Duchenne Muscular Dystrophy (DMD) Prevalence Data — dat.crisafulli2020 • metadat

26 studies reporting estimates of the birth prevalence of Duchenne muscular dystrophy.

dat.crisafulli2020

Format

The data frame contains the following columns:

study	`character`	study label (first author, year)
pubyear	`integer`	publication year
country	`factor`	origin of investigated population
from, to	`integer`	time span of investigation (years)
cases	`integer`	number of DMD cases
total	`integer`	corresponding total population

Details

Duchenne muscular dystrophy (DMD) is a rare disease that is caused by a genetic mutation and is characterized by impairment through muscle weakness and a reduced life expectancy.

Crisafulli et al. (2020) reported on a systematic review of data on the epidemiology of DMD, including estimates of the birth prevalence (which is of the order of a few per ten thousand). One of the originally reported studies (Koenig, 2019) is omitted here, as it constitutes an obvious outlier, and the reliability of the reported data is doubtful; Crisafulli et al. (2020) pointed out that “Concerning birth prevalence, Koenig et al. were found to be outliers. This study had problems with data collection in the last study year, as due to privacy issues, DMD cases were under-reported.”

Source

Crisafulli, S., Sultana, J., Fontana, A., Salvo, F., Messina, S., & Trifiro, G. (2020). Global epidemiology of Duchenne muscular dystrophy: an updated systematic review and meta-analysis. Orphanet Journal of Rare Diseases, 15, 141. https://doi.org/10.1186/s13023-020-01430-8

Author

Christian Roever, christian.roever@med.uni-goettingen.de

Concepts

medicine, epidemiology, proportions, dose-response models

Examples

# show (some) data
head(dat.crisafulli2020)
#>              study pubyear country from   to cases  total
#> 1    Brooks (1977)    1977      UK 1953 1968    47 177413
#> 2   Danieli (1977)    1977      IT 1952 1972    66 234396
#> 3 Takeshita (1977)    1977      JP 1956 1970    19  91157
#> 4  Drummond (1979)    1979      NZ   NA   NA     2  10000
#> 5     Cowan (1980)    1980      AU 1960 1971    99 532302
#> 6   Danieli (1980)    1980      IT 1952 1972   105 371698

# compute logarithmic proportions and associated standard errors
library(metafor)
logp <- escalc(measure="PLN",
               xi=cases, ni=total, slab=study,
               data=dat.crisafulli2020)

# perform meta-analysis
rma01 <- rma.uni(logp)

# show results
rma01
#> 
#> Random-Effects Model (k = 26; tau^2 estimator: REML)
#> 
#> tau^2 (estimated amount of total heterogeneity): 0.0130 (SE = 0.0094)
#> tau (square root of estimated tau^2 value):      0.1139
#> I^2 (total heterogeneity / total variability):   41.79%
#> H^2 (total variability / sampling variability):  1.72
#> 
#> Test for Heterogeneity:
#> Q(df = 25) = 37.4118, p-val = 0.0527
#> 
#> Model Results:
#> 
#> estimate      se       zval    pval    ci.lb    ci.ub      
#>  -8.4014  0.0386  -217.6251  <.0001  -8.4770  -8.3257  *** 
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 

# illustrate in a forest plot
forest(rma01, header=TRUE, xlim=c(-12,-5))