Exploratory Analysis on the Impact of Morbidities on Colorectal Cancer Screening Uptake
Condition: Colorectal Cancer
MeSH terms: Colorectal Neoplasms
Study type: Observational

Summary: 
      The primary aim of this project is to examine the association between having a long-term
      condition (morbidity) and screening uptake for colorectal cancer. Whilst this project will
      consider all morbidity and co-morbidities, there will be a particular focus on common mental
      health disorders, such as depression and anxiety.

      The secondary aim of this project is to examine other factors that may influence uptake rate.
      Information on a wide array of potential factors is available for this project. These include
      demographics (age, gender, ethnicity), socio-economic status (deprivation, education status)
      and lifestyle (smoking status, drinking patterns, degree of exercise). In addition, any
      potential moderating effect of these factors on the association between morbidity and
      screening uptake shall be explored.

      In summary, the following shall be explored:

        -  Uptake rates by type of mental health disorder.

        -  Uptake rates by chronic physical health problems.

        -  Associations between uptake, morbidity (both physical and mental) and broader health
           determinants such as demographics, socio-economic status and lifestyle.
    

Detailed description: 
      Linkage of datasets and ethical issues.

      The South Yorkshire Cohort (SYC) data (including patient identifiers) are held by the
      Clinical Trials Research Unit (CTRU) at the University of Sheffield, whilst Bowel Cancer
      Screening Programme (BCSP) data are held by the NHS Cancer Screening Programmes (part of
      Public Health England).

      Linkage will be based on the subset of respondents to the SYC who have given their consent
      for the SYC researchers to look at their NHS health records, and are eligible to be invited
      for colorectal cancer (CRC) screening. The proposed method for linkage will be as follows:

        -  For those respondents who have given this consent, the CTRU shall send their NHS number
           and their SYC study ID to the BCSP.

        -  Using the NHS number, the BCSP shall extract data on if the individual was invited for
           screening, and if so whether or not the individual accepted screening.

        -  The BCSP shall then send the screening data along with the SYC study ID to the CTRU. NHS
           number shall not be included in the data that is sent from the BCSP (in other words it
           will be pseudo-anonymised).

        -  The CTRU shall use the pseudo-ID to link the screening programme data to the SYC data. A
           pseudo-anonymised dataset shall then be released to the research team.

      In this way, the only patient-identifiable data that the BCSP would receive is data that they
      already hold (NHS number), whilst the CTRU would not receive any additional
      patient-identifiable data.

      It should also be stressed that the research team undertaking this project would not have
      access to patient-identifiable data at any stage of the project.

      Data used.

      The following data shall be used within this project:

      • Exposure variables. The exposure variables are the presence of long-standing conditions.
      These are collected within the SYC as self-reported long-standing conditions. Twelve named
      conditions are recorded, along with an "other" condition, which includes free-text to allow
      the respondent to state the condition. These conditions (and their prevalence amongst a
      preliminary sample of people aged 60 to 74 in the SYC) are: Depression (8%), Anxiety (10%),
      Fatigue (19%), Pain (28%), Insomnia (8%), Diabetes (10%), Breathing problems (13%), High
      blood pressure (31%), Heart disease (10%), Osteoarthritis (16%), Stroke (3%), Cancer (5%) and
      Other (29%).

      All of the long-standing conditions shall be considered, with the exception of cancer (as
      this will include CRC, and patients with this are not eligible for screening) and free-text
      descriptions (these shall be analysed as "other").

        -  Outcome variable. The outcome variable will be if the individual attended CRC screening.
           This will be linked to SYC data using data supplied by the National Bowel Cancer
           Screening Programme. It should be noted that CRC screening relates to the first time an
           individual is screened (prevalent screening). The main analysis will look at if an
           individual has had a CRC screen (irrespective of number of invitations to screening that
           were sent). Depending on the available evidence, a secondary analysis would consider the
           outcome variable of 'individual accepted CRC screening after refusing the initial
           invitation for CRC screening.'

        -  Confounding variables. The following potentially-confounding variables shall be
           assessed: Age, gender, ethnicity, highest level of education, levels of physical
           exercise, smoking status, alcohol consumption, and IMD deprivation score.

        -  Additional descriptive variables. The following variables will not be used within the
           statistical models, but shall be included when performing a descriptive comparison of
           individuals who do attend screening with those who do not attend screening:
           self-reported EQ-5D summary score, self-reported healthcare use in the last 3 months
           (this includes 22 named categories, it is anticipated that due to small numbers some of
           these will need to be grouped. Possible groupings to consider are: Accident and
           emergency (A&E), Hospital use (excluding A&E), Health-carer: GP, Health-carer: Nurse,
           Other health-carers, Other carers, Alternative therapists.

      Statistical analysis.

      The statistical analysis shall include the following sections:

      • Initial exploratory analysis. This initial analysis shall provide an overview of the
      available data, and will highlight any issues that may need to be addressed within the
      statistical modelling.

      A descriptive analysis shall compare the characteristics of people who attend CRC screening
      with those who do not attend. This comparison will include the exposure variables,
      confounding variables and additional descriptive variables (as detailed in the previous
      section). Comparisons will be tested for statistical significance, with the caveat that as no
      specific differences were hypothesised a priori, resulting p-values should be interpreted
      with caution. T-tests will be used to compare continuous variables, Fisher's exact test will
      be used to compare binary variables, and the Kruskal-Wallis test will be used to compare
      ordinal variables. Any p-values less than 5% will be taken to indicate a statistically
      significant association.

      In addition to the descriptive analysis, the functional form of the association between any
      continuous variables and the outcome shall be visually assessed using smoothing methods. If a
      non-linear functional form is indicated then the use of non-linear functions (fractional
      polynomials, natural splines) shall be considered.

        -  Handling missing data. The amount of missing data shall determine the strategy employed
           (Harrell, 2001). If the total proportion of people with any missing data is less than 5%
           then single imputation of missing values shall be carried out. Otherwise, multiple
           imputation of missing values shall be carried out.

        -  Statistical modelling. Screening uptake rates for CRC shall be analysed using logistic
           regression, with patient demographics, socioeconomic status, lifestyle factors and
           morbidities as the potentially explanatory variables.

      The primary interest is which morbidities affect uptake rate, with particular interest in
      mental morbidities (of which depression and anxiety are measured in the SYC). Because of
      this, only interactions with these two mental morbidities shall be considered. To examine the
      association between the mental morbidities and uptake, and to see how the other variables
      influence this association, a series of models shall be presented:

        -  Model 1 (univariate-model): this shall include only the long-standing conditions
           (exposure variables) as the independent variables.

        -  Model 2 (demographics-adjusted model): this shall include the independent variables from
           Model 1, along with the demographic variables (age, gender and ethnicity).

        -  Model 3 (full model): this shall include all the variables considered in this study.
           Interactions shall not be considered.

        -  Model 4 (exploratory model): this shall apply subset-selection to Model 3
           (backwards-stepwise, with a probability-to-remove of 0.05). Interactions shall be
           considered after applying subset-selection.

      The purpose of displaying a series of models will be to show the un-adjusted association
      between morbidities and screening uptake, and the highlight the degree to which these
      associations are mediated by patient characteristics. A distinction is made between
      'intrinsic' characteristics (age, gender and ethnicity), which are (generally) beyond a
      person's control to change, and the remaining characteristics, over-which a person has more
      control

      Power calculations.

      Power analyses were conducted using G*Power 3.1.9. The required sample size to detect a
      significant effect of a pre-specified variable was calculated. For this analyses an alpha
      level of 5% and a two-tailed test were used. There were a number of additional factors that
      needed to be estimated or chosen:

        -  Required power: values of 95% and 80% were used.

        -  Odds ratio: values between 1.2 and 3 were tested.

        -  Probability of uptake amongst those without any morbidity: the overall uptake of 47%
           observed between 2008 and 2011 of the BCSP was used.

        -  Degree of correlation amongst the potentially explanatory variables: values of 20% and
           40% were tested (this is denoted by 'R2' in the table below).

        -  Prevalence of the pre-specified variable: a value of 8% was used, corresponding to the
           prevalence of depression in a preliminary analysis of the SYC data. In this analysis,
           74% of the sample had a morbidity, with an average of 1.8 long-term conditions per
           person.

      Table 1: Sample size required as a function of power, correlation, and odds ratio.

      Power = 80% Power = 95% Odds Ratio R2 = 0.2 R2 = 0.4 R2 = 0.2 R2 = 0.4 1.2 16,066 21,422
      26,605 35,473 1.3 7,780 10,373 12,876 17,168 1.4 4,751 6,335 7,857 10,476 1.5 3,292 4,389
      5,437 7,249 1.6 2,466 3,289 4,069 5,425 1.8 1,603 2,137 2,637 3,516 2.0 1,174 1,566 1,925
      2,567 2.2 926 1,234 1,513 2,017 2.4 767 1,022 1,248 1,665 2.6 658 877 1,067 1,422 2.8 579 772
      935 1,247 3.0 519 693 837 1,116

      The sample size available is expected to be approximately 7,500 indicating that any odds
      ratios of 1.4 or greater will be detected with 80% power, whilst any odds ratios of 1.5 or
      greater will be detected with 95% power.

      An example of a change in uptake rates relating to an odds ratio of 1.5 would be a decrease
      in uptake from 60% to 57%. Further examples are presented in Table 2.

      Table 2: Decreases in uptake rate corresponding to an odds ratio of 1.5 Uptake rate in group
      1: 90% 80% 70% 60% Uptake rate in group 2: 81% 72% 64% 57%

      An alternative method for estimating samples sizes was also tested (Campbell, Julious, and
      Altman 1996). This uses published tables which estimate the sample size (per group) required
      to identify a pre-specified difference in two proportions at at a 5% significance level with
      80% power. The group sizes are then adjusted to take into account any differences in group
      size. For example, if the group with depression represents 8% of the total population, then
      to detect a difference in uptake of 65% amongst people without depression and 60% amongst
      people with depression, an overall sample size of 425 is required, of which 34 would need to
      have depression and 391 would not have depression. The overall sample size required for a
      range of differences in uptake rates are presented in Table 3 (the uptake rates are all
      multiples of 5% as these values are used in the published tables).

      Table 3: Sample sizes required to detect pre-specified differences in uptake. Uptake in group
      A Uptake in group B Total sample size 60% 55% 1 188 65% 60% 425 70% 65% 213 75% 70% 138

      * Assuming that the comparison is between people with depression (prevalence 8%,
      corresponding to group B) and without depression.
    

Intervention name(s): Exposure to having a long-term condition.
Intervention type(s): Other
Intervention description(s): Long-term conditions are self-reported: twelve named conditions are recorded, along with an "other" condition - these shall all be considered with the exception of the named condition "cancer".
Observational model: Case-Control
Time perspective: Cross-Sectional
Sampling method: Non-Probability Sample
Study population: 
        This study shall be restricted to participants in the South Yorkshire Cohort who have
        ticked both boxes on the Consent Form for their data to be used (the boxes being labelled
        "May we look at your health records?" and "May we use the information you provide to look
        at the benefit of health treatments?"), and are eligible for screening in the NHS Bowel
        Cancer Screening Programme (ages 60 to 69).
      
Keywords: Colorectal Cancer; Mass Screening; Uptake; Morbidity; Mental Health