BackgroundThe importance of viewing health from a broader perspective than the mere presence or absence of disease is critical at primary healthcare level. However, there is scanty evidence-based stratification of population health using other criteria than morbidity-related indicators in developing countries. We propose a novel stratification of population health based on cognitive, functional and social disability and its covariates at primary healthcare level in DR Congo.MethodWe conducted a community-based cross-sectional study in adults with diabetes or hypertension, mother-infant pairs with child malnutrition, their informal caregivers and randomly selected neighbours in rural and sub-urban health zones in South-Kivu Province, DR Congo. We used the WHO Disability Assessment Schedule 2.0 (WHODAS) to measure functional, cognitive and social disability. The study outcome was health status clustering derived from a principal component analysis with hierarchical clustering around the WHODAS domains scores. We calculated adjusted odds ratios (AOR) using mixed-effects ordinal logistic regression.ResultsOf the 1609 respondents, 1266 had WHODAS data and an average age of 48.3 (SD: 18.7) years. Three hierarchical clusters were identified: 9.2% of the respondents were in cluster 3 of high dependency, 21.1% in cluster 2 of moderate dependency and 69.7% in cluster 1 of minor dependency. Associated factors with higher disability clustering were being a patient compared to being a neighbour (AOR: 3.44; 95% CI: 1.93–6.15), residency in rural Walungu health zone compared to semi-urban Bagira health zone (4.67; 2.07–10.58), female (2.1; 1.25–2.94), older (1.05; 1.04–1.07), poorest (2.60; 1.22–5.56), having had an acute illness 30 days prior to the interview (2.11; 1.24–3.58), and presenting with either diabetes or hypertension (2.73; 1.64–4.53) or both (6.37; 2.67–15.17). Factors associated with lower disability clustering were being informally employed (0.36; 0.17–0.78) or a petty trader/farmer (0.44; 0.22–0.85).ConclusionHealth clustering derived from WHODAS domains has the potential to suitably classify individuals based on the level of health needs and dependency. It may be a powerful lever for targeting appropriate healthcare service provision and setting priorities based on vulnerability rather than solely presence of disease.Electronic supplementary materialThe online version of this article (10.1186/s12889-019-6431-z) contains supplementary material, which is available to authorized users.

Despite many attempts to evaluate the effectiveness of case management for frail older people, systematic reviews including experimental designs show inconsistent results. Starting from the view that case management is a complex intervention occurring in multilayered realities, we conducted a realist evaluation of case management in Belgium, where this type of intervention is new. Realist approaches are particularly well suited to evaluate complex interventions as they seek to investigate iteratively the literature and empirical data to uncover mid-range theories underpinning the intervention under study. As such, realist evaluations are works in progress which provide tools to describe how, why and for whom an intervention is supposed to work. In this paper, we describe two mid-range theories that can explain why case management can help frail older people to remain at home, through the lens of capacity and social support.

The methodological challenges to effectiveness evaluation of complex interventions has been widely discussed. Bottom-up case management for frail older person was implemented in Belgium, and indeed, it was evaluated as a complex intervention. This paper presents the methodological approach we developed to respond to four main methodological challenges regarding the evaluation of case management: (1) the standardization of the interventions, (2) stratification of the frail older population that was used to test various modalities of case management with different risks groups, (3) the building of a control group, and (4) the use of multiple outcomes in evaluating case management. To address these challenges, we developed a mixed-methods approach that (1) used multiple embedded case studies to classify case management types according to their characteristics and implementation conditions; and (2) compared subgroups of beneficiaries with specific needs (defined by Principal Component Analysis prior to cluster analysis) and a control group receiving ‘usual care’, to evaluate the effectiveness of case management. The beneficiaries’ subgroups were matched using propensity scores and compared using generalized pairwise comparison and the hurdle model with the control group. Our results suggest that the impact of case management on patient health and the services used varies according to specific needs and categories of case management. However, these equivocal results question our methodological approach. We suggest to reconsider the evaluation approach by moving away from a viewing case management as an intervention. Rather, it should be considered as a process of interconnected actions taking place within a complex system.

Background Optimizing the organization of care for community-dwelling frail older people is an important issue in many Western countries. In Belgium, a series of complex, innovative, bottom-up interventions was recently designed and implemented to help frail older people live at home longer. As the effectiveness of these interventions may vary between different population groups according to their long-term care needs, they must be evaluated by comparison with a control group that has similar needs. Methods The goal was to identify target groups for these interventions and to establish control groups with similar needs and to explore, per group, the extent to which the utilization of long-term care is matched to needs. We merged two databases: a clinical prospective database and the routine administrative database for healthcare reimbursements. Through Principal Component Analysis followed by Clustering, the intervention group was first stratified into disability profiles. Per profile, comparable control groups for clinical variables were established, based on propensity scores. Using chi-squared tests and logistic regression analysis, long-term care utilization at baseline was then compared per profile and group studied. Results Stratification highlighted five disability profiles: people with low-level limitations; people with limitations in instrumental activities of daily life and low-level of cognitive impairment; people with functional limitations; people with functional and cognitive impairments; and people with functional, cognitive, and behavioral problems. These profiles made it possible to identify long-term care needs. For instance, at baseline, those who needed more assistance with hygiene tasks also received more personal nursing care ( P < 0.05). However, there were some important discrepancies between the need for long-term care and its utilization: while 21% of patients who were totally dependent for hygiene tasks received no personal nursing care, personal nursing care was received by 33% of patients who could perform hygiene tasks. Conclusions The disability profiles provide information on long-term care needs but not on the extent to which those needs are met. To assess the effectiveness of interventions, controls at baseline should have similar disability profiles and comparable long-term care utilization. To allow for large comparative effectiveness studies, these dimensions should ideally be available in routine databases. Electronic supplementary material The online version of this article (10.1186/s12913-019-4240-9) contains supplementary material, which is available to authorized users.

Background The eastern Democratic Republic of Congo (DRC) has experienced decades-long armed conflicts which have had a negative impact on population’s health. Most research in public health explores measures that focus on a specific health problem rather than overall population health status. The aim of this study was to assess the health status of the population and its predictors in conflict settings of South Kivu province, using the World Health Organization Disability Assessment Schedule (WHODAS). Methods Between May and June 2019, we conducted a community-based cross-sectional survey among 1440 adults in six health zones (HZ), classified according to their level of armed conflict intensity and chronicity in four types (accessible and stable, remote and stable, intermediate and unstable). The data were collected by a questionnaire including socio-demographic data and the WHODAS 2.0 tool with 12 items. The main variable of the study was the WHODAS summary score measuring individual’s health status and synthesize in six domains of disability (household, cognitive, mobility, self-care, social and society). Univariate analysis, correlation and comparison tests as well as hierarchical multiple linear regression were performed. Results The median WHODAS score in the accessible and stable (AS), remote and stable (RS), intermediate (I) and unstable (U) HZ was 6.3 (0–28.6); 25 (6.3–41.7); 22.9 (12.5–33.3) and 39.6 (22.9–54.2), respectively. Four of the six WHODAS domain scores (household, cognitive, mobility and society) were the most altered in the UHZs. The RSHZ and IHZ had statistically comparable global WHODAS scores. The stable HZs (accessible and remote) had statistically lower scores than the UHZ on all items. In regression analysis, the factors significantly associated with an overall poor health status (or higher WHODAS score) were advanced age, being woman, being membership of an association; being divorced, separated or widower and living in an unstable HZ. Conclusions Armed conflicts have a significantly negative impact on people’s perceived health, particularly in crisis health zones. In this area, we must accentuate actions aiming to strengthen people’s psychosocial well-being.