Obesity Prevention in a City State: Lessons from New York City during the Bloomberg Administration
Bibliographic record
Abstract
OBJECTIVE: To illuminate the key components of multi-sector reform to address the obesogenic environment in New York City during the administration of Mayor Michael Bloomberg from 2002 to 2013, we conducted a case study consisting of interviews with and a critical analysis of the experiences of leading decision makers and implementers. METHOD: Key informant interviews (N = 41) conducted in 2014 were recorded, transcribed, coded, and thematically analyzed. Participants included officials from the Health Department and other New York City Government agencies, academics, civil society members, and private sector executives. RESULTS: Participants described Mayor Bloomberg as a data-driven politician who wanted to improve the lives of New Yorkers. He appointed talented Commissioners and encouraged them and their staff to be bold, innovative, and collaborative. Multiple programs spanning multiple sectors, with varied approaches and targets, were supported. This study found that much of the work relied on loose coalitions across City Government, with single agencies responsible for their own agendas, some with health co-benefits. Many policies were implemented through non-legislative mechanisms such as executive orders and the Health Code. Despite support from academic and some civil society groups, strong lobbying from industry and an unfavorable media led to some reforms being modified, legally challenged or blocked completely, particularly food environment modifiers. In contrast, reforms of the physical environment were described as highly consultative across and outside government and resulted in slower but more sustained reform. CONCLUSION: The Bloomberg administration was a "window of opportunity" with the imprimatur of the executive to progress a long-term, multi-faceted obesity prevention strategy, which has successfully reversed childhood trends. Through the involvement of external researchers and the extensive use of empirical data from a wide range of participants, this study offers a unique insight into the ways in which this was achieved. While some of the aspects of the reforms in New York City are unique to that setting at that time, there are important lessons that are transferable to other urban settings. These include: strong and consistent leadership; a commitment to innovative approaches and cross-sectoral collaboration; and a context to support and encourage this approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".