Spatio-temporal heterogeneity of malaria morbidity in Ghana: Analysis of routine health facility data

被引:25
|
作者
Awine, Timothy [1 ,2 ]
Malm, Keziah [3 ]
Peprah, Nana Yaw [3 ]
Silal, Sheetal P. [1 ,4 ]
机构
[1] Univ Cape Town, Dept Stat Sci, Modelling & Simulat Hub, Cape Town, South Africa
[2] Univ Stellenbosch, South African Dept Sci & Technol, Natl Res Fdn Ctr Excellence Epidemiol Modelling &, Stellenbosch, South Africa
[3] Minist Hlth, Natl Malaria Control Program, Accra, Ghana
[4] Univ Oxford, Nuffield Dept Med, Trop Dis Modelling, Oxford, England
来源
PLOS ONE | 2018年 / 13卷 / 01期
基金
新加坡国家研究基金会;
关键词
SCALING-UP; CLIMATE; IMPACT;
D O I
10.1371/journal.pone.0191707
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background Malaria incidence is largely influenced by vector abundance. Among the many interconnected factors relating to malaria transmission, weather conditions such as rainfall and temperature are known to create suitable environmental conditions that sustain reproduction and propagation of anopheles mosquitoes and malaria parasites. In Ghana, climatic conditions vary across the country. Understanding the heterogeneity of malaria morbidity using data sourced from a recently setup data repository for routine health facility data could support planning. Methods Monthly aggregated confirmed uncomplicated malaria cases from the District Health Information Management System and average monthly rainfall and temperature records obtained from the Ghana Meteorological Agency from 2008 to 2016 were analysed. Univariate time series models were fitted to the malaria, rainfall and temperature data series. After pre-whitening the morbidity data, cross correlation analyses were performed. Subsequently, transfer function models were developed for the relationship between malaria morbidity and rainfall and temperature. Results Malaria morbidity patterns vary across zones. In the Guinea savannah, morbidity peaks once in the year and twice in both the Transitional forest and Coastal savannah, following similar patterns of rainfall at the zonal level. While the effects of rainfall on malaria morbidity are delayed by a month in the Guinea savannah and Transitional Forest zones those of temperature are delayed by two months in the Transitional forest zone. In the Coastal savannah however, incidence of malaria is significantly associated with two months lead in rainfall and temperature. Conclusion Data captured on the District Health Information Management System has been used to demonstrate heterogeneity in the dynamics of malaria morbidity across the country. Timing of these variations could guide the deployment of interventions such as indoor residual spraying, Seasonal Malaria Chemoprevention or vaccines to optimise effectiveness on zonal basis.
引用
收藏
页数:17
相关论文
共 50 条
  • [31] On multifactorial drivers for malaria rebound in Brazil: a spatio-temporal analysis
    Mario J. C. Ayala
    Leonardo S. Bastos
    Daniel A. M. Villela
    Malaria Journal, 21
  • [32] A spatio-temporal analysis to identify the drivers of malaria transmission in Bhutan
    Wangdi, Kinley
    Xu, Zhijing
    Suwannatrai, Apiporn T.
    Kurscheid, Johanna
    Lal, Aparna
    Namgay, Rinzin
    Glass, Kathryn
    Gray, Darren J.
    Clements, Archie C. A.
    SCIENTIFIC REPORTS, 2020, 10 (01)
  • [33] Spatio-Temporal Analysis for Smart City Data
    Bermudez-Edo, Maria
    Barnaghi, Payam
    COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), 2018, : 1841 - 1845
  • [34] Interactive exploratory analysis of spatio-temporal data
    Dreesman, JM
    COMPSTAT 2002: PROCEEDINGS IN COMPUTATIONAL STATISTICS, 2002, : 407 - 412
  • [35] Fuzzy cluster analysis of spatio-temporal data
    Liu, ZJ
    George, R
    COMPUTER AND INFORMATION SCIENCES - ISCIS 2003, 2003, 2869 : 984 - 991
  • [36] A spatio-temporal analysis to identify the drivers of malaria transmission in Bhutan
    Kinley Wangdi
    Zhijing Xu
    Apiporn T. Suwannatrai
    Johanna Kurscheid
    Aparna Lal
    Rinzin Namgay
    Kathryn Glass
    Darren J. Gray
    Archie C. A. Clements
    Scientific Reports, 10
  • [37] On multifactorial drivers for malaria rebound in Brazil: a spatio-temporal analysis
    Ayala, Mario J. C.
    Bastos, Leonardo S.
    Villela, Daniel A. M.
    MALARIA JOURNAL, 2022, 21 (01)
  • [38] Spatio-temporal heterogeneity analysis of energy use in residential buildings
    Zhang, Yan
    Teoh, Bak Koon
    Zhang, Limao
    Chen, Jiayu
    JOURNAL OF CLEANER PRODUCTION, 2022, 352
  • [39] Spatio-temporal heterogeneity of the snow cover from data of the penetrometer SnowMicroPen
    Komarov, A. Y.
    Seliverstov, Y. G.
    Grebennikov, P. B.
    Sokratov, S. A.
    LED I SNEG-ICE AND SNOW, 2018, 58 (04): : 473 - 485
  • [40] Estimating the local spatio-temporal distribution of malaria from routine health information systems in areas of low health care access and reporting
    Hyde, Elizabeth
    Bonds, Matthew H.
    Ihantamalala, Felana A.
    Miller, Ann C.
    Cordier, Laura F.
    Razafinjato, Benedicte
    Andriambolamanana, Herinjaka
    Randriamanambintsoa, Marius
    Barry, Michele
    Andrianirinarison, Jean Claude
    Andriamananjara, Mauricette N.
    Garchitorena, Andres
    INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS, 2021, 20 (01)