Classification of hospital pathways in the management of cancer: Application to lung cancer in the region of burgundy

Nuemi, G.; Afonso, Filipe; Roussot, Adrien; Billard, L.; Cottenet, Jonathan; Combier, Évelyne; Diday, Edwin; Quantin, Catherine

doi:10.1016/j.canep.2013.06.007

Cited by 17 publications

(17 citation statements)

References 17 publications

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“…However, the object of this study was not to investigate individual propensity to develop both conditions but to explore spatial tendencies for a whole country. Several spatial studies using the same type of data and scale of analysis have demonstrated the robustness of this type of analysis [21,67,68]. Second, we could not explore obesity and depression in primary care, but our study probably identified the most severe cases of depression and obesity that required in-hospital health services.…”

Section: Discussionmentioning

confidence: 99%

Depression and obesity, data from a national administrative database study: Geographic evidence for an epidemiological overlap

et al. 2019

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BackgroundDepression and obesity are two major conditions with both psychological and somatic burdens. Some data suggest strong connections between depression and obesity and more particularly associated prevalence of both disorders. However, little is known about the geographical distribution of these two diseases. This study aimed to determine if there is spatial overlap between obesity and depression using data from the entire French territory.MethodsData for 5,627 geographic codes for metropolitan France were collected from the two national hospital databases (PMSI-MCO and RIM-P) for the year 2016. We identified people who were depressed, obese or both registered in the two public medico-administrative databases, and we assessed their location. In addition, a multivariable analysis was performed in order to determine geographic interactions between obesity and depression after controlling for age, sex, environmental and socio-economic factors (social/material deprivation, urbanicity/rurality).Results1,045,682 people aged 18 years and older were identified. The mapping analysis showed several cold and hot regional clusters of coinciding obesity and depression. The multivariable analysis demonstrated significant geographic interactions, with an increasing probability of finding a high prevalence of obesity in regions with major depression (OR 1.29 95% CI 1.13–1.49, p = 0.0002) and an increased probability of finding a high prevalence of depression in regions with a high ration of obesity (OR 1.32, 95% CI 1.15–1.52, p<0.0001).ConclusionOur study confirms the significant bidirectional relationships between obesity and depression at a group level. French geographic patterns reveal a partial overlap between obesity and depression, suggesting these two diseases can be included in a common approach. Further studies should be done to increase the understanding of this complex comorbidity.

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Section: Discussionmentioning

confidence: 99%

Depression and obesity, data from a national administrative database study: Geographic evidence for an epidemiological overlap

et al. 2019

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“…A study conducted in the Bourgogne region of France has shown that patient flow was centered towards the university hospital that admitted patients from the entire region and based on the regional proximity of the patients’ residence and patients also sought care in two of the closest main healthcare hubs for specialized care (Paris or Lyon). [ 30 ] Highly connected hospitals may harbor more MRSA and MRSA bacteremia cases and may have the most potential to transmit HAIs in the entire network. [ 12 , 13 , 31 , 32 ] HAIs may spread at a higher rate than expected at random due to the centralization of patient movement and due to the small average number of transfers required for patients to move throughout the network.…”

Section: Discussionmentioning

confidence: 99%

Spread of hospital-acquired infections: A comparison of healthcare networks

et al. 2017

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Hospital-acquired infections (HAIs), including emerging multi-drug resistant organisms, threaten healthcare systems worldwide. Efficient containment measures of HAIs must mobilize the entire healthcare network. Thus, to best understand how to reduce the potential scale of HAI epidemic spread, we explore patient transfer patterns in the French healthcare system. Using an exhaustive database of all hospital discharge summaries in France in 2014, we construct and analyze three patient networks based on the following: transfers of patients with HAI (HAI-specific network); patients with suspected HAI (suspected-HAI network); and all patients (general network). All three networks have heterogeneous patient flow and demonstrate small-world and scale-free characteristics. Patient populations that comprise these networks are also heterogeneous in their movement patterns. Ranking of hospitals by centrality measures and comparing community clustering using community detection algorithms shows that despite the differences in patient population, the HAI-specific and suspected-HAI networks rely on the same underlying structure as that of the general network. As a result, the general network may be more reliable in studying potential spread of HAIs. Finally, we identify transfer patterns at both the French regional and departmental (county) levels that are important in the identification of key hospital centers, patient flow trajectories, and regional clusters that may serve as a basis for novel wide-scale infection control strategies.

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“…To our knowledge, the methods used up to now have been derived from informatics, and they have been used to study in-hospital pathways by mining multi-dimensional itemset sequential patterns and symbolic data analysis. 15,16 However, these methods are not commonly used in statistics and epidemiology. Moreover, a requirement for specific knowledge or skills in programming may limit their use in the study of care pathways, including ambulatory care.…”

Section: Introductionmentioning

confidence: 99%

Use of state sequence analysis for care pathway analysis: The example of multiple sclerosis

Roux

Grimaud

Leray

2018

Stat Methods Med Res

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The concept of care pathways is increasingly being used to enhance the quality of care and to optimize the use of resources for health care. We here propose an innovative method in epidemiology that is derived from social sciences: state sequence analysis (SSA). This method takes into account the chronology of care consumption and allows for identification of specific patterns. A process for using SSA in the health area is proposed and discussed. The main steps are: data coding, measurement of dissimilarities between sequences (focusing on optimal matching methods and the choice of related costs), and application of a clustering method to obtain a typology of sequence patterns. As an example of its use in the health area, SSA was employed to analyse care pathways of a random sample of patients with multiple sclerosis. This sample has been selected from the main French healthcare database covering the period 2007 to 2013 ( n = 1 000). A five-cluster typology was obtained which allowed distinction of care consumption groups. Overall, about half of the patients had low care consumption, about one quarter had medium to high consumption, and another quarter had high consumption. We conclude that state sequence analysis is an innovative and flexible methodology that is worth considering in health care research.

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Classification of hospital pathways in the management of cancer: Application to lung cancer in the region of burgundy

Cited by 17 publications

References 17 publications

Depression and obesity, data from a national administrative database study: Geographic evidence for an epidemiological overlap

Depression and obesity, data from a national administrative database study: Geographic evidence for an epidemiological overlap

Spread of hospital-acquired infections: A comparison of healthcare networks

Use of state sequence analysis for care pathway analysis: The example of multiple sclerosis

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