Public Perceptions of Cancer Risk using Analytic Hierarchy Process
This study aims to propose an analytical approach to rank risk levels of cancer. Analytic Hierarchy Process (AHP) model which incorporates five risk factors is constructed to rank five cancer types. A case study of perceptions of cancer risk levels is presented and the proposed model is applied to facilitate the decision process. A twenty five items questionnaire is employed to collect data from public at a residential area in Malaysia. The results show that lung cancer is ranked as the highest risk and prostate cancer is ranked as the lowest risk among the five cancer types. The overall ranking reflects the extent of awareness of cancer types and their risk factors among Malaysian public.
One of the most current significant discussions in medical fraternity is health
problems related to cancer diseases. Cancer becomes one of the major health
issues in the world and normally associated with life expectancy. In a very
much frightening situation, cancer has been perceived as leading factor to premature
death. In Malaysia, cancer is now the fourth leading cause of death among medically
certified deaths. In the absence of a nationwide population-based cancer registry,
the burden of cancer can only be estimated by extrapolating from regional surveys.
According to Lim (2002), in a regional population-based
cancer registry survey carried out between 1988 and 1990, the incidence
rates for males and females were 56.3 and 56.9 per 100,000, respectively.
This was probably an underestimate, as another regional cancer registry
survey carried out in Penang in 1994 demonstrated that the incidence
rates for all cancers was 115.9 per 100,000 for males and 119.7 per
100,000 for females. As under-reporting is known to be a significant
problem in such surveys, the likely estimate is probably close to 150
per 100,000. The annual incidence of cancer in Malaysia has been estimated to
be 30,000 (Lim and Lim, 1993). In 1998, Malaysias
population was 21.4 million, of whom 4% were aged 65 years and above. The incidence
of cancer is expected to rise with an increase in aging population. The proportion
aged more than 60 years was 4.6% in 1957, increased to 5.7% in 1990 and is projected
to be 9.8% in 2020 (Karim,1997).
In a regional cancer registry surveyed by Kasri (1993),
10 leading cancer among males were lung, nasopharynx, stomach, urinary bladder,
rectum, non-Hodgkins lymphoma, larynx, liver, colon and oesophagus. Whereas,
the ten leading cancer among females were cervix, breast, ovary, lung, nasopharynx,
oesophagus, thyroid, colon, rectum and non-Hodgkins lymphoma. In a national
childhood cancer survey, the commonest childhood tumours were leukemias, tumours
of the brain and spinal cord, lymphomas, neuroblastoma, gonadal and germ cell
tumours, kidney tumours, soft tissue sarcomas and retinoblastomas. The crude
incidence rate of paediatric malignancies in Malaysia was 77.4 per million children
aged less than 15 years. Cancers with racial differences in incidence include
nasopharyngeal cancer and oral cavity cancer. The incidence of nasopharyngeal
cancer in males by race was 0.79 per 100,000 in Malays, 15.9 per 100,000 in
Chinese and 1.1 per 100,000 in Indians while the corresponding incidence in
females was 0.8, 4.1 and 0 per 100,000, respectively.
Despite its long alarming figures, what causes cancer or factors that associated
with cancer are far from conclusive. Number of factors was believed to be associated
with cancer and it varies depending on cancer types. For example, nasopharyngeal
carcinoma revealed that smoking, working under poor ventilation, use of nasal
balms or oil for nasal and throat troubles, use of herbal drugs and anti-EBV
antibody titter were found statistically associated (Lin
et al., 2006). It is also reported that increased body mass index
levels are associated with an increased risk of cancer (Calle
and Thun, 2004). And recently Song et al. (2008)
reported that alcohol consumption may increase the risk of gastric cancer
in women. Some other factors such as chemical exposure, family history of cancer
and growing older are said to be among the factors that linked to cancer. But
again, there are no reliable and concrete evidences to show the specific factors
that can be linked to cancer. No single factor was claimed to be largely contribute
to cancer. In other words there was no clear cut evidences to provide specific
risk factor to cancer. In the midst of uncertainty, public may come with various
unscientific presumptions toward factors that cause cancer. Public has been
exposed to various information which is often conflicting or misleading concerning
cancer risks. Little is known about the ways in which this information may have
influenced public perceptions of cancer. It warrants further research to extract
public perceptions of cancer and factors associated with cancer.
Since, perception is something intangible and linked with human judgment, thus
a comparative evaluation model is proposed in decision making process. Based
on mathematics and human psychology, Analytic Hierarchy Process (AHP) firstly
proposed by Saaty (1980) and has been widely used to solve
multiple-criteria decision-making problems. AHP, since its invention, has been
a tool at the hands of decision makers and researchers and it is one of the
most widely used multiple criteria decision-making tools (Vaidya
and Kumar, 2006). The AHP provides a comprehensive and rational framework
for structuring a problem, for representing and quantifying its elements, for
relating those elements to overall goals and for evaluating alternative solutions.
Another important advantage of the AHP is that it allows for inconsistency in
judgment and measures the degree to which the judgments are inconsistent and
establishes an acceptable tolerance level for the degree of inconsistency. An
extensive description of AHP in addressing comparisons and multi-attribute utility
theory can be found by Dyer (1990a, b),
Harker and Vargas (1990), Saaty (1990)
and Winkler (1990). Therefore, this study aims to rank
of the five cancer types in accordance with their risk levels from public perceptions
using Analytic Hierarchy Process (AHP) which one of the structured techniques
for dealing with comparative or ranking decisions.
Study of perceptions is one of the typical studies in many areas including
health sciences. It has been studied with a number of methods from qualitative
to quantitative approaches. In a study to find association between cancer risk
perception and screening behavior among diverse women, Kim
et al. (2008) use cross-sectional telephone and in-person interviews
of women aged 50 to 80 years. The results from 1160 women were presented in
percentages and confidence interval. Hay et al. (2002)
surveyed oral cancer risk perception and risk behaviours among participants
in a free oral-cancer screening. Again method of descriptive statistics and
confidence level were presented to report the perception of smokers and lifetime
tobacco exposure to the risk for cancer. Metcalfe and Narod
(2002) study breast cancer risk perception among women who have undergone
prophylactic bilateral mastectomy. Risk estimates were compared using Wilcoxons
signed-rank test and Pearsons product-moment correlation analysis.
Besides statistical approaches, there have been many studies in perceptions
used qualitative approaches. For example, a qualitative approach was used by
McMullin et al. (2008) to evaluate perception
perceptions of cancer. Qualitative data from cancer related questions were collected
via completed face-to-face, semi-structured interviews. Another qualitative
research by Walker et al. (2007) aim to elicit
the opinions of women who received standardized ear acupuncture protocol treatment
delivered in small group clinics as an option to manage these side effects.
The present study takes a different perspective. Rather than use a typical
statistical inferences and qualitative model, the approach advocated here uses
a comparative evaluation model. The AHP technique uses a process of pair-wise
comparisons to determine the relative importance or priority of alternatives
in a multi-criteria decision making problem. The technique has been used in
fields such as government, business, industry and healthcare. Chow
and Luk (2005) used AHP framework to measure service quality in fast food
restaurant industry. The AHP procedure provided a ranking order of firms with
respect to the dimensions that define service quality. In a business related
research, Kim and Hwang (2005) applied the analytic hierarchy
process to the evaluation of customer-oriented success factors in mobile commerce.
This study aimed explain the factors that affect success in mobile commerce
and then evaluate and rate these factors by analyzing components of commercial
activity in the mobile Internet environment using the AHP. Recently, Sambasivan
and Fei (2008) used the AHP to find the relative weights and priorities
of critical success factors and benefits among Malaysian companies in the electrical
and electronics sector. The results were presented in the order of importance
of critical success factors. The technique also extended to healthcare analysis
and several studies have applied the AHP for the evaluation of health care facilities
and in health care policy analysis. Very recently, Liberatore
and Nydick (2008) reviewed extensively the application of AHP in medical
and health care decision making.
Obviously, AHP has been successfully ranked all factors related to field of
study. Users of the AHP first decompose their decision problem into a hierarchy
of more easily comprehended sub-problems, each of which can be analyzed independently.
The elements of the hierarchy can relate to any aspect of the decision problem
tangible or intangible, carefully measured or roughly estimated, well or poorly
understood anything at all that applies to the decision at hand. Once, the hierarchy
is built, the decision makers systematically evaluate its various elements,
comparing them to one another in pairs. In making the comparisons, the decision
makers can use concrete data about the elements, or they can use their judgments
about the elements relative meaning and importance. In short, AHP is a
multi-criteria decision method that utilizes structured pair-wise comparisons
among systems of similar alternative strategies to produce a scale of preference.
METHOD OF AHP
The AHP is a selection process that consists of five steps as follow:
||Step 1: Decide upon the factor for selection:
||Rate the relative importance of factors using pair-wise comparisons. Set
up a matrix to compare each criterion to the others
where, aij is integer and 0< a<10 and a is aij,
then aij = 1/a, then aij = 1 if I = j
||Rank the degree of association of each criterion relative
to the others, using the scale of association from 1 to 9
||Step 2: Find the eigenvector by normalised the pair-wise
||Divide each entry by the total of its column:
||Divide total of row by the total of number of row:
||Step 3: Rate each factor relative to each other factor
on the basis of degree of risk for each selection factor. This is achieved
by performing pair-wise comparisons of the choices
||Step 4: Normalised the pair-wise comparisons
||Step 5: Combine the ratings derived in steps 2 and 4 to obtain
an overall relative rating for each potential choice:
||Overall relative rating for factor I
||Average normalised weight for factor I
||Average normalised rating for type j with respect to factor I
AN EMPIRICAL STUDY
In accordance with the purpose and method of this research, criteria and factors
were identified. This research focuses on the Malaysian perceptions about the
risk of getting cancer based on five selected factors. The five cancer types
viz., breast, lung, throat, mouth and prostate were needed to be ranked according
to their degree of risk based on the five selected factors. Each cancer type
was perceived based on the premise that these five factors have been contributed
to cancer. The five selected factors are alcohol, tobacco, ion radiation, obesity
and genetic. The hierarchical structure of risk factors and cancer types are
shown in Fig. 1. The first level stated the goal of the AHP.
The second level addressed the relative risk of five cancer types. Respondents
were asked to compare pairs of cancer types (for example breast vs. Lung) and
to indicate whether they felt that one cancer type was equal risk to or absolute
risk another cancer type. The third level of hierarchy compared the association
of risk factors with respect to cancer type. The respondents were asked to state
their preference for the risk factors in a pair-wise manner on a nine-point
degree of association scale.
||Hierarchical structure of model in application
Data were collected at a residential area administrated by Kuantan Municipal
Council in Peninsular Malaysia using a questionnaire prepared by the researhers.
Data collection was conducted between 25 November 2007 and 22 December 2007
by the researchers. The twenty five items questionnaire was designed to meet
the purpose of the study. One hundred and forty seven respondents were selected
from different genders, ages, occupations and ethnics participated in a survey.
The questionnaire consists of two parts with the first part contains the basic
information of respondents. The second part of the questionnaire contains five
questions to evaluate public perception of five factors with respect to the
five cancer types. The perceptions were based on a nine-point relational scale
of degree of association originally proposed by Saaty (1980).
According to the scale used in this study, 1 represented equal associated, 2
represented equally associated to somewhat associated, 3 represented somewhat
more associated, 4 represented somewhat associated to moderately associated,
5 represented moderately associated, 6 represented moderately associated to
very associated 7 represented very associated 8 represented very associated
to extremely associated to 9 represented extremely associated. Within each of
these five questions, there were five sub-questions that compared between two
cancer types. Again, the perceptions were based a nine point relational scale
of degree of risks. The relationship between the factors and cancer types are
explained using AHP. The steps as prescribed in the section Method of AHP are
utilized throughout computations. The results are presented according to the
Step 1: The assigning weights as in the linguistic terms are used to find the pair-wise comparison of factors. These are shown in Table 1. The values reflect the association of the selected factors that possibly cause cancer.
Step 2: The weights in Table 1 are then normalised
by dividing each entry in a column by the sum of all the entries in that column
(Eq. 1, 2). By using the Eq.
3, the weights are averaged across the rows to give an average weight for
each factor. The normalised pair-wise comparison of factors is shown in Table
Step 3: The next step is the pair-wise comparison of the cancer types
to enumerate how well the respondents rate with each factor. For each pair within
each factor, the risk of cancer types is awarded a rating on a scale between
1 (equal risk) and 9 (absolute risk), whilst the other method in the pairing
is awarded a rating equal to the reciprocal of this value. The results for the
alcohol factor are given in Table 3.
|| Pair-wise comparison for the risk factors
|| Normalized pair-wise comparison for the risk factors
|| Pair-wise comparison for cancer types
||Average-normalized pair-wise comparison for cancer types and
Each entry in this matrix records how well the method corresponding to its
row meets the alcohol factor when compared to the corresponding column. For
example, the mouths cancer was found to be a far more risk for alcoholic
than breast cancer. The step 3 is executed for another four risk factors. Due
to the limited space, the detailed results are not shown in this study.
Step 4: The ratings in these comparison matrices are normalised and averaged across the rows to give an average normalised rating by criterion for each cancer type. The step 4 is executed and the results are shown in Table 4.
Step 5: Combine the average normalized cancer type ratings with the average normalised factor weights to generate an overall rating for each cancer (Eq. 4). The extent to which the cancer type associates the factor is weighted according to the relative risk of the factor. Table 5 gives the results of this final step.
These results clearly show that lung cancer is the highest risk among the five
cancer types based on the five selected factors.
|| Ranking of risk levels by cancer types
Throat cancer ranked at second place followed by mouth and breast cancer. Prostate
cancer ranked at the lowest among the five cancer types.
The perception of risk to five cancer types has established by utilizing a
model in decision making. The model is a method for formalizing decision making
where there are a limited number of choices but each has a number of factors
and it is difficult to formalize some of those factors. The proposed model is
used in determining the ranking of the cancer types according to the selected
factors. In this experiment, data were collected based on phrases such as much
more risk than to extract the decision makers preferences. The AHP has successfully
applied in the process to decide the risk levels of cancer types based on the
selected factors from respondents perspective. The results are drawn based on
the ranking after executing several steps in AHP. Lung cancer perceived as the
highest risk while prostate cancer perceived as the lowest risk. These results
reflect the variability in perceptions about cancer and also the factors that
may lead to cancer. Overall, this sample of the Malaysian population showed
some degree of awareness of the various cancer types and the factors that might
influence cancer risk. The awareness of cancer needs to be inculcated to public
and more educational activities to be geared up in an effort to be at the best
of quality of life of Malaysian specifically and the world population as a whole.
The authors thank to the Ministry of Higher Education, Malaysia under the Fundamental Research Grant Scheme, No. 59114 for financing this research.
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