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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.

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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 .

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Cross-Sectional Study in Research

Introduction

What is a cross-sectional study in research, what is the difference between cross-sectional and longitudinal research, cross-sectional study examples, types of cross-sectional studies, benefits of cross-sectional studies, challenges of cross-sectional studies.

Cross-sectional studies are a fundamental research method used across various fields to analyze data at a specific point in time. By comparing different subjects without considering the time variable, these studies can provide valuable insights into the prevalence and characteristics of phenomena within a population.

This article explores the concept of cross-sectional research, outlining its key features, applications, and how it differs from longitudinal studies. We will also examine examples of cross-sectional data, discuss the various types of cross-sectional studies, and highlight both the advantages and challenges associated with this research method. Understanding when and how to employ research methods for a cross-sectional study design is crucial for researchers aiming to draw accurate and meaningful conclusions from their data .

A cross-sectional study is a type of observational research design that analyzes data from a population, or a representative subset, at one specific point in time. Unlike longitudinal studies that observe the same subjects over a period of time to detect changes, cross-sectional studies focus on finding relationships and prevalences within a predefined snapshot. This method is particularly useful for understanding the current status of a phenomenon or to identify associations between variables without inferring causal relationships.

In practice, cross-sectional studies collect data across a wide range of subjects at a single moment, aiming to capture a comprehensive picture of a particular research question. Researchers might analyze various factors, including demographic information, behaviors, conditions, or outcomes, to discern patterns or correlations within the population studied.

Though these studies cannot determine cause and effect, they are invaluable for generating hypotheses or propositions, informing policy decisions, and guiding future research. Their descriptive nature and relative ease of execution make cross-sectional studies a common starting point in many research endeavors, providing a foundational understanding of the context and variables of interest.

The primary distinction between cross-sectional and longitudinal research lies in how and when the data is collected. Cross-sectional studies differ in that they capture data at a single point in time, offering a snapshot that helps to identify the prevalence and relationships between variables within a specific moment that further research might be able to explore. In contrast, a longitudinal study involves collecting data from the same subjects repeatedly over an extended period of time, enabling the observation of changes and developments in the variables of interest.

While cross-sectional studies are efficient for gathering data at one point in time and are less costly and time-consuming than longitudinal studies, they fall short in tracking changes over time or establishing cause-and-effect relationships. On the other hand, longitudinal studies excel in observing how variables evolve, providing insights into dynamics and causal pathways. However, longitudinal data collection requires more resources, time, and a rigorous design to manage participant attrition and ensure consistent data collection over the study period.

Another key difference is in the potential for cohort effects. A cross-sectional analysis might conflate age-related changes with generational effects because different age groups are compared at one particular point in time. Longitudinal research, by observing the same individuals over time, can differentiate between aging effects and cohort effects, offering a clearer view of how specific and multiple variables change throughout an individual's life or over time.

Cross-sectional studies are employed across various disciplines to investigate multiple phenomena at a specific point in time. These studies offer insights into the prevalence, distribution, and potential associations between variables within a defined population.

Below are three examples from different fields illustrating how cross-sectional research is applied to glean valuable findings.

Healthcare: Prevalence of a medical condition

In medical research, cross-sectional studies are frequently used to determine the prevalence of diseases or health outcomes in a population. For instance, a study might collect cross-sectional data from a diverse sample of individuals to assess the current prevalence of diabetes. By analyzing factors such as age, lifestyle, and comorbidities, researchers can identify patterns and risk factors associated with the disease, aiding in public health planning and intervention strategies.

Education: Analyzing student performance

Educational researchers often use a cross-sectional design to evaluate student performance across different grades or age groups at a single point in time. Such a study could compare test scores to analyze trends and disparities in educational achievement. By examining variables like socio-economic status, teaching methods, and school resources, educators and policymakers can identify areas needing improvement or intervention.

Economics: Employment trends analysis

In economics, a cross-sectional survey can provide snapshots of employment trends within a specific region or sector. An example might involve analyzing the employment rates, job types, and economic sectors in a country at a given time. This data can reveal insights into the economic health, workforce distribution, and potential areas for economic development or policy focus, informing stakeholders and guiding decision-making processes.

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Cross-sectional studies can be categorized into different types based on their objectives and methodologies . These variations allow researchers to adapt the cross-sectional approach to suit specific research questions and contexts.

By understanding the different types of cross-sectional studies, researchers can select the most appropriate design to obtain reliable and relevant data. Below are four common types of cross-sectional studies, each with its unique focus and application.

Descriptive cross-sectional studies

Descriptive cross-sectional studies aim to provide a detailed snapshot of a population or phenomenon at a particular point in time. These studies focus on 'what exists' or 'what is prevalent' without delving into relationships between variables or concepts.

For example, a descriptive research study might catalog various health behaviors within a specific demographic group to inform public health initiatives. The primary goal is to describe characteristics, frequencies, or distributions as they exist in the study population.

Analytical cross-sectional studies

Unlike descriptive studies that focus on prevalence and distribution, analytical cross-sectional studies aim to uncover potential associations between variables. These studies often compare different groups within the population to identify factors that may correlate with certain outcomes.

For instance, an analytical cross-sectional study might investigate the relationship between lifestyle choices and blood pressure levels across various age groups. While these studies can suggest associations, they do not establish cause and effect.

Exploratory cross-sectional studies

Exploratory cross-sectional studies are conducted to explore potential relationships or hypotheses when little is known about a subject. These studies are particularly useful in emerging fields or for new phenomena. By examining available data, they can generate hypotheses for further research without committing extensive resources to long-term studies.

An example might be exploring the usage patterns of a new technology within a population to identify trends and areas for in-depth study.

Explanatory cross-sectional studies

Explanatory cross-sectional studies go beyond identifying associations; they aim to explain why certain patterns or relationships are observed. These studies often incorporate theoretical frameworks or models to analyze the data within a broader context, providing deeper insights into the underlying mechanisms or factors.

For example, an explanatory cross-sectional study could investigate why certain educational strategies are associated with better student outcomes, integrating theories of learning and cognition.

Cross-sectional studies are a crucial tool in the repertoire of research methodologies , offering unique advantages that make them particularly suitable for various research contexts. These studies are instrumental in providing a snapshot of a specific point in time, which can be invaluable for understanding the status quo and informing future research directions. Below, we explore three significant benefits of employing cross-sectional studies in research endeavors.

Cost-effectiveness

One of the primary benefits of cross-sectional studies is their cost-effectiveness compared to longitudinal studies . Since they are conducted at a single point in time and do not require follow-ups, the financial resources, time, and logistical efforts needed are considerably lower. This efficiency makes cross-sectional studies an appealing option for researchers with limited budgets or those seeking preliminary data before committing to more extensive research.

Cross-sectional studies are inherently timely, providing quick snapshots that are especially valuable in fast-paced research areas where timely data is crucial. They allow researchers to collect and analyze data relatively quickly, offering insights that are current and relevant. This timeliness is particularly beneficial for informing immediate policy decisions or for studies in fields where trends may change rapidly, such as technology or public health.

Versatility

The versatility of cross-sectional studies is evident in their wide applicability across various fields and purposes. They can be designed to explore numerous variables and their interrelations within different populations and settings. This flexibility enables researchers to tailor studies to specific research questions, making cross-sectional studies a versatile tool for exploratory research, hypothesis generation , or situational analysis across disciplines.

Despite their utility in various fields of research, cross-sectional studies face distinct challenges that can affect the validity and applicability of their findings. Understanding these limitations is crucial for researchers to design robust studies and for readers to interpret results appropriately. Here are three key challenges commonly associated with cross-sectional studies.

Causality determination

One of the inherent limitations of cross-sectional studies is their inability to establish causality. Since data is collected at a single point in time, it is challenging to ascertain whether a relationship between two variables is causal or merely correlational. This limitation necessitates cautious interpretation of results, as establishing temporal precedence is essential for causal inference, which cross-sectional designs cannot provide.

Selection bias

Selection bias can occur in cross-sectional studies if the sample is not representative of the population from which it was drawn. This can happen due to non-random sampling methods or non-response, leading to skewed results that do not accurately reflect the broader population. Such bias can compromise the generalizability of the study's findings, making it critical to employ rigorous sampling methods and consider potential biases during analysis.

Cross-sectional confounding

Cross-sectional studies can also be susceptible to confounding, where an external variable influences both the independent and dependent variables , creating a spurious association. Without longitudinal data , it is difficult to control for or identify these confounding factors, which can lead to erroneous conclusions. Researchers must carefully consider potential confounders and employ statistical methods to adjust for these variables where possible.

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Cross-Sectional Study: Definition, Designs & Examples

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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.

Cross-Sectional Research Design

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This chapter addresses the peculiarities, characteristics, and major fallacies of cross-sectional research designs. The major advantage of cross-sectional research lies in cross-case analysis. A cross-sectional study aims at describing generalized relationships between distinct elements and conditions. The specific case and its particularities are not the focus, but all instances and cases. So cross-sectional studies try to establish general models that link a combination of elements with other elements under certain conditions. The results are tested (or rejected) theories about these relationships. Also, researchers find relevant information on how to write a cross-sectional research design paper and learn about typical methodologies used for this research design. The chapter closes with referring to overlapping and adjacent research designs.

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Allen, M. (2017). Cross-Sectional Design. The SAGE encyclopedia of communication research methods . SAGE Publications, Inc.

Google Scholar  

Lauren, T. (2020). What is a cross-sectional study? Retrieved June 14, 2021, from https://www.scribbr.com/methodology/cross-sectional-study/ .

Maxwell, S. E., & Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12 , 23–44.

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Ployhart, R. E., & Vandenberg, R. J. (2010). Longitudinal Research: The theory, design, and analysis of change. Journal of Management, 36 , 94–120.

Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis . Oxford University Press.

USC University of Southern California (2021). Research guides . Retrieved April 05, 2021, from https://libguides.usc.edu/writingguide/researchdesigns .

Williams, J. J., & Seaman, A. E. (2002). Management accounting systems change and departmental performance: The influence of managerial information and task uncertainty. Management Accounting Research, 13 (4), 419–445.

Ziliak, S. T., & McCloskey, D. (2008). The cult of statistical significance: How the standard error costs Us jobs, justice, and lives . The University of Michigan Press.

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Quantitative study designs: Cross-Sectional Studies

Quantitative study designs.

• Introduction
• Cohort Studies
• Randomised Controlled Trial
• Case Control
• Cross-Sectional Studies
• Study Designs Home

Cross-Sectional Study

The Australian Census run by the Australian Bureau of Statistics, is an example of a whole of population cross-sectional study.

Data on a number of aspects of the Australian population is gathered through completion of a survey within every Australian household on the same night. This provides a snapshot of the Australian population at that instance.

Cross-sectional studies look at a population at a single point in time, like taking a slice or cross-section of a group, and variables are recorded for each participant.

This may be a single snapshot for one point in time or may look at a situation at one point in time and then follow it up with another or multiple snapshots at later points; this is then termed a repeated cross-sectional data analysis. 

The stages of a Cross-Sectional study

Repeated Cross-Sectional Data Analysis

Which clinical questions does a Cross-Sectional study best answer?

Please note the Introduction , where there is a table under "Which study type will answer my clinical question?" .  You may find that there are only one or two question types that your study answers – that’s ok. 

Cross-sectional study designs are useful when:

• Answering questions about the incidence or prevalence of a condition, belief or situation.
• Establishing what the norm is for a specific demographic at a specific time. For example: what is the most common or normal age for students completing secondary education in Victoria?
• Justifying further research on a topic. Cross-sectional studies can infer a relationship or correlation but are not always sufficient to determine a direct cause. As a result, these studies often pave the way for other investigations.  

What are the advantages and disadvantages to consider when using a Cross-Sectional study design?

What does a strong cross-sectional study look like.

• Appropriate recruitment of participants. The sample of participants must be an accurate representation of the population being measured.
• Sample size. As is the case for most study types a larger sample size gives greater power and is more ideal for a strong study design. Within a cross-sectional study a sample size of at least 60 participants is recommended, although this will depend on suitability to the research question and the variables being measured.
• A suitable number of variables. Cross-sectional studies ideally measure at least three variables in order to develop a well-rounded understanding of the potential relationships of the two key conditions being measured.

What are the pitfalls to look for?

Cross-sectional studies are at risk of participation bias, or low response rates from participants. If a large number of surveys are sent out and only a quarter are completed and returned then this becomes an issue as those who responded may not be a true representation of the overall population.

Critical appraisal tools 

To assist with critically appraising cross-sectional studies there are some tools / checklists you can use.

• Axis Appraisal Tool for Cross Sectional Studies
• Critical Appraisal Tool for Cross- Sectional Studies (CAT-CSS)
• Critical Appraisal of a Cross-Sectional Study on Environmental Health
• Critical appraisal tool for cross-sectional studies using biomarker data (BIOCROSS)
• CEBM Critical Appraisal of a Cross-Sectional Study (Survey)
• JBI Critical Appraisal checklist for analytical cross-sectional studies
• Specialist Unit for Review Evidence (SURE) 2018. Questions to assist with the critical appraisal of cross sectional studies
• STROBE Checklist for cross-sectional studies

Real World Examples

The Australian National Survey of Mental Health and Wellbeing (NSMHWB)

https://www.abs.gov.au/statistics/health/mental-health/national-survey-mental-health-and-wellbeing-summary-results/2007

A widely known example of cross-sectional study design, the Australian National Survey of Mental Health and Wellbeing (NSMHWB). This study was a national epidemiological survey of mental disorders investigating the questions: How many people meet DSM-IV and ICD-10 diagnostic criteria for the major mental disorders? How disabled are they by their mental disorders? And, how many have seen a health professional for their mental disorder?

References and Further Reading

Australian Government Department of Health. (2003). The Australian National Survey of Mental Health and Wellbeing (NSMHWB). 2019, from https://www.abs.gov.au/statistics/health/mental-health/national-survey-mental-health-and-wellbeing-summary-results/2007

Bowers, D. a., Bewick, B., House, A., & Owens, D. (2013). Understanding clinical papers (Third edition. ed.): Wiley Blackwell.

Gravetter, F. J. a., & Forzano, L.-A. B. (2012). Research methods for the behavioral sciences (Fourth edition. ed.): Wadsworth Cengage Learning.

Greenhalgh, T. a. (2014). How to read a paper : the basics of evidence-based medicine (Fifth edition. ed.): John Wiley & Sons Inc.

Hoffmann, T. a., Bennett, S. P., & Mar, C. D. (2017). Evidence-Based Practice Across the Health Professions (Third edition. ed.): Elsevier.

Howitt, D., & Cramer, D. (2008). Introduction to research methods in psychology (Second edition. ed.): Prentice Hall.

Kelly, P. J., Kyngdon, F., Ingram, I., Deane, F. P., Baker, A. L., & Osborne, B. A. (2018). The Client Satisfaction Questionnaire‐8: Psychometric properties in a cross‐sectional survey of people attending residential substance abuse treatment. Drug and Alcohol Review, 37(1), 79-86. doi: 10.1111/dar.12522

Lawrence, D., Hancock, K. J., & Kisely, S. (2013). The gap in life expectancy from preventable physical illness in psychiatric patients in Western Australia: retrospective analysis of population based registers. BMJ: British Medical Journal, 346(7909), 13-13.

Nasir, B. F., Toombs, M. R., Kondalsamy-Chennakesavan, S., Kisely, S., Gill, N. S., Black, E., Ranmuthugala, G., Ostini, R., Nicholson, G. C., Hayman, N., & Beccaria, G.. (2018). Common mental disorders among Indigenous people living in regional, remote and metropolitan Australia: A cross-sectional study. BMJ Open , 8 (6). https://doi.org/10.1136/bmjopen-2017-020196

Robson, C., & McCartan, K. (2016). Real world research (Fourth Edition. ed.): Wiley.

Sedgwick, P. (2014). Cross sectional studies: advantages and disadvantages. BMJ : British Medical Journal, 348, g2276. doi: 10.1136/bmj.g2276

Setia, M. S. (2016). Methodology Series Module 3: Cross-sectional Studies. Indian journal of dermatology, 61(3), 261-264. doi: 10.4103/0019-5154.182410

Shafiei, T., Biggs, L. J., Small, R., McLachlan, H. L., & Forster, D. A. (2018). Characteristics of women calling the panda perinatal anxiety & depression australia national helpline: A cross-sectional study. Archives of Women's Mental Health. doi: 10.1007/s00737-018-0868-4

Van Heyningen, T., Honikman, S., Myer, L., Onah, M. N., Field, S., & Tomlinson, M. (2017). Prevalence and predictors of anxiety disorders amongst low-income pregnant women in urban South Africa: a cross-sectional study. Archives of Women's Mental Health(6), 765. doi: 10.1007/s00737-017-0768-z

Vogt, W. P. (2005). Dictionary of statistics & methodology : a nontechnical guide for the social sciences (Third edition. ed.): Sage Publications.

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Methodology Series Module 3: Cross-sectional Studies

Affiliation.

• 1 Department of Epidemiologist, MGM Institute of Health Sciences, Navi Mumbai, Maharashtra, India.
• PMID: 27293245
• PMCID: PMC4885177
• DOI: 10.4103/0019-5154.182410

Cross-sectional study design is a type of observational study design. In a cross-sectional study, the investigator measures the outcome and the exposures in the study participants at the same time. Unlike in case-control studies (participants selected based on the outcome status) or cohort studies (participants selected based on the exposure status), the participants in a cross-sectional study are just selected based on the inclusion and exclusion criteria set for the study. Once the participants have been selected for the study, the investigator follows the study to assess the exposure and the outcomes. Cross-sectional designs are used for population-based surveys and to assess the prevalence of diseases in clinic-based samples. These studies can usually be conducted relatively faster and are inexpensive. They may be conducted either before planning a cohort study or a baseline in a cohort study. These types of designs will give us information about the prevalence of outcomes or exposures; this information will be useful for designing the cohort study. However, since this is a 1-time measurement of exposure and outcome, it is difficult to derive causal relationships from cross-sectional analysis. We can estimate the prevalence of disease in cross-sectional studies. Furthermore, we will also be able to estimate the odds ratios to study the association between exposure and the outcomes in this design.

Keywords: Cross-sectional studies; design; limitations; strengths.

• What is a cross-sectional study?

Last updated

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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 Study: What it is + Free Examples

A cross-sectional study is used to collect data from a population simultaneously. It is a snapshot of the population at a particular moment rather than a study that tracks changes over time. This design is often used in fields such as public health, sociology, and psychology to gather information about the characteristics, attitudes, and behaviors of a group of individuals .

This blog will discuss what cross-sectional studies are. We’ll review examples and explain the types of cross-sectional studies you might perform. We’ll also take a closer look at the benefits of this valuable research for the work you do.

What is a Cross-Sectional Study?

A cross-sectional study is a type of observational research that analyzes data of variables collected at one given point in time across a sample population or a pre-defined subset.

This study type is also known as cross-sectional analysis, transverse study, or prevalence study. Although this research does not involve conducting experiments, researchers often use it to understand outcomes in the physical and social sciences and many business industries.

Characteristics of Cross-Sectional Studies

When researchers conduct cross-sectional studies, they look at a specific group of people at a single point in time. Here are some simple characteristics of cross-sectional studies that might help you understand them better:

• Researchers can conduct cross-sectional studies with the same set of variables over a set period.
• Similar research may look at the same variable of interest, but each study observes a new set of subjects.
• The cross-sectional analysis assesses topics during a single instance with a defined start and stopping point, unlike longitudinal studies, where variables can change during extensive research.
• Cross-sectional studies allow the researcher to look at one independent variable and one or more dependent variables as the focus of the cross-sectional study.

Want a fitting metaphor? Think of a snapshot of a group of people at one event, say a family reunion. The people in that extended family are used to determine what is happening in real-time at the moment.

All people have at least one variable in common – being related – and multiple variables they do not share. You could make all kinds of observations and analyses from that starting point. Hence, this research type “takes the pulse” of population data at any given time.

You can also use this type of research to map prevailing variables that exist at a particular given point—for example, cross-sectional data on past drinking habits and a current diagnosis of liver failure.

Cross-Sectional Study Examples

The data collected in cross-sectional studies involves subjects or participants who are similar in all variables – except the one that is under review. This variable remains constant throughout the study. This is unlike a longitudinal study , where variables can change throughout the research. Consider these examples for more clarity:

• Retail: In retail, this research can be conducted on men and women in a specific age range to reveal similarities and differences in spending trends related to gender.
• Education : Cross-sectional studies in school are beneficial in understanding how students who scored within a particular grade range in the same preliminary courses perform with a new curriculum .
• Healthcare: Scientists in healthcare may use cross-sectional studies to understand how children ages 2-12 across the United States are prone to calcium deficiency.
• Business: In business, researchers can study how people of different socio-economic statuses from one  geographic segment  respond to one change in an offering.
• Psychology: The cross-sectional study definition in psychology is research that involves different groups of people who do not share the same variable of interest (like the variable you’re focusing on) but who do share other relevant variables. These could include age range, gender identity, socio-economic status, and so on.

This research allows scholars and strategists to quickly collect cross-sectional data that helps in decision-making and offering products or services.

Types of Cross-Sectional Studies

When you conduct a cross-sectional research study, you will engage in one or both types of research: descriptive or analytical. Read their descriptions to see how they might apply to your work.

• Descriptive Research:  A cross-sectional study may be entirely descriptive research . A cross-sectional descriptive survey assesses how frequently, widely, or severely the variable of interest occurs throughout a specific demographic . Please think of the retail example we mentioned above. In that example, researchers make focused observations to identify spending trends. They might use those findings to develop products and services and market existing offerings. They aren’t necessarily looking at why these gendered trends occur in the first place.
• Analytical Research: A cross-sectional survey investigates the association between two related or unrelated parameters. This research isn’t entirely foolproof, though, because outside variables and outcomes are simultaneous, and their studies are, too. For example, to validate whether coal miners could develop bronchitis, look only at the variables in a mine. What it doesn’t account for is that a predisposition to bronchitis could be hereditary, or this health condition could be present in the coal workers before their employment in the mine. Other medical research has shown that coal mining is detrimental to the lungs, but you don’t want those assumptions to bias your current study.

Researchers usually use descriptive and analytical research methods in real-life cross-sectional studies.

Benefits of a Cross-Sectional Study

Are you curious whether this research is the right approach for your next study? A Cross-Sectional Survey is an efficient and revealing way to collect data. Check out some of the critical advantages of conducting online research using cross-sectional studies and see if it’s a good fit for your needs.

• Relatively quick to conduct.
• Researchers can collect all variables at a point in time.
• Multiple outcomes can be researched at once.
• Prevalence for all factors can be measured.
• Suitable for descriptive analysis .
• Researchers can use it as a springboard for further research.

If you are looking for an approach that studies subjects and variables over time, you might prefer a longitudinal study. Additionally, you could follow your research with a longitudinal study. It is easy to confuse the two research methods, so we’ve broken it down here:

We recently published a blog that talks about Causal Research ; why don’t you check it out for more ideas?

Cross-Sectional vs. Longitudinal Studies

Although they are both quantitative research methods, there are a few differences when comparing and contrasting cross-sectional and  longitudinal studies .

Researchers prefer cross-sectional studies to find common points between variables. Still, they use longitudinal studies, due to their nature, to dissect the research from the cross-sectional studies for further research.

Examples of Cross-Sectional Data

Now that you have a better understanding of what cross-sectional research is and how to perform your studies, let’s look at two examples in more detail:

Example 1: Gender and Phone Sales

Phone companies rely on advanced and innovative features to drive sales.  Research  by a phone manufacturer throughout the target demographic market validates the expected adoption rate and potential phone sales. In cross-sectional studies, researchers enroll men and women across regions and age ranges for research. 

If the results show that Asian women would not buy the phone because it is bulky, the mobile phone company can tweak the design to make it less bulky. They can also develop and market a smaller phone to appeal to a more inclusive group of women.

Example 2: Men and Cancer

Another example of a cross-sectional study would be a medical study examining the prevalence of cancer amongst a defined population. The researcher can evaluate people of different ages, ethnicities, geographical locations, and social backgrounds.

If a significant number of men from a particular age group are more prone to have the disease, the researcher can conduct further studies to understand the reasons. A longitudinal study is best used, in this case, to study the same participants over time.

Create and Analyse a Cross-Sectional Study Survey

It’s your turn! Whether you’re building a marketing strategy or performing a cutting-edge medical study, you can get started by creating an intuitive survey from QuestionPro. Please choose from one of our 350+ survey templates, or build your own and leverage our reporting tools to discover deep insights to apply to your best work.

You can use single-ease questions . A single-ease research question is a straightforward query that elicits a concise and uncomplicated response.

Also, you can find advanced data analysis tools such as trend analysis and dashboards to visualize your information and do your own cross-sectional studies simply and efficiently.

A cross-sectional study provides valuable insights into a population’s characteristics, attitudes, and behaviors at a single point in time. As with any research design , cross-sectional studies should be used with other research methods to provide a complete study. Overall, cross-sectional studies can be a valuable tool for researchers looking to understand a population quickly.

With QuestionPro, you can conduct cross-sectional studies with ease. QuestionPro provides various tools for analyzing your collected data, cross-tabulation, and more. Whether you’re a researcher, marketer, or business professional, QuestionPro can help you gather the data you need to make informed decisions.

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Frequently Asked Questions (FAQ)

A cross-sectional study is a type of research that collects data from a group of people at a single point in time to analyze characteristics and relationships.

They are valuable for understanding the current status of a condition or behavior within a population, making them great for initial assessments.

Cross-sectional studies capture data at a one-time point, while longitudinal studies track the same individuals over an extended period to observe changes.

It’s cost-effective, quick to conduct, and provides a broad view of a population’s characteristics or behaviors at a specific time.

The primary goal of a cross-sectional study is to examine and analyze the relationships or associations between different variables within a population at a specific point in time.

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What is an Analytical Cross-Sectional Study?

Pro tips: analytical cross-sectional study checklist, articles on cross-sectional study design and methodology.

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An analytical cross-sectional study is a type of quantitative, non-experimental research design. These studies seek to "gather data from a group of subjects at only one point in time" (Schmidt & Brown, 2019, p. 206).  The purpose is to measure the association between an exposure and a disease, condition or outcome within a defined population.  Cross-sectional studies often utilize surveys or questionnaires to gather data from participants (Schmidt & Brown, 2019, pp. 206-207).  

Schmidt N. A. & Brown J. M. (2019). Evidence-based practice for nurses: Appraisal and application of research  (4th ed.). Jones & Bartlett Learning. 

Each JBI Checklist provides tips and guidance on what to look for to answer each question.   These tips begin on page 4. 

Below are some additional  Frequently Asked Questions  about the Analytical Cross-Sectional Studies  Checklist  that have been asked students in previous semesters. 

For more help:  Each JBI Checklist provides detailed guidance on what to look for to answer each question on the checklist.  These explanatory notes begin on page four of each Checklist. Please review these carefully as you conduct critical appraisal using JBI tools. 

Kesmodel U. S. (2018). Cross-sectional studies - what are they good for?   Acta Obstetricia et Gynecologica Scandinavica ,  97 (4), 388–393. https://doi.org/10.1111/aogs.13331

Pandis N. (2014). Cross-sectional studies .  American Journal of Orthodontics and Dentofacial Orthopedics ,  146 (1), 127–129. https://doi.org/10.1016/j.ajodo.2014.05.005

Savitz, D. A., & Wellenius, G. A. (2023). Can cross-sectional studies contribute to causal inference? It depends .  American Journal of Epidemiology ,  192 (4), 514–516. https://doi.org/10.1093/aje/kwac037

Wang, X., & Cheng, Z. (2020). Cross-sectional studies: Strengths, weaknesses, and recommendations .  Chest ,  158 (1S), S65–S71. https://doi.org/10.1016/j.chest.2020.03.012

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A cross-sectional study is a research design used to gather data from a population or sample at a specific point in time. It aims to provide a snapshot of a particular phenomenon or explore the relationship between variables at a given moment. Unlike longitudinal studies that track individuals over time, cross-sectional studies focus on a single interval.

Whether you are investigating an unstudied topic or just can’t afford to spend too much time on research, cross sectional designs can work wonders. All you need is a single time and many different participants. Sounds easy, right? And it should be if you follow this guide from our writing service . Get ready for lots of insights and useful examples as you read our blog post. But first things first – let’s begin with the basics. 

What Is a Cross Sectional Study: Definition

A cross-sectional study is a type of observational research that allows assembling data from many different subjects at one point. Scientists usually rely on specific variables to pick the participants. As descriptive research, a cross-sectional study is used to observe something that already exists in a cohort. Thus, you won’t need to adjust or change variables.  Here are the main attributes that set cross-sectional studies apart from other types of research:

• The population members are observed only once.
• Various traits can be examined simultaneously.
• Researchers don’t control the variables.
• Method allows investigating predominant qualities within a group.

As a rule, cross sectional studies are carried out in developmental psychology. However, research paper writers also widely use this type of study in economics, education, medicine and social sciences. 

Cross-Sectional Study: When to Use

A cross-sectional study is used to explore the characteristics that are dominant in a specific group of people at a particular time. Researchers opt for this method when having to choose between time or expenses. It’s a time-wise option, especially if the data you have was gathered only once. Cross-sectional studies don’t require repeated experiments, and, thus, are budget-friendly.

Example of cross sectional study use case You want to find out how many people currently work remotely in your district. You only should learn the current number of individuals who work from home. For this reason, a cross-sectional study is preferred.

Descriptive Cross Sectional Study vs Analytical Cross Sectional Study

Depending on their main purpose, cross sectional studies can be either descriptive or analytical.  A descriptive cross sectional study is aimed at the prevalence of some characteristics in a population. It only describes the outcome. An analytical cross sectional study requires that you look for a relationship between the cause and outcome.

Example You are examining the occurrence of cardiovascular disease. In a descriptive research design, you will look for the prevalence of cardiovascular disease in older individuals. Meanwhile, in analytical research you will focus on recent radiation exposure as the main reason for heart diseases.

Cross-Sectional Study: How to Implement

There are two ways to conduct a cross sectional study design:

• Use the data collected by another researcher/ organization.
• Run your own research.

In the first case, you can use national or local government’s registers, surveys or reports by international organizations. Such data is easy to retrieve from official websites. On the flip side, your research question may differ, so do variables.  If you decide to do your own cross-sectional research, make sure you follow these steps:

• Select participants using inclusion and exclusion criteria. Include only those subjects that have necessary attributes that will help to answer your research question. Consider such factors as age, gender , social status to include individuals in your research.
• Examine the influence and results at the same time. Try to find an association between variables of interest. Sometimes, researchers may observe only the outcomes in subjects.
• Measure the prevalence of traits within your population. Collect and analyze data about traits that prevail in your chosen group. You can also estimate odd ratios to explore the relation between variables.

Conducting a cross-sectional research study undeniably requires much effort. Sometimes, it’s better to buy research paper online or ask professionals to ‘ write my research paper .’

Cross Sectional Study vs Longitudinal Study

Now let’s look at the difference between a cross sectional study and a longitudinal study . Cross-sectional studies are executed to gather information from a population only once. Meanwhile, longitudinal studies are used to examine a small group of participants a number of times.

Longitudinal research is more time-consuming and requires more resources. For this reason, you should be 100% sure that there is some kind of correlation between variables. And that’s exactly what cross-sectional research can help you with. As a cheap and easy option, it allows you to collect initial information about the subjects. This should be enough to decide whether it’s worth continuing further research. 

Advantages and Disadvantages of Cross Sectional Study

Cross sectional research is the best choice when it comes to gathering some basic information about some population. Advantages of cross sectional studies include:

• Cheap data collection methods
• Timesaving research option
• Measurements of several variables at a time
• Guidance to further experimental studies.

Limited time is one of the disadvantages of cross sectional studies. As an experiment that takes place only once, it also has some other limitations: 

• Difficulty to identify causal relationship
• No opportunity for long-lasting observation
• Cohort effect among participants who share experience.

Cross Sectional Study Example

Now that you know all ins and outs, let’s review an example of cross sectional study. It should give you an idea of what this type of research should focus on.

Cross-sectional research example Researchers examine the influence of vitamin C  consumption on blood vessels. They first conduct cross-sectional research to identify if there is any change in blood vessels in those individuals who take in vitamin C. If there is some impact, researchers will want to explore this further.

Cross Sectional Research: A Word From StudyCrumb

Like any other research method, a cross-sectional study takes practice. Not that much as you would need to complete a longitudinal research, though… And yet, you should remember that this method won’t work if you want to identify a cause-and-effect relationship. Opt for this type of research if you want to run an initial experiment or just lack time.

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FAQ About Cross Sectional Research Design

1. what is cross sectional correlational study.

As a correlational study, cross-sectional research is used to examine the association between two or more variables. However, as with any other correlational research, you won’t have any chance to manipulate the cause (an independent variable). 

2. Is cross-sectional study qualitative?

In most instances, a cross-sectional study involves working with numbers and specific measurements, and, thus, is quantitative. However, sometimes researchers can also use this method to collect qualitative data or analyze both types of data.

3. How is cross sectional data collected?

Cross sectional data is usually gathered with the help of surveys, polls or self-administered questionnaires. These tools allow researchers to quickly collect information from a large population. However, surveys aren't always accurate and can lead to invalid results.

4. What evidence level is a cross-sectional study?

Based on the validity and overall quality, the evidence level of a cross-sectional study is rather low. The design takes the VI place in the hierarchy since it offers evidence from a single study.

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Original research article, dietary micronutrients intake and its effect on haemoglobin levels of pregnant women for clinic visit in the mount cameroon health area: a cross-sectional study.

• 1 Department of Animal Biology and Conservation, University of Buea, Buea, Cameroon
• 2 International Centre for Agricultural Research in the Dry Areas, ICARDA, Cairo, Egypt
• 3 Department of Biochemistry and Molecular Biology, University of Buea, Buea, Cameroon
• 4 Department of Biomedical Sciences, University of Bamenda, Bamenda, Cameroon
• 5 Department of Microbiology and Immunology, College of Medicine, Drexel University, Philadelphia, PA, United States

Background: Nutritional deficiencies and its consequences such as anaemia are frequent among pregnant women residing in under resource settings. Hence, this study sought to investigate specific dietary micronutrient inadequacy and its effect on maternal haemoglobin levels.

Methods: This institution based cross-sectional survey enrolled 1,014 consenting pregnant women consecutively. Data on socio-demographic, economic and antenatal characteristics were recorded using a structured questionnaire. Minimum dietary diversity for women (MDD-W) was assessed using the 24-h recall method and haemoglobin (Hb) concentration (g/dL) determined using a portable Hb metre. Significant levels between associations was set at p  < 0.05.

Results: Among those enrolled, 40.9% were anaemic while 89.6% had inadequate dietary nutrient intake. In addition, uptake of blood supplements, haem iron, plant and animal-based foods rich in vitamin A were 71.5, 86.2, 35.5 and 12.6%, respectively. Moreover, anaemia prevalence was significantly ( p  < 0.05) lower in women who took iron-folic acid along with food groups rich in haem iron (38.5%) or both plant and animal vitamin A (29.0%). Besides, mean maternal Hb levels was significantly ( p  < 0.001) higher in women who consumed haem iron (11.08 ± 1.35) and vitamin A food groups (11.34 ± 1.30) when compared with their counterparts who did not consume haem iron (10.54 ± 1.19) and vitamin A food groups (10.74 ± 1.31).

Conclusion: Dietary uptake of foods rich in haem-iron and vitamin A significantly improves Hb levels in Cameroonian pregnant women. Our findings underscore the importance of improving maternal nutritional awareness and counselling during antenatal period to reduce the anaemia burden.

Introduction

Micronutrients are vital to health as they ensure normal growth, metabolism and physical wellbeing ( 1 , 2 ). Although required in small amounts, the impact of their deficiency is severe ( 3 ). Globally, more than 2 billion people suffer from micronutrient deficiencies, with the main being iron, zinc, iodine, vitamins A and B ( 4 , 5 ). During pregnancy, these deficiencies which results from; lack of consumption of nutrient-dense food groups, poor understanding of the importance of a diverse diet and inefficient utilisation of available micronutrients ( 6 , 7 ) can lead to a myriad of adverse maternal and perinatal outcomes including; anaemia, increased susceptibility to infectious diseases, low birth weight, preterm birth, increased risk of maternal and neonatal mortality as well as cognitive deficit in the baby later in life ( 2 , 8 ).

Anaemia is a widespread public health problem that has significant consequence for human health, social development, and economic growth ( 9 – 11 ). According to the World Health Organization (WHO), anaemia is a condition in which the haemoglobin concentration within the red blood cells are lower than normal and consequently their oxygen carrying capacity is insufficient to meet the physiological demands of the body ( 12 , 13 ). This results in symptoms such as; body weakness, fatigue, dizziness, palpitations and shortness of breath ( 13 , 14 ). In 2019, the prevalence rates of anaemia was estimated at 29.9% among women of reproductive ages (WRA) and 36.5% in pregnant women ( 15 ). Though preventable, in pregnancy it is still one of the leading causes of maternal and neonatal morbidity and mortality ( 16 , 17 ). Apart from nutritional deficiencies of which iron deficiency is the most prevalent cause of anaemia, other conditions such as folate, zinc, vitamin A and B deficiencies, chronic inflammation, infectious diseases and inherited haemoglobin disorders can as well lead to anaemia ( 12 , 18 , 19 ).

Over the past decade, awareness for anaemia and its consequences for maternal and infant health has increased. For instance, in 2012, the 65th World Health Assembly (WHA) approved global targets for maternal, infant and young child nutrition with a commitment to reduce to half the prevalence of anaemia among WRA (15–49 years) by 2025 ( 20 , 21 ). Ensuing this, the WHO and United Nations Children’s Fund (UNICEF) proposed extending this target to 2030 to align with the United Nations (UN) Sustainable Development Goals ( 21 , 22 ). With this in mind, Cameroon has been committed to curb the burden of maternal anaemia through malaria prophylaxis and haematinic supplementation ( 16 ). Despite efforts, anaemia prevalence rates have not changed over the years as it is still a severe (≥ 40%) health problem in WRA ( 23 , 24 ). An explanation to this high prevalence rates could be an underestimation of the role of dietary micronutrient inadequacy on anaemia. Besides, data on micronutrients are limited in the study area and are thus needed, to design and implement public health programmes targeted at reducing anaemia. Hence, this study aimed to investigate intake of dietary nutrients and its effect on maternal haemoglobin levels in the Mount Cameroon health area.

Materials and methods

This study was conducted at the antenatal care units of various health facilities located in the Buea and Tiko Health Districts of the Mount Cameroon area. The characteristic of the study settings has been described in detail by Jugha et al. ( 25 ). More so, the different health facilities in these health districts were chosen based on their accessibility as well as the localities they serve ( 25 , 26 ).

The tropical equatorial climate of the Mount Cameroon region is made up of a long rainy season accompanied by high rainfall (2,000–10,000 mm) and average temperatures conducive for agriculture, the principal economic activity in the region ( 27 , 28 ). Irrespective of the agricultural biodiversity, starchy staple is the most commonly consumed food group ( 25 ). In addition, malaria is endemic in the area and transmission is perennial ( 29 ) with Plasmodium falciparum accounting for over 90% of malaria parasite infection ( 30 ). Also, anaemia prevalence among pregnant women (≥ 40%) over the years in the area has not changed ( 16 , 25 , 31 ).

Study design, and population

This cross-sectional survey enrolled consenting pregnant women in any trimester of gestation consecutively. Study sample size was estimated using the Cochrane formulae for cross-sectional studies based on the prevalence of anaemia (40%) in the study area ( 25 , 32 ). After adding for a 10% non-response rate (NRR) the overall number of women to be enrolled from both health district was 1,014.

A structured questionnaire (pre-tested) through a face-to-face interview was used to obtain maternal socio-demographic data (setting, age, marital status), educational level, household number, and antenatal clinic data (number of antenatal care visits, gestational age, parity, IPTp-SP and iron-folic acid uptake). Information relating to household wealth that is; housing type, house ownership, toilet type, possession of basic amenities (radio, car, bicycle, television, motorcycle and mobile phone) and source of drinking water were also documented. These indicators of household wealth were subjected to principal component analysis (PCA) in order to determine maternal wealth status ( 33 ).

Dietary micronutrients assessment

The minimum dietary diversity for women (MDD-W) questionnaire, a proxy indicator of micronutrient adequacy was used to determine maternal dietary nutrient intake ( 34 , 35 ). During questionnaire survey, each study respondent was requested to describe all food groups and drinks consumed day and/or night 24 - h before the survey. These food groups (FGs) included: starchy staples; pulses; nuts and seeds; dairy; meat, poultry and fish; eggs; dark green leafy vegetables; vitamin A-rich fruits and vegetables; other vegetables and other fruits ( 25 , 34 ). A score of 1 was attributed to the consumption of any food item within any food group as per the FAO guidelines ( 34 ). Dietary diversity score was obtained by summing up the FGs consumed among the 10 required FGs ( 34 ). Participants were then categorised as having adequate dietary nutrient intake if they consumed at least 5 of more food groups a day prior to the study ( 25 , 34 ).

Moreover, the FGs; dark green leafy vegetables, vitamin A-rich fruits and vegetables, Meat (including organ meat), poultry, fish, eggs and milk products were further reclassified as vitamin A-rich plant foods (dark green leafy vegetables, vitamin A-rich fruits and vegetables), vitamin A-rich animal foods (organ meat, eggs and milk products) and foods rich in haem iron (meat, poultry and fish) as per the FAO guidelines ( 36 ).

Sample collection and laboratory analysis

Venous blood (2 mL) was collected from each pregnant woman using sterile techniques. Maternal Hb concentration (g/dL) was determined in the field using a portable URIT - 12 Hb metre (URIT Medical Electronics Co., Ltd. Guangxi, China). In this study, anaemia status was defined as Hb < 11 g/dL for gravid women in the first and third trimester and Hb < 10.5 g/dL for those in the second trimester of gestation ( 25 , 37 ).

Ethical considerations

Ethical clearance (Ref No: 2019/967-05/UB/SG/IRB/FHS) was obtained from the Faculty of Health Science Institutional Review Board (IRB), University of Buea whereas administrative authorization was gotten from the South West Regional Delegation of Public Health, District Medical and Chief Medical Officers in charge of the health districts and medical facilities, respectively. After sensitising the women on the study objectives, potential risks and benefits, those who gave their consent signed a written informed consent form and were thus included into the study whereas, those presenting with complicated pregnancy or a history of diabetes, hypertensive disorders or pre-eclampsia were not eligible to partake in the study and were therefore, excluded. In addition, participation in the study was voluntary.

Data analysis

Data was analysed using the IBM-Statistical Package for Social Sciences (SPSS) version 23. Continuous data were checked for normality and expressed as means and standard deviation (SD). Descriptive statistics such as mean, SD, frequency and percentages were used to describe data. Furthermore, the Pearson Chi - square test (χ 2 ) was used to evaluate the differences in proportions between uptake of iron - folic acid (IFA), haem iron, vitamin - A food groups and maternal anaemia status. In addition, comparison between the continuous variable (Hb levels) and group parameters (intake of haem iron and vitamin A food groups) was done using the student’s paired t - test. Statistical test was two - tailed and the level of significance set at p  < 0.05.

Characteristics of the study participants

As shown in Table 1 , mean maternal age (± SD) and household size (± SD) of those enrolled was 26.72 (± 5.48) years and 4.44 (± 2.20) persons. Besides, over 50% of the women were married and had a household size of at least four and more members. Furthermore, most (33.9%) of the study participants were within the age group 25–29 years followed by those aged 19–24 years (30.4%; Table 1 ).

Table 1 . Sociodemographic and economic characteristics of the women.

Antenatal care characteristics of the study participants

Of those enrolled, mean gestational age (± SD) was 27.60 (± 7.61) weeks. In addition, gravid women with parity 1–2 constituted 43.3% of the study population. Besides, over 70% of the women had received blood supplements in the form of iron - folic acid. Moreover, 35.5, 12.6 and 86.2% of the women had consumed plant foods rich in vitamin A, animal foods rich in vitamin A and haem iron, respectively ( Table 2 ).

Table 2 . Maternal obstetric characteristics and frequency of dietary micronutrient intake.

Association between uptake of iron-folic acid, haem iron, vitamin A foods and maternal anaemia

As shown in Table 3 , anaemia prevalence rates were lowest in women who took blood supplements (iron - folic acid) alongside food groups rich in haem iron (38.5%, p  = 0.031) as well as both plant and animal vitamin A (29.0%, p  < 0.001) when compared with their respective contemporaries who relied on IFA only ( Table 3 ).

Table 3 . Association between uptake of iron-folic acid, haem iron, vitamin A foods and maternal anaemia.

Intake of haem iron and vitamin A food groups on haemoglobin levels

As illustrated on Figure 1 , mean maternal haemoglobin (Hb) levels was significantly ( p  < 0.001) high in women who consumed haem iron (11.08 ± 1.35), plant (11.25 ± 1.29) and animal foods rich in vitamin A (11.82 ± 1.30) when compared with their counterparts who did not consume haem iron (10.54 ± 1.19), plant (10.87 ± 1.34) and animal foods rich in vitamin A (10.88 ± 1.30; Figure 1 ).

Figure 1 . Average maternal Hb levels Vs intake of (A) Haem iron food groups, (B) Plant vitamin A food groups, (C) Animal vitamin A food groups, (D) Combine plant and animal vitamin A food groups.

In Cameroon, anaemia prevalence among women is still severe (≥ 40%) ( 16 , 24 , 25 ). This high prevalence rate may represent significant constraint for achieving the Global Nutrition Target endorsed by the World Health Assembly of halving anaemia prevalence among WRA by 2025 ( 20 ). This study therefore aimed to evaluate dietary micronutrient intake and their effect on haemoglobin levels of pregnant Cameroonian women.

In order to reduce the risk of anaemia during pregnancy, the WHO recommends a daily oral dose of 60 mg of iron along with 400 μg of folic acid throughout pregnancy and as part of the routine antenatal care services ( 37 ). In Cameroon, iron supplementation is the main strategy for anaemia control and prevention ( 16 , 38 ). In addition, several studies have shown that iron-folic acid uptake during this critical period prevents maternal anaemia while reducing the risk of preterm labour, low birthweight, premature delivery, postpartum haemorrhage ( 39 – 41 ). The observed anaemia prevalence rate (40.9%) among study respondents in the study area despite uptake of iron-folic acid (71.5%) might be due to poor adherence, an aspect this study did not assess. Poor adherence to iron supplements may be as a result of inadequate supply of iron tablets, poor utilisation of prenatal health-care services, gastrointestinal discomfort accompanied with the drug, inability to purchase the tablet, forgetfulness, poor counselling by health care providers regarding the usefulness of the tablet as well as maternal knowledge and beliefs surrounding the tablet ( 42 – 44 ). Besides, this study further showed that combine uptake of iron-folic acid with a diet rich in haem iron or vitamin A food groups is more efficient in reducing the burden of anaemia than iron-folic acid taken alone.

Although diet holds great importance for maternal and neonatal health, inadequate proportions are often consumed most especially by women residing in low - and - middle income countries and study participants in the Mount Cameroon area were no exception (89.6%) ( 25 , 45 , 46 ). According to the WHO, the most common micronutrient deficiencies are; iron, vitamin A and iodine deficiencies ( 2 , 47 ). In this study, 86.2% of the women consumed foods rich in iron specifically haem iron a day before the survey. Dietary iron is present in two forms that is haem iron, which is obtained from animal products such as meat, fish and poultry whereas non-haem iron is obtained from cereals, fruits and vegetables ( 36 , 48 , 49 ). Furthermore, it was observed in this study that consumption of haem iron was associated with increased haemoglobin levels of pregnant women. This finding is in line with observations from Jakarta ( 50 ), Ethiopia ( 51 ) and Pakistan ( 52 ). The increased haemoglobin levels among women who consumed meat, fish and poultry might be due to the fact that, foods rich in haem iron are absorbed from the gut with greater efficiency thus, making their iron content (the main component of haemoglobin) readily available for red blood cell production ( 51 , 53 ).

Adequate vitamin A during pregnancy is essential for maternal and infant health ( 54 , 55 ). Dietary vitamin A is available from two main sources that is, plants (provitamin A) and animals (preformed vitamin A) ( 55 ). Animal foods rich in vitamin A include; eggs, organ meat and dairy products while dark green leafy vegetables, vitamin A rich fruits and vegetables are plant foods rich in vitamin A ( 36 , 56 , 57 ). In this survey, 35.5 and 12.6% of the respondents enrolled consumed plant and animal food groups rich in vitamin A, respectively. The observed low intake of vitamin A animal food groups among study respondents might be due to the inability of the women to purchase eggs, organ meat and milk products. Furthermore, intake of foods rich in vitamin A was associated with maternal haemoglobin levels. Similar correlations have been described elsewhere ( 18 , 58 – 60 ). Inadequate vitamin A intake is thought to cause anaemia through; reduction of the body’s immune response to infectious diseases which in turn leads to anaemia of infection, modulation of erythropoiesis and iron metabolism ( 6 , 58 , 61 ). Besides, vitamin A deficiency is known to increase the risk of iron deficient erythropoiesis and subsequently anaemia by altering absorption, storage, release and transport of iron to the bone marrow ( 62 ). This phenomenon might explain the low Hb levels observed among those who did not consume foods rich in vitamin A.

The current study had some limitations. Firstly, its cross-sectional nature could not establish the cause - and - effect relationship between dietary components and anaemia. In addition, this study did not measure biomarkers of micronutrient deficiency and other indicators of anaemia such as; mean corpuscular haemoglobin concentration (MCHC), mean corpuscular volume (MCV), reticulocyte count. In contrast, this study has as strength in its sample size as well as minimised recall bias by employing the use of the 24 - h recall method to assess dietary diversity. Moreover, this study further demonstrates the effect haem iron and vitamin A rich food groups has on haemoglobin levels. Besides, this study sets the basis for future works determining the association and comparative influence of iron and vitamin A on Hb levels.

Overall, the prevalence of anaemia (40.9%) was high despite adequate uptake of iron supplement (71.5%). Moreover, dietary diversity was inadequate (89.6%). In addition, anaemia prevalence rate was significantly ( p  < 0.05) lower in women who took IFA coupled with a diet rich in haem iron (38.5%) and vitamin A (29.0%). Furthermore, mean haemoglobin levels were significantly (< 0.001) higher in women who consumed haem iron (11.08 ± 1.35) and vitamin-A (11.34 ± 1.30) rich foods a day before the survey when compared with their respective contemporaries who did not. Thus, apart from focusing on iron - folic acid supplementation alone to curb the burden of maternal anaemia, public health authorities and health care givers should improve maternal nutritional awareness on the importance of a diversified diet as this would in turn enhance uptake of foods rich in haematopoietic nutrients thereby reducing anaemia prevalence rate.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Ethics statement

The studies involving humans were approved by Institutional Review Board (IRB), Faculty of Health Science, University of Buea, Cameroon. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.

Author contributions

VJ: Formal analysis, Conceptualization, Data curation, Investigation, Writing – original draft, Writing – review & editing. JA: Formal analysis, Validation, Writing – review & editing. DS-F: Formal analysis, Validation, Writing – review & editing. GT: Formal analysis, Validation, Writing – review & editing. HK: Conceptualization, Supervision, Validation, Writing – review & editing. JA-K: Conceptualization, Supervision, Validation, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

The authors are grateful to all the pregnant women who gave their consent to partake in the study. We are equally thankful to the administrative staffs, midwives, nurses and laboratory technicians of the different health facilities where this study was conducted for their collaboration and assistance.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. Baker, BC, Hayes, DJ, and Jones, RL. Effects of micronutrients on placental function: evidence from clinical studies to animal models. Reproduction . (2018) 156:R69–82. doi: 10.1530/REP-18-0130

PubMed Abstract | Crossref Full Text | Google Scholar

2. Fite, MB, Tura, AK, Yadeta, TA, Oljira, L, Wilfong, T, Mamme, NY, et al. Co-occurrence of iron, folate, and vitamin a deficiency among pregnant women in eastern Ethiopia: a community-based study. BMC Nutrition . (2023) 9:1–8. doi: 10.1186/s40795-023-00724-x

Crossref Full Text | Google Scholar

3. Organization WH. WHO antenatal care recommendations for a positive pregnancy experience: Nutritional interventions update: Multiple micronutrient supplements during pregnancy . Geneva, Switzerland. (2020).

Google Scholar

4. Bailey, RL, West, KP Jr, and Black, RE. The epidemiology of global micronutrient deficiencies. Ann Nutr Metab . (2015) 66:22–33. doi: 10.1159/000371618

5. Littlejohn, PT, Bar-Yoseph, H, Edwards, K, Li, H, Ramirez-Contreras, CY, Holani, R, et al. Multiple micronutrient deficiencies alter energy metabolism in host and gut microbiome in an early-life murine model. Front Nutr . (2023) 10:670. doi: 10.3389/fnut.2023.1151670

6. Abizari, A-R, Azupogo, F, and Brouwer, ID. Subclinical inflammation influences the association between vitamin A-and iron status among schoolchildren in Ghana. PloS one . (2017) 12:e0170747. doi: 10.1371/journal.pone.0170747

7. Afata, TN, Mekonen, S, and Tucho, GT. Serum concentration of zinc, copper, iron, and its associated factors among pregnant women of small-scale farming in western Ethiopia. Sci Rep . (2023) 13:4197. doi: 10.1038/s41598-023-30284-w

8. Glosz, CM, Schaffner, AA, Reaves, SK, Manary, MJ, and Papathakis, PC. Effect of nutritional interventions on micronutrient status in pregnant malawian women with moderate malnutrition: a randomized, controlled trial. Nutrients . (2018) 10:879. doi: 10.3390/nu10070879

9. Organization WH. The world health report 2002: Reducing risks, promoting healthy life . Geneva, Switzerland: World Health Organization (2002).

10. Ntenda, PAM, Chilumpha, S, Mwenyenkulu, ET, Kazambwe, JF, and El-Meidany, W. Clinical malaria and the potential risk of anaemia among preschool-aged children: a population-based study of the 2015–2016 Malawi micronutrient survey. Infect Dis Poverty . (2019) 8:95–11. doi: 10.1186/s40249-019-0607-8

11. Kare, AP, and Gujo, AB. Anemia among pregnant women attending ante natal care clinic in Adare general hospital, southern Ethiopia: prevalence and associated factors. Health Services Insights . (2021) 14:11786329211036303. doi: 10.1177/11786329211036303

12. Organization WH. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity World Health Organization (2011).

13. Chaparro, CM, and Suchdev, PS. Anemia epidemiology, pathophysiology, and etiology in low-and middle-income countries. Ann N Y Acad Sci . (2019) 1450:15–31. doi: 10.1111/nyas.14092

14. Lema, EJ, and Seif, SA. Prevalence of anemia and its associated factors among pregnant women in Ilala municipality-Tanzania: analytical cross-sectional study. Medicine . (2023):102. doi: 10.1097/MD.0000000000033944

15. World Health Organization (WHO). Anaemia in women and children: who global anaemia estimates . Geneva, Switzerland: World Health Organization. (2001). Available at: https://www.who.int/data/gho/data/themes/topics/anaemia_in_women_and_children

16. Anchang-Kimbi, JK, Nkweti, VN, Ntonifor, HN, Apinjoh TOChi, HF, Tata, RB, et al. Profile of red blood cell morphologies and causes of anaemia among pregnant women at first clinic visit in the Mount Cameroon area: a prospective cross sectional study. BMC Res Notes . (2017) 10:1–7. doi: 10.1186/s13104-017-2961-6

17. Bwana, VM, Rumisha, SF, Mremi, IR, Lyimo, EP, and Mboera, LE. Patterns and causes of hospital maternal mortality in Tanzania: a 10-year retrospective analysis. PloS one . (2019) 14:e0214807. doi: 10.1371/journal.pone.0214807

18. Wirth, JP, Rohner, F, Woodruff, BA, Chiwile, F, Yankson, H, Koroma, AS, et al. Anemia, micronutrient deficiencies, and malaria in children and women in Sierra Leone prior to the Ebola outbreak-findings of a cross-sectional study. PLoS One . (2016) 11:e0155031. doi: 10.1371/journal.pone.0155031

19. McGann, PT, Williams, AM, Ellis, G, McElhinney, KE, Romano, L, Woodall, J, et al. Prevalence of inherited blood disorders and associations with malaria and anemia in Malawian children. Blood Adv . (2018) 2:3035–44. doi: 10.1182/bloodadvances.2018023069

20. WHO. Global nutrition targets 2025: Anaemia policy brief . Geneva: WHO/NMH/NHD (2014).

21. Sappani, M, Mani, T, Asirvatham, ES, Joy, M, Babu, M, and Jeyaseelan, L. Trends in prevalence and determinants of severe and moderate anaemia among women of reproductive age during the last 15 years in India. PLoS One . (2023) 18:e0286464. doi: 10.1371/journal.pone.0286464

22. Branca, F, Grummer-Strawn, L, Borghi, E, Blössner, M, and Onis, M. Extension of the WHO maternal, infant and young child nutrition targets to 2030. SCN News . (2015) 41:55–8.

23. WHO. The global prevalence of anaemia in 2011 . Geneva: WHO (2015).

24. National Institute of StatisticsICF. Cameroon Demographic and Health Survey, Yaoundé, Cameroon and Rockville . Maryland, USA: NIS and ICF. (2018).

25. Jugha, VT, Anchang-Kimbi, JK, Anchang, JA, Mbeng, KA, and Kimbi, HK. Dietary diversity and its contribution in the etiology of maternal anemia in conflict hit Mount Cameroon area: a cross-sectional study. Front Nutr . (2021) 7:625178. doi: 10.3389/fnut.2020.625178

26. Anchang-Kimbi, JK, Nkweti, VN, Ntonifor, HN, Apinjoh TOTata, RB, Chi, HF, et al. Plasmodium falciparum parasitaemia and malaria among pregnant women at first clinic visit in the Mount Cameroon area. BMC Infect Dis . (2015) 15:1–10. doi: 10.1186/s12879-015-1211-6

27. Wanji, S, Tanke, T, Atanga, SN, Ajonina, C, Nicholas, T, and Fontenille, D. Anopheles species of the Mount Cameroon region: biting habits, feeding behaviour and entomological inoculation rates. Trop Med Int Health . (2003) 8:643–9. doi: 10.1046/j.1365-3156.2003.01070.x

28. Wanji, S, Kengne-Ouafo, AJ, Eyong, EEJ, Kimbi, HK, Tendongfor, N, Ndamukong-Nyanga, JL, et al. Genetic diversity of plasmodium falciparum merozoite surface protein-1 block 2 in sites of contrasting altitudes and malaria endemicities in the Mount Cameroon region. American J Tropical Med Hygiene . (2012) 86:764–74. doi: 10.4269/ajtmh.2012.11-0433

29. Anchang-Kimbi, JK, Kalaji, LN, Mbacham, HF, Wepnje, GB, Apinjoh TONgole Sumbele, IU, et al. Coverage and effectiveness of intermittent preventive treatment in pregnancy with sulfadoxine–pyrimethamine (IPTp-SP) on adverse pregnancy outcomes in the Mount Cameroon area, South West Cameroon. Malaria J . (2020) 19:1–12. doi: 10.1186/s12936-020-03155-2

30. Sumbele, IUN, Bopda, OSM, Kimbi, HK, Ning, TR, and Nkuo-Akenji, T. Nutritional status of children in a malaria meso endemic area: cross sectional study on prevalence, intensity, predictors, influence on malaria parasitaemia and anaemia severity. BMC Public Health . (2015) 15:1099–9. doi: 10.1186/s12889-015-2462-2

31. Fokam, EB, Ngimuh, L, Anchang-Kimbi, JK, and Wanji, S. Assessment of the usage and effectiveness of intermittent preventive treatment and insecticide-treated nets on the indicators of malaria among pregnant women attending antenatal care in the Buea Health District, Cameroon. Malar J . (2016) 15:1–7. doi: 10.1186/s12936-016-1228-3

32. Cochran, WG. Sampling techniques . 3rd ed. New York, NY: John Wiley & Sons (1977). 442 p.

33. Jugha, VT, Anchang, JA, Taiwe, GS, Kimbi, HK, and Anchang-Kimbi, JK. Association between malaria and undernutrition among pregnant women at presentation for antenatal care in health facilities in the Mount Cameroon region. PLoS One . (2023) 18:e0292550. doi: 10.1371/journal.pone.0292550

34. FAF. Minimum dietary diversity for women: a guide for measurement . Rome: Food and nutrition technical assistance (FANTA III) (2016). 82 p.

35. FAO. Minimum dietary diversity for women . Rome: FAO (2021). 176 p.

36. Kennedy, G, Ballard, T, and Dop, MC. Guidelines for measuring household and individual dietary diversity . US: Nutrition and Consumer Protection Division, Food and Agriculture Organization of the United Nations (2013).

37. WHO. WHO recommendations on antenatal care for a positive pregnancy experience . Geneva. (2016). Available at: https://iris.who.int/bitstream/handle/10665/250796/9789241549912-eng.pdf?sequence=1

38. Fouelifack, FY, Sama, JD, and Sone, CE. Assessment of adherence to iron supplementation among pregnant women in the Yaounde gynaeco-obstetric and paediatric hospital. Pan Afr Med J . (2019) 34:211. doi: 10.11604/pamj.2019.34.211.16446

39. Bhutta, ZA, Darmstadt, GL, Hasan, BS, and Haws, RA. Community-based interventions for improving perinatal and neonatal health outcomes in developing countries: a review of the evidence. Pediatrics . (2005) 115:519–617. doi: 10.1542/peds.2004-1441

40. Abu-Ouf, NM, and Jan, MM. The impact of maternal iron deficiency and iron deficiency anemia on child’s health. Saudi Med J . (2015) 36:146–9. doi: 10.15537/smj.2015.2.10289

41. Malek, L, Umberger, W, Makrides, M, and Zhou, SJ. Poor adherence to folic acid and iodine supplement recommendations in preconception and pregnancy: a cross-sectional analysis. Aust N Z J Public Health . (2016) 40:424–9. doi: 10.1111/1753-6405.12552

42. Taye, B, Abeje, G, and Mekonen, A. Factors associated with compliance of prenatal iron folate supplementation among women in Mecha district, Western Amhara: a cross-sectional study. Pan Afr Med J . (2015) 20:43. doi: 10.11604/pamj.2015.20.43.4894

43. Titilayo, A, Palamuleni, M, and Omisakin, O. Sociodemographic factors influencing adherence to antenatal iron supplementation recommendations among pregnant women in Malawi: analysis of data from the 2010 Malawi demographic and health survey. Malawi Med J . (2016) 28:1–5. doi: 10.4314/mmj.v28i1.1

44. Moshi, FV, Millanzi, WC, and Mwampagatwa, I. Factors associated with uptake of iron supplement during pregnancy among women of reproductive age in Tanzania: an analysis of data from the 2015 to 2016 Tanzania demographic and health survey and malaria indicators survey. Front Public Health . (2021) 9:604058. doi: 10.3389/fpubh.2021.604058

45. Garcia-Casal, MN, Estevez, D, and De-Regil, LM. Multiple micronutrient supplements in pregnancy: implementation considerations for integration as part of quality services in routine antenatal care. Objectives, results, and conclusions of the meeting. Matern Child Nutr . (2018) 14:e12704. doi: 10.1111/mcn.12704

46. Victora, CG, Christian, P, Vidaletti, LP, Gatica-Domínguez, G, Menon, P, and Black, RE. Revisiting maternal and child undernutrition in low-income and middle-income countries: variable progress towards an unfinished agenda. Lancet . (2021) 397:1388–99. doi: 10.1016/S0140-6736(21)00394-9

47. Allen, L, de Benoist, B, Dary, O, and Richard, H. Guidelines on food fortification with micronutrients Geneva, Switzerland: World Health Organization, Food and Agricultural Organization of the United Nations (2006).1–376. Available at: https://iris.who.int/bitstream/handle/10665/43412/9241594012_eng.pdf

48. Abbaspour, N, Hurrell, R, and Kelishadi, R. Review on iron and its importance for human health. J Res Medical Sci: Official J Isfahan University of Med Sci . (2014) 19:164–74.

PubMed Abstract | Google Scholar

49. Young, I, Parker, HM, Rangan, A, Prvan, T, Cook, RL, Donges, CE, et al. Association between haem and non-haem iron intake and serum ferritin in healthy young women. Nutrients . (2018) 10:81. doi: 10.3390/nu10010081

50. Ferdi, J, Bardosono, S, and Medise, BE. Iron intake and its correlation to ferritin and hemoglobin level among children aged 24–36 months in Jakarta in 2020. World Nutrition J . (2021) 5:106–12. doi: 10.25220/WNJ.V05.i1.0014

51. Hailu, T, Kassa, S, Abera, B, Mulu, W, and Genanew, A. Determinant factors of anaemia among pregnant women attending antenatal care clinic in Northwest Ethiopia. Tropical Dis, Travel Med Vaccines . (2019) 5:13–7. doi: 10.1186/s40794-019-0088-6

52. Baig-Ansari, N, Badruddin, SH, Karmaliani, R, Harris, H, Jehan, I, Pasha, O, et al. Anemia prevalence and risk factors in pregnant women in an urban area of Pakistan. Food Nutr Bull . (2008) 29:132–9. doi: 10.1177/156482650802900207

53. Kumar, A, Sharma, E, Marley, A, Samaan, M, and Brookes, M. Iron deficiency Anaemia: pathophysiology, assessment, practical Management. BMJ Open Gastroenterol . (2022) 9:e000759. doi: 10.1136/bmjgast-2021-000759

54. Spiegler, E, Kim, Y-K, Wassef, L, Shete, V, and Quadro, L. Maternal–fetal transfer and metabolism of vitamin a and its precursor β-carotene in the developing tissues. Biochimica et Biophysica Acta (BBA)-molecular and cell biology of. Lipids . (2012) 1821:88–98. doi: 10.1016/j.bbalip.2011.05.003

55. Bastos Maia, S, Rolland Souza, AS, Costa Caminha, MF, Lins da Silva, S, Callou Cruz, RSBL, Carvalho dos Santos, C, et al. Vitamin a and pregnancy: a narrative review. Nutrients . (2019) 11:681. doi: 10.3390/nu11030681

56. FAO WA. Vitamin and mineral requirements in human nutrition . 2nd ed. Geneva, Switzerland: WHO (2004). 362 p.

57. Tanumihardjo, SA, Russell, RM, Stephensen, CB, Gannon, BM, Craft, NE, Haskell, MJ, et al. Biomarkers of nutrition for development (BOND)—vitamin a review. J Nutr . (2016) 146:1816S–48S. doi: 10.3945/jn.115.229708

58. Semba, R, and Bloem, M. The anemia of vitamin a deficiency: epidemiology and pathogenesis. Eur J Clin Nutr . (2002) 56:271–81. doi: 10.1038/sj.ejcn.1601320

59. Engle-Stone, R, Aaron, GJ, Huang, J, Wirth, JP, Namaste, SM, Williams, AM, et al. Predictors of anemia in preschool children: biomarkers reflecting inflammation and nutritional determinants of Anemia (BRINDA) project. Am J Clin Nutr . (2017) 106:402S–15S. doi: 10.3945/ajcn.116.142323

60. Sunardi, D, Bardosono, S, Basrowi, RW, Wasito, E, and Vandenplas, Y. Dietary determinants of anemia in children aged 6–36 months: a cross-sectional study in Indonesia. Nutrients . (2021) 13:2397. doi: 10.3390/nu13072397

61. Fishman, SM, Christian, P, and West, KP. The role of vitamins in the prevention and control of anaemia. Public Health Nutr . (2000) 3:125–50. doi: 10.1017/S1368980000000173

62. Bloem, MW. Interdependence of vitamin a and iron: an important association for programmes of anaemia control. Proc Nutr Soc . (1995) 54:501–8. doi: 10.1079/PNS19950018

Keywords: dietary diversity, micronutrients, haem iron, vitamin A, haemoglobin levels, pregnant women, Mt. Cameroon area, cross-sectional study

Citation: Jugha VT, Anchang JA, Sofeu-Feugaing DD, Taiwe GS, Kimbi HK and Anchang-Kimbi JK (2024) Dietary micronutrients intake and its effect on haemoglobin levels of pregnant women for clinic visit in the Mount Cameroon health area: a cross-sectional study. Front. Nutr . 11:1341625. doi: 10.3389/fnut.2024.1341625

Received: 20 November 2023; Accepted: 10 April 2024; Published: 07 May 2024.

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Copyright © 2024 Jugha, Anchang, Sofeu-Feugaing, Taiwe, Kimbi and Anchang-Kimbi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Vanessa Tita Jugha, [email protected]

This article is part of the Research Topic

Dietary Diversity Indicators: Cultural Preferences and Health Outcomes