Predicting parent health-related quality of life: evaluating conceptual models

Defenderfer, Ellen K.; Rybak, Tiffany M.; Davies, W. Hobart; Berlin, Kristoffer S.

doi:10.1007/s11136-016-1491-3

Cited by 14 publications

(13 citation statements)

References 39 publications

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“…This term is commonly used in research to describe the influence of disease on physical and mental well‐being, thus taking into account contextual and personal factors . This concept can also be applied to caregivers of ill children, given that the caregiving process—as opposed to having a disease oneself—is considered an important health determinant . To systematically assess parental HRQoL in this study, we chose to use an adjusted version of the conceptual model of Raina et al (Figure ).…”

Section: Introductionmentioning

confidence: 99%

“…13 This concept can also be applied to caregivers of ill children, given that the caregiving process-as opposed to having a disease oneself-is considered an important health determinant. 14,15 To systematically assess parental HRQoL in this study, we chose to use an adjusted version of the conceptual model of Raina et al (Figure 1). This model relates background and contextual factors, child characteristics, caregiver strain/stress, intrapsychic factors, and coping factors to caregivers' HRQoL.…”

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confidence: 99%

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Gender‐specific differences in parental health‐related quality of life in childhood cancer

Rensen

Steur

Schepers

et al. 2019

Pediatric Blood & Cancer

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Background Parents of children with cancer are at risk for impaired health‐related quality of life (HRQoL). Most prior research has focused on the HRQoL of mothers. The aim of this study is to describe HRQoL in mothers and fathers, and determine the influence of sociodemographic, medical, and psychosocial factors. Procedure In a cross‐sectional study, both parents completed questionnaires on sociodemographics, distress, and HRQoL. Parental HRQoL was compared to healthy population values. Differences between mothers and fathers were evaluated with multilevel analysis. Gender‐specific HRQoL determinants were assessed via multiple linear regression analysis. Results Parents (202 mothers, 150 fathers; comprising 121 couples) of 231 children with different cancer diagnoses (mean time since diagnosis 3.3 ± 1.4 years, 90% posttreatment) participated. Compared to healthy women and men, mothers and fathers reported significantly impaired HRQoL on the following domains: cognitive functioning, sleep, daily activities, and vitality (Cohen's d = 0.3‐0.9). Additionally, maternal HRQoL was reduced on the domains gross motor functioning, pain, social functioning, sexuality, and depressive emotions. Mothers scored worse than fathers on six of 12 domains. Risk factors for adverse outcomes in both parents were higher distress, emotional and parenting problems, little social support, medication use, and active treatment of the child. Other determinants in mothers were non‐Dutch background and unemployment, while lower HRQoL in fathers was predicted by their child's diagnosis type, shorter time since diagnosis, and treatment intensity. Conclusion These outcomes illustrate the need for family‐centered care. Future interventions aimed at improving parental functioning should take into account gender‐specific differences in HRQoL to reach optimal efficacy.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Gender‐specific differences in parental health‐related quality of life in childhood cancer

Rensen

Steur

Schepers

et al. 2019

Pediatric Blood & Cancer

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“…Recent research has attempted to predict caregiver outcomes using patient health data—for example, researchers have used data collected for measuring the family impact of meningitis to estimate changes in carers’ health based on changes in patients’ health [ 30 ], and have also focused on predicting parents’ health-related quality of life based on health information about their ill children [ 31 ]. Although these studies provide compelling examples of how to include caregivers’ health-related quality of life data in economic evaluations, they are rather restrictive in the type of data they use, and they also report poor predictive performance for their statistical models.…”

Section: Introductionmentioning

confidence: 99%

Estimating Informal Caregiving Time from Patient EQ-5D Data: The Informal CARE Effect (iCARE) Tool

Gheorghe¹,

Hoefman

Versteegh³

et al. 2018

PharmacoEconomics

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The iCARE algorithm developed in this study allows researchers who only have patient-level EQ-5D data to estimate the mean hours of informal care received per week, including a 95% Bayesian credible interval. Caregiver time can be multiplied with a monetary value for caregiving, enabling the inclusion of informal care costs in economic evaluations. We recommend using the tool for samples that fall within the confidence intervals of the characteristics of our samples: men (age range 47.0-104.2 years), women (age range 55-103 years).

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“…We collected data at two time points which enabled us to observe and predict changes in health status for individual carers. Previous studies have adopted cross sectional designs focused on predicting outcomes at a single point in time [ 51 , 52 ]. Collecting data at two time points however provides a more direct, and potentially valuable, test of predictive ability of models as it is ultimately changes in health status (rather than the health status per se) that are used to estimate benefits within economic evaluation.…”

Section: Methodsmentioning

confidence: 99%

Predicting carer health effects for use in economic evaluation

2017

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BackgroundIllnesses and interventions can affect the health status of family carers in addition to patients. However economic evaluation studies rarely incorporate data on health status of carers.ObjectivesWe investigated whether changes in carer health status could be ‘predicted’ from the health data of those they provide care to (patients), as a means of incorporating carer outcomes in economic evaluation.MethodsWe used a case study of the family impact of meningitis, with 497 carer-patient dyads surveyed at two points. We used regression models to analyse changes in carers’ health status, to derive predictive algorithms based on variables relating to the patient. We evaluated the predictive accuracy of different models using standard model fit criteria.ResultsIt was feasible to estimate models to predict changes in carers’ health status. However, the predictions generated in an external testing sample were poorly correlated with the observed changes in individual carers’ health status. When aggregated, predictions provided some indication of the observed health changes for groups of carers.ConclusionsAt present, a ‘one-size-fits-all’ predictive model of carer outcomes does not appear possible and further research aimed to identify predictors of carer’s health status from (readily available) patient data is recommended. In the meanwhile, it may be better to encourage the targeted collection of carer data in primary research to enable carer outcomes to be better reflected in economic evaluation.

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Predicting parent health-related quality of life: evaluating conceptual models

Cited by 14 publications

References 39 publications

Gender‐specific differences in parental health‐related quality of life in childhood cancer

Gender‐specific differences in parental health‐related quality of life in childhood cancer

Estimating Informal Caregiving Time from Patient EQ-5D Data: The Informal CARE Effect (iCARE) Tool

Predicting carer health effects for use in economic evaluation

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