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Verma M, Panwar S, Sahoo SS, Grover GS, Aggarwal S, Tripathy JP, Shah J, Kakkar R. Mapping the stability of febrile illness hotspots in Punjab from 2012 to 2019- a spatial clustering and regression analysis. BMC Public Health 2023; 23:2014. [PMID: 37845663 PMCID: PMC10580620 DOI: 10.1186/s12889-023-16930-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 10/07/2023] [Indexed: 10/18/2023] Open
Abstract
INTRODUCTION Febrile illnesses (FI) represent a typical spectrum of diseases in low-resource settings, either in isolation or with other common symptoms. They contribute substantially to morbidity and mortality in India. The primary objective was to study the burden of FI based on Integrated Disease Surveillance Programme (IDSP) data in Punjab, analyze geospatial and temporal trends and patterns, and identify the potential hotspots for effective intervention. METHODS A retrospective ecological study used the district-level IDSP reports between 2012 and 2019. Diseases responsible for FI on a large scale, like Dengue, Chikungunya, Malaria (Plasmodium Falciparum, P. Vivax), Enteric fever, and Pyrexia of Unknown Origin (PUO), were included in the analysis. The digital map of Punjab was obtained from GitHub. Spatial autocorrelation and cluster analysis were done using Moran's I and Getis-Ord G* to determine hotspots of FI using the incidence and crude disease numbers reported under IDSP. Further, negative binomial regression was used to determine the association between Spatio-temporal and population variables per the census 2011. Stable hotspots were depicted using heat maps generated from district-wise yearly data. RESULTS PUO was the highest reported FI. We observed a rising trend in the incidence of Dengue, Chikungunya, and Enteric fever, which depicted occasional spikes during the study period. FI expressed significant inter-district variations and clustering during the start of the study period, with more dispersion in the latter part of the study period. P.Vivax malaria depicted stable hotspots in southern districts of Punjab. In contrast, P. Falciparum malaria, Chikungunya, and PUO expressed no spatial patterns. Enteric Fever incidence was high in central and northeastern districts but depicted no stable spatial patterns. Certain districts were common incidence hotspots for multiple diseases. The number of cases in each district has shown over-dispersion for each disease and has little dependence on population, gender, or residence as per regression analysis. CONCLUSIONS The study demonstrates that information obtained through IDSP can describe the spatial epidemiology of FI at crude spatial scales and drive concerted efforts against FI by identifying actionable points.
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Affiliation(s)
- Madhur Verma
- Department of Community and Family Medicine, All India Institute of Medical Sciences, Bathinda, Punjab, India.
| | - Shweta Panwar
- Centre for Technology Alternatives for Rural Areas (CTARA), Indian Institute of Technology Bombay, Mumbai, Maharashtra, India
| | - Soumya Swaroop Sahoo
- Department of Community and Family Medicine, All India Institute of Medical Sciences, Bathinda, Punjab, India
| | - Gagandeep Singh Grover
- Directorate of Health and Family Welfare Punjab and State Programme Officer, Integrated Disease Surveillance Program Punjab, Chandigarh, India
| | - Seema Aggarwal
- Directorate of Health and Family Welfare Punjab and State Programme Officer, Integrated Disease Surveillance Program Punjab, Chandigarh, India
| | - Jaya Prasad Tripathy
- Department of Community and Family Medicine, All India Institute of Medical Sciences, Nagpur, Maharashtra, India
| | - Jitendra Shah
- Centre for Technology Alternatives for Rural Areas (CTARA), Indian Institute of Technology Bombay, Mumbai, Maharashtra, India
| | - Rakesh Kakkar
- Department of Community and Family Medicine, All India Institute of Medical Sciences, Bathinda, Punjab, India
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Kimuyu JS. Comparative spatial–temporal analysis and predictive modeling of climate change-induced malaria vectors’ invasion in new hotspots in Kenya. SN APPLIED SCIENCES 2021. [DOI: 10.1007/s42452-021-04722-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Abstract
AbstractClimate change/variability is a major driving factor among others that contribute to the spread of suitable malaria vectors’ geographical extent. The current study employed comparative spatial–temporal analysis using bioclimatic envelope modeling to predict and quantify the possible surge of suitable malaria vectors’ habitats in new hotspots in Kenya. BIOCLIM and BIOCLIM True/False models were run with model data from HADCM3, CCCMA and SCIRO IPCC future climatic projections under A2a scenario. Prediction and projection of the malaria vectors’ prevalence and distribution were done for the whole country. Spatial–temporal models were generated for the baseline climate, and projections were done to depict how the vectors are likely to be distributed by the years 2020, 2050 and 2080 under the influence of climate change. The results showed that the highest suitable malaria vectors’ habitats by area was 227, 092 km2 obtained when prediction was done with HADCM3 future climate by the year 2050. The least suitable habitats by area was 80, 060 km2 which was predicted with CCCMA projection by the year 2050. Ecological niche prediction from HADCM3 and CSIRO showed a similar trend although at different magnitudes. The prediction results portrayed high likelihood of shift in some suitable habitats that could turn unsuitable, while new hotspots are likely to emerge. The BIOCLIM prediction with the three future climate models showed that the current endemic zones of the lake region and south coastal strip of the Indian Ocean will still remain suitable habitats but with a decline shift in spatial extent by the year 2020, then start to expand by the years 2050–2080. Predictions from HADCM3 by the year 2050 has shown possible wide spread of malaria spatial extents in counties like Narok, Kajiado, Kitui, Makueni, Machakos, Meru, Marsabit, Isiolo, Samburu, Baringo, West Pokot, Turkana and Mandera, while a few others of lower extent might have some emerging isolated hotspots. Laikipia County might become unsuitable habitat for malaria vectors by the year 2050, and the case may remain the same by the year 2080. The malaria burden is likely to shift from Laikipia to the neighboring counties of Baringo, Isiolo, Meru and Turkana. In conclusion, malaria vectors are likely to spread in new continuous and isolated hotspots with future likely increase in malaria prevalence hence possible epidemic upsurge by the year 2050. Consequently, evidence-based scientific research can be utilized to guide policy for sustainable development in the health agenda.
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Debebe Y, Hill SR, Tekie H, Dugassa S, Hopkins RJ, Ignell R. Malaria hotspots explained from the perspective of ecological theory underlying insect foraging. Sci Rep 2020; 10:21449. [PMID: 33293574 PMCID: PMC7722757 DOI: 10.1038/s41598-020-78021-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 11/19/2020] [Indexed: 01/16/2023] Open
Abstract
Hotspots constitute the major reservoir for residual malaria transmission, with higher malaria incidence than neighbouring areas, and therefore, have the potential to form the cornerstone for successful intervention strategies. Detection of malaria hotspots is hampered by their heterogenous spatial distribution, and the laborious nature and low sensitivity of the current methods used to assess transmission intensity. We adopt ecological theory underlying foraging in herbivorous insects to vector mosquito host seeking and modelling of fine-scale landscape features at the village level. The overall effect of environmental variables on the density of indoor mosquitoes, sporozoite infected mosquitoes, and malaria incidence, was determined using generalized linear models. Spatial analyses were used to identify hotspots for malaria incidence, as well as malaria vector density and associated sporozoite prevalence. We identify household occupancy and location as the main predictors of vector density, entomological inoculation rate and malaria incidence. We propose that the use of conventional vector control and malaria interventions, integrated with their intensified application targeting predicted hotspots, can be used to reduce malaria incidence in endemic and residual malaria settings.
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Affiliation(s)
- Yared Debebe
- Department of Zoological Sciences, Addis Ababa University, PO. Box 1176, Addis Ababa, Ethiopia
| | - Sharon Rose Hill
- Unit of Chemical Ecology, Department of Plant Protection Biology, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Habte Tekie
- Department of Zoological Sciences, Addis Ababa University, PO. Box 1176, Addis Ababa, Ethiopia
| | - Sisay Dugassa
- Aklilu Lemma Institute of Pathobiology, Addis Ababa University, PO. Box 1176, Addis Ababa, Ethiopia
| | | | - Rickard Ignell
- Unit of Chemical Ecology, Department of Plant Protection Biology, Swedish University of Agricultural Sciences, Alnarp, Sweden.
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4
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Hamre KES, Hodges JS, Ayodo G, John CC. Lack of Consistent Malaria Incidence Hotspots in a Highland Kenyan Area During a 10-Year Period of Very Low and Unstable Transmission. Am J Trop Med Hyg 2020; 103:2198-2207. [PMID: 33124534 DOI: 10.4269/ajtmh.19-0821] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
The use of spatial data in malaria elimination strategies is important to understand whether targeted interventions against malaria can be used, particularly in areas with limited resources. We previously documented consistent areas of increased malaria incidence in the epidemic-prone area of Kipsamoite in highland Kenya from 2001 to 2004. In this area and a neighboring subcounty (Kapsisiywa), malaria incidence decreased substantially in 2005, going from peak incidence of 31.7 per 1,000 persons in June 2004 to peak incidence of 7.4 per 1,000 persons in May 2005. Subsequently, the use of indoor residual spraying and artemisinin combination therapy malaria treatment led to a possible interruption of malaria transmission for a 13-month period from 2007 to 2008, after which the incidence returned to very low levels until an epidemic in April-July 2013. In the present study, we used novel kernel density estimation methods to determine whether areas of increased malaria incidence were consistent in six periods of peak incidence from 2003 to 2013, and to assess patterns of incidence in the period before versus. after the period of possible interruption. Areas of highest incidence differed during peak malaria transmission periods over the years 2003-2013, and differed before and after the potential malaria interruption. In this epidemic-prone region with very low malaria transmission, consistent malaria "hotspots" identified in a time of higher transmission are no longer present. Ongoing assessment of spatial malaria epidemiology to identify and target current areas of elevated malaria risk may be important in campaigns to control or eliminate malaria in epidemic-prone areas.
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Affiliation(s)
- Karen E S Hamre
- CDC Foundation, Atlanta, Georgia.,Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota.,Division of Global Pediatrics, University of Minnesota, Minneapolis, Minnesota
| | - James S Hodges
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota
| | - George Ayodo
- Jaramogi Oginga Odinga University of Science and Technology, Bondo, Kenya.,Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Chandy C John
- Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya.,Division of Global Pediatrics, University of Minnesota, Minneapolis, Minnesota.,Department of Pediatrics, Indiana University, Indianapolis, Indiana.,Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota
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5
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Larsen DA, Martin A, Pollard D, Nielsen CF, Hamainza B, Burns M, Stevenson J, Winters A. Leveraging risk maps of malaria vector abundance to guide control efforts reduces malaria incidence in Eastern Province, Zambia. Sci Rep 2020; 10:10307. [PMID: 32587283 PMCID: PMC7316765 DOI: 10.1038/s41598-020-66968-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 02/10/2020] [Indexed: 01/30/2023] Open
Abstract
Although transmission of malaria and other mosquito-borne diseases is geographically heterogeneous, in sub-Saharan Africa risk maps are rarely used to determine which communities receive vector control interventions. We compared outcomes in areas receiving different indoor residual spray (IRS) strategies in Eastern Province, Zambia: (1) concentrating IRS interventions within a geographical area, (2) prioritizing communities to receive IRS based on predicted probabilities of Anopheles funestus, and (3) prioritizing communities to receive IRS based on observed malaria incidence at nearby health centers. Here we show that the use of predicted probabilities of An. funestus to guide IRS implementation saw the largest decrease in malaria incidence at health centers, a 13% reduction (95% confidence interval = 5-21%) compared to concentrating IRS geographically and a 37% reduction (95% confidence interval = 30-44%) compared to targeting IRS based on health facility incidence. These results suggest that vector control programs could produce better outcomes by prioritizing IRS according to malaria-vector risk maps.
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Affiliation(s)
| | | | | | - Carrie F Nielsen
- US President's Malaria Initiative, US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | | | - Jennifer Stevenson
- Macha Research Trust, Choma, Zambia
- Johns Hopkins Malaria Research Institute, Baltimore, MD, USA
| | - Anna Winters
- Akros Research, Lusaka, Zambia
- University of Montana, Missoula, MT, USA
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6
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Zhao X, Thanapongtharm W, Lawawirojwong S, Wei C, Tang Y, Zhou Y, Sun X, Cui L, Sattabongkot J, Kaewkungwal J. Malaria Risk Map Using Spatial Multi-Criteria Decision Analysis along Yunnan Border During the Pre-elimination Period. Am J Trop Med Hyg 2020; 103:793-809. [PMID: 32602435 PMCID: PMC7410425 DOI: 10.4269/ajtmh.19-0854] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
In moving toward malaria elimination, finer scale malaria risk maps are required to identify hotspots for implementing surveillance–response activities, allocating resources, and preparing health facilities based on the needs and necessities at each specific area. This study aimed to demonstrate the use of multi-criteria decision analysis (MCDA) in conjunction with geographic information systems (GISs) to create a spatial model and risk maps by integrating satellite remote-sensing and malaria surveillance data from 18 counties of Yunnan Province along the China–Myanmar border. The MCDA composite and annual models and risk maps were created from the consensus among the experts who have been working and know situations in the study areas. The experts identified and provided relative factor weights for nine socioeconomic and disease ecology factors as a weighted linear combination model of the following: ([Forest coverage × 0.041] + [Cropland × 0.086] + [Water body × 0.175] + [Elevation × 0.297] + [Human population density × 0.043] + [Imported case × 0.258] + [Distance to road × 0.030] + [Distance to health facility × 0.033] + [Urbanization × 0.036]). The expert-based model had a good prediction capacity with a high area under curve. The study has demonstrated the novel integrated use of spatial MCDA which combines multiple environmental factors in estimating disease risk by using decision rules derived from existing knowledge or hypothesized understanding of the risk factors via diverse quantitative and qualitative criteria using both data-driven and qualitative indicators from the experts. The model and fine MCDA risk map developed in this study could assist in focusing the elimination efforts in the specifically identified locations with high risks.
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Affiliation(s)
- Xiaotao Zhao
- Yunnan Institute of Parasitic Diseases, Pu'er, P. R. China.,Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Weerapong Thanapongtharm
- Department of Livestock Development, Veterinary Epidemiological Center, Bureau of Disease Control and Veterinary Services, Bangkok, Thailand
| | - Siam Lawawirojwong
- Geo-Informatics and Space Technology Development Agency, Bangkok, Thailand
| | - Chun Wei
- Yunnan Institute of Parasitic Diseases, Pu'er, P. R. China
| | - Yerong Tang
- Yunnan Institute of Parasitic Diseases, Pu'er, P. R. China
| | - Yaowu Zhou
- Yunnan Institute of Parasitic Diseases, Pu'er, P. R. China
| | - Xiaodong Sun
- Yunnan Institute of Parasitic Diseases, Pu'er, P. R. China
| | - Liwang Cui
- Division of Infectious Diseases and Internal Medicine, Department of Internal Medicine, University of South Florida, Tampa, Florida
| | - Jetsumon Sattabongkot
- Mahidol Vivax Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Jaranit Kaewkungwal
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.,Center of Excellence for Biomedical and Public Health Informatics (BIOPHICS), Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
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7
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Dieng S, Ba EH, Cissé B, Sallah K, Guindo A, Ouedraogo B, Piarroux M, Rebaudet S, Piarroux R, Landier J, Sokhna C, Gaudart J. Spatio-temporal variation of malaria hotspots in Central Senegal, 2008-2012. BMC Infect Dis 2020; 20:424. [PMID: 32552759 PMCID: PMC7301493 DOI: 10.1186/s12879-020-05145-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 06/10/2020] [Indexed: 12/01/2022] Open
Abstract
Background In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal and to identify the meteorological, environmental, and preventive factors that influence this variation. Methods This study analysed the weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population approximately 500,000) as part of a trial of seasonal malaria chemoprevention (SMC). Data on weekly rainfall and annual vegetation types were obtained for each village through remote sensing. The time series of weekly malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected during each transmission period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model. Results The malaria incidence for the entire area varied between 0 and 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR = 0.48, IC95%: 0.33–0.68). The association between rainfall and hotspot status was non-linear and depended on both vegetation type and amount of rainfall. The association between village location in the study area and hotspot status was also shown. Conclusion In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. By taking into consideration the environmental and meteorological characteristics common to all hotspots, monitoring of these factors could lead targeted public health interventions at the local level. Moreover, spatial hotspots and foci of malaria persisting during LTPs need to be further addressed. Trial registration The data used in this work were obtained from a clinical trial registered on July 10, 2008 at www.clinicaltrials.gov under NCT00712374.
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Affiliation(s)
- Sokhna Dieng
- Aix Marseille Univ, IRD, INSERM, SESSTIM, Marseille, France. .,Ecole des Hautes Etudes en Santé Publique, Rennes, France.
| | - El Hadj Ba
- UMR VITROME, Campus International IRD-UCAD de l'IRD, Dakar, Sénégal
| | - Badara Cissé
- Institut de Recherche en Santé, de Surveillance Épidémiologique et de Formation (IRESSEF) Diamniadio, Dakar, Sénégal
| | - Kankoe Sallah
- Aix Marseille Univ, IRD, INSERM, SESSTIM, Marseille, France.,AP-HP, Hôpital Bichat, Unité de Recherche Clinique PNVS, Paris, France
| | - Abdoulaye Guindo
- Aix Marseille Univ, IRD, INSERM, SESSTIM, Marseille, France.,Research and Training Center - Ogobara K Doumbo, FMOS-FAPH, Mali-NIAID-ICER, Université des Sciences, des Techniques et des Technologies de Bamako, Bamako, Mali
| | - Boukary Ouedraogo
- Aix Marseille Univ, IRD, INSERM, SESSTIM, Marseille, France.,Direction des Systèmes d'Information en santé, Ministère de la santé, Ouagadougou, Burkina Faso
| | - Martine Piarroux
- French Armed Forces Center for Epidemiology and Public Health (CESPA), Marseille, France
| | - Stanislas Rebaudet
- APHM, Assistance Publique - Hôpitaux de Marseille, Marseille, France.,Hôpital Européen, Marseille, France
| | - Renaud Piarroux
- Sorbonne Université, INSERM, Institut Pierre-Louis d'Epidémiologie et de Santé Publique, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Jordi Landier
- Aix Marseille Univ, IRD, INSERM, SESSTIM, Marseille, France
| | - Cheikh Sokhna
- UMR VITROME, Campus International IRD-UCAD de l'IRD, Dakar, Sénégal
| | - Jean Gaudart
- Aix Marseille Univ, APHM, INSERM, IRD, SESSTIM, Hop Timone, BioSTIC, Biostatistic & ICT, Marseille, France
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Dieng S, Michel P, Guindo A, Sallah K, Ba EH, Cissé B, Carrieri MP, Sokhna C, Milligan P, Gaudart J. Application of Functional Data Analysis to Identify Patterns of Malaria Incidence, to Guide Targeted Control Strategies. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17114168. [PMID: 32545302 PMCID: PMC7312547 DOI: 10.3390/ijerph17114168] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 06/05/2020] [Accepted: 06/06/2020] [Indexed: 11/16/2022]
Abstract
We introduce an approach based on functional data analysis to identify patterns of malaria incidence to guide effective targeting of malaria control in a seasonal transmission area. Using functional data method, a smooth function (functional data or curve) was fitted from the time series of observed malaria incidence for each of 575 villages in west-central Senegal from 2008 to 2012. These 575 smooth functions were classified using hierarchical clustering (Ward’s method), and several different dissimilarity measures. Validity indices were used to determine the number of distinct temporal patterns of malaria incidence. Epidemiological indicators characterizing the resulting malaria incidence patterns were determined from the velocity and acceleration of their incidences over time. We identified three distinct patterns of malaria incidence: high-, intermediate-, and low-incidence patterns in respectively 2% (12/575), 17% (97/575), and 81% (466/575) of villages. Epidemiological indicators characterizing the fluctuations in malaria incidence showed that seasonal outbreaks started later, and ended earlier, in the low-incidence pattern. Functional data analysis can be used to identify patterns of malaria incidence, by considering their temporal dynamics. Epidemiological indicators derived from their velocities and accelerations, may guide to target control measures according to patterns.
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Affiliation(s)
- Sokhna Dieng
- Sciences Economiques et Sociales de la Santé et Traitement de de l'Information Médicale (SESSTIM), Institut de Recherche pour le Développement (IRD), Institut National de la Santé et de la Recherche médicale (INSERM), Aix Marseille Université, 13005 Marseille, France
| | - Pierre Michel
- Aix Marseille School of Economics (AMSE), Centrale Marseille, Ecoles des Hautes Etudes en Sciences Sociales (EHESS), Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université, 13001 Marseille, France
| | - Abdoulaye Guindo
- Sciences Economiques et Sociales de la Santé et Traitement de de l'Information Médicale (SESSTIM), Institut de Recherche pour le Développement (IRD), Institut National de la Santé et de la Recherche médicale (INSERM), Aix Marseille Université, 13005 Marseille, France
- Mère et Enfant face aux Infections Tropicales (MERIT), Institut de Recherche pour le Développement (IRD), Université Paris 5, 75006 Paris, France
| | - Kankoe Sallah
- Sciences Economiques et Sociales de la Santé et Traitement de de l'Information Médicale (SESSTIM), Institut de Recherche pour le Développement (IRD), Institut National de la Santé et de la Recherche médicale (INSERM), Aix Marseille Université, 13005 Marseille, France
- Unité de Recherche Clinique Paris Nord Val de Seine (PNVS), Hôpital Bichat, Assistance Publique-Hôpitaux de Paris (AP-HP), 75018 Paris, France
| | - El-Hadj Ba
- Unité Mixte de Recherche (UMR), Vecteurs-Infections Tropicales et Méditerranéennes (VITROME), Campus International Institut de Recherche pour le Développement-Université Cheikh Anta Diop (IRD-UCAD) de l'IRD, Dakar CP 18524, Senegal
| | - Badara Cissé
- Institut de Recherche en Santé, de Surveillance Épidémiologique et de Formation (IRESSEF) Diamniadio, Dakar BP 7325, Senegal
| | - Maria Patrizia Carrieri
- Sciences Economiques et Sociales de la Santé et Traitement de de l'Information Médicale (SESSTIM), Institut de Recherche pour le Développement (IRD), Institut National de la Santé et de la Recherche médicale (INSERM), Aix Marseille Université, 13005 Marseille, France
| | - Cheikh Sokhna
- Unité Mixte de Recherche (UMR), Vecteurs-Infections Tropicales et Méditerranéennes (VITROME), Campus International Institut de Recherche pour le Développement-Université Cheikh Anta Diop (IRD-UCAD) de l'IRD, Dakar CP 18524, Senegal
| | - Paul Milligan
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Jean Gaudart
- Aix Marseille Université, Assistance Publique-Hôpitaux de Marseille(APHM), INSERM, IRD, SESSTIM, Hop Timone, BioSTIC, Biostatistic and ICT, 13005 Marseille, France
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9
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Peprah S, Tenge C, Genga IO, Mumia M, Were PA, Kuremu RT, Wekesa WN, Sumba PO, Kinyera T, Otim I, Legason ID, Biddle J, Reynolds SJ, Talisuna AO, Biggar RJ, Bhatia K, Goedert JJ, Pfeiffer RM, Mbulaiteye SM. A Cross-Sectional Population Study of Geographic, Age-Specific, and Household Risk Factors for Asymptomatic Plasmodium falciparum Malaria Infection in Western Kenya. Am J Trop Med Hyg 2019; 100:54-65. [PMID: 30457091 DOI: 10.4269/ajtmh.18-0481] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
The burden of Plasmodium falciparum (Pf) malaria in Kenya is decreasing; however, it is still one of the top 10 causes of morbidity, particularly in regions of western Kenya. Between April 2015 and June 2016, we enrolled 965 apparently healthy children aged 0-15 years in former Nyanza and Western Provinces in Kenya to characterize the demographic, geographic, and household risk factors of asymptomatic malaria as part of an epidemiologic study to investigate the risk factors for endemic Burkitt lymphoma. The children were sampled using a stratified, multistage cluster sampling survey design. Malaria was assessed by rapid diagnostic test (RDT) and thick-film microscopy (TFM). Primary analyses of Pf malaria prevalence (pfPR) are based on RDT. Associations between weighted pfPR and potential risk factors were evaluated using logistic regression, accounting for the survey design. Plasmodium falciparum malaria prevalence was 36.0% (27.5%, 44.5%) by RDT and 22.3% (16.0%, 28.6%) by TFM. Plasmodium falciparum malaria prevalence was positively associated with living in the lake-endemic area (adjusted odds ratio [aOR] 3.46; 95% confidence interval [95% CI] 1.63, 7.37), paternal occupation as peasant farmer (aOR 1.87; 1.08, 3.26) or manual laborer (aOR 1.83; 1.00, 3.37), and keeping dogs (aOR 1.62; 0.98-2.69) or cows (aOR 1.52; 0.96-2.40) inside or near the household. Plasmodium falciparum malaria prevalence was inversely associated with indoor residual insecticide spraying (IRS) (aOR 0.44; 0.19, 1.01), having a household connected to electricity (aOR 0.47; 0.22, 0.98), and a household with two (aOR 0.45; 0.22, 0.93) or ≥ three rooms (aOR 0.41; 0.18, 0.93). We report high but geographically heterogeneous pfPR in children in western Kenya and significant associations with IRS and household-level socioeconomic factors.
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Affiliation(s)
- Sally Peprah
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
| | | | - Isaiah O Genga
- EMBLEM Study, Academic Model Providing Access to Healthcare (AMPATH), Eldoret, Kenya
| | - Mediatrix Mumia
- EMBLEM Study, Academic Model Providing Access to Healthcare (AMPATH), Eldoret, Kenya
| | - Pamela A Were
- EMBLEM Study, Academic Model Providing Access to Healthcare (AMPATH), Eldoret, Kenya
| | | | | | | | - Tobias Kinyera
- EMBLEM Study, African Field Epidemiology Network, Kampala, Uganda
| | - Isaac Otim
- EMBLEM Study, African Field Epidemiology Network, Kampala, Uganda
| | - Ismail D Legason
- EMBLEM Study, African Field Epidemiology Network, Kampala, Uganda
| | - Joshua Biddle
- Stanford Hospitals and Clinics, University of Stanford, Pao Alto, California
| | - Steven J Reynolds
- National Institutes of Health/Uganda Project Entebbe, National Institute of Allergy and Infectious Diseases, Rockville, Maryland
| | - Ambrose O Talisuna
- World Health Organization, Regional Office for Africa, Brazzaville, Congo
| | - Robert J Biggar
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
| | - Kishor Bhatia
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
| | - James J Goedert
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
| | - Ruth M Pfeiffer
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
| | - Sam M Mbulaiteye
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
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10
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Stresman G, Bousema T, Cook J. Malaria Hotspots: Is There Epidemiological Evidence for Fine-Scale Spatial Targeting of Interventions? Trends Parasitol 2019; 35:822-834. [PMID: 31474558 DOI: 10.1016/j.pt.2019.07.013] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 07/29/2019] [Accepted: 07/29/2019] [Indexed: 12/20/2022]
Abstract
As data at progressively granular spatial scales become available, the temptation is to target interventions to areas with higher malaria transmission - so-called hotspots - with the aim of reducing transmission in the wider community. This paper reviews literature to determine if hotspots are an intrinsic feature of malaria epidemiology and whether current evidence supports hotspot-targeted interventions. Hotspots are a consistent feature of malaria transmission at all endemicities. The smallest spatial unit capable of supporting transmission is the household, where peri-domestic transmission occurs. Whilst the value of focusing interventions to high-burden areas is evident, there is currently limited evidence that local-scale hotspots fuel transmission. As boundaries are often uncertain, there is no conclusive evidence that hotspot-targeted interventions accelerate malaria elimination.
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Affiliation(s)
- Gillian Stresman
- Infection Biology Department, London School of Hygiene and Tropical Medicine, London, UK.
| | - Teun Bousema
- Radboud University Medical Centre, Department of Microbiology, HB Nijmegen, The Netherlands.
| | - Jackie Cook
- Medical Research Council (MRC) Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, UK
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11
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Peprah S, Dhudha H, Ally H, Masalu N, Kawira E, Chao CN, Genga IO, Mumia M, Were PA, Kinyera T, Otim I, Legason ID, Biggar RJ, Bhatia K, Goedert JJ, Pfeiffer RM, Mbulaiteye SM. A population-based study of the prevalence and risk factors of low-grade Plasmodium falciparum malaria infection in children aged 0-15 years old in northern Tanzania. Trop Med Int Health 2019; 24:571-585. [PMID: 30843638 PMCID: PMC6499672 DOI: 10.1111/tmi.13225] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVES Northern Tanzania experiences significant malaria-related morbidity and mortality, but accurate data are scarce. We update the data on patterns of low-grade Plasmodium falciparum malaria infection among children in northern Tanzania. METHODS Plasmodium falciparum malaria prevalence (pfPR) was assessed in a representative sample of 819 children enrolled in 94 villages in northern Tanzania between October 2015 and August 2016, using a complex survey design. Individual- and household-level risk factors for pfPR were elicited using structured questionnaires. pfPR was assessed using rapid diagnostic tests (RDTs) and thick film microscopy (TFM). Associations with pfPR, based on RDT, were assessed using adjusted odds ratios (aOR) and confidence intervals (CI) from weighted survey logistic regression models. RESULTS Plasmodium falciparum malaria prevalence (pfPR) was 39.5% (95% CI: 31.5, 47.5) by RDT and 33.4% (26.0, 40.6) by TFM. pfPR by RDT was inversely associated with higher-education parents, especially mothers (5-7 years of education: aOR 0.55; 95% CI: 0.31, 0.96, senior secondary education: aOR 0.10; 95% CI: 0.02, 0.55), living in a house near the main road (aOR 0.34; 95% CI: 0.15, 0.76), in a larger household (two rooms: aOR 0.40; 95% CI: 0.21, 0.79, more than two rooms OR 0.35; 95% CI: 0.20, 0.62). Keeping a dog near or inside the house was positively associated with pfPR (aOR 2.01; 95% CI: 1.26, 3.21). pfPR was not associated with bed-net use or indoor residual spraying. CONCLUSIONS Nearly 40% of children in northern Tanzania had low-grade malaria antigenaemia. Higher parental education and household metrics but not mosquito bed-net use were inversely associated with pfPR.
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Affiliation(s)
- Sally Peprah
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - Herry Dhudha
- EpideMiology of Burkitt Lymphoma in East African Children and Minors (EMBLEM) Study, Bugando Medical Center, Mwanza, Tanzania
| | - Hillary Ally
- EpideMiology of Burkitt Lymphoma in East African Children and Minors (EMBLEM) Study, Bugando Medical Center, Mwanza, Tanzania
| | - Nestory Masalu
- EpideMiology of Burkitt Lymphoma in East African Children and Minors (EMBLEM) Study, Bugando Medical Center, Mwanza, Tanzania
| | - Esther Kawira
- EMBLEM Study, Shirati Health and Educational Foundation, Shirati, Tanzania
| | - Colin N Chao
- EpideMiology of Burkitt Lymphoma in East African Children and Minors (EMBLEM) Study, Bugando Medical Center, Mwanza, Tanzania
| | - Isaiah O. Genga
- EMBLEM Study, Academic Model Providing Access to Healthcare, Eldoret, Kenya
| | - Mediatrix Mumia
- EMBLEM Study, Academic Model Providing Access to Healthcare, Eldoret, Kenya
| | - Pamela A. Were
- EMBLEM Study, Academic Model Providing Access to Healthcare, Eldoret, Kenya
| | - Tobias Kinyera
- EMBLEM Study, African Field Epidemiology Network, Kampala, Uganda
| | - Isaac Otim
- EMBLEM Study, African Field Epidemiology Network, Kampala, Uganda
| | | | - Robert J. Biggar
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - Kishor Bhatia
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - James J. Goedert
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - Ruth M. Pfeiffer
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - Sam M. Mbulaiteye
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
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