Letter from Louisa L. Riley, Plainfield, New Jersey, to Adeline Manning, Boston, Massachusetts, 1905 April 4

https://repository.wellesley.edu/whitney_correspondence/2844/thumbnail.jp

Letter from Harriet L. Scudder, Macugnaga, Italy, to Anne Whitney, 1906 July 9

https://repository.wellesley.edu/whitney_correspondence/2854/thumbnail.jp

Letter from Louisa L. Riley, Plainfield, New Jersey, to Anne Whitney, Plymouth, Massachusetts, 1906 August 6

https://repository.wellesley.edu/whitney_correspondence/2847/thumbnail.jp

Letter from Louisa L. Riley, Plainfield, New Jersey, to Adeline Manning, Boston, Massachusetts, 1906 March 23

https://repository.wellesley.edu/whitney_correspondence/2846/thumbnail.jp

Research & Action Spring/Summer 2013

In this issue: Commentary: Women, Employment, & Health by Nancy Marshall Open Circle: Celebrating 25 Years of Getting to the Heart of Learning Q&A with Nan Stein, Ed.D.https://repository.wellesley.edu/researchandactionreport/1022/thumbnail.jp

Letter from Emma L. Leighton, Shelburne, New Hampshire, to Anne Whitney, Boston, Massachusetts, 1912 February 20

https://repository.wellesley.edu/whitney_correspondence/2695/thumbnail.jp

Geographic variation and localised clustering of congenital anomalies in Great Britain

Background: Environmental pollution as a cause of congenital anomalies is sometimes suspected because of clustering of anomalies in areas of higher exposure. This highlights questions around spatial heterogeneity (clustering) in congenital anomaly rates. If spatial variation is endemic, then any one specific cluster is less remarkable, though the presence of uncontrolled geographically clustered risk factors is suggested. If rates are relatively homogeneous across space other than around specific hazards, then evidence for these hazards causing the clusters is strengthened. We sought to estimate the extent of spatial heterogeneity in congenital anomaly rates in the United Kingdom. Methods: The study population covered about one million births from five registers in Britain from 1991–1999. We estimated heterogeneity across four geographical levels: register area, hospital catchment, electoral ward, and enumeration district, using a negative binomial regression model. We also sought clusters using a circular scan statistic. Results: Congenital anomaly rates clearly varied across register areas and hospital catchments (p 0.2). Adjusting for socioeconomic deprivation and maternal age made little difference to the extent of geographical variation for most congenital anomaly subtypes. The two most significant circular clusters (of four ano-rectal atresias and six congenital heart diseases) contained two or more siblings. Conclusion: The variation in rates between registers and hospital catchment area may have resulted in part from differences in case ascertainment, and this should be taken into account in geographical epidemiological studies of environmental exposures. The absence of evidence for variation below this level should be interpreted cautiously in view of the low power of general heterogeneity tests. Nevertheless, the data suggest that strong localised clusters in congenital anomalies are uncommon, so clusters around specific putative environmental hazards are remarkable when observed. Negative binomial models applied at successive hierarchical levels provide an approach of intermediate complexity to characterising geographical heterogeneity

Towards environmentally sustainable human behaviour: targeting non-conscious and conscious processes for effective and acceptable policies.

Meeting climate change targets to limit global warming to 2°C requires rapid and large reductions in demand for products that most contribute to greenhouse gas (GHG) emissions. These include production of bulk materials (e.g. steel and cement), energy supply (e.g. fossil fuels) and animal source foods (particularly ruminants and their products). Effective strategies to meet these targets require transformative changes in supply as well as demand, involving changes in economic, political and legal systems at local, national and international levels, building on evidence from many disciplines. This paper outlines contributions from behavioural science in reducing demand. Grounded in dual-process models of human behaviour (involving non-conscious and conscious processes) this paper considers first why interventions aimed at changing population values towards the environment are usually insufficient or unnecessary for reducing demand although they may be important in increasing public acceptability of policies that could reduce demand. It then outlines two sets of evidence from behavioural science towards effective systems-based strategies, to identify interventions likely to be effective at: (i) reducing demand for products that contribute most to GHG emissions, mainly targeting non-conscious processes and (ii) increasing public acceptability for policy changes to enable these interventions, targeting conscious processes.This article is part of the themed issue 'Material demand reduction'

Creating a population-based cohort of children born with and without congenital anomalies using birth data matched to hospital discharge databases in 11 European regions: Assessment of linkage success and data quality

Linking routinely collected healthcare administrative data is a valuable method for conducting research on morbidity outcomes, but linkage quality and accuracy needs to be assessed for bias as the data were not collected for research. The aim of this study was to describe the rates of linking data on children with and without congenital anomalies to regional or national hospital discharge databases and to evaluate the quality of the matched data. Eleven population-based EUROCAT registries participated in a EUROlinkCAT study linking data on children with a congenital anomaly and children without congenital anomalies (reference children) born between 1995 and 2014 to administrative databases including hospital discharge records. Odds ratios (OR), adjusted by region, were estimated to assess the association of maternal and child characteristics on the likelihood of being matched. Data on 102,654 children with congenital anomalies were extracted from 11 EUROCAT registries and 2,199,379 reference children from birth registers in seven regions. Overall, 97% of children with congenital anomalies and 95% of reference children were successfully matched to administrative databases. Information on maternal age, multiple birth status, sex, gestational age and birthweight were &gt;95% complete in the linked datasets for most regions. Compared with children born at term, those born at ≤27 weeks and 28-31 weeks were less likely to be matched (adjusted OR 0.23, 95% CI 0.21-0.25 and adjusted OR 0.75, 95% CI 0.70-0.81 respectively). For children born 32-36 weeks, those with congenital anomalies were less likely to be matched (adjusted OR 0.78, 95% CI 0.71-0.85) while reference children were more likely to be matched (adjusted OR 1.28, 95% CI 1.24-1.32). Children born to teenage mothers and mothers ≥35 years were less likely to be matched compared with mothers aged 20-34 years (adjusted ORs 0.92, 95% CI 0.88-0.96; and 0.87, 95% CI 0.86-0.89 respectively). The accuracy of linkage and the quality of the matched data suggest that these data are suitable for researching morbidity outcomes in most regions/countries. However, children born preterm and those born to mothers aged &lt;20 and ≥35 years are less likely to be matched. While linkage to administrative databases enables identification of a reference group and long-term outcomes to be investigated, efforts are needed to improve linkages to population groups that are less likely to be linked.</p