Read in Data

Upload our data
Transform Data so that all checked variables = 1 and unchecked = 0
Record ID 312 being omitted because sex = NA, Omit other data per the list omar sent

Omarallergy<- as.data.frame(read.csv("NEWAIDATA.csv"))
record_ids_to_exclude <- c(20, 39, 58, 134, 142, 188, 189, 234, 273, 304, 312, 329, 331, 344, 355, 368,444,511)

Omarallergy <- Omarallergy[!(Omarallergy$Record.ID %in% record_ids_to_exclude), ]

psych::describe(Omarallergy)

Omarallergy$Race..ethnicity..choice.otherRace. <- Omarallergy$Race..ethnicity..choice.other.
Omarallergy$Race..ethnicity..choice.other. <- NULL

##changing the variable names for ease
names(Omarallergy)<- gsub("Race..ethnicity..choice.", "", names(Omarallergy))

names(Omarallergy)<- gsub("shellfish.that.the.patient.reacted.to..or.suspected.reacted.to...choice.", "", names(Omarallergy))

Omarallergy[][Omarallergy[] == "Checked"] <- 1

Omarallergy[][Omarallergy[] == "Unchecked"] <- 0

First step, make race categories, categorize everyone as either white, black, or other

col_names <- colnames(Omarallergy)
duplicate_cols <- col_names[duplicated(col_names)]

Omarallergy <- mutate(Omarallergy, pdemrace = ifelse(White. == 1, "1", ifelse(Black.or.African.American. == 1, "2", ifelse(American.Indian.or.Alaska.Native. == 1, "3", ifelse(Asian. == 1, "3", ifelse(Native.Hawaiian.or.Other.Pacific.Islander. == 1, "3", ifelse(not.documented. == 1, "3", 
                                                                        ifelse(otherRace. == 1, "3", 
                                                                               ifelse(not.documented..1 == 1, 3, "")))))))))

                                                                        
# table(Omarallergy$pdemrace) 

filtered_data <- Omarallergy %>%
  filter(pdemrace != 3)


z <- table(filtered_data$pdemrace)
rownames(z) <- c( "White","Black")


kbl(z) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

Var1	Freq
White	96
Black	114

No Difference in Age by Race

filtered_data$race <- dplyr::recode(filtered_data$pdemrace, "1" = "White", "2" = "Black")

View(filtered_data)
filtered_data$pdemrace <- as.factor(filtered_data$pdemrace)
# anova(lm(age.in.years~ pdemrace, data=filtered_data))


wilcox.test(age.in.years ~ pdemrace, data = filtered_data, 
        exact = FALSE, alternative = "two.sided")

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  age.in.years by pdemrace
## W = 5806.5, p-value = 0.4463
## alternative hypothesis: true location shift is not equal to 0

filtered_data %>%
  group_by(race) %>%
  get_summary_stats(age.in.years, type = "common")

ggboxplot(filtered_data, x = "race", y = "age.in.years", 
          color ="race", palette = c("#00AFBB", "#E7B800"),
          ylab = "age (years)", xlab = "Race") + theme(legend.position = "none") + ggtitle("Figure 1. No Difference in Age in Years by Race") +   theme(title  = element_text(face="bold", size = 15))

No Difference in Age of Onset by Race

# anova(lm(shellfish.age.of.allergy.onset ~ pdemrace, data=filtered_data))

wilcox.test(shellfish.age.of.allergy.onset ~ pdemrace, data = filtered_data, 
        exact = FALSE, alternative = "two.sided")

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  shellfish.age.of.allergy.onset by pdemrace
## W = 1458.5, p-value = 0.6326
## alternative hypothesis: true location shift is not equal to 0

filtered_data %>%
  group_by(race) %>%
  get_summary_stats(shellfish.age.of.allergy.onset, type = "common")

ggboxplot(filtered_data, x = "race", y = "shellfish.age.of.allergy.onset", 
          color ="race", palette = c("#00AFBB", "#E7B800"),
          ylab = "age of onset of allergy (years)", xlab = "Race") + theme(legend.position = "none") + ggtitle("Figure 2. No Racial differences in SF Allergy Onset Age") + theme(title  = element_text(face="bold", size = 15))

Difference in Sex by Race

proportion of females differs by sex (i.e., more black females) at greater than chance levels

filtered_data$Sex <- dplyr::recode(filtered_data$Sex, female = "0", male = "1", na.action = "na.omit")


filtered_data$Sex <- as.factor(filtered_data$Sex)

# table(filtered_data$Sex)

cont_tablesex <- table(filtered_data$pdemrace, filtered_data$Sex)
fisher_resultsex <- fisher.test(cont_tablesex)




rownames(cont_tablesex) <- c( "White","Black")
colnames(cont_tablesex) <- c( "Female","Male")

fisher_resultsex

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_tablesex
## p-value = 0.0005618
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.1783935 0.6538752
## sample estimates:
## odds ratio 
##  0.3446718

kbl(cont_tablesex) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	Female	Male
White	54	42
Black	90	24

No Difference in Roach by Race

filtered_data$rhinitis.specific.sensitization..skin.test.or.serum.IgE...choice.cockroach. <- as.factor(filtered_data$rhinitis.specific.sensitization..skin.test.or.serum.IgE...choice.cockroach.)



df1 = filtered_data %>% 
  count(race, rhinitis.specific.sensitization..skin.test.or.serum.IgE...choice.cockroach.) %>% 
  group_by(race) %>% 
  mutate(prop=prop.table(n))


cont_table <- table(filtered_data$pdemrace, filtered_data$rhinitis.specific.sensitization..skin.test.or.serum.IgE...choice.cockroach.)
fisher_result <- fisher.test(cont_table)



rownames(cont_table) <- c( "White","Black")
colnames(cont_table) <- c( "Not","Sensitized")


kbl(cont_table) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	Not	Sensitized
White	65	31
Black	74	40

fisher_result

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table
## p-value = 0.7698
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.613361 2.102886
## sample estimates:
## odds ratio 
##   1.132715

ggplot(df1, aes(race, fill = rhinitis.specific.sensitization..skin.test.or.serum.IgE...choice.cockroach.))+
geom_bar(aes(y = prop*100),
    position = "dodge", stat = "identity") + scale_fill_discrete(name = "Roach Sensitization", labels = c("No", "Yes"),type=c("cornflowerblue","red")) +theme_classic() + ylab("Proportion (%)") + xlab("Race") + ggtitle("Figure 3. No Racial Differences in Roach Sensitization") + theme(title  = element_text(face="bold", size = 15))

No Difference in Mites by Race

filtered_data$rhinitis.specific.sensitization..skin.test.or.serum.IgE...choice.dust.mite. <- as.factor(filtered_data$rhinitis.specific.sensitization..skin.test.or.serum.IgE...choice.dust.mite.)


df1 = filtered_data %>% 
  count(race, rhinitis.specific.sensitization..skin.test.or.serum.IgE...choice.dust.mite.) %>% 
  group_by(race) %>% 
  mutate(prop=prop.table(n))



cont_table <- table(filtered_data$pdemrace, filtered_data$rhinitis.specific.sensitization..skin.test.or.serum.IgE...choice.dust.mite.)
fisher_result <- fisher.test(cont_table)



rownames(cont_table) <- c( "White","Black")
colnames(cont_table) <- c( "Not","Sensitized")


kbl(cont_table) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	Not	Sensitized
White	46	50
Black	44	70

fisher_result

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table
## p-value = 0.208
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.813804 2.632418
## sample estimates:
## odds ratio 
##   1.460926

ggplot(df1, aes(race, fill = rhinitis.specific.sensitization..skin.test.or.serum.IgE...choice.dust.mite.))+
geom_bar(aes(y = prop*100),
    position = "dodge", stat = "identity") + scale_fill_discrete(name = "Dust Mite Sensitization", labels = c("No", "Yes"),type=c("cornflowerblue","red")) +theme_classic() + ylab("Proportion (%)") + xlab("Race") + ggtitle("Figure 4. No Racial Differences in \n Dust Mite Sensitization") + theme(title  = element_text(face="bold", size = 15))

Difference in Symptoms

Hives/angioedema - Marginal proportional difference by race

non-significant difference in hives; black individuals >> white individuals

filtered_data$shellfish.symptoms.with.any.prior.reaction..choice.Skin..hives.or.angioedema.. <- as.factor(filtered_data$shellfish.symptoms.with.any.prior.reaction..choice.Skin..hives.or.angioedema..)

cont_table <- table(filtered_data$pdemrace, filtered_data$shellfish.symptoms.with.any.prior.reaction..choice.Skin..hives.or.angioedema..)
fisher_result <- fisher.test(cont_table)



rownames(cont_table) <- c( "White","Black")
colnames(cont_table) <- c( "No","Yes")


kbl(cont_table) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	23	73
Black	15	99

fisher_result

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table
## p-value = 0.04892
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.9595547 4.5926514
## sample estimates:
## odds ratio 
##   2.072129

df1 = filtered_data %>% 
  count(race, shellfish.symptoms.with.any.prior.reaction..choice.Skin..hives.or.angioedema..) %>% 
  group_by(race) %>% 
  mutate(prop=prop.table(n))

ggplot(df1, aes(race, fill = shellfish.symptoms.with.any.prior.reaction..choice.Skin..hives.or.angioedema..))+
geom_bar(aes(y = prop*100),
    position = "dodge", stat = "identity") + scale_fill_discrete(name = "SkinSymptom", labels = c("No", "Yes"),type=c("cornflowerblue","red")) +theme_classic() + ylab("Proportion (%)") + xlab("Race") + ggtitle("Figure 5. Marginal Racial Difference in Hives/Angioedema") + theme(title  = element_text(face="bold", size = 15))

GI, Emesis/Diarrhea - No difference by race

filtered_data$shellfish.symptoms.with.any.prior.reaction..choice.GI..nausea..vomiting..or.diarrhea.. <- as.factor(filtered_data$shellfish.symptoms.with.any.prior.reaction..choice.GI..nausea..vomiting..or.diarrhea..)


cont_table <- table(filtered_data$pdemrace, filtered_data$shellfish.symptoms.with.any.prior.reaction..choice.GI..nausea..vomiting..or.diarrhea..)
fisher_result <- fisher.test(cont_table)



rownames(cont_table) <- c( "White","Black")
colnames(cont_table) <- c( "No","Yes")


kbl(cont_table) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	70	26
Black	93	21

fisher_result

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table
## p-value = 0.1392
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.2990554 1.2294264
## sample estimates:
## odds ratio 
##  0.6094171

df1 = filtered_data %>% 
  count(race, shellfish.symptoms.with.any.prior.reaction..choice.GI..nausea..vomiting..or.diarrhea..) %>% 
  group_by(race) %>% 
  mutate(prop=prop.table(n))

ggplot(df1, aes(race, fill = shellfish.symptoms.with.any.prior.reaction..choice.GI..nausea..vomiting..or.diarrhea..))+
geom_bar(aes(y = prop*100),
    position = "dodge", stat = "identity") + scale_fill_discrete(name = "GI", labels = c("No", "Yes"),type=c("cornflowerblue","red")) +theme_classic() + ylab("Proportion (%)") + xlab("Race") + ggtitle("Figure 6. No Racial Differences in GI Symptoms") + theme(title  = element_text(face="bold", size = 15))

Respiratory - No Racial differences by race

filtered_data$shellfish.symptoms.with.any.prior.reaction..choice.Respiratory..wheezing..stridor..voice.change..or.shortness.of.breath.. <- as.factor(filtered_data$shellfish.symptoms.with.any.prior.reaction..choice.Respiratory..wheezing..stridor..voice.change..or.shortness.of.breath..)

filtered_data$respiratory <- as.factor(filtered_data$shellfish.symptoms.with.any.prior.reaction..choice.Respiratory..wheezing..stridor..voice.change..or.shortness.of.breath..)


cont_table <- table(filtered_data$pdemrace, filtered_data$respiratory)
fisher_result <- fisher.test(cont_table)



rownames(cont_table) <- c( "White","Black")
colnames(cont_table) <- c( "No","Yes")


kbl(cont_table) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	40	56
Black	53	61

fisher_result

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table
## p-value = 0.4898
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.4578537 1.4737504
## sample estimates:
## odds ratio 
##  0.8228744

df1 = filtered_data %>% 
  count(race, respiratory) %>% 
  group_by(race) %>% 
  mutate(prop=prop.table(n))

ggplot(df1, aes(race, fill = respiratory))+
geom_bar(aes(y = prop*100),
    position = "dodge", stat = "identity") + scale_fill_discrete(name = "Respiratory", labels = c("No", "Yes"),type=c("cornflowerblue","red")) +theme_classic() + ylab("Proportion (%)") + xlab("Race") + ggtitle("Figure 7. No Racial Differences in Respiratory Symptoms") + theme(title  = element_text(face="bold", size = 15))

Cardiac - No Racial differences by race

filtered_data$shellfish.symptoms.with.any.prior.reaction..choice.Cardiovascular..hypotension..syncope..lightheartedness.. <- as.factor(filtered_data$shellfish.symptoms.with.any.prior.reaction..choice.Cardiovascular..hypotension..syncope..lightheartedness..)

filtered_data$cardiacsymp <- as.factor(filtered_data$shellfish.symptoms.with.any.prior.reaction..choice.Cardiovascular..hypotension..syncope..lightheartedness..)


cont_table <- table(filtered_data$pdemrace, filtered_data$cardiacsymp)
fisher_result <- fisher.test(cont_table)



rownames(cont_table) <- c( "White","Black")
colnames(cont_table) <- c( "No","Yes")


kbl(cont_table) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	91	5
Black	110	4

fisher_result

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table
## p-value = 0.7349
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.1276666 3.1801351
## sample estimates:
## odds ratio 
##  0.6631358

df1 = filtered_data %>% 
  count(race, cardiacsymp) %>% 
  group_by(race) %>% 
  mutate(prop=prop.table(n))

ggplot(df1, aes(race, fill = cardiacsymp))+
geom_bar(aes(y = prop*100),
    position = "dodge", stat = "identity") + scale_fill_discrete(name = "Cardiac Symptoms", labels = c("No", "Yes"),type=c("cornflowerblue","red")) +theme_classic() + ylab("Proportion (%)") + xlab("Race") + ggtitle("Figure 8. No Racial Differences in Cardiac Symptoms") + theme(title  = element_text(face="bold", size = 15))

Neurological - No Racial differences by race

filtered_data$shellfish.symptoms.with.any.prior.reaction..choice.Neurologic..headache..parasthesias.. <- as.factor(filtered_data$shellfish.symptoms.with.any.prior.reaction..choice.Neurologic..headache..parasthesias..)

filtered_data$neurosymp <- as.factor(filtered_data$shellfish.symptoms.with.any.prior.reaction..choice.Neurologic..headache..parasthesias..)


cont_table <- table(filtered_data$pdemrace, filtered_data$neurosymp)
fisher_result <- fisher.test(cont_table)



rownames(cont_table) <- c( "White","Black")
colnames(cont_table) <- c( "No","Yes")


kbl(cont_table) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	91	5
Black	112	2

fisher_result

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table
## p-value = 0.2503
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.03043896 2.05201183
## sample estimates:
## odds ratio 
##  0.3266769

df1 = filtered_data %>% 
  count(race, neurosymp) %>% 
  group_by(race) %>% 
  mutate(prop=prop.table(n))

ggplot(df1, aes(race, fill = neurosymp))+
geom_bar(aes(y = prop*100),
    position = "dodge", stat = "identity") + scale_fill_discrete(name = "Neuro Symptoms", labels = c("No", "Yes"),type=c("cornflowerblue","red")) +theme_classic() + ylab("Proportion (%)") + xlab("Race") + ggtitle("Figure 9. No Racial Differences in Neuro Symptoms") + theme(title  = element_text(face="bold", size = 15))

Medical Comorbidities

Allergic Rhinitis Comorbidity - No differences by Race

cont_table_medcomorbidAR <- table(filtered_data$pdemrace, filtered_data$medical.comorbidities.at.the.time.of.food.allergy.diagnosis..choice.allergic.rhinitis)



fisher.test(cont_table_medcomorbidAR)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table_medcomorbidAR
## p-value = 0.6334
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.6103689 2.3413828
## sample estimates:
## odds ratio 
##   1.195792

rownames(cont_table) <- c( "White","Black")
colnames(cont_table) <- c( "No","Yes")


kbl(cont_table) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	91	5
Black	112	2

Asthma Comorbidity - No differences by Race

cont_table_medcomorbid <- table(filtered_data$pdemrace, filtered_data$medical.comorbidities.at.the.time.of.food.allergy.diagnosis..choice.asthma)



fisher.test(cont_table_medcomorbid)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table_medcomorbid
## p-value = 0.2556
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.7684675 2.5723880
## sample estimates:
## odds ratio 
##   1.400706

rownames(cont_table_medcomorbid) <- c( "White","Black")
colnames(cont_table_medcomorbid) <- c( "No","Yes")


kbl(cont_table_medcomorbid) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	64	32
Black	67	47

Eczema Comorbidity - No differences by Race

cont_table_medcomorbidEC<- table(filtered_data$pdemrace, filtered_data$medical.comorbidities.at.the.time.of.food.allergy.diagnosis..choice.eczema)
fisher.test(cont_table_medcomorbidEC)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table_medcomorbidEC
## p-value = 0.5794
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.5573043 2.8476393
## sample estimates:
## odds ratio 
##   1.244902

rownames(cont_table_medcomorbidEC) <- c( "White","Black")
colnames(cont_table_medcomorbidEC) <- c( "No","Yes")


kbl(cont_table_medcomorbidEC) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	82	14
Black	94	20

Contact Derm Comorbidity - No differences by Race

cont_table_medcomorbidCD<- table(filtered_data$pdemrace, filtered_data$medical.comorbidities.at.the.time.of.food.allergy.diagnosis..choice.contact.dermatitis)
fisher.test(cont_table_medcomorbidCD)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table_medcomorbidCD
## p-value = 0.4155
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.03653308 2.95085260
## sample estimates:
## odds ratio 
##  0.4124236

rownames(cont_table_medcomorbidCD) <- c( "White","Black")
colnames(cont_table_medcomorbidCD) <- c( "No","Yes")


kbl(cont_table_medcomorbidCD) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	92	4
Black	112	2

Oral Allergy Syndrome Comorbidity - No differences by Race

cont_table_medcomorbidOAS<- table(filtered_data$pdemrace, filtered_data$medical.comorbidities.at.the.time.of.food.allergy.diagnosis..choice.oral.allergy.syndrome)
fisher.test(cont_table_medcomorbidOAS)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table_medcomorbidOAS
## p-value = 0.1849
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##   0.5642122 30.8965892
## sample estimates:
## odds ratio 
##   3.059937

rownames(cont_table_medcomorbidOAS) <- c( "White","Black")
colnames(cont_table_medcomorbidOAS) <- c( "No","Yes")


kbl(cont_table_medcomorbidOAS) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	94	2
Black	107	7

Anxiety Comorbidity - No differences by Race

cont_table_medcomorbidANXS<- table(filtered_data$pdemrace, filtered_data$medical.comorbidities.at.the.time.of.food.allergy.diagnosis..choice.anxiety)
fisher.test(cont_table_medcomorbidANXS)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table_medcomorbidANXS
## p-value = 0.406
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.2751168 1.7067296
## sample estimates:
## odds ratio 
##  0.6903187

rownames(cont_table_medcomorbidANXS) <- c( "White","Black")
colnames(cont_table_medcomorbidANXS) <- c( "No","Yes")


kbl(cont_table_medcomorbidANXS) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	82	14
Black	102	12

Other Psych Comorbidities - Proportion of White individuals > Proportion of Black Individuals

filtered_data$otherpsych <- as.factor(filtered_data$medical.comorbidities.at.the.time.of.food.allergy.diagnosis..choice.other.psychological.diagnosis..depression..bipolar..schizophrenia..etc.)




cont_table_medcomorbidPSY<- table(filtered_data$pdemrace, filtered_data$medical.comorbidities.at.the.time.of.food.allergy.diagnosis..choice.other.psychological.diagnosis..depression..bipolar..schizophrenia..etc.)
fisher.test(cont_table_medcomorbidPSY)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table_medcomorbidPSY
## p-value = 0.003038
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.1109345 0.7002356
## sample estimates:
## odds ratio 
##  0.2900308

rownames(cont_table_medcomorbidPSY) <- c( "White","Black")
colnames(cont_table_medcomorbidPSY) <- c( "No","Yes")


kbl(cont_table_medcomorbidPSY) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	74	22
Black	105	9

df1 = filtered_data %>% 
  count(race, otherpsych) %>% 
  group_by(race) %>% 
  mutate(prop=prop.table(n))

ggplot(df1, aes(race, fill = otherpsych))+
geom_bar(aes(y = prop*100),
    position = "dodge", stat = "identity") + scale_fill_discrete(name = "other psych", labels = c("No", "Yes"),type=c("cornflowerblue","red")) +theme_classic() + ylab("Proportion (%)") + xlab("Race") + ggtitle("Figure 10. White Individuals Have \n Greater Proportion of Comorbid Other Psych DX") + theme(title  = element_text(face="bold", size = 15))

Cardiac Comorbidities - No differences by Race

cont_table_medcomorbidhypertension<- table(filtered_data$pdemrace, filtered_data$medical.comorbidities.at.the.time.of.food.allergy.diagnosis..choice.cardiac..hypertension..coronary.artery.dz..etc.)
fisher.test(cont_table_medcomorbidhypertension)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table_medcomorbidhypertension
## p-value = 0.08685
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.8944657 3.4935575
## sample estimates:
## odds ratio 
##   1.749169

rownames(cont_table_medcomorbidhypertension) <- c( "White","Black")
colnames(cont_table_medcomorbidhypertension) <- c( "No","Yes")


kbl(cont_table_medcomorbidhypertension) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	76	20
Black	78	36

GI Comorbidity - No Racial differences

cont_table_GI<- table(filtered_data$pdemrace, filtered_data$medical.comorbidities.at.the.time.of.food.allergy.diagnosis..choice.gastrointestinal)
fisher.test(cont_table_GI)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table_GI
## p-value = 0.6595
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.64163 2.21424
## sample estimates:
## odds ratio 
##   1.188207

rownames(cont_table_GI) <- c( "White","Black")
colnames(cont_table_GI) <- c( "No","Yes")


kbl(cont_table_GI) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	66	30
Black	74	40

Alpha Gal Comorbidity - Proportion of White Individuals > Proportion of Black Individuals

cont_table_alphagal<- table(filtered_data$pdemrace, filtered_data$medical.comorbidities.at.the.time.of.food.allergy.diagnosis..choice.alpha.gal.allergy)
fisher.test(cont_table_alphagal)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table_alphagal
## p-value = 0.04219
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.000000 1.257693
## sample estimates:
## odds ratio 
##          0

rownames(cont_table_alphagal) <- c( "White","Black")
colnames(cont_table_alphagal) <- c( "No","Yes")


kbl(cont_table_alphagal) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	92	4
Black	114	0

filtered_data$alphagal <- as.factor(filtered_data$medical.comorbidities.at.the.time.of.food.allergy.diagnosis..choice.alpha.gal.allergy)

df1 = filtered_data %>% 
  count(race, alphagal) %>% 
  group_by(race) %>% 
  mutate(prop=prop.table(n))

ggplot(df1, aes(race, fill = alphagal))+
geom_bar(aes(y = prop*100),
    position = "dodge", stat = "identity") + scale_fill_discrete(name = "alpha gal", labels = c("No", "Yes"),type=c("cornflowerblue","red")) +theme_classic() + ylab("Proportion (%)") + xlab("Race") + ggtitle("Figure 10. White Individuals Have \n Greater Proportion of Comorbid Alpha Gal") + theme(title  = element_text(face="bold", size = 15))

Other Food Allergy - No Racial differences

cont_table_otherfoodallergy<- table(filtered_data$pdemrace, filtered_data$medical.comorbidities.at.the.time.of.food.allergy.diagnosis..choice.other.food.allergy)
fisher.test(cont_table_otherfoodallergy)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table_otherfoodallergy
## p-value = 0.4461
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.6882361 2.5162859
## sample estimates:
## odds ratio 
##   1.309079

rownames(cont_table_otherfoodallergy) <- c( "White","Black")
colnames(cont_table_otherfoodallergy) <- c( "No","Yes")

kbl(cont_table_otherfoodallergy) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	71	25
Black	78	36

Drug Allergy Comorbidity - No Racial differences, marginal trend that proportion of white individuals > proportion of black individuals

filtered_data$drugallergy <- as.factor(filtered_data$medical.comorbidities.at.the.time.of.food.allergy.diagnosis..choice.drug.allergy)


cont_table_otherdrugallergy<- table(filtered_data$pdemrace, filtered_data$medical.comorbidities.at.the.time.of.food.allergy.diagnosis..choice.drug.allergy)
fisher.test(cont_table_otherdrugallergy)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table_otherdrugallergy
## p-value = 0.09723
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.3482513 1.1232258
## sample estimates:
## odds ratio 
##  0.6269876

rownames(cont_table_otherdrugallergy) <- c( "White","Black")
colnames(cont_table_otherdrugallergy) <- c( "No","Yes")


kbl(cont_table_otherdrugallergy) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	47	49
Black	69	45

df1 = filtered_data %>% 
  count(race, drugallergy) %>% 
  group_by(race) %>% 
  mutate(prop=prop.table(n))

ggplot(df1, aes(race, fill = drugallergy))+
geom_bar(aes(y = prop*100),
    position = "dodge", stat = "identity") + scale_fill_discrete(name = "drug allergy", labels = c("No", "Yes"),type=c("cornflowerblue","red")) +theme_classic() + ylab("Proportion (%)") + xlab("Race") + ggtitle("Figure 11. White Individuals Have \n Greater Proportion of Comorbid Drug Allergy") + theme(title  = element_text(face="bold", size = 15))

Insect Allergy - racial differences; Prop White Individuals > Black Individuals

filtered_data$insectallergy <- as.factor(filtered_data$medical.comorbidities.at.the.time.of.food.allergy.diagnosis..choice.stinging.insect.allergy..hymenoptera.)


cont_table_insectallergy<- table(filtered_data$pdemrace, filtered_data$medical.comorbidities.at.the.time.of.food.allergy.diagnosis..choice.stinging.insect.allergy..hymenoptera.)
fisher.test(cont_table_insectallergy)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_table_insectallergy
## p-value = 0.1165
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.04963743 1.29201374
## sample estimates:
## odds ratio 
##  0.2989531

rownames(cont_table_insectallergy) <- c( "White","Black")
colnames(cont_table_insectallergy) <- c( "No","Yes")


kbl(cont_table_insectallergy) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	88	8
Black	111	3

df1 = filtered_data %>% 
  count(race, insectallergy) %>% 
  group_by(race) %>% 
  mutate(prop=prop.table(n))

ggplot(df1, aes(race, fill = insectallergy))+
geom_bar(aes(y = prop*100),
    position = "dodge", stat = "identity") + scale_fill_discrete(name = "insect allergy", labels = c("No", "Yes"),type=c("cornflowerblue","red")) +theme_classic() + ylab("Proportion (%)") + xlab("Race") + ggtitle("Figure 12. White Individuals Have \n Greater Proportion of Comorbid Insect Allergy") + theme(title  = element_text(face="bold", size = 15))

Medications at Time of DX

PPI - No Racial differences

PPI <- table(filtered_data$pdemrace, filtered_data$medications.prescribed.at.time.of.food.allergy.diagnosis..choice.proton.pump.inhibitor.)
fisher.test(PPI)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  PPI
## p-value = 0.5865
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.371189 1.781090
## sample estimates:
## odds ratio 
##  0.8133125

Beta Blockers - No Racial differences

BB <- table(filtered_data$pdemrace, filtered_data$medications.prescribed.at.time.of.food.allergy.diagnosis..choice.beta.blocker.)
fisher.test(BB)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  BB
## p-value = 0.2567
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.6360769 5.4420692
## sample estimates:
## odds ratio 
##   1.775266

ACE/ARB - No Racial differences

AceArb <- table(filtered_data$pdemrace, filtered_data$medications.prescribed.at.time.of.food.allergy.diagnosis..choice.ACE.ARB.)
fisher.test(AceArb)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  AceArb
## p-value = 0.3931
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.6181428 4.2617530
## sample estimates:
## odds ratio 
##   1.574869

NSAID - No Racial differences

NSAID <- table(filtered_data$pdemrace, filtered_data$medications.documented.at.the.time.of.chart.review..PMHx.section...not.already.included.in.previous.medication.question...choice.NSAID..prescribed.or.documented.in.note..)
fisher.test(NSAID)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  NSAID
## p-value = 0.2915
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.7102191 3.3203993
## sample estimates:
## odds ratio 
##   1.513872

Sulfasalazine - noone taking it

Sulfasalazine <- table(filtered_data$pdemrace, filtered_data$medications.documented.at.the.time.of.chart.review..PMHx.section...not.already.included.in.previous.medication.question...choice.sulfasalazine..azulfidine..or.mesalamine..5.ASA..)

Immune modulator - No Racial differences

ImmuneMod <- table(filtered_data$pdemrace, filtered_data$medications.documented.at.the.time.of.chart.review..PMHx.section...not.already.included.in.previous.medication.question...choice.immune.modulator..immune.suppressant..biologic..cyclophsophamide..MMF..calcineurin.inhibitor..mTOR.inhibitor..methotrexate..azathioprene..)
fisher.test(ImmuneMod)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  ImmuneMod
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.3301762 4.1135168
## sample estimates:
## odds ratio 
##   1.131406

Oral steroid - No Racial differences

oralsteroid <- table(filtered_data$pdemrace, filtered_data$medications.prescribed.at.time.of.food.allergy.diagnosis..choice.daily.oral.steroid.)
fisher.test(oralsteroid)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  oralsteroid
## p-value = 0.2943
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##   0.4511193 26.9498950
## sample estimates:
## odds ratio 
##   2.600139

Shellfish allergy severity

No difference in antihistamine only or undocumented any prior reaction

notxantihisonly <- table(filtered_data$pdemrace, filtered_data$shellfish.severity.of.any.prior.reaction..choice.no.treatment.or.antihistamine.only)
fisher.test(notxantihisonly)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  notxantihisonly
## p-value = 0.3096
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.3996987 1.3650616
## sample estimates:
## odds ratio 
##  0.7394829

notxantihisonly

##    
##      0  1
##   1 60 36
##   2 79 35

notdocument <- table(filtered_data$pdemrace, filtered_data$shellfish.severity.of.any.prior.reaction..choice.not.documented)
fisher.test(notdocument)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  notdocument
## p-value = 0.2692
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.4080485 1.3108219
## sample estimates:
## odds ratio 
##  0.7324531

                                                  notdocument

##    
##      0  1
##   1 49 47
##   2 67 47

Severity, Requiring ED and/or EPI, Black individuals >> White Individuals

filtered_data$epied <- as.factor(filtered_data$shellfish.severity.of.any.prior.reaction..choice.presented.to.ED.and..or.epinephrine.use)



EDepinephrine <- table(filtered_data$pdemrace, filtered_data$shellfish.severity.of.any.prior.reaction..choice.presented.to.ED.and..or.epinephrine.use)
fisher.test(EDepinephrine)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  EDepinephrine
## p-value = 0.01306
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  1.180884 6.217303
## sample estimates:
## odds ratio 
##    2.62465

EDepinephrine

##    
##      0  1
##   1 85 11
##   2 85 29

df1 = filtered_data %>% 
  count(race, epied) %>% 
  group_by(race) %>% 
  mutate(prop=prop.table(n))

df1

ggplot(df1, aes(race, fill = epied))+
geom_bar(aes(y = prop*100),
    position = "dodge", stat = "identity") + scale_fill_discrete(name = "Epi-ED", labels = c("No", "Yes"),type=c("cornflowerblue","red")) +theme_classic() + ylab("Proportion (%)") + xlab("Race") + ggtitle("Figure 13. Greater Proportion of  Black Individuals W/ \n Reaction Requiring Presenting to ED/Epi Use") + theme(title  = element_text(face="bold", size = 15))

Prescribed EPI at Time of DX - No Racial differences by Race

EPIDX <- table(filtered_data$pdemrace, filtered_data$prescribed.epinephrine.at.time.of.food.allergy.diagnosis)
fisher.test(EPIDX)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  EPIDX
## p-value = 0.1914
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##   0.6384991 17.8471320
## sample estimates:
## odds ratio 
##   2.895754

EPIDX

##    
##      no yes
##   1   7  89
##   2   3 111

Shellfish differences

# 
# clam <- table(filtered_data$pdemrace, filtered_data$clam.)
# fisher.test(clam)
# 
# crab <- table(filtered_data$pdemrace, filtered_data$crab.)
# fisher.test(crab)
# 
# 
# lobster <- table(filtered_data$pdemrace, filtered_data$lobster.)
# fisher.test(lobster)
# 
# scallops <- table(filtered_data$pdemrace, filtered_data$scallops.)
# fisher.test(scallops)
# 
# 
# shrimp <- table(filtered_data$pdemrace, filtered_data$shrimp.)
# fisher.test(shrimp)

filtered_data$clam. <- as.numeric(filtered_data$clam.)
filtered_data$oyster. <- as.numeric(filtered_data$oyster.)
filtered_data$scallops. <- as.numeric(filtered_data$scallops.)
filtered_data$crab. <- as.numeric(filtered_data$crab.)
filtered_data$shrimp. <- as.numeric(filtered_data$shrimp.)
filtered_data$lobster. <- as.numeric(filtered_data$lobster.)




filtered_data$mollusks <- ifelse(filtered_data$clam. | filtered_data$oyster. | filtered_data$scallops., 1, 0)

filtered_data$crustaceans <- ifelse(filtered_data$crab. | filtered_data$shrimp. | filtered_data$lobster., 1, 0)

Crustaceans - No difference

crustaceans <- table(filtered_data$pdemrace, filtered_data$crustaceans)
fisher.test(crustaceans)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  crustaceans
## p-value = 0.2347
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.7873682 2.7459666
## sample estimates:
## odds ratio 
##    1.46753

Mollusks - Prop White Individuals > Black Individuals

mollusks <- table(filtered_data$pdemrace, filtered_data$mollusks)
fisher.test(mollusks)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  mollusks
## p-value = 0.01145
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.08565154 0.79344774
## sample estimates:
## odds ratio 
##  0.2794467

rownames(mollusks) <- c( "White","Black")
colnames(mollusks) <- c( "No","Yes")


kbl(mollusks) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	80	16
Black	108	6

filtered_data$molluskss <- as.factor(filtered_data$mollusks)

df1 = filtered_data %>% 
  count(race, molluskss) %>% 
  group_by(race) %>% 
  mutate(prop=prop.table(n))

ggplot(df1, aes(race, fill = molluskss))+
geom_bar(aes(y = prop*100),
    position = "dodge", stat = "identity") + scale_fill_discrete(name = "mollusks allergy", labels = c("No", "Yes"),type=c("cornflowerblue","red")) +theme_classic() + ylab("Proportion (%)") + xlab("Race") + ggtitle("Figure 14.  White Individuals Have Greater Proportion of Mollusks Allergy") + theme(title  = element_text(face="bold", size = 15))

+ difference is driven by oyster

Oyster - Prop White Individuals > Black Individuals

oyster <- table(filtered_data$pdemrace, filtered_data$oyster.)
fisher.test(oyster)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  oyster
## p-value = 0.01884
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.0000000 0.8987685
## sample estimates:
## odds ratio 
##          0

rownames(oyster) <- c( "White","Black")
colnames(oyster) <- c( "No","Yes")


kbl(oyster) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	91	5
Black	114	0

filtered_data$oysteral <- as.factor(filtered_data$oyster.)

df1 = filtered_data %>% 
  count(race, oysteral) %>% 
  group_by(race) %>% 
  mutate(prop=prop.table(n))

ggplot(df1, aes(race, fill = oysteral))+
geom_bar(aes(y = prop*100),
    position = "dodge", stat = "identity") + scale_fill_discrete(name = "oyster allergy", labels = c("No", "Yes"),type=c("cornflowerblue","red")) +theme_classic() + ylab("Proportion (%)") + xlab("Race") + ggtitle("Figure 14.  White Individuals Have Greater Proportion of Oyster Allergy") + theme(title  = element_text(face="bold", size = 15))

Food Allergy

No diff in peanut, treenut, fish, egg, soy, or wheat

peanut <- table(filtered_data$pdemrace, filtered_data$Food.allergy..choice.peanut.)

fisher.test(peanut)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  peanut
## p-value = 0.07539
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.895861 5.702772
## sample estimates:
## odds ratio 
##   2.175016

rownames(peanut) <- c( "White","Black")
colnames(peanut) <- c( "No","Yes")

kbl(peanut) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	87	9
Black	93	21

treenut <- table(filtered_data$pdemrace, filtered_data$Food.allergy..choice.tree.nut.)

fisher.test(treenut)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  treenut
## p-value = 0.6994
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.518347 2.828239
## sample estimates:
## odds ratio 
##   1.196095

rownames(treenut) <- c( "White","Black")
colnames(treenut) <- c( "No","Yes")

kbl(treenut) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	83	13
Black	96	18

fish <- table(filtered_data$pdemrace, filtered_data$Food.allergy..choice.fish.)

fisher.test(fish)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  fish
## p-value = 0.355
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.6410381 3.3394545
## sample estimates:
## odds ratio 
##   1.439202

rownames(fish) <- c( "White","Black")
colnames(fish) <- c( "No","Yes")

kbl(fish) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	83	13
Black	93	21

milk <- table(filtered_data$pdemrace, filtered_data$Food.allergy..choice.milk.)
fisher.test(milk)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  milk
## p-value = 0.7349
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.1276666 3.1801351
## sample estimates:
## odds ratio 
##  0.6631358

rownames(milk) <- c( "White","Black")
colnames(milk) <- c( "No","Yes")

kbl(milk) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	91	5
Black	110	4

egg <- table(filtered_data$pdemrace, filtered_data$Food.allergy..choice.egg.)

fisher.test(egg)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  egg
## p-value = 0.1914
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.05603141 1.56617297
## sample estimates:
## odds ratio 
##  0.3453331

rownames(egg) <- c( "White","Black")
colnames(egg) <- c( "No","Yes")

kbl(egg) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	89	7
Black	111	3

soy <- table(filtered_data$pdemrace, filtered_data$Food.allergy..choice.soy.)

fisher.test(soy)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  soy
## p-value = 0.5936
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.006995213 8.138635974
## sample estimates:
## odds ratio 
##  0.4176305

rownames(soy) <- c( "White","Black")
colnames(soy) <- c( "No","Yes")

kbl(soy) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	94	2
Black	113	1

sesame <- table(filtered_data$pdemrace, filtered_data$Food.allergy..choice.sesame.)
rownames(sesame) <- c( "White","Black")
colnames(sesame) <- c( "No")
kbl(sesame) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No
White	96
Black	114

wheat <- table(filtered_data$pdemrace, filtered_data$Food.allergy..choice.wheat.)
fisher.test(wheat)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  wheat
## p-value = 0.1808
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.004102636 2.117339256
## sample estimates:
## odds ratio 
##  0.2049072

rownames(wheat) <- c( "White","Black")
colnames(wheat) <- c( "No","Yes")


kbl(wheat) %>%
  kable_paper(full_width = F) %>% kable_styling(bootstrap_options = "striped", font_size = 15)

	No	Yes
White	92	4
Black	113	1

filtered_data$wheatallergy <- as.factor(filtered_data$Food.allergy..choice.wheat.)

df1 = filtered_data %>% 
  count(race, wheatallergy) %>% 
  group_by(race) %>% 
  mutate(prop=prop.table(n))

ggplot(df1, aes(race, fill = wheatallergy))+
geom_bar(aes(y = prop*100),
    position = "dodge", stat = "identity") + scale_fill_discrete(name = "wheat allergy", labels = c("No", "Yes"),type=c("cornflowerblue","red")) +theme_classic() + ylab("Proportion (%)") + xlab("Race") + ggtitle("Figure 15. Wheat Allergy Greater in White Individuals") + theme(title  = element_text(face="bold", size = 15))

Omar AI project

NME

Read in Data

First step, make race categories, categorize everyone as either white, black, or other

No Difference in Age by Race

No Difference in Age of Onset by Race

Difference in Sex by Race

No Difference in Roach by Race

No Difference in Mites by Race

Difference in Symptoms

Hives/angioedema - Marginal proportional difference by race

GI, Emesis/Diarrhea - No difference by race

Respiratory - No Racial differences by race

Cardiac - No Racial differences by race

Neurological - No Racial differences by race

Medical Comorbidities

Allergic Rhinitis Comorbidity - No differences by Race

Asthma Comorbidity - No differences by Race

Eczema Comorbidity - No differences by Race

Contact Derm Comorbidity - No differences by Race

Oral Allergy Syndrome Comorbidity - No differences by Race

Anxiety Comorbidity - No differences by Race

Other Psych Comorbidities - Proportion of White individuals > Proportion of Black Individuals

Cardiac Comorbidities - No differences by Race

GI Comorbidity - No Racial differences

Alpha Gal Comorbidity - Proportion of White Individuals > Proportion of Black Individuals

Other Food Allergy - No Racial differences

Drug Allergy Comorbidity - No Racial differences, marginal trend that proportion of white individuals > proportion of black individuals

Insect Allergy - racial differences; Prop White Individuals > Black Individuals

Medications at Time of DX

PPI - No Racial differences

Beta Blockers - No Racial differences

ACE/ARB - No Racial differences

NSAID - No Racial differences

Sulfasalazine - noone taking it

Immune modulator - No Racial differences

Oral steroid - No Racial differences

Shellfish allergy severity

No difference in antihistamine only or undocumented any prior reaction

Severity, Requiring ED and/or EPI, Black individuals >> White Individuals

Prescribed EPI at Time of DX - No Racial differences by Race

Shellfish differences

Crustaceans - No difference

Mollusks - Prop White Individuals > Black Individuals

Oyster - Prop White Individuals > Black Individuals

Food Allergy

No diff in peanut, treenut, fish, egg, soy, or wheat