@article{fdi:010061424,
  title = {{E}vidence for overdispersion in the distribution of malaria parasites and leukocytes in thick blood smears},
  author = {{H}ammami, {I}. and {G}arcia, {A}ndr{\'e} and {N}uel, {G}.},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground: {M}icroscopic examination of stained thick blood smears ({TBS}) is the gold standard for routine malaria diagnosis. {P}arasites and leukocytes are counted in a predetermined number of high power fields ({HPF}s). {D}ata on parasite and leukocyte counts per {HPF} are of broad scientific value. {H}owever, in published studies, most of the information on parasite density ({PD}) is presented as summary statistics (e. g. {PD} per microlitre, prevalence, absolute/assumed white blood cell counts), but original data sets are not readily available. {B}esides, the number of parasites and the number of leukocytes per {HPF} are assumed to be {P}oisson-distributed. {H}owever, count data rarely fit the restrictive assumptions of the {P}oisson distribution. {T}he violation of these assumptions commonly results in overdispersion. {T}he objectives of this paper are to investigate and handle overdispersion in field-collected data. {M}ethods: {T}he data comprise the records of three {TBS}s of 12-month-old children from a field study of {P}lasmodium falciparum malaria in {T}ori {B}ossito, {B}enin. {A}ll {HPF}s were examined systemically by visually scanning the film horizontally from edge to edge. {T}he numbers of parasites and leukocytes per {HPF} were recorded and formed the first dataset on parasite and leukocyte counts per {HPF}. {T}he full dataset is published in this study. {T}wo sources of overdispersion in data are investigated: latent heterogeneity and spatial dependence. {U}nobserved heterogeneity in data is accounted for by considering more flexible models that allow for overdispersion. {O}f particular interest were the negative binomial model ({NB}) and mixture models. {T}he dependent structure in data was modelled with hidden {M}arkov models ({HMM}s). {R}esults: {T}he {P}oisson assumptions are inconsistent with parasite and leukocyte distributions per {HPF}. {A}mong simple parametric models, the {NB} model is the closest to the unknown distribution that generates the data. {O}n the basis of model selection criteria {AIC} and {BIC}, {HMM}s provided a better fit to data than mixtures. {O}rdinary pseudo-residuals confirmed the validity of {HMM}s. {C}onclusion: {F}ailure to take overdispersion into account in parasite and leukocyte counts may entail important misleading inferences when these data are related to other explanatory variables (malariometric or environmental). {I}ts detection is therefore essential. {I}n addition, an alternative {PD} estimation method that accounts for heterogeneity and spatial dependence should be seriously considered in epidemiological studies with field-collected parasite and leukocyte data.},
  keywords = {{P}arasite and leukocyte counts per {HPF} ; {P}oisson distribution ; overdispersion ; negative binomial distribution ; mixture models ; {HMM}s ; {EM} algorithm ; {AIC} ; {BIC} ; {O}rdinary pseudo-residuals},
  booktitle = {},
  journal = {{M}alaria {J}ournal},
  volume = {12},
  numero = {},
  pages = {398},
  ISSN = {1475-2875},
  year = {2013},
  DOI = {10.1186/1475-2875-12-398},
  URL = {https://www.documentation.ird.fr/hor/fdi:010061424},
}