A novel child-centred core palliative care outcome measure for use in clinical practice and research: findings from a multinational validation study

doi:10.21203/rs.3.rs-4853681/v1

Download PDF

Research Article

A novel child-centred core palliative care outcome measure for use in clinical practice and research: findings from a multinational validation study

https://doi.org/10.21203/rs.3.rs-4853681/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background: Outcome measurement is pivotal to person-centred assessment, quality improvement and research. Children and young people with life-limiting and -threatening illness have high needs and service use, yet there is a lack of evidence for interventions and care models. Efforts to strengthen paediatric palliative care (PPC) services has been hampered by the lack of an appropriate outcome measure.

Objective: To determine the validity, reliability, measurement invariance, responsiveness, acceptability, and interpretability properties of the novel Children’s Palliative care Outcome Scale (C-POS).

Methods: We recruited children (0-17 years) with life-limiting illness and their families in Kenya, Uganda and South Africa. Using C-POS repeated measurement using over four timepoints. we assessed: 1 The objectives were to determine: 1) construct validity (structural properties, discriminant validity, known groups validity, measurement invariance, differential item functioning by country), 2) reliability (internal consistency and test re-test), 3) responsiveness, 4) acceptability (time to complete) and 5) interpretability.

Results: We recruited a cohort of 434 children (response rate 94%). Of these, 302 participated in the repeated measures component and 279 (92%) completed four datapoints.

We found evidence for face and content validity as the C-POS items mapped on to themes developed from qualitative interviews, including: pain and other symptoms, pyscho-social well-being, and family wellbeing that matter to children and their families. We confirmed the two-factor structure (child and family subscales). We confirmed discriminant and known groups validity, as well as construct equivalence for the child and proxy versions. Controlling for age, we found no differential item functioning by country setting. 2)The sub-scale internal consistency was moderate, given the multi-dimensional nature of the C-POS self and proxy report versions omega scores (0.67 and 0.73, respectively). The test characteristic curve information confirmed the moderate internal subscale consistency scores between 0.3 to 0.9 for the proxy version and 0.3-0.5 for the self-report version. Test-retest reliability was acceptable for all items, with weighted kappa range for scores: self-report (0.43-0.57) and proxy version (0.35-0.64) and family items (0.51-0.71). 3)Responsiveness was demonstrated, except for the feeding item. 4)Median completion time at the last visit was 10 minutes for both versions with minimal missing data. 5)The minimum important difference was 3 for the self and proxy report versions on a scale of 0-30 and 4 for the child and family scale on a scale of 0-55.

Conclusions and relevance: The C-POS has good psychometric properties. To optimise C-POS, from the data we identified items for potential removal, and further domains for which developmental age-appropriate items are needed. C-POS has potential to evaluate and improve person-centred children’s palliative care in both research and routine clinical practice.

Paediatric palliative care

outcome measurement

children

Africa

Worldwide, there are approximately 21 million children and young people (hereafter “children”) aged 0–19 years with life-limiting and life-threatening conditions (LLCs)[1]. There are almost 400 conditions that affect children for which palliative care could be beneficial[2, 3].

Due to medical advances, children are living longer with life-limiting conditions[2, 4]. Children with LLCs face a high burden of multidimensional symptoms and concerns, spanning physical (e.g. pain) psychological (e.g. anxiety), social (e.g. education, isolation) and spiritual (e.g. meaning and uncertainty)[5, 6]. Although referral to specialist paediatric palliative care may reduce hospital admissions[7], the impact on their person-centred outcomes (i.e. their symptoms and concerns) is largely unknown.

Palliative care for adults is effective and cost-effective, reducing unplanned admissions and futile treatments[8–10], while improving quality of life, care quality and survival[11–13]. However, evidence for effectiveness of children’s palliative care is limited in part due to a lack of a valid and reliable person-centred outcome measure (PCOM)[14, 15]. Systematic reviews report that there are no valid patient-centred outcome measures (PCOMs) for paediatric palliative care[16–18]. Development of such a measure has repeatedly been identified as a research priority[19–21].

Person-centredness is holistic healthcare and a core commitment of the World Health Organisation[22–25]. To deliver child-centred paediatric healthcare, it is essential to understand what is important to children and their families[26, 27]. The United Nations Convention on the Rights of the Child emphasises the importance of children being involved in matters that affect them[28].

Patient-reported information is central to improving care and quality of life, and evidence demonstrates that children can reliably self-report[27, 29]. However, their voices have not been prioritised in clinical care or research[30]. Person-centred, ideally patient-reported, outcome measures are key to assessment, problem measurement, demonstrating the effectiveness of care, and driving healthcare quality and equity[31]. Minimal evidence has been published on the outcomes of children with life-limiting or threatening conditions[17]. This evidence is affected by the use also of methods which majorly use retrospective and proxy methods, and lacking a validated tool to collect outcome data[32, 33]. Progress on documenting young people’s palliative care outcomes is therefore limited compared to advances in adult outcome measurement[34].

The Palliative care Outcome Scale (POS) was developed to measure adult patient-reported outcomes in palliative care [35],[36] and is now used worldwide in multiple versions with widely reported clinical and research use [37–40]. An adult African version was developed and validated [41, 42] and has been used widely to measure and improve care quality for adults[40].

The vast majority of children with LLCs (98%) live in low- and middle-income countries[43], where late presentation, fewer curative options, and higher prevalence of infectious diseases lead to the urgent need for evidence of optimal and effective care delivery models[44]. A review of the status of children’s palliative care in sub-Saharan Africa identified the need for a validated outcome measure[45]. Consequently, the first outcome measure for children with LLCs was developed and piloted: the APCA African Children’s Palliative care Outcome Scale (hereafter referred to as C-POS)[46, 47]. This early version was revised by a multidisciplinary cross-national expert panel. This process integrated findings from the pilot with a new systematic review of the evidence for outcomes meaningful to children with life-limiting and life-threatening illness[6]. Further primary qualitative data for children’s needs in sub-Saharan Africa generated to improve face and content validity[48]. We previously reported content validation (mapping C-POS items onto an evidence-based framework from prior evidence), comprehensiveness, comprehensibility, acceptability feasibility, and implementability from qualitative in-depth and cognitive interviews with children, families and health care professionals in three sub-Saharan African countries[49]. Assessment of those properties concluded that C-POS items capture the core symptoms and concerns that matter to children and their families, and that it is feasible, comprehensible, and acceptable for use in clinical settings.

We present here the results from the full quantitative psychometric validation. This validation study aimed to determine the psychometric properties of a novel child- and family-centred palliative care core outcome measure in sub-Saharan Africa for use in research and clinical practice.

The objectives were to determine: 1) construct validity (structural properties, discriminant validity, known groups validity, measurement invariance, differential item functioning by country), 2) reliability (internal consistency and test re-test), 3) responsiveness, 4) acceptability (time to complete) and 5) interpretability.

Design

We applied the COSMIN taxonomy of best practices in tool development and validation[50] [51] and outcome measure guidance in palliative care[41, 52, 53]. The overview of our study methods is show in Fig. 1; we present methods and findings by phase of study. The overall construct being measured was symptoms and concerns among children and families facing life-limiting or life-threatening conditions.

Setting

Three clinical sites delivering palliative care (a children’s HIV outpatient service in Uganda, a teaching and referral hospital-one provincial hospital in Kenya, and a national children’s hospital in South Africa) in line with the WHO definition of paediatric palliative care[54] and ability to recruit at least 30 new patients per month to allow for timely study completion.

Inclusion/exclusion criteria

Inclusion: aged between birth and 17 years receiving care for a LLC, with parent or legal guardian present to consent to study participation.

Exclusion: We excluded children who were deemed by their clinician to be too ill or had cognitive impairments of a severity that precluded meaningful participation. Families were excluded if their child’s clinician felt the family member was either too unwell or distressed to take part. We defined a family caregiver as a family member who took care of the child for at least 50% of the time.

Recruitment and consent

Carers were approached by a member of the clinical team and informed of the study objectives and procedures. All participating children aged 8 years and above gave assent, and adult caregivers gave informed written consent for children to participate.

Data collection

Data collection occurred between February 2012 to November 2012. Study instruments, information and consent forms were forward and back translated from English[55] into local languages: Uganda (Luganda, Runyakore-Rukiga); Kenya (Kikuyu, Luo, Kiswahili); South Africa (Xhosa, Zulu, Pedi Sesotho, South Sotho). This was followed by the reconciliation of the two forward translations through dialogue and consultation with content experts.

Due to varying levels of respondent literacy, the research nurses read the questions aloud in all instances and recorded responses from children or carers. We trained all the research nurses on how to administer the study instruments and gave each nurse a copy of the standard operating procedures for administering the study tools. Patients could score the C-POS using a hand or verbal scale. In our earlier phases of developing the C-POS, children were asked about preferred scales and hand and verbal were most preferred and self-reporting children could interpret hand and verbal scales. Children aged 7 years and above were allowed to respond on their own if possible given evidence shows they can self-report on health and wellbeing[56, 57]. For test-retest, the C-POS was re-administered to sub-cohort of in-patients whose well-being the clinical team did not expect to change significantly in a period of 24 hours.

Measures

The C-POS

The C-POS addresses young people’s symptoms and concerns, drawing on the “total pain” construct that drives assessment and intervention in palliative care (i.e. physical and pyscho-social, spiritual, practical and emotional concerns, and needs of the family) (Appendix Fig. 2). Scores use Likert scales from 0 to 5. Questions 1–7 of the C-POS are directed at patients/children; these questions can be asked directly to children (either self-report or proxy) or proxies can be observer informants. The tool is self-reported for children aged 7 years and above[58] where literacy is a problem, staff can (read aloud the items and responses with respondents choosing the best response options, on their own). Proxies can respond where a child is unable because of age (e.g. the 0-6-year-old) or those with advanced disease, although research with adults demonstrated that family members and professionals’ ratings may differ from the patient[59]. For assessment, the C-POS was administered four times, and each point the completion time was recorded.

Paediatric Quality of Life Inventory (PedsQL)

The PedsQL[60], is a 23 item measure of quality of life designed to measure the core dimensions of health and role (school) functioning. Given that the PedsQL is generic function-based measure of quality of life we selected it to test convergence from C-POS measure of palliative care-related symptoms and concerns in children with life-limiting and life-threatening conditions.

Eastern Cooperative Oncology Group Scale (ECOG)

This single item measure of functional performance[61] ranges from 0 (fully active) to 4 (completely disabled). It is used as a proxy for disease progression and its effect on daily living abilities [61]. In line with guidance, we used ECOG with children aged 5 and above [62].

Socio-demographics

A study-specific questionnaire collected data on: child’s age, sex, first language, carer’s relationship to the child, household size, primary diagnosis, phase of illness, place of care (inpatient/outpatient), and reason for referral to palliative care.

Sample size

Sample size estimation was based on factor analysis and structural equation modelling (SEM) as they required the largest sample. Ideally 10 cases/observations per indicator variable are recommended for factor analysis[63], while sample sizes of 100–150 are recommended for SEM[64], based on Monte and Carlo simulations with a power of 80%, p value 0.05, a sample size of > 200 is recommended for robust weighted Least Squares or maximum likelihood for both binary or ordinal data[65, 66]. We therefore deemed a sample size > 200 for the child and proxy versions of the C-POS as adequate for modelling.

Data management

Data were entered into a pre-designed Epidata database and exported to Stata version 15 and Mplus 8.3 for analysis. The C-POS scores were explored for out-of-range scores and missing values, and items (1–4, & 8) were reversed so in all instances a score of “0” represented the least severity and “5” the most severe.

Analysis

1) Construct validity

We assessed for the following aspects of construct validity: a) structural properties; b) discriminant validity; c) known groups validity; d) measurement invariance; e) differential item functioning by country.

1a) Structural properties: theoretically the C-POS has two sub-scales, i.e., child items outcomes (n = 7) and family-items (n = 5). We conducted multi-level confirmatory analysis using the weighted least squares to confirm this factor structure[51]. This approach is recommended where a pre-existing theory exists as confirmatory factor analysis tests a hypothesis and is hence more robust[51]. We compared competing models to identify the model with the best fit using the flowing model fit coefficients; i)the chi-square and the associated degrees of freedom and the associated P values, the comparative fit index (CFI) > 0.9 is recommended, the Tucker-Lewis Index (TLI) - ≥ 0.9–0.95 and the RMSEA -recommended cut off is ≤ 0.05 but sometimes < .08 acceptable[67].

1b) Discriminant validity: We hypothesized that we would find a low-moderate correlation between C-POS and PEDSQL (i.e. <0.6). The following were calculated: PedsQL psychosocial subscale scores vs C-POS psychosocial items (felt happy + felt like playing), and PedsQL physical health subscale vs the C-POS physical items (pain + other symptoms items). We used the Spearman Rank Correlation (Spearman’s rho) to assess for the strength of correlation between the C-POS and PedsQL scores[68] (0 < 0.3 – low correlation; 0.3–0.5 – moderate correlation; >0.5 - strong correlation)[69].

1c) Known groups validity: We defined our known groups based on functional performance scores i.e. the principle that certain specified groups of patients are anticipated to score differently. Dividing the study population into known groups by functional performance status (for children aged 5 and above for whom it was possible to use the ECOG). We hypothesised that children with poor physical function would report more palliative care-related problems. For the self and proxy versions of the C-POS, we used analysis of variances to assess statistical significance of mean differences in the C-POS child item total scores across the levels of functional performance as measured by the ECOG.

1d) Measurement invariance: We fitted a configural model to explore the extent to which the C-POS self and proxy versions measure a similar construct, then a metric invariance model to determine the equivalence between the two versions (self and proxy report) at factor loading or structural level and then use of the scale or threshold level. At each stage, the model fit indices were examined to identify problematic items (i.e., those with large residual errors, and the level at which variance occurs). Sensitivity analyses were conducted to the explore the effect of excluding such items or allowing them to vary on the overall model fit.

1e) Differential item functioning by country setting: We used differential item functioning analysis to assess for cultural differences in the functionality of the C-POS items across Uganda, Kenya and South Africa. This was achieved using Multiple Indicator, Multiple Cause (MIMIC) modelling controlling for the effect of age. We fit three models: Model 1 self-report version of the C-POS child items; Model 2 Proxy report version of the C-POS child items; Model 3 Family items.

2) Reliability

a) Internal consistency

As C-POS is a multidimensional measure, we did not assess the internal consistency of the tool as a whole, as results can be misleading[70]. Following confirmation of the hypothesised factor structure, internal consistency for the child and family subscales was assessed using the omega composite reliability coefficient. The latter statistic is robust in case of violation of the unequal factor loadings in a factorial model[71]. For items tapping a single construct, coefficients above 0.7 are acceptable and for multi-dimensional measures low coefficients of up to 0.5 are acceptable [72].

Further analysis was undertaken using item response theory to test the precision of the selected items and to identify areas for improvement along the latent construct continuum. We fitted a partial graded model and examined the internal consistency with additional information on the extent to which the various items contributed to our understanding of the variation in the latent construct. We inspected item and test information functions graphically to reflect how reliably the individual items and the test estimate the construct over the entire scale range. Values can be converted into an estimate of reliability (using this formula reliability ¼ 1 _ [1 /information]) extrapolating from Cronbach’s alpha rule of thumb of 0.70 to 0.90 for interpreting reliability; with the test function curve, these values correspond to acceptable item curve information scores of 3.3 to 10[63].

To identify potential areas for improving internal consistency, we also examined the functionality of the items; good items should contribute to our understanding of the variation in the latent construct between − 2 and + 2 standard deviations. Items with poor discrimination power (i.e., those with low information curves) could also be targeted for removal, replacement or rephrasing.

2b) Test-retest reliability

We used the weighted kappa coefficient to assess the level of agreement between the two time point scores. More subjective items will generally show relatively low reliability, and physical outcomes may be more consistent[72, 73]. Lower test-retest coefficients were expected for the child self-report version, as test-retest reliability is affected by child developmental age[74, 75]. Interpretation of coefficients was: less than 0.2 poor agreement; 0.21–0.40 slight; 0.41–0.60 moderate 0.61–0.8 good and 0.81 -1 very high [75]. We adopted a score of 0.3 for lowest acceptable level of agreement.

3) Responsiveness

Responsiveness is defined as the ability of a measure to detect change[76]. A good instrument should be able to respond to changes in a patient’s condition but it is important to note that responsiveness contributes more to our understanding of the variance of the population being assessed and is not a property of an instrument in the strict sense[77]. Using Wilcoxon paired sign rank sum test, we compared differences in paired scores at the following time points; 1 vs 3, 2 vs 3, 3vs 4 and 1 vs 4. The initial test-retest measures were 24 hours apart, after which time points were a mean of three days apart. The test-retest data were excluded in the responsiveness analysis. We also used generalised linear regression models to assess for change in total C-POS scores for the child (self and proxy versions) and family subscales over time controlling for age and country setting. The latter approach is more robust as it uses all observations, as opposed to the selected paired [78].

4) Acceptability

We measured both time and extent of completion are indicators of measure acceptability[41]. For feasible clinical use in palliative care populations, measures should be short[79]. Although missing values commonly range from 4–18%, a threshold of 8% is acceptable[80].

5) Interpretability

Studies have consistently shown that there is little variability in the standard deviations derived from between-subject differences at baseline, change scores or difference between change in scores[81]. We therefore used the 0.5 standard deviation of baseline scores to compute the minimum important difference for the child and family total scores[82]. For the two versions of the C-POS, we computed the total C-POS scores for child and family items. To assess Cross -cultural validity we fitted three multiple indicator cause models (self-report child items; proxy child items and family subscale) controlling for age to explore differential item functioning by country setting. We set a stringent P value of 0.001 or < 0.001 as a requirement for statistical significance considering Bonferroni corrections for multiple testing and coefficients of at least 0.64. Items with differential item functioning would be included in the scale to avoid inflating the type II error.

Sample characteristics

Of 454 children approached, none refused and 20 were not enrolled because they were deemed too ill to participate. Therefore 434 children and 431 family carers were recruited (95.4% of those approached). The most common diagnoses were HIV/AIDS (50.0%), sickle cell (14.6%) and cancer (12.2%). Where the child completed the child-oriented items alone, the child’s mean age was 9.7 years (SD = 3.4). The socio-demographic characteristics of the children are presented in Table 1.

Table 1

Participants’ characteristics for the quantitative cohort (n = 434)
Variable		Kenya (n = 99)		Uganda (n = 233)		South Africa (n = 102)
Age	Mean (SD)- self report version	7.9(2.5)		10.8(3.7)		7.4(3.9)
Age	Mean (SD)-proxy report version	3(2.4)		2.6(2.3)		2.8(3.4)
sex		n	%	n	%	n	%
	Female	44	44.4	114	48.9	63	61.76
	Male	55	55.6	119	51.1	39	38.2
place of care
	In patient	42	42.4	70	30.0	101	99.0
	Out patient	57	57.6	163	69.96	1	1.0
Primary diagnosis	HIV/AIDS	44	44.4	177	76.3	7	6.9
	Cancer	16	16.2	52	22.4	21	20.6
	Sickle cell	39	39.4	1	0.4	4	3.9
	Epilepsy	0	0	2	0.9	23	22.5
	Renal failure	0	0	0	0	3	2.9
	Cardiac disease	0	0	0	0	19	18.6
	TB	0	0	0	0	6	5.9
	other	0	0	0	0	19	18.6
Location of home
	Rural	47	47.5	156	66.9	13	12.7
	Urban	52	52.5	77	33.1	89	87.3
Relationship of caregiver with child	Mother	82	82.8	117	50.2	87	85.3
	Father	7	7.1	15	6.4	6	5.9
	Other relative	5	5.1	32	13.7	2	2.0
	Grand parents	4	4.0	37	15.9	3	2.9
	Auntie/uncle	1	1.0	32	13.7	4	3.9
Language used	Luo	76	76.8
	Kiswahili	21	21.2
	English	2	2.0	58	25	44	43.1
	Zulu	0	0	0	0	31	30.4
	Xhosa	0	0	0	0	27	26.5
	Luganda	0	0	174	75	0	0

1) Construct validity

1a) Structural properties

Item 7 (having questions about your illness answered) had a high number of missing values on the proxy version (item coverage 0.73) and was therefore omitted in the analysis of the C-POS proxy version (see Table 2)

Table 2

Completeness of Data based on the repeated measures cohort
	n(children)
Item	T1 (n = 302)	T2(n = 297)		T3 (n = 285	T4(n = 279)
Child-oriented items	Missing values: child-reported(caregiver-reported)	Missing values: child-reported(caregiver-reported)	Missing values: child-reported(caregiver-reported)		Missing values: child-reported(caregiver-reported)
Item 1 pain	0 (0)	0(0)	0 (3)		0 (0)
Item 2 other body	0 (0)	0 (0)	0 (0)		2 (0)
Item 3 eating	0 (0)	0(0)	0(0)		2(0)
Item 4 cried	1 (0)	1 (0)	1 (0)		3 (0)
Item 5 happy	0 (1)	0 (0)	0 (0)		2 (1)
Item 6 playing	1 (2)	0 (1)	6(1)		7 (1)
Item 7 questions answered	3(57)	2(*)	8(*)		8(*)
	n (family carers)
Caregiver/family-oriented items	N = 299	N = 282	N = 272		N = 270
Item 8 worry	2	0	3		5
Item 9 Shared own feelings	2	0	4		5
Item 10 Received adequate info	1	0	5		5
Item 11 Received enough help and advice	5	2	0		0
Item 13 Family confidence	1	1	3		6
*no further analysis was conducted because of the high number of missing values resulting into a low item coverage

For the self-report version, from model 1, item 3 (eating) had a highest residual variance and therefore was removed from the model, and model fit was examined further. From Table 3, model 2, which excludes the “feeding” item, has a much better fit compared to model 1 (CFI index is 0.941, TLI is 0.901 and RMSEA is 0.043). On average, the indices moderately satisfy the cut offs quoted by Hu and Bentler of 0.95 for the comparative fit index, 0.06 for the standardized root mean square residual[83].

For the proxy version, model 1 of the proxy version has 6 items (pain, other body problems, feeding, cried, happy and play). Inspecting the model modification indices, the feeding item showed a residual variance. Removing the feeding item from the model did not meaningfully impact on fitness indices (see Table 3). Given that feeding was an emergent theme from the qualitative interviews, and the fact that it is negatively worded and hence susceptible to construct distortion bias, we decided to retain it and the wording problem can be addressed.

For the family subscale, the model with 5 items showed moderate fit, any further changes in content did not improve the model fit.

Table 3

Confirmatory factor analyses model fit indices for the models evaluated
Model	n	X (df)	Comparative fit index	Tucker- Lewis index (TLI)	Root mean square error of approximation (RMSEA)	RMSEA − 90%CI	Standardized Root Mean Square Residual (SRMR)
Self-report version
Model 1 with all 7 items	221	34.553*(14)	0.933	0.899	0.062	0.048–0.116	0.046
Model 2 minus feeding item (n for items = 6)	221	25.888*(9)	0.941	0.901	0.043	0.052–0.135	0.044
Proxy-report
Model 1 with 6 items Excludes item on questions answered (n for items = 6)	213	52.733(9)	0.925	0.876	< 0.001	0.113–0.192	0.056
Model 2 Excludes feeding and questions answered items (n of items = 5)	213	39.950(5)	0.936	0.871	< 0.001	0.132–0.235	0.050
Assessing the self and proxy report versions for invariance by type of respondent
Configural	434	63.218*(10)	0.933	0.866	0.157	0.121–0.195	0.048
Metric	Model does not converge due to item 4 invariance item
Scalar	434	146.091(33)	0.858	0.914	0.126	0.105–0.147	0.065
Metric invariance model – allowing item 4 to vary
Metric invariance model – allowing item 4 to vary	434	63.218(10)	0.933	0.866	0.157	0.121–0.195	0.048
Family version – 5 items	215	15.892(5)	0.862	0.724	0.057	0.048–0.158	0.044
Family version – 4 items Less item 12	215	12.312(6)	0.918	0.864	0.234	0.000 -0.126	0.045

df: degrees of freedom; CFI: comparative fit index; TLI: Tucker–Lewis index; RMSEA: root mean square error of approximation; 95% CI: 95% confidence intervals.

1b) Discriminant validity

A total of 302 children/family carers completed the C-POS and the PedsQL. The Spearman’s rho correlation coefficients for the selected best fit C-POS and PedsQL subscales scores for child items (self and proxy) and the PedsQL scores were low as hypothesized (all < 0.6) (see Table 4).

Table 4

Correlations of PedsQL and C-POS subscale summary scores for discriminant validity
PedsQL summary scores	C-POS subscale scores	Spearman’s correlation (proxy version)	Spearman’s correlation (self-report version)
Total PedsQL score	C-POS patient items	0.308	0.117
PedsQL Physical health summary score	Sum- C-POS (Pain)+ (other problems)	0.291	0.029
PedsQL Psychosocial health summary score	Sum C-POS (felt happy) + Q6(felt like playing) + Q (questions about illness)	0.256	0.133
C-POS items –includes 6 patient items, we excluded one item (questions answered because of the high number of missing values)

1c) Known groups’ comparison

For both the child and proxy versions, patients with worse functional performance (ECOG) reported more severe palliative care-related symptoms and concerns (CPOS) compared to those with better functional performance (see Fig. 1).

Higher scores indicate more palliative care related concerns

1d) Measurement invariance

We found partial metric variance due to item 4 (cried). We inspected the source of variance and noted it was at the threshold level and carers were more likely to rate cried highly or severely as compared to the children. Allowing item 4 to vary depending on type of respondent improved the model fit, comparing the scalar model fit indices with the model that allows item 4 to vary by type of respondent, shows that the latter had a better fit (see Table 3).

1e) Differential item functioning

For our MIMIC model assessing item functioning variation by country controlling for age, at a P value cut off of < 0.001, we did not find any item functioning variation by country setting for the self-report and proxy report versions (see supplementary results e-table S1 and S2).

2) Reliability

2a) Internal consistency

For both versions of the C-POS, the omega reliability coefficients for the child item sub-scale showed acceptable internal consistency (omega coefficients proxy version- 0.71 and child version 0.67. We also found moderate internal consistency for the family sub-scale omega coefficient (0.49). Inspecting the item information functions, C-POS proxy report (achieved satisfactory internal consistency, item information range 3–9), the self-report version, item information range was 3–5, can could be strengthened by including more items that addressed concerns of children with less severe palliative care problems. The family subscale could be strengthened by including items that reflect the concerns of carers for children with more severe palliative care-related concerns, as that is where the standard error is biggest. For item information (i.e., reliability ¼ 1 _ [1 /information]) the values of 3.3 to 10[43], correspond to the recommended Cronbach’s alpha scores of 0.70 to 0.90 for interpreting reliability. (see Fig. 2 for details).

2b) Test-re-test reliability

The C-POS was completed by 297 children at two visits; the test-retest analysis is based on a sub-sample of 152 inpatients whom we hypothesised remained stable during the test-retest period. The weighted Kappa scores were good for all items, with more subjective items generally showing lower reliability than the physical outcomes (see Table 5).

Table 5

Test-retest reliability (children n = 152, carers n = 127)
In patients (152)
Child version (n = 33)					Proxy version (n = 119)
Child items				Child items
	Weighted Kappa statistic	Agreement within one score	Expected agreement		Weighted Kappa statistic	Agreement within one score	Expected agreement
Item 1 pain	0.5364	80.63%	58.20%	Q1	0.5239	81.87%	61.91%
Item 2 other body symptoms	0.4995	80.63%	61.29%	Q2	0.4791	77.33%	56.49%
Item 3 eating	0.5741	82.50%	58.91%	Q3	0.6421	83.73%	54.55%
Item 4 cried	0.4237	86.72%	76.95%	Q4	0.3477	76.53%	64.02%
Item 5 happy	0.4863	78.75%	58.63%	Q5	0.5950	81.74%	54.92%
Item 6 playing	0.4348	73.75%	53.55%	Q6	0.5861	81.08%	54.29%
Item 7 questions answered	0.5224	80.00%	58.12%	Q7	0.3613	71.91%	56.03%
Family items (n = 127)
Item 8 worry	0.5047	76.52%	52.60%
Item 9 Shared own feelings	0.6814	88.63%	64.33%
Item 10 Received adequate info	0.6487	88.55%	67.41%
Item 11 Received enough help and advice	0.6942	88.89%	63.67%
Item 12 Family confidence	0.7086	95.68%	85.19%

3) Responsiveness

The GEE analysis showed a positive change (improvement/reduction in palliative care related concerns) for most items (5/7 questions for child self-report version and 6/7 questions for the proxy report version) (see Table 6). For both versions, the tool did not demonstrate responsiveness for the feeding item. For the self-report version, the tool did not demonstrate responsiveness on the items of cried, felt happy and questions about illness answered.

Table 6

Responsiveness analysis results using Wilcoxon rank sum test for matched pairs
					P values for differences between paired observations
	T1 Mean (SD) Median (IQR)	T2 Mean (SD) Median (IQR)	T3 Mean (SD) Median (IQR)	T4 Mean (SD) Median (IQR)	T1 -T3	T2-T3	T3-T4	T1-T4
Self-report version (n = 71)
Item1 Pain	2.4(1.9) 2(0–4)	1.9(1.7) 1(0–3)	1.4(1.7) 1(0–3)	1.3(1.6) 0(0–2)	< 0.001*	0.055	0.4452	0.0001*
Item2 other body problems	2.1(1.8) 2(0–4)	1.7(1.8) 1(0–3)	1.5(1.7) 1(0–3)	1.5(1.8) 1(0–3)	0.008*	0.065	0.963	0.011*
Item 3 Feeding	2.5(2.0) 3(0–5)	2.3(1.9) 2(0–4)	2.3(2.2) 2(0–5)	2.3(2.2) 2(0–5)	0.402	0.887	0.3652	0.417
Item 4 Cried	0.7(1.4) 0(0–1)	2.1(1.6) 2(1–3)	0.6(1.2) 0(0–1)	0.6(1.5) 0(0–0)	0.855	0.433	0.786	0.224
Item 5 felt happy	1.6(1.7) 1(0–3)	1.6(1.8) 1(0–3)	1.3(1.6) 1(0–3)	1.3(1.6) 0.5(0–2)	0.150	0.142	0.884	0.217
Item 6 playing	2.2(2.2) 2(0–5)	2.0(2.1) 1(0–5)	1.6(2.2) 0(0–3)	1.9(2.5) 0(0–3)	0.007*	0.073	0.861	0.130
Item 7 questions answered	1.7(1.9) 1(0–3)	1.6(1.9) 1(0–3)	1.5(2.2) 0(0–3)	1.6(2.4) 0(0–3)	0.112	0.191	0.897	0.631
Proxy version (n = 159)
Item1 Pain	2.1(1.8) 2(0–3)	1.3(1.6) 1(0–3)	1.4(1.7) 1(0–3)	1.3(1.7) 0(0–3)	< 0.001*	0.2089	0.3550	< 0.001*
Item2 other body problems	2.6(1.9) 3(1–4)	2.0(1.7) 2(0–3)	1.6(1.7) 1(0–3)	1.5(1.6) 1(0–3)	< 0.001*	0.005*	0.1987	< 0.001*
Item 3 Feeding	2.7(2.0) 3(0–5)	2.7(2.0) 3(0–5)	2.8(2.0) 3(0–5)	2.8(2.1) 3(0–5)	0.402	0.678	0.5237	0.459
Item 4 Cried	2.1(1.6) 2(1–3)	1.0(1.7) 0(0–2)	1.6(1.6) 1(0–3)	1.3(1.5) 1(0–2)	0.001*	0.4186	0.001*	0.001*
Item 5 felt happy	2.4(1.9) 3(0–4)	2.2(1.9) 3(0–4)	1.9(1.8) 2(0–3)	1.7(1.8) 1(0–3)	0.001*	0.032	0.0602	0.001*
Item 6 playing	2.3(2.0) 2(0–5)	2.3(1.9) 2(0–4)	2.1(2.0) 2(0–4)	1.7(2.0) 1(0–3)	0.062	0.148	0.001*	0.001*
Item7 questions answered	2.7(1.9) 3(1–5)	2.3(1.9) 3(0–4)	2.1(2.0) 2(0–4)	2.4(2.4) 2(0–4)	0.002*	0.1458	0.901	< 0.001*
Family (n = 293)
Item 8 worry(n = 293)	3(2.1) 3(1–5)	2.8(2.0) 3(1–5)	2.9(1.8) 3(2–5)	2.7(2.0) 3(1–5)
Item 9 Shared own feelings (n = 272)	1.5(1.9) 0(0–3)	1.5(1.9) 1(0–3)	1.5(1.9) 0(0–2)	1.1(1.8) 0(0–2)	0.030*	0.014*	< 0.001*	< 0.001*
Item 10 Received adequate information (n = 294)	1.6(1.8) 1(0–3)	1.4(1.7) 1(0–2)	1.4(1.7) 1(0–2)	1.2(1.7) 0(0–2)	< 0.001*	0.066	0.006*	< 0.001*
Item 11 Received enough help and advice (n = 290)	2.2(2) 2(0–5)	1.9(1.8) 2(0–3)	1.9(1.9) 1(0-3.5)	1.8(1.8) 1(0–3)	0.001*	0.272	0.0006	< 0.001*
Item 12 Family confidence (n = 268)	0.9(1.6) 0(0–1)	0.8(1.4) 0(0–1)	0.6(1.2) 0(0–1)	0.5(1.1) 0(0–1)	0.001*	0.023*	0.004*	0.001*

Statistically significant p values are bolded - Significant at *5% **1% ***0.01% levels.

Time difference between T1 and T2 is 1 day, T2 and T3 is 3 days T3 and T4 is 3 days

Controlling for country and age, generalised linear regression analysis showed statistically significant reductions in palliative care concerns over time for both the self and proxy report versions of the C-POS and the family sub-scale. We found no differences due to age and country setting variation in palliative care-related concerns over time for self-report version of the C-POS (see supplementary table 3).

4) Acceptability of the C-POS

For the C-POS child self-report version, the median completion time reduced from 25 minutes (IQR 20–30) at visit 1 to 10.5 (IQR (10–15) at visit 4. For the C-POS proxy version, the median time for completion reduced from 20 (IQR 15–25) at visit 1 to 10 minutes (IQR 7–13 minutes) at visit 4. The C-POS items were acceptable to participants, as shown by the infrequent missing values for any time point (0% for “pain” to 2.8% for “questions answered”). Data coverage for item 7 – question about illness answered was 0.72, suggesting that the item is problematic. Details of these findings were presented in Table 2 at point of assessing item level data completeness.

5) Interpretability

The mean scores for the child and proxy versions were 14.07 (SD- 6.8)-proxy and 10.69 (6.03) respectively. Therefore, the minimum important difference for total score on the six child items alone is 3 on a scale of 0–30. For the family 11 item sub-scale, the mean scores were 20.1 (SD 8.6) (proxy version) and 23.9 (SD8.2) (self-report version) yielding a minimum important difference of 4, on a scale of 0–55.

This study provides evidence for the validity, reliability, responsiveness to change and acceptability, and interpretability of both the child and proxy versions of the novel C-POS. This comprehensive multi-dimensional person-centred outcome measure for use in children’s palliative provides an important contribution to the advancement of the science and practice of outcome measurement for children and young people.

Due to the absence of a gold standard multi-dimensional outcome measure, we selected a comparative tool: the PedsQL. We found evidence for the divergent validity of the C-POS using the PedsQL. The C-POS total scores and subscale scores showed low/moderate correlation to the PedsQL, suggesting the instrument discriminates dissimilar constructs. The PedsQL is a function-oriented quality of life measure while the C-POS is a multidimensional outcome measure and therefore measures different constructs from existing measures[84, 85]. Whilst the PedsQL has been found not to be valid in palliative care in the USA[86], it has been reported valid in typical palliative care patients in African countries. The difference in the utility of this tool could be due to differences in models of care, different health conditions and the lower prevalence of associated cognitive delay in HIV, cancer and sickle cell compared to the more prevalent neurological conditions in high income countries.

Although the psychometric properties of the C-POS were found to acceptable, a low test-retest reliability score was obtained for the ‘cried’ and ‘questions answered’ items on the proxy version of the C-POS. This could be because of the subjectivity of the items or an indication of measuring difficulties of very subjective constructs in young populations[72, 87]. For example, children with advanced cognitive development age may show other emotions beyond crying. For the self-report version C-POS, responsiveness was not demonstrated for the two items of “cried” and “felt happy.” This could be explained by the fact that the two items are inversely related, the happiness item within this subsample will tend to have a more restricted range of scores than for the full sample. Also, these items had floor and ceiling effects which limits variability and hence ability to show change over time. Lastly, the proxy version of the C-POS did not demonstrate change for the item of feeding, possibly due to the challenge of scoring feeding.

The acceptability of the C-POS is demonstrated by its minimum patient burden in terms of its completion time and very limited missing data. Moreover, the C-POS items were acceptable, the scale was judged as relevant to the population, and instructions were clear. Item 7 (“how much have questions about your sickness have answered since yesterday”) had the highest number of missing values on the proxy version. It might be explained by common difficulties proxies face in reporting unobservable constructs without input from patients themselves[88]. We suggest that this question is asked directly to children or and observable indicators can be used for the proxy version, the question should be avoided for very young children who cannot ask questions given their cognitive development age.

Themes developed from the qualitative interviews highlighted spiritual and existential concerns particularly amongst older children[6]. This finding demonstrates the robustness of the spiritual /existential domain of health and well-being which is equally important in children as for adults[89]. Consideration of the inclusion of an item to cover this domain. The themes mirrored how patients and families adjusted to self, community and environment as it related to the perceived purpose of life, life satisfaction and life experiences[90]. The theme of financial problems was raised by adolescents but should be interpreted with caution as respondents commonly anticipate financial support if they present such difficulties. As countries move towards integrating palliative care into healthcare systems using a public health approach, additional items or modules might be developed and validated to add as necessary to this core outcome measure. This approach has been used for newer versions of the POS, such as an expanded symptom module and for specific diagnostic groups.

Measure refinement

Based on the findings, we recommend the following minor revisions:

First, remove the feeding item as it provides the least information explaining variation in the latent construct; moreover, it has considerable overlap with the “other body problems” item for the self-report version. This item was, however, negatively worded, and this could affect its functionality; re-wording it may therefore be an option also worth exploring.

Second, all negatively worded items should be re-worded as evidence has consistently shown that such items tap different constructs. Item 8 (family worry) and item 3 (feeding), fall in this category.

Third, item 7 (questions about illness answered) should be revisited; it can be posed to the children, but an observable alternative may be more useful for the proxy version. Moreover, the question should be omitted for very young children who cannot ask questions because of developmental/cognitive age problems.

Fourth, the crying item showed metric variance should be revisited as older children, who complete the self-report version, are more likely to express emotions such as sadness or feel like crying as opposed to crying.

Fifth, we suggest additional items to address the following conceptual gaps: spiritual/existential and normalcy. Items with small discriminating power could be replaced or additional items added to maximise impact. The developmental and chronological age differences in themes should be addressed, age group-specific prototypes should be considered, and the current questions can be modified slightly to address this gap.

Strengths and limitations

The C-POS has been developed according to highest scientific principles and guidelines for tool development and validation. Importantly this measure has been developed across a range of countries, settings (inpatient, outpatient and daycare) and diagnostic and cultural groups. Second, the self-report version of the C-POS was not used in children who were too ill to complete the tool, a common occurrence in paediatric palliative care. These children may be unable to self-report due to young age, advanced illness, type of illness or fatigue as they near death[91] Proxy reporting from family carers is acceptable in such situations, given the time spent together during their illness and this can be adopted as best practice[58].

There are several limitations of this study that need to be taken into consideration. First, the development of person-centred outcome measures is an evolutionary process, and we identified some areas for improvement which should be considered. Second, we also did not explore minimum important clinical difference, which is useful for interpreting changes in scores in clinical settings. Third, grouping children by stage of disease trajectory would be useful for informing construct validity assessment by known groups but this is complicated by the unpredictability of disease trajectories being mindful of the 400 conditions that require palliative care in young people[92]. More so, due to language diversity in the countries, the interviews were conducted in different languages which were later translated into English and combined in the analysis. This could introduce some language level biases.

With the minimal changes proposed above, the C-POS has potential to measure person-centred outcomes in paediatric palliative care. The vast majority of evidence for the concept of practice of person-centredness in serious physical illness has focused on adults[93, 94]. The data in this study expands this to conceptualising and measuring child- and family-centred outcomes. The palliative care of children and young people has become a global health priority[95]. It is therefore important that resources are allocated for adaptation of the C-POS where required, and sustained implementation[96] to improve outcomes and inform a culture shift to outcome-focused services.

APCA

African Palliative Care Association

CFI

Comparative Fit Index

C-POS

Children’s Palliative Care Outcome Scale

ECOG

Eastern Cooperative Oncology Group Scale

LLCs

Life-limiting and life-threatening conditions

MIMIC

Multiple Indicator, Multiple Cause Modelling Controlling

PedsQL

Paediatric Quality of Life Inventory

PCOM/PCOMs

Person-Centred Outcome Measure(S)

RMSEA

Root Mean Square Error of Approximation

SEM

Structural Equation Modelling

Standard Deviation

SRMR

Standardized Root Mean Square Residual

TLI

Tucker-Lewis Index

Disclaimer

The views expressed in this publication by author RAP are his own and not necessarily those of the NIHCR or the Department of Health and Social Care, London, UK

Ethical approval and consent to participate

Ethical approval was obtained from King’s College London, Research Ethics Office (LRS-15/16-3524) and in the three countries: Uganda National Council for Science and Technology (UNCST SS2366), Kenya Medical Research Institute (KEMRI/RES/7/3/1), and the South Africa Hospice and Palliative Care Association (HPCA 03/10).

All participating children aged 8 years and above gave assent, while adult caregivers gave informed written consent for children to participate.

Competing interests

The authors declare that they have no competing interests to declare.

Funding

We thank AIDSTAR-One- grant number AIDSTAR-One -001 for funding a project that partly contributed to the development of this core outcome measure in children. We thank the Open Society Foundations and Cicely Saunders International for funding EN’s PhD studentship.

Authors’ contributions

Study concept and design, analysis and interpretation of the data drafting of the manuscript critical revision of the manuscript for important intellectual content: EN, KB, FEM, JD , MA , MS,RH . Analysis and interpretation of the data, drafting of the manuscript, critical revision of the manuscript for important intellectual content: IJH , LF, MA, FM-P, RAP Study supervision, drafting of manuscript, administrative, technical, and material support: ZA , MM .

All authors read and approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

The authors express their sincere gratitude to the following sites that participated in the development and validation of the C-POS: Nyahururu Hospice – Kenya, Nyanza Provincial General Hospital children’s oncology ward, through Kisumu Hospice, Kenya, Jaramogi Oginga Odinga Teaching and Referral Hospital, Isibani Sitembi – South Africa, Soweto Hospice – South Africa, Red Cross Children’s hospital –South Africa, Little Hospice Hoima – Uganda, Hospice Africa Uganda, Mildmay Uganda, Mulago National Teaching and Referral Hospital, Kawempe Home Care, Makerere Palliative Care Unit, Uganda, Oshakati Hospital, Namibia , and Island Hospice, Harare, Zimbabwe. We also thank the research assistants who undertook data collection and the children, their carers and facility staff who provided the data on which these findings are based. The authors are grateful to Dr Chong Chua, for his guidance on the statistical analysis, Professor Richard Siegert for his comments on the statistical analysis plan and to Dr Simon Wandiembe, for his comments on the statistical analysis plan and the quantitative results.

Connor, S.R., J. Downing, and J. Marston, Estimating the Global Need for Palliative Care for Children: A Cross-sectional Analysis. J Pain Symptom Manage, 2017. 53(2): p. 171-177.
Mitchell, S., et al., Specialist paediatric palliative care services: What are the benefits? Archives of Disease in Childhood, 2017. 102(10): p. 923-929.
Hain, R., et al., Paediatric palliative care: development and pilot study of a 'Directory' of life-limiting conditions. BMC Palliat Care, 2013. 12(1): p. 43.
Fraser, L.K., et al., Estimating the current and future prevalence of life-limiting conditions in children in England. Palliative Medicine, 2020.
Scott, H.M., et al., Spiritual, religious, and existential concerns of children and young people with life-limiting and life-threatening conditions: A qualitative interview study. Palliat Med, 2023: p. 2692163231165101.
Namisango, E., et al., Symptoms and Concerns Among Children and Young People with Life-Limiting and Life-Threatening Conditions: A Systematic Review Highlighting Meaningful Health Outcomes. Patient, 2019. 12(1): p. 15-55.
Fraser, L.K., et al., Does referral to specialist paediatric palliative care services reduce hospital admissions in oncology patients at the end of life? Br J Cancer, 2013. 108(6): p. 1273-9.
Gomes, B., et al., Effectiveness and cost-effectiveness of home palliative care services for adults with advanced illness and their caregivers. Cochrane Database Syst Rev, 2013(6): p. CD007760.
Gomes, B., N. Calanzani, and I.J. Higginson, Benefits and costs of home palliative care compared with usual care for patients with advanced illness and their family caregivers. JAMA, 2014. 311(10): p. 1060-1.
Higginson, I.J. and C.J. Evans, What is the evidence that palliative care teams improve outcomes for cancer patients and their families? Cancer J, 2010. 16(5): p. 423-35.
Temel, J.S., et al., Early palliative care for patients with metastatic non-small-cell lung cancer. N Engl J Med, 2010. 363(8): p. 733-42.
Smith, S., et al., Evidence on the cost and cost-effectiveness of palliative care: a literature review. Palliat Med, 2014. 28(2): p. 130-50.
Farquhar, M.C., et al., Is a specialist breathlessness service more effective and cost-effective for patients with advanced cancer and their carers than standard care? Findings of a mixed-method randomised controlled trial. BMC Med, 2014. 12: p. 194.
Dawson, J., et al., The routine use of patient reported outcome measures in healthcare settings. BMJ, 2010. 340: p. c186.
Coombes, L.H., et al., Health-related quality-of-life outcome measures in paediatric palliative care: A systematic review of psychometric properties and feasibility of use. Palliat Med, 2016. 30(10): p. 935-949.
Friedel, M., et al., Instruments to Measure Outcomes in Pediatric Palliative Care: A Systematic Review. Pediatrics, 2019. 143(1).
Coombes, L.H., et al., Health-related quality-of-life outcome measures in paediatric palliative care: A systematic review of psychometric properties and feasibility of use. Palliat Med, 2016. 31: p. 0269216316649155.
Huang, I.C., et al., Measuring quality of life in pediatric palliative care: challenges and potential solutions. Palliative Medicine, 2010. 24(2): p. 175-182.
Friedel, M., et al., Instruments to Measure Outcomes in Pediatric Palliative Care: A Systematic Review. Pediatrics, 2018: p. e20182379.
Medical Research Council, Patient- Reported Outcome Measures (PROMs): Identifying UK Research Priorities. 2009, MRC: London.
Booth, A., et al., Research prioritisation exercises related to the care of children and young people with life-limiting conditions, their parents and all those who care for them: A systematic scoping review. Palliat Med, 2018. 32(10): p. 1552-1566.
Kitson, A., et al., What are the core elements of patient-centred care? A narrative review and synthesis of the literature from health policy, medicine and nursing. Journal of Advanced Nursing, 2013. 69(1): p. 4-15.
Mead, N. and P. Bower, Patient-centredness: a conceptual framework and review of the empirical literature. Social Science & Medicine, 2000. 51(7): p. 1087-1110.
Coulter, A. and J. Oldham, Person-centred care: what is it and how do we get there? Future Hospital Journal, 2016. 3(2): p. 114-116.
World Health Organisation, People-Centred Health Care: A Policy Framework. 2007: Geneva, Switzerland.
Namisango, E., et al., Towards person-centred quality care for children with life-limiting and life-threatening illness: Self-reported symptoms, concerns and priority outcomes from a multi-country qualitative study. Palliative Medicine, 2020. 34(3): p. 319-335.
Hinds, P.S., J.C. Menard, and S.S. Jacobs, The child's voice in pediatric palliative and end-of-life care. Progress in Palliative Care, 2012. 20(6): p. 337-342.
United Nations, United Nations Convention on the Rights of the Child. 1989, Unicef.
Coombes, L., et al., Enhancing validity, reliability and participation in self-reported health outcome measurement for children and young people: a systematic review of recall period, response scale format, and administration modality. Quality of Life Research, 2021.
Namisango, E., et al., Symptoms and Concerns Among Children and Young People with Life-Limiting and Life-Threatening Conditions: A Systematic Review Highlighting Meaningful Health Outcomes. The Patient - Patient-Centered Outcomes Research, 2019. 12(1): p. 15-55.
Dawson, J., et al., The routine use of patient reported outcome measures in healthcare settings. British Medical Journal, 2010. 340.
Drake, R., J. Frost, and J.J. Collins, The symptoms of dying children. Journal of Pain & Symptom Management, 2003. 26(1): p. 594-603.
Wolfe, J., Suffering in children at the end of life: recognizing an ethical duty to palliate. Journal of Clinical Ethics, 2000. 11(2): p. 157-63.
Knapp, C. and V. Madden, Conducting outcomes research in pediatric palliative care. American Journal of Hospice & Palliative Medicine, 2010. 27(4): p. 277-81.
Hearn, J. and I.J. Higginson, Outcome measures in palliative care for advanced cancer patients: A review. Journal of Public Health Medicine, 1997. 19(2): p. 193-199.
Palliative Care Outcome Scale. www.pos-pal.org 2012 [cited May 2016
Bausewein, C., et al., The use of two common palliative outcome measures in clinical care and research: A systematic review of POS and STAS. Palliative Medicine, 2011. 25(4): p. 304-313.
Harding, R. and I.J. Higginson, PRISMA: a pan-European co-ordinating action to advance the science in end-of-life cancer care. Eur J Cancer, 2010. 46(9): p. 1493-501.
Harding, R., Special Section: The PRISMA Symposium Foreword. Journal of Pain and Symptom Management, 2011. 42(4): p. 491-492.
Defilippi K and Downing J, Feedback from African palliative care practioners on the use of the APCA POS International Journal of Palliative Nursing, 2013 19(12): p. 577-581.
Harding, R., et al., Validation of a core outcome measure for palliative care in Africa: the APCA African Palliative Outcome Scale. Health and Quality of Life Outcomes, 2010. 8(10).
Powell, R.A., et al., Development of the APCA African palliative outcome scale. Journal of Pain and Symptom Management, 2007. 33(2): p. 229-232.
WHPCA/WHO, Global Atlas of Palliative Care, S. Connor, Editor. 2020, WHPCA/WHO: London.
Harding, R., et al., Research into palliative care in sub-Saharan Africa. Lancet Oncology, 2013. 14(4): p. E183-E188.
Harding, R., L. Sherr, and R. Albertyn, The Status of Paediatric palliative care in sub-Saharan Africa: An appraisal . . 2010, The Diana Princess of Wales Memorial Fund London
Downing, J., et al., A palliative care outcome measure for children in sub-Saharan Africa. EUROPEAN JOURNAL OF PALLIATIVE CARE, 2012. 19(6): p. 292-295.
Murtagh, F.E.M., J.M. Addington-Hall, and I.J. Higginson, The value of cognitive interviewing techniques in palliative care research. Palliative Medicine, 2007. 21(2): p. 87-93.
Namisango, E., et al., Towards person-centred quality care for children with life-limiting and life-threatening illness: Self-reported symptoms, concerns and priority outcomes from a multi-country qualitative study. Palliat Med, 2020. 34(3): p. 319-335.
Namisango, E., et al., Face and content validity, acceptability, feasibility, and implementability of a novel outcome measure for children with life-limiting or life-threatening illness in three sub-Saharan African countries. Palliat Med, 2022. 36(7): p. 1140-1153.
De Civita, M., et al., Evaluating health-related quality-of-life studies in paediatric populations - Some conceptual, methodological and developmental considerations and recent applications. Pharmacoeconomics, 2005. 23(7): p. 659-685.
Streiner. D.L; Norman. G.R; Cairney. J, Health measurement scales: a practical guide to their development and use. 2014: Oxford university press.
Hearn, J., I.J. Higginson, and P. Palliative Care Core Audit, Development and validation of a core outcome measure for palliative care: the palliative care outcome scale. Quality in Health Care, 1999. 8(4): p. 219-227.
Blum, D., et al., Self-report measurement of pain & symptoms in palliative care patients: a comparison of verbal, visual and hand scoring methods in Sub-Saharan Africa. Health and Quality of Life Outcomes, 2014. 12.
World Health Organisation. Definition of Palliative Care 2013 [cited April 2019 Definition of palliative care ]. Available from: http://www. who.int/cancer/palliative/definition/en/.
Bowden, A. and J.A. Fox-Rushby, A systematic and critical review of the process of translation and adaptation of generic health-related quality of life measures in Africa, Asia, Eastern Europe, the Middle East, South America. Social Science & Medicine, 2003. 57(7): p. 1289-306.
Riley, A.W., Evidence that school-age children can self-report on their health. Ambulatory Pediatrics, 2004. 4(4): p. 371-376.
Coombes, L., et al., Enhancing validity, reliability and participation in self-reported health outcome measurement for children and young people: a systematic review of recall period, response scale format, and administration modality. Qual Life Res, 2021.
Matza, L.S., et al., Assessment of health-related quality of life in children: A review of conceptual, methodological, and regulatory issues. Value in Health, 2004. 7(1): p. 79-92.
Higginson, I.J. and W. Gao, Caregiver assessment of patients with advanced cancer: concordance with patients, effect of burden and positivity. Health and Quality of Life Outcomes, 2008. 6.
Varni, J.W., M. Seid, and P.S. Kurtin, PedsQL (TM) 4.0: Reliability and validity of the pediatric quality of life Inventory (TM) Version 4.0 generic core scales in healthy and patient populations. Medical Care, 2001. 39(8): p. 800-812.
Oken, M.M., et al., Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol, 1982. 5(6): p. 649-55.
Sullivan, M.C. and M.E. Msall, Functional performance of preterm children at age 4. J Pediatr Nurs, 2007. 22(4): p. 297-309.
Nunnally, J.C., Psychometric theory. 1978, New York: McGraw-Hill.
Tabachnick B. G. and Fidell L. S., Using multivariate statistics, ed. A. Bacon. 2007, Boston, MA.
Forero, C.G., A. Maydeu-Olivares, and D. Gallardo-Pujol, Factor Analysis with Ordinal Indicators: A Monte Carlo Study Comparing DWLS and ULS Estimation. Structural Equation Modeling: A Multidisciplinary Journal, 2009. 16(4): p. 625-641.
Bandalos, D.L., Relative Performance of Categorical Diagonally Weighted Least Squares and Robust Maximum Likelihood Estimation. Structural Equation Modeling: A Multidisciplinary Journal, 2014. 21(1): p. 102-116.
Hu, L., Bentler, P., Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 1999. 6(1): p. 1-55.
Kirkwood, R.B. and J.A.C. Sterne Essential Medical Statistics 2nd ed, ed. B.w.p. Ltd. 2003, UK
J., C., Statistical Power Analysis for the Behavioral Sciences 2nd edition ed. 1988, Hillsdale, NJ:: Lawrence Erlbaum Associates, Publishers.
Streiner, D.L., Being inconsistent about consistency: when coefficient alpha does and doesn't matter. J Pers Assess, 2003. 80(3): p. 217-22.
Cronbach, L.J., COEFFICIENT ALPHA AND THE INTERNAL STRUCTURE OF TESTS. Psychometrika, 1951. 16(3): p. 297-334.
Fayers. P.; Machin. D, Quality of life: the assessment, analysis and interpretation of patient-reported outcomes. 2nd ed. 2007, England John Wiley & Sons.
Streiner. DL, Norman. GR, and Cairney. J, Health Measurement Scales: a practical guide to their development and use 4th ed. 2008, New York Oxford University Press
Cocchetti, D.V., Sample size requirements for increasing the precision of reliability estimates: problems and proposed solutions. J Clin Exp Neuropsychol, 1999. 21(4): p. 567-70.
Fayers P.M. and D. Machin, Quality of Life second edition ed. The assessment, analysis and interpretation of patient reported outcomes 2000.
Mokkink, L.B., et al., The COSMIN checklist for evaluating the methodological quality of studies on measurement properties: A clarification of its content. BMC Medical Research Methodology, 2010. 10.
Norman, G.R., P. Stratford, and G. Regehr, Methodological problems in the retrospective computation of responsiveness to change: The lesson of Cronbach. Journal of Clinical Epidemiology, 1997. 50(8): p. 869-879.
Husted, J.A., et al., Methods for assessing responsiveness: a critical review and recommendations. J Clin Epidemiol, 2000. 53(5): p. 459-68.
Evans, C.J., et al., The selection and use of outcome measures in palliative and end-of-life care research: the MORECare International Consensus Workshop. J Pain Symptom Manage, 2013. 46(6): p. 925-37.
Fayers, P.M., D. Curran, and D. Machin, Incomplete quality of life data in randomized trials: missing items. Stat Med, 1998. 17(5-7): p. 679-96.
Norman, G.R., et al., Relation of distribution- and anchor-based approaches in interpretation of changes in health-related quality of life. Med Care, 2001. 39(10): p. 1039-47.
Norman, G.R., J.A. Sloan, and K.W. Wyrwich, Interpretation of changes in health-related quality of life - The remarkable universality of half a standard deviation. Medical Care, 2003. 41(5): p. 582-592.
P., H.L.B., Cutoff criteria for fit indexes in covariance structure analysis: Convetional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 1999. 61(1): p. 1-55.
Davis, E., et al., Paediatric quality of life instruments: a review of the impact of the conceptual framework on outcomes. Developmental Medicine and Child Neurology, 2006. 48(4): p. 311-318.
World Health Organisation. WHO Definition of Palliative Care. 2002; Available from: http://www.who.int/cancer/palliative/definition/en/.
Huang, I.C., et al., Measuring quality of life in pediatric palliative care: challenges and potential solutions. Palliative Medicine, 2010. 24(2): p. 175-82.
Eiser, C. and R. Morse, Can parents rate their child's health-related quality of life? Results of a systematic review. Qual Life Res, 2001. 10(4): p. 347-57.
Davis, E., et al., Parent-proxy and child self-reported health-related quality of life: using qualitative methods to explain the discordance. Quality of Life Research, 2007. 16(5): p. 863-871.
Bluebond-Langner, M., DeCicco, and M.N. Schwallie, Children’s views of death, in Oxford Textbook of Palliative Care for Children, R. Goldman, R. Hain, and S. Liben, Editors. 2012, Oxford University Press: Oxford.
Paloutzian, R. and C. ; Ellison, Manual for spiritual well-being scale. 1991.
Widger, K., et al., Initial development and psychometric testing of an instrument to measure the quality of children's end-of-life care. BMC Palliat Care, 2015. 14(1): p. 1.
Hain, R., et al., Paediatric palliative care: development and pilot study of a 'Directory' of life-limiting conditions. BMC Palliative Care, 2013. 12.
Giusti, A., et al., The empirical evidence underpinning the concept and practice of person-centred care for serious illness: a systematic review. BMJ Glob Health, 2020. 5(12).
Giusti, A., et al., Developing a global practice-based framework of person-centred care from primary data: a cross-national qualitative study with patients, caregivers and healthcare professionals. BMJ Glob Health, 2022. 7(7).
Harding, R. and I.J. Higginson, Inclusion of end-of-life care in the global health agenda. Lancet Global Health, 2014. 2(7): p. E375-E376.
Antunes, B., R. Harding, and I.J. Higginson, Implementing patient-reported outcome measures in palliative care clinical practice: a systematic review of facilitators and barriers. Palliat Med, 2014. 28(2): p. 158-75.

No competing interests reported.

supplementarymaterial1and2.docx

Download PDF

Reviewers invited by journal
08 Aug, 2024
Editor assigned by journal
07 Aug, 2024
Submission checks completed at journal
07 Aug, 2024
First submitted to journal
03 Aug, 2024

You are reading this latest preprint version

A novel child-centred core palliative care outcome measure for use in clinical practice and research: findings from a multinational validation study

Status:

Version 1

Abstract

Figures

Background

Methods

Design

Setting

Inclusion/exclusion criteria

Recruitment and consent

Data collection

Measures

The C-POS

Paediatric Quality of Life Inventory (PedsQL)

Eastern Cooperative Oncology Group Scale (ECOG)

Socio-demographics

Sample size

Data management

a) Internal consistency

2b) Test-retest reliability

Results

Sample characteristics

1a) Structural properties

1b) Discriminant validity

1c) Known groups’ comparison

Higher scores indicate more palliative care related concerns

1d) Measurement invariance

1e) Differential item functioning

2a) Internal consistency

2b) Test-re-test reliability

3) Responsiveness

Discussion

Measure refinement

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1