OralHealthNL: Oral Health in Children in The Netherlands

In countreg: Count Data Regression

Oral Health in Children in The Netherlands

Description

Data from a study on oral health status and the preventive dental behaviors of 9-year-old children in The Netherlands.

Usage

data("OralHealthNL")

Format

A data frame containing 440 observations on 8 variables.

dmfs

Numeric index of decayed, missing, and filled surfaces (DMFS) in deciduous teeth.

education

Factor indicating whether the highest completed education level of the mother is "high" (senior general secondary education, HAVO, or higher) or "low".

gender

Factor indicating gender of the child ("female" or "male").

ethnicity

Factor indicating whether the mother is "immigrant" (born abroad) or "native" (born in The Netherlands).

brushing

Factor indicating whether the frequency of brushing teeth is "< 2" or ">= 2" times per day.

breakfast

Factor indicating whether the frequency of having breakfast is "7" or "< 7" days per week.

fooddrink

Factor indicating whether the frequency of food and drinks in addition to the three main meals is "<= 7" or "> 7" times per day.

corah

Factor indicating whether Corah's Dental Anxiety score is "< 13" or ">= 13" (see also below).

Details

The data are from the study “Oral Health in Children and Adolescents in The Netherlands” (Schuller et al. 2011). The aim of this study was to describe the oral health status and the preventive dental behaviors of children from different age groups (Dusseldorp et al. 2015). Here, the subset of children at the age of 9 years is provided as analyzed by Hofstetter et al. (2016).

The data collection consisted of a clinical oral examination and a questionnaire survey, using a repeated cross-sectional design. Data contained information about demographic variables (ethnicity and educational level), nutrition, children's dental attendance, oral self-care, and dental anxiety. The score on Corah's Dental Anxiety Questionnaire was used as a measure of dental anxiety. This questionnaire consists of four questions with answer categories from 1 (low anxiety) to 5 (high anxiety). A total Corah score was computed by taking the sum of the four items and then dichotomized into ‘lower than 13’ and ‘higher than or equal to 13’.

Source

Supplementary materials for Hofstetter et al. (2016). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1159/000448197")}

References

Dusseldorp E, Kamphuis M, Schuller AA (2015). “Impact of Lifestyle Factors on Caries Experience in Three Different Age Groups: 9, 15, and 21-Year-Olds”, Community Dentistry and Oral Epidemiology, 43(1), 9–16. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/cdoe.12123")}

Hofstetter H, Dusseldorp E, Zeileis A, Schuller AA (2016). “Modeling Caries Experience: Advantages of the Use of the Hurdle Model”, Caries Research, 50(6), 517–526. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1159/000448197")}

Schuller AA, Poorterman JHG, van Kempen CPF, Dusseldorp E, van Dommelen P, Verrips GHW (2011). Kies voor tanden: Een onderzoek naar mondgezondheid en preventief tandheelkundig gedrag van jeugdigen. Tussenmeting 2009, een vervolg op de reeks TJZ-onderzoeken. TNO, Leiden.

Examples

## Load data and omit NAs and one dmfs outlier
data("OralHealthNL", package = "countreg")
head(OralHealthNL)
OralHealthNL <- na.omit(subset(OralHealthNL, dmfs < 40))

## Visualization: Is dmfs > 0?
par(mfrow = c(2, 4))
plot(factor(dmfs > 0, levels = c(TRUE, FALSE), labels = c("> 0", "= 0")) ~ .,
  data = OralHealthNL, ylab = "dmfs")

## Count: How large is log(dmfs) given dmfs > 0?
par(mfrow = c(2, 4))
plot(log(dmfs) ~ ., data = OralHealthNL, subset = dmfs > 0, ylab = "dmfs")

## Relevel the factor variables so that non-risk group is the reference
OralHealthNL <- transform(OralHealthNL,
  ethnicity = relevel(ethnicity, ref = "native"),
  brushing = relevel(brushing, ref = ">= 2"),
  breakfast = relevel(breakfast, ref = "7")
)

## Count regression models
zinb <- zeroinfl(dmfs ~ ., data = OralHealthNL, dist = "negbin")
zip  <- zeroinfl(dmfs ~ ., data = OralHealthNL, dist = "poisson")
hnb  <-   hurdle(dmfs ~ ., data = OralHealthNL, dist = "negbin")
hp   <-   hurdle(dmfs ~ ., data = OralHealthNL, dist = "poisson")

## Model comparisons (Table 3)
## Information criteria
cbind(AIC(hnb, zinb, hp, zip), BIC = BIC(hnb, zinb, hp, zip)[, 2])
## Negative binomial vs. Poisson
if(require("lmtest")) lrtest(hnb, hp)
if(require("lmtest")) lrtest(zinb, zip)
## Zero-inflation vs. hurdle
if(require("nonnest2")) vuongtest(zinb, hnb)

## Coefficients, odds ratios, and rate ratios
## Negative binomial hurdle model (Table 3)
summary(hnb)
exp(confint(hnb))
## Negative binomial zero-inflated model (Table 4)
summary(hnb)
exp(confint(zinb))

## Rootograms (top left: Figure 1)
if(require("topmodels")) {
par(mfrow = c(2, 2))
rootogram(lm(OralHealthNL$dmfs ~ 1),
  style = "standing", scale = "raw",
  breaks = 0:23 - 0.5, xlim = c(-0.5, 22.5),
  xlab = "dmfs", main = "Normal distribution")
rootogram(hnb,
  style = "standing", scale = "raw",
  width = 1, xlim = c(-0.5, 22.5),
  xlab = "dmfs", main = "Negative binomial hurdle model")
rootogram(lm(OralHealthNL$dmfs ~ 1),
  breaks = 0:23 - 0.5, xlim = c(-0.5, 22.5),
  xlab = "dmfs", main = "Normal distribution")
abline(h = c(-1, 1), lty = 2)
rootogram(hnb,
  width = 1, xlim = c(-0.5, 22.5),
  xlab = "dmfs", main = "Negative binomial hurdle model")
abline(h = c(-1, 1), lty = 2)
par(mfrow = c(1, 1))
}

## Number of zeros
c(dmfs = sum(OralHealthNL$dmfs == 0),
  ZINB = sum(predict(zinb, type = "prob")[,1]),
  Hurdle = sum(predict(hnb, type = "prob")[,1]))
## Correlation of observations and fitted means
cor(cbind(dmfs = OralHealthNL$dmfs,
  ZINB = fitted(zinb), HNB = fitted(hnb)))

## Bias-reduced logistic regression (due to separation)
if(require("brglm2")) {
br <- glm(
  factor(dmfs == 0, levels = c(TRUE, FALSE), labels = c("= 0", "> 0")) ~ .,
  data = OralHealthNL, family = binomial, method = "brglmFit")
print(coeftest(br), digits = 1)
}

countreg documentation built on Dec. 4, 2023, 3:09 a.m.