 million or more, the location of principal city populations within the MSA. The 2013 and 2006 NCHS schemes were applied to data from the National Vital Statistics System (NVSS) and National Health Interview Survey (NHIS) to illustrate differences in selected health measures by urbanization level and to assess the magnitude of differences between estimates from the two schemes. RESULTS AND CONCLUSIONS: County urban-rural assignments under the 2013 NCHS scheme are very similar to those under the 2006 NCHS scheme. Application of the updated scheme to NVSS and NHIS data demonstrated the continued usefulness of the six categories for assessing and monitoring health differences among communities across the full urbanization spectrum. Residents of large central and large fringe metro counties differed substantially on many health measures, illustrating the importance of continuing to separate these counties. Residents of large fringe metro counties generally fared better than residents of less urban counties. Estimates obtained from the 2013 and 2006 schemes were similar.

Effect of the regional environment on the skin properties and the early wrinkles in young Chinese women.

BACKGROUND: There are ethnic differences in the skin characteristics, also the skin is susceptible to be influenced by the external environment such as UV radiation and the climates. It can be shown that the skin in same race or twins varies by the environment. OBJECTIVES: This study was designed to investigate the skin characteristics and the early wrinkles of young Chinese women from four different regions, and to identify the correlation among the wrinkles, the other skin characteristics, and environmental conditions. METHODS: A total of 441 healthy Chinese women aged between 20 and 35 years participated in the study: 110 from Beijing, 110 from Shanghai, 111 from Wuhan, and 110 from Guangzhou. The skin hydration, sebum contents, TEWL, pH, elasticity, and wrinkles were measured on the crow's feet area. RESULTS: There were regional differences in the skin characteristics and the wrinkles. Beijing women had dry skin and more wrinkles, but Guangzhou women had high sebum contents, low pH, and less wrinkles (P < 0.01). Shanghai women's TEWL and Wuhan's women's skin elasticity were higher compared with that of women from other regions. The wrinkles' form (area, depth, and length) was different from region to region. Beijing women's wrinkles were deep and large, but Guangzhou women's wrinkles were shallow and small. The skin physical parameters that influenced the wrinkles were low sebum content and hydration, high TEWL, and pH (P < 0.05). CONCLUSION: In the Chinese women aged 20-35 years, the skin was influenced by the climates, so they had regionally a different skin. The skin hydration, sebum contents, TEWL, and pH can affect the early wrinkle formation than skin elasticity.

Perinatal influences on the development of asthma and atopy in childhood.

BACKGROUND: In most children with asthma and atopy, onset of disease occurs early in life, indicating a crucial role of in utero and early childhood environment. However, only a small part of this burden of disease established early in life has been explained. OBJECTIVE: To examine the effects of early environmental exposures on the development of asthma and atopy within the setting of an affluent urban population. METHODS: The authors followed 526 German children from birth to 5 years of age. Parental interviews in pregnancy and then yearly assessed the health of the child and environmental characteristics. Endotoxin and allergens in house dust were measured at 3 months. Atopic sensitization was assessed at 1 and 5 years. RESULTS: In atopic mothers, acute atopic symptoms during pregnancy were associated with increased risk of early atopic dermatitis (adjusted odds ratio [aOR] 1.74, 95% confidence interval [CI] 1.00-3.02) and allergic rhinitis at 5 years (aOR 2.11, 95% CI 1.01-4.41). Further, maternal illnesses during pregnancy (ie, repeated common colds) increased the risk of asthma at 5 years (aOR 2.31, 95% CI 1.12-4.78). Endotoxin in the child's mattress was inversely associated with atopic sensitization (aOR 0.79, 95% CI 0.64-0.97) and asthma (aOR 0.71, 95% CI 0.55-0.93). A contrasting effect of early endotoxin and mite exposure was observed for mite sensitization: mite exposure increased the risk of mite sensitization at 5 years (aOR 1.30, 95% CI 1.11-1.53), whereas endotoxin exposure was inversely associated with mite sensitization (aOR 0.73, 95% CI 0.57-0.95). CONCLUSION: Factors affecting the in utero environment, such as maternal atopy and infections, and bacterial exposure in pregnancy or early life may act as immunomodulators enhancing or inhibiting the development of asthma and atopy in childhood.

Urban economies and occupation space: can they get "there" from "here"?

Much of the socioeconomic life in the United States occurs in its urban areas. While an urban economy is defined to a large extent by its network of occupational specializations, an examination of this important network is absent from the considerable body of work on the determinants of urban economic performance. Here we develop a structure-based analysis addressing how the network of interdependencies among occupational specializations affects the ease with which urban economies can transform themselves. While most occupational specializations exhibit positive relationships between one another, many exhibit negative ones, and the balance between the two partially explains the productivity of an urban economy. The current set of occupational specializations of an urban economy and its location in the occupation space constrain its future development paths. Important tradeoffs exist between different alternatives for altering an occupational specialization pattern, both at a single occupation and an entire occupational portfolio levels.

The geography of happiness: connecting twitter sentiment and expression, demographics, and objective characteristics of place.

We conduct a detailed investigation of correlations between real-time expressions of individuals made across the United States and a wide range of emotional, geographic, demographic, and health characteristics. We do so by combining (1) a massive, geo-tagged data set comprising over 80 million words generated in 2011 on the social network service Twitter and (2) annually-surveyed characteristics of all 50 states and close to 400 urban populations. Among many results, we generate taxonomies of states and cities based on their similarities in word use; estimate the happiness levels of states and cities; correlate highly-resolved demographic characteristics with happiness levels; and connect word choice and message length with urban characteristics such as education levels and obesity rates. Our results show how social media may potentially be used to estimate real-time levels and changes in population-scale measures such as obesity rates.

NCHS urban-rural classification scheme for counties.

OBJECTIVES: This report details the National Center for Health Statistics' (NCHS) development of the 2006 NCHS Urban-Rural Classification Scheme for Counties and provides some examples of how the scheme can be used to describe differences in health measures by urbanization level. METHODS: The 2006 NCHS urban-rural classification scheme classifies all U.S. counties and county-equivalents into six levels--four for metropolitan counties and two for nonmetropolitan counties. The Office of Management and Budget's delineation of metropolitan and nonmetropolitan counties forms the foundation of the scheme. The NCHS scheme also uses the cut points of the U.S. Department of Agriculture Rural-Urban Continuum Codes to subdivide the metropolitan counties based on the population of their metropolitan statistical area (MSA): large, for MSA population of 1 million or more; medium, for MSA population of 250,000-999,999; and small, for MSA population below 250,000. Large metro counties were further separated into large central and large fringe metro categories using classification rules developed by NCHS. Nonmetropolitan counties were assigned to two levels based on the Office of Management and Budget's designated micropolitan or noncore status. The 2006 scheme was applied to data from the National Vital Statistics System (NVSS) and the National Health Interview Survey (NHIS) to illustrate its ability to capture health differences by urbanization level. RESULTS AND CONCLUSIONS: Application of the 2006 NCHS scheme to NVSS and NHIS data shows that it identifies important health disparities among communities, most notably those for inner city and suburban communities. The design of the NCHS Urban-Rural Classification Scheme for Counties makes it particularly well-suited for assessing and monitoring health differences across the full urbanization continuum.

The foodscape: classification and field validation of secondary data sources across urban/rural and socio-economic classifications in England.

BACKGROUND: In recent years, alongside the exponential increase in the prevalence of overweight and obesity, there has been a change in the food environment (foodscape). This research focuses on methods used to measure and classify the foodscape. This paper describes the foodscape across urban/rural and socio-economic divides. It examines the validity of a database of food outlets obtained from Local Authority sources (secondary level & desk based), across urban/rural and socio-economic divides by conducting fieldwork (ground-truthing). Additionally this paper tests the efficacy of using a desk based classification system to describe food outlets, compared with ground-truthing. METHODS: Six geographically defined study areas were purposively selected within North East England consisting of two Lower Super Output Areas (LSOAs; a small administrative geography) each. Lists of food outlets were obtained from relevant Local Authorities (secondary level & desk based) and fieldwork (ground-truthing) was conducted. Food outlets were classified using an existing tool. Positive predictive values (PPVs) and sensitivity analysis was conducted to explore validation of secondary data sources. Agreement between 'desk' and 'field' based classifications of food outlets were assessed. RESULTS: There were 438 food outlets within all study areas; the urban low socio-economic status (SES) area had the highest number of total outlets (n = 210) and the rural high SES area had the least (n = 19). Differences in the types of outlets across areas were observed. Comparing the Local Authority list to fieldwork across the geographical areas resulted in a range of PPV values obtained; with the highest in urban low SES areas (87%) and the lowest in Rural mixed SES (79%). While sensitivity ranged from 95% in the rural mixed SES area to 60% in the rural low SES area. There were no significant associations between field/desk percentage agreements across any of the divides. CONCLUSION: Despite the relatively small number of areas, this work furthers our understanding of the validity of using secondary data sources to identify and classify the foodscape in a variety of geographical settings. While classification of the foodscape using secondary Local Authority food outlet data with information obtained from the internet, is not without its difficulties, desk based classification would be an acceptable alternative to fieldwork, although it should be used with caution.

The meaning of 'rural' in rural health: a review and case study from Brazil.

Health disparities between rural and urban populations are an important global health concern, although ascertaining what constitutes a rural context is a complicated undertaking. This article summarises theoretical contributions that help to explain how uncritical use of rural classifications may interfere with epidemiological data and health policies. Bonfim, a community located in Rio de Janeiro state, Brazil, illustrates the discussion. Bonfim is classified as urban by the Brazilian census, although the community contains farmland, parkland and rural social groups such as family farmers and ecotourism employees. The (mis)classification of Bonfim as urban further complicates the meaning of rural, and thus also what is meant by rural health. Researchers have developed some new rurality indexes to overcome the rural-urban dichotomy and to help understand local scale health determinants. But the obstacles for large-scale studies and government decision-making are still many. 'Rural' is an epidemiological variable that unites in a single indicator diverse life aspects relevant for health purposes. Therefore, to facilitate allocation of health resources based on objective criteria, governments and policy makers must acknowledge the difficulty of defining what rural is and work to improve the definitions they use.

Monitoring gender equity in health using gender-sensitive indicators: a cross-national study.

BACKGROUND: As gender is known to be a major determinant of health, monitoring gender equity in health systems remains a vital public health priority. Focusing on a low-income (Peru), middle-income (Colombia), and high-income (Canada) country in the Americas, this study aimed to (1) identify and select gender-sensitive health indicators and (2) assess the feasibility of measuring and comparing gender-sensitive health indicators among countries. METHODS: Gender-sensitive health indicators were selected by a multidisciplinary group of experts from each country. The most recent gender-sensitive health measures corresponding to selected indicators were identified through electronic databases (CINAHL, PsycINFO, MEDLINE, Embase, LILACS, LIPECS, Latindex, and BIREME) and expert consultation. Data from population-based studies were analyzed when indicator information was unavailable from reports. RESULTS: Twelve of the 17 selected gender-sensitive health indicators were feasible to measure in at least two countries, and 9 of these were comparable among all countries. Indicators that were available were not stratified or adjusted by age, education, marital status, or wealth. The largest between-country difference was maternal mortality, and the largest gender inequity was mortality from homicides. CONCLUSIONS: This study shows that gender inequities in health exist in all countries, regardless of income level. Economic development seemed to confer advantages in the availability of such indicators; however, this finding was not consistent and needs to be further explored. Future initiatives should include identifying health system factors and risk factors associated with disparities as well as assessing the cost-effectiveness of including the routine monitoring of gender inequities in health.