ArticlePharmacoEconomics2007
Do drugs reduce utilisation of other healthcare resources?
Article in PharmacoEconomics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Use of healthcare services and expenditure in the US in 2025: The effect of obesity and morbid obesity.PloS one · 2018Article
- The current state of drug discovery and a potential role for NMR metabolomics.Journal of medicinal chemistry · 2014Review
- Pharmacoeconomics and aging.Drugs & aging · 2009Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDrug expenditures per capita have drastically increased over the last quarter century in Canada, with a share of overall healthcare costs rising from 8.8% in 1980 to 16.8% in 2002. Pressure to curb expenditure on drugs has increased accordingly, but containing drug expenditure might increase costs elsewhere in the healthcare sector.
objectiveTo measure substitution patterns between drugs and other healthcare resources over the last 25 years and thus assess whether containing drug costs might result in higher expenditure elsewhere in the healthcare system. METHODS AND DATA: A production function approach was used, in which life expectancy was modelled as a function of per capita drugs and non-drug healthcare resources, among other factors. Estimates are used to calculate a marginal rate of substitution, or trade-off, between drugs and non-drug healthcare resources, for a given level of life expectancy in the population. The model is estimated from a societal perspective, with panel data techniques using Canadian provincial-level data on health expenditure and spending on physicians per capita for the period 1980-2002, as well as individual survey data on lifestyle habits such as cigarette consumption and body mass index.
resultUsing life expectancy at birth for males as the production function, increasing drug spending by Can 1.00 dollars (constant 2003 values) was accompanied by a decrease of Can 1.48 dollars in non-drug, non-physician healthcare resources over the study period, without affecting life expectancy at birth. Results using life expectancy at birth for females as the production function showed a decrease of Can 1.05 dollars in non-drug, non-physician healthcare resources over the same period.
conclusionUsing life expectancy as a general health indicator, results suggest that increases in drug spending could be more than offset by decreases in other healthcare spending without affecting the health of the population. This suggests that better access to drugs may be an effective strategy to decrease overall healthcare costs. Freeing up healthcare dollars by reallocating spending towards drugs could provide opportunities for overall healthcare cost savings without negatively impacting the health of the population.
Indexed as
Identifiers
17335307What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.