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Summary Multimorbidity constitutes a serious challenge on the healthcare systems in the world, due to its association with poorer health-related outcomes, more complex clinical management, increases in health service utilization and costs, but a decrease in productivity. However, to date, most evidence on multimorbidity is derived from cross-sectional studies that have limited capacity to understand the pathway of multimorbid conditions. In this article, we present an innovative perspective on analyzing longitudinal data within a statistical framework of survival analysis of time-to-event recurrent data. The proposed methodology is based on a joint frailty modeling approach with multivariate random effects to account for the heterogeneous risk of failure and the presence of informative censoring due to a terminal event. We develop a generalized linear mixed model method for the efficient estimation of parameters. We demonstrate the capacity of our approach using a real cancer registry data set on the multimorbidity of melanoma patients and document the relative performance of the proposed joint frailty model to the natural competitor of a standard frailty model via extensive simulation studies. Our new approach is timely to advance evidence-based knowledge to address increasingly complex needs related to multimorbidity and develop interventions that are most effective and viable to better help a large number of individuals with multiple conditions.

BackgroundThe aims of this study were (1) to estimate 10-year trends in disability-free life expectancy (DFLE) by area-level social disadvantage and (2) to examine how incidence, recovery and mortality transitions contributed to these trends.MethodsData were drawn from the nationally representative Household Income and Labour Dynamics in Australia survey. Two cohorts (baseline age 50+ years) were followed up for 7 years, from 2001 to 2007 and from 2011 to 2017, respectively. Social disadvantage was indicated by the Socio-Economic Indexes for Areas (SEIFA). Two DFLEs based on a Global Activity Limitation Indicator (GALI) and difficulties with activities of daily living (ADLs) measured by the 36-Item Short Form Survey physical function subscale were estimated by cohort, sex and SEIFA tertile using multistate models.ResultsPersons residing in the low-advantage tertile had more years lived with GALI and ADL disability than those in high-advantage tertiles. Across the two cohorts, dynamic equilibrium for GALI disability was observed among men in mid-advantage and high-advantage tertiles, but expansion of GALI disability occurred in the low-advantage tertile. There was expansion of GALI disability for all women irrespective of their SEIFA tertile. Compression of ADL disability was observed for all men and for women in the high-advantage tertile. Compared to the 2001 cohort, disability incidence was lower for the 2011 cohort of men within mid-advantage and high-advantage tertiles, whereas recovery and disability-related mortality were lower for the 2011 cohort of women within the mid-advantage tertile.ConclusionOverall, compression of morbidity was more common in high-advantage areas, whereas expansion of morbidity was characteristic of low-advantage areas. Trends also varied by sex and disability severity.

We present a multilevel frailty model for handling serial dependence and simultaneous heterogeneity in survival data with a multilevel structure attributed to clustering of subjects and the presence of multiple failure outcomes. One commonly observes such data, for example, in multi-institutional, randomized placebo-controlled trials in which patients suffer repeated episodes (eg, recurrent migraines) of the disease outcome being measured. The model extends the proportional hazards model by incorporating a random covariate and unobservable random institution effect to respectively account for treatment-by-institution interaction and institutional variation in the baseline risk. Moreover, a random effect term with correlation structure driven by a first-order autoregressive process is attached to the model to facilitate estimation of between patient heterogeneity and serial dependence. By means of the generalized linear mixed model methodology, the random effects distribution is assumed normal and the residual maximum likelihood and the maximum likelihood methods are extended for estimation of model parameters. Simulation studies are carried out to evaluate the performance of the residual maximum likelihood and the maximum likelihood estimators and to assess the impact of misspecifying random effects distribution on the proposed inference. We demonstrate the practical feasibility of the modeling methodology by analyzing real data from a double-blind randomized multi-institutional clinical trial, designed to examine the effect of rhDNase on the occurrence of respiratory exacerbations among patients with cystic fibrosis.

The recent development of making secondary school education free in Ghana has raised concerns about the level of preparedness of teachers to teach students with diverse needs in one classroom. Sign...

Purpose The purpose of this paper is to assess the differences pertaining to the resources presently accessible for problem-based learning (PBL) among six colleges of Kwame Nkrumah University of Science and Technology (KNUST) in Ghana. Design/methodology/approach Data for the study are the cross-sectional type drawn from 1,020 students. Poisson and zero-inflated Poisson (ZIP) models were implemented on the data to ascertain the variations regarding the extent of resources available for PBL across the colleges of the university. Findings The study outlines the specific resources accessible for PBL across college levels of KNUST. On aggregate, 25.7 per cent reported that their respective colleges have sufficient resources, while 74.3 per cent indicated otherwise. The ZIP model exhibited superiority over the Poisson model, when compared under a Vuong test. As per the ZIP model, none of the colleges appeared to differ significantly in terms of having sufficient resource for PBL. Practical implications Findings are applicable to informed decision-making which targets achieving quality education through the use of PBL. Access to sufficient resources that meet the needs of colleges or departments of a University is emphasized. Originality/value The application of Poisson and ZIP models to aggregated count data in a PBL setting is novel.

Purpose The purpose of this paper is to measure the spatial accessibility of primary healthcare facility in Ghanaian rural areas, by determining the barriers to healthcare accessibilities in the Amansie Central District. Design/methodology/approach Both network and proximity analyses were performed on the digitized data such as road networks, settlements, population, district boundary, natural resources (rivers, streams and forest) and site location (health facilities). To quantify the population who have access to healthcare the authors used the Ghana Health Service access criteria that health facility should be accessible to an estimated population within 8 km radius from the facility. Findings The overall mean distance to the nearest health facility in the district was 8.9 km. Fiankoma sub-district recorded the highest mean distance whereas Tweapease sub-district recorded the least. In general, 31.2 percent of the district population has no access to healthcare facility. Transportation was identified to be one of the major hindrances to healthcare accessibility and this was as a result of poor road network in the district. Research limitations/implications The study was restricted to the Amansie Central District of Ghana. This limits the extent of generalization of results. Originality/value The study proposed additional sites for siting new health facilities base on criteria such as population, distance, centrality and existing infrastructural development. This will consequently improve healthcare accessibility and utilization by increasing total coverage closer to 100 percent.

We report the first analysis of inequalities in Disability-Free Life Expectancy (DFLE) trends for Australia, based on two cohorts of the nationally representative Household Income and Labour Dynamics in Australia survey. Each cohort was aged 45+ at baseline with 7-years of annual follow-up (Older cohort: 2001-2007, n=6363; Younger cohort: 2011-2017, n=8197). Disability was defined by a Global Activity Limitation Indicator, and socioeconomic position (SEP) by an area-level index of disadvantage. Compared to men in high advantage areas, men residing in low advantage areas experienced smaller gains in life expectancy (3.0 vs 4.6 years at age 65), DFLE (0.6 vs 1.8 years) and years with disability (2.4 vs 2.8 years). In contrast, for women in low advantage areas all years gained in life expectancy (2.6 years) were years with disability, whereas women in high advantage areas experienced gains in DFLE (1.7 years) and even more years with disability (2.7 years).

Many medical studies yield data on recurrent clinical events from populations which consist of a proportion of cured patients in the presence of those who experience the event at several times (uncured). A frailty mixture cure model has recently been postulated for such data, with an assumption that the random subject effect (frailty) of each uncured patient is constant across successive gap times between recurrent events. We propose two new models in a more general setting, assuming a multivariate time-varying frailty with an AR(1) correlation structure for each uncured patient and addressing multilevel recurrent event data originated from multi-institutional (multi-centre) clinical trials, using extra random effect terms to adjust for institution effect and treatment-by-institution interaction. To solve the difficulties in parameter estimation due to these highly complex correlation structures, we develop an efficient estimation procedure via an EM-type algorithm based on residual maximum likelihood (REML) through the generalised linear mixed model (GLMM) methodology. Simulation studies are presented to assess the performances of the models. Data sets from a colorectal cancer study and rhDNase multi-institutional clinical trial were analyzed to exemplify the proposed models. The results demonstrate a large positive AR(1) correlation among frailties across successive gap times, indicating a constant frailty may not be realistic in some situations. Comparisons of findings with existing frailty models are discussed.

In the study of multiple failure time data with recurrent clinical endpoints, the classical independent censoring assumption in survival analysis can be violated when the evolution of the recurrent events is correlated with a censoring mechanism such as death. Moreover, in some situations, a cure fraction appears in the data because a tangible proportion of the study population benefits from treatment and becomes recurrence free and insusceptible to death related to the disease. A bivariate joint frailty mixture cure model is proposed to allow for dependent censoring and cure fraction in recurrent event data. The latency part of the model consists of two intensity functions for the hazard rates of recurrent events and death, wherein a bivariate frailty is introduced by means of the generalized linear mixed model methodology to adjust for dependent censoring. The model allows covariates and frailties in both the incidence and the latency parts, and it further accounts for the possibility of cure after each recurrence. It includes the joint frailty model and other related models as special cases. An expectation-maximization (EM)-type algorithm is developed to provide residual maximum likelihood estimation of model parameters. Through simulation studies, the performance of the model is investigated under different magnitudes of dependent censoring and cure rate. The model is applied to data sets from two colorectal cancer studies to illustrate its practical value.

There has been little investigation of how life-course social mobility is linked to Disability-Free Life Expectancy (DFLE). We report novel analysis of the HILDA survey examining how DFLE trends differs by three markers of socio-economic position (SEP): early-life (educational attainment), midlife (occupational level), and late-life (area-disadvantage). All women, irrespective of their educational level, gained years with disability (Age 65: Low education=1.5 and High education=2.5 years). Similar results were obtained by level of occupation, but women with low occupation showed small declines in LE (-0.8 years), all being losses in DFLE. Only women in the most advantaged areas gained DFLE. For men, higher levels of any marker of SEP were associated with DFLE gains that were larger than, or comparable to, gains in years lived with disability, although lower education was associated only with gains in years lived with disability. DFLE trends differ by SEP marker more in women than men.

Background The latest review of studies on multimorbidity patterns showed high heterogeneity in the methodology for identifying groups of multimorbid conditions. However, it is unclear how analytical methods used influence the identified multimorbidity patterns. Methods We undertook a systematic review of analytical methods used to identify multimorbidity patterns in PubMed and EMBASE from their inception to January 2017. We conducted a comparison analysis to assess the effect the analytical methods had on the multimorbidity patterns identified, using the Australian National Health Survey (NHS) 2007-08 data. Results We identified 13 194 studies and excluded 13 091 based on titles/abstracts. From the full-text reviews of the 103 remaining publications, we identified 41 studies that used five different analytical methods to identify multimorbid conditions in the studies. Thirty-seven studies (90%) adopted either the factor-analysis or hierarchical-clustering methods, but heterogeneity arises for the use of different proximity measures within each method to form clusters. Our comparison analysis showed the variation in identified groups of multimorbid conditions when applying the methods to the same NHS data. We extracted main similarities among the groupings obtained by the five methods: (i) cardiovascular and metabolic diseases, (ii) mental health problems and (iii) allergic diseases. Conclusion We showed the extent of effects for heterogeneous analytical methods on identification of multimorbidity patterns. However, more work is needed to guide investigators for choosing the best analytical method to improve the validity and generalizability of findings. Investigators should also attempt to compare results obtained by various methods for a consensus grouping of multimorbid conditions.

We present a multilevel frailty model for handling serial dependence and simultaneous heterogeneity in survival data with a multilevel structure attributed to clustering of subjects and the presence of multiple failure outcomes. One commonly observes such data, for example, in multi-institutional, randomized placebo-controlled trials in which patients suffer repeated episodes (eg, recurrent migraines) of the disease outcome being measured. The model extends the proportional hazards model by incorporating a random covariate and unobservable random institution effect to respectively account for treatment-by-institution interaction and institutional variation in the baseline risk. Moreover, a random effect term with correlation structure driven by a first-order autoregressive process is attached to the model to facilitate estimation of between patient heterogeneity and serial dependence. By means of the generalized linear mixed model methodology, the random effects distribution is assumed normal and the residual maximum likelihood and the maximum likelihood methods are extended for estimation of model parameters. Simulation studies are carried out to evaluate the performance of the residual maximum likelihood and the maximum likelihood estimators and to assess the impact of misspecifying random effects distribution on the proposed inference. We demonstrate the practical feasibility of the modeling methodology by analyzing real data from a double-blind randomized multi-institutional clinical trial, designed to examine the effect of rhDNase on the occurrence of respiratory exacerbations among patients with cystic fibrosis.

In most epidemiological data sets one cannot be certain that all risk factors are measured or observed. This paper studies the risk for mortality associated with diseases and deals with heterogeneity in mortality owing to unobserved covariates. We extracted medical records of 186 hospitalized patients from an urban health facility in Ghana. Patients with at least 2 diagnoses of chronic diseases were considered multimorbid. Using age of patients at death as survival time we executed our analysis with and without incorporating frailty effect to Cox proportional hazards (PH) model. The Cox PH model with Gaussian frailty fitted the data better when compared to the standard Cox PH model and Cox PH model with gamma frailty. On average patients were aged 62.315.3 years, with 66.1% being multimorbid. Varying degrees of mortality risks were found for different diseases, with the highest associated with having pulmonary valve disorders (HR 7.99, 95% CI 1.45-44.0). Heterogeneity in mortality resulting from unmeasured factors was insignifi- cant. This study demonstrates that patients share similar risk with respect to unobserved factors, but varying risk when conditioned on observables.