Health status and healthy life measures for population health need assessment: modelling variability and uncertainty

A scoping review on patient heterogeneity in economic evaluations of precision medicine based on basket trials

INTRODUCTION

Considerable challenges in the economic evaluation of precision medicines have been mentioned in previous studies. However, they have not addressed how an economic assessment would be conducted based on basket trials (novel studies for evaluation of precision medicine effects) in which the included populations have specific biomarkers and various cancers. Since basket trial populations have remarkable heterogeneity, this study aims to investigate the concept of heterogeneity and specific method(s) for considering it in economic evaluations through guidelines and studies that could be applicable in economic evaluation based on basket trials.

AREA COVERED

We searched PubMed, Web of Science, Scopus, Google Scholar, and Google to find studies and pharmacoeconomics guidelines. The inclusion criteria included subjects of patient heterogeneity and suggested explicit method(s). Thirty-nine guidelines and 43 studies were included and evaluated. None of these materials mentioned disease types in a target population as a factor causing heterogeneity. Moreover, in economic evaluations, patient heterogeneity has been considered with four general approaches subgroup analysis, individual-based models, sensitivity analysis, and regression models.

EXPERT OPINION

Type of disease is not considered a contributing factor in population heterogeneity, and the probable appropriate method for this issue could be individual-based models.

Population health needs assessment and healthcare services use in a 3 years follow-up on administrative and clinical data: results from the Brisighella Heart Study

INTRODUCTION

A large number of epidemiological trials clearly show the impact of the main cardiovascular disease risk factors in term of hospitalization and related cost, but relatively less frequently if this reflect the health needs of a given population.

AIM

To develop a model for the health needs-assessment that will be applied to verify if and how the prevalence of some classical risk factors for cardiovascular disease predicts mortality and hospitalisation episodes at 3 years, and if it could express the health need of that population. The long-life clinical record of 1,704 subjects, recruited during the 2004 Brisighella Heart Study survey, has been monitored. We defined the health profile of these subjects at 2004 (based on clinical history, smoking and dietary habits, physical activity, drug use, anthropometric data, blood pressure, and hematological data) and then sampled data relative to their hospitalisations, mortality, and general medical assistance.

RESULTS

Our results shows that age over 65 years (OR 4.08; 95 % CI 2.74-6.08), hypertension (OR 3.44; 95 % CI 2.36-5.01) and hypercholesterolemia (OR 1.33; 95 % CI 0.92-1.94) increase the probability to get hospitalised. Furthermore, the burden of care was defined and computed for our sample. Vascular and respiratory diseases [Burden of health care (Bc) = 24.5 and 36.5, respectively] are the most costly DRGs which means that the biggest part of our resources directed to cardiovascular patients were provided for these diagnoses.

CONCLUSION

The application of the proposed model could help policy makers and researchers in directing resources and workforce in the treatment of cardiovascular diseases.

Shared component modelling as an alternative to assess geographical variations in medical practice: gender inequalities in hospital admissions for chronic diseases

BACKGROUND

Small area analysis is the most prevalent methodological approach in the study of unwarranted and systematic variation in medical practice at geographical level. Several of its limitations drive researchers to use disease mapping methods -deemed as a valuable alternative. This work aims at exploring these techniques using - as a case of study- the gender differences in rates of hospitalization in elderly patients with chronic diseases.

METHODS

Design and study setting: An empirical study of 538,358 hospitalizations affecting individuals aged over 75, who were admitted due to a chronic condition in 2006, were used to compare Small Area Analysis (SAVA), the Besag-York-Mollie (BYM) modelling and the Shared Component Modelling (SCM). Main endpoint: Gender spatial variation was measured, as follows: SAVA estimated gender-specific utilization ratio; BYM estimated the fraction of variance attributable to spatial correlation in each gender; and, SCM estimated the fraction of variance shared by the two genders, and those specific for each one.

RESULTS

Hospitalization rates due to chronic diseases in the elderly were higher in men (median per area 21.4 per 100 inhabitants, interquartile range: 17.6 to 25.0) than in women (median per area 13.7 per 100, interquartile range: 10.8 to 16.6). Whereas Utilization Ratios showed a similar geographical pattern of variation in both genders, BYM found a high fraction of variation attributable to spatial correlation in both men (71%, CI95%: 50 to 94) and women (62%, CI95%: 45 to 77). In turn, SCM showed that the geographical admission pattern was mainly shared, with just 6% (CI95%: 4 to 8) of variation specific to the women component.

CONCLUSIONS

Whereas SAVA and BYM focused on the magnitude of variation and on allocating where variability cannot be due to chance, SCM signalled discrepant areas where latent factors would differently affect men and women.

Toward a needs based mechanism for capitation purposes in Italy: the role of socioeconomic level in explaining differences in the use of health services

The paper investigated differences in the use of hospital care, out-patient care and pharmaceutical care in Piemonte, a region of northern Italy with 4,000,000 inhabitants, taking into account factors of need and supply, for capitation purposes. The study used a geographical design, with the municipalities as statistical units, and was based on integrated data from health and health service information systems, the population census and on the geographical distances among municipalities. Hierarchical regression models were fitted with the utilisation of services as the outcome variable and a set of direct and indirect factors of need and supply indicators as covariates. Higher health service consumption rates were observed for the most disadvantaged employment categories, in addition to the elderly. Distance from hospital was inversely correlated with the hospitalisation rate. A formula for determining capitation can be developed using age and indirect factors of need as weights.

Deprivation indices, population health and geography: an evaluation of the spatial effectiveness of indices at multiple scales

Area-based deprivation indices (ABDIs) have become a common tool with which to investigate the patterns and magnitude of socioeconomic inequalities in health. ABDIs are also used as a proxy for individual socioeconomic status. Despite their widespread use, comparably less attention has been focused on their geographic variability and practical concerns surrounding the Modifiable Area Unit Problem (MAUP) than on the individual attributes that make up the indices. Although scale is increasingly recognized as an important factor in interpreting mapped results among population health researchers, less attention has been paid specifically to ABDI and scale. In this paper, we highlight the effect of scale on indices by mapping ABDIs at multiple census scales in an urban area. In addition, we compare self-rated health data from the Canadian Community Health Survey with ABDIs at two census scales. The results of our analysis confirm the influence of spatial extent and scale on mapping population health-with potential implications for health policy implementation and resource distribution.

A census-based socio-economic status (SES) index as a tool to examine the relationship between mental health services use and deprivation

This paper discusses the development and application of a socio-economic status (SES) index, created to explore the relationship between socio-economic variables and psychiatric service use. The study was conducted in a community-based mental health service (CMHS) in Verona, Northern Italy, utilising service use data from 1996. An ecological SES index was constructed through a factor analysis from 1991 Census data, at census block level. Three factors reflected the following domains: the educational-employment sector (with four components), the relational network (with three components) and the material conditions (with three components). All service users were assigned a SES value, according to their place of residence in 1996. When these data were explored spatially, using ArcView 8.3, an association was observed between socio-economic deprivation and psychiatric service use. The SES index was then successfully validated using occupational status at the individual level. This study confirms the usefulness of developing and validating an ecological census-based SES index, for service planning and resource allocation in an area with a community-based system of mental health care.