The overarching goal of public health informatics is to apply computer science and information technology to promote health and minimize disease and injury at the population level.

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The overarching goal of public health informatics is to apply computer science and information technology to promote health and minimize disease and injury at the population level. Public health informatics uses data from surveys, vital statistics, hospital and clinical statistics, private and public data sources, and government data sources for analysis in improving population health. The goal of technology in public health is to improve case reporting of potential outbreaks early and improve surveillance methods by investigating trends of diseases at a local, national, and global level.

Key concepts of public health informatics is to convert data into information and then knowledge using:

  • Data collection
  • Storage
  • Retrieval

Key functions of knowledge management, which assist public health professionals include:

  • Monitor health status.
  • Diagnose and investigate health problems.
  • Inform and educate the public communities.
  • Develop polies and programs to support community health.
  • Enforce laws and regulations.
  • Link communities to needed health services.
  • Assure that the health professionals are well trained and competent in their field.
  • Evaluate the effectiveness and quality of health services.
  • Research new insights into community health problems.

Tasks:

  • Explore the Chronic Disease Indicators website. Next, select one chronic disease indicator of interest to you. Using the required readings and websites for this week, respond to the following:
    • Describe the most significant functions of knowledge management associated with the health indicator selected.
    • List and describe the various web-based data query programs that are available in your state or another state.
    • Compare and contrast the individual state, CDC, and WHO websites and state the similarities and differences between the types of public health data available with both these organizations.
    • Examine some of the challenges knowledge management presents to public health professionals using tools such as surveillance systems and other technology advancements.

Have to be written in APA format

Applied Biostatistics 1

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PUH 5302, Applied Biostatistics 1

Course Learning Outcomes for Unit VIII

Upon completion of this unit, students should be able to:

3. Evaluate study designs and statistical tests for public health research and analysis. 3.1 Compare and contrast various types of tests used in nonparametric methods. 3.2 Analyze the use of data visualization methods in public health.

Course/Unit Learning Outcomes

Learning Activity

3.1 Unit Lesson Chapter 10 Unit VIII Assessment

3.2 Unit Lesson Chapter 12 Unit VIII Assessment

Reading Assignment

Chapter 10: Nonparametric Tests

Chapter 12: Data Visualization

Unit Lesson

Welcome to Unit VIII. In the previous unit, we learned how to analyze public health information and interpret results of biostatistical analysis. We also defined examples of the dependent and independent variables and closed with a discussion on multivariable methods.

In this unit, we will discuss how to select appropriate study designs and statistical methods for public health. In doing so, we will compare and contrast various methods used in nonparametric statistics and close with some information on data presentation and visualization methods.

Nonparametric Methods

Statistical methods have different forms of classifications such as descriptive and inferential statistics and parametric and nonparametric methods, to list a few. We will concentrate on nonparametric methods, but let’s briefly review some points about parametric methods.

 Parametric methods work best with normally distributed populations (or close to normal populations).

 They use two parameters to achieve normal distributions, namely mean and standard deviation.

 They rely on assumptions made about a given population such as confidence interval for a population with known and unknown standard deviation, confidence interval for a population variance, and confidence interval of two means with unknown standard deviation.

In contrast with parametric methods, nonparametric methods do not have to make any assumptions about the population under study. They do not have any dependence on population under study, do not have fixed parameters, and are distribution-free methods. Many researchers have shown interest in nonparametric methods because, aside from the characteristics described above, they are easy to apply and understand, and they do not have any constraints.

UNIT VIII STUDY GUIDE

Selecting the Appropriate Study Design

 

 

 

PUH 5302, Applied Biostatistics 2

UNIT x STUDY GUIDE

Title

Comparing Parametric and Nonparametric Methods (Summary)

 Parametric statistics depend on normal distribution, but nonparametric statistics do not.

 There are less assumptions made in parametric than nonparametric statistics.

 Parametric statistics use simpler formulae in comparison to nonparametric statistics.

 Parametric statistics are used for normal or close to normal distribution. Nonparametric methods are used for data that are not normally distributed.

 Parametric statistics are commonly used in preliminary data analysis, while nonparametric statistics are not used as often and generally only apply to special cases (Sullivan, 2018).

Applications of Nonparametric Methods Nonparametric methods are mostly used in studies involving populations with attributes that can be ranked. Data that can be ranked with no clear numerical underpinnings or interpretations are normally used in nonparametric analysis. Ordinal data are examples of such. Nonparametric methods are applied widely because they make fewer assumptions about the population under study. In addition, because there are fewer assumptions, they facilitate robust statistics by seeking to provide methods that follow popular statistical methods. However, one of the differences is that nonparametric methods are not affected by outliers or values that are plus or minus a few departures from the mean. Nonparametric methods have been associated with simplicity because they save the researcher from committing to other analyses to justify the use of parametric methods. However, this simplicity may serve as a weakness in that, in cases where a parametric test would be appropriate, the researcher may decide to choose the parametric method over the nonparametric method. Types of Data and Tests Used in Nonparametric Statistics Nonparametric statistics are used on nominal or ordinal data or scales of measurement. The table below gives you a summary of the type of tests used in nonparametric testing. The Chi-square statistics and their modifications are used for nominal data. All other nonparametric statistics are appropriate only when data are measured on an ordinal scale of measurement. See table below for examples of the different tests used for nominal and ordinal data.

Nominal Data Ordinal Data

Chi-Square Goodness-of-Fit Test Mann-Whitney U Test

Chi-Square Test of Independence Wilcoxon Signed Rank Test

McNemar’s Test Kruskal-Wallis Test Friedman Two-way Analysis of Variance (ANOVA) by Ranks Test Spearman rs

For a more comprehensive look at some of the tests above as well as other nonparametric tests read the information below:

 The Chi-Square Goodness-of-Fit is a nonparametric test deployed to establish the significant difference between the observed value and the expected value. It helps to discern how the theoretical value fits the calculated value. It is most used to compare samples involving intervals.

 The Fisher Exact Probability is used to test the statistical significance in certain samples of data. It falls in one of the classes of exact tests because the exact significance of the deviation from the null hypothesis can be calculated instead of approximated. The Fisher Exact Probability test is useful for categorical data to examine the significance of the association between the two categories.

 The Mann-Whitney U test is the nonparametric counterpart of the parametric t-test. It does not require a normal distribution for its calculation, and it is equally effective as the t-test. In order to calculate the Mann-Whitney U test, some assumptions must be made: All observations are independent, they have ordinal data, distributions are equal for null hypothesis, and distributions are not equal for the alternative hypothesis. With these assumptions, the researcher can effectively conduct the test with reliable results.

 The Wilcoxon Signed-Rank test is a nonparametric test used in evaluating the differences in two groups that are correlated. The basic requirement for using this test is that the data must be matched,

 

 

 

PUH 5302, Applied Biostatistics 3

UNIT x STUDY GUIDE

Title

the dependent variable must be continuous, and there should be no ties between the samples. It mostly works with the median of data samples.

 The Kruskal-Wallis test is the nonparametric test for its parametric counterpart, analysis of variance (ANOVA). The two tests are used to examine significant differences between a continuous variable and a categorical variable. The continuous variable must be the dependent variable, and the categorical variable must be the independent variable with two or more groups. Unlike ANOVA, where assumptions of normality are assumed for the dependent variable, the Kruskall-Wallis test does not have such assumptions.

 The Friedman Two-way Analysis of Variance (ANOVA) by Ranks test is also a nonparametric test similar to ANOVA and is used to examine differences across multiple samples using ranking. It is similar to the Kruskal-Wallis test.

 The Kolmogorov-Smirnov test attempts to determine if two datasets are significantly different. It is a distribution-free test and makes no assumption about the distribution of the data. The Kolmogorov- Smirnov test may serve another purpose. It can be modified to function as a goodness-of-fit test, but it has been found to be less powerful in its function as a test for normality compared to other tests.

 The Anderson-Darling test is a modification of the Kolmogorov-Smirnov test and is more powerful. The Anderson-Darling test uses specific distribution to calculate critical values and is more sensitive, making it an advantageous test. The limitation to the test is that it requires calculation of the critical values for each distribution.

In order to choose any of these tests for analysis, we must examine our samples in terms of number and relationship between variables. That is, the researcher must conduct exploratory data analysis or prepare the data for testing. Nonparametric tests, as opposed to parametric tests, use ranking. As an example of ranking, let’s examine the pain scale. The pain scale is often measured from 0 to 10, with 0 representing no pain and 10 representing agonizing pain. Sometimes pain scales use visual anchors such as smiling or crying faces that rank the intensity of the pain. Nonparametric testing uses ranks to compare data without taking the normality of the data into consideration. Let’s now examine the various steps in nonparametric testing. Steps in Nonparametric Testing Like parametric testing (discussed in Chapter 7), the nonparametric testing follows the same five steps of hypothesis testing. Please see page 227 of your textbook for further discussion of these steps. Data Visualization Data visualization is a graphical representation of information communicated to an audience. The information is encoded into visual graphics including charts, lines, and bar graphs. The goal here is to help the researcher communicate information clearly and effectively via graphical means that should stimulate the viewer’s attention (Sullivan, 2018). Recipients of the results of scientific findings need clear and accurate reporting of data and statistical results. These graphic presentations may help to generate interest and provoke the thoughts of the audience. Different Formats of Graphical Presentations Information is presented in different formats including texts, tables, figures, and pie charts. Chapter 12 in the textbook gives a clear picture of each of these various formats. One thing that is common with all of these formats is that they must be labeled effectively to provide meaning and interpretation centered on the information they represent. The chart below gives some of the characteristics of these various formats.

 

 

 

PUH 5302, Applied Biostatistics 4

UNIT x STUDY GUIDE

Title

Texts Tables Figures

 A few numbers

 Data that are secondary or ancillary to main analysis

 Many data points to present and exact values

 Main findings (often readers turn to tables before reading text)

 

 Complex relationships among variables

 Trends over time

 Geographic variation

 Main findings (often readers turn to figures before reading text)

(Sullivan, 2018)

Importance of Data Visualizations To the researcher and the consumer, data visualization is significant because it aids the quick absorption of information. It also helps to save time by looking at the big picture instead of pieces of information and shows patterns and trends in the data. Many consumers have become interested in reading research findings or materials that are presented in graphical forms because the graphical nature of the material helps hold their interest longer. Furthermore, data visualization makes data more accessible and less confusing and helps the researcher share his or her insights with everyone. In many cases, data visualization quickly reveals the outliers in the data and helps researcher or presenters memorize the important insights (Tandon, 2017). Summary Statistical analysis is key to researchers and consumers of the reports advanced from scientific studies. Two main forms of scientific data analysis commonly used in research are parametric and nonparametric methods. Both the parametric and nonparametric follow the same pattern when it comes to data analysis. However, the major difference lies with nonparametric methods not requiring normality of data for analysis. The results of these analyses are sometimes best presented in visual forms for easy and clear presentation to the consumer.

References Sullivan, L. M. (2018). Essentials of biostatistics in public health (3rd ed.). Burlington, MA: Jones & Bartlett

Learning. Tandon, D. (2017, March 14). The Importance of data visualization in your business and 10 ways to pull it off

easily [Blog post]. Retrieved from https://thekinigroup.com/importance-data-visualization/

Reflective Practice Assignment (based on Australia)

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Reflective Practice Assignment (based on Australia)

This is an individual task assessment. You will be required to write a reflective essay (1500-2500 words). You will reflect on the application of your learning related to the readings, lectures, tutorials and prior assessments. You will be tasked to address these two points:

· Your positionality and what you thought to be true in relation to public health research

· How you would seek to undertake research with Indigenous communities

· This is a marking guide for the reflective essay.

—————————————————

Grading criteria

Application of reflection framework

Demonstrates a developing sense of self in relation to research positionality, building on prior experiences to respond to public health research.

Maximum score

15

Knowledge of Indigenous worldviews

Develops clear understanding of undertaking Indigenous research by reflecting on class materials and own experiences.

Maximum score

15

Reflective Practice Assignment (based on Australia)

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Reflective Practice Assignment (based on Australia)

This is an individual task assessment. You will be required to write a reflective essay (1500-2500 words). You will reflect on the application of your learning related to the readings, lectures, tutorials and prior assessments. You will be tasked to address these two points:

· Your positionality and what you thought to be true in relation to public health research

· How you would seek to undertake research with Indigenous communities

· This is a marking guide for the reflective essay.

—————————————————

Grading criteria

Application of reflection framework

Demonstrates a developing sense of self in relation to research positionality, building on prior experiences to respond to public health research.

Maximum score

15

Knowledge of Indigenous worldviews

Develops clear understanding of undertaking Indigenous research by reflecting on class materials and own experiences.

Maximum score

15

 Define public health nursing. Discuss the following three core functions of the public health nurse: assessment, policy development and assurance

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 Define public health nursing. Discuss the following three core functions of the public health nurse: assessment, policy development and assurance. Explain how population-focused nursing practice is different from clinical nursing care delivered in the community. Include a minimum of 250-300 words and two professional references, one of which, may be Stanhope. Initial post due by  Thursday 2330; respond to two of your peers by Monday at 2330.

A paper and powerpoint is needed for this assignment.

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Please see attached powerpoint for instruction.

A paper and powerpoint is needed for this assignment.

This is a three part project, part 1 and 2 have already been completed. I’ve attached part 2 (assignment 2)

Feedback for assignment 2 from professor:

(a) The following 2 statements only have one variable in each research question,

(b) the vulnerability to diabetes is an outcome and can’t be considered as your variable affecting diabetes.

(c) Which variable in the secondary dataset will be used to identify your SES?

(d) each research question needs at lease 2 variables plus the variable “year”

  1. To what extent does the ethnicity of the people of Bronx impact their vulnerability to diabetes?
  2. Is the social-economic status of the people of Bronx a contributing factor to their developing diabetes?

student can use the variable “insurance” to justify the relationship with SES.

Discussion on Data/Statistical Analysis for the Bronx diabetes data

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Virtual class: March 2022 Discussion on Data/Statistical Analysis for the Bronx diabetes data

The third assignment (submission- NO later than Sunday April 10th before 5pm Period.)

at least 2 charts and 2 tables of the five-year trend analyses.

In all charts and tables, you should include the field of “Year” plus at least 2 other fields.

detail explanations of your research findings on these charts and analyses.

Compare 2015 data (treat it as baseline) with the rest of 4 years. Describe the pattern of changes in these five-year trends.

Example: what happened of your findings? Why or how does it happened? When, and where etc. Be creative to describe your research findings from your data analyses.

 

 

 

The Final Examination Power Point and Presentation during class (no later than April 11st Monday Morning before noon, period):

A complete project paper including “at least” a. Introduction, b. Research Findings with at least 2 charts and tables from the third assignment, and c. Conclusion.

(assuming the first 2 assignments was the proposal of applying research grant)

 

Of curse, the more creative the better , you can add more paragraphs , such as your recommendations, etc., etc.

 

Put your project paper on the Power Point. You have 10 mins to present your project to the class.

 

 

Discussion on Statistical Analysis for the Bronx diabetic data *REQUIREMENTS:* A. Required: Five year-trends for the comparison purpose is required for all charts. You can treat 2015 data as the baseline, compare with the rest of the 4-year trends. B. You have to submit “at least” 2 analyses with 2 charts and 2 tables for your third assignment and your final examination: Power Point Presentation Suggestion: C. Run frequency tables of the fields (variables) you pick to see how clean are the data

Data analysis guidelines: You can pick whatever variables you think are significant or logical for this data analysis.

 

Example : Not a requirements

 

1. Discuss what dataset you want to focus on: Inpatient , Outpatient, Emergency Department or all three.

2. Report card for each facilities ; Comparing Facilities on ED use, or mortality rate, readmission rate?

 

3.compare the changing pattern; (any before and after intervention? Such as wellness program)

Discussion on Statistical Analysis for the Bronx diabetic data 4. Focus on all kind of demographic characteristics, gender, race, age group zip codes etc 5. If you have clinical background, you can compare different type of diabetics- Diagnosis descriptions, e.g. Type I, Type II or severity of the illness 6.Using the census data, NYCDOH or NYSDOH data to figure out the target population of diabetic in Bronx as denominator, using the Bronx RHIO data as the numerator. (Hint: to calculate the number of in-care or unmet need etc)

Discussion on Statistical Analysis for the Bronx diabetic data 7. Based on the meaningful use of “pay for performance” think about how to create a report card (Hint: by hospitals) 8.Think about the relationship between the number of encounter and the number of unique (unduplicated count of patient) . (Hint: level of utilization, frequency of utilization) 9.Duration of service (Hint: LOS, ALOS)

Discussion on Statistical Analysis for the Bronx diabetic data 10. Mortality rate (identify the denominator; target population, which hospital) 11.Comparing type of insurance, Medicaid, Medicare, Private 12.How often patient use ED, who and which hospital provided more ED services

Discussion on Statistical Analysis for the Bronx diabetic data 13. Comparing age GROUP, (how to break down age groups) by race, gender etc 14.Categorize type of services by demographic characteristics 15.comparing zip codes by type of services, race, languages 16.Does language and zip codes has any relationship

Discussion on Statistical Analysis for the Bronx diabetic data 17. diagnosis are (1) more significant, (2) in which zip codes, (3) affecting which demographic characteristics (4) utilization of ED (5) ALOS (Hint: Descending order of diagnosis 18. How to compare the baseline data with the trends of other years. 19. Set the baseline of ED utilization, compare the ED use with the rest of the years 20. City by ED utilization, mortality rate, type of insurance, diagnosis description, ALOS, languages etc.,

Graphical Representation of Data

11

Health IT Workforce Curriculum Version 3.0/Spring 2012

 

p <0.001

Look at this graphic. There clearly has been a statistically significant improvement before and after the change.

But what other information can you gather from this graph?

Was the improvement due to the change?

Is the improvement holding over time?

It is very difficult with this type of graphic representation of data to determine what happened overtime and to get a good understanding of what is happening to the system.

 

11

Run Charts

12

Health IT Workforce Curriculum Version 3.0/Spring 2012

 

These three scenarios reflect the data in the histogram. They all have a pre-change average of 70 and a post change average of 30. However, as you examine the display of the outcome over time you will realize they tell very different stories.

In the blue chart you can see that the outcome hovered around 70 before the change and although there is forty point range the outcomes after the change hovered around 30. The change seems to have produced an improvement in the outcome.

In the green chart there is a progressive decrease of the value of the outcome that started before the implementation of the change. Although there seems to have been an improvement, it’s not due to the change implemented.

Finally, in the maroon chart there is an improvement after the change, but it seems to be short lived since, after the March measure, the outcome seems to worsen again.

 

12

Discuss how the concepts in this course can be applied to real-world   situations and increase your chances of career or life success.

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Discuss how the concepts in this course can be applied to real-world   situations and increase your chances of career or life success.

The journal entry must be at least 300 words in length. APA format   required.

See attached study guide for reference materials. 

Find a media article (NOT a scientific study) pertaining to epidemiology or public health and prepare a 5-minute oral presentation based on its content.

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Find a media article (NOT a scientific study) pertaining to epidemiology or public health and prepare a 5-minute oral presentation based on its content.

1) Give a reference of the article you used at the top of the first page. The reference must be in correct APA format.

Write your answers to the questions using full sentences (minimum 250 words) and upload the document to Moodle. The document does not have to be in APA format, aside from the reference at the top of the page.

3) Answer the assigned questions in your presentation and be prepared to answer additional questions after your presentation.

Presentations will take place in Weeks 4-8. There will be sign-ups for presentation dates in Moodle.

Submit in either Microsoft Word or as a PDF. Do not use Pages or turn in handwritten work. Any assignment not submitted in either Microsoft Word or PDF format will not be accepted.

Assigned Questions

1. What is the problem? Is it an epidemic? Why or why not?

2. Who is affected?

3. What is/are the risk factor(s)?

4. What regulatory or public health bodies are involved?

5. What control measures have been taken (disease control, risk control, prevention)?

6. What obstacles to control are there, if any?

7. What kind of testing, if any, was done?

8. What research questions and study designs might be applicable to this situation and why?

Vitamin A and D intake in pregnancy, infant supplementation, and asthma development: the Norwegian Mother and Child Cohort

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Vitamin A and D intake in pregnancy, infant supplementation, and asthma development: the Norwegian Mother and Child Cohort

Christine L Parr,1,4 Maria C Magnus,1,5,6 Øystein Karlstad,1 Kristin Holvik,1 Nicolai A Lund-Blix,1,7 Margareta Haugen,2

Christian M Page,1 Per Nafstad,1,8 Per M Ueland,9,10 Stephanie J London,11 Siri E Håberg,1,3 and Wenche Nystad1

1Division of Mental and Physical Health; 2Department of Exposure and Risk Assessment; and 3Center for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway; 4Department of Nursing and Health Promotion, OsloMet–Oslo Metropolitan University, Oslo, Norway; 5Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom; 6Department of Population Health Sciences, Bristol Medical School, Bris- tol, United Kingdom; 7Division of Pediatric and Adolescent Medicine, Department of Pediatrics, Oslo University Hospital, Oslo, Norway; 8Department of Community Medicine, University of Oslo, Oslo, Norway; 9Department of Clinical Science, University of Bergen, Bergen, Norway; 10Laboratory of Clini- cal Biochemistry, Haukeland University Hospital, Bergen, Norway; and 11Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Department of Health and Human Services, Research Triangle Park, NC

ABSTRACT Background: Western diets may provide excess vitamin A, which is potentially toxic and could adversely affect respiratory health and counteract benefits from vitamin D. Objective: The aim of this study was to examine child asthma at age 7 y in relation to maternal intake of vitamins A and D during preg- nancy, infant supplementation with these vitamins, and their potential interaction. Design: We studied 61,676 school-age children (born during 2002– 2007) from the Norwegian Mother and Child Cohort with data on maternal total (food and supplement) nutrient intake in pregnancy (food-frequency questionnaire validated against biomarkers) and in- fant supplement use at age 6 mo (n = 54,142 children). Linkage with the Norwegian Prescription Database enabled near-complete follow- up (end of second quarter in 2015) for dispensed medications to clas- sify asthma. We used log-binomial regression to calculate adjusted RRs (aRRs) for asthma with 95% CIs. Results: Asthma increased according to maternal intake of to- tal vitamin A [retinol activity equivalents (RAEs)] in the highest (≥2031 RAEs/d) compared with the lowest (≤779 RAEs/d) quin- tile (aRR: 1.21; 95% CI: 1.05, 1.40) and decreased for total vitamin D in the highest (≥13.6 µg/d) compared with the lowest (≤3.5 µg/d) quintile (aRR: 0.81; 95% CI: 0.67, 0.97) during pregnancy. No as- sociation was observed for maternal intake in the highest quintiles of both nutrients (aRR: 0.99; 95% CI: 0.83, 1.18) and infant supple- mentation with vitamin D or cod liver oil. Conclusions: Excess vitamin A (≥2.5 times the recommended in- take) during pregnancy was associated with increased risk, whereas vitamin D intake close to recommendations was associated with a re- duced risk of asthma in school-age children. No association for high intakes of both nutrients suggests antagonistic effects of vitamins A and D. This trial was registered at http://www.clinicaltrials.gov as NCT03197233. Am J Clin Nutr 2018;107:789–798.

Keywords: food-frequency questionnaire, dietary supplements, pregnant women, infants, vitamin A, vitamin D, pediatric asthma,

prescriptions, Norwegian Prescription Database, Norwegian Mother and Child Cohort

INTRODUCTION

Asthma is currently among the top 5 chronic conditions con- tributing to the global burden of disease in children aged 5–14 y (1). Unfavorable changes in diet have been hypothesized to in- crease the susceptibility to asthma (2) and dietary exposures in utero and infancy could play a role, in particular for childhood onset of the disease (3).

Fat-soluble vitamins have a broad range of effects related to antioxidant properties (4), immune function (5), and lung devel- opment (6). In particular, vitamin D has attracted much interest because of widespread deficiency in Western populations (7).

The Norwegian Mother and Child Cohort Study is supported by the Norwe- gian Ministry of Health and Care Services and the Ministry of Education and Research, NIH/National Institute of Environmental Health Sciences (contract no. N01-ES-75558), and NIH/National Institute of Neurological Disorders and Stroke (grant nos. 1 UO1 NS 047537-01 and 2 UO1 NS 047537-06A1). This work was also supported by the Norwegian Research Council (grant no. 221097; to WN) and by the Intramural Research Program of the NIH, Na- tional Institute of Environmental Health Sciences (ZO1 ES49019; to SJL). The funders of the study had no role in study design, data collection, data

analysis and interpretation, writing of the report, or the decision to submit the article for publication. Supplemental Figure 1 and Supplemental Tables 1–8 are available from the

“Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/ajcn/. Address correspondence to CLP (e-mail: christine-louise.parr@fhi.no). Abbreviations used: FFQ, food-frequency questionnaire; MoBa, Nor-

wegian Mother and Child Cohort Study; NorPD, Norwegian Prescription Database; RAE, retinol activity equivalent. Received June 13, 2017. Accepted for publication January 17, 2018. First published online April 20, 2018; doi: https://doi.org/10.1093/ajcn/

nqy016.

Am J Clin Nutr 2018;107:789–798. Printed in USA. © 2018 American Society for Nutrition. This work is written by (a) US Government employee(s) and is in the public domain in the US. 789

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nloaded from https://academ

ic.oup.com /ajcn/article-abstract/107/5/789/4979668 by guest on 15 O

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790 PARR ET AL.

Studies that used Mendelian randomization do not support that genetically lowered 25-hydroxyvitamin D is a risk factor for asthma (8). However, randomized trials (9, 10) and a meta- analysis of birth cohort studies (11) suggest that prenatal vita- min D supplementation above the regular dose (9, 10), and higher maternal circulating 25-hydroxyvitamin D (11), may reduce the susceptibility to asthma in the offspring, although follow-up of children to school age is not yet available in the trials.

Vitamin A deficiency poses a public health problem in parts of the world, but westernized diets may provide excess vitamin A (12–14) from increasing intakes of animal products and fortified foods and the use of dietary supplements. High dietary vitamin A has been associated with increased asthma severity in a murine model (15), but human studies are limited by potential toxic effects and a lack of feasible biomarkers for assessing adequate or subtoxic status (16). Observational studies, rather than trials, are therefore important to examine unintended health effects of vitamin A excess at the population level. Previous observational studies of vitamin A and asthma have mainly focused on the antioxidant properties of carotenoids (3) and have not included retinol, the most potent form of vitamin A. Vitamin A supplemen- tation trials have been conducted in areas with endemic deficiency (17, 18) where the effects on respiratory outcomes could differ from those in well-nourished populations due to differences in baseline vitamin A status (19). Few studies, to our knowledge, have examined the risk of child asthma in relation to prenatal con- centrations of vitamin A, including retinol, outside of deficient populations (20, 21) or the importance of prenatal compared with early postnatal exposure. Furthermore, high vitamin A intake could potentially counteract the beneficial effects of vitamin D, due to competition for the nuclear retinoid X receptor (22).

Our objective was to investigate the association of maternal intakes of vitamins A and D during pregnancy, infant exposure to dietary supplements containing these nutrients, and potential nutrient interaction, with current asthma at school age when the diagnosis is more reliable than at earlier ages. Norway offers advantages for the study of high intakes of vitamin A during pregnancy because of a generally high intake from food sources in addition to the widespread use of cod liver oil as a dietary supplement.

METHODS

Study population

The study included participants in the Norwegian Mother and Child Cohort Study (MoBa), a population-based pregnancy co- hort (births during 1999–2009) administered by the Norwegian Institute of Public Health (23, 24). Women were recruited na- tionwide (41% participation) at ∼18 wk of gestation when a pre- natal screening is offered to all pregnant women. For the cur- rent study we linked MoBa file version 9 (115,398 children and 95,248 mothers) with the Medical Birth Registry of Norway (hereafter referred to as the birth registry) and the Norwegian Prescription Database (NorPD), with follow-up to the end of the second quarter of 2015. The current study was registered at http://www.clinicaltrials.gov as NCT03197233. Eligible children (Figure 1) had available data on maternal dietary intake in preg- nancy from a validated food-frequency questionnaire (FFQ) ad- ministered at ∼20 gestational weeks and prescription follow-up

for ≥12 mo from age 6 y (n = 61,676; born 2002–2007), of whom 89% (n = 55,142) had data on infant supplement use at 6 mo. We used a random subsample of 2244 births from 2002–2003 to compare maternal dietary intake with plasma concentrations of fat-soluble vitamins at 18 gestational weeks.

Ethical approval

The MoBa study has been approved by the Norwegian Data Inspectorate (reference 01/4325) and the Regional Committee for Medical Research Ethics (refererence S-97045, S-95). All of the participants gave written informed consent at the time of enrollment. The current study was approved by the Regional Committee for Medical Research Ethics of South/East Norway.

Dietary exposure assessment and biomarker comparisons

Total (food and supplement) nutrient intakes during pregnancy were estimated from the FFQ, which queried about intake since becoming pregnant. The FFQ has been validated against a 4-d weighed food diary and with selected biomarkers (25, 26). To- tal vitamin A (sum of total retinol and total β-carotene) was ex- pressed as daily retinol activity equivalents (RAEs) per day by using the conversion factors 1 μg retinol (from diet or supple- ments) = 12 μg β-carotene from diet = 2 μg β-carotene from supplements to account for differences in bioavailability (27). To- tal vitamin D (micrograms per day) included vitamin D3 from foods and vitamins D2 and D3 from supplements. Nutrient intake was calculated by using the Norwegian Food Composition Ta- ble (28) and a compiled database of dietary supplements, mainly based on the manufacturers’ information. Maternal plasma retinol and 25-hydroxyvitamin D2 and D3 were measured at Bevital AS laboratories in Bergen, Norway (www.bevital.no), in a single, nonfasting venous blood sample drawn at ∼18 wk of gestation. The frequency of infant supplement use (never, sometimes, or daily) was assessed from a follow-up questionnaire mailed at 6 mo of age. We analyzed the use of the following supplement categories containing vitamins A or D or both: vitamin D only (liquid oil-based formula), cod liver oil, multivitamins, and any vitamin D supplement, excluding multivitamins. The latter cat- egory included vitamin D only, cod liver oil, and less common supplements (fish oil with added vitamin D, liquid vitamin A and vitamin D formula, vitamin D with fluoride, and other vitamin D combinations).

Outcome measures of children’s asthma

We examined current asthma in children at ∼7 y of age, defined as having ≥2 pharmacy dispensations of asthma medication in the NorPD within a 12-mo interval, the first prescription being dis- pensed between ages 6 and 7 y. Noncases were all children who did not meet these criteria. Asthma medications were inhaled β2- agonists, inhaled glucocorticoids, combination inhalers with β2- agonists and glucocorticoids, or leukotriene receptor antagonists.

Covariates

Potential confounders and covariates were based on data from the birth registry (maternal age at delivery, parity, region of de- livery, mode of delivery, child’s sex, birth weight, and gestational

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VITAMINS A AND D AND ASTHMA DEVELOPMENT 791

FIGURE 1 Sample selection and eligibility criteria. FFQ, food-frequency questionnaire; MoBa, Norwegian Mother and Child Cohort Study.

age) or MoBa questionnaires completed at approximately gesta- tional weeks 18 (inclusion), 20 (FFQ), and 30 and when the child was aged 6 mo.

Because cod liver oil and other omega-3 supplements con- tribute to the intake of vitamins A and D in many MoBa women (13), we also evaluated maternal intakes of other nutrients pro- vided by these supplements, including vitamin E (preservative, antioxidant) and long-chain n–3 fatty acids (EPA, docosapen- taenoic acid, and DHA). In addition, we included vitamin C as a measure of fruit and vegetable intake (29), folate intake (30), and total energy intake. In sensitivity analyses, we also eval- uated maternal zinc intake (3) and birth year to control for a potential cohort effect. To assess potential confounding by UV exposure in the analysis of vitamin D intake, we included leisure- time physical activity (0, ≤1, 2–4, or ≥5 times/wk) and solar- ium use (0, 1–5, or ≥6 total times) in pregnancy, geographical region of delivery within Norway (South and East, West, Mid,

North) as a proxy for latitude of residence, and season of deliv- ery (January–March, April–June, July–September, or October– December). Maternal histories of asthma and allergic disorders (separate variables) were defined as ever reports at week 18 of asthma or hay fever, atopic dermatitis, animal hair allergies, or “other” allergies.

Many clinical practice guidelines recommend the use of di- etary supplements, including multivitamins, to ensure adequate nutrient supply to low-birth-weight or premature infants (31). To adjust for child frailty, which could be related to both supple- ment use (therapeutic or nontherapeutic) and later asthma suscep- tibility, we included low birth weight (<2500 g), premature birth (gestational age <37 wk), and postnatal exposures in the first 6 mo to full breastfeeding (number of months), respiratory tract infections (no or yes), and maternal smoking (no, sometimes, or daily) in the main analysis. In sensitivity analyses, we addition- ally included child’s sex, birth season, cesarean delivery (no or

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yes), and use of paracetamol or acetaminophen (no or yes) and antibiotics (no or yes) in the first 6 mo.

Statistical analysis

We examined associations of maternal vitamin A and D intake during pregnancy (exposures) and infant supplement use (expo- sures) with children’s asthma (outcome) by using log binomial regression. We calculated RRs with 95% CIs on the basis of ro- bust cluster variance estimation and controlled for potential con- founding by multivariable adjustment. The NorPD linkage en- abled near-complete follow-up for asthma.

Our regression models were based on a directed acyclic graph for the hypothesized causal relations (Supplemental Figure 1). According to the graph, the effects of maternal intake and infant supplementation on children’s asthma can be estimated indepen- dently when potential confounding factors and mediators are ad- justed for. In the analysis of maternal intake (model 1), vitamins A and D were mutually adjusted for (Spearman correlation of 0.53, continuous data), and we additionally adjusted for total intakes of other nutrients (vitamin E; sum of the n–3 fatty acids EPA, do- cosapentaenoic acid, and DHA; vitamin C; and folate) and energy during pregnancy, maternal prenatal factors (age at delivery, par- ity, prepregnancy BMI, education, history of asthma and atopy, and smoking in pregnancy), and birth weight and prematurity as potential mediators. In the analysis of infant supplementation (model 2), we mutually adjusted for the different supplements given and included all model 1 factors and postnatal child factors (months of full breastfeeding, child respiratory tract infections in the first 6 mo, and maternal smoking since birth). Missing val- ues in individual covariates were <5% (Supplemental Table 1) and handled by multiple imputation by using chained equations (10 imputations). For 10.6% of the main study sample with miss- ing questionnaire follow-up at age 6 mo (6534 of 61,676), we assessed the effect of imputing the infant supplement exposure data before performing multivariable adjustments.

All of the maternal nutrient intake variables were included as quintiles to account for a potential nonlinear association with children’s asthma. We tested for linearity by including the quin- tile values (ordinal scale) as a continuous variable. To examine the potential interaction between vitamins A and D in the mother, we created a binary variable for high (highest quintile) compared with low (all lower quintiles) intakes of each vitamin and 4 mu- tually exclusive exposure categories for the following combina- tions: low vitamin A and low vitamin D, high vitamin A and low vitamin D, high vitamin D and low vitamin A, and high vitamin A and high vitamin D. To account for multiple supplement use in children, we created 6 mutually exclusive categories for daily or sometimes compared with never use of the following: 1) vitamin D only; 2) cod liver oil only; 3) multivitamin only; 4) any vitamin D supplement, including cod liver oil, combined with a multivi- tamin; 5) multiple vitamin D supplements (e.g., vitamin D only combined with a fish-oil supplement containing vitamin D); and 6) none of the categories (reference).

In sensitivity analyses, we added more covariates to our main multivariable regression models, as described in Results, and we performed propensity score matching as an alternative method of controlling for potential confounding (32). We tested for multi- plicative interaction between maternal intakes of vitamin A and vitamin D, taking potential nonlinearity into account by including

all spline term combinations from restricted cubic spline models with 4 knots. We also assessed the potential influence of unmea- sured confounding by using a recently published framework de- veloped by Ding and VanderWeele (33). The significance level was 5% for all tests. The analyses were conducted in Stata 14.0 (StataCorp LP).

RESULTS

Participant selection is shown in Figure 1, and selected partic- ipant characteristics are shown in Table 1 (mothers) and Table 2 (children). Characteristics were similar for the main study sam- ple, the subsample with questionnaire follow-up at 6 mo, and the biomarker subsample (Supplemental Table 1).

Characteristics of mothers and children

Associations between maternal characteristics and dietary in- take in pregnancy (n = 61,676) were generally in the same di- rection for vitamins A and D. High intakes were associated with older age, higher education, primiparity, lower BMI, less smok- ing, and supplement use (Table 1).

Supplementation with cod liver oil at age 6 mo was related to high maternal intakes of both vitamins A and D (Table 1) and was higher in children with positive health indicators (birth weight ≥2500 g, term birth, breastfeeding ≥6 mo, and no respiratory tract infections or postnatal maternal smoking) (Table 2). The use of multivitamins (percentage) was much higher among low–birth weight (45%) and premature (31%) children, indicating therapeu- tic use according to clinical practice guidelines (31), and was as- sociated with shorter breastfeeding and more postnatal maternal smoking (Table 2).

Maternal intakes of vitamins A and D and child asthma

The prevalence of current asthma at age 7 y, based on prescrip- tion registry data, was 4.1% (2546 of 61,676). Children born to women in the highest compared with the lowest quintile of total vitamin A intake during pregnancy had a slightly higher preva- lence of asthma (4.9% compared with 4.1%), and the adjusted RR was 20% higher (Table 3). We observed the lowest preva- lence of asthma (3.6%) in the second quintile of total vitamin A (780–1102 RAEs/d) in which intake was close to, or slightly above, the public recommendation for pregnant women of 800 RAEs/d in Nordic countries (34), which is similar to other na- tional recommendations (35). Relative to the second quintile, the adjusted RR of asthma was 32% higher (95% CI: 1.15, 1.51) in the highest quintile. The effect of total vitamin A (retinol and β- carotene) was only marginally stronger than for total retinol. Total β-carotene showed a weak, but positive association with asthma after adjustment for total retinol. The adjusted RR for the high- est (≥4007 µg/d) compared with the lowest (≤1360 µg/d) quin- tile of β-carotene was 1.11 (95% CI: 0.98, 1.27) (Supplemental Table 2). The Spearman correlation between total retinol and to- tal β-carotene (continuous data) was 0.12. A high intake of vita- min A from food was not associated with asthma when the study sample was restricted to nonusers of retinol-containing supple- ments (712 cases; n = 16,924). The adjusted RR was 1.05 (95% CI: 0.81, 1.36) for the highest (≥1462 RAEs/d) compared with

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TABLE 1 Distribution of maternal characteristics according to the lowest (Q1) and highest (Q5) quintiles of total vitamin A and D intake in pregnancy1

Vitamin A Vitamin D3

Q1 (≤779 RAEs/d) Q5 (≥2031 RAEs/d) Q1 (≤3.5 µg/d) Q5 (≥13.6 µg/d) n 12,331 12,346 12,089 12,378 Maternal age at delivery, %

<25 y 12.9 11.5 13.6 9.5 25–30 y 43.0 42.0 42.3 40.1 >30 y 44.0 46.5 44.1 50.4

Previous children, % 0 43.5 46.3 38.7 49.2 1 36.8 34.6 39.1 33.2 ≥2 19.7 19.2 22.2 17.6

Maternal education, % Less than high school 9.5 8.3 10.7 6.5 High school 33.4 30.3 35.4 27.0 ≤4 y of college 38.6 40.8 37.7 41.8 >4 y of college 18.0 20.1 15.8 24.3 Missing 0.5 0.4 0.4 0.4

Maternal prepregnancy BMI (kg/m2), % <18.5 2.3 3.1 2.2 3.2 18.5–24.9 57.8 64.9 56.0 67.9 25.0–29.9 25.0 20.3 25.9 19.5 ≥30 11.8 9.1 12.9 7.1 Missing 3.0 2.6 3.1 2.4

Maternal smoking in pregnancy, % No 74.8 76.2 73.0 78.7 Stopped in pregnancy 15.6 15.8 16.0 14.7 Yes 9.6 8.0 11.0 6.6 Missing <0.01 0.00 <0.01 0.02

Maternal history of asthma, % yes 7.2 8.0 8.0 7.2 Maternal history of atopy, % yes 29.9 32.5 30.0 32.1 Supplement use in pregnancy, % yes Cod liver oil 12.4 31.8 2.2 70.2 Other n–3 supplement 28.4 42.8 21.6 22.5 Multivitamin 19.2 67.3 10.0 69.4 Folic acid 39.7 75.9 32.3 75.7

Child supplement use at 6 mo (n = 55,142), % yes Cod liver oil 40.9 51.4 38.4 59.3 Vitamin D drops 24.5 24.8 23.2 23.9 Multivitamins 9.1 8.9 9.6 7.1

1n = 61,676. Q, quintile; RAE, retinol activity equivalent.

the lowest (≤97 RAEs/d) quintile of food vitamin A intake (re- sults not shown).

A high intake of vitamin D during pregnancy was associated with less-frequent asthma (3.9% compared with 4.4% for the highest compared with the lowest quintile), and the adjusted RR was ∼20% lower in the highest compared with the lowest quintile (Table 3). We observed no adverse effect of high vitamin A, or a protective effect of vitamin D, for intakes in the highest quintiles of both nutrients (Table 4).

Food and supplement contributions to maternal intake of total vitamins A and D

The use of supplements containing retinol, including cod liver oil, was common (73% overall compared with 86% in the high- est quintile). The median intake of supplemental retinol among users was ≥300 µg/d in the third through fifth quintiles of to- tal vitamin A intake, indicating that many pregnant women take

more than the standard daily dose of 250 µg, or combine multi- ple supplements. However, food retinol contributed most to total vitamin A (Supplemental Table 3). The main food sources were sandwich meats, including liver spread, fortified margarine, and dairy products. In Norway, dairy products are not fortified with retinol. Low-fat milk is fortified with low amounts of vitamin D, but food intake of vitamin D varied little, and the use of supple- mental vitamin D (76% overall compared with 99% in the highest quintile) was an important contributor to total vitamin D intake (Supplemental Table 4).

Biomarker comparisons

In the biomarker subsample (n = 2244), maternal plasma vitamin D3 concentration increased across each quintile of total vitamin D intake (medians: 68, 72, 74, 75, and 82 nmol/L for the first through the fifth quintile, respectively; see Supplemental Table 4). The overall plasma-diet Spearman correlation

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TABLE 2 Distribution of child characteristics according to any (sometimes or daily) postnatal supplement use in the first 6 mo1

n % Cod liver oil, % Vitamin D drops, % Multivitamin, %

Child birth weight, g <2500 1473 2.7 43.1 12.8 44.5 2500–4500 51,223 92.9 54.3 29.3 8.4 ≥4501 2424 4.4 54.0 25.5 8.1 Missing 22 0.04 40.9 27.3 22.7

Preterm birth No (≥37 wk gestation) 52,346 94.9 54.4 29.1 8.3 Yes (≤36 wk gestation) 2577 4.7 46.7 19.9 30.7 Missing 219 0.4 49.8 22.8 12.8

Months of full breastfeeding 0 658 1.2 47.6 18.2 13.2 1 to <4 21,295 38.6 51.4 27.7 11.1 4 to <6 25,354 46.0 55.2 29.9 8.9 ≥6 7835 14.2 57.7 27.9 5.9

Respiratory tract infections in the first 6 mo

No 50,838 92.2 54.1 28.9 9.2 Yes, not hospitalized 1645 3.0 51.2 26.7 12.2 Yes, hospitalized 1033 1.9 50.3 24.2 12.7 Missing 1626 2.9 56.0 25.5 8.9

Postnatal maternal smoking in the first 6 mo

No 45,680 82.8 54.9 29.7 8.8 Some 3095 5.6 51.1 28.4 9.6 Daily 4149 7.5 46.3 21.0 14.8 Missing 2218 4.0 54.4 21.6 11.3

1n = 55,142.

(continuous) for vitamin D varied with the season of blood draw, from 0.15 in summer to 0.32 in winter. Associations with indicators of UV exposure were in the expected direction (Supplemental Table 5): plasma vitamin D3 increased with leisure-time physical activity and tanning bed use in pregnancy and from North to South for geographical region of delivery. The maternal plasma retinol concentration (median: 1.64 µmol/L; IQR: 1.46–1.83 µmol/L) varied little with vitamin A intake

(see Supplemental Table 3), also as expected, due to its strict homeostatic control.

Infant supplementation and child asthma

Daily infant supplementation with vitamin D only or cod liver oil was not associated with the risk of asthma at school age. Daily use of multivitamins was associated with a 19% higher RR after

TABLE 3 Total vitamin A and vitamin D intake in pregnancy and RR estimates (95% CIs) for current asthma at age 7 y1

Quintiles of intake Cases/total n Prevalence, % Crude RR Adjusted RR2

Total vitamin A (RAEs/d) Q1 (≤779) 506/12,331 4.1 1 (ref) 1 (ref) Q2 (780–1102) 445/12,323 3.6 0.88 (0.78, 1.00) 0.92 (0.80, 1.05) Q3 (1103–1479) 475/12,331 3.9 0.94 (0.83, 1.06) 0.99 (0.86, 1.13) Q4 (1480–2030) 520/12,345 4.2 1.03 (0.91, 1.16) 1.08 (0.93, 1.24) Q5 (≥2031) 600/12,346 4.9 1.18 (1.05, 1.33) 1.21 (1.05, 1.40) P-trend <0.001 0.001

Total vitamin D (µg/d) Q1 (≤3.5) 531/12,089 4.4 1 (ref) 1 (ref) Q2 (3.6–5.7) 485/12,487 3.9 0.88 (0.78, 1.00) 0.90 (0.79, 1.02) Q3 (5.8–8.6) 496/12,393 4.0 0.91 (0.81, 1.03) 0.89 (0.77, 1.03) Q4 (8.7–13.5) 556/12,329 4.5 1.03 (0.91, 1.15) 0.96 (0.82, 1.12) Q5 (≥13.6) 478/12,378 3.9 0.88 (0.78, 0.99) 0.81 (0.67, 0.97) P-trend 0.46 0.03

1n = 61,676. RRs are from a log binomial regression model. Q, quintile; RAE, retinol activity equivalent; ref, reference. 2Adjusted for maternal total intakes of vitamins A or D (mutual adjustment), vitamin E, vitamin C, folate, and sum of n–3 fatty acids (all in quintiles) and

total energy intake (continuous); the following maternal prenatal factors: age at delivery (continuous), parity (0, 1, or ≥2), education (less than high school, high school, ≤4 y of college/university, or >4 y of college/university), prepregnancy BMI (kg/m2; <18.5, 18.5–24.9, 25.0–29.9, or ≥30), history of asthma (no or yes), history of atopy (no or yes), and smoking in pregnancy (no, quit, or yes); and the following mediators: birth weight (<2500, 2500–4500, or ≥4500 g) and prematurity (no or yes). Missing values in covariates were handled by multiple imputation (m = 10) by using chained equations.

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TABLE 4 Combined effect of total vitamin A and vitamin D intake in pregnancy and RR estimates (95% CIs) for current asthma at age 7 y1

Total vitamin A (RAEs/d) Total vitamin D (µg/d) Cases/total n Prevalence, % Crude RR Adjusted RR2

Low (≤2030) Low (≤13.5) 1687/41,903 4.0 1 (ref) 1 (ref) High (≥2031) Low (≤13.5) 381/7395 5.2 1.28 (1.15, 1.43) 1.21 (1.08, 1.36) Low (≤2030) High (≥13.6) 259/7427 3.5 0.87 (0.76, 0.98) 0.86 (0.73, 1.00) High (≥2031) High (≥13.6) 219/4951 4.4 1.10 (0.96, 1.26) 0.99 (0.83, 1.18)

1n = 61,676. RRs are from a log binomial regression model. A high intake corresponds to the highest quintile (Q5) and low intake to all lower quintiles (Q1–Q4) in Table 3. Q, quintile; RAE, retinol activity equivalent; ref, reference.

2Adjusted for maternal total intake of vitamins A or D (mutual adjustment), vitamin E, vitamin C, folate, and sum of n–3 fatty acids (all in quintiles) and total energy intake (continuous); the following maternal prenatal factors: age at delivery (continuous), parity (0, 1, or ≥2), education (less than high school, high school, ≤4 y of college/university, or >4 y of college/university), prepregnancy BMI (kg/m2; <18.5, 18.5–24.9, 25.0–29.9, or ≥30), history of asthma (no or yes), history of atopy (no or yes), and smoking in pregnancy (no, quit, or yes); and the following mediators: birth weight (<2500, 2500–4500, or ≥4500 g) and prematurity (no or yes). Missing values in covariates were handled by multiple imputation (m = 10) by using chained equations.

multivariable adjustment (Table 5). However, there was no in- creased risk for any (daily or sometimes) use of multivitamins in infants who were given an additional vitamin D–containing sup- plement.

Maternal and child sensitivity analyses

Results on maternal intake (Table 3) were robust to a range of sensitivity analyses including additional adjustment for total zinc intake, proxy variables for UV exposure during pregnancy (leisure-time physical activity, tanning bed use, and geographical region of delivery) in the vitamin D analysis, or birth year to

control for a potential cohort effect (Supplemental Table 6). The results from the nonlinear analysis of multiplicative interaction were not significant (P-interaction from 0.59 to 0.94 in the multivariable model). Confounder adjustment by multivariable regression and propensity score matching gave similar results (Supplemental Table 7). From our main model (Table 3), we estimated the direct effect of maternal intake not mediated through low birth weight and prematurity; however, the total effect, not adjusting for these mediators, was similar (results not shown). Results on infant supplement use (Table 5) were little affected by additional adjustment for indicators of child frailty or asthma susceptibility (child’s sex, birth season, delivery by

TABLE 5 Infant supplement use in the first 6 mo and crude and adjusted RR estimates (95% CIs) for current asthma at age 7 y1

Cases/total n Prevalence, % Crude RR2 Crude RR3 Adjusted RR3,4

Cod liver oil No 1095/25,365 4.3 1 (ref) 1 (ref) 1 (ref) Sometimes 428/11,579 3.7 0.86 (0.77, 0.96) 0.86 (0.77, 0.97) 0.91 (0.81, 1.02) Daily 721/18,198 4.0 0.92 (0.84, 1.01) 0.92 (0.84, 1.01) 0.97 (0.87, 1.09)

Vitamin D only No 1617/39,343 4.1 1 (ref) 1 (ref) 1 (ref) Sometimes 152/3746 4.1 0.99 (0.84, 1.16) 1.02 (0.87, 1.19) 1.05 (0.89, 1.23) Daily 475/12,053 3.9 0.96 (0.87, 1.06) 0.99 (0.90, 1.10) 0.97 (0.86, 1.09)

Multivitamins No 2008/50,363 4.0 1 (ref) 1 (ref) 1 (ref) Sometimes 81/2129 3.8 0.95 (0.77, 1.19) 0.97 (0.78, 1.21) 0.88 (0.71, 1.10) Daily 155/2650 5.9 1.47 (1.25, 1.72) 1.45 (1.24, 1.70) 1.19 (1.01, 1.41)

Combined use (sometimes/daily) Neither category 410/9397 4.3 1 (ref) 1 (ref) 1 (ref) Cod liver oil only 936/24,545 3.8 0.89 (0.80, 1.00) 0.90 (0.80, 1.01) 0.97 (0.86, 1.09) Vitamin D only 524/12,978 4.0 0.95 (0.83, 1.08) 0.97 (0.85, 1.10) 1.00 (0.88, 1.15) Multivitamin only 149/2493 6.0 1.40 (1.16, 1.69) 1.39 (1.15, 1.67) 1.19 (0.98, 1.43) Any vitamin D supplement and multivitamin 108/2541 4.3 1.00 (0.81, 1.23) 1.03 (0.84, 1.27) 0.94 (0.76, 1.15) Multiple vitamin D supplements 126/3188 4.0 0.93 (0.76, 1.13) 0.99 (0.81, 1.21) 1.02 (0.83, 1.26)

1n = 61,676. ref, reference. 2RRs were from a log binomial regression model. Sample included participants with a follow-up questionnaire at 6 mo (n = 55,142). 3RRs were from a log binomial regression model. Analysis included all eligible children (n = 61,676) with child supplement use imputed for 10.6% of

the sample with missing follow-up at age 6 mo. Missing values were handled by multiple imputation (m = 10) by using chained equations. 4Infant supplements (vitamin D only, cod liver oil, multivitamins) were mutually adjusted for with additional adjustments for maternal total intake of

vitamins A, D, E, and C; folate; sum of n–3 fatty acids (all in quintiles); and total energy (continuous); the following maternal prenatal factors: age at delivery (continuous), parity (0, 1, or ≥2), education (less than high school, high school, ≤4 y of college/university, or >4 y of college/university), prepregnancy BMI (kg/m2; <18.5, 18.5–24.9, 25.0–29.9, or ≥30), history of asthma (no or yes), history of atopy (no or yes), and smoking in pregnancy (no, quit, or yes); and the following postnatal child factors: birth weight (<2500, 2500–4500, or ≥4500 g), prematurity (no or yes), months of full breastfeeding (0, 1 to <4, 4 to <6, or ≥6 mo), child respiratory tract infections in first 6 mo (no or yes), and maternal smoking since birth (none, sometimes, or daily). Missing values in covariates were handled by multiple imputation (m = 10) by using chained equations.

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cesarean section, and antibiotics and paracetamol use) or the exclusion of 5.5% (3399 of 61,676) of premature or low–birth weight children (Supplemental Table 8). Maternal and child risk estimates were also unaffected by the exclusion of 20% of controls (11,828 of 59,130) who had been dispensed any asthma medication by the age of 8 y (see Supplemental Tables 6 and 8).

Ding and VanderWeele’s (33) approach for assessing the po- tential influence of unmeasured confounding showed that to completely explain an RR of 1.2 (as we observed for a high maternal intake of total vitamin A and for infant supplemen- tation with multivitamins) it would take an unmeasured con- founder with a strength ≥1.7, which is stronger than what we observed for all of our measured confounders, except for maternal asthma.

DISCUSSION

In this large population–based pregnancy cohort study, a high maternal intake of vitamin A during pregnancy was associated with more asthma and a high intake of vitamin D was associated with less asthma in children at age 7 y, independent of infant sup- plement use in the first 6 mo. The RR for intake in the highest compared with the lowest quintile was ∼20% higher for vitamin A and 20% lower for vitamin D. In agreement with the hypothesis that vitamin A may antagonize actions of vitamin D, we observed no protective effect of vitamin D when the intake of vitamin A was high and likewise no adverse effect of high vitamin A in the face of high vitamin D. We found no protective effect of infant supplementation with vitamin D only, or cod liver oil, on asthma at school age.

Total vitamin A intake in the highest quintile (≥2031 RAEs/d), in which we observed more frequent asthma, corresponds to ∼2.5 times the recommended intake for pregnant women of 800 RAEs/d (34, 35). In comparison, the cutoff for the upper quintile of vitamin D (≥13.6 µg/d), in which we observed less asthma, was close to the Nordic (10 µg/d) and US (15 µg/d) rec- ommendations for pregnant women.

Comparison with other studies

Few other studies have assessed asthma development in school-age children in relation to pregnancy intake of vitamin A, including retinol, outside of populations at risk of deficiency. In a study from the Danish National Birth Cohort with half the sample size of the current study, the association of total vitamin A intake with the risk of asthma at age 7 y was only borderline significant (21), but the magnitude (8% higher risk per 1000-µg/d increase) is compatible with our finding of a 20% increased risk in the high- est quintile. A study from Finland of maternal antioxidant intake during pregnancy showed positive, but nonsignificant, relations of total intake of carotenoids (α and β) and retinol from food (0.2% retinol supplement use was ignored) with child asthma at age 5 y (20). Our results support that intakes of β-carotene or food vitamin A alone (results shown in Supplemental Table 2) are not sufficient, or high enough, in most women, to increase asthma risk. Furthermore, vitamin A supplementation trials conducted in areas of Nepal with endemic deficiency reported better lung func- tion in children of supplemented mothers (17, 18). A prospective study in Norwegian adults reported that daily intake of cod liver oil was associated with increased incidence of asthma (36). The

authors attributed the association to the high retinol content of Norwegian cod liver oil at the time (1000 µg/5 mL before 1999), combined with a traditional diet rich in vitamin A. Thus, the risk of adverse effects of vitamin A appears to be greater in Western populations who consume supplemental retinol in the face of high food retinol intake.

A high intake of vitamin D from our FFQ was reflected in higher maternal circulating 25-hydroxyvitamin D, which has been associated with a lower risk of asthma in a recent meta- analysis of birth cohort studies (11), including a case-cohort study in younger MoBa children (37). The findings of this review and our current study are in keeping with recent reports from 2 tri- als of prenatal vitamin D supplementation, which suggest an in- verse association between prenatal exposure to vitamin D and child asthma (38). Our results suggested that the protective ef- fect of high vitamin D intake was attenuated among those with vitamin A intake in the highest quintile. Likewise, there was no adverse effect of high vitamin A intake when vitamin D intake was high. Other studies support that retinol and vitamin D may have antagonistic effects that affect health outcomes. A large, nested, case-control study of colorectal cancer found that the pro- tective effect of high circulating vitamin D disappeared in sub- jects with a high retinol intake (≥1000 µg/d) (39); however, vi- tamin D may also reduce toxicity from retinol. In a review of case-reports of vitamin A toxicity, the median dose of retinol as- sociated with toxicity was higher in cases who had also taken vitamin D (40).

Strengths and limitations of this study

Our study has several strengths. We used a validated FFQ and few previous studies have estimated the total intake of vitamin A from foods and supplements during pregnancy outside of de- ficient populations (20, 21). In addition, we had high statistical power (2546 cases) to study asthma, and the prescription reg- istry linkage enabled near-complete follow-up to school age. As in other large, nationwide, population-based studies, we were not able to classify asthma on the basis of clinical examination, and we cannot rule out some misclassification in our asthma outcome. We expect that any bias in our RR estimates would be in the di- rection of slight attenuation, because the risk of outcome misclas- sification should be low and independent of maternal exposure (nondifferential error). Norway has universal health care and pre- scription coverage, so undiagnosed or untreated asthma should be rare. In addition, in a validation study of the MoBa 7-y question- naire items with regard to asthma, we found that even a single dispensing of asthma medication was very rare in the absence of the maternal report of a doctor’s diagnosis of asthma (41). A pre- scription for asthma medication requires a physician’s evaluation, and we required ≥2 prescriptions to increase the positive predic- tive value of our asthma definition (42). Furthermore, we would not expect high maternal intakes of vitamin A and vitamin D to be associated with asthma in opposite directions, if high intakes just reflected differences in health consciousness and health-seeking behavior.

A limitation of this study is that we did not have data on nutrient intake from supplements in infants, but we were able to compare different supplements. Our results suggested more asthma among children who were given multivitamins but not cod liver oil. Both supplements provide similar doses of vitamin A

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(typically 200–250 µg) and vitamin D (typically the recom- mended dose of 10 µg), and cod liver oil also contains vitamin E and n–3 fatty acids. A potential explanation for this difference is that liquid multivitamins for children contain water-miscible or emulsified retinol, which could be more toxic than retinol in oil- based solutions such as cod liver oil (40). Interestingly, a Swedish study found an increased risk of asthma and allergy in infants supplemented with vitamins A and D in water-based but not oil- based formula (43). In our study, the lack of association between any multivitamin use and the risk of asthma in infants who were given an additional supplement containing vitamin D could be explained by an antagonistic effect of vitamin D on retinol. How- ever, it is also possible that these infants had a lower intake of multivitamins than infants in the multivitamin-only category due to alternating use of a vitamin D supplement. Other vitamins or minerals in a multivitamin formula, potentially folic acid, could also affect asthma development (30). Unmeasured confounding is always of concern in observational studies, but Ding and Van- derWeele’s (33) framework provides some reassurance that even a modest RR of 1.2 is relatively robust to unmeasured confound- ing. Last, we did not assess the potential influence of vitamin A and D exposures at other time points, such as during lactation or after age 6 mo, on our results.

Potential mechanisms

Asthma is characterized by chronic airway inflammation and has been associated with atopy and a T-helper 2 (Th2)–dominated cytokine profile. Vitamin A exerts many of its effects through retinoic acid–mediated gene transcription, and retinoic acid may have a Th2 cell–promoting effect (44). Although vitamin A is mainly stored in the liver, excess vitamin A also accumulates in the lung (15), where retinoid metabolites may cause asthma- like symptoms (45). In the rat lung, vitamin A supplementation with higher and intermediate doses increases markers of oxidative stress (46), which also may impair lung function. We found no in- dication that antioxidant properties of β-carotene protect against asthma. The effect of β-carotene was weaker but in the same di- rection as retinol. However, many aspects related to the maternal– fetal transfer of retinoids and carotenoids, their metabolism in the developing tissues, and homeostatic control in the face of exces- sive maternal dietary vitamin A intake are still poorly understood (47). Our results suggest that little, if any, of the effects of vitamin A and D intake during pregnancy on child asthma were mediated through low birth weight or prematurity. We found some indi- cation that the adverse effects associated with excess vitamin A were mitigated by having a sufficient intake of vitamin D. This observation is in line with mechanistic studies in myeloid cells, which showed that vitamin D represses retinoic acid transcrip- tional activity, but the action is 2 way, which also explains how vitamin A can attenuate vitamin D activity (22).

Conclusions

In this study, we found that a diet naturally high in vitamin A combined with the use of supplements containing retinol during pregnancy place women at risk of vitamin A excess, which was associated with increased susceptibility to asthma in school-age children. We observed this effect for intakes that were ≥2.5 times the recommended dose, which is below the tolerable upper intake level for retinol of 3000 µg/d during pregnancy (27). Vitamin D

intake close to recommendations was associated with a reduced risk of asthma at school age but not when maternal intake of vi- tamin A was high. Thus, the balance of vitamin A and vitamin D intake during pregnancy could be of importance to asthma sus- ceptibility in the offspring. A high intake of dietary retinol com- bined with a low intake of vitamin D is seen in many Western populations (12) in which child asthma is common.

The authors’ responsibilities were as follows—WN and CLP: were respon- sible for the study conception, design, and data acquisition; CLP, ØK, NAL-B, and MH: contributed to the data analysis; CLP: wrote the manuscript and had primary responsibility for the final content; and all authors: contributed to the interpretation of data, revised the manuscript for intellectual content, and read and approved the final manuscript. The authors had no conflicts of interest to disclose.

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