The impact of income-support interventions on life course risk factors and health outcomes during childhood: a systematic review in high income countries
Bibliographic record
Abstract
BACKGROUND: In high income countries one in five children still lives in poverty, which is known to adversely shape the life course health trajectory of these children. However, much less is understood on whether social and fiscal policies have the capacity to reverse this damage, which intervention is likely to be most effective and when these interventions should be delivered to maximise their impact. This systematic review attempts to address these questions by looking at the impact of income-support interventions, delivered during the first 1,000 days of life, on cardiovascular, metabolic, respiratory and mental health outcomes. METHODS: The review was restricted to experimental or quasi experimental studies conducted in high income countries. Studies were retrieved from multidisciplinary databases as well as health, economic, social sciences-specific literature browsers. All papers retrieved through the search strategy were double screened at title, abstract and full text stage. Relevant data of the selected studies were extracted and collected in tables, then summarised via narrative synthesis approach. Robustness of findings was assessed by tabulating impact by health outcome, type of intervention and study design. RESULTS: Overall, 16 relevant papers were identified, including 15 quasi-experimental studies and one randomized control trial (RCT). Income-support interventions included were unconditional/conditional cash transfers, income tax credit and minimum wage salary policies. Most studies were conducted in United States and Canada. Overall, the evidence suggested limited effect on mental health indicators but a positive, albeit small, effect of most policies on birth weight outcomes. Despite this, according to few studies that tried to extrapolate the results into public health terms, the potential number of negative outcomes averted might be consistent. CONCLUSIONS: Income-support interventions can positively affect some of the health outcomes of interest in this review, including birth weight and mental health. Given the large number of people targeted by these programs, one could infer that - despite small - the observed effect may be still relevant at population level. Nonetheless, the limited generalisability of the evidence gathered hampers firm conclusions. For the future, the breadth and scope of this literature need to be broadened to fully exploit the potential of these interventions and understand how their public health impact can be maximised.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".