Design of a prospective cohort study to assess ethnic inequalities in patient safety in hospital care using mixed methods
BMC Health Services Research
Design of a prospective cohort study to assess ethnic inequalities in patient safety in hospital care using mixed methods
Floor van Rosse 0 1
Martine C de Bruijne 0
Cordula Wagner 0 2
Karien Stronks 1
Marie-Louise Essink-Bot 1
0 Department of Public and Occupational Health, VU University Medical Center (VUmc), EMGO Institute for Health and Care Research , Amsterdam , The Netherlands
1 Department of Public Health, Academic Medical Center , Meibergdreef 9, Amsterdam, AZ 1105 , The Netherlands
2 NIVEL, Netherlands Institute for Health Services Research , Utrecht , The Netherlands
Background: While US studies show a higher risk of adverse events (AEs) for ethnic minorities in hospital care, in Europe ethnic inequalities in patient safety have never been analysed. Based on existing literature and exploratory research, our research group developed a conceptual model and empirical study to increase our understanding of the role ethnicity plays in patient safety. Our study is designed to (1) assess the risk of AEs for hospitalised patients of non-Western ethnic origin in comparison to ethnic Dutch patients; (2) analyse what patient-related determinants affect the risk of AEs; (3) explore the mechanisms of patient-provider interactions that may increase the risk of AEs; and (4) explore possible strategies to prevent inequalities in patient safety. Methods: We are conducting a prospective mixed methods cohort study in four Dutch hospitals, which began in 2010 and is running until 2013. 2000 patients (1000 ethnic Dutch and 1000 of non-Western ethnic origin, ranging in age from 45-75 years) are included. Survey data are collected to capture patients' explanatory variables (e.g., Dutch language proficiency, health literacy, socio-economic status (SES)-indicators, and religion) during hospital admission. After discharge, a two-stage medical record review using a standardized instrument is conducted by experienced reviewers to determine the incidence of AEs. Data will be analysed using multilevel multivariable logistic regression. Qualitative interviews with providers and patients will provide insight into the mechanisms of AEs and potential prevention strategies. Conclusion: This study uses a robust study plan to quantify the risk difference of AEs between ethnic minority and Dutch patients in hospital care. In addition we are developing an in-depth description of the mechanisms of excess risk for some groups compared to others, while identifying opportunities for more equitable distributions of patient safety for all.
Patient safety, defined as the lack of preventable injury, is
the minimum prerequisite for good quality of care. Safety
should be equally achievable for all patients, independent
of their backgrounds. 11% of the population currently
living in the Netherlands is considered to be of non-Western
origin [see Table 1], and this percentage is increasing, as it
is all over Europe. International literature outside Europe
shows ethnic inequalities in patient safety [1-12]. To our
knowledge, ethnic inequalities in patient safety have not
been studied before in any European contexts.
This paper is laid out in three parts. It starts with an
overview of published empirical studies outside of Europe
assessing ethnic inequalities in patient safety. We also
describe two exploratory studies conducted by our research
group. Secondly, we present a conceptual model that has
helped us theorize the possible association between
ethnicity and patient safety. Finally, we describe the design of
our current study that assesses and describes possible
ethnic inequalities in patient safety in Dutch hospital care.
Ethnic inequalities in patient safety empirical literature
Additional file 1 provides an overview of studies that
have examined ethnicity in relation with patient safety in
Table 1 Ethnic background classification
Non-Western ethnic origin
Classification of country of origin for
A patient is classified as Dutch when the patient and one or both parents of the patient were born in
the Netherlands, or when the patient was born outside the Netherlands while both parents were born in
A patient is classified as non-Western when the patient and one or both parents of the patient were
born in a non-Western countrya, or when both parents were born in a non-western country, irrespective
of the country of birth of the patient.
When a patient and one or both parents were born in the same country, e.g. Turkey, that country is the
country of origin, the patient will be classified as Turkish. When both parents of the patient were born in
the same country, which is different from the country of birth of the patient, the country of birth of both
parents is the country of origin. When a patient and his/her parents were born in three different
countries, the country of birth of the mother will indicate the country of origin of the patient.
An exception exists for our ethnic origin definition regarding Javanese Surinamese patients who were
born in Suriname and whose both parents were born in Indonesia. According to the CBS definition they
would be classified as western. But they are included in our non-western study sample and classified as
a Non-Western countries are: Turkey, all countries in Africa, all countries in South America, and all countries in Asia except Japan and Indonesia.
hospital care [1-12]. Those published to date have
documented the U.S. and New Zealands healthcare systems.
Because both of these contexts differ from the Dutch
and other European systems, and different definitions of
ethnicity are also employed, results are not easily
generalised and a study of European inequalities can make an
In advance of our current study, our research group
performed two exploratory studies about ethnic
inequalities in patient safety in the Netherlands. First, we
conducted a nationwide retrospective record linkage study
to explore whether ethnicity was associated with excess
lengths of stay (LOS) and unplanned readmission rates
. We determined that, overall, ethnic minority
groups had more unplanned readmissions and excess
LOS compared to ethnic Dutch after controlling for
demographics and patient mix (significant Odds Ratios
(ORs) varying from 1.04-1.14). These differences were
partly but not largely accounted for by socio-economic
indicators, meaning that variations in socio-economic
status (SES) explained part but not all of the risk
differences between ethnic groups. In interpreting our
findings, we determined that excess LOS and unplanned
readmission might be distal indicators of AEs, suggesting
a differential risk of poor patient safety with ethnic
background. The interpretation was inconclusive, however,
because the differences could also be explained by each
groups healthcare needs.
Secondly, we conducted a qualitative interview study
with care providers, to better understand the process
underlying ethnic differences in patient safety .
Three patterns of interactions between professional and
patient that can contribute to ethnic differences in
patient safety were identified: inappropriate responses by
health care providers to objective characteristics of
immigrant patients, such as low Dutch proficiency;
misunderstandings between patients and care providers due to
differences in illness perceptions and expectations about
health care; and inappropriate care because of providers
prejudices against or stereotypical ideas about patients
of non-Dutch ethnic origin.
Ethnic differences in patient safety conceptual model
Existing literature and our exploratory work suggest the
possibility of a higher risk of AEs among ethnic minority
patient groups, but valid epidemiological evidence for
the situation outside of the U.S. and New Zealand is
lacking. Based on the exploratory studies, we developed
a conceptual model to understand the role ethnicity may
play in patient safety (Figure 1). Our model identifies
three domains: 1) Patient characteristics (such as
language proficiency): here, we relied on Stonks et al. 
to define relevant patient characteristics that possibly
explain ethnic differences in patient safety. 2) Healthcare
characteristics (such as a protocolled use of interpreters):
we also relied upon Stronks et al. along with the
CLAS standards to define healthcare characteristics .
3) Patient-care provider interaction: we used the work of
Suurmond et al. .
Besides measuring differences in risk, throughout our
study we are collecting data to identify the determinants
and mechanisms that potentially increase the risk of AEs
for ethnic minority groups.
Study objectives are to:
1) determine and compare incidence, type, impact, and
preventability of AEs in non-Western hospital
patients compared to ethnic Dutch hospital patients.
2) assess the contribution of patient-related
determinants (language proficiency, health literacy,
SES indicators, cultural distance to the Dutch
healthcare system and religion), and the contribution
of healthcare-related determinants to the risk of
experiencing an AE in hospital care.
3) explore the mechanisms of patient-provider
interactions that may increase the risk of AEs.
Patient-care provider interaction
- Hospital services (like protocollized use of interpreters)
- Cultural competence of wards (e.g. cultural sensitivity of care providers)
- Registration of patient characteristics such as language proficiency,
Figure 1 Conceptual model.
4) explore possible strategies to prevent inequalities in
We are using a mixed methods design combining
quantitative and qualitative methods. Quantitative methods
include the use of a survey-based questionnaire and a
medical record review to compare the incidence of AEs
in Dutch and non-Western patients (See Table 1) and to
assess the contribution of several determinants to the
risk of AEs. We use a qualitative interview-based
component to examine the mechanisms underlying AEs, and
to explore possible prevention strategies. Figure 2
provides a schematic overview of the study design and
outcome. Data collection began in 2010, and is projected to
be completed in 2013.
Patient recruitment is taking place in four urban
hospitals in three cities in the Netherlands with a high density
of inhabitants classified as being of non-Western ethnic
origin. Two of the hospitals are teaching hospitals. In
each of the hospitals, a total of 500 patients will be
included, equally divided between ethnic Dutch and
non-Western patients (See Table 1).
We are in the process of recruiting all 2000 patients, of
whom approximately half are considered ethnically
Dutch, and the other half are considered of
nonWestern ethnic origin (Table 1). To define Dutch origin
and non-Western origin, we are using ethnicity
indicators based on country of birth criteria as defined by the
Dutch Central Bureau of Statistics  (See Table 1). In
the Netherlands and elsewhere in continental Europe,
van Rosse et al. BMC Health Services Research 2012, 12:450
Inclusion of 2000 patients of whom 1000 Dutch and 1000 non-Western
Questionnaire-patient self assessment
*Includes additional researcher assessment of
Dutch language proficiency
Phase 1: Nurse review
Phase 2: Physician review
Figure 2 Flow chart of measurements and primary outcome.
In depth interviews
ethnic origin based on country of birth is commonly
used as the ethnicity indicator. This is distinct from the
UK, which uses self-identified ethnicity, and in the U.S.,
where both ethnicity and race are used to classify groups
Place of birth of patients, and their parents, is not
registered in Dutch hospitals in a standard way. This
information, along with patient-specific variables (Dutch language
proficiency, SES-indicators, religion, health literacy, and
cultural distance to the Dutch healthcare system) are
obtained in the patient self-assessment questionnaire.
Background of ethnic minorities in the Netherlands
The three largest groups of residents of non-Western
ethnic origin living in the Netherlands are of Turkish,
Surinamese, and Moroccan origin. The Turkish and Moroccan
groups mainly came to the Netherlands between 1960 and
1980 in the wave of economic labour migration. Migrants
generally had a low level of education at the time of entry,
which persists today. The Surinamese group migrated from
Suriname, a former Dutch colony in South America. The
Surinamese population is often divided into two principal
subgroups: one of African (Creole) and the other of South
Asian (Hindustani) decent. Migration to the Netherlands
peaked at the time of Surinamese independence in 1975.
Most people of Surinamese origin speak the Dutch
language. Those of non-Western ethnic origin that we
describe primarily live in the three largest Dutch cities
(Amsterdam, Rotterdam, and The Hague) and often cluster
in certain neighbourhoods.
Inclusion and exclusion criteria
We include older patients and clinical admissions rather
than day-admissions in our study. Patient safety research
shows that incidence of AEs is higher among older
patients and among those who undergo invasive or
complex care . Also, the 2009 record linkage study
showed that ethnic differences in LOS and readmission
were larger among older patients , and so we
therefore include patients between ages 45 and 75 years of
age. The lower limit of 45 was chosen because from this
age, the hospital admission rate increases. By choosing
45 years as a lower limit, around 98% of the
nonWestern patients in our study will be first generation
immigrants, meaning they were born in their country of
origin that designates their ethnic classification. The
upper age limit of 75 years was chosen because in the
Netherlands, there were few non-Western inhabitants
prior to the above-described waves of migration.
For logistical reasons, patient recruitment has been
limited to three large wards in each hospital: surgery
(including orthopaedic surgery), internal medicine
(including cardiology and pulmonology), and neurology. To
enhance comparability between ethnic Dutch and
nonWestern patients regarding type of condition and reason
for admittance, we have ensured that we enrol
approximately the same number of representatives of each
group in each of the wards, i.e., no more than 10 more
in one group compared to the other per ward.
Because subjects of non-Western origin in the studys
age group on average score lower in variable categories
that potentially increase the risk of AEs (e.g., Dutch
language proficiency and health literacy) compared to
people of Western but non-Dutch ethnic origin, we have
selected to exclude the latter group. By doing so, the
contrast between the two study groups in differential
risk of AEs should be increased (See Table 1).
A sample size of 1000 patients per group allows us to
detect an incidence difference of 2% by an incidence of
8%, for which we need at least 800 patients per group.
In order to create enough power to investigate the
association between the five explanatory patient-related
variables (See Figure 1) and an AE, we need 200 extra
patients per group. The sample size calculation is based
on those from prior record review studies to measure
differences in AE incidences per hospital type, and based
on the results of the most recent Dutch record review
We informed wards through an oral presentation and an
information letter before the study started. Only limited
efforts have been required of ward staff. On recruitment
days, we ask the co-operation of a senior nurse to
identify which patients selected by the researchers can be
approached and have no severe medical
contraindications (See Patient recruitment). We have asked the
ward management to provide basic ward characteristics
(see Measurements-healthcare level). A limited number
of care providers are invited for an interview. (See
Patients are recruited on the wards during their clinical
admissions. To reach recruitment targets, each ward is
visited once or twice a week, depending on patient
turnover. At each visit, the presence of patients meeting the
inclusion criteria is assessed by the researchers using the
wards admissions information. Then a senior nurse
verifies whether these eligible patients are available to be
approached by the researcher or her assistants. The
senior nurse may only advise against approaching
prospective patients for serious reasons, defined by the
medical condition or the patient being busy with or
recovering from a test or intervention. If possible, these
patients are approached on a subsequent visit, which is
most of the times successful.
At each ward visit, we approach the latest admitted
eligible Dutch and non-Western patient, followed by the
second to last admitted eligible Dutch and non-Western
patient, and so on as a way of avoiding selection bias.
Before a patient is approached, his/her ethnic
background is estimated by the researcher based on surname,
phenotypical characteristics, and the presumptions of a
senior nurse. Ethnic origin is subsequently confirmed by
patient through the self-assessment part in the
questionnaire, which asks the patient about country of birth as
well as his/her parents. To ensure not missing any
eligible patients, researchers use an approach that initially
includes patients who may ultimately be excluded.
An information letter as well as oral information are
provided when the researcher approaches the patient.
Patients willing to participate must sign an informed
consent form. By signing the form, the patient gives
researchers permission to review the patient record after
discharge, and to use the data from the questionnaire.
Informed consent can also be given orally, in which case
the consent is taped. After the consent process, the
questionnaire is given. Patients who are able to do so fill
out the questionnaire independently. When necessary,
the researcher helps the patients by reading out the
questions and/or writing down the answers. Information
letters, consent forms, and questionnaires are available
in Dutch, English and Turkish. We have no written
translation in Arabic, because we anticipated that many
Arabic-speaking patients would not read or write in
Arabic. Further, the Berber dialect spoken by Moroccan
patients is not a written language. We use trained
bilingual (Turkish and Arabic speaking) research assistants
to ensure the participation of those who do not speak
Dutch or English. A limited number of patients may be
invited to participate in an interview after their
discharge. (See Qualitative component).
Non-response or missing
Throughout, we monitor the number of patients who we
could not approach due to the advice of the senior
nurse, and, if applicable, the reason for advising against
approaching. We are also monitoring the number of
patients who refuse to participate, and, when possible,
the reason for refusal (e.g., not comfortable with record
review, already participating in other studies, feeling too
sick to fill out a questionnaire). Finally, we monitor the
number of patients we could not approach at all because
they were off-ward for a scan or other procedure.
The data sources for the first two study objectives
include patient questionnaires and patient records.
Ethnic origin is assessed by country of birth of the
patient and birth of the patients parents .
Additionally, patients of Surinamese ethnic origin are
asked to classify to which subgroup they belong (e.g.
Hindustani or Creole) .
The two variables on migration history are length of
time of residence in the host country (measured by
year of arrival in the Netherlands), and reason for
migration (patients can choose between: work/study,
family, political, or other).
The questionnaire also contains items about all of the
patient-related determinants shown in the conceptual
model (See Figure 1).
Dutch language proficiency is measured by asking
patients to rank their own proficiency in
understanding, speaking, reading, and writing the
Dutch language (classified as not at all, a little,
moderately, good), and by asking which language is
spoken at home.
Religion is measured by asking whether patients are
religious, and if so, which religion they practise/were
brought up with. Also, we assess whether patients
practise their religion by asking if they visit religious
services and how often.
SES-indicators are measured with items about
education, occupation, and income. Educational level is
measured by asking about the number of completed
years of education since six years of age and the highest
completed grade level. We use reply-assistance
reference cards depicting educational systems of the
Netherlands, Suriname, Turkey, and Morocco.
Occupation is measured by asking the patients current
employment status, and, if applicable the employment
status of his/her partner. Because of privacy reasons,
we will not ask for income amounts, but rather if and
how easily patients are able to run their household
based on their income.
Health literacy is measured with three items using
Chews Set of Brief Screening Questions (SBSQ). The
questions are: "How often do you have someone help
you read hospital materials?"; "How confident are you
filling out medical forms by yourself?"; and "How often
do you have problems learning about your medical
condition because of difficulty understanding written
information?" The combined item-responses result in a
subjective health literacy score [22-24]. We use the
Dutch adaptation of the SBSQ .
Cultural distance to the Dutch healthcare system is
measured with five newly developed items in which
patients are asked to compare the Dutch healthcare
system with the healthcare system in the country of
their ethnic origin. When applicable, patients are asked
to indicate which system they prefer.
Questions on migration history and cultural distance
to the Dutch healthcare system are only provided to
Because we approach patients during hospital
admission, a time when they may feel ill, we made the
questionnaire as short as possible to lessen the burden of
participation as much as possible.
In addition to the patient-self assessment, the researcher
reports his or her perception of the patients Dutch
language proficiency and health literacy for all enrolled
patients. All communication problems due to language
barriers, the patients medical condition, or other factors
are reported. Furthermore, because phenotypical
characteristics such as skin colour may be related to stereotyping
in healthcare, these are also reported by the researcher.
Data entry of questionnaires is performed in BlaiseW.
During data collection, data checks are performed on a
regular basis (e.g. inconsistencies, missing data, out of
Measurements healthcare level
At the hospital level, we collect characteristics such as
the number of beds, admissions per year, and the type
(i.e., teaching hospital). Furthermore we investigate the
different hospital-specific services that are available (e.g.,
interpreters, spiritual counselors, dietary services).
At ward level, characteristics and cultural competence
data are collected including basic information about
employees (e.g., distribution of age, sex, and ethnic
origin), number of beds, and patients average lengths of
stay. Cultural competence data include the use of
interpreters, special meals, and spiritual counselors in the
After four months after the patients discharge, the medical
record is screened for the presence of AEs in a two-stage
review process based on the Harvard Medical Practise
Study (HMPS)  and Dutch patient safety studies
[20,21]. In the first stage, nurses review the complete
nursing record for the presence of one or more of 16 triggers
known to be sensitive to the presence of an AE (See
Table 2). If one or more triggers are found in the nursing
record, the record is forwarded to the second stage of the
review procedure. When no triggers are found in the
nursing record, the nurse screens the medical record for the
presence of triggers. When triggers are present, the nurse
decides the specialty most appropriate to review the
record in the second phase, choosing between internal
disease, surgery, or neurology.
Adverse event assessment
The specialist determines whether an AE has occurred,
and whether it was preventable.
The determination of AEs is based on three criteria
 (See Table 3). First, the specialist determines if
unintended injury occurred. A 6-point scale is used to
determine whether the injury was caused by healthcare
management rather than by the patients disease. To
structure the review process, the causation scale is
preceded by 13 questions to facilitate the final judgement
(See Additional file 2). Causation scores 4-6 are
classified as AEs and further analysed. In addition, the degree
of preventability (6-point scale) is determined, again
preceded by 13 facilitating questions. Furthermore, timing,
involved specialties, parts of the care process (e.g..,
diagnosis, medication, discharge) and causes (e.g. technical,
human factors) of AEs are determined [20,21].
Characteristics of the hospital admission
To describe the patient mix, the following variables are
also collected from the record: length of stay, admission
status (e.g., elective, urgent), admission and discharge
diagnosis, admission specialty, and discharge status (e.g.,
to home, to home with outpatient care). We collect
ICD-9-CM codes of the primary diagnosis of all patient
To assess the reliability of screening for triggers by
nurses, 5% of the records are screened independently by
a second nurse. To assess reliability of AE
determination, 10% of the second phase records are reviewed
independently by a second specialist. The second reviewer
is blind to the outcome of the first review.
Table 3 Adverse event definition
Record review data entry is performed in a highly
secured web-based program. During data collection, data
checks are performed on a regular basis for
inconsistencies, missing data, and out of range answers.
Reviewer recruitment and training
We recruited experienced reviewers who have
participated in other Dutch record review studies [20,21], and
who were chosen by using strict selection criteria (at
least ten years post graduate general clinical experience
for specialists and a minimum of five years of clinical
experience for nurses). Reviewers do not review records in
hospitals where they are currently working, or where
they have worked in the past 20 years.
During data collection, reviewers can discuss
problems, and they also use a regularly updated Frequently
Asked Questions document. Reflection days are also
organised for all reviewers. Reviewers are compensated
for their review activities at an hourly rate, and expenses
To satisfy our third and fourth research objectives, we
use qualitative research methods to explore the
mechanisms that play a role in AEs in patients of different
ethnic origins, and we also examine what prevention
strategies might be developed to minimize differences.
This quantitative part shows us the contribution of
several determinants to the risk of an AE, and examines the
relationships between determinants and occurrences of
AEs. We are trying to identify the barriers in
patientprovider interactions, to find opportunities to improve
communication and minimize the risk for AEs.
We are selecting a maximum of 60 admissions by
purposive and theoretical sampling based on the admissions
where an AE has been determined. We sample from a
range of patients with different variables, such as
language proficiency, health literacy, etc. For each AE, the
patient and care provider are interviewed separately
using a semi-structured topic list. The care provider can
be either a doctor or a nurse, as long as he/she has been
a Any disadvantage for the patient that leads to prolonged or strengthened treatment, temporary or permanent (physical or mental) impairment or death.
An unintended injurya that results in disability that results in temporary or permanent disability, death, or prolonged hospital
stay and is caused by health care management rather than by the patients underlying disease process.
In our study, determination of the presence of an adverse event was based on 3 criteria:
1. An unintended (physical and/or mental) injury which
2. Results in temporary or permanent disability, death or prolongation of hospital stay, and is
3. caused by health care management rather than the patients disease
An adverse event resulting from an error in management due to failure to follow accepted practice at an individual or
system level. Accepted practice is the current level of expected performance for the average practitioner or system that
manages the condition in question.
closely involved with the patients care process. The
interview takes place soon after the patients discharge
to ensure the patient and provider will remember the
details of the admission. In the interview, both patients
and providers are asked to explain the AE and the care
process in detail. We ask about the perceived barriers
that may underlie an AE, and ask interviewees for
suggested strategies to address them.
Interviews will be transcribed and qualitatively
analysed based on the conceptual model. Transcribed
interviews will be coded and text-parts with the same code
will be grouped to identify sub-themes. A second
researcher will be involved in identifying codes and
Data will be analysed using SPSS 16.0 for Windows or
Baseline comparability will be investigated by descriptive
statistics including age, gender, admission status and
primary diagnosis of the patients. Also, we will compare
some hospital- and ward characteristics such as the
number of patients included per ward. Response rates in both
groups will be compared. General characteristics of
nonresponders will be compared to those of responders.
To answer our first research question regarding
differences in AE rates between non-Western and ethnic
Dutch patients, we will first describe the nature,
preventability, causes and consequences of AEs. We will show
preventability of AEs per group (both treated as a
categorical and dichotomous variable) in order to find out
whether differences in the degree of preventability are
present. We will tabulate the distribution of AEs across
ethnic groups, medical specialties, medical process
involved (e.g. diagnosis, medication), impact of AEs, and
causes of AEs (e.g. technical, organizational).
Next, incidence rates of AEs and preventable AEs will
be calculated for both groups with 95% confidence
intervals. Multilevel analysis will be performed to assess and
adjust for variance at the hospital and ward level.
Potential confounders like age, sex, and primary diagnosis will
be entered stepwise into the model.
To answer our second research question, we will
perform multilevel multivariable logistic regression analysis
to investigate the contribution of the patient-related
independent explanatory variables (SES indicators, Dutch
language proficiency, health literacy, cultural distance,
and religion) to the risk of experiencing an AE.
Covariables will be entered stepwise into the model,
starting with potential confounders (age, gender, primary
diagnosis), followed by possible explanatory variables.
Outcomes will be presented in odds ratios.
To analyse inter-rater reliability, we will calculate the
percentage of records for which there was agreement on
the presence of screening criteria for nurses and on the
presence and preventability for physicians. Also, we will
calculate a kappa-statistic.
To prepare for the main analyses, we will conduct
exploratory analyses to optimize our dataset and statistical
models. To describe our exploratory determinants (Dutch
language proficiency, Health literacy, SES-indicators,
religion and cultural distance) we have measured several
variables. Exploratory analyses will lead us to determine
which data to use in further analyses, the cut-off points,
and the number of categories the explanatory
determinants should be divided into. For example, to measure
Dutch language proficiency we have the patient-self
assessment of his/her proficiency in understanding,
speaking, reading, and writing Dutch, all on a 4-point
scale (from not at all to good). Furthermore, we know
the language the patient speaks at home, and we also have
information on language proficiency from the patient
record and the subjective researcher assessment of the
patients Dutch language proficiency. After exploratory
analyses we can decide cut-off points and the number of
language proficiency categories, and we can validate our
categorization with the researcher assessment. We will
use the same method for the health literacy determinant.
We also have three SES-indicators (education, occupation,
and a proxy for income), and we will choose the optimal
ones by refining the SES-measure after exploratory
This will be the first study to investigate possible ethnic
inequalities in Europe. The results of this study will
show whether, to what extent, and how ethnic
inequalities affect patient safety in Dutch hospital care. Findings
will show which determinants and mechanisms
potentially increase the risk for an AE, and which of these can
be associated with a potential increased risk for ethnic
minority groups. With this information, prevention
strategies can be formulated, and health care can be
made safer for ethnic minorities, and also for all patient
In this study we focus on different explanatory
variables that are often applicable to ethnic minority
patients rather than analyzing patient safety in specific
ethnic groups living in the Netherlands. By identifying
specific patient related factors related to AEs, the results
will be generalizable to all patients, irrespective of their
specific ethnic origin.
Strengths and limitations
We have made efforts to involve all patients in our study
even though they may not have mastered the Dutch
language, or be literate. We provide translations of letters,
consent forms and questionnaires, use bilingual research
assistants, and also ask relatives of patients to interpret
when possible. Even with all these efforts, we might miss
some patients because they speak a language we are not
able to cover. To quantify this possible selection bias, we
are closely monitoring how many patients we are unable
to approach because of communication difficulties.
Record review has been cited as a strong method to
study the frequency and types of AEs, and has high face
validity among health care workers. The most important
advantages of this method are its utilization of readily
available data and its common international use. Other
ways of studying AEs are possible, but have important
limitations; morbidity and mortality conferences analysis,
malpractice claims analysis, and error reporting system
analysis are three methods prone to reporting bias, while
observation of patient care and clinical surveillance are
very expensive and not suitable for detecting latent errors
. A weakness of record review is hindsight bias .
Knowing the outcome and its severity may influence
judgement of causation and preventability. However, this
will affect both the groups equally, which means that
comparability between them is unaffected. Because we
compare two patient groups, assessment bias is another
potential limitation. Unfortunately, it is impossible to
completely blind reviewers for the patient groups to which
the records they review belong. However, this assessment
bias can go in both directions. Both over- and
underidentification of AEs can occur. Also, because of the
standardized format and the number of support questions
helping the reviewer make their determinations, we think
the risk for this assessment bias is low.
Even with the carefully structured review process and
the experience of the reviewers, variation in the
judgements of reviewers can be present. We limit variation in
judgements by organising reflection days and using
frequently updated FAQ-lists. In this study we will have 5%
of the first phase and 10% of the second phase records
double-reviewed to be able to check inter-reviewer
To study causes and mechanisms underlying AEs,
record review does not provide complete information . In
the present study, patient characteristics and
patientprovider interaction are very important. The addition of
questionnaires and in-depth interviews to our record
review constitutes a major strength of this studys approach.
This study uses a robust study plan to quantify the risk
difference of AEs between ethnic minority and Dutch
patients in hospital care. In addition we are developing
an in-depth description of the mechanisms of excess risk
for some groups compared to others, while identifying
opportunities for more equitable distributions of patient
safety for all.
The study protocol was reviewed and approved by the
ethical review board of the Academic Medical Centre in
Amsterdam, the Netherlands. All participating hospitals
granted approval to participate.
FvR, MCdeB and MLE-B designed the study. FvR drafted the manuscript and
will collect and analyze the data. MCdB and MLE-B provide daily supervision
of the study and the manuscript. KS and CW supervised study design and
provided comments on subsequent versions of the manuscript. All authors
read and approved the final manuscript.
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