Density plot over disposable household income per adult and choice of PHCC.

Density plot over disposable household income per adult and choice of PHCC.

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Objective To assess socioeconomic differences between patients registered with private and public primary healthcare centres. Design Population-based cross-sectional study controlling for municipality and household. Setting Swedish population-based socioeconomic data collected from Statistics Sweden linked with individual registration data from a...

Contexts in source publication

Context 1
... individuals living in municipalities without alternative PHCCs to choose from were excluded (samples 1 and 2), the results show the same trends but with a slightly higher magnitude. To further assess the robustness of the results, a density plot was generated (see figure 2). ...
Context 2
... density plot in figure 2 reveals that the likelihood of registering with a private PHCC is consistently based on disposable income, that is, up until a certain level of income an individual is more likely to register with a public PHCC. After that level, individuals are more likely to register with a private PHCC. ...

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... As summarized in a couple of reviews (Burstrom et al., 2017;Fredriksson and Isaksson, 2022), several signs of inequitable consequences have been reported, such as those relating to the location of new private health care centers (Isaksson et al., 2016), composition of patients (Isaksson et al., 2018), and PHC utilization (Beckman and Anell, 2013). Evaluations of PHC-related population impact of the reform are so far sparse, with only two recent studies, reporting no effects on avoidable hospitalizations (Dietrichson et al., 2020;Mosquera et al., 2021), but with worse outcomes in regions with long-term dominance of private providers (Mosquera et al., 2021). ...
... An increasing number of private health providers and the free right to establish care centers in any location has, in recent years, reduced the number of child and maternal care units in poorer areas where the need is greater [24]. There is also evidence of a preference for establishing healthcare facilities in high-income areas among private providers [25]. Also, the virtualization of primary care likely contribute to HCU inequalities, considering higher rates of digital HCU among individuals with higher education and income, even after adjusting for age and prevalence of chronic diseases [26]. ...
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Background Despite universal healthcare, socioeconomic differences in healthcare utilization (HCU) persist in modern welfare states. However, little is known of how HCU inequalities has developed over time. The aim of this study is to assess time trends of differences in utilization of primary and specialized care for the lowest (Q1) and highest (Q5) income quantiles and compare these to mortality. Methods and findings Using a repeated cross-sectional register-based study design, data on utilization of (i) primary; (ii) specialized outpatient; and (iii) inpatient care, as well as (iv) cause of death, were linked to family income and sociodemographic control variables (for instance, country of origin and marital status). The study sample comprised all individuals 16 years or older residing in Sweden any year during the study period and ranged from 7.1 million in year 2004 to 8.0 million year 2017. HCU and mortality for all disease as well as for the 5 disease groups causing most deaths were compared for the Q1 and Q5 using logistic regression, adjusting for sex, age, marital status, and birth country. The primary outcome measures were adjusted odds ratios (ORs), and regression coefficients of annual changes in these ORs log-transformed. Additionally, we conducted negative binominal regression to calculate adjusted rate ratios (RRs) comparing Q1 and Q5 with regard to number of disease specific healthcare encounters ≤5 years prior to death. In 2017, for all diseases combined, Q1 utilized marginally more primary and specialized outpatient care than Q5 (OR 1.07, 95% CI [1.07, 1.08]; p < 0.001, and OR 1.04, 95% CI [1.04, 1.05]; p < 0.001, respectively), and considerably more inpatient care (OR 1.44, 95% CI [1.43, 1.45]; p < 0.001). The largest relative inequality was observed for mortality (OR 1.78, 95% CI [1.74, 1.82]; p < 0.001). This pattern was broadly reproduced for each of the 5 disease groups. Time trends in HCU inequality varied by level of care. Each year, Q1 (versus Q5) used more inpatient care and suffered increasing mortality rates. However, utilization of primary and specialized outpatient care increased more among Q5 than in Q1. Finally, group differences in number of healthcare encounters ≤5 years prior to death demonstrated a similar pattern. For each disease group, primary and outpatient care encounters were fewer in Q1 than in Q5, while inpatient encounters were similar or higher in Q1. A main limitation of this study is the absence of data on self-reported need for care, which impedes quantifications of HCU inequalities each year. Conclusions Income-related differences in the utilization of primary and specialized outpatient care were considerably smaller than for mortality, and this discrepancy widened with time. Facilitating motivated use of primary and outpatient care among low-income groups could help mitigate the growing health inequalities.
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... This may indicate that the reform led to a decrease in geographical equity because more affluent areas got more new providers. In line with this, another study by Isaksson et al. [53] analysed socioeconomic differences in patients registered with private compared to public PHC centres. The results confirmed that individuals with higher socioeconomic status were more likely to be registered with a private PHC centre than individuals with lower socioeconomic status. ...
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