Cystic Fibrosis Clinical Trial
Official title:
The Influence of Social Determinants and Access to Health Care on the Outcome of Children With Chronic Health Problems in British Columbia
Anyone who practices clinical medicine will understand that socially disadvantaged children
will have worse health outcomes, no matter what the underlying condition might be. There is
limited prospective data on the effects of social deprivation on children in BC and there is
none concerning the effects of social deprivation on children with chronic diseases. In order
to generate relevant data for those who manage children with chronic diseases in BC, the
investigators wish to perform an observational study of the relationship between
questionnaire-derived social variables and measured outcomes in children with cystic
fibrosis, type 1 diabetes, and chronic kidney disease.
Our working hypothesis is that there is an association between social determinants of health
(income, education, race) and health outcomes in children with cystic fibrosis, type 1
diabetes and chronic renal failure, that is independent of access to health care (assessed by
distance to nearest specialty clinic and number of clinic visits in the last year).
1. Cystic fibrosis (CF). Cystic fibrosis is a genetic condition that causes slowly
progressive lung disease. It occurs in people of all social class so the disease itself
is independent of social factors. There is considerable variation in the outcome of CF
in different children. There are many potential causes for these differences but the
available literature would suggest that a significant part of this variability is due to
social factors. While there are no prospective clinical studies, the limited information
from database reviews suggest that socially deprived children have significantly worse
outcomes. In the US, children living in poverty are twice as likely to be underweight
and over three times more likely to die (25). UK data is similar - disadvantaged
children have worse growth parameters and lower lung function compared to richer
children (26).
2. Type 1 diabetes (T1D). Type 1 diabetes is a multi-factorial disease. It is not an
autosomal recessive disease like CF but genetic predisposition can be one of its
predisposing features. As with the data on CF, socio-economic deprivation has been shown
to have a significant adverse effect on outcome in type 1 diabetes. In the US, glycemic
control amongst young adults was significantly worse amongst disadvantaged young adults
(27). A retrospective review of New Zealand children with type 1 diabetes also showed
that poor socio-economic status and Polynesian ethnicity were significantly associated
with poor glycemic control and long term complications (28).
3. Chronic kidney disease (CKD). Chronic kidney disease may be due to congenital, acquired,
or hereditary causes. Within all these groups, socioeconomic status, gender, and race
may influence disease progression and outcomes. It has been shown in children with
chronic kidney disease, primarily from the United States, that it was more common in
low- and middle- income families to have an abnormal birth history such as prematurity,
low birth weight or small for age which predisposes to the development of renal disease
(29). In addition, blood pressure control and height deficits improve faster in children
of families with higher income. In Canada, Aboriginals have a higher prevalence of
severe chronic kidney disease and a 77% increased mortality risk (30,31) and a lower
likelihood of nephrology clinic visits (32). Aboriginal children and young adults with
chronic kidney disease are more likely to have glomerulonephritis as a cause of kidney
disease compared to Caucasians and are more likely to reach end stage renal disease
(33). In contrast, lower mortality risks are seen in East Asian and Indo Asian Canadian
adults starting dialysis (34). There is still much to be studied on the influence of
these various factors in renal health and disease.
STUDY DESIGN.
1. Study Objective. We wish to determine the influence of education, income, race and
access to health care upon health outcomes in children with chronic diseases in British
Columbia.
2. Justification. While the effects of social deprivation on health outcomes are commonly
discussed, it is surprising how little prospective research there is to quantify the
adverse effects of social inequity. There is limited prospective data on the effects of
social deprivation on children in BC and there is none concerning the effects of social
deprivation on children with chronic diseases. In order to generate firm data relevant
for those who manage children with chronic diseases in BC, we wish to perform a cross
sectional observational study of the relationship between social variables and outcome
in children with cystic fibrosis, type 1 diabetes and chronic kidney disease.
3. General design. All families in the three clinics will initially be informed of the
study by letter. When the child is due for regular follow-up assessment, the parents
will be approached during the clinic visit and invited to join the study. Their
participation will only involve the completion of a questionnaire. The research
assistant will collect the data at interview and will subsequently (with parental
permission) retrieve laboratory results from records of previous clinic visits to assess
measured health trends over time. The study protocol does not add any further
investigations other than the questionnaire. Apart from the variable time needed for
parent information and consent, the questionnaire itself will require no more than about
30 minutes to complete.
4. Questionnaire. We will use a socio-economic assessment questions that we are already
using in our study of quality of life in parents caring for children needing home
ventilation. It is a conventional form assessing education level, employment, income,
partnership status and ethnicity. This last is very important because of the poorer
health outcomes already known to be associated with Aboriginal background. The proxy for
access to health care will be distance to the main specialty clinic and number of visits
in the last year.
5. Analysis. The relationship between socio-economic variables plus ethnicity and outcome
will be examined using multiple linear regression analysis. In the absence of relevant
literature, there are too many unknowns to allow any reliable form of power analysis or
patient number calculation to be performed. One accepted 'rule of thumb' is that there
should be at least 10 patients for each predictive variable in multiple linear
regression analysis. We will concentrate on 6 to 8 of the main social factors and will
try to enroll over 100 patients within each sub-specialty - enough to ensure 15 to 20
patient outcomes for each variable.
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