Socioeconomic Inequalities In Receipt Research Articles

Socioeconomic Inequalities in Care Receipt at Older Ages: A Comparative European Study

This study is among the first to investigate whether and how socioeconomic inequalities in the receipt of formal and/or informal care by disabled older adults vary across long-term care (LTC) systems. We link data from the SHARE survey with LTC system indicators for 136 regions in 12 European countries in 2015. Using multinomial multilevel models with cross-level interactions, we test whether and how income and wealth gradients in the receipt of only informal, only formal, and mixed care vary with the number of beds in residential LTC facilities across regions. We find pro-rich inequalities in the receipt of formal and mixed care only in regions with low or intermediate numbers of LTC beds, and no inequalities in regions with greater availability of residential beds. Our findings suggest that the presence of extensive formal LTC services may lead to a fairer distribution of formal and mixed care use across socioeconomic groups.

Comorbidity prevalence among cancer patients: a population-based cohort study of four cancers

BackgroundThe presence of comorbidity affects the care of cancer patients, many of whom are living with multiple comorbidities. The prevalence of cancer comorbidity, beyond summary metrics, is not well known. This study aims to estimate the prevalence of comorbid conditions among cancer patients in England, and describe the association between cancer comorbidity and socio-economic position, using population-based electronic health records.MethodsWe linked England cancer registry records of patients diagnosed with cancer of the colon, rectum, lung or Hodgkin lymphoma between 2009 and 2013, with hospital admissions records. A comorbidity was any one of fourteen specific conditions, diagnosed during hospital admission up to 6 years prior to cancer diagnosis. We calculated the crude and age-sex adjusted prevalence of each condition, the frequency of multiple comorbidity combinations, and used logistic regression and multinomial logistic regression to estimate the adjusted odds of having each condition and the probability of having each condition as a single or one of multiple comorbidities, respectively, by cancer type.ResultsComorbidity was most prevalent in patients with lung cancer and least prevalent in Hodgkin lymphoma patients. Up to two-thirds of patients within each of the four cancer patient cohorts we studied had at least one comorbidity, and around half of the comorbid patients had multiple comorbidities. Our study highlighted common comorbid conditions among the cancer patient cohorts. In all four cohorts, the odds of having a comorbidity and the probability of multiple comorbidity were consistently highest in the most deprived cancer patients.ConclusionsCancer healthcare guidelines may need to consider prominent comorbid conditions, particularly to benefit the prognosis of the most deprived patients who carry the greater burden of comorbidity. Insight into patterns of cancer comorbidity may inform further research into the influence of specific comorbidities on socio-economic inequalities in receipt of cancer treatment and in short-term mortality.

The role of receipt and timeliness of treatment in socioeconomic inequalities in lung cancer survival: population-based, data-linkage study

BackgroundLung cancer survival is socioeconomically patterned, and socioeconomic inequalities in receipt of treatment have been demonstrated. In England, there are target waiting times for the referral (14 days) and treatment...

The role of patient, tumour and system factors in socioeconomic inequalities in lung cancer treatment: population-based study.

Background:Reducing socioeconomic inequalities in lung cancer treatment may reduce survival inequalities. However, the reasons for treatment variation are unclear.Methods:Northern and Yorkshire cancer registry, Hospital Episode Statistics and lung cancer audit data sets were linked. Logistic regression was used to explore the role of stage, histology, performance status and comorbidity in socioeconomic inequalities in lung cancer treatment, for 28 733 lung cancer patients diagnosed in 2006–2010, and in a subgroup with stage recorded (n=7769, 27%).Results:Likelihood of receiving surgery was significantly lower in the most deprived group (odds ratio (OR)=0.75, 95% confidence interval (CI) 0.65–0.86); however, the OR was attenuated when including histological subtype (OR=0.82, 95% CI 0.71–0.96). Patients in the most deprived group were significantly less likely to receive chemotherapy in the fully adjusted full cohort model including performance status (OR=0.64, 95% CI 0.58–0.72) but not in the staged subgroup model when performance status was included (OR=0.88, 95% CI 0.72–1.08). Socioeconomic inequalities in radiotherapy were not found.Interpretation:Socioeconomic inequalities in performance status statistically explained socioeconomic inequalities in receipt of chemotherapy in the selective staged subgroup, but not in the full cohort. Socioeconomic variation in histological subtype may account for some of the socioeconomic inequalities in surgery.

Socio-economic inequalities in patient, primary care, referral, diagnostic, and treatment intervals on the lung cancer care pathway: protocol for a systematic review and meta-analysis

BackgroundEarly diagnosis and treatment of cancer is thought to be important for improving survival. Longer time between the onset of cancer symptoms and receipt of treatment may help explain the poorer survival of UK cancer patients compared to that in other countries.Socio-economic inequalities in receipt of, and time to, treatment may contribute to socio-economic differences in cancer survival. Socio-economic inequalities in receipt of lung cancer treatment have been shown in a recent systematic review. However, no systematic review of the evidence for socio-economic inequalities in time to presentation (patient interval), time to first investigation (primary care interval), time to secondary care investigation (referral interval), time to diagnosis (diagnostic interval), and time to treatment (treatment interval) has been conducted.This review aims to assess the published and grey literature evidence for socio-economic inequalities in the length of time spent on the lung cancer diagnostic and treatment pathway, examining interim intervals on the pathway where inequalities might occur.MethodsSystematic methods will be used to identify relevant studies, assess study eligibility for inclusion, and evaluate study quality. The online databases of MEDLINE, EMBASE, and CINAHL will be searched to locate cohort studies of adults with a primary diagnosis of lung cancer; where the outcome is mean or median time to the interval endpoint (or a suitable proxy measure of this), or the likelihood of longer or shorter time to the endpoint; analysed by a measure of socio-economic position. Meta-analysis will be conducted if there are sufficient studies available with suitable data.DiscussionThis review will systematically determine if there are socio-economic inequalities in time from symptom onset to treatment for lung cancer. If such inequalities are present, our review evidence will help inform the development of interventions to reduce the time to diagnosis and treatment, ultimately helping to reduce socio-economic inequalities in survival.Trial registrationPROSPERO CRD42014007145

The effect of socioeconomic inequalities in receipt of, and time to, treatment on socioeconomic inequalities in lung cancer survival: an observational, data-linkage study

BackgroundSocioeconomic inequalities in lung cancer survival have been suggested to be at least partly caused by inequalities in receipt of treatment. In England, there are target waiting times for referral (14 days to first hospital appointment) and treatment (31 days from diagnosis) for suspected cancer. In a systematic review and meta-analysis, we found evidence of socioeconomic inequalities in receipt of lung cancer treatment in the UK, USA, and elsewhere. Northern and Yorkshire Cancer Registry and Information Centre (NYCRIS), Hospital Episode Statistics (HES), and lung cancer audit (LUCADA) data were linked via NHS number to investigate the effect of socioeconomic inequalities in receipt of and time to treatment on lung cancer survival. MethodsNYCRIS data for 22 967 patients diagnosed in 2006–09 with a primary diagnosis of lung cancer (ICD 10 C33 and C34) were analysed, including information about referral date and type and date of treatment. Socioeconomic position (SEP) was measured with the income domain of the index of multiple deprivation. Stage and performance status (a measure of patient wellbeing) were recorded in LUCADA for 5233 (23%) of the cohort. Comorbidity score was obtained from HES for 17 621 (77%). Survival time was from date of diagnosis to death or end of follow-up on Dec 31, 2011. Cox regression models were used to calculate hazard of death and logistic regression to examine likelihood of being alive 2 years after diagnosis, by SEP, in the full cohort and the subset with stage recorded. Findings3513 (15%) of 22 967 patients were alive 2 years after diagnosis. This proportion increased to 1562 (70%) of 2236 who had surgery. Patients referred (odds ratio [OR] 0·76, 95% CI 0·69–0·84; p<0·0001) and treated (0·37, 0·34–0·41; p<0·0001) within guideline targets had a lower likelihood of surviving to 2 years compared with those referred and treated later. Socioeconomic inequalities in survival were found in a multivariable analysis controlling for stage and performance status, with those in the lowest socioeconomic group significantly less likely to be alive after 2 years compared with those in the highest group (OR 0·78, 95% CI 0·69–0·88; p<0·0001). Timely referral did not change this result. However, when receipt of treatment was included, the association was no longer significant (OR 0·87, 95% CI 0·75–1·00; p=0·06). Timeliness of treatment did not alter the conclusion. InterpretationSocioeconomic inequalities in survival from lung cancer were statistically explained by socioeconomic inequalities in receipt of treatment, but not by inequalities in timeliness of referral and treatment. We were unable to examine smoking status and there might be residual confounding from this and other factors. Missing stage and performance status data are an important limitation. However, patterns of survival in the subset of patients with stage data were similar to those in the full cohort. Research into unexplained variance in treatment rates is needed to develop interventions that address socioeconomic inequalities in receipt of treatment and survival. This factor might have a greater effect on improvement of survival than the present clinical focus on time-interval targets. FundingLFF (ESRC studentship ES/I020926/1) is a PhD student funded by ESRC as a member of Fuse, the Centre for Translational Research in Public Health (www.fuse.ac.uk). JA, MW, and GR are funded in part as a staff member (JA), director (MW), and senior investigator (GR) of Fuse. Fuse is a UK Clinical Research Collaboration (UKCRC) Public Health Research Centre of Excellence. Funding for Fuse from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, MRC, NIHR, under the auspices of the UKCRC, is acknowledged. The views expressed do not necessarily represent those of the funders or UKCRC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

OP50 Socio-Economic Inequalities in Lung Cancer Treatment: The Role of Histological Subtype and Performance Status

BackgroundSocio-economic inequalities in receipt of lung cancer treatment have been demonstrated in our recently published systematic review and meta-analysis, in both universal (UHCS) and non-universal healthcare systems. These findings could...

Why are Socioeconomic Inequalities in Receipt of Treatment Found for Lung Cancer?

177 ISSN 1758-1966 10.2217/LMT.13.12 © 2013 Future Medicine Ltd Lung Cancer Manage. (2013) 2(3), 177–180 Intervention-generated inequalities We would hope that interventions to improve health would also reduce health inequalities, but this is not always the case [1]. Evidence is accumulating that effective treatments for lung cancer are not equally received by individuals across the socioeconomic spectrum [2]. Intervention-generated inequalities (IGIs) have been described as “unintended (and unwanted) variations in outcomes, across population subgroups, that result from any element of any health intervention” [3], where, although overall population health may improve as the result of an intervention, differences in access to the intervention, differential uptake, delays in uptake and differential compliance with, or differences in effectiveness of, an intervention may result in inequalities in outcome [3,4]. The IGI framework expands on previous equity hypotheses that have attempted to describe variations in the provision and uptake of interventions. The ‘inverse care law’ states that “the availability of good quality healthcare is inversely related to need in the population served” [5]. The ‘inverse prevention law’ suggests that a similar socioeconomic gradient also exists in health prevention [6], and the ‘inverse equity hypothesis’ proposed that public health interventions initially widen socioeconomic inequalities due to preferential uptake by more advantaged groups before the less advantaged follow suit, eventually improving health overall [7]. However, none of the above equity laws/hypotheses consider how interventions can be designed to reduce inequality. In order to expand these equity laws into an evidence-based framework and model of intervention inequality, it is necessary to examine all the possible stages where inequalities may be introduced and consider ways in which these can be addressed, as it is likely that IGIs contribute to overall socioeconomic inequalities in morbidity and mortality [3].

Socioeconomic Inequalities in Lung Cancer Treatment: Systematic Review and Meta-Analysis

Intervention-generated inequalities are unintended variations in outcome that result from the organisation and delivery of health interventions. Socioeconomic inequalities in treatment may occur for some common cancers. Although the incidence and outcome of lung cancer varies with socioeconomic position (SEP), it is not known whether socioeconomic inequalities in treatment occur and how these might affect mortality. We conducted a systematic review and meta-analysis of existing research on socioeconomic inequalities in receipt of treatment for lung cancer. MEDLINE, EMBASE, and Scopus were searched up to September 2012 for cohort studies of participants with a primary diagnosis of lung cancer (ICD10 C33 or C34), where the outcome was receipt of treatment (rates or odds of receiving treatment) and where the outcome was reported by a measure of SEP. Forty-six papers met the inclusion criteria, and 23 of these papers were included in meta-analysis. Socioeconomic inequalities in receipt of lung cancer treatment were observed. Lower SEP was associated with a reduced likelihood of receiving any treatment (odds ratio [OR] = 0.79 [95% CI 0.73 to 0.86], p<0.001), surgery (OR = 0.68 [CI 0.63 to 0.75], p<0.001) and chemotherapy (OR = 0.82 [95% CI 0.72 to 0.93], p = 0.003), but not radiotherapy (OR = 0.99 [95% CI 0.86 to 1.14], p = 0.89), for lung cancer. The association remained when stage was taken into account for receipt of surgery, and was found in both universal and non-universal health care systems. Patients with lung cancer living in more socioeconomically deprived circumstances are less likely to receive any type of treatment, surgery, and chemotherapy. These inequalities cannot be accounted for by socioeconomic differences in stage at presentation or by differences in health care system. Further investigation is required to determine the patient, tumour, clinician, and system factors that may contribute to socioeconomic inequalities in receipt of lung cancer treatment.

PL03 Socio-Economic Inequalities in Lung Cancer Treatment: A Systematic Review and Meta-Analysis

BackgroundIntervention-generated inequalities in health result from the way that health interventions are organised and delivered. There is some evidence that socio-economic inequalities in care may occur for some common cancers...