The joint influence of area income, income inequality, and immigrant density on adverse birth outcomes: a population-based study

<p>Abstract</p> <p>Background</p> <p>The association between area characteristics and birth outcomes is modified by race. Whether such associations vary according to social class indicators beyond race has not been assessed.</p> <p>Methods</p> <p>This study evaluated effect modification by maternal birthplace and education of the relationship between neighbourhood characteristics and birth outcomes of newborns from 1999–2003 in the province of Québec, Canada (N = 353,120 births). Areas (N = 143) were defined as administrative local health service delivery districts. Multi-level logistic regression was used to model the association between three area characteristics (median household income, immigrant density and income inequality) and the two outcomes preterm birth (PTB) and small-for-gestational age (SGA) birth. Effect modification by social class indicators was evaluated in analyses stratified according to maternal birthplace and education.</p> <p>Results</p> <p>Relative to the lowest tertile, high median household income was associated with SGA birth among Canadian-born mothers (odds ratio (OR) 1.13, 95% confidence interval (CI) 1.06, 1.20) and mothers with high school education or less (OR 1.13, 95% CI 1.02, 1.24). Associations between median household income and PTB were weaker. Relative to the highest tertile, low immigrant density was associated with a lower odds of PTB among foreign-born mothers (OR 0.79, 95% CI 0.63, 1.00) but a higher odds of PTB among Canadian-born mothers (OR 1.14, 95% CI 1.07, 1.21). Associations with income inequality were weak or absent.</p> <p>Conclusion</p> <p>The association between area factors and birth outcomes is modified by maternal birthplace and education. Studies have found that race interacts in a similar manner. Public health policies focussed on perinatal health must consider the interaction between individual and area characteristics.</p

Systematic Neighborhood Observations at High Spatial Resolution: Methodology and Assessment of Potential Benefits

There is a growing body of public health research documenting how characteristics of neighborhoods are associated with differences in the health status of residents. However, little is known about how the spatial resolution of neighborhood observational data or community audits affects the identification of neighborhood differences in health. We developed a systematic neighborhood observation instrument for collecting data at very high spatial resolution (we observe each parcel independently) and used it to collect data in a low-income minority neighborhood in Dallas, TX. In addition, we collected data on the health status of individuals residing in this neighborhood. We then assessed the inter-rater reliability of the instrument and compared the costs and benefits of using data at this high spatial resolution. Our instrument provides a reliable and cost-effect method for collecting neighborhood observational data at high spatial resolution, which then allows researchers to explore the impact of varying geographic aggregations. Furthermore, these data facilitate a demonstration of the predictive accuracy of self-reported health status. We find that ordered logit models of health status using observational data at different spatial resolution produce different results. This implies a need to analyze the variation in correlative relationships at different geographic resolutions when there is no solid theoretical rational for choosing a particular resolution. We argue that neighborhood data at high spatial resolution greatly facilitates the evaluation of alternative geographic specifications in studies of neighborhood and health

Neighborhood fast food restaurants and fast food consumption: A national study

<p>Abstract</p> <p>Background</p> <p>Recent studies suggest that neighborhood fast food restaurant availability is related to greater obesity, yet few studies have investigated whether neighborhood fast food restaurant availability promotes fast food consumption. Our aim was to estimate the effect of neighborhood fast food availability on frequency of fast food consumption in a national sample of young adults, a population at high risk for obesity.</p> <p>Methods</p> <p>We used national data from U.S. young adults enrolled in wave III (2001-02; ages 18-28) of the National Longitudinal Study of Adolescent Health (n = 13,150). Urbanicity-stratified multivariate negative binomial regression models were used to examine cross-sectional associations between neighborhood fast food availability and individual-level self-reported fast food consumption frequency, controlling for individual and neighborhood characteristics.</p> <p>Results</p> <p>In adjusted analysis, fast food availability was not associated with weekly frequency of fast food consumption in non-urban or low- or high-density urban areas.</p> <p>Conclusions</p> <p>Policies aiming to reduce neighborhood availability as a means to reduce fast food consumption among young adults may be unsuccessful. Consideration of fast food outlets near school or workplace locations, factors specific to more or less urban settings, and the role of individual lifestyle attitudes and preferences are needed in future research.</p

Optimizing the two-step floating catchment area method for measuring spatial accessibility to medical clinics in Montreal

<p>Abstract</p> <p>Background</p> <p>Reducing spatial access disparities to healthcare services is a growing priority for healthcare planners especially among developed countries with aging populations. There is thus a pressing need to determine which populations do not enjoy access to healthcare, yet efforts to quantify such disparities in spatial accessibility have been hampered by a lack of satisfactory measurements and methods. This study compares an optimised and the conventional version of the two-step floating catchment area (2SFCA) method to assess spatial accessibility to medical clinics in Montreal.</p> <p>Methods</p> <p>We first computed catchments around existing medical clinics of Montreal Island based on the shortest network distance. Population nested in dissemination areas were used to determine potential users of a given medical clinic. To optimize the method, medical clinics (supply) were weighted by the number of physicians working in each clinic, while the previous year's medical clinic users were computed by ten years age group was used as weighting coefficient for potential users of each medical clinic (demand).</p> <p>Results</p> <p>The spatial accessibility score (SA) increased considerably with the optimisation method. Within a distance of 1 Km, for instance, the maximum clinic accessible for 1,000 persons is 2.4 when the conventional method is used, compared with 27.7 for the optimized method. The t-test indicates a significant difference between the conventional and the optimized 2SFCA methods. Also, results of the differences between the two methods reveal a clustering of residuals when distance increases. In other words, a low threshold would be associated with a lack of precision.</p> <p>Conclusion</p> <p>Results of this study suggest that a greater effort must be made ameliorate spatial accessibility to medical clinics in Montreal. To ensure that health resources are allocated in the interest of the population, health planners and the government should consider a strategy in the sitting of future clinics which would provide spatial access to the greatest number of people.</p

Los modelos de niveles múltiples: una estrategia analítica para el estudio de los problemas de salud de la población Multilevel models: an analysis strategy for the study of health problems in society

Se presenta una discusión teórico metodológica sobre la aplicabilidad de modelos de niveles múltiples para el estudio de los procesos de salud/enfermedad, sus determinantes y condicionantes, en función de la estratificación de la sociedad y de las condiciones de vida de sus habitantes. Se recupera una noción de población según la perspectiva de la teoría de los sistemas complejos jerárquicos que busca no reducir la realidad, sino una construcción del problema procurando identificar distintos niveles de abstracción para su abordaje. Estos modelos constituyen una opción que supera las experiencias previas, con la aplicación de técnicas estadísticas convencionales, dado que permiten analizar simultáneamente distintos niveles de agregación conservando su estructura jerárquica. Se consideran la influencia de las variables teniendo en cuenta su pertenencia a unidades mayores y la asociación potencialmente existente entre las unidades de un mismo nivel, es decir, la correlación intraclase entre variables relativas a individuos, familias, grupos, próximos entre sí, que comparten condiciones semejantes. Se evita de este modo sobredimensionar el efecto de las variables de macro nivel. Los modelos de niveles múltiples resultan particularmente adecuados para valorar desigualdades en el proceso salud/enfermedad/atención de los grupos poblacionales y analizar cómo los contextos sociales afectan los resultados y los riesgos de salud individuales. Se destaca la necesidad de desarrollar estrategias de producción de información y de análisis que posibiliten reconocer niveles de explicación y de intervención, para proveer insumos y desencadenar acciones adecuadas a las especificidades locales, a nivel de las micro-áreas, con miras a lograr una mayor equidad en salud.<br>This paper presents the theoretical-methodological discussion about the applicability of multiple level models in the study of the health/sickness process, its determinants and conditioning factors, as a function of the stratification of society and the living conditions of its inhabitants. It goes back to the concept of population according to the theory of hierarchical complex systems, which seeks not to reduce reality, but rather to build the problem trying to identify different levels of abstraction in its approach. These models are options to overcome prior experiences, with the application of conventional statistical techniques, given that they make it possible to simultaneously analyze different levels of aggregation, while keeping its hierarchical structure. They consider the influence of the variables taking into account their belonging to lager units and the potential association existing between the units of a same level, that is, the intraclass correlation among variables relative to individuals, families and groups, close amongst themselves, which share similar conditions. In this manner, it tries to avoid oversizing the effect of macro level variables. The multiple level models are particularly appropriate to evaluate inequalities in the health/sickness/care process of the population groups and to analyze how social contexts affect the results and health risks of people. It highlights the need to develop information production strategies and analyses that make it possible to recognize levels of explanation and intervention to provide inputs and trigger actions suited to local specificities, at the level of micro-areas, so as to have more equity in healthcare