Childhood Allergies: Prevalence, Demographics by Kaggle | Healthcare

About this Dataset

Childhood Allergies: Prevalence, Demographics

Childhood Allergies: Prevalence, Diagnosis, and Treatment Outcomes

Investigating Allergy Prevalence, Treatment Outcomes, and Patient Demographics

About this dataset

This dataset contains the power to help us better understand the prevalence and treatment outcomes of childhood allergies over an extended period of time. Not only does it publicize the number of individuals currently suffering from asthma, atopic dermatitis, allergic rhinitis and food allergies through retrospective data as reported by healthcare providers - but it also features a set of columns which allow us to gain valuable insights into how these outcomes differ across different demographics such as gender, race and ethnicity. By further examining this data, we can start to recognize patterns in trends among the diagnosed cases - paving way for new treatments and prevention strategies that could prevent severe allergic reactions for many children all around the world

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How to use the dataset

Assess what kind of questions you want to answer using this data - do you want to focus on one particular type of allergy or analyze them together? Do you want a descriptive analysis or would an analysis that looks for correlations between conditions be more appropriate?

Once you have determined your research question(s), identify what variables from the dataset are pertinent to your inquiry and assess any outliers that might need further investigation or filtering out during your analysis. Also consider any independent variables or confounding factors which might affect your results as well as any existing hypotheses related to the topic that might help guide your research project expectations

Be aware of potential sources of bias when using self-reported healthcare provider information such as difficulties in disease identification (i.e allergies may be misdiagnosed). Additionally note that many allergy cases may go unreported/unrecorded due issues such as lack access/awareness about healthcare etc). A good way combat bias is by sample size - use largest possible datasets whenever available!

Begin collecting relevant data from columns pertaining medical history (allergy diagnosis start & end date etc.), patient demographic information (gender factor ,ethnicity factor etc.), treatment trends & outcomes( first Asthma RX date , last asthma RX date , NUM asthma rx etc ). To get the most insights outta thisdata all these factors must be taken into account – if there isn’t enough evidence then explore other reliable sources too

Structure & organize collected data so they can me easily accessed later – maybe create separate sheets/tabs with different categories i.e patient/treatment information OR create individual sheets for each subject depending upon how much info needs collecting .Designing formulaic functions will not only make life easier but critically save time & energy when it comes analyzing vast amounts data stored within workbook ! Remember larger sample sizes provide more

Research Ideas

Use the dataset to identify risk factors or patterns in childhood allergies that can inform preventative and treatment measures.

Investigate the correlation between demographic characteristics (e.g., age, gender) and diagnosis or severity of childhood allergies by using cross-tabs or other statistical techniques on the data provided in this dataset.

Analyze longitudinal trends in treatment outcomes for various types of childhood allergy, such as asthma, atopic dermatitis and food allergy by comparing patient results over time (i.e., looking at pre-treatment diagnosis and post-treatment diagnoses)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: food-allergy-analysis-Zenodo.csv

Column name	Description
BIRTH_YEAR	Year of birth of the patient. (Integer)
GENDER_FACTOR	Gender of the patient. (String)
RACE_FACTOR	Race of the patient. (String)
ETHNICITY_FACTOR	Ethnicity of the patient. (String)
PAYER_FACTOR	Insurance coverage of the patient. (String)
ATOPIC_MARCH_COHORT	Cohort of the patient. (String)
AGE_START_YEARS	Age of the patient at the start of the study. (Integer)
AGE_END_YEARS	Age of the patient at the end of the study. (Integer)
SHELLFISH_ALG_START	Shellfish allergy status at the start of the study. (String)
SHELLFISH_ALG_END	Shellfish allergy status at the end of the study. (String)
MILK_ALG_START	Milk allergy status at the start of the study. (String)
MILK_ALG_END	Milk allergy status at the end of the study. (String)
SOY_ALG_START	Soy allergy status at the start of the study. (String)
SOY_ALG_END	Soy allergy status at the end of the study. (String)
EGG_ALG_START	Egg allergy status at the start of the study. (String)
EGG_ALG_END	Egg allergy status at the end of the study. (String)
WHEAT_ALG_START	Wheat allergy status at the start of the study. (String)
WHEAT_ALG_END	Wheat allergy status at the end of the study. (String)
PEANUT_ALG_START	Peanut allergy status at the start of the study. (String)
PEANUT_ALG_END	Peanut allergy status at the end of the study. (String)
SESAME_ALG_START	Sesame allergy status at the start of the study. (String)
SESAME_ALG_END	Sesame allergy status at the end of the study. (String)
TREENUT_ALG_START	Tree nut allergy status at the start of the study. (String)
TREENUT_ALG_END	Tree nut allergy status at the end of the study. (String)
WALNUT_ALG_START	Walnut allergy status at the start of the study. (String)
WALNUT_ALG_END	Walnut allergy status at the end of the study. (String)
PECAN_ALG_START	Pecan allergy status at the start of the study. (String)
PECAN_ALG_END	Pecan allergy status at the end of the study. (String)
PISTACH_ALG_START	Pistachio allergy status at the start of the study. (String)
PISTACH_ALG_END	Pistachio allergy status at the end of the study. (String)
ALMOND_ALG_START	Almond allergy status at the start of the study. (String)
ALMOND_ALG_END	Almond allergy status at the end of the study. (String)
BRAZIL_ALG_START	Brazil nut allergy status at the start of the study. (String)
BRAZIL_ALG_END	Brazil nut allergy status at the end of the study. (String)
HAZELNUT_ALG_START	Hazelnut allergy status at the start of the study. (String)
HAZELNUT_ALG_END	Hazel
ATOPIC_DERM_START	Atopic dermatitis status at the start of the study. (String)
ATOPIC_DERM_END	Atopic dermatitis status at the end of the study. (String)
ALLERGIC_RHINITIS_START	Allergic rhinitis status at the start of the study. (String)
ALLERGIC_RHINITIS_END	Allergic rhinitis status at the end of the study. (String)
ASTHMA_START	Asthma status at the start of the study. (String)
ASTHMA_END	Asthma status at the end of the study. (String)
FIRST_ASTHMARX	First asthma medication prescribed. (String)
LAST_ASTHMARX	Last asthma medication prescribed. (String)
NUM_ASTHMARX	Number of asthma medications prescribed. (Integer)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

Food Allergy Analysis Zenodo

@kaggle.thedevastator_childhood_allergies_prevalence_diagnosis_and_tre.food_allergy_analysis_zenodo

5.88 MB
333200 rows
50 columns


CREATE TABLE food_allergy_analysis_zenodo (
  "subject_id" BIGINT,
  "birth_year" BIGINT,
  "gender_factor" VARCHAR,
  "race_factor" VARCHAR,
  "ethnicity_factor" VARCHAR,
  "payer_factor" VARCHAR,
  "atopic_march_cohort" BOOLEAN,
  "age_start_years" DOUBLE,
  "age_end_years" DOUBLE,
  "shellfish_alg_start" DOUBLE,
  "shellfish_alg_end" DOUBLE,
  "fish_alg_start" DOUBLE,
  "fish_alg_end" DOUBLE,
  "milk_alg_start" DOUBLE,
  "milk_alg_end" DOUBLE,
  "soy_alg_start" DOUBLE,
  "soy_alg_end" DOUBLE,
  "egg_alg_start" DOUBLE,
  "egg_alg_end" DOUBLE,
  "wheat_alg_start" DOUBLE,
  "wheat_alg_end" DOUBLE,
  "peanut_alg_start" DOUBLE,
  "peanut_alg_end" DOUBLE,
  "sesame_alg_start" DOUBLE,
  "sesame_alg_end" DOUBLE,
  "treenut_alg_start" DOUBLE,
  "treenut_alg_end" VARCHAR,
  "walnut_alg_start" DOUBLE,
  "walnut_alg_end" DOUBLE,
  "pecan_alg_start" DOUBLE,
  "pecan_alg_end" DOUBLE,
  "pistach_alg_start" DOUBLE,
  "pistach_alg_end" DOUBLE,
  "almond_alg_start" DOUBLE,
  "almond_alg_end" DOUBLE,
  "brazil_alg_start" DOUBLE,
  "brazil_alg_end" DOUBLE,
  "hazelnut_alg_start" DOUBLE,
  "hazelnut_alg_end" DOUBLE,
  "cashew_alg_start" DOUBLE,
  "cashew_alg_end" DOUBLE,
  "atopic_derm_start" DOUBLE,
  "atopic_derm_end" DOUBLE,
  "allergic_rhinitis_start" DOUBLE,
  "allergic_rhinitis_end" DOUBLE,
  "asthma_start" DOUBLE,
  "asthma_end" DOUBLE,
  "first_asthmarx" DOUBLE,
  "last_asthmarx" DOUBLE,
  "num_asthmarx" DOUBLE
);