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
  • [1] SPATIO-TEMPORAL HETEROGENEITY OF MALARIA MORBIDITY IN GHANA: ANALYSIS OF ROUTINE HEALTH FACILITY DATA
    Awine, Timothy
    Malm, Keziah
    Peprah, Nana
    Silal, Sheetal
    AMERICAN JOURNAL OF TROPICAL MEDICINE AND HYGIENE, 2018, 99 (04): : 98 - 98
  • [2] Spatio-temporal Bayesian models for malaria risk using survey and health facility routine data in Rwanda
    Semakula, Muhammed
    Niragire, Francois
    Faes, Christel
    TROPICAL MEDICINE & INTERNATIONAL HEALTH, 2023, 28 : 122 - 123
  • [3] Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania
    Sumaiyya G. Thawer
    Monica Golumbeanu
    Samwel Lazaro
    Frank Chacky
    Khalifa Munisi
    Sijenunu Aaron
    Fabrizio Molteni
    Christian Lengeler
    Emilie Pothin
    Robert W. Snow
    Victor A. Alegana
    Scientific Reports, 13
  • [4] Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania
    Thawer, Sumaiyya G.
    Golumbeanu, Monica
    Lazaro, Samwel
    Chacky, Frank
    Munisi, Khalifa
    Aaron, Sijenunu
    Molteni, Fabrizio
    Lengeler, Christian
    Pothin, Emilie
    Snow, Robert W.
    Alegana, Victor A.
    SCIENTIFIC REPORTS, 2023, 13 (01)
  • [5] Modelling heterogeneity in malaria transmission using large sparse spatio-temporal entomological data
    Rumisha, Susan Fred
    Smith, Thomas
    Abdulla, Salim
    Masanja, Honorath
    Vounatsou, Penelope
    GLOBAL HEALTH ACTION, 2014, 7 : 1 - 13
  • [6] Spatio-Temporal Routine Mining on Mobile Phone Data
    Qin, Tian
    Shangguan, Wufan
    Song, Guojie
    Tang, Jie
    ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, 2018, 12 (05)
  • [7] A Bayesian Spatio-Temporal Analysis of Malaria in the Greater Accra Region of Ghana from 2015 to 2019
    Donkor, Elorm
    Kelly, Matthew
    Eliason, Cecilia
    Amotoh, Charles
    Gray, Darren J.
    Clements, Archie C. A.
    Wangdi, Kinley
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2021, 18 (11)
  • [8] Bayesian spatio-temporal analysis of breastfeeding practices in Ghana
    Gayawan, Ezra
    Adjei, Christiana Nyarko
    GEOJOURNAL, 2021, 86 (04) : 1943 - 1955
  • [9] Bayesian spatio-temporal analysis of breastfeeding practices in Ghana
    Ezra Gayawan
    Christiana Nyarko Adjei
    GeoJournal, 2021, 86 : 1943 - 1955
  • [10] Spatio-temporal modelling of malaria incidence for evaluation of public health policy interventions in Ghana, West Africa
    Appiah, S. K.
    Mueller, U.
    Cross, J.
    19TH INTERNATIONAL CONGRESS ON MODELLING AND SIMULATION (MODSIM2011), 2011, : 676 - 682