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The purpose of this study is to determine if healthier neighbourhood food environments are associated with healthier diet quality.
This was a cross-sectional study using linear regression models to analyse data from the Maastricht Study. Diet quality was assessed using data collected with a FFQ to calculate the Dutch Healthy Diet (DHD). A buffer zone encompassing a 1000 m radius was created around each participant home address. The Food Environment Healthiness Index (FEHI) was calculated using a Kernel density analysis within the buffers of available food outlets. The association between the FEHI and the DHD score was analysed and adjusted for socio-economic variables.
The region of Maastricht including the surrounding food retailers in the Netherlands.
7367 subjects aged 40–75 years in the south of the Netherlands.
No relationship was identified between either the FEHI (B = 0·62; 95 % CI = –2·54, 3·78) or individual food outlets, such as fast food (B = –0·07; 95 % CI = –0·20, 0·07) and diet quality. Similar null findings using the FEHI were identified at the 500 m (B = 0·95; 95 % CI = –0·85, 2·75) and 1500 m (B = 1·57; 95 % CI = –3·30, 6·44) buffer. There was also no association between the food environment and individual items of the DHD including fruits, vegetables and sugar-sweetened beverages.
The food environment in the Maastricht area appeared marginally unhealthy, but the differences in the food environment were not related to the quality of food that participants reported as intake.
Low dietary guideline adherence is persistent, but there is limited understanding of how individuals with varying socio-economic backgrounds reach a certain dietary intake. We investigated how quantitative and qualitative data on dietary guidelines adherence correspond and complement each other, to what extent determinants of guideline adherence in quantitative data reflect findings on determinants derived from qualitative data and which of these determinants emerged as interdependent in the qualitative data.
This mixed-methods study used quantitative questionnaire data (n 1492) and qualitative data collected via semi-structured telephone interviews (n 24). Quantitative data on determinants and their association with total guideline adherence (scored 0–150) were assessed through linear regression. Directed content analysis was used for qualitative data.
Dutch urban areas.
Adults aged 18–65 years.
A range of determinants emerged from both data sources, for example higher levels of cognitive restraint (β 5·6, 95 % CI 4·2, 7·1), habit strength of vegetables (β 4·0, 95 % CI 3·3, 4·7) and cooking skills (β 4·7, 95 % CI 3·5, 5·9), were associated with higher adherence. Qualitative data additionally suggested the influence of food prices, strong dietary habits and the social aspect of eating, and for the determinants cognitive restraint, habit strength related to vegetables, food prices and home cooking, some variation between interviewees with varying socio-economic backgrounds emerged in how these determinants affected guideline adherence.
This mixed-methods exploration provides a richer understanding of why adults with varying socio-economic backgrounds do or do not adhere to dietary guidelines. Results can guide future interventions promoting healthy diets across populations.
Studies on neighbourhood characteristics and depression show equivocal results.
This large-scale pooled analysis examines whether urbanisation, socioeconomic, physical and social neighbourhood characteristics are associated with the prevalence and severity of depression.
Cross-sectional design including data are from eight Dutch cohort studies (n= 32 487). Prevalence of depression, either DSM-IV diagnosis of depressive disorder or scoring for moderately severe depression on symptom scales, and continuous depression severity scores were analysed. Neighbourhood characteristics were linked using postal codes and included (a) urbanisation grade, (b) socioeconomic characteristics: socioeconomic status, home value, social security beneficiaries and non-Dutch ancestry, (c) physical characteristics: air pollution, traffic noise and availability of green space and water, and (d) social characteristics: social cohesion and safety. Multilevel regression analyses were adjusted for the individual's age, gender, educational level and income. Cohort-specific estimates were pooled using random-effects analysis.
The pooled analysis showed that higher urbanisation grade (odds ratio (OR) = 1.05, 95% CI 1.01–1.10), lower socioeconomic status (OR = 0.90, 95% CI 0.87–0.95), higher number of social security beneficiaries (OR = 1.12, 95% CI 1.06–1.19), higher percentage of non-Dutch residents (OR = 1.08, 95% CI 1.02–1.14), higher levels of air pollution (OR = 1.07, 95% CI 1.01–1.12), less green space (OR = 0.94, 95% CI 0.88–0.99) and less social safety (OR = 0.92, 95% CI 0.88–0.97) were associated with higher prevalence of depression. All four socioeconomic neighbourhood characteristics and social safety were also consistently associated with continuous depression severity scores.
This large-scale pooled analysis across eight Dutch cohort studies shows that urbanisation and various socioeconomic, physical and social neighbourhood characteristics are associated with depression, indicating that a wide range of environmental aspects may relate to poor mental health.
In the review ‘the art of successful implementation of psychosocial interventions in residential dementia care: a systematic review of the literature based on the RE-AIM framework, the email for the corresponding author is incorrect. The correct email address is Petra.Boersma@Inholland.nl
In the past decades many psychosocial interventions for elderly people with dementia have been developed and implemented. Relatively little research has been done on the extent to which these interventions were implemented in the daily care. The aim of this study was to obtain insight into strategies for successful implementation of psychosocial interventions in the daily residential dementia care. Using a modified RE-AIM framework, the indicators that are considered important for effective and sustainable implementation were defined.
A systematic literature search was undertaken in PubMed, PsycINFO, and Cinahl, followed by a hand search for key papers. The included publications were mapped based on the dimensions of the RE-AIM framework: Reach, Effectiveness, Adoption, Implementation, and Maintenance.
Fifty-four papers met the inclusion criteria and described various psychosocial interventions. A distinction was made between studies that used one and studies that used multiple implementation strategies. This review shows that to improve their knowledge, caregivers needed at least multiple implementation strategies, only education is not enough. For increasing a more person-centered attitude, different types of knowledge transfer can be effective. Little consideration is given to the adoption of the method by caregivers and to the long-term sustainability (maintenance).
This review shows that in order to successfully implement a psychosocial method the use of multiple implementation strategies is recommended. To ensure sustainability of a psychosocial care method in daily nursing home care, innovators as well as researchers should specifically pay attention to the dimensions Adoption, Implementation, and Maintenance of the RE-AIM implementation framework.
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