healthmedicinet

All analyses were performed using SPSS (version 19.0 for Windows, SPSS Inc., Chicago,
IL, USA).

Study setting

For this study, data was obtained from IVF patients and healthcare professionals in
8 public IVF units in public hospitals in Israel. The patients in this sampling were
infertile couples, 142 women and 62 men, who had undergone or were currently undergoing
IVF treatment. Healthcare professionals were enlisted to ask patients for their cooperation.
Out of 300 questionnaires distributed, 204 were filled in and returned, i.e., a response
rate of 68 %.

The sample of healthcare professionals consisted of gynecologists, and fertility nurses
from the same 8 public IVF units. Out of 24 questionnaires distributed, 19 were valid.
Respondents were asked to answer all of the questions without exception: questionnaires
that were not filled out in their entirety were disqualified, yielding a response
rate of 79 %.

All the questionnaires were printed out and delivered manually, for both IVF patients
and healthcare professionals.

Ethical approval was obtained in advance from the Ethics Committees (Helsinki committees)
in each public hospital.

Written informed consent for participation in the study was obtained from each participant
in the IVF patient group and from each participant in the group of healthcare professionals.

Procedure

The research instruments were questionnaires that were constructed for the study by
Spiegel et al. see 37] in a three-stage process: (i) For the initial exploratory stage conducting in-depth
interviews with eight fertility experts and 40 IVF patients, 30 women and ten men,
to identify which items would be included in the research questionnaires; (ii) Pilot
study of 40 IVF patients, five from each hospital; (iii) Main survey: Based on findings
of the pilot study, the research questionnaires were revised and modified.

The same version of the research questionnaire was distributed to all the healthcare
professionals (see also Aarts et al., 32]). In filling out the questionnaire, the professionals were asked to consider the
fertility patients who were treated in their clinic.

This paper focuses on the following issues:

Evaluation of treatment

In order to evaluate patient satisfaction with IVF treatments and perceptions of it
by professionals, this study was based on the research of Gerteis et al. 38] and on the Picker survey instruments that measure the patient’s experience of care
in eight dimensions of patient-centeredness (www.pickerinstitute.org).

The following three major dimensions were tested:

1. Coordination and integration of care: Professionalism of fertility clinic staff;
attitude and sensitivity of fertility clinic staff and their relationship with patients;
no personnel changes in the fertility clinic staff from beginning of treatment to
the end; provision of consulting services and follow-up support – (medical, social
and psychological factors).

2. Information: Information on the chances of success (taking home a baby); information
on prognosis, different treatment options, clinical aspects, and possible side effects
of treatment; information about medical issues during pregnancy (multiple pregnancies,
ectopic pregnancies, miscarriages, etc.); information about potential health problems
of “test tube babies” – defects, prematurity; information on treatment costs.

3. Access to care and Physical conditions: Geographical accessibility; physical conditions
in the operating room – new/old medical equipment; physical conditions in the recovery
room (number of beds, personal bedside cabinet, location of bathroom, privacy); physical
conditions in the waiting room (new/old furniture, drinks available, reading material,
newsletters, atmosphere); waiting times; standby time on the waiting list.

The patients’ Evaluation of treatment questions were presented on a 7-point Likert
scale in which 1 represents ‘Completely dissatisfied’; 2 represents ‘Mostly dissatisfied’;
3 represents ‘Somewhat dissatisfied’; 4 represents ‘neither satisfied or dissatisfied’,
5 represents ‘Somewhat satisfied’; 6 represents ‘Mostly satisfied’ and 7 represents
‘Completely satisfied’.

In order to enable a comparison of the three major dimensions of satisfaction – of
patients and fertility professionals – two methods were used for processing the data:

1. Principal Component Analysis (PCA)

2. Indices construction

Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is the most widely used extraction method of component
analysis and is most appropriate when the purpose is to reduce the number of items
to a smaller number of representative components 39], 40].

The number of components to retain is determined by the criteria, which are that each
PC explain at least 5 % (5 %-10 %) of the variance; the cumulative variance is at
least 50 % .The literature varies on how much variance should be explained before
the number of factors is sufficient. The majority suggest that 75–90 % of the variance
should be accounted for 41], 42]. However, some indicate as little as 50 % of the variance explained is acceptable
43], and eigenvalues, which indicate the amount of variance explained by each component
42], are greater than one (Kaiser criterion) 44].

All the items relating to satisfaction with treatment were analyzed using PCA, and
the analysis yielded three factors of satisfaction: (i) Human factor: satisfaction
with coordination and integration of care; (ii) Information factor: satisfaction with
information; (iii) Physical factor: satisfaction with access to care and physical
conditions

Indices construction

Following Van Empel et al. 8], a sum score was calculated adding up the accompanying item scores. The dimension
sum scores with diverse maxima were transformed into indices from 1.00 (worst possible)
to 10.00 (best possible), using the same formula of Van Empel et al. (8], p.144): “satisfaction index?=?9* [(actual sum score – lowest possible sum score)/
(highest possible sum score – lowest possible sum score)] +1.”

Three satisfaction indices were defined here: (i) Human Satisfaction index (ii) Information
Satisfaction index (iii) Physical Satisfaction index.

General psychological responses of the respondents

The experience of infertile couples is described in the literature as emotionally
taxing 45]–47]. The unique psychological factors of in-vitro fertilization (IVF) have been examined
and assessed in order to discover whether psychological variables are correlated to
patient satisfaction in these factors.

The psychological items were formulated as questions, such as ‘To what degree do you
experience the following feelings at these times: guilt, success, etc.?’ Each item
was analyzed individually and then graded on a five-point Likert scale in which: 1
represents ‘very slightly or not at all’, 2 ‘a little’, 3 ‘moderately’, 4 ‘a lot’
and 5 ‘extremely’

A Principal Component Analysis (PCA) of the psychological responses was conducted;
this analysis yielded three psychological factors (i) Pessimism (ii) Activeness: active
involvement in obtaining information and making decisions during treatment, taking
initiative, and accepting full responsibility for the stages of treatment and results
(iii) Shame.

Monetary evaluation of a treatment cycle – what is the maximum amount a respondent
is willing to pay for a cycle of IVF treatment

The instrument chosen for economic evaluation of IVF treatment was the willingness
to pay (WTP) 48].

The foremost economic theory in decision making by consumers posits that individuals
try to maximize the utility of the goods and services they receive (subject to certain
constraints). According to Lancaster 49], 50], the utility derived by each consumer from the characteristics of the good is different
than the utility derived from the good as a whole.

Ryan 51] applied Lancaster’s utility approach to the field of health economics, using the
contingent valuation methodology (CVM), which allows the assessment of a non-market
good with a complex utility function. This assessment is made using a technique known
as Willingness-to-Pay (WTP), where respondents are asked questions directly in a survey
about their “Maximum WTP” – the maximum amount which they would be willing to pay
for a service/product or an attribute of a service/product not available in a regular
market, or non-priced goods and services. WTP is based on the assumption that “the
maximum amount of money an individual is willing to pay for a commodity is an indicator
of the value to him/her of that commodity” (52], p. 182).

The respondents were asked to state ‘what is the amount of money they are willing
to pay for one IVF treatment?’

The present study sought to check whether the dimensions of patient satisfaction are
correlated with the willingness to pay for IVF treatment.

Demographic, socio-economic and health characteristics

Questions about socioeconomic position, number of children not from IVF, number of
children from IVF, years of infertility, diagnosed infertility, and number of fertility
treatments were derived from the baseline questionnaire.

Statistical analysis

Using a Pearson correlation, each of the three satisfaction factors were correlated
with the demographic, socio-economic, health characteristics, psychological factors,
and the WTP variable. P-values??0.05 were considered statistically significant.

Gender differences in the satisfaction indices were assessed using a t-test. Another
comparison was made between patients’ experiences and professionals’ perceptions of
these experiences. The mean scores of patients and of professionals were compared
using t-tests to detect any statistical differences. The group of professionals was
taken as one group rather than broken down into physicians and nurses which would
have made the group sizes too small. As for significance, P??0.05 was considered statistically significant.

In order to assess the demographic, socio-economic, health characteristics, and psychological
factor influence on the satisfaction indices, an Ordinary least squares (OLS) regression
was used. As with OLS regression, F Value is the F-statistic signifying the Mean Square
Model divided by the Mean Square Error. The F value should be with a p value (Pr??F)
smaller than the standard criterion of 0.05.

R-Square is the proportion of variance in the dependent variable which can be explained
by the independent variables. This is an overall measure of the strength of association
and does not reflect the extent to which any particular independent variable is associated
with the dependent variable.

In the social sciences, low R-squared values are common and expected. “Micro data
on individuals, families, or households tend to have low R-squared because there is
so much variation in individual behavior. Low R-squared do not necessarily mean that
the model is poor” 53]; p 43. For example, Levitt 54] reports R-squared in the range of 0.06 and 0.37. In the present study, the acceptable
R-squared were in the range of 0.04 and 0.1.

Adj R-Sq is a modification of the R-squared that penalizes the addition of external
predictors to the model. In the social sciences, Adjusted R-squared is also used for
a measure of effect size 55]: small effect 0.0196, medium effect 0.1300, and large effect 0.2600. Savage 56] reports adjusted R-squared in the range of 0.05 to 0.1. In the present study, the
acceptable adjusted R-squared values were in the range of 0.03 and 0.1.