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• Cross-Sectional Study | Definition, Uses & Examples

Cross-Sectional Study | Definition, Uses & Examples

Published on May 8, 2020 by Lauren Thomas . Revised on June 22, 2023.

A cross-sectional study is a type of research design in which you collect data from many different individuals at a single point in time. In cross-sectional research, you observe variables without influencing them.

Researchers in economics, psychology, medicine, epidemiology, and the other social sciences all make use of cross-sectional studies in their work. For example, epidemiologists who are interested in the current prevalence of a disease in a certain subset of the population might use a cross-sectional design to gather and analyze the relevant data.

Table of contents

Cross-sectional vs longitudinal studies, when to use a cross-sectional design, how to perform a cross-sectional study, advantages and disadvantages of cross-sectional studies, other interesting articles, frequently asked questions about cross-sectional studies.

The opposite of a cross-sectional study is a longitudinal study . While cross-sectional studies collect data from many subjects at a single point in time, longitudinal studies collect data repeatedly from the same subjects over time, often focusing on a smaller group of individuals that are connected by a common trait.

Both types are useful for answering different kinds of research questions . A cross-sectional study is a cheap and easy way to gather initial data and identify correlations that can then be investigated further in a longitudinal study.

Prevent plagiarism. Run a free check.

When you want to examine the prevalence of some outcome at a certain moment in time, a cross-sectional study is the best choice.

Sometimes a cross-sectional study is the best choice for practical reasons – for instance, if you only have the time or money to collect cross-sectional data, or if the only data you can find to answer your research question was gathered at a single point in time.

As cross-sectional studies are cheaper and less time-consuming than many other types of study, they allow you to easily collect data that can be used as a basis for further research.

Descriptive vs analytical studies

Cross-sectional studies can be used for both analytical and descriptive purposes:

• An analytical study tries to answer how or why a certain outcome might occur.
• A descriptive study only summarizes said outcome using descriptive statistics.

To implement a cross-sectional study, you can rely on data assembled by another source or collect your own. Governments often make cross-sectional datasets freely available online.

Prominent examples include the censuses of several countries like the US or France , which survey a cross-sectional snapshot of the country’s residents on important measures. International organizations like the World Health Organization or the World Bank also provide access to cross-sectional datasets on their websites.

However, these datasets are often aggregated to a regional level, which may prevent the investigation of certain research questions. You will also be restricted to whichever variables the original researchers decided to study.

If you want to choose the variables in your study and analyze your data on an individual level, you can collect your own data using research methods such as surveys . It’s important to carefully design your questions and choose your sample .

Like any research design , cross-sectional studies have various benefits and drawbacks.

• Because you only collect data at a single point in time, cross-sectional studies are relatively cheap and less time-consuming than other types of research.
• Cross-sectional studies allow you to collect data from a large pool of subjects and compare differences between groups.
• Cross-sectional studies capture a specific moment in time. National censuses, for instance, provide a snapshot of conditions in that country at that time.

Disadvantages

• It is difficult to establish cause-and-effect relationships using cross-sectional studies, since they only represent a one-time measurement of both the alleged cause and effect.
• Since cross-sectional studies only study a single moment in time, they cannot be used to analyze behavior over a period of time or establish long-term trends.
• The timing of the cross-sectional snapshot may be unrepresentative of behavior of the group as a whole. For instance, imagine you are looking at the impact of psychotherapy on an illness like depression. If the depressed individuals in your sample began therapy shortly before the data collection, then it might appear that therapy causes depression even if it is effective in the long term.

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Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

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Cross-Sectional Study: Definition, Designs & Examples

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BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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A cross-sectional study design is a type of observational study, or descriptive research, that involves analyzing information about a population at a specific point in time.

This design measures the prevalence of an outcome of interest in a defined population. It provides a snapshot of the characteristics of the population at a single point in time.

It can be used to assess the prevalence of outcomes and exposures, determine relationships among variables, and generate hypotheses about causal connections between factors to be explored in experimental designs.

Typically, these studies are used to measure the prevalence of health outcomes and describe the characteristics of a population.

In this study, researchers examine a group of participants and depict what already exists in the population without manipulating any variables or interfering with the environment.

Cross-sectional studies aim to describe a variable , not measure it. They can be beneficial for describing a population or “taking a snapshot” of a group of individuals at a single moment in time.

In epidemiology and public health research, cross-sectional studies are used to assess exposure (cause) and disease (effect) and compare the rates of diseases and symptoms of an exposed group with an unexposed group.

Cross-sectional studies are also unique because researchers are able to look at numerous characteristics at once.

For example, a cross-sectional study could be used to investigate whether exposure to certain factors, such as overeating, might correlate to particular outcomes, such as obesity.

While this study cannot prove that overeating causes obesity, it can draw attention to a relationship that might be worth investigating.

Cross-sectional studies can be categorized based on the nature of the data collection and the type of data being sought.

Analytical Studies

In analytical cross-sectional studies, researchers investigate an association between two parameters. They collect data for exposures and outcomes at one specific time to measure an association between an exposure and a condition within a defined population.

The purpose of this type of study is to compare health outcome differences between exposed and unexposed individuals.

Descriptive Studies

• Descriptive cross-sectional studies are purely used to characterize and assess the prevalence and distribution of one or many health outcomes in a defined population.
• They can assess how frequently, widely, or severely a specific variable occurs throughout a specific demographic.
• This is the most common type of cross-sectional study.
• Evaluating the COVID-19 positivity rates among vaccinated and unvaccinated adolescents
• Investigating the prevalence of dysfunctional breathing in patients treated for asthma in primary care (Wang & Cheng, 2020)
• Analyzing whether individuals in a community have any history of mental illness and whether they have used therapy to help with their mental health
• Comparing grades of elementary school students whose parents come from different income levels
• Determining the association between gender and HIV status (Setia, 2016)
• Investigating suicide rates among individuals who have at least one parent with chronic depression
• Assessing the prevalence of HIV and risk behaviors in male sex workers (Shinde et al., 2009)
• Examining sleep quality and its demographic and psychological correlates among university students in Ethiopia (Lemma et al., 2012)
• Calculating what proportion of people served by a health clinic in a particular year have high cholesterol
• Analyzing college students’ distress levels with regard to their year level (Leahy et al., 2010)

Simple and Inexpensive

These studies are quick, cheap, and easy to conduct as they do not require any follow-up with subjects and can be done through self-report surveys.

Minimal room for error

Because all of the variables are analyzed at once, and data does not need to be collected multiple times, there will likely be fewer mistakes as a higher level of control is obtained.

Multiple variables and outcomes can be researched and compared at once

Researchers are able to look at numerous characteristics (ie, age, gender, ethnicity, and education level) in one study.

The data can be a starting point for future research

The information obtained from cross-sectional studies enables researchers to conduct further data analyses to explore any causal relationships in more depth.

Limitations

Does not help determine cause and effect.

Cross-sectional studies can be influenced by an antecedent consequent bias which occurs when it cannot be determined whether exposure preceded disease. (Alexander et al.)

Report bias is probable

Cross-sectional studies rely on surveys and questionnaires, which might not result in accurate reporting as there is no way to verify the information presented.

The timing of the snapshot is not always representative

Cross-sectional studies do not provide information from before or after the report was recorded and only offer a single snapshot of a point in time.

It cannot be used to analyze behavior over a period of time

Cross-sectional studies are designed to look at a variable at a particular moment, while longitudinal studies are more beneficial for analyzing relationships over extended periods.

Cross-Sectional vs. Longitudinal

Both cross-sectional and longitudinal studies are observational and do not require any interference or manipulation of the study environment.

However, cross-sectional studies differ from longitudinal studies in that cross-sectional studies look at a characteristic of a population at a specific point in time, while longitudinal studies involve studying a population over an extended period.

Longitudinal studies require more time and resources and can be less valid as participants might quit the study before the data has been fully collected.

Unlike cross-sectional studies, researchers can use longitudinal data to detect changes in a population and, over time, establish patterns among subjects.

Cross-sectional studies can be done much quicker than longitudinal studies and are a good starting point to establish any associations between variables, while longitudinal studies are more timely but are necessary for studying cause and effect.

Alexander, L. K., Lopez, B., Ricchetti-Masterson, K., & Yeatts, K. B. (n.d.). Cross-sectional Studies. Eric Notebook. Retrieved from https://sph.unc.edu/wp-content/uploads/sites/112/2015/07/nciph_ERIC8.pdf

Cherry, K. (2019, October 10). How Does the Cross-Sectional Research Method Work? Verywell Mind. Retrieved from https://www.verywellmind.com/what-is-a-cross-sectional-study-2794978

Cross-sectional vs. longitudinal studies. Institute for Work & Health. (2015, August). Retrieved from https://www.iwh.on.ca/what-researchers-mean-by/cross-sectional-vs-longitudinal-studies

Leahy, C. M., Peterson, R. F., Wilson, I. G., Newbury, J. W., Tonkin, A. L., & Turnbull, D. (2010). Distress levels and self-reported treatment rates for medicine, law, psychology and mechanical engineering tertiary students: cross-sectional study. The Australian and New Zealand journal of psychiatry, 44(7), 608–615.

Lemma, S., Gelaye, B., Berhane, Y. et al. Sleep quality and its psychological correlates among university students in Ethiopia: a cross-sectional study. BMC Psychiatry 12, 237 (2012).

Wang, X., & Cheng, Z. (2020). Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations. Chest, 158(1S), S65–S71.

Setia M. S. (2016). Methodology Series Module 3: Cross-sectional Studies. Indian journal of dermatology, 61 (3), 261–264.

Shinde S, Setia MS, Row-Kavi A, Anand V, Jerajani H. Male sex workers: Are we ignoring a risk group in Mumbai, India? Indian J Dermatol Venereol Leprol. 2009;75:41–6.

Further Information

• Setia, M. S. (2016). Methodology series module 3: Cross-sectional studies. Indian journal of dermatology, 61(3), 261.
• Sedgwick, P. (2014). Cross sectional studies: advantages and disadvantages. Bmj, 348.

1. Are cross-sectional studies qualitative or quantitative?

Cross-sectional studies can be either qualitative or quantitative , depending on the type of data they collect and how they analyze it. Often, the two approaches are combined in mixed-methods research to get a more comprehensive understanding of the research problem.

2. What’s the difference between cross-sectional and cohort studies?

A cohort study is a type of longitudinal study that samples a group of people with a common characteristic. One key difference is that cross-sectional studies measure a specific moment in time, whereas  cohort studies  follow individuals over extended periods.

Another difference between these two types of studies is the subject pool. In cross-sectional studies, researchers select a sample population and gather data to determine the prevalence of a problem.

Cohort studies, on the other hand, begin by selecting a population of individuals who are already at risk for a specific disease.

3. What’s the difference between cross-sectional and case-control studies?

Case-control studies differ from cross-sectional studies in that case-control studies compare groups retrospectively and cannot be used to calculate relative risk.

In these studies, researchers study one group of people who have developed a particular condition and compare them to a sample without the disease.

Case-control studies are used to determine what factors might be associated with the condition and help researchers form hypotheses about a population.

4. Does a cross-sectional study have a control group?

A cross-sectional study does not need to have a control group , as the population studied is not selected based on exposure.

In a cross-sectional study, data are collected from a sample of the target population at a specific point in time, and everyone in the sample is assessed in the same way. There isn’t a manipulation of variables or a control group as there would be in an experimental study design.

5. Is a cross-sectional study prospective or retrospective?

A cross-sectional study is generally considered neither prospective nor retrospective because it provides a “snapshot” of a population at a single point in time.

Cross-sectional studies are not designed to follow individuals forward in time ( prospective ) or look back at historical data ( retrospective ), as they analyze data from a specific point in time.

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• Knowledge Base
• Methodology
• Cross-Sectional Study | Definitions, Uses & Examples

Cross-Sectional Study | Definitions, Uses & Examples

Published on 5 May 2022 by Lauren Thomas .

A cross-sectional study is a type of research design in which you collect data from many different individuals at a single point in time. In cross-sectional research, you observe variables without influencing them.

Researchers in economics, psychology, medicine, epidemiology, and the other social sciences all make use of cross-sectional studies in their work. For example, epidemiologists who are interested in the current prevalence of a disease in a certain subset of the population might use a cross-sectional design to gather and analyse the relevant data.

Table of contents

Cross-sectional vs longitudinal studies, when to use a cross-sectional design, how to perform a cross-sectional study, advantages and disadvantages of cross-sectional studies, frequently asked questions about cross-sectional studies.

The opposite of a cross-sectional study is a longitudinal study . While cross-sectional studies collect data from many subjects at a single point in time, longitudinal studies collect data repeatedly from the same subjects over time, often focusing on a smaller group of individuals connected by a common trait.

Both types are useful for answering different kinds of research questions . A cross-sectional study is a cheap and easy way to gather initial data and identify correlations that can then be investigated further in a longitudinal study.

Prevent plagiarism, run a free check.

When you want to examine the prevalence of some outcome at a certain moment in time, a cross-sectional study is the best choice.

Sometimes a cross-sectional study is the best choice for practical reasons – for instance, if you only have the time or money to collect cross-sectional data, or if the only data you can find to answer your research question were gathered at a single point in time.

As cross-sectional studies are cheaper and less time-consuming than many other types of study, they allow you to easily collect data that can be used as a basis for further research.

Descriptive vs analytical studies

Cross-sectional studies can be used for both analytical and descriptive purposes:

• An analytical study tries to answer how or why a certain outcome might occur.
• A descriptive study only summarises said outcome using descriptive statistics.

To implement a cross-sectional study, you can rely on data assembled by another source or collect your own. Governments often make cross-sectional datasets freely available online.

Prominent examples include the censuses of several countries like the US or France , which survey a cross-sectional snapshot of the country’s residents on important measures. International organisations like the World Health Organization or the World Bank also provide access to cross-sectional datasets on their websites.

However, these datasets are often aggregated to a regional level, which may prevent the investigation of certain research questions. You will also be restricted to whichever variables the original researchers decided to study.

If you want to choose the variables in your study and analyse your data on an individual level, you can collect your own data using research methods such as surveys . It’s important to carefully design your questions and choose your sample .

Like any research design , cross-sectional studies have various benefits and drawbacks.

• Because you only collect data at a single point in time, cross-sectional studies are relatively cheap and less time-consuming than other types of research.
• Cross-sectional studies allow you to collect data from a large pool of subjects and compare differences between groups.
• Cross-sectional studies capture a specific moment in time. National censuses, for instance, provide a snapshot of conditions in that country at that time.

Disadvantages

• It is difficult to establish cause-and-effect relationships using cross-sectional studies, since they only represent a one-time measurement of both the alleged cause and effect.
• Since cross-sectional studies only study a single moment in time, they cannot be used to analyse behavior over a period of time or establish long-term trends.
• The timing of the cross-sectional snapshot may be unrepresentative of behaviour of the group as a whole. For instance, imagine you are looking at the impact of psychotherapy on an illness like depression. If the depressed individuals in your sample began therapy shortly before the data collection, then it might appear that therapy causes depression even if it is effective in the long term.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyse behaviour over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cite this Scribbr article

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Thomas, L. (2022, May 05). Cross-Sectional Study | Definitions, Uses & Examples. Scribbr. Retrieved 2 April 2024, from https://www.scribbr.co.uk/research-methods/cross-sectional-design/

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How Do Cross-Sectional Studies Work?

Gathering Data From a Single Point in Time

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Steven Gans, MD is board-certified in psychiatry and is an active supervisor, teacher, and mentor at Massachusetts General Hospital.

What Is a Cross-Sectional Study?

• Defining Characteristics

Advantages of Cross-Sectional Studies

Challenges of cross-sectional studies, cross-sectional vs. longitudinal studies.

Verywell / Jessica Olah

A cross-sectional study looks at data at a single point in time. The participants in this type of study are selected based on particular variables of interest. Cross-sectional studies are often used in developmental psychology , but this method is also used in many other areas, including social science and education.

Cross-sectional studies are observational in nature and are known as descriptive research, not causal or relational, meaning that you can't use them to determine the cause of something, such as a disease. Researchers record the information that is present in a population, but they do not manipulate variables .

This type of research can be used to describe characteristics that exist in a community, but not to determine cause-and-effect relationships between different variables. This method is often used to make inferences about possible relationships or to gather preliminary data to support further research and experimentation.

Example: Researchers studying developmental psychology might select groups of people who are different ages but investigate them at one point in time. By doing this, any differences among the age groups can be attributed to age differences rather than something that happened over time.

Defining Characteristics of Cross-Sectional Studies

Some of the key characteristics of a cross-sectional study include:

• The study takes place at a single point in time
• It does not involve manipulating variables
• It allows researchers to look at numerous characteristics at once (age, income, gender, etc.)
• It's often used to look at the prevailing characteristics in a given population
• It can provide information about what is happening in a current population

Think of a cross-sectional study as a snapshot of a particular group of people at a given point in time. Unlike longitudinal studies, which look at a group of people over an extended period, cross-sectional studies are used to describe what is happening at the present moment.This type of research is frequently used to determine the prevailing characteristics in a population at a certain point in time. For example, a cross-sectional study might be used to determine if exposure to specific risk factors might correlate with particular outcomes.

A researcher might collect cross-sectional data on past smoking habits and current diagnoses of lung cancer, for example. While this type of study cannot demonstrate cause and effect, it can provide a quick look at correlations that may exist at a particular point.

For example, researchers may find that people who reported engaging in certain health behaviors were also more likely to be diagnosed with specific ailments. While a cross-sectional study cannot prove for certain that these behaviors caused the condition, such studies can point to a relationship worth investigating further.

Cross-sectional studies are popular because they have several benefits that are useful to researchers.

Inexpensive and Fast

Cross-sectional studies typically allow researchers to collect a great deal of information quickly. Data is often obtained inexpensively using self-report surveys . Researchers are then able to amass large amounts of information from a large pool of participants.

For example, a university might post a short online survey about library usage habits among biology majors, and the responses would be recorded in a database automatically for later analysis. This is a simple, inexpensive way to encourage participation and gather data across a wide swath of individuals who fit certain criteria.

Can Assess Multiple Variables

Researchers can collect data on a few different variables to see how they affect a certain condition. For example, differences in sex, age, educational status, and income might correlate with voting tendencies or give market researchers clues about purchasing habits.

Might Prompt Further Study 

Although researchers can't use cross-sectional studies to determine causal relationships, these studies can provide useful springboards to further research. For example, when looking at a public health issue, such as whether a particular behavior might be linked to a particular illness, researchers might utilize a cross-sectional study to look for clues that can spur further experimental studies.

For example, researchers might be interested in learning how exercise influences cognitive health as people age. They might collect data from different age groups on how much exercise they get and how well they perform on cognitive tests. Conducting such a study can give researchers clues about the types of exercise that might be most beneficial to the elderly and inspire further experimental research on the subject.

No method of research is perfect. Cross-sectional studies also have potential drawbacks.

Difficulties in Determining Causal Effects

Researchers can't always be sure that the conditions a cross-sectional study measures are the result of a particular factor's influence. In many cases, the differences among individuals could be attributed to variation among the study subjects. In this way, cause-and-effect relationships are more difficult to determine in a cross-sectional study than they are in a longitudinal study. This type of research simply doesn't allow for conclusions about causation.

For example, a study conducted some 20 years ago queried thousands of women about their consumption of diet soft drinks. The results of the study, published in the medical journal Stroke , associated diet soft drink intake with stroke risk that was greater than that of those who did not consume such beverages. In other words, those who drank lots of diet soda were more prone to strokes. However, correlation does not equal causation. The increased stroke risk might arise from any number of factors that tend to occur among those who drink diet beverages. For example, people who consume sugar-free drinks might be more likely to be overweight or diabetic than those who drink the regular versions. Therefore, they might be at greater risk of stroke—regardless of what they drink.

Cohort Differences

Groups can be affected by cohort differences that arise from the particular experiences of a group of people. For example, individuals born during the same period might witness the same important historical events, but their geographic regions, religious affiliations, political beliefs, and other factors might affect how they perceive such events.

Report Biases

Surveys and questionnaires about certain aspects of people's lives might not always result in accurate reporting. For example, respondents might not disclose certain behaviors or beliefs out of embarrassment, fear, or other limiting perception. Typically, no mechanism for verifying this information exists.

Cross-sectional research differs from longitudinal studies in several important ways. The key difference is that a cross-sectional study is designed to look at a variable at a particular point in time. A longitudinal study evaluates multiple measures over an extended period to detect trends and changes.

Evaluates variable at single point in time

Participants less likely to drop out

Uses new participant(s) with each study

Measures variable over time

Requires more resources

More expensive

Subject to selective attrition

Follows same participants over time

Longitudinal studies tend to require more resources; these are often more expensive than those used by cross-sectional studies. They are also more likely to be influenced by what is known as selective attrition , which means that some individuals are more likely to drop out of a study than others. Because a longitudinal study occurs over a span of time, researchers can lose track of subjects. Individuals might lose interest, move to another city, change their minds about participating, etc. This can influence the validity of the study.

One of the advantages of cross-sectional studies is that data is collected all at once, so participants are less likely to quit the study before data is fully collected.

A Word From Verywell

Cross-sectional studies can be useful research tools in many areas of health research. By learning about what is going on in a specific population, researchers can improve their understanding of relationships among certain variables and develop additional studies that explore these conditions in greater depth.

Levin KA. Study design III: Cross-sectional studies . Evid Based Dent . 2006;7(1):24-5. doi:10.1038/sj.ebd.6400375 

Morin JF, Olsson C, Atikcan EO, eds.  Research Methods in the Social Sciences: An A-Z of Key Concepts . Oxford University Press; 2021.

Abbasi J. Unpacking a recent study linking diet soda with stroke risks .  JAMA . 2019;321(16):1554-1555. doi:10.1001/jama.2019.2123

Setia MS. Methodology series module 3: Cross-sectional studies . Indian J Dermatol . 2016;61(3):261-4. doi:10.4103/0019-5154.182410

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

What (Exactly) Is A Cross-Sectional Study?

A plain-language explanation & definition (with examples).

By: Derek Jansen (MBA) | June 2020

If you’ve just started out on your dissertation, thesis or research project and it’s your first time carrying out formal research, you’ve probably encountered the terms “cross-sectional study” and “cross-sectional research” and are wondering what exactly they mean. In this post, we’ll explain exactly :

• What a cross-sectional study is (and what the alternative approach is)
• What the main advantages of a cross-sectional study are
• What the main disadvantages of a cross-sectional study are
• Whether you should use a cross-sectional or longitudinal study for your research

What (exactly) is a cross-sectional study?

A cross-sectional study (also referred to as cross-sectional research) is simply a study in which data are collected at one point in time . In other words, data are collected on a snapshot basis, as opposed to collecting data at multiple points in time (for example, once a week, once a month, etc) and assessing how it changes over time.

Example: Cross-Sectional vs Longitudinal 

Here’s an example of what this looks like in practice:

Cross-sectional study: a study which assesses a group of people’s attitudes and feelings towards a newly elected president, directly after the election happened.

Longitudinal study: a study which assesses how people’s attitudes towards the president changed over a period of 3 years after the president is elected, assessing sentiment every 6 months.

As you can probably see, while both these studies are analysing the same topic (people’s sentiment towards the president), they each have a different focus. The cross-sectional study is interested in what people are feeling and thinking “ right now ”, whereas the longitudinal study is interested in not just what people are feeling and thinking, but how those thoughts and feelings change over time .

What are the advantages of a cross-sectional study?

There are many advantages to taking a cross-sectional approach, which makes it the more popular option for dissertations and theses. Some main advantages are:

• Speed – given the nature of a cross-sectional study, you can complete your research relatively quickly, as information only needs to be gathered once.
• Cost – because information only needs to be collected once, the cost is lower than a longitudinal approach.
• Control – because the data are only collected at one point in time, you have a lot more control over the measurement process (i.e. you don’t need to worry about measurement instruments changing over a period of years).
• Flexibility – using a cross-sectional approach, you can measure multiple factors at once. Your study can be descriptive (assessing the prevalence of something), analytical (assessing the relationship between two or more things) or both.

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What are the disadvantages of a cross-sectional study?

While the cross-sectional approach to research has many advantages, it (naturally) has its limitations and disadvantages too. Some of the main disadvantages are:

• Static – cross-sectional studies cannot establish any sequence of events, as they only assess data with a snapshot view.
• Causality – because cross-sectional studies look at data at a single point in time (no sequence of events), it’s sometimes difficult to understand which way causality flows – for example, does A cause B, or does B cause A? Without knowing whether A or B came first, it’s not always easy to tell which causes which.
• Sensitivity to timing – the exact time at which data are collected can have a large impact on the results, and therefore the findings of the study may not be representative.

Should I use a cross-sectional study or longitudinal study design?

It depends… Your decision to use a cross-sectional or longitudinal approach needs to be informed by your overall research aims, objectives and research questions . As with most research design choices, the research aims will heavily influence your approach.

For example, if your research objective is to get a snapshot view of something, then a cross-sectional approach should work well for you. However, if your research aim is to understand how something has changed over time, a longitudinal approach might be more appropriate.

If you’re trying to make this decision for a dissertation or thesis, you also need to consider the practical limitations such as time and access to data. Chances are, you won’t have the luxury of conducting your research over a period of a few years, so you might be “forced” into a cross-sectional approach due to time restrictions.

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• What is a cross-sectional study?

Last updated

6 February 2023

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Miroslav Damyanov

Read on to learn about cross-sectional studies. We’ll explore examples, types, advantages, and limitations of cross-sectional studies, plus when you might use them.

Analyze cross-sectional studies

Dovetail streamlines cross-sectional studies to help you uncover and share actionable insights

A cross-sectional study is also known as a prevalence or transverse study. It’s a tool that allows researchers to collect data across a pre-defined subset or sample population at a single point in time. The information is typically about many individuals with multiple variables, such as gender and age. Although researchers get to analyze these variables, they do not manipulate them.

This study type is commonly used in clinical research, business-related studies, and population studies.

Once the researcher has selected the ideal study period and participant group, the study usually takes place as a survey or physical experiment.

• Characteristics of cross-sectional studies

Primary characteristics of cross-sectional studies include the following:

Consistent variables : Researchers carry out a cross-sectional study over a specific period with the same set of variables (income, gender, age, etc.).

Observational nature : Researchers record findings about a specific population but do not alter variables—they just observe.

Well-defined extremes : The analysis includes defined start and stop points which allow all variables to stay the same.

Singular instances : Only one topic or instance can be analyzed with a cross-sectional study. This allows for more accurate data collection .

• Examples of cross-sectional studies

Variables remain the same during a cross-sectional study. This makes it a useful research tool in various sectors and circumstances across multiple industries.

Here are some examples to give you better clarity:

Healthcare : Scientists might leverage cross-sectional research to assess how children aged 3–10 are prone to calcium deficiency.

Retail : Researchers use cross-sectional studies to identify similarities and differences in spending habits between men and women within a specific age group.

Education : These studies help reveal how students with a specific grade range perform when schools introduce a new curriculum.

Business: Researchers might leverage cross-sectional studies to understand how a geographic segment responds to offers and discounts.

• Types of cross-sectional studies

We can categorize cross-sectional studies into two distinct types: descriptive and analytical research. However, the researcher may use one or both types to gather and analyze data.

Here is a description of the two to help you understand how they may apply to your work.

Descriptive research

A descriptive cross-sectional survey or study assesses how commonly or frequently the primary variable occurs within a select demographic. This enables you to identify any problem areas within the group.

Descriptive research makes trend identification easy, facilitating the development of products and services that fit a particular population.

Analytical research

An analytical cross-sectional study investigates the relationship between two related or unrelated parameters. Outside variables may affect the study while the investigation is ongoing, however.

Note that the original results and data are studied together simultaneously in an analytical cross-sectional study.

• Cross-sectional versus longitudinal studies

Although longitudinal and cross-sectional studies are both observational, they are relatively different types of research design.

Below are the main differences between cross-sectional and  longitudinal studies :

Sample group

A cross-sectional study will include several variables and sample groups, meaning it will collect data for all the different sample groups at once. However, in longitudinal studies, the same groups with similar variables can be observed repeatedly.

Cross-sectional studies are usually cheaper to conduct than longitudinal studies, so they are ideal if you have a limited budget.

Participants in longitudinal studies have to commit for an extended period, which significantly increases costs. Cross-sectional studies, on the other hand, are shorter and require less effort.

Data is collected only once in cross-sectional research. In contrast, longitudinal research takes considerable time because data is collected across numerous periods (potentially decades).

Researchers don’t necessarily seek causation in longitudinal research. This means the data will lack context regarding previous participant behavior.

Longitudinal research, on the other hand, clearly shows how data evolves. This means you can infer cause-and-effect relationships.

• How to perform a cross-sectional study

You will need to follow these steps to conduct a cross-sectional study:

Formulate research questions and hypotheses . You will also need to identify your target population at this stage.

Design the research . You will need to leverage observation rather than experiments when collecting data. However, you can always use non-experimental techniques such as questionnaires or surveys. As a result, this type of research will let you collect both quantitative and qualitative data .

Conduct the research . You can collect your data or assemble it from another source. In most instances, governments make cross-sectional datasets available to the public (through censuses) that can help with your research. The World Bank and World Health Organization also provide cross-sectional datasets on their websites.

Analyze the data . Data analysis will depend on the type of data collection method you use.

• Advantages and disadvantages of cross-sectional studies

Are you considering whether a cross-sectional study is an ideal approach for your next research? It’s an efficient and effective way to gather data. Check out some of the key advantages and disadvantages of cross-sectional studies.

Advantages of cross-sectional research

Quick to conduct

Multiple outcomes are researched at once

Relatively inexpensive

Used as a basis for further research

Researchers gather all variables at a single point in time

It’s possible to measure the prevalence of all factors

Ideal for descriptive analysis

Disadvantages of cross-sectional research

Preventing other variables from influencing the study is challenging

Researchers cannot infer cause-and-effect relationships

Requires large, heterogeneous samples, which increases the chances of sampling bias

The select population and period may not be representative

• When to use a cross-sectional design

Cross-sectional studies are useful when:

You need answers to questions regarding the prevalence and incidence of a situation, belief, or condition.

Establishing the norm in a particular demographic at a specified time. For instance, what is the average age for completing studies in Dallas?

Justifying the need to conduct further research on a specific topic. With cross-sectional research, you can infer a correlation without determining a direct cause. This makes it easier to justify conducting other investigations.

• The bottom line

A cross-sectional study is essential when researching the prevailing characteristics in a given population at a single point in time. Cross-sectional studies are often used to analyze demography, financial reports, and election polls. You could also use them in medical research or when building a marketing strategy, for instance.

Are cross-sectional studies quantitative or qualitative?

Cross-sectional research can be both qualitative and quantitative.

Do cross-sectional studies have control groups?

Cross-sectional studies don’t need a control group as the selected population is not based on exposure.

What are the limitations of cross-sectional studies?

Limitations of cross-sectional studies include the inability to make causal inferences, study rare illnesses, and access incidence. Researchers select a subject sample from a large and heterogeneous population.

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Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations

Affiliations.

• 1 Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH. Electronic address: [email protected].
• 2 Department of Respiratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China.
• PMID: 32658654
• DOI: 10.1016/j.chest.2020.03.012

Cross-sectional studies are observational studies that analyze data from a population at a single point in time. They are often used to measure the prevalence of health outcomes, understand determinants of health, and describe features of a population. Unlike other types of observational studies, cross-sectional studies do not follow individuals up over time. They are usually inexpensive and easy to conduct. They are useful for establishing preliminary evidence in planning a future advanced study. This article reviews the essential characteristics, describes strengths and weaknesses, discusses methodological issues, and gives our recommendations on design and statistical analysis for cross-sectional studies in pulmonary and critical care medicine. A list of considerations for reviewers is also provided.

Keywords: bias; confounding; cross-sectional studies; prevalence; sampling.

Copyright © 2020 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

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• Cross-Sectional Studies / standards
• Cross-Sectional Studies / statistics & numerical data*
• Guidelines as Topic

Cross-Sectional Research Designs

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Baltes, P. B. (1968). Longitudinal and cross-sectional sequences in the study of age and generation effects. Human Development, 11 , 145–171.

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Baltes, P. B., Reese, H. W., & Nesselroade, J. R. (1988). Life-span developmental psychology: Introduction to research methods (2nd ed.). Hillsdale: Erlbaum.

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Schaie, K. W. (1994). Developmental designs revisited. In S. H. Cohen & H. W. Reese (Eds.), Life-span developmental psychology (pp. 45–64). Hillsdale: Erlbaum.

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Sleep quality and psychological health in patients with pelvic and acetabulum fractures: a cross-sectional study

• Khan Akhtar Ali   ORCID: orcid.org/0000-0003-0463-197X 1 ,
• LingXiao He 1 ,
• Wenkai Li 1 ,
• Weikai Zhang 1 &
• Hui Huang 1  

BMC Geriatrics volume  24 , Article number:  314 ( 2024 ) Cite this article

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Background and objectives

It is known that difficulty sleeping after a fracture can have negative effects on both mental and physical health and may prolong the recovery process. The objective of this study is to explore how sleep quality and psychological health are linked in patients with pelvic and acetabulum fractures.

A study was conducted on 265 patients between 2018 and 2022 who had suffered pelvic and acetabulum fractures. The study examined various factors, including age, gender, cause of injury, post-operative complications, and injury severity. The study employed ordinal logistic regression to examine the relationship between various pelvic fractures and seven subscales of the Majeed Pelvic Score (MPS), as well as the Sleep Disorder Questionnaire (SDQ) and Beck Depression Inventory (BDI). The study focused on the postoperative outcome one year after surgery, and each patient was assessed at the one-year mark after surgical intervention. Additionally, the study evaluated the functional outcome, sleep quality, and psychological disorders of the patients.

From 2018 to 2022, a total of 216 patients suffered from pelvic and acetabulum fractures. Among them, 6.6% experienced borderline clinical depression, and 45.2% reported mild mood disturbances. Anxiety was found to be mild to moderate in 46% of Tile C and posterior acetabulum wall fracture patients. About 24.8% of patients reported insomnia, while 23.1% reported sleep movement disorders. However, no significant correlation was found between fracture types and sleep disorders. The mean Majeed pelvic score (MPS) was 89.68.

Conclusions

Patients with pelvic and acetabular fractures typically experience functional improvement, but may also be at increased risk for insomnia and sleep movement disorders, particularly for certain types of fractures. Psychological well-being varies between fracture groups, with signs of borderline clinical depression observed in some cases. However, anxiety levels do not appear to be significantly correlated with pelvic and acetabular fractures.

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Introduction

The term “pelvic fracture” refers to both acetabular and pelvic ring fractures originating from high- and low-energy trauma. In young patients, these fractures typically result from high-energy trauma, but in elderly patients, they more frequently result from low-energy trauma [ 1 , 2 ]. Pelvic and acetabular trauma frequently manifests as polytrauma and may result in fatal hemodynamic instability [ 3 ]. After a pelvis fracture, the functional result and health-related quality of life (HRQOL) are not excellent; most patients do not resume their previous activities [ 4 ]. After an acute orthopedic injury, nearly one-third of patients experience depression, and more than one-quarter experience PTSD [ 5 ]. After pelvic ring fractures, chronic posttraumatic pelvic pain hurts concerns with quality of life [ 6 ]. Patients with orthopedic trauma injuries are significantly more likely to experience psychological distress [ 7 ]. In addition to these physiologic studies, posttraumatic psychological issues like posttraumatic stress disorder, chronic pain, depression, and/or anxiety seem to play a role in the negative functional outcomes [ 7 , 8 , 9 ]. Patients with depression and those with chronic pelvic pain have various sleep patterns [ 10 ]. A significant number of patients with post-traumatic stress disorder (PTSD)following injury also have depression, according to studies. One study found that four months after injury, 16 of 37 PTSD patients also had major depression. Patients who have both major depression and PTSD simultaneously seem to experience more problems than those who only have one of the two conditions. In comparison to people who only have PTSD or major depression, Shalev et al. discovered that patients with comorbid PTSD and depression reported more symptoms, felt more distress from those symptoms, and performed worse in daily life [ 11 , 12 ]. Orthopedic trauma patients often suffer from anxiety and depression, which can lead to negative surgical outcomes. Psychological distress, chronic pain, and traumatic limb amputation also contribute to adverse mental health outcomes [ 13 ].Depression is a widespread mental health disorder that affects a significant portion of the world’s population, leading to a heavy burden on society [ 14 , 15 , 16 ]. Following orthopedic trauma, the patient psychological status has received less attention [ 17 ]. Sleep is crucial for rest, recovery, information processing, and memory consolidation. After surgery, sleep deprivation can cause changes in the sleep cycle, such as the absence of rapid eye movement (REM) due to pain caused by inflammation. Surgical trauma may also lead to immunosuppression, which increases the risk of infection [ 18 , 19 , 20 , 21 ]. High levels of the cytokine IL-6 at night can cause disrupted and superficial sleep, while low levels are linked to deep and restful sleep. Surgical inflammation may also contribute to postoperative sleep disturbances, with major surgeries leading to the most significant disruption [ 22 , 23 , 24 ]This cross-sectional study aims to investigate how sleep quality and psychological health affect patients with pelvic and acetabulum fractures. The study evaluates the functional outcome, sleep quality, and psychological disorders of the patients one year after surgical intervention. The study also examines the relationship between various pelvic fractures and seven subscales of the Majeed Pelvic Score (MPS), as well as the Sleep Disorder Questionnaire (SDQ) and Beck Depression Inventory (BDI). The hypothesis is that patients with pelvic and acetabular fractures typically experience functional improvement but may also be at an increased risk for insomnia and sleep movement disorders, particularly for certain types of fractures. psych logicalizes that psychological well-being varies between fracture groups, with signs of borderline clinical depression observed in some cases.

Patients and methods

The hospital database initially had 265 people, but due to some unfortunate circumstances, contact information for 48 patients was lost or modified, and one patient passed away. This left us with only 216 patients, or 82% of the initial group, who were eligible for the study. We contacted these patients and asked them some questions before starting the interview using related questionnaires. To ensure a controlled study, we confirmed whether the patients were currently taking any anti-psychotic drugs or NSAIDs. We also asked if they had any psychological or sleep disorders problems prior to their pelvic fractures. After selecting the patients who met the criteria, we interviewed them using questionnaires to assess sleep problems, anxiety, and depression. 82% of patients completed the questionnaires with no significant difference in age, gender, Tile categorization or injury severity score (ISS) compared to those not contacted. The data of the remaining 18% were analyzed retrospectively due to lost or changed contact information, including age, gender, tile categorization, and ISS. The study evaluated various factors, including age, gender, injury severity, and post-operative complications, and assessed the functional outcome, sleep quality, and psychological disorders of the patients. The study aimed to clarify the impact of sleep and psychiatric disorders on patients with pelvic and acetabular fractures. To evaluate the functional outcome of pelvic fractures and acetabulum and to correlate them with sleep and psychological disorders, we used the Majeed pelvic score. To minimize the negative impact of concurrent injuries on the health of patients, we assessed them one year after surgical intervention for pelvic and acetabulum fractures. To ensure accuracy, patients with severe and multiple injuries related to pelvic trauma were excluded from the study based on our criteria. The Majeed Pelvic Score (MPS) is a widely used health-related quality of life instrument that assesses pain, work, sitting, sexual intercourse, standing, walking aids, unaided gait, and walking distance. The MPS score ranges from 16 (worst health state) to 100 (best health state) [ 25 , 26 , 27 ]. Baker et al. published the Injury Severity Score (ISS) in 1974, which describes the severity and death probability in patients with multiple injuries. The ISS compares injuries and outcomes retrospectively and is easily accessible to clinicians and researchers. The ISS has been the standard for trauma scoring for over 20 years [ 28 ]. Tile classification: Tile developed a classification system for pelvis bone fractures based on the observed injury mechanisms, which include anterior-posterior compression, lateral compression, and vertical shear. His classification shows that the mortality rates increase from type A to type C fractures, with the highest mortality rates occurring after C2 injuries. Additionally, B3 fractures have comparable mortality rates to C-type fractures [ 29 ]. The Judet and Letournel classification system is widely used by orthopedic surgeons to determine the appropriate surgical approach for acetabular fractures. Certain fracture patterns in the classification have worse prognostic outcomes [ 30 ].

Inclusion exclusion criteria

The study included cases of pelvic and acetabulum fractures classified according to Tile and Judet and Letournel classification, which included Tile A, B, C, Ant wall and column, Posterior wall, Transverse and Both columns’ fractures. However, the study excluded patients with multiple severe trauma, spinal cord injuries, patients who were conservatively treated for pelvic trauma, patients under the age of 18, chain smokers and alcoholics. Additionally, patients who were on anti-psychotic or anti-epileptic drugs, had neurological disorders, were using Parkinson or Anti-Parkinsonian drugs, and those on sleeping pills and antidepressive drugs were also excluded from this study.(Fig.  1 ).

Study flow chart

Sleeping disorders questionnaire

According to the International Classification of Sleeping Disorders (ICSD3), sleep disorders are classified mainly into parasomnias and insomnia which are further divided into sub types [ 31 ] sleep disorders classification. (Fig.  2 ). SLEEP DISORDERS QUESTIONNAIRE: This questionnaire is designed to assist doctors in screening for insomnia and identifying potential sleep disorders. However, a more comprehensive clinical assessment is required, and a referral to a specialist may be necessary. We have utilized questions 1–13, as shown in Fig.  3 . The Diagnostic Domains are as follows: (1) Insomnia: Q1-5. (2) Psychiatric Disorders: Q6-9. (3) Circadian Rhythm Disorder: Q10. (4) Movement disorders: Q11-12. (5) Parasomnias: Q13. (detailed questionnaire in supplementary files).

International classification of sleep disorders (ICSD3)

Sleep disorders questionaries

The zung self-rating anxiety scale

SAS is an assessment tool comprising of 20 questions aimed at evaluating anxiety levels in individuals [ 32 ]. The questions are categorized into four groups based on the experienced symptoms: cognitive, autonomic, motor, and central nervous system. It is a self-reporting test, which means that the individual completes it themselves. The scoring interpretations are as follows: 20–44 Normal Range, 45–59 Mild to Moderate Anxiety Levels, 60–74 Marked Severe Anxiety Levels, and 75–80 Extreme Anxiety Levels. (detailed questionnaire in supplementary files). Lindsay and Michie published a study in 1988 in the Journal of Mental Deficiency Research (32, 485–490), where they adopted the Zung Self-Rating Anxiety Scale (SAS) for individuals with intellectual disabilities (ID) [ 33 ]. Research has shown that individuals with intellectual disabilities (ID) often experience high levels of anxiety [ 34 , 35 , 36 ]. The SAS-ID can be useful for research and clinical purposes, such as assessing treatment effectiveness over time [ 37 ].

The Beck depression inventory

(BDI, BDI-1 A, BI-II), created by Aaron T. Beck, is a 21-question choice self; one of the most widely used psychometric tests for measuring the severity of depression [ 38 ] 0.0–9: Indicates minimal depression,10–18: Indicates mild depression,19–29: Indicates moderate depression,30–63: Indicates severe. (detailed questionnaire in supplementary files). The Beck Depression Inventory (BDI) is a popular self-assessment tool to measure depression. It consists of 21 items and has been used in over 7,000 studies worldwide. The BDI was first proposed by Beck et al. and has undergone two major revisions: the BDI-IA in 1978 and the BDI-II in 1996 [ 39 , 40 , 41 , 42 , 43 ]. The BDI-II is a reliable and cost-effective psychometric tool that can distinguish between depressed and non-depressed individuals. It has improved validity, making it suitable for measuring depression severity in research and clinical settings worldwide [ 44 ].

Statistical analysis

The sample size for this study was estimated using the following formula: n = Zα/2^2 x P (1-P)/d^2. Based on a previous studies [ 45 , 46 , 47 , 48 , 49 ], the prevalence of sleep disorders in patients with orthopedic trauma was estimated to be 40%. With a margin of error of 5%, a confidence level of 95%, and a non-response rate of 10%, the estimated sample size was 265.

A post-hoc power analysis was conducted to evaluate the statistical power of the study. The analysis showed that the study had a power of 80% to detect a significant difference in sleep disorders between different fracture types, assuming a significance level of 0.05.

In this study, several statistical analysis models and methods were used to examine the relationship between various factors and the outcomes of interest. The study employed ordinal logistic regression analysis to examine the relationship between various pelvic fractures and seven subscales of the Majeed Pelvic Score (MPS), as well as the Sleep Disorder Questionnaire (SDQ) and Beck Depression Inventory (BDI). The purpose of the ordinal logistic regression analysis was to determine whether there was a significant relationship between the independent variables (such as age, gender, cause of injury, post-operative complications, and injury severity) and the dependent variables (the MPS subscales, SDQ, and BDI).

Descriptive statistics were used to summarize the mean scores for each domain of the MPS questionnaire. The mean scores were calculated based on the responses of the patients to the questions in each domain of the MPS questionnaire.

Chi-square tests were used to determine whether there were significant differences in the prevalence of sleep disorders and psychiatric disorders among the different categories of pelvic fractures. The study used correlation analysis to examine the relationship between different factors, such as the correlation between the MPS scores and the SDQ and BDI scores.

In summary, the statistical analysis models and methods used in this study included ordinal logistic regression analysis, descriptive statistics, chi-square tests, and correlation analysis. These methods were used to examine the relationship between various factors and the outcomes of interest and to determine whether there were significant differences among the different categories of pelvic fractures. The results are presented as β-coefficients (B) with 95% CIs. All statistical analyses were performed using SPSS, version 22 (SPSS Inc, Chicago, IL, USA), with consultation from statistical experts. A p-value of 0.05 was considered statistically significant. Future studies with larger sample sizes and more robust statistical power are needed to further investigate the relationship between pelvic and acetabular fractures, sleep disorders, and psychological well-being.

Demographic data

This table shows insights on patients with pelvic fractures and the associated risk factors. The study evaluated 216 patients with different types of pelvic fractures based on age, gender, ISS, and mechanism of injury. Most patients were male, with car accidents and falls from heights being the most common. MIPPO was the most common surgical approach used. The included patients’ average age was 48.24 years (SD 14.98), and the average ISS was 15.37 years (SD 8.07). Sixteen acetabulum fractures and 148 pelvic fractures (Tiles a, b, and c) were treated using the Mippo method. Four transverse acetabulum fractures were treated using the (MIPPO + Kocher-Langenbeck) approach, 28 posterior acetabulum wall fractures were treated using the KL approach, two pelvic Tile c-type fractures, seven pelvic and eight acetabulum fractures were treated using the ilioinguinal approach. 20 patients (9%) who arrived in the ER were hemodynamically unstable (shock class 3 or higher). Complex fracture patients had a markedly higher ISS and shock class and were more frequently operated on and were more frequently treated with operation/surgery). In 58% of patients, concurrent injuries were found. In 44 patients (32.4%), there were concurrent injuries to the lower extremities. 47 patients (34.5%) had neurological damage, of whom 28 (20.6%) had severe head trauma. Nine patients (6.6%) had focal neurological deficits. One Tile B patient had a urethral injury. The average follow-up time was two years, ranging from four to one. 39 patients diagnosed with deep vein thrombosis (DVT) due to pelvic fractures, and 8 patients with acetabulum fractures were treated according to hospital protocols for DVT, and all patients were stabilized. Two patients in Tile A, three in Tile B, and one in Tile C, as well as three patients with posterior wall fracture, reported experiencing numbness or irritation in their lower limbs. The Mippo Technique was used to treat Tiles A, B, and C, which involves a pelvic incision that can affect the skin and thigh. Posterior wall fracture and its surgical treatment can lead to sciatic nerve damage. Most of the symptoms went away with functional training or six months after surgery. However, one patient from Tile B reported experiencing some wound irritation one year after the surgery, but it had improved compared to the symptoms experienced six months after the surgery. (Patient characteristics are listed in (Table  1 ).

Sleep disorders results

According to the sleep disorder questionnaires, neither somatization nor circadian rhythm disorder was noted in any pelvic or acetabulum fractures group. The rate of Insomnia was relatively higher in Tile B 24 (40%), and posterior acetabulum wall fractures 6(16%). Out of the 17 patients with pelvic fractures, those with Tile C had a higher incidence of sleep movement disorders. Similarly, among the 18 patients with acetabulum fractures, those with a posterior acetabular wall had a higher likelihood of experiencing sleep movement disorders Table 4 .

Depression and anxiety results

The Table 4 provides information on various sleep and mental health measures such as the Sleep Disorder Questionnaire (SDQ), Beck Depression Inventory (BDI), and Zung Self-Rating Anxiety Scale, along with some demographic information about the participants in each group such as gender and number of individuals. The results are presented differently depending on the measure being reported. For instance, the SDQ provides the percentages of participants with insomnia, psychiatric disorders, and movement disorders, while the BDI and Zung Self-Rating Anxiety Scale report mean scores and the percentage of individuals with different levels of depression or anxiety severity. The percentage of participants reporting insomnia and psychiatric disorders is relatively high across all groups, ranging from 16 to 40% and 5–10%, respectively. Some groups have higher mean scores on the BDI and Zung Self-Rating Anxiety Scale, indicating higher levels of depression or anxiety compared to other groups. The normal percentage of BDI and Zung Self-Rating Anxiety Scale scores is high for most of the groups, implying that many participants in each group are not experiencing significant levels of depression or anxiety. The contingency table reveals that the distributions of SDQ and Zung are different when there are different fracture groups, with respective p-values of (X2 = 29.255, p0.05), and (X2 = 25.958, p0.05). While there was no distinguishable correlation between anxiety and pelvic and acetabular fractures, Tile B and posterior acetabular wall fractures were more likely to have mild mood disturbance. Transverse acetabulum fracture (27%) and Tile A, B, and C (9%, 7%, and 10%), as well as the posterior wall (11%) all showed signs of borderline clinical depression.The study also investigated the relationship between various pelvic fractures and sleep disorders using the SDQ and BDI. The logistic regression was binary, with the outcome variable being whether or not the patient had a sleep disorder or depression. Table  4 shows the Sleep Disorder Questionnaire (SDQ), Beck Depression Inventory (BDI), and Zung Self-Rating Anxiety Scale.

MPS functional outcome scores

The table presents the results of an Ordinal logistic regression analysis of Majeed pelvic score (MPS) with various factors such as pain, work, sitting, sexual intercourse, standing, gait unaided, walking distance, age, sex, ISS, and fracture type. The table is divided into two sections with the first section presenting the regression coefficients, 95% confidence intervals, and p-values for pain, work, sitting, and sexual intercourse for each of the factors. The second section presents the regression coefficients, 95% confidence intervals, and p-values for standing, gait unaided, walking distance, and MPS score for each of the factors. The fracture type is further divided into four categories, Tile A, Tile B, Tile C, and Acetabular, and the table provides the regression coefficients, 95% confidence intervals, and p-values for each fracture type. The table compares the various fracture types and their impact on pain, work, sitting, sexual intercourse, standing, gait unaided, walking distance, and MPS score. The study found that fractures classified as Tile A, B, and C are associated with lower mobility issues, self-care problems, pain and discomfort scores, and fewer problems with usual activity when compared to acetabular fractures. Specifically, Tile B fractures were found to have a significantly lower rate of usual activity issues compared to acetabular fractures. Additionally, after surgery, a high percentage of patients with each type of fracture were able to return to work − 88% for Tile A, 80% for Tile B, and 86% for Tile C. Overall, the study suggests that Tile A, B, and C fractures may have better outcomes in terms of mobility, self-care, and pain compared to acetabular fractures. The beta coefficients for mobility, self-care, usual activity, and pain and discomfort were − 1.448 (95%CI: -2.221-0.674), -1.259 (95%CI: -2.191-0.326), -1.020 (95%CI: -1.795-0.244), and − 1.037 (95% CI: -1.771-0.303), respectively, for Tile A fractures. The corresponding beta coefficients for Tile B fractures were − 2.545 (95%CI: -3.511-1.579), -1.828 (95%CI: -2.865-0.792), -1.020 (95%CI: -1.795-0.244), and − 1.641 (95% CI: -2.402-0.879), respectively. For Tile C fractures, the beta coefficients were − 1.997 (95%CI: -3.049-0.945), -1.496 (95%CI: -2.648-0.343), and − 1.332 (95% CI: -2.243-0.420) for mobility, self-care, and pain and discomfort, respectively. Each of the 216 patients finished the MPS, and the median score was 89.68 ± 10.04. After an hour of walking, more than 50% of patients with every fracture type displayed a slight limp. However, following surgery, the majority of patients were able to return to their jobs, with 88% of Tile A, 80% of Tile B, and 86% of Tile C patients successfully returning to their jobs. The confidence intervals provide a range of plausible values for the true effect size or beta coefficient, which can help us assess the level of uncertainty in the results.

According to our findings, a positive relationship exists between age and the degree to which pain, work, sexual activity, and walking distance are affected. At the same time, there is a negative relationship between age and the total MPS score. The average MPS scores did not significantly differ between the various types of fractures. dummy variables were used in the logistic regression analysis to represent the different types of pelvic fractures. (Majeed Pelvic Score (MPS) questionnaire results and employed ordinal logistic regression of MPS. Tables ( 2 and 4 ).

Our study found that many patients with Tile b c and posterior acetabulum wall fractures experienced mild mood disturbances. Compare to study of Martin MP et al. higher levels of depression and anxiety symptoms were associated with poorer functional outcomes in patients with Tile C pelvic injuries [ 50 ] but we didn’t find though no severe depression, anxiety or somatization disorders were observed, some types of fractures may be associated with borderline clinical depression. In a diverse cohort of orthopedic trauma patients, clinically relevant depression was prevalent at a rate of close to 45%. Depression and overall disability have a strong relationship. The risk of depression may also rise in the presence of an open fracture [ 51 ]. Chronic pelvic pain is unknowingly linked to sleep issues, depression, and anxiety [ 52 ].In our study, we found that Insomnia was more common in Tile B 24, affecting 40% of the patients. We also observed that sleep movement disorders were more frequent in patients with Tile C pelvic fractures. Similarly, patients with posterior acetabular wall fractures were more likely to experience sleep movement disorders. Our study results were similar to LU K et al’s regarding sleeping disturbance, but with gender specificity and time difference in their study results. Our study’s results were taken one year postoperative, and more than half of all patients still reported having trouble sleeping. LU K’s study, on the other hand, found that sleep disturbances were more likely to affect women than men, but their results were taken three months after surgery [ 49 ]. Women may be more susceptible to insomnia after trauma, with a strong association found among women but not men (Nicole A. et al.) [ 53 ]. In a study by Matthew C Swann et al. their findings suggest that sleep disturbance is both highly prevalent in Pittsburgh sleep quality index (86%) and severe (54.6% ) in patients recovering from a traumatic orthopedic injury [ 47 ]. Stephen Breazeale et al. discovered four symptom cluster profiles that they categorized as Physical Symptoms Only, Mild, Moderate, and Severe Psychological Distress in orthopedic trauma patients. Pelvic injuries can cause long-lasting physical pain and mental health issues. Participants in a study conducted by Kenleigh R reported higher levels of PTSD, depression, and problematic alcohol use one year after injury [ 54 ]. A study by Zhen Hong et al. found that 28.20% of 468 patients with traumatic fractures had acute stress disorder (ASD) [ 55 ].

Another study by Shalev et al. (2017) explored the relationship between comorbid PTSD and depression on psychological well-being and functional outcomes following orthopedic trauma. The study found that patients with comorbid PTSD and depression experienced more symptoms, felt more distress from those symptoms, and performed worse in daily life than patients with only one of these conditions. While our study did not find a significant correlation between anxiety and pelvic and acetabular fractures, the study by Shalev et al. highlights the importance of exploring comorbid conditions in orthopedic trauma patients [ 56 ]. When compared to acetabular fracture, Tile B fractures are less likely to cause problems with work, sitting, and sexual activity According to our findings, there is a positive association between age and the degree to which a person experiences pain, works, has sex, and walks a distance, whereas there is a negative correlation between age and the total MPS score. Studies have shown that age-related changes in the body, such as hormonal changes and decreased muscle mass, can lead to these issues [ 57 ]. Chronic pain is a common health problem in older adults, with prevalence rates ranging from 25–76% [ 58 ]. As people age, their body tissues may become less resilient, leading to increased risk of injury and chronic pain. Chronic pain can limit physical activity and impair mobility, making it difficult for older adults to engage in work or leisure activities [ 59 ].Reduced mobility and physical activity are also common in older adults, with studies showing that physical activity levels decline with age [ 60 ]. Sexual dysfunction is another issue that becomes more common as people age, with studies indicating that up to 40% of older adults experience sexual problems [ 61 ]. Patients with pelvic ring injuries have reasonable long-term physical functioning and quality of life, but it is significantly lower compared to other groups in the general population [ 62 ]. Screening injured patients and providing timely intervention for posttraumatic stress disorder (PTSD) and depression could improve outcomes and quality of life [ 63 ].Our study revealed that patients with pelvic fractures had better results in the MPS scores compared to those with acetabular fractures. The pelvic fracture group demonstrated superior performance in walking, distances, work, sitting, and sexual activities compared to the acetabular fracture group. These findings suggest that pelvic fractures may have a better prognosis and improved functional outcomes compared to acetabular fractures.As per our earlier results, patients who had acetabular fractures recorded lower scores on the Majeed Pelvic Score (MPS) in comparison to patients with other types of pelvic fractures. The complex anatomy of the acetabulum and the difficulty involved in surgical repair may contribute to worse outcomes. Additionally, acetabular fractures are often caused by high-energy trauma and can be associated with other injuries or complications [ 64 ]. Some people may have negative reactions to surgical implants, such as allergies to metals, methacrylate’s, and antibiotics those with a history of material reactions should undergo pre-implant testing to explore alternative options [ 65 ]. In our study some individuals reported numbness and irritation in the implant area and their thighs. Identification of the root cause and appropriate treatment to alleviate any numbness or irritation is crucial. A study by Katherine F et al. found that sexual function was notably reduced after experiencing a pelvic fracture, with a significant decrease in the quality of life. Sexual dysfunction is an independent risk factor for decreased quality of life following the injury [ 66 ]. However, many of the patients included in our study did not answer questions related to sexual activities, which is considered an essential factor for of life as included in our questionnaires.

Limitations

Due to the small number of patients with acetabulum subgroup fractures and the fact that most patients’ information was either missing or inaccurate, we did not perform a comparative study of various approaches to treating pelvic and acetabulum fractures. The questionnaires did not address the socioeconomic problems that have a significant impact on people’s psychological well-being. The concurrent injuries were present in 58% of the patients, which could act as a major confounding factor and may have influenced the results and conclusions of the study. Further comparative studies are needed to confirm the psychological and health-related issues and reduction quality in pelvic and acetabulum fractures treated with different approaches. Most middle-aged patients and patients over 60 or 70 years old didn’t respond to sex questions due to the culture and privacy.

Our study found that 80% of patients showed better mobility and comfort in performing daily activities after surgical intervention for pelvic and acetabular fractures. However, older and middle-aged patients may experience anxiety and depression. Also, certain types of fractures were associated with an increased risk for insomnia and sleep movement disorders. Pelvic and acetabular fracture patients may experience borderline clinical depression. Anxiety levels do not seem to be significantly associated with these fractures. Understanding these psychological challenges can aid medical professionals in creating personalized treatment plans. Our study highlights the importance of a multidisciplinary approach to care for orthopedic trauma patients with pelvic injuries. Psychological screening and intervention should be integrated into their recovery. It is especially important to monitor patients with posterior acetabulum wall fractures and Tile-C pelvic fractures.

Data availability

Researchers who make a valid request to the corresponding author will be given access to the data.

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Ali, K.A., He, L., Li, W. et al. Sleep quality and psychological health in patients with pelvic and acetabulum fractures: a cross-sectional study. BMC Geriatr 24 , 314 (2024). https://doi.org/10.1186/s12877-024-04929-y

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Oral health knowledge, attitudes and practices of primary healthcare workers of Lucknow district: A cross-sectional study

Omveer singh.

1 Department of Public Health Dentistry, Career Dental College, Lucknow, UP, India

Devina Pradhan

2 Department of Public Health Dentistry, Rama Dental College, Hospital and Research Centre Kanpur, UP, India

Lokesh Sharma

3 Department of Public Health Dentistry, Sardar Patel Post Graduate Institute of Dental and Medical Sciences, Lucknow, UP, India

Rahul Srivastava

4 Department of Oral Medicine and Radiology, Rama Dental College, Hospital and Research Centre Kanpur, UP, India

Introduction:

Oral diseases are considered a public health problem due to their high prevalence. It is the primary concern of oral health educators to impart positive oral health knowledge and behavior in the society. Health workers’ knowledge, attitude, and practices (KAP) toward oral health to a great extent influence the community as they can extend health education at the first contact in the community. Thus, the aim of the study was to assess the knowledge, attitude, and practices of health care workers.

Material and Methods:

A descriptive cross-sectional survey was designed to assess the knowledge, attitude, and practices towards oral health among health care workers. The study was conducted among health care workers aged between 20 and 60 years working in Primary Healthcare Centres and Community Healthcare Centres of Lucknow district. Informed consent was obtained from health care workers before the start of the study. The data were collected via a predesigned and pretested questionnaire. The data were analyzed using IBM SPSS Statistics-version 21.

The results of the study showed that 70.2% of the respondents ever visited the dentist due to somereason of which 19.2% visited once in a year. In addition, 38.9% of the respondents were daily smokers of which the majority belonged to the age group 20–40 years. A total of 63.9% of the respondents were daily chewable tobacco users, and 12.4% were routine users of alcohol.

Conclusion:

The present study gives a brief insight into the oral health knowledge, attitude, and practices of health care workers which were of fair degree.

Introduction

Health is one of the most valuable assets one can possess. Health continues to be a neglected entity despite continuous efforts for health promotion worldwide. “If wealth is lost nothing is lost but if health is lost everything is lost.” Always humans take health for granted, and its value is understood when it is lost.[ 1 ]

Oral health is now recognized as equally important to general health. Oral health may be defined as a standard of health of the oral and related tissues which enables an individual to eat, speak, and socialize without active disease, discomfort, or embarrassment and which contributes to general well-being. Oral diseases can be considered a public health problem due to their high prevalence and significant social impact.[ 2 ]

The oral tissue forms an integral part of the human and is extremely vulnerable to disease as it is in intimate relation with the external environment and is constantly subjected to mechanical, chemical, and bacterial interactions. The most common oral health complaints across the world are tooth decay, gum problems, bad breath, etc.[ 3 ]

It is the primary concern of oral health educators to impart positive oral health knowledge and behavior in the society. This knowledge is usually derived from information and the information when believed translates into action. Behavior is the outcome when that action is sustained. However, only a weak relationship exists between knowledge and behavior. Nonetheless, there are reports that there is an association between increased knowledge and better oral health.[ 4 ] Thus, primary health care physicians play an important role in achieving this as they are the primary level providers and can help people with counseling and motivation.

Several factors may affect the oral health behavior of an individual, among which are, acquisition of Western education, values, and cultures, etc., Oral diseases are related to behavior, and that the prevalence of dental caries and periodontal disease decreases with improvements in oral hygiene and a decrease in the consumption of sugar. In contrast to twice daily tooth brushing in Western countries, this behavior is lacking in developing nations.[ 4 ]

Oral health is concerned with maintaining the health of the craniofacial complex, the teeth, and gums as well as the tissue of the face and head that surrounds the mouth. Loss of teeth and deterioration of oral tissue substantially reduce the quality of life.[ 5 ]

Prevalence and severity of periodontal diseases vary from individual to individual and are affected by age, gender, education, and socioeconomic status. Most chronic periodontal pathologies are directly related to lifestyle and are considered a major public health problem due to their high prevalence and significant social impact. Chronic periodontitis typically leads to tooth loss, and in some cases has physical, emotional, and economic impacts – physical appearance and diet are often worsened, and the patterns of daily life and social relations are often negatively affected. These impacts lead in turn to reduced welfare and quality of life.[ 6 ]

Tobacco use has been directly implicated in numerous oral morbidities including oral cancer, stomatitis nicotina, oral leukoplakia, periodontitis, gingival recession, and soft tissue changes. Hence, we are in a unique position to provide salient, proximal information about tobacco use and oral health, which can motivate tobacco users to quit.[ 7 ]

Knowledge of oral health is considered to be a prerequisite for health-related behavior. It has been shown that the rural Indian community, which constitutes more than 70% of the Indian population has a low level of oral health awareness and practice when compared to urban. Health workers’ knowledge, attitude, and practices (KAP) toward oral health to a great extent influence the community as they can extend health education at the first contact in the community and hence should possess good oral health. Literature on the oral health knowledge and oral hygiene status of health care professionals of India is almost nonexistent.[ 2 ]

Thus, this study was carried out to assess the knowledge, attitude, and practices of health care workers in Lucknow district.

Materials and Methods

A descriptive cross-sectional survey was designed to assess the knowledge, attitude, and practices towards oral health among health care workers. The study was conducted among health care workers aged between 20 and 60 years working in PHCs and CHCs of Lucknow district. Before the start of the study, a list of PHCs, CHCs, and sub-centers located within the Lucknow was obtained from the office of Chief Medical Officer (CMO), and the necessary permission was taken from Director General Hospital and Health services from the Ministry of Health government of Uttar Pradesh along with related government authorities and the heads of the health centers. The inclusion criteria were all the health care workers who were present on the day of the study, and the exclusion criteria were all the health care workers who were not present on the scheduled date of the study.

A pilot study was conducted using the proforma on 90 health care workers attending health center to check the validity of the questionnaire and operational feasibility of the study. Cronbach’s alpha was applied for the reliability of the questionnaire for assessing the knowledge on oral health problems as the questionnaire items were analyzed for difficulty in understanding, interpretation, and answering correctly without any difficulty. The same set of the questionnaire was asked to the same group of health care workers a week after the first administration of the questionnaire. These two sets of responses (i.e. the first and second administration) were then used in calculating the alpha coefficient for internal consistency which was found to be 0.84. No adjustments were found to be necessary. These workers were not part of the final study sample.

The formula for determining the size of the sample is:

N = 4pq/L 2

• p = prevalence
• L = allowance error
• N = 4 * 0.68 (1-0.68)/0.05 * 0.05 = 816

The calculation of sample size was performed to seek the results at a 95% confidence level. The allowable error taken was e = 0.05.

In the pilot study done, the prevalence was found to be 0.68 (prevalence of dental caries). The sample size was estimated as 816 which was rounded off to a final sample of 900.

Before the commencement of the study, ethical clearance was obtained from the institutional ethical committee of Sardar Patel Post Graduate Institute of Dental and Medical Sciences, Lucknow. Approvals were taken from the Director General Hospital and Ministry of health government of Uttar Pradesh. Informed consent was obtained from primary health care workers before the start of the study.

The data were collected via a predesigned and pretested questionnaire. The questionnaire consisted of three sections:

• The first section included gathering demographic details like name, age, the official address of Health center from the health care workers.
• In the second section, the socio-economic status (Kuppuswamy Socio- Economic Status Scale, Revised for 2014) was reported.
• In the third section, the health care workers were interviewed using the WHO Oral Health Survey 1997 Questionnaire.

The data were analyzed using IBM SPSS Statistics-version 21 (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp.) Descriptive statistics included the calculation of percentages, mean, median, and standard deviation. The data distribution was assessed for normality using the Shapiro-Wilk test. Associations between the dependent and independent variables of categorical (discrete) type were tested by Chi-square (χ 2 ) test. All values were considered statistically significant for a value of P ≤ 0.05.

Table 1 : shows the majority of the respondents were aged 20–40 years (54.4%) followed by 41–60 years (44.1%) and were female (75.7%). The mean age of the respondents was 41.40 ± 10.44 years.

Age & Sex Distribution of Respondents

Table 2 : shows the majority of respondents were married (86.3%) and having family income greater than Rs 2000 and belonged to the – Upper Lower (55.0%) socioeconomic class. Regarding education, the majority of respondents were in almost equal proportion in High School (30.0%), ntermediate (33.6%), and graduate (30.2%). Regarding occupation, the majority of respondents were in almost equal proportion of semi-skilled (26.8%), skilled (29.7%), and semi-professional (24.0%). Most of the respondents belonged to the Upper lower (42.1%) and Lower middle (31.3%) socio-economic classes.

Distribution of Respondents according to Social Characteristics

Table 3 : shows the majority of the respondents have a frequency of cleaning teeth once a day (59.8%) followed by twice or more a day (29.8%). There were 5.1% of the respondents who cleaned their teeth only once a month. Also, the majority of the respondents used a toothbrush for cleaning of their teeth (97.4%) followed by miswak (6.2%). Some used thread (4.4%), wooden toothpicks (3.4%), and other methods (1.8%).

Distribution of Respondents According to Cleaning Practice of Teeth

Table 4 Depicts that 70.2% of the respondents ever visited a dentist due to some reason of which 19.2% visited once in a year to 2 years. A total of 29.8% of the respondents never visited the dentist.

Distribution of Respondents according to Visit to Dentist

Table 5 : Depicts that the majority of the participants (49.2%) consumed fruit every day (49.2%), and 50.2% of the participants consumed biscuits every day. Because of teeth/mouth problems most of the respondents faced various major and minor problems say difficulty in biting (48.9%), difficulty in chewing (57.4%), sleep interrupted (40.0%), and so on.

Eating Habit of Respondents

Table 6 : shows that 38.9% of the respondents were daily smokers of which the majority belonged to the age group 20–40 years. A total of 63.9% of the respondents were daily chewable tobacco users, and 12.4% of the respondents were routine users of alcohol.

Agewise dental fluorosis status of respondents

Table 7 : shows that only in 31.1% of the respondents, fluorosis was found to be normal. It was questionable in 35.1% of the respondents, very mild in 19.9%, and mild in 13.9% of the respondents.

Tobacco & Alcohol Habit of Respondents

Oral health which is an integral part of general health may be defined as “standard of health of the oral and related tissues which enables an individual to eat, speak, and socialize without active disease, discomfort, or embarrassment and which contributes to general well-being.”

The study was conducted on 900 health care workers, and a pilot study was performed on 90 subjects within the department before the start of the study to check the validity of the questionnaire and for operational feasibility of the study along with proper calibration and standardization.

In the present study, the age of the health care workers ranged from 20 to 60 years with a mean age of 41.4 ± 10.4yrs. A similar study was conducted by Aggnur M et al . (2014)[ 2 ] in that, the majority (53.6) of the health care workers in the study belonged to the age group between 20 and 40 years which is also similar to the study by Prathibha B et al . (2010).[ 8 ]

The total study subjects (900) were distributed according to Kuppuswamy’s classification of socioeconomic class within its three parameters i.e. education, occupation, and income. Distribution of the study subjects according to education revealed that 30.2% had completeded graduation/postgraduation, 33.6% had completeded intermediate, and 30.0% were educated till high school. Most of the study subjects (29.7%) were skilled workers, 26.8% subjects were semi-skilled, 30.0% were semi-professional. The majority of the participants (30.1%) had a monthly income between Rs. 17415 and 34829, 29.7% had monthly income between Rs. 13029 and 17414, and 22.3% had monthly income between Rs. 8707 and 13028, whereas the remaining 8.1% had between Rs. 5224 and 8706. The majority of the participants (42.1%) belonged to the upper lower class, 31.3% belonged to the lower middle class, whereas the remaining 15.2% belonged to the upper-middle class, wherein the education and occupation along with the total family income was recorded using Kuppuswamy’s SES scale that has been employed previously by Pushpanjali K et al . (2011).[ 9 ]

Health care workers who were more knowledgeable about the development of dental caries might also be more aware of the importance of oral health care. Therefore, the health care workers need to be educated about the importance of fluoridated toothpaste which has been similarly (96.7%) done in a study by Gangwar et al . (2014).[ 10 ]

In response to utilization of dental services which was measured by a visit to the dentist, more than half of health care workers (70.2%) visited a dentist which is similar to the study by Yadav K et al . (2016)[ 11 ] who found that 76.8% of the health care workers visited a dentist and also with the study by Baseer MA et al . (2012).[ 12 ] These findings are in contrast with the study by Kaur S et al . (2015)[ 13 ] which showed only 50% of the health care workers visited the dentist, with the study by Gangwar C et al . (2014).[ 10 ]

The distribution of responses to the reason for dental visits among health care workers was analyzed, and 46.8% of health care workers visited a dentist because of pain. The finding highlights the similar picture depicted by Yadav K et al . (2016)[ 11 ] who found that a high proportion (35%) of health care workers visited a dentist because of pain. These findings are in contrast with the study by Gangwar C et al . (2014).[ 10 ]

Surveys done in many parts of the world have found tooth brushing to be the best way to maintain oral health.[ 14 , 15 ] To prevent oral health problems, the American Dental Association recommends tooth brushing at least once a day.

This shows that respondents had lacked in practicing oral hygiene measures for their oral hygiene maintenance as it is believed that behavior and practices may be as a result of cultural or local influences.

The present study also showed that some of the health care workers (6.2%) used indigenous products instead of toothpaste and toothbrush as oral hygiene aid which is in contrast with the study by Baseer MA et al . (2012)[ 12 ] in which 1.2% health professionals used the products for cleaning their teeth.

An interesting piece of information from the participants reported that 63.9% of the health care workers had a habit of chewing tobacco everyday followed by 38.9% of the participants used smoking tobacco every day. These findings are similar to the study by Gangwar C et al . (2014)[ 10 ] which showed that 88.5% of the health care workers were unaware of the harmful effect of tobacco consumption. The findings of the current study are in contrast with the study by Aggnur M et al . (2014)[ 2 ] which showed that 98% of the health care workers were aware of the fact that excessive smoking can pre–dispose the person to oral and lung cancer.

This information showed that despite the best efforts of the Government of India to spread awareness on the harmful effects of tobacco in its various forms, there is ignorance among health care workers.[ 10 ]

It is difficult to fathom the causes for this rampant addictive habit. One of the reasons cited is the habit of paying wages on tobacco which might have got them hooked to the habit as well as they were unaware of the harmful effects of tobacco consumption and oral cancer. It is the poor knowledge of health care workers in the present study regarding the harmful effect of tobacco on health and in particular oral health.[ 10 ]

Thus, the findings of this present study also have imperative implications for public health and provide much-needed information to target wider interventions on a community level.

The present study gives a brief insight into the oral health knowledge, attitude, and practices of health care workers which were of fair degree. The study findings concluded that there was an evident gap between their knowledge and what they were really practicing.

Proper oral hygiene practices along with the frequency of using oral hygiene aid along with the use of toothbrushes and fluoridated toothpaste is a necessity for having good oral hygiene. Hence, there is a need to educate health care workers about the harmful effects of consuming tobacco as it causes oral cancer and as having sufficient knowledge regarding oral hygiene but not implementing it into their practices is also a major issue that should be taken care of.

Declaration of patient consent

The authors certify that they have obtained all appropriate patient consent forms. In the form the patient(s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

• Open access
• Published: 01 April 2024

Knowledge, attitudes, and practice toward postoperative cognitive dysfunction among anesthesiologists in China: a cross-sectional study

• Li Hu 1   na1 ,
• Shuai Kang 1   na1 ,
• Qiaoyi Peng 2 ,
• Erdan An 1 ,
• Jian Lu 1 ,
• Hao Yang 3 ,
• Hongmei Zhou 1 &
• Bin Zhang 1  

BMC Medical Education volume  24 , Article number:  359 ( 2024 ) Cite this article

140 Accesses

Metrics details

To investigate the knowledge, attitudes, and practice (KAP) toward postoperative cognitive dysfunction (POCD) among anesthesiologists in China.

This cross-sectional study was conducted nationwide among Chinese anesthesiologists between December 2022 and January 2023. The demographic information and KAP scores of the respondents were collected using a web-based questionnaire. The mean KAP dimension scores  ≥  60% were considered good.

This study enrolled 1032 anesthesiologists (51.2% male). The mean total scores of knowledge, positive attitude, and positive practice were 9.3 ± 1.2 (max 12), 34.8 ± 3.3 (max 40), and 30.6 ± 6.7 (max 40), respectively. The knowledge items with correctness scores < 60% were “the anesthetic drugs that tend to cause POCD” (23.3%) and “Treatment of POCD” (40.3%). Multivariable analysis showed that ≥  40 years old, master’s degree or above, intermediate professional title (i.e., attending physician), senior professional title (i.e., chief physician), and working in tertiary hospitals were independently associated with adequate knowledge. Multivariable analysis showed that the attitude scores, middle professional title, and ≥  16 years of experience were independently associated with good practice.

Conclusions

These results suggest that Chinese anesthesiologists have good knowledge, favorable attitudes, and good practice toward POCD. Still, some points remain to be improved (e.g., the drugs causing POCD and managing POCD) and should be emphasized in training and continuing education.

Trial registration

ChiCTR2200066749.

Peer Review reports

Postoperative cognitive dysfunction (POCD) is characterized clinically by subtle symptom onset (typically noticed weeks to months after surgery), mild cognitive decline with improvement within weeks to months of surgery (rarely persists for years), impairment involving memory, learning, concentration, attention, and/or psychomotor performance, and alert mental status with the maintenance of orientation to person, place, and time [ 1 ]. POCD might be reversible in days to months, but if it persists beyond > 12 months postoperatively, the standard DSM 5 nomenclature is suggested (mild or major neurocognitive disorder) without the use of the “postoperative” classifier [ 2 ]. The reported incidence is 5-55% in older patients, but the wide range is due to the surgery type and the definitions used [ 1 ]. POCD may occur in patients of any age but is most common in older patients. General anesthesia exposure is associated with an increased risk of intraoperative hypotension in adults aged > 50 years [ 3 , 4 ]. General and regional anesthesia might be associated with similar cognitive outcomes  ≥  7 days postoperatively in adults [ 5 ]. Bispectral index-guided anesthesia might reduce the risk of POCD [ 6 ]. Bispectral index-guided anesthesia may reduce the risk of POCD, and the type of anesthetic used may also influence the occurrence of POCD depending on the patient’s characteristics and the type of surgery [ 7 ]. Besides selecting the anesthetic agents [ 8 , 9 ], effective non-pharmacological measures are available to decrease the risk of POCD and shorten, including getting the patient out of bed as soon as possible after surgery and maintaining continuous contact with other people [ 10 ]. Preventing POCD while avoiding polypharmacy and providing adequate pain control are important goals for anesthesiologists [ 11 ]. Therefore, anesthesiologists must be aware of POCD.

Knowledge, attitudes, and practice (KAP) studies are surveys designed to provide quantitative and qualitative data to identify gaps that could be barriers to a specific activity in a specific population [ 12 , 13 ]. KAP studies are particularly useful for planning behavioral change interventions [ 12 , 13 ]. A German study showed that the awareness of physicians toward POCD, especially persisting POCD, was low [ 14 ]. Still, the exact KAP toward POCD among Chinese anesthesiologists is unknown.

Therefore, this study aimed to investigate the KAP toward POCD among Chinese anesthesiologists and explore the factors influencing KAP.

This cross-sectional study was conducted nationwide from December 2022 to January 2023. The inclusion criteria were (1) board-certified anesthesiologists and (2) anesthetic practice for at least 6 months. This study was ethically approved by the Ethics Committee of the Second Affiliated Hospital of Jiaxing University (approval No: 2022ZFYJ245-01). Written informed consent was obtained from each participant before he/she completed the questionnaire. This study was registered with the Chinese Clinical Trials Registry (registration No. ChiCTR2200066749). Registration link https://www.chictr.org.cn/showproj.html?proj=186869 .

Data collection

The questionnaire was designed after referring to the relevant literature [ 15 , 16 ] and was revised based on the comments made by three senior experts. Two rounds of correspondence were conducted for content validity. In the first round, two anesthesiologists and one epidemiologist were selected, and according to their opinions, we added questions about POCD attitude and practice. In the second round, one anesthesiologist and one epidemiologist were selected, and the questions about POCD pathogenesis, treatment, and prevention were changed from single-choice to multiple-choice according to their opinions. Forty-three anesthesiologists were randomly selected to perform an internal consistency reliability test with a Cronbach’s α of 0.86. The questionnaires were edited and handled through the Sojump online platform ( https://www.wjx.cn/app/survey.aspx ). Publicity was sent to the anesthesiologists through newsletters, with a QR code to the questionnaire. The anesthesiologists interested in completing and returning the questionnaire simply had to scan the QR code. Participants were assured of anonymity during the survey process. At the beginning of the questionnaire, the respondents were asked for informed consent. If the participants had to tick “yes” to the statement “I consent to participate in this survey and to my data being used for research purposes” to access the questionnaire. Not ticking the “yes” box indicated that the participant did not consent to participate in the study and could not complete the questionnaire. Only one questionnaire could be submitted from an IP address. Incomplete questionnaires, those with obvious filling patterns (e.g., all first choices), questionnaires with logic errors, and those that took < 3 min to complete were excluded.

The final self-administered anonymous questionnaire was in Chinese and contained four dimensions: demographic information (age, gender, education, professional title, years of anesthetic practice after obtaining board certification, and hospital grade), knowledge dimension, attitude dimension, and practice dimension. Physician title is divided into four levels: primary title (physician and physician resident), intermediate title (attending physician), deputy senior title (deputy chief physician), and senior title (chief physician).

The knowledge dimension consisted of 12 questions, scored 1 point for correct answers and 0 points for incorrect or unclear answers, ranging from 0 to 12 points. The attitude dimension consisted of eight questions using a 5-point Likert scale, with positive attitude questions assigned 5 to 1 point from “Strongly Agree” to “Strongly Disagree” and negative attitude questions (items A5 and A7) were assigned points in reverse; the total score ranged from 8 to 40 points. The practice dimension contained eight questions, also using a 5-point Likert scale, ranging from “Always” (5 points) to “Never” (1 point), and ranging from 8 to 40 points. The mean KAP dimension scores  ≥  60% were considered good [ 17 ] (i.e., > 7.2 for knowledge, > 24 for attitude, and > 24 for practice). The mean values of the KAP scores were used as the cut-off values, and anesthesiologists with scores above the mean value were considered to have adequate knowledge, positive attitude, and good practice.

Statistical analysis

The normal distribution of the continuous data was confirmed using the Kolmogorov-Smirnov test. The continuous variables were expressed as mean ± standard deviations (SD) and analyzed using Student’s t-test and ANOVA. Categorical data were expressed as n (%) and analyzed using the chi-square test. Univariable and multivariable analyses were conducted using logistic regression to analyze the factors influencing KAP. The enter method was used to screen the variables, and the variables with P  < 0.05 in the univariable analyses were included in the multivariable analysis. All statistical analyses were performed using SPSS 26.0 (IBM, Armonk, NY, USA). Two-sided P-values < 0.05 were considered statistically significant.

Characteristics of the participants

A total of 1092 questionnaires were received in this study. After excluding 18 participants who refused to participate in the study, 27 questionnaires had logical errors in the answers, and 15 questionnaires completed the survey within 3 min. Finally, 1032 questionnaires (1032 participants) were valid and included in the analysis. The highest frequencies of participants were observed in the following categories: 31–40 years old (40.2%), male (51.2%), with a master’s degree or above (35.2%), with a senior title (37.5%), with ≥  16 years of experience (40.1%) and working in tertiary hospitals (72.6%) (Table  1 ). Among them, 537 participants were from Zhejiang, 198 were from Jiangsu, and 57 were from Shanghai (Supplementary Table  S1 ).

• Knowledge, attitudes, and practice

The mean knowledge score was 9.3 ± 1.2 (max 12). Knowledge scores were associated with age ( P  < 0.001), education ( P  < 0.001), professional title ( P  < 0.001), experience ( P  < 0.001), and hospital level ( P  < 0.001) (Table  1 ). The knowledge items with correctness scores < 60% were “the anesthetic drugs that tend to cause POCD” (23.26%) and “Treatment of POCD” (40.31%). All other items were correctly answered by ≥  60% of the participants (Table  2 ).

The mean attitude score was 34.8 ± 3.3 (max 40). The attitude scores were associated with professional titles ( P  = 0.018) (Table  1 ). Supplementary Table  S2 shows the distribution of the attitudes.

The mean practice score was 30.6 ± 6.7 (max 40). The practice scores were associated with age ( P  < 0.001), professional titles ( P  < 0.001), and experience ( P  < 0.001) (Table  1 ). The distribution of the practice evaluation is presented in Supplementary Table  S3 .

Factors associated with knowledge, attitudes, and practice

The multivariable analysis showed that ≥  40 years old (OR = 0.440, 95%CI: 0.201–0.963, P  = 0.040), master’s degree or above (vs. bachelor’s degree or below, OR = 1.405, 95%CI: 1.042–1.895, P  = 0.026), middle professional title (OR = 2.185, 95%CI: 1.009–4.733, P  = 0.047), senior professional title (OR = 4.704, 95%CI: 1.988–11.127, P  < 0.001), and working in tertiary hospitals (OR = 1.567, 95%CI: 1.167–2.102, P  = 0.003) were independently associated with adequate knowledge of anesthesiologists toward POCD (Table  3 ). Moreover, according to the results shown in Table  4 , only the knowledge score was associated with a favorable attitude in the univariable analysis (OR = 1.319, 95%CI: 1.220–1.423, P  < 0.001). Therefore, no multivariable analysis was further conducted. Finally, the multivariable analysis revealed that good practice was independently associated with attitude scores (OR = 1.144, 95% CI: 1.098–1.192, P  < 0.001), middle professional title (OR = 0.397, 95% CI: 0.185–0.854, P  = 0.018), and more than 16 years of experience (OR = 2.714, 95% CI: 1.172–6.285, P  = 0.020) (Table  5 ).

The study findings showed that Chinese anesthesiologists possess good knowledge, hold favorable attitudes, and have active practices toward POCD. However, there are areas where improvement is needed. The results of this study could be used to design training programs that will improve the KAP of anesthesiologists concerning the prevention and management of POCD.

POCD is an important medical issue in patients undergoing surgery [ 1 , 18 ]. The present study showed good KAP toward POCD in Chinese anesthesiologists, while a German study showed that the awareness of POCD was higher in nurses than in physicians [ 14 ]. Similar results were observed in Sweden [ 19 ]. The present study generally showed that the knowledge of POCD improved with experience and professional title, which are often interrelated. Importantly, POCD can be prevented by non-pharmacologic approaches [ 10 ]. Still, the present study showed that Chinese anesthesiologists had poor knowledge of the drugs that cause POCD and how to manage it once it occurs. Hence, training programs will have to emphasize these two aspects in the future. Indeed, intraoperative hypotension is a clear cause of POCD [ 3 , 4 ], while several anesthetics and clinical parameters are considered possible risk factors of POCD, including fentanyl, ketamine, lidocaine, magnesium sulfate infusion, piracetam, steroids, benzodiazepines, general vs. regional anesthesia, bispectral index, and monitoring based on copeptin levels, inflammatory markers, and glycemia, among others [ 5 , 20 , 21 , 22 , 23 , 24 , 25 ]. Of note, a study in Germany showed that the implementation of mandatory training on the cognitive impacts of surgery was low [ 26 ]. In addition, existing strategies focus on screening and therapy after surgery or intensive care [ 19 , 27 ], but a holistic approach appears to be missing [ 28 ]. In addition, guidelines do not address who is responsible for risk detection and communication [ 19 , 27 ]. Therefore, authoritative organizations and decision-makers must also play a role in improving healthcare providers’ knowledge of POCD.

Although the survey showed that anesthesiologists have favorable attitudes and good practice toward POCD, only correct knowledge can lead to correct actions [ 29 ]. In fact, there is a lack of guidance on POCD in clinical practice, which makes anesthesiologists take different approaches toward POCD. Previous studies showed that the KAP of anesthesiologists toward POCD is low [ 14 , 19 ], probably due to rare systematic POCD education and training. Therefore, some authors suggest that knowledge about perioperative brain dysfunction should be included in the basic education curriculum of anesthesiology training. Unfortunately, there are no relevant guidelines or consensuses in China. Such consensuses should be reached by experts.

In terms of training methods, anesthesiologists could use online learning software for POCD-specific knowledge through network platforms to facilitate fragmented learning, and continuous education activities could be designed and implemented. In terms of training content, the impact of anesthetic drugs on POCD and POCD treatment should be advocated as the focus of education and training, and the core principles of POCD prevention and treatment should be emphasized to improve the fundamental anesthesiologist’s management ability of POCD. The present study highlighted that the anesthesiologists had poorer knowledge regarding the drugs that can cause POCD and how to manage POCD. Such knowledge should be improved. In terms of quality control, it is suggested to attach importance to POCD management, establish relevant assessment mechanisms, and improve the standardized management level of anesthesiologists’ KAP toward POCD.

According to the KAP theory [ 12 , 13 ], knowledge is the theoretical basis for practice, while attitude is the force driving practice. Still, in the present study, knowledge did not directly affect practice. Several reasons could account for that. It could be related to the way the questions are formulated. A KAP survey is a questionnaire that skims the general KAP toward a subject without going into details. It could also be because some actions are performed out of habits or according to teaching but without knowing the exact theory of why it is performed. The present study was not designed to determine why knowledge was not directly related to practice. For example, knowledge items pertaining to the drugs causing POCD and how to manage POCD showed poor knowledge about those two items, but there are no practice questions about the drugs, and the only practice question about POCD management is about referral to attending physicians and neurologists. Additional studies will be necessary to examine that issue.

A strength of this study was its nationwide nature. Still, it also had limitations. Although this study was advertised in newsletters, it was clear that older, more experienced anesthesiologists working at tertiary hospitals were enrolled, probably biasing the results. In addition, because the anesthesiologists interested in participating in the study simply had to scan the QR code, the response rate could not be calculated. Even though tertiary hospitals more frequently undertake specialized procedures than non-tertiary hospitals (primary and secondary hospitals), the emergent or routine procedures that require general anesthesia and are performed in non-tertiary hospitals also involve a risk of POCD [ 20 ]. Furthermore, KAP surveys essentially record an “opinion” based on the survey statements. Therefore, the KAP survey reveals what was said, but there is a possibility of considerable gaps between what was said and what was done.

Chinese anesthesiologists have good knowledge, favorable attitudes, and active practice toward POCD. Nevertheless, some areas of KAP (e.g., the drugs causing POCD and managing POCD) were identified as needing improvements. Those areas will have to be included and highlighted in future training programs during residency or as continuing education activities.

Data availability

All data generated or analyzed during this study are included in this published article.

Abbreviations

knowledge, attitudes, and practice

postoperative cognitive dysfunction

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Acknowledgements

Not applicable.

This study was funded by the Zhejiang Medical Health Science and Technology Program (2020KY318) and the Jiaxing City Science and Technology Plan (2019AY32016).

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Li Hu MD and Shuai Kang MD authors contributed equally to this work.

Authors and Affiliations

Department of Anesthesiology, The Second Affiliated Hospital of Jiaxing University, Jiaxing, China

Li Hu, Shuai Kang, Erdan An, Jian Lu, Hongmei Zhou & Bin Zhang

Zhejiang Chinese Medical University, Hangzhou, China

Qiaoyi Peng

Department of Anesthesiology, Shanghai Pudong New Area People’s Hospital, Shanghai, China

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LH: This author helped carry out the studies, participated in collecting data, and drafted the manuscript, SK: This author helped carry out the studies, participated in collecting data, and drafted the manuscript, EDA: This author helped perform the statistical analysis and participated in its design, Jian Lu: This author helped perform the statistical analysis and participated in its design, QYP: This author helped perform the statistical analysis and participated in its design, Hao Yang: This author helped perform the statistical analysis and participated in its design, BZ: This author helped participate in the acquisition, analysis, or interpretation of data and draft the manuscript, HMZ: This author helped participate in the acquisition, analysis, or interpretation of data and draft the manuscript, All authors read and approved the final manuscript.

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Correspondence to Hongmei Zhou or Bin Zhang .

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The study was carried out after the protocol was approved by the Ethics Committee of the Second Hospital of Jiaxing (2022ZFY245-01). I confirm that all methods were performed in accordance with the relevant guidelines. All procedures were performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments, and informed consent was obtained from all participants.

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This study was registered with the Chinese Clinical Trials Registry (registration No. ChiCTR2200066749). Registration link https://www.chictr.org.cn/showproj.html?proj=186869 .

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Hu, L., Kang, S., Peng, Q. et al. Knowledge, attitudes, and practice toward postoperative cognitive dysfunction among anesthesiologists in China: a cross-sectional study. BMC Med Educ 24 , 359 (2024). https://doi.org/10.1186/s12909-024-05358-6

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Received : 29 August 2023

Accepted : 27 March 2024

Published : 01 April 2024

DOI : https://doi.org/10.1186/s12909-024-05358-6

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