Search

Background: Stroke registers are recommended as a key priority by the Lancet Neurology World Stroke Organization Commission for Stroke, 2023, and the African Stroke Leaders' Summit, 2022. Aims: This scoping review aims to map where stroke registers have been implemented in Sub-Saharan Africa (SSA). The article then compares and critiques the methods and definitions used and summarizes key results from the registers. The scoping review searched EMBASE, MEDLINE, and CABI Global Health databases and included all studies with a prospective longitudinal design in SSA, where adult acute stroke was the primary condition studied. Articles were screened against inclusion and exclusion criteria independently by two authors. Summary: We identified 42 unique stroke registers from 48 individual studies. The registers were located in 19 countries, with 19 from East Africa, 15 West Africa, 6 Central Africa, and 2 from Southern Africa. Cumulatively, the registers recruited 12,345 participants with stroke, the median number of participants was 183 (interquartile range (IQR): 121–312), and the range was 50–1018. Only one study was a population-based register, and 41 were hospital-based registers. Of the hospital-based registers, 29 were single site, 10 were conducted at two sites, and 2 at three sites. Twenty-three (54.7%) of the registers were located in the capital city of their respective country, and only one of the hospital-based registers was in a self-described rural area. Length of recruitment ranged from 4 months to 6 years; the median length of recruitment was 12 months. Methodology and definitions were heterogenous between the registers. Only seven (19.4%) registers referenced the WHO STEPwise approach to implementing stroke registers. Twenty-seven (64.3%) registers used the WHO definition of stroke. The mean neuroimaging rate was 84%, and ranged from 0% to 100%. Stroke severity was measured using the National Institute of Health Stroke Scale (NIHSS) in 22 (52.4%) registers, four registers used the Glasgow Coma Scale (GCS), two registers used the miniNIHSS, one used the Scandinavian Stroke Scale, one modified Rankin Scale (mRS), and 11 registers did not report a stroke severity measure. Seventeen (40.5%) registers used the mRS to measure function, six registers used Barthel Index alone, and three registers used both mRS and Barthel Index. Only two registers included a quality-of-life measure, the EQ-5D. Eight registers included a quality-of-care measure, and 26 (61.9%) registers recorded socioeconomic status or a socioeconomic status proxy, most frequently educational attainment. Conclusions: This scoping review found high heterogeneity of methods and definitions used by stroke registers, with low uptake of the WHO stepwise method of stroke surveillance. A drive to standardize methodology would improve the comparability of stroke data in SSA. The shared use of educational attainment by registers in our review may enable future meta-analyses of inequities in stroke in SSA. Incorporating health-related quality-of-life measures, such as EQ-5D, into stroke registers should be encouraged, bringing a patient perspective, and allow the estimation of quality-adjusted life years lost to stroke. Agreement on a standardized register methodology or further promotion and uptake of the WHO stepwise method is essential to produce comparable data to improve stroke prevention and care. [ABSTRACT FROM AUTHOR]

Background: Stroke is a leading cause of mortality and negatively affects health-related quality of life (HRQoL). HRQoL after stroke is understudied in Africa and there are no reports of quality-adjusted life years after stroke (QALYs) in African countries. We determined the impact of stroke on HRQoL after stroke in Sierra Leone. We calculated QALYs at 1 year post-stroke and determined sociodemographic and clinical variables associated with HRQoL and QALYs in this population. Methods: A prospective stroke register was established at the two-principal adult tertiary government hospitals in Freetown, Sierra Leone. Participants were followed up at 7, 90 days, and 1 year post-stroke to capture all-cause mortality and EQ-5D-3L data. QALYs were calculated at the patient level using EQ-5D-3L utility values and survival data from the register, following the area under the curve method. Utilities were based on the UK and Zimbabwe (as a sensitivity analysis) EQ-5D value sets, as there is no Sierra Leonean or West African value set. Explanatory models were developed based on previous literature to assess variables associated with HRQoL and QALYs at 1 year after stroke. To address missing values, Multiple Imputation by Chained Equations (MICE), with linear and logistic regression models for continuous and binary variables, respectively, were used. Results: EQ-5D-3L data were available for 373/460 (81.1%), 360/367 (98.1%), and 299/308 (97.1%) participants at 7, 90 days, and 1 year after stroke. For stroke survivors, median EQ-5D-3L utility increased from 0.20 (95% CI: −0.16 to 0.59) at 7 days post-stroke to 0.76 (0.47 to 1.0) at 90 days and remained stable at 1 year 0.76 (0.49 to 1.0). Mean QALYs at 1 year after stroke were 0.28 (SD: 0.35) and closely associated with stroke severity. Older age, lower educational attainment, patients with subarachnoid hemorrhage and undetermined stroke types all had lower QALYs and lower HRQoL, while being the primary breadwinner was associated with higher HRQoL. Sensitivity analysis with the Zimbabwe value set did not significantly change regression results but did influence the absolute values with Zimbabwe utility values being higher, with fewer utility values less than 0. Conclusion: We generated QALYs after stroke for the first time in an African country. QALYs were significantly lower than studies from outside Africa, partially explained by the high mortality rate in our cohort. Further research is needed to develop appropriate value sets for West African countries and to examine QALYs lost due to stroke over longer time periods. Data availability: The Stroke in Sierra Leone anonymized dataset is available on request to researchers, see data access section. [ABSTRACT FROM AUTHOR]

Background: 'High-cost' individuals with multimorbidity account for a disproportionately large share of healthcare costs and are at most risk of poor quality of care and health outcomes. Aim: To compare high-cost with lower-cost individuals with multimorbidity and assess whether these populations can be clustered based on similar disease patterns. Design and setting: A cross-sectional study based on 2019/2020 electronic medical records from adults registered to primary care practices (n = 41) in a London borough. Method: Multimorbidity is defined as having ≥2 long-term conditions (LTCs). Primary care costs reflected consultations, which were costed based on provider and consultation types. High cost was defined as the top 20% of individuals in the cost distribution. Descriptive analyses identified combinations of 32 LTCs and their contribution to costs. Latent class analysis explored clustering patterns. Results: Of 386 238 individuals, 101 498 (26%) had multimorbidity. The high-cost group (n = 20 304) incurred 53% of total costs and had 6833 unique disease combinations, about three times the diversity of the lower-cost group (n = 81 194). The trio of anxiety, chronic pain, and depression represented the highest share of costs (5%). High-cost individuals were best grouped into five clusters, but no cluster was dominated by a single LTC combination. In three of five clusters, mental health conditions were the most prevalent. Conclusion: High-cost individuals with multimorbidity have extensive heterogeneity in LTCs, with no single LTC combination dominating their primary care costs. The frequent presence of mental health conditions in this population supports the need to enhance coordination of mental and physical health care to improve outcomes and reduce costs. [ABSTRACT FROM AUTHOR]

Women of reproductive age are a group of particular concern with regards to vaccine uptake, related to their unique considerations of menstruation, fertility, and pregnancy. To obtain vaccine uptake data specific to this group, we obtained vaccine surveillance data from the Office for National Statistics, linked with COVID-19 vaccination status from the National Immunisation Management Service, England, from 8 Dec 2020 to 15 Feb 2021; data from 13,128,525 such women at population-level, were clustered by age (18–29, 30–39, and 40–49 years), self-defined ethnicity (19 UK government categories), and index of multiple deprivation (IMD, geographically-defined IMD quintiles). Here we show that among women of reproductive age, older age, White ethnicity and being in the least-deprived index of multiple deprivation are each independently associated with higher vaccine uptake, for first and second doses; however, ethnicity exerts the strongest influence (and IMD the weakest). These findings should inform future vaccination public messaging and policy.Women of reproductive age may have specific concerns relating to perceived impacts on fertility and menstrual cycles that make them hesitant to receive COVID-19 vaccination. In this study, the authors explore COVID-19 vaccine uptake rates in women of reproductive age using linked data for ~13 million women in England. [ABSTRACT FROM AUTHOR]

Background: People with multimorbidity have complex healthcare needs. Some co-occurring diseases interact with each other to a larger extent than others and may have a different impact on primary care use.Aim: To assess the association between multimorbidity clusters and primary care consultations over time.Design and Setting: A retrospective longitudinal (panel) study design was used. Data comprised electronic primary care health records of 826 166 patients registered at GP practices in an ethnically diverse, urban setting in London between 2005 and 2020.Method: Primary care consultation rates were modelled using generalised estimating equations. Key controls included the total number of long-term conditions, five multimorbidity clusters, and their interaction effects, ethnic group, and polypharmacy (proxy for disease severity). Models were also calibrated by consultation type and ethnic group.Results: Individuals with multimorbidity used two to three times more primary care services than those without multimorbidity (incidence rate ratio 2.30, 95% confidence interval = 2.29 to 2.32). Patients in the alcohol dependence, substance dependence, and HIV cluster (Dependence+) had the highest rate of increase in primary care consultations as additional long-term conditions accumulated, followed by the mental health cluster (anxiety and depression). Differences by ethnic group were observed, with the largest impact in the chronic liver disease and viral hepatitis cluster for individuals of Black or Asian ethnicity.Conclusion: This study identified multimorbidity clusters with the highest primary care demand over time as additional long-term conditions developed, differentiating by consultation type and ethnicity. Targeting clinical practice to prevent multimorbidity progression for these groups may lessen future pressures on primary care demand by improving health outcomes. [ABSTRACT FROM AUTHOR]

Background: Managing multimorbidity is complex for both patients and healthcare systems. Patients with multimorbidity often use a variety of primary and secondary care services. Country-specific research exploring the healthcare utilisation and cost consequences of multimorbidity may inform future interventions and payment schemes in the UK.Aim: To assess the relationship between multimorbidity, healthcare costs, and healthcare utilisation; and to determine how this relationship varies by disease combinations and healthcare components.Design and Setting: A systematic review.Method: This systematic review followed the bidirectional citation searching to completion method. MEDLINE and grey literature were searched for UK studies since 2004. An iterative review of references and citations was completed. Authors from all articles selected were contacted and asked to check for completeness of UK evidence. The National Institutes of Health National Heart, Lung, and Blood Institute quality assessment tool was used to assess risk of bias. Data were extracted, findings synthesised, and study heterogeneity assessed; meta-analysis was conducted when possible.Results: Seventeen studies were identified: seven predicting healthcare costs and 10 healthcare utilisation. Multimorbidity was found to be associated with increased total costs, hospital costs, care transition costs, primary care use, dental care use, emergency department use, and hospitalisations. Several studies demonstrated the high cost of depression and of hospitalisation associated with multimorbidity.Conclusion: In the UK, multimorbidity increases healthcare utilisation and costs of primary, secondary, and dental care. Future research is needed to examine whether integrated care schemes offer efficiencies in healthcare provision for multimorbidity. [ABSTRACT FROM AUTHOR]

Quality of care worries and rising costs have resulted in a widespread interest in enhancing the efficiency of health care delivery. One area of increasing interest is in promoting teamwork as a way of coordinating efforts to reduce costs and improve quality, and identifying the characteristics of the work environment that support teamwork. Relational climate is a measure of the work environment that captures shared employee perceptions of teamwork, conflict resolution, and diversity acceptance. Previous research has found a positive association between relational climate and quality of care, yet its relationship with costs remains unexplored. We examined the influence of primary care relational climate on health care costs incurred by diabetic patients at the U.S. Department of Veterans Affairs between 2008 and 2012. We found that better relational climate is significantly related to lower costs. Clinics with the strongest relational climate saved $334 in outpatient costs per patient compared with facilities with the weakest score in 2010. The total outpatient cost saving if all clinics achieved the top 5% relational climate score was $20 million. Relational climate may contribute to lower costs by enhancing diabetic treatment work processes, especially in outpatient settings. [ABSTRACT FROM AUTHOR]

Correction to: I Nature Communications i https://doi.org/10.1038/s41467-023-36125-8 published online 22 February 2023 The original version of this Article omitted a statement in the Acknowledgements recognising the UK Health Security Agency for providing data. This has been added to the Acknowledgements in both the PDF and HTML versions of the Article. [Extracted from the article]