It seems that income-related inequality and inequity in the use of dental services narrowed only temporarily after the reform.

The use of oral health care services rose and age did not seem to be a barrier to the use of oral health care services after the reform, as was the aim of the reform. No change in the association of household income with the use of oral health care services was seen after the OHCR.

Objectives In Finland, a dental subsidization reform, implemented in 2001-2002, abolished age restrictions on subsidized dental care. The aim of this study was to investigate income-related inequality in the perceived oral health and its determinants among adult Finns before and after the reform. Materials and methods Three identical cross-sectional nationally representative postal surveys, concerning perceived oral health and the use of dental services among people born before 1971, were conducted in 2001 (n = 2157), in 2004 (n = 1814) and in 2007 (n = 1671). Three measures of perceived oral health were used: toothache or oral discomfort during the past 12 months, current need for dental care and self-reported oral health status. Concentration index was used to analyse the income-related inequalities. Its decomposition was used to study factors related to the inequalities. Results The proportion of respondents reporting need for dental care decreased from 2001 to 2007, while no changes were seen in reports of toothache or self-reported oral health status. Income-related inequalities in reports of toothache and perceived need for care widened, while the inequality in self-reported oral health remained stable. Most of the inequalities were related to income itself, perceived general health and the time since the last visit to dental care. Conclusions It seems that the income-related inequalities in perceived oral health remained or even widened after the reform.

Background Healthcare costs are rising, and a substantial proportion of medical care is of little value. De-implementation of low-value practices is important for improving overall health outcomes and reducing costs. We aimed to identify and synthesize randomized controlled trials (RCTs) on de-implementation interventions and to provide guidance to improve future research. Methods MEDLINE and Scopus up to May 24, 2021, for individual and cluster RCTs comparing de-implementation interventions to usual care, another intervention, or placebo. We applied independent duplicate assessment of eligibility, study characteristics, outcomes, intervention categories, implementation theories, and risk of bias. Results Of the 227 eligible trials, 145 (64%) were cluster randomized trials (median 24 clusters; median follow-up time 305 days), and 82 (36%) were individually randomized trials (median follow-up time 274 days). Of the trials, 118 (52%) were published after 2010, 149 (66%) were conducted in a primary care setting, 163 (72%) aimed to reduce the use of drug treatment, 194 (85%) measured the total volume of care, and 64 (28%) low-value care use as outcomes. Of the trials, 48 (21%) described a theoretical basis for the intervention, and 40 (18%) had the study tailored by context-specific factors. Of the de-implementation interventions, 193 (85%) were targeted at physicians, 115 (51%) tested educational sessions, and 152 (67%) multicomponent interventions. Missing data led to high risk of bias in 137 (60%) trials, followed by baseline imbalances in 99 (44%), and deficiencies in allocation concealment in 56 (25%). Conclusions De-implementation trials were mainly conducted in primary care and typically aimed to reduce low-value drug treatments. Limitations of current de-implementation research may have led to unreliable effect estimates and decreased clinical applicability of studied de-implementation strategies. We identified potential research gaps, including de-implementation in secondary and tertiary care settings, and interventions targeted at other than physicians. Future trials could be improved by favoring simpler intervention designs, better control of potential confounders, larger number of clusters in cluster trials, considering context-specific factors when planning the intervention (tailoring), and using a theoretical basis in intervention design. Registration OSF Open Science Framework hk4b2