@article{fdi:010080976,
  title = {{E}stimating the local spatio-temporal distribution of malaria from routine health information systems in areas of low health care access and reporting},
  author = {{H}yde, {E}. and {B}onds, {M}. {H}. and {I}hantamalala, {F}. {A}. and {M}iller, {A}. {C}. and {C}ordier, {L}. {F}. and {R}azafinjato, {B}. and {A}ndriambolamanana, {H}. and {R}andriamanambintsoa, {M}. and {B}arry, {M}. and {A}ndrianirinarison, {J}. {C}. and {A}ndriamananjara, {M}. {N}. and {G}architorena, {A}ndres},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground {R}eliable surveillance systems are essential for identifying disease outbreaks and allocating resources to ensure universal access to diagnostics and treatment for endemic diseases. {Y}et, most countries with high disease burdens rely entirely on facility-based passive surveillance systems, which miss the vast majority of cases in rural settings with low access to health care. {T}his is especially true for malaria, for which the {W}orld {H}ealth {O}rganization estimates that routine surveillance detects only 14% of global cases. {T}he goal of this study was to develop a novel method to obtain accurate estimates of disease spatio-temporal incidence at very local scales from routine passive surveillance, less biased by populations' financial and geographic access to care. {M}ethods {W}e use a geographically explicit dataset with residences of the 73,022 malaria cases confirmed at health centers in the {I}fanadiana {D}istrict in {M}adagascar from 2014 to 2017. {M}alaria incidence was adjusted to account for underreporting due to stock-outs of rapid diagnostic tests and variable access to healthcare. {A} benchmark multiplier was combined with a health care utilization index obtained from statistical models of non-malaria patients. {V}ariations to the multiplier and several strategies for pooling neighboring communities together were explored to allow for fine-tuning of the final estimates. {S}eparate analyses were carried out for individuals of all ages and for children under five. {C}ross-validation criteria were developed based on overall incidence, trends in financial and geographical access to health care, and consistency with geographic distribution in a district-representative cohort. {T}he most plausible sets of estimates were then identified based on these criteria. {R}esults {P}assive surveillance was estimated to have missed about 4 in every 5 malaria cases among all individuals and 2 out of every 3 cases among children under five. {A}djusted malaria estimates were less biased by differences in populations' financial and geographic access to care. {A}verage adjusted monthly malaria incidence was nearly four times higher during the high transmission season than during the low transmission season. {B}y gathering patient-level data and removing systematic biases in the dataset, the spatial resolution of passive malaria surveillance was improved over ten-fold. {G}eographic distribution in the adjusted dataset revealed high transmission clusters in low elevation areas in the northeast and southeast of the district that were stable across seasons and transmission years. {C}onclusions {U}nderstanding local disease dynamics from routine passive surveillance data can be a key step towards achieving universal access to diagnostics and treatment. {M}ethods presented here could be scaled-up thanks to the increasing availability of e-health disease surveillance platforms for malaria and other diseases across the developing world.},
  keywords = {{MADAGASCAR} ; {IFANADIANA} {DISTRICT}},
  booktitle = {},
  journal = {{I}nternational {J}ournal of {H}ealth {G}eographics},
  volume = {20},
  numero = {1},
  pages = {8 [17 ]},
  ISSN = {1476-072{X}},
  year = {2021},
  DOI = {10.1186/s12942-021-00262-4},
  URL = {https://www.documentation.ird.fr/hor/fdi:010080976},
}