How Is the Family of a Person With Sickle Cell Disease Affected

Qual Life Res. 2009; 18(1): 5–13.

Impact of family income and sickle cell disease on the health-related quality of life of children

Julie A. Panepinto

¹Department of Pediatrics, The Children's Research Institute of the Children'south Infirmary of Wisconsin/Medical College of Wisconsin, Milwaukee, WI Usa

ⁱⁱDepartment of Pediatrics, Hematology/Oncology/Os Marrow Transplantation, MFRC, Medical Higher of Wisconsin , 8701 Watertown Plank Road, Milwaukee, WI 53226 USA

Nicholas M. Pajewski

^threeDepartment on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL The states

Lisa 1000. Foerster

⁴Illinois School of Professional person Psychology, Chicago, IL USA

Svapna Sabnis

¹Department of Pediatrics, The Children's Research Institute of the Children's Hospital of Wisconsin/Medical Higher of Wisconsin, Milwaukee, WI United states

Raymond Yard. Hoffmann

^oneSection of Pediatrics, The Children's Research Institute of the Children's Hospital of Wisconsin/Medical College of Wisconsin, Milwaukee, WI USA

Received 2008 Jun 27; Accepted 2008 Oct xv.

Abstruse

Purpose

The objective of this written report was to make up one's mind the affect of family income and sickle cell disease on the wellness-related quality of life (HRQL) of children.

Methods

This was a cross-sectional study of children with and without sickle cell disease. Participants completed the PedsQL™ generic core scales parent-proxy or child self-report questionnaire during a routine dispensary visit. HRQL was the primary outcome measured. Family income and sickle cell disease were the principal independent variables of interest.

Results

A total of 104 children with sickle cell disease and 74 without disease participated in the study. After adjusting for family income, patient historic period, and the presence of co-morbidities, children with severe sickle jail cell disease had increased odds of worse overall HRQL (parent-proxy HRQL written report odds ratio [OR] 4.0) and physical HRQL (parent-proxy report OR v.67, kid cocky-written report OR 3.33) compared to children without sickle cell disease.

Conclusions

Children with sickle jail cell disease have significantly impaired HRQL, even afterward considering the potential detrimental effect of family income on HRQL. Targeted interventions to better these children's HRQL are warranted.

Keywords: Sickle prison cell illness, Wellness-related quality of life, Socioeconomic factors, Children, Family income

Introduction

Health-related quality of life (HRQL) is a complex patient-reported outcome that provides an assessment of how an illness, its complications, and its treatment affect the patient. It has become increasingly important to measure patient-reported outcomes such every bit HRQL [1] to evaluate prognostic factors, to identify problems that tin be targeted by an intervention, to compare therapies, and to classify resources.

The HRQL of children with sickle cell disease is generally poor [2–4]. Yet, the extent to which the disease itself impacts the HRQL of these children is not clear, since these children are at chance for poor HRQL due to other factors, such as depression family income. In the United States, children with sickle cell disease are largely African American. Information technology is well known that African American children are more likely to live in poverty and reside in families with lower family income in the United States [five], a risk factor that was institute to be associated with worse HRQL in healthy, urban school age children [6]. Therefore, it is of import to appraise the outcome of sickle cell disease on HRQL while acknowledging that children with sickle prison cell disease are likely to be impoverished.

The objective of this report was to determine the touch of family income and sickle prison cell disease on the HRQL of children. Our hypothesis was that children with sickle cell disease have worse HRQL than children without sickle jail cell disease, fifty-fifty after because the potential detrimental outcome of lower family income on HRQL.

Patients and methods

Study setting and subjects

This was a cross-sectional study conducted from January 2006 through June 2007. Two groups of children aged 2–18 years were eligible for the study: (1) children with sickle cell disease who presented for a routine bank check-upwards at the Midwest Sickle Cell Center (MSCC) clinic and (ii) children without sickle prison cell disease who presented for a routine check-upwards at the Downtown Health Center in Milwaukee, Wisconsin. Children were excluded from the study if they had an acute disease or were hospitalized within the last month.

The MSCC serves over 300 children with sickle prison cell illness and is based inside an bookish children's hospital. The Downtown Health Center is an urban-based clinic that provides main care to over 4,000 children a year. The majority of patients who regularly nourish this clinic are African American (80%) and take public insurance (34% Medicaid, 58% Medicaid-HMO).

The demographic data were parent-reported or obtained from the child'south medical tape. Race data for the children was collected using a modified United states of america Demography classification and reflect parent report based on the following choices: White, Black, native Hawaiian or other Pacific Islander, Asian, American Indian or Alaskan native, other or unknown.

The Institutional Review Board of the Children'southward Hospital of Wisconsin/Medical College of Wisconsin approved the study. Informed consent was obtained from the parent and assent from children seven years of age or older.

Main consequence

The main outcome was HRQL measured with the PedsQL™ generic core scales parent-proxy and child self-report questionnaire. The PedsQL is a 23-item generic HRQL questionnaire that has a parent-proxy report for children anile two–18 years and a child self-written report questionnaire for children anile 5–18 years [seven]. The questionnaire yields data on the physical, emotional, social, and school functioning of the kid during the previous 4 weeks. Information technology has been extensively tested in good for you children [eight, nine], children with chronic disease [x–13], and was recently validated in children with sickle cell illness by our group [14]. Mean scores are calculated based on a 5-point response scale for each particular and transformed to a 0–100 scale, with a higher score representing better quality of life. There are four scale scores: physical functioning, emotional performance, social functioning, and school functioning. In add-on, the PedsQL yields three summary scores: a total scale score, a physical health summary score, and a psychosocial health summary score. The full score is comprised of the average of all items in the questionnaire. The psychosocial summary score is comprised of the average of the items in the emotional, social, and schoolhouse functioning scales. The concrete health summary score is comprised of the boilerplate of the items in the concrete functioning scale and is the aforementioned as the physical functioning scale score. Missing items were handled based on the developer's recommendation, which allows a scale score to be calculated if at least 50% of the items in each calibration are answered [7].

Variables

Our primary covariates of interest were family unit income and the presence of sickle prison cell disease. Withal, because the severity of sickle prison cell disease (divers beneath) [4], age, and the presence of other chronic conditions (medical and neurobehavioral co-morbidities) [3, 4, 12, xv, 16] could besides affect HRQL, we also examined the effect of these variables on HRQL.

Family unit income

The parents/primary caregivers were asked to provide their total household income on a categorical calibration for the family income variable. For those respondents that did not provide a family income (northward = 41), the median household income within their demography block group utilizing street addresses was used every bit a proxy for their income level. The demography block grouping was identified using the U.S. Census Bureau'due south American Fact Finder. Data from the 2000 Census Summary File 3 were downloaded for each of the block groups identified. Census data were merged with survey information by block group.

Family income was and so categorized into iii groups (<$20,000, $20,000–40,000, and >$xl,000), based on piece of work done in a prior evaluation of HRQL and income [half dozen]. We used $twenty,000 every bit our lowest category of family income, which is consistent with the weighted average 2006 poverty threshold ($20,614) for a family unit of four (representing household income where all members living in the home are included, i.e., three adults and i child, one adult and three children) [v].

Affliction severity

For children with sickle jail cell disease, disease status was classified a priori as mild or severe illness, regardless of the child's sickle cell disease blazon, which is consistent with how nosotros have classified disease severity in our prior work [14, 17]. Children with a history of a sickle cell-related stroke, astute chest syndrome, three or more hospitalizations for vasoocclusive painful events in the prior 3 years, and/or recurrent priapism were classified equally having astringent disease based on criteria used for intervention with hydroxyurea or bone marrow transplantation [18–20]. All others were classified as having mild disease. The type of sickle cell disease (hemoglobin SS versus SC, etc.) was not used as a marking of disease severity, as it is well known that there is inherent variability in the phenotypic expression and disease manifestations within particular sickle jail cell genotypes. Because HRQL is meant to reflect the well being and functioning of individuals, information technology is the experience of illness complications and morbidity in individuals that more accurately reflects their affliction condition and the potential impact that this volition accept on HRQL.

Other chronic conditions

Parents were asked to study whether they had ever been told by a health intendance provider that their kid had any of the following medical conditions: asthma, chronic allergies/sinus problem, chronic orthopedic/os/joint bug, chronic rheumatic illness, diabetes, epilepsy, or other chronic medical condition. Patients were classified as having a medical co-morbidity if they reported one or more of the above chronic medical conditions.

In add-on, parents were asked to report whether they had ever been told by a health care provider that their kid had whatever of the following neurobehavioral weather: anxiety, attentional or behavioral bug, depression, developmental delay or mental retardation, learning problems, or speech problems. Patients were classified as having a neurobehavioral co-morbidity if they reported ane or more of the in a higher place-noted neurobehavioral atmospheric condition.

Age

Age was examined using the age categories of the PedsQL questionnaire: 2–iv years, 5–7 years, 8–12 years, and 13–18 years.

Statistical analysis

Descriptive statistics were used to compare the distribution of demographic factors betwixt children with and without sickle cell disease. Continuous factors were compared using two-sample t-tests and non-parametric Wilcoxon rank-sum tests, where appropriate. Categorical factors were compared using Chi-square tests and Fisher–Freeman–Halton tests, where appropriate.

To examine the combined furnishings of sickle jail cell severity and family income adjusted for medical co-morbidities, neurobehavioral co-morbidities, and historic period on HRQL, each calibration or subscale was divided into categories of impairment based on published population data from Varni et al. [nine]. This categorization was performed because of skewed outcome distributions in the presence of ceiling effects, especially amidst the control population. Varni et al. define an impaired HRQL score equally less than the population mean − one standard departure (SD). Thus for each scale, the mean and SD were used to produce 4 categories of decreasing impairment: highly impaired (≤hateful – two SD), impaired (>hateful – 2 SD but ≤mean – ane SD), average (>mean – one SD but ≤mean), and above boilerplate (>hateful). For example, in the instance of the parent-proxy report total score, this led to cutpoints at scores of 49.fifty, 65.42, and 81.34 on the 100-bespeak scale. An ordinal logistic regression model was then fitted to the scale effect with disease group, disease severity, family income, and the presence of medical and neurobehavioral co-morbidities equally contained predictors. The presented odds ratios (OR) from the ordinal logistic regression model correspond the odds of scoring lower on the ordinal scale, implying worse HRQL.

The imputation of family unit income based on census data for the 41 respondents with missing income has the potential to bias our results. Consequently, we examined models with missing information as a divide poverty category to test whether the missing data had any effect on the results. Since information technology did not, we merely nowadays the results with the imputed data in the results section.

Predicted probabilities of impaired HRQL (≤population mean – 1 SD) were calculated using the fitted values from the ordinal logistic regression models. All analyses were performed using SAS v9.one.3 (SAS Inc., Cary, NC). An alpha level of 0.05 was used throughout to announce statistical significance.

Results

Report population

We recruited a convenience sample of patients from both clinics (Fig.ane). Thirty-three subjects with sickle cell disease refused participation in the study, mostly due to non having an interest in participating or stating that they did not have time to participate. Nine children completed a child self-study of the PedsQL but the parent did non.

Description of the available PedsQL parent-proxy reports and child self-reports for children with sickle cell disease (SCD) and control populations

Xx subjects without sickle cell disease refused to participate, again due to the lack of interest or not having time for the study. Our concluding sample size was 178 subjects; 104 with sickle prison cell disease and 74 without sickle cell disease.

Parent and kid demographics

The majority of parents in both groups were African American, although the parents of children with sickle cell disease were more likely to exist African American compared to the parents of children without sickle cell disease (Tablei). In addition, parents of children without sickle cell disease had a higher percentage of reporting the lowest family income (Tablei). Children with sickle cell disease had the post-obit types of sickle cell disease: 66 hemoglobin SS, 26 hemoglobin SC, ix hemoglobin Sβ⁺, one hemoglobin Sβ⁰, and 2 other sickle cell disease variants.

Table ane

Sociodemographic characteristics of the disease and command children

Variable	Children with sickle jail cell illness	Children without sickle cell affliction	P-value
Parent-proxy report total sample
north	104	74	–
Age (years)			0.036
ii–4	27 (26.0)	26 (35.1)
five–7	fourteen (xiii.5)	15 (20.3)
8–12	26 (25.0)	21 (28.4)
13–xviii	37 (35.6)	12 (16.2)
Gender			0.374
Male	52 (fifty.0)	32 (43.ii)
Race/ethnicity			<0.001
African American	98 (94.2)	58 (78.4)
Other	1 (one.0)	fourteen (xviii.9)
Unreported	5 (iv.8)	ii (two.seven)
Disease status			–
Mild disease	50 (48.1)	–
Severe disease	54 (51.9)	–
Parent-reported family unit income level			0.016
>$forty,000	22 (21.ii)	5 (half dozen.viii)
>$xx,000 and ≤$40,000	26 (25.0)	13 (17.6)
≤$20,000	36 (34.6)	35 (47.3)
Unknown	20 (19.two)	21 (28.4)
Median census household income
Mean ± SD	28,678 ± 1,143	27,798 ± 1,557	0.253*
Child cocky-written report total sample
n	69	40	–
Illness condition			–
Mild affliction	26 (37.7)	–
Severe disease	43 (62.3)	–
Parent-reported family income level			0.037
>$40,000	xviii (26.1)	2 (5.0)
>$xx,000 and ≤$40,000	19 (27.five)	9 (22.v)
≤$20,000	21 (30.four)	16 (40.0)
Unknown	11 (xv.nine)	13 (32.5)

* P-value based on the Wilcoxon rank-sum test

Asthma was the most mutual medical co-morbidity reported for children in both groups, while attentional and behavioral problems were the most common neurobehavioral co-morbidity (Tableii). The children without sickle cell disease reported more medical co-morbidities than the group of children with sickle prison cell disease (Table2).

Table 2

Medical and neurobehavioral co-morbidities of disease and control children

	Sickle cell disease (north = 104)	Controls (n = 74)	P-value
Medical co-morbidities
Any	27 (26.0)	35 (47.three)	0.003
Asthma	20 (19.2)	28 (37.eight)
Chronic allergies	6 (5.eight)	11 (xiv.9)
Diabetes	1 (1.0)	1 (1.iv)
Chronic orthopedic, bone, or joint problems	9 (viii.7)	three (4.ane)
Epilepsy	4 (three.9)	1 (1.4)
Rheumatic illness	i (1.0)	0 (0.0)
Other	2 (1.9)	five (6.8)
Neurobehavioral co-morbidities
Whatsoever	42 (40.four)	35 (47.3)	0.359
Feet problems	6 (5.8)	9 (12.ii)
Attentional problems	24 (23.1)	xviii (24.3)
Behavioral bug	xx (19.2)	18 (24.3)
Low	8 (7.seven)	6 (8.i)
Developmental filibuster or mental retardation	4 (iii.9)	7 (9.v)
Learning bug	21 (xx.ane)	14 (xviii.9)
Speech problems	10 (ix.6)	10 (13.five)

HRQL: parent-proxy (for children aged 2–18 years) and child self-study (for children aged 5–eighteen years)

We have previously published the reliability, feasibility, and HRQL summary and scale scores in these populations showing very niggling missing data and the poor HRQL of children with sickle jail cell illness compared to children without illness [14]. Briefly, based on the parent-proxy study, children with sickle cell illness displayed significantly lower scores on all three summary scores, besides every bit on the social operation and school functioning scale scores. In contrast, for the child cocky-reports, children with sickle cell disease only reported significantly lower scores for the physical health summary score [14].

Children with sickle cell disease have worse HRQL than children who practice non have sickle cell disease after bookkeeping for family unit income

Parent-proxy HRQL

When the outcome of sickle prison cell disease on HRQL was examined taking into account other potential take a chance factors, children with severe sickle cell affliction had a 4.00 times higher odds of having worse total HRQL. Family income at the lowest income level was also indicative of having worse full HRQL for all children (OR 2.88). Similar to our prior piece of work and that of others in sickle cell illness [3, four], having other co-morbidities and being of older age were besides associated with worse HRQL (Table3).

Tabular array three

Ordinal logistic regression for PedsQL parent-proxy written report

Issue	Full score		Physical health		Psychosocial health
	OR*	95% CI	OR*	95% CI	OR*	95% CI
Age (years)
2–4 (referent)	–	–	–	–	–	–
5–7	1.85	(0.75, 4.75)	1.69	(0.68, iv.xix)	i.51	(0.62, 3.69)
8–12	ii.61	(i.19, v.75)	two.55	(1.thirteen, 5.76)	ii.63	(1.18, 5.84)
thirteen–18	2.74	(1.19, half dozen.29)	ii.70	(ane.thirteen, vi.44)	2.42	(1.05, 5.57)
Sickle cell illness
No affliction (referent)	–	–	–	–	–	–
Balmy sickle cell disease	ii.11	(1.00, four.43)	2.26	(1.05, 4.87)	ii.27	(1.08, iv.78)
Severe sickle cell illness	iv.00	(1.92, 8.31)	v.67	(two.68, 11.97)	2.81	(one.37, 5.77)
Family unit income
>$40,000 (referent)	–	–	–	–	–	–
>$20,000 and ≤$40,000	1.69	(0.72, three.96)	2.78	(1.xvi, 6.65)	ane.37	(0.59, 3.19)
≤$twenty,000	two.88	(1.22, 6.79)	3.87	(1.62, 9.26)	2.20	(0.94, 5.15)
Medical co-morbidity	two.30	(i.25, 4.24)	two.39	(one.28, 4.47)	3.04	(1.62, 5.70)
Neurobehavioral co-morbidity	one.95	(i.07, 3.57)	1.34	(0.73, 2.46)	2.31	(1.25, four.27)

When the data were further examined past looking at the upshot of these covariates on the physical HRQL summary score, children with sickle cell disease had an increased odds of worse physical HRQL. Also, having medical co-morbidities, being of older age, and lower family income were also associated with worse concrete HRQL.

When examining psychosocial HRQL, children with sickle jail cell disease had an increased odds of a worse psychosocial HRQL independent of the other co-variates. In add-on, older children and having medical or neurobehavioral co-morbidities were associated with worse psychosocial HRQL.

To examination whether illness differentially affects HRQL across poverty levels, we too examined models including interaction terms for disease group and poverty level. Since none of the interactions were significant, the detailed results are not shown.

Child self-report HRQL

When the child self-report of HRQL was examined for the event of sickle cell disease in the regression model taking into account the other variables, children with severe sickle jail cell disease had a 3.33 times increased odds of having worse physical HRQL. Unlike in the parent-proxy report of HRQL, none of the variables considered displayed a meaning upshot on psychosocial HRQL in the child self-report sample (Tablefour).

Table 4

Ordinal logistic regression for PedsQL child-cocky-report

Result	Total score		Physical health		Psychosocial health
	OR*	95% CI	OR*	95% CI	OR*	95% CI
Age (years)
v–7 (referent)	–	–	–	–	–	–
8–12	0.41	(0.16, 1.03)	0.63	(0.25, 1.lx)	0.41	(0.15, 1.07)
13–xviii	0.43	(0.17, 1.12)	0.threescore	(0.23, 1.53)	0.38	(0.15, 1.00)
Sickle prison cell disease
No disease (referent)	–	–	–	–	–	–
Mild sickle jail cell affliction	i.99	(0.77, 5.15)	2.59	(0.98, 6.84)	1.15	(0.42, iii.14)
Severe sickle prison cell disease	ii.18	(0.95, 4.99)	3.33	(ane.39, vii.99)	ane.80	(0.78, iv.18)
Income level
>$xl,000 (referent)	–	–	–	–	–	–
>$20,000 and ≤$twoscore,000	ii.07	(0.74, v.78)	i.70	(0.60, 4.78)	ane.95	(0.68, 5.59)
≤$20,000	ii.01	(0.72, five.64)	1.62	(0.58, iv.54)	1.54	(0.53, iv.53)
Medical co-morbidity	1.58	(0.77, 3.25)	1.19	(0.57, 2.49)	1.73	(0.82, 3.65)
Neurobehavioral co-morbidity	ane.52	(0.75, 3.09)	1.24	(0.threescore, 2.56)	i.39	(0.68, 2.83)

Predicted probability of impaired physical HRQL

The nomogram in Fig.2 shows the predicted probability of an impaired physical health summary score to illustrate the bear on of all of the variables on the HRQL of children. These probabilities, based on the ordinal regression model, represent the probability of parent-reported concrete HRQL falling more one SD beneath the population hateful reported in Varni et al. [ix]. The number of points for each condition is determined by matching the condition, due east.yard., aye on co-morbidities, to the corresponding vertical location (50 points). For example, children with severe sickle cell disease (100 points) who are at the everyman income level (lxxx points), who are from the oldest historic period grouping (58 points), and who have a neurobehavioral co-morbidity (17 points) and a medical co-morbidity (l points) represent the highest gamble group (305 points summed). Using the total points assessed of 305 on the total points line, they would have approximately an eighty% predicted probability (bottom line) of having an dumb physical HRQL. On the other hand, if a child has just mild sickle cell disease (50 points) but is from the everyman family income grouping (eighty points), is in the everyman age group (0 points), has no neurobehavioral co-morbidities (0 points), and also has asthma (a medical co-morbidity—50 points), the predicted probability (from the total of 180 points) of having an impaired physical HRQL is reduced to roughly 43%. It is important to note that nosotros have, for simplicity, omitted estimates of the variability surrounding the predicted probabilities. As discussed by Iasonos et al. [21], information technology is possible that 2 individuals with the same predicted probability have differential variability surrounding that gauge, depending on the components of the risk. Therefore, if such models are to be used as a prognostic tool for identifying children with sickle prison cell disease at risk for dumb HRQL, this variability will need to be deemed for in the future.

Nomogram for the predicted probability (which denotes the probability that physical health-related quality of life (HRQL) is ≤1 SD below the population mean reported by Varni et al. [nine]) of impaired physical HRQL: parent-proxy report

Discussion

The bear upon of sickle cell disease on the HRQL of children is credible, despite whether the children are living in homes with the lowest family income. Because many of these children practice live in poverty and have other medical or neurobehavioral co-morbidities in addition to sickle cell disease, the touch of sickle prison cell disease on HRQL is fifty-fifty more severe. There are no similar chronic diseases that primarily affect those from an impoverished, minority groundwork and our findings highlight how this uniquely affects children with sickle cell disease.

Our study is the first that we know of to examine the commonage effect of family income and affliction severity on the HRQL of children with sickle prison cell disease. Other markers of income, such every bit education of the parent and work status, accept been examined and are institute to have conflicting results on HRQL in children with sickle cell affliction [3, four]. All the same, this prior work has shown the negative upshot that disease severity has on the HRQL of the kid with sickle cell disease [3, four, 17]. Because sickle cell disease predominately affects African Americans who are probable to have lower family unit income [5], it was important to examine the impact of sickle cell disease and family income on the HRQL of these children. Although our control patients had lower family unit income than our patients with sickle cell disease, we still demonstrated a significant consequence of family income on the HRQL of children with sickle cell disease.

Prior work has demonstrated the negative touch on of lower socioeconomic condition on the HRQL of healthy schoolhouse children [6, 8, 22, 23]. In addition, there has been some work examining the impact of markers of socioeconomic status on the HRQL of children with chronic disease [ix, 23–25]. In children with asthma, lower income was a significant, independent predictor of worse HRQL, whereas disease severity was not [24]. Our study found like results regarding income, simply also constitute the pregnant bear on that sickle cell affliction has on the HRQL of these children.

In addition to the touch on of poverty, many of the children with sickle jail cell affliction take some other co-morbidity, such as asthma or attentional issues, which also affects their well being. The boosted challenges of poverty and having other chronic conditions demand to exist considered in the context of care for these children. As health care providers caring for these children, we have very limited power to amend their family income. However, future research to make up one's mind how handling for co-morbidities and comeback in disease condition affect well beingness, especially concrete well being, are needed as health care providers are able to provide therapy that improves disease status for medical co-morbidities and sickle cell disease.

Nosotros did non encounter the same significant results from the regression models for the parent-proxy and child-self-reports. This may largely be a part of the decreased sample size of kid-reports. The betoken estimates of the odds ratios from each model display the aforementioned human relationship across both parent-proxy and child-self-reports. All the same, some caste of disagreement between parent and child reports is not an unexpected result, as at that place has been consistent evidence indicating that proxy reports of HRQL practise non necessarily match with child/patient assessments [4, 26, 27]. Two prior studies in children with asthma that utilized child self-report of affliction-specific HRQL (simply no parent-proxy written report of HRQL) found lower socioeconomic status [25] and lower household income [24] to be associated with worse HRQL in children. In that location are a few prior studies that accept utilized both a parent and kid cocky-study of generic HRQL [8, 23] and examined the impact of socioeconomic factors. Ane study using a generic HRQL tool found that chronic disease and a family'south financial situation were associated with parent-proxy report of the child'due south HRQL [23]. Yet, this study did not discover an issue of chronic disease on HRQL in the child cocky-report but did find an association with the child cocky-written report of HRQL and the family unit's financial situation [23]. Some other study, using the generic PedsQL parent-proxy and child self-reports, found that children with chronic disease had worse HRQL compared to healthy children when socioeconomic status was controlled for in their analysis [8]. This written report did not examine the independent effects of both chronic disease and socioeconomic condition together. Furthermore, the lack of a disease-specific tool may have prevented usa from detecting the bear on of disease and family income on the child self-report of HRQL in children with sickle cell affliction. In the report by Van Dellen et al. [25] examining asthma, in that location were no significant associations found betwixt socioeconomic status and generic HRQL, even though, as noted above, they found differences in HRQL by household income when using the disease-specific asthma measure of HRQL. This written report did not report parent-proxy HRQL information. Hereafter research will need to investigate whether illness and poverty practise, somehow, differentially affect a child's perception of their HRQL.

Other limitations of our study are its cross-sectional design and that it does not take into account family income over fourth dimension. Other measures of socioeconomic condition were not assessed, such every bit material impecuniousness, which may also touch on a kid's HRQL. In addition, not all families reported their total family unit income, so demography block group data was used to gauge their family income. The census block group data were obtained by utilizing the street address of both groups of children. This method of determining family income has been shown to be valid [28, 29]. Additionally, since the census income did not differ significantly betwixt groups, this should be a reasonable guess of family income for those families who did not cocky-report the data. However, because we have more than missing information on family income within our command population, our family income data may be biased and may have led to an over or under estimation of the issue of family income. Lastly, we drew our study population from a convenience sample, which may take biased our results and made them less generalizable to other populations.

Conclusions

In conclusion, children with sickle cell affliction have worse health-related quality of life (HRQL) compared to children without sickle prison cell illness after adjusting for income level and other pregnant covariates. Older children with severe sickle prison cell disease who accept the everyman family income and other co-morbidities take the worst HRQL.

Acquittance

This piece of work was supported by grants from the National Institutes of Health (K23 HL80092 and General Clinical Research Center grant M01-RR00058 from the National Eye for Research Resources).

Open up Access

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial apply, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Abbreviations

HRQL	Health-related quality of life

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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2840660/

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