1
|
Bisanzio D, Keita MS, Camara A, Guilavogui T, Diallo T, Barry H, Preston A, Bangoura L, Mbounga E, Florey LS, Taton JL, Fofana A, Reithinger R. Malaria trends in districts that were targeted and not-targeted for seasonal malaria chemoprevention in children under 5 years of age in Guinea, 2014-2021. BMJ Glob Health 2024; 9:e013898. [PMID: 38413098 PMCID: PMC10900330 DOI: 10.1136/bmjgh-2023-013898] [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: 09/07/2023] [Accepted: 01/26/2024] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND Seasonal malaria chemoprevention (SMC) is a main intervention to prevent and reduce childhood malaria. Since 2015, Guinea has implemented SMC targeting children aged 3-59 months (CU5) in districts with high and seasonal malaria transmission. OBJECTIVE We assessed the programmatic impact of SMC in Guinea's context of scaled up malaria intervention programming by comparing malaria-related outcomes in 14 districts that had or had not been targeted for SMC. METHODS Using routine health management information system data, we compared the district-level monthly test positivity rate (TPR) and monthly uncomplicated and severe malaria incidence for the whole population and disaggregated age groups (<5 years and ≥5 years of age). Changes in malaria indicators through time were analysed by calculating the district-level compound annual growth rate (CAGR) from 2014 to 2021; we used statistical analyses to describe trends in tested clinical cases, TPR, uncomplicated malaria incidence and severe malaria incidence. RESULTS The CAGR of TPR of all age groups was statistically lower in SMC (median=-7.8%) compared with non-SMC (median=-3.0%) districts. Similarly, the CAGR in uncomplicated malaria incidence was significantly lower in SMC (median=1.8%) compared with non-SMC (median=11.5%) districts. For both TPR and uncomplicated malaria incidence, the observed difference was also significant when age disaggregated. The CAGR of severe malaria incidence showed that all age groups experienced a decline in severe malaria in both SMC and non-SMC districts. However, this decline was significantly higher in SMC (median=-22.3%) than in non-SMC (median=-5.1%) districts for the entire population, as well as both CU5 and people over 5 years of age. CONCLUSION Even in an operational programming context, adding SMC to the malaria intervention package yields a positive epidemiological impact and results in a greater reduction in TPR, as well as the incidence of uncomplicated and severe malaria in CU5.
Collapse
Affiliation(s)
- Donal Bisanzio
- RTI International, Washington, District of Columbia, USA
| | | | - Alioune Camara
- Programme National de la Lutte contre le Paludisme, Ministère de la Santé et de l'Hygiène Publique, Conakry, Guinea
| | | | | | | | | | - Lamine Bangoura
- President's Malaria Initiative, US Agency for International Development, Conakry, Guinea
| | - Eliane Mbounga
- President's Malaria Initiative, US Agency for International Development, Conakry, Guinea
| | - Lia S Florey
- US Agency for International Development, Washington, District of Columbia, USA
| | | | | | | |
Collapse
|
2
|
Thawer SG, Golumbeanu M, Lazaro S, Chacky F, Munisi K, Aaron S, Molteni F, Lengeler C, Pothin E, Snow RW, Alegana VA. Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania. Sci Rep 2023; 13:10600. [PMID: 37391538 PMCID: PMC10313820 DOI: 10.1038/s41598-023-37669-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 06/26/2023] [Indexed: 07/02/2023] Open
Abstract
As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incomplete nature of information can result in lower administrative units without empirical data. To overcome geographic sparsity of data and its representativeness, geo-spatial models can leverage routine information to predict risk in un-represented areas as well as estimate uncertainty of predictions. Here, a Bayesian spatio-temporal model was applied on malaria test positivity rate (TPR) data for the period 2017-2019 to predict risks at the ward level, the lowest decision-making unit in mainland Tanzania. To quantify the associated uncertainty, the probability of malaria TPR exceeding programmatic threshold was estimated. Results showed a marked spatial heterogeneity in malaria TPR across wards. 17.7 million people resided in areas where malaria TPR was high (≥ 30; 90% certainty) in the North-West and South-East parts of Tanzania. Approximately 11.7 million people lived in areas where malaria TPR was very low (< 5%; 90% certainty). HF data can be used to identify different epidemiological strata and guide malaria interventions at micro-planning units in Tanzania. These data, however, are imperfect in many settings in Africa and often require application of geo-spatial modelling techniques for estimation.
Collapse
Affiliation(s)
- Sumaiyya G Thawer
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland.
- University of Basel, Basel, Switzerland.
| | - Monica Golumbeanu
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Samwel Lazaro
- Ministry of Health, Dodoma, Tanzania
- National Malaria Control Programme, Dodoma, Tanzania
| | - Frank Chacky
- Ministry of Health, Dodoma, Tanzania
- National Malaria Control Programme, Dodoma, Tanzania
| | - Khalifa Munisi
- Ministry of Health, Dodoma, Tanzania
- National Malaria Control Programme, Dodoma, Tanzania
| | - Sijenunu Aaron
- Ministry of Health, Dodoma, Tanzania
- National Malaria Control Programme, Dodoma, Tanzania
| | - Fabrizio Molteni
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
- National Malaria Control Programme, Dodoma, Tanzania
| | - Christian Lengeler
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Emilie Pothin
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
- Clinton Health Access Initiative, New York, USA
| | - Robert W Snow
- Population Health Unit, KEMRI-Welcome Trust Research Programme, Nairobi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Victor A Alegana
- World Health Organization, Regional Office for Africa, Brazzaville, Republic of Congo
| |
Collapse
|
3
|
Katale RN, Gemechu DB. Spatio-temporal analysis of malaria incidence and its risk factors in North Namibia. Malar J 2023; 22:149. [PMID: 37149600 PMCID: PMC10163860 DOI: 10.1186/s12936-023-04577-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 04/25/2023] [Indexed: 05/08/2023] Open
Abstract
BACKGROUND Millions of dollars have been spent in fighting malaria in Namibia. However, malaria remains a major public health concern in Namibia, mostly in Kavango West and East, Ohangwena and Zambezi region. The primary goal of this study was to fit a spatio-temporal model that profiles spatial variation in malaria risk areas and investigate possible associations between disease risk and environmental factors at the constituency level in highly risk northern regions of Namibia. METHODS Malaria data, climatic data, and population data were merged and Global spatial autocorrelation statistics (Moran's I) was used to detect the spatial autocorrelation of malaria cases while malaria occurrence clusters were identified using local Moran statistics. A hierarchical Bayesian CAR model (Besag, York and Mollie's model "BYM") known to be the best model for modelling the spatial and temporal effects was then fitted to examine climatic factors that might explain spatial/temporal variation of malaria infection in Namibia. RESULTS Average rainfall received on an annual basis and maximum temperature were found to have a significant spatial and temporal variation on malaria infection. Every mm increase in annual rainfall in a specific constituency in each year increases annual mean malaria cases by 0.6%, same to average maximum temperature. The posterior means of the time main effect (year t) showed a visible slightly increase in global trend from 2018 to 2020. CONCLUSION The study discovered that the spatial temporal model with both random and fixed effects best fit the model, which demonstrated a strong spatial and temporal heterogeneity distribution of malaria cases (spatial pattern) with high risk in most of the Kavango West and East outskirt constituencies, posterior relative risk (RR: 1.57 to 1.78).
Collapse
Affiliation(s)
- Remember Ndahalashili Katale
- Department of Mathematics, Statistics, and Actuarial Science, Faculty of Health, Natural Resources and Applied Sciences, School of Natural and Applied Sciences, Namibia University of Science and Technology, Windhoek, Namibia
| | - Dibaba Bayisa Gemechu
- Department of Mathematics, Statistics, and Actuarial Science, Faculty of Health, Natural Resources and Applied Sciences, School of Natural and Applied Sciences, Namibia University of Science and Technology, Windhoek, Namibia.
| |
Collapse
|
4
|
Bisanzio D, Lalji S, Abbas FB, Ali MH, Hassan W, Mkali HR, Al-Mafazy AW, Joseph JJ, Nyinondi S, Kitojo C, Serbantez N, Reaves E, Eckert E, Ngondi JM, Reithinger R. Spatiotemporal dynamics of malaria in Zanzibar, 2015-2020. BMJ Glob Health 2023; 8:bmjgh-2022-009566. [PMID: 36639160 PMCID: PMC9843203 DOI: 10.1136/bmjgh-2022-009566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 12/21/2022] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Despite high coverage of malaria interventions, malaria elimination in Zanzibar remains elusive, with the annual number of cases increasing gradually over the last 3 years. OBJECTIVE The aims of the study were to (1) assess the spatiotemporal dynamics of malaria in Zanzibar between 2015 and 2020 and (2) identify malaria hotspots that would allow Zanzibar to develop an epidemiological stratification for more effective and granular intervention targeting. METHODS In this study, we analysed data routinely collected by Zanzibar's Malaria Case Notification (MCN) system. The system collects sociodemographic and epidemiological data from all malaria cases. Cases are passively detected at health facilities (ie, primary index cases) and through case follow-up and reactive case detection (ie, secondary cases). Analyses were performed to identify the spatial heterogeneity of case reporting at shehia (ward) level during transmission seasons. RESULTS From 1 January 2015 to 30 April 2020, the MCN system reported 22 686 index cases. Number of cases reported showed a declining trends from 2015 to 2016, followed by an increase from 2017 to 2020. More than 40% of cases had a travel history outside Zanzibar in the month prior to testing positive for malaria. The proportion of followed up index cases was approximately 70% for all years. Out of 387 shehias, 79 (20.4%) were identified as malaria hotspots in any given year; these hotspots reported 52% of all index cases during the study period. Of the 79 hotspot shehias, 12 were hotspots in more than 4 years, that is, considered temporally stable, reporting 14.5% of all index cases. CONCLUSIONS Our findings confirm that the scale-up of malaria interventions has greatly reduced malaria transmission in Zanzibar since 2006. Analyses identified hotspots, some of which were stable across multiple years. Malaria efforts should progress from a universal intervention coverage approach to an approach that is more tailored to a select number of hotspot shehias.
Collapse
Affiliation(s)
- Donal Bisanzio
- RTI International, Washington, District of Columbia, USA
| | - Shabbir Lalji
- RTI International, Dar es Salaam, United Republic of Tanzania
| | - Faiza B Abbas
- Zanzibar Malaria Elimination Programme, Ministry of Health, Stone Town, Zanzibar, United Republic of Tanzania
| | - Mohamed H Ali
- Zanzibar Malaria Elimination Programme, Ministry of Health, Stone Town, Zanzibar, United Republic of Tanzania
| | - Wahida Hassan
- Zanzibar Malaria Elimination Programme, Ministry of Health, Stone Town, Zanzibar, United Republic of Tanzania
| | | | | | - Joseph J Joseph
- RTI International, Dar es Salaam, United Republic of Tanzania
| | - Ssanyu Nyinondi
- RTI International, Dar es Salaam, United Republic of Tanzania
| | - Chonge Kitojo
- U.S. President’s Malaria Initiative, U.S. Agency for International Development, Dar es Salaam, United Republic of Tanzania
| | - Naomi Serbantez
- U.S. President’s Malaria Initiative, U.S. Agency for International Development, Dar es Salaam, United Republic of Tanzania
| | - Erik Reaves
- U.S. President’s Malaria Initiative, U.S. Centers for Disease Control, Dar es Salaam, United Republic of Tanzania
| | - Erin Eckert
- RTI International, Washington, District of Columbia, USA
| | | | | |
Collapse
|
5
|
Dlamini SN, Fall IS, Mabaso SD. Bayesian Geostatistical Modeling to Assess Malaria Seasonality and Monthly Incidence Risk in Eswatini. J Epidemiol Glob Health 2022; 12:340-361. [PMID: 35976542 PMCID: PMC9382628 DOI: 10.1007/s44197-022-00054-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 08/06/2022] [Indexed: 11/30/2022] Open
Abstract
Eswatini is on the brink of malaria elimination and had however, had to shift its target year to eliminate malaria on several occasions since 2015 as the country struggled to achieve its zero malaria goal. We conducted a Bayesian geostatistical modeling study using malaria case data. A Bayesian distributed lags model (DLM) was implemented to assess the effects of seasonality on cases. A second Bayesian model based on polynomial distributed lags was implemented on the dataset to improve understanding of the lag effect of environmental factors on cases. Results showed that malaria increased during the dry season with proportion 0.051 compared to the rainy season with proportion 0.047 while rainfall of the preceding month (Lag2) had negative effect on malaria as it decreased by proportion − 0.25 (BCI: − 0.46, − 0.05). Night temperatures of the preceding first and second month were significantly associated with increased malaria in the following proportions: at Lag1 0.53 (BCI: 0.23, 0.84) and at Lag2 0.26 (BCI: 0.01, 0.51). Seasonality was an important predictor of malaria with proportion 0.72 (BCI: 0.40, 0.98). High malaria rates were identified for the months of July to October, moderate rates in the months of November to February and low rates in the months of March to June. The maps produced support-targeted malaria control interventions. The Bayesian geostatistical models could be extended for short-term and long-term forecasting of malaria supporting-targeted response both in space and time for effective elimination.
Collapse
Affiliation(s)
- Sabelo Nick Dlamini
- Department of Geography, University of Eswatini, Kwaluseni, Manzini, M200, Eswatini. .,World Health Organization, 27 Geneva, Geneva, Switzerland.
| | | | - Sizwe Doctor Mabaso
- Department of Geography, University of Eswatini, Kwaluseni, Manzini, M200, Eswatini
| |
Collapse
|
6
|
Muchiri SK, Muthee R, Kiarie H, Sitienei J, Agweyu A, Atkinson PM, Edson Utazi C, Tatem AJ, Alegana VA. Unmet need for COVID-19 vaccination coverage in Kenya. Vaccine 2022; 40:2011-2019. [PMID: 35184925 PMCID: PMC8841160 DOI: 10.1016/j.vaccine.2022.02.035] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 01/30/2022] [Accepted: 02/07/2022] [Indexed: 11/30/2022]
Abstract
COVID-19 has impacted the health and livelihoods of billions of people since it emerged in 2019. Vaccination for COVID-19 is a critical intervention that is being rolled out globally to end the pandemic. Understanding the spatial inequalities in vaccination coverage and access to vaccination centres is important for planning this intervention nationally. Here, COVID-19 vaccination data, representing the number of people given at least one dose of vaccine, a list of the approved vaccination sites, population data and ancillary GIS data were used to assess vaccination coverage, using Kenya as an example. Firstly, physical access was modelled using travel time to estimate the proportion of population within 1 hour of a vaccination site. Secondly, a Bayesian conditional autoregressive (CAR) model was used to estimate the COVID-19 vaccination coverage and the same framework used to forecast coverage rates for the first quarter of 2022. Nationally, the average travel time to a designated COVID-19 vaccination site (n = 622) was 75.5 min (Range: 62.9 - 94.5 min) and over 87% of the population >18 years reside within 1 hour to a vaccination site. The COVID-19 vaccination coverage in December 2021 was 16.70% (95% CI: 16.66 - 16.74) - 4.4 million people and was forecasted to be 30.75% (95% CI: 25.04 - 36.96) - 8.1 million people by the end of March 2022. Approximately 21 million adults were still unvaccinated in December 2021 and, in the absence of accelerated vaccine uptake, over 17.2 million adults may not be vaccinated by end March 2022 nationally. Our results highlight geographic inequalities at sub-national level and are important in targeting and improving vaccination coverage in hard-to-reach populations. Similar mapping efforts could help other countries identify and increase vaccination coverage for such populations.
Collapse
Affiliation(s)
- Samuel K Muchiri
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya.
| | - Rose Muthee
- Department of Health Informatics, Monitoring and Evaluation, Ministry of Health, Nairobi, Kenya
| | - Hellen Kiarie
- Department of Health Informatics, Monitoring and Evaluation, Ministry of Health, Nairobi, Kenya
| | - Joseph Sitienei
- Department of Health Informatics, Monitoring and Evaluation, Ministry of Health, Nairobi, Kenya
| | - Ambrose Agweyu
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme Nairobi, Kenya
| | - Peter M Atkinson
- Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK; Geography and Environmental Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK; Institute of Geographic Sciences and Natural Resource Research, Chinese Academy of Sciences, Beijing 100101, China
| | - C Edson Utazi
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK; Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, UK
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Victor A Alegana
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya; Geography and Environmental Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK
| |
Collapse
|
7
|
Alegana VA, Macharia PM, Muchiri S, Mumo E, Oyugi E, Kamau A, Chacky F, Thawer S, Molteni F, Rutazanna D, Maiteki-Sebuguzi C, Gonahasa S, Noor AM, Snow RW. Plasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratification. PLOS GLOBAL PUBLIC HEALTH 2021; 1:e0000014. [PMID: 35211700 PMCID: PMC7612417 DOI: 10.1371/journal.pgph.0000014] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 11/15/2021] [Indexed: 11/18/2022]
Abstract
The High Burden High Impact (HBHI) strategy for malaria encourages countries to use multiple sources of available data to define the sub-national vulnerabilities to malaria risk, including parasite prevalence. Here, a modelled estimate of Plasmodium falciparum from an updated assembly of community parasite survey data in Kenya, mainland Tanzania, and Uganda is presented and used to provide a more contemporary understanding of the sub-national malaria prevalence stratification across the sub-region for 2019. Malaria prevalence data from surveys undertaken between January 2010 and June 2020 were assembled form each of the three countries. Bayesian spatiotemporal model-based approaches were used to interpolate space-time data at fine spatial resolution adjusting for population, environmental and ecological covariates across the three countries. A total of 18,940 time-space age-standardised and microscopy-converted surveys were assembled of which 14,170 (74.8%) were identified after 2017. The estimated national population-adjusted posterior mean parasite prevalence was 4.7% (95% Bayesian Credible Interval 2.6-36.9) in Kenya, 10.6% (3.4-39.2) in mainland Tanzania, and 9.5% (4.0-48.3) in Uganda. In 2019, more than 12.7 million people resided in communities where parasite prevalence was predicted ≥ 30%, including 6.4%, 12.1% and 6.3% of Kenya, mainland Tanzania and Uganda populations, respectively. Conversely, areas that supported very low parasite prevalence (<1%) were inhabited by approximately 46.2 million people across the sub-region, or 52.2%, 26.7% and 10.4% of Kenya, mainland Tanzania and Uganda populations, respectively. In conclusion, parasite prevalence represents one of several data metrics for disease stratification at national and sub-national levels. To increase the use of this metric for decision making, there is a need to integrate other data layers on mortality related to malaria, malaria vector composition, insecticide resistance and bionomic, malaria care-seeking behaviour and current levels of unmet need of malaria interventions.
Collapse
Affiliation(s)
- Victor A. Alegana
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
- Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | - Peter M. Macharia
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
- Centre for Health Informatics, Computing, and Statistics, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
| | - Samuel Muchiri
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
| | - Eda Mumo
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
| | - Elvis Oyugi
- Division of National Malaria Programme, Ministry of Health, Nairobi, Kenya
| | - Alice Kamau
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
| | - Frank Chacky
- National Malaria Control Programme, Ministry of Health, Community Development, Gender, Elderly and Children, Dodoma, Tanzania
| | - Sumaiyya Thawer
- National Malaria Control Programme, Ministry of Health, Community Development, Gender, Elderly and Children, Dodoma, Tanzania
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Fabrizio Molteni
- National Malaria Control Programme, Ministry of Health, Community Development, Gender, Elderly and Children, Dodoma, Tanzania
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Damian Rutazanna
- National Malaria Control Division, Ministry of Health, Kampala, Uganda
| | - Catherine Maiteki-Sebuguzi
- National Malaria Control Division, Ministry of Health, Kampala, Uganda
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | | | - Abdisalan M. Noor
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Robert W. Snow
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
8
|
Ruktanonchai CW, Lai S, Utazi CE, Cunningham AD, Koper P, Rogers GE, Ruktanonchai NW, Sadilek A, Woods D, Tatem AJ, Steele JE, Sorichetta A. Practical geospatial and sociodemographic predictors of human mobility. Sci Rep 2021; 11:15389. [PMID: 34321509 PMCID: PMC8319369 DOI: 10.1038/s41598-021-94683-7] [Citation(s) in RCA: 4] [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: 04/09/2021] [Accepted: 07/13/2021] [Indexed: 11/08/2022] Open
Abstract
Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.
Collapse
Affiliation(s)
- Corrine W Ruktanonchai
- Population Health Sciences, College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, USA.
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Chigozie E Utazi
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Alex D Cunningham
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Patrycja Koper
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Grant E Rogers
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Nick W Ruktanonchai
- Population Health Sciences, College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, USA
| | | | - Dorothea Woods
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Jessica E Steele
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Alessandro Sorichetta
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| |
Collapse
|
9
|
Smith JL, Mumbengegwi D, Haindongo E, Cueto C, Roberts KW, Gosling R, Uusiku P, Kleinschmidt I, Bennett A, Sturrock HJ. Malaria risk factors in northern Namibia: The importance of occupation, age and mobility in characterizing high-risk populations. PLoS One 2021; 16:e0252690. [PMID: 34170917 PMCID: PMC8232432 DOI: 10.1371/journal.pone.0252690] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 05/20/2021] [Indexed: 11/19/2022] Open
Abstract
In areas of low and unstable transmission, malaria cases occur in populations with lower access to malaria services and interventions, and in groups with specific malaria risk exposures often away from the household. In support of the Namibian National Vector Borne Disease Program's drive to better target interventions based upon risk, we implemented a health facility-based case control study aimed to identify risk factors for symptomatic malaria in Zambezi Region, northern Namibia. A total of 770 febrile individuals reporting to 6 health facilities and testing positive by rapid diagnostic test (RDT) between February 2015 and April 2016 were recruited as cases; 641 febrile individuals testing negative by RDT at the same health facilities through June 2016 were recruited as controls. Data on socio-demographics, housing construction, overnight travel, use of malaria prevention and outdoor behaviors at night were collected through interview and recorded on a tablet-based questionnaire. Remotely-sensed environmental data were extracted for geo-located village residence locations. Multivariable logistic regression was conducted to identify risk factors and latent class analyses (LCA) used to identify and characterize high-risk subgroups. The majority of participants (87% of cases and 69% of controls) were recruited during the 2016 transmission season, an outbreak year in Southern Africa. After adjustment, cases were more likely to be cattle herders (Adjusted Odds Ratio (aOR): 4.46 95%CI 1.05-18.96), members of the police or other security personnel (aOR: 4.60 95%CI: 1.16-18.16), and pensioners/unemployed persons (aOR: 2.25 95%CI 1.24-4.08), compared to agricultural workers (most common category). Children (aOR 2.28 95%CI 1.13-4.59) and self-identified students were at higher risk of malaria (aOR: 4.32 95%CI 2.31-8.10). Other actionable risk factors for malaria included housing and behavioral characteristics, including traditional home construction and sleeping in an open structure (versus modern structure: aOR: 2.01 95%CI 1.45-2.79 and aOR: 4.76 95%CI: 2.14-10.57); cross border travel in the prior 30 days (aOR: 10.55 95%CI 2.94-37.84); and outdoor agricultural work at night (aOR: 2.09 95%CI 1.12-3.87). Malaria preventive activities were all protective and included personal use of an insecticide treated net (ITN) (aOR: 0.61 95%CI 0.42-0.87), adequate household ITN coverage (aOR: 0.63 95%CI 0.42-0.94), and household indoor residual spraying (IRS) in the past year (versus never sprayed: (aOR: 0.63 95%CI 0.44-0.90). A number of environmental factors were associated with increased risk of malaria, including lower temperatures, higher rainfall and increased vegetation for the 30 days prior to diagnosis and residing more than 5 minutes from a health facility. LCA identified six classes of cases, with class membership strongly correlated with occupation, age and select behavioral risk factors. Use of ITNs and IRS coverage was similarly low across classes. For malaria elimination these high-risk groups will need targeted and tailored intervention strategies, for example, by implementing alternative delivery methods of interventions through schools and worksites, as well as the use of specific interventions that address outdoor transmission.
Collapse
Affiliation(s)
- Jennifer L. Smith
- Malaria Elimination Initiative, Global Health Group, University of California San Francisco (UCSF), San Francisco, California, United States of America
| | - Davis Mumbengegwi
- Multidisciplinary Research Centre, University of Namibia, Windhoek, Namibia
| | - Erastus Haindongo
- School of Medicine, Faculty of Health Sciences, University of Namibia, Windhoek, Namibia
| | - Carmen Cueto
- Malaria Elimination Initiative, Global Health Group, University of California San Francisco (UCSF), San Francisco, California, United States of America
| | - Kathryn W. Roberts
- Malaria Elimination Initiative, Global Health Group, University of California San Francisco (UCSF), San Francisco, California, United States of America
| | - Roly Gosling
- Malaria Elimination Initiative, Global Health Group, University of California San Francisco (UCSF), San Francisco, California, United States of America
| | - Petrina Uusiku
- National Ministry of Health and Social Services, Windhoek, Namibia
| | - Immo Kleinschmidt
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Adam Bennett
- Malaria Elimination Initiative, Global Health Group, University of California San Francisco (UCSF), San Francisco, California, United States of America
| | - Hugh J. Sturrock
- Malaria Elimination Initiative, Global Health Group, University of California San Francisco (UCSF), San Francisco, California, United States of America
| |
Collapse
|
10
|
Odhiambo JN, Kalinda C, Macharia PM, Snow RW, Sartorius B. Spatial and spatio-temporal methods for mapping malaria risk: a systematic review. BMJ Glob Health 2021; 5:bmjgh-2020-002919. [PMID: 33023880 PMCID: PMC7537142 DOI: 10.1136/bmjgh-2020-002919] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 08/23/2020] [Accepted: 08/24/2020] [Indexed: 12/21/2022] Open
Abstract
Background Approaches in malaria risk mapping continue to advance in scope with the advent of geostatistical techniques spanning both the spatial and temporal domains. A substantive review of the merits of the methods and covariates used to map malaria risk has not been undertaken. Therefore, this review aimed to systematically retrieve, summarise methods and examine covariates that have been used for mapping malaria risk in sub-Saharan Africa (SSA). Methods A systematic search of malaria risk mapping studies was conducted using PubMed, EBSCOhost, Web of Science and Scopus databases. The search was restricted to refereed studies published in English from January 1968 to April 2020. To ensure completeness, a manual search through the reference lists of selected studies was also undertaken. Two independent reviewers completed each of the review phases namely: identification of relevant studies based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, data extraction and methodological quality assessment using a validated scoring criterion. Results One hundred and seven studies met the inclusion criteria. The median quality score across studies was 12/16 (range: 7–16). Approximately half (44%) of the studies employed variable selection techniques prior to mapping with rainfall and temperature selected in over 50% of the studies. Malaria incidence (47%) and prevalence (35%) were the most commonly mapped outcomes, with Bayesian geostatistical models often (31%) the preferred approach to risk mapping. Additionally, 29% of the studies employed various spatial clustering methods to explore the geographical variation of malaria patterns, with Kulldorf scan statistic being the most common. Model validation was specified in 53 (50%) studies, with partitioning data into training and validation sets being the common approach. Conclusions Our review highlights the methodological diversity prominent in malaria risk mapping across SSA. To ensure reproducibility and quality science, best practices and transparent approaches should be adopted when selecting the statistical framework and covariates for malaria risk mapping. Findings underscore the need to periodically assess methods and covariates used in malaria risk mapping; to accommodate changes in data availability, data quality and innovation in statistical methodology.
Collapse
Affiliation(s)
| | - Chester Kalinda
- Discipline of Public Health Medicine, University of KwaZulu-Natal, Durban, South Africa.,Faculty of Agriculture and Natural Resources, University of Namibia, Windhoek, Namibia
| | - Peter M Macharia
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
| | - Robert W Snow
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya.,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Benn Sartorius
- Discipline of Public Health Medicine, University of KwaZulu-Natal, Durban, South Africa.,Department of Disease Control, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| |
Collapse
|
11
|
Okunlola OA, Oyeyemi OT, Lukman AF. Modeling the relationship between malaria prevalence and insecticide-treated bed net coverage in Nigeria using a Bayesian spatial generalized linear mixed model with a Leroux prior. Epidemiol Health 2021; 43:e2021041. [PMID: 34098626 PMCID: PMC8510838 DOI: 10.4178/epih.e2021041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 06/04/2021] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVES To evaluate malaria transmission in relation to insecticide-treated net (ITN) coverage in Nigeria. METHODS We used an exploratory analysis approach to evaluate variation in malaria transmission in relation to ITN distribution in 1,325 Demographic and Health Survey clusters in Nigeria. A Bayesian spatial generalized linear mixed model with a Leroux conditional autoregressive prior for the random effects was used to model the spatial and contextual variation in malaria prevalence and ITN distribution after adjusting for environmental variables. RESULTS Spatial smoothed maps showed the nationwide distribution of malaria and ITN. The distribution of ITN varied significantly across the 6 geopolitical zones (p<0.05). The North-East had the least ITN distribution (0.196±0.071), while ITN distribution was highest in the South-South (0.309±0.075). ITN coverage was also higher in rural areas (0.281±0.074) than in urban areas (0.240±0.096, p<0.05). The Bayesian hierarchical regression results showed a non-significant negative relationship between malaria prevalence and ITN coverage, but a significant spatial structured random effect and unstructured random effect. The correlates of malaria transmission included rainfall, maximum temperature, and proximity to water. CONCLUSIONS Reduction in malaria transmission was not significantly related to ITN coverage, although much could be achieved in attempts to curtail malaria transmission through enhanced ITN coverage. A multifaceted and integrated approach to malaria control is strongly advocated.
Collapse
Affiliation(s)
- Oluyemi A Okunlola
- Department of Mathematics, University of Medical Sciences, Ondo, Nigeria
| | - Oyetunde T Oyeyemi
- Department of Biological Sciences, University of Medical Sciences, Ondo, Nigeria
| | - Adewale F Lukman
- Department of Physical Sciences, Landmark University, Omu-Aran, Nigeria
| |
Collapse
|
12
|
Lee SA, Jarvis CI, Edmunds WJ, Economou T, Lowe R. Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions. J R Soc Interface 2021; 18:20210096. [PMID: 34034534 PMCID: PMC8150046 DOI: 10.1098/rsif.2021.0096] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 04/26/2021] [Indexed: 12/14/2022] Open
Abstract
Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.
Collapse
Affiliation(s)
- Sophie A. Lee
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Christopher I. Jarvis
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - W. John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| |
Collapse
|
13
|
Epstein A, Namuganga JF, Kamya EV, Nankabirwa JI, Bhatt S, Rodriguez-Barraquer I, Staedke SG, Kamya MR, Dorsey G, Greenhouse B. Estimating malaria incidence from routine health facility-based surveillance data in Uganda. Malar J 2020; 19:445. [PMID: 33267886 PMCID: PMC7709253 DOI: 10.1186/s12936-020-03514-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 11/19/2020] [Indexed: 12/03/2022] Open
Abstract
Background Accurate measures of malaria incidence are essential to track progress and target high-risk populations. While health management information system (HMIS) data provide counts of malaria cases, quantifying the denominator for incidence using these data is challenging because catchment areas and care-seeking behaviours are not well defined. This study’s aim was to estimate malaria incidence using HMIS data by adjusting the population denominator accounting for travel time to the health facility. Methods Outpatient data from two public health facilities in Uganda (Kihihi and Nagongera) over a 3-year period (2011–2014) were used to model the relationship between travel time from patient village of residence (available for each individual) to the facility and the relative probability of attendance using Poisson generalized additive models. Outputs from the model were used to generate a weighted population denominator for each health facility and estimate malaria incidence. Among children aged 6 months to 11 years, monthly HMIS-derived incidence estimates, with and without population denominators weighted by probability of attendance, were compared with gold standard measures of malaria incidence measured in prospective cohorts. Results A total of 48,898 outpatient visits were recorded across the two sites over the study period. HMIS incidence correlated with cohort incidence over time at both study sites (correlation in Kihihi = 0.64, p < 0.001; correlation in Nagongera = 0.34, p = 0.045). HMIS incidence measures with denominators unweighted by probability of attendance underestimated cohort incidence aggregated over the 3 years in Kihihi (0.5 cases per person-year (PPY) vs 1.7 cases PPY) and Nagongera (0.3 cases PPY vs 3.0 cases PPY). HMIS incidence measures with denominators weighted by probability of attendance were closer to cohort incidence, but remained underestimates (1.1 cases PPY in Kihihi and 1.4 cases PPY in Nagongera). Conclusions Although malaria incidence measured using HMIS underestimated incidence measured in cohorts, even when adjusting for probability of attendance, HMIS surveillance data are a promising and scalable source for tracking relative changes in malaria incidence over time, particularly when the population denominator can be estimated by incorporating information on village of residence.
Collapse
Affiliation(s)
- Adrienne Epstein
- Department of Medicine, University of California, San Francisco, 550 16th Street, San Francisco, CA, 94158, USA.
| | | | | | - Joaniter I Nankabirwa
- Infectious Diseases Research Collaboration, Kampala, Uganda.,Department of Internal Medicine, Makerere University College of Health Sciences, Kampala, Uganda
| | - Samir Bhatt
- Department of Infectious Disease Epidemiology, St Marys Hospital, Imperial College, London, UK
| | - Isabel Rodriguez-Barraquer
- Department of Medicine, University of California, San Francisco, 550 16th Street, San Francisco, CA, 94158, USA
| | | | - Moses R Kamya
- Infectious Diseases Research Collaboration, Kampala, Uganda.,Department of Internal Medicine, Makerere University College of Health Sciences, Kampala, Uganda
| | - Grant Dorsey
- Department of Medicine, University of California, San Francisco, 550 16th Street, San Francisco, CA, 94158, USA
| | - Bryan Greenhouse
- Department of Medicine, University of California, San Francisco, 550 16th Street, San Francisco, CA, 94158, USA.,Chan Zuckerberg Biohub, San Francisco, CA, USA
| |
Collapse
|
14
|
Milusheva S. Managing the spread of disease with mobile phone data. JOURNAL OF DEVELOPMENT ECONOMICS 2020; 147:102559. [PMID: 33144750 PMCID: PMC7561616 DOI: 10.1016/j.jdeveco.2020.102559] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 08/19/2020] [Accepted: 08/20/2020] [Indexed: 06/04/2023]
Abstract
While human mobility has important benefits for economic growth, it can generate negative externalities. This paper studies the effect of mobility on the spread of disease in a low-incidence setting when people do not internalize their risks to others. Using malaria as a case study and 15 billion mobile phone records across nine million SIM cards, this paper quantifies the relationship between travel and the spread of disease. The estimates indicate that an infected traveler contributes to 1.66 additional cases reported in the health facility at the traveler's destination. This paper develops a simulation-based policy tool that uses mobile phone data to inform strategic targeting of travelers based on their origins and destinations. The simulations suggest that targeting informed by mobile phone data could reduce the caseload by 50 percent more than current strategies that rely only on previous incidence.
Collapse
|
15
|
Lim JT, Han Y, Sue Lee Dickens B, Ng LC, Cook AR. Time varying methods to infer extremes in dengue transmission dynamics. PLoS Comput Biol 2020; 16:e1008279. [PMID: 33044957 PMCID: PMC7595636 DOI: 10.1371/journal.pcbi.1008279] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 10/29/2020] [Accepted: 08/20/2020] [Indexed: 11/18/2022] Open
Abstract
Dengue is an arbovirus affecting global populations. Frequent outbreaks occur, especially in equatorial cities such as Singapore, where year-round tropical climate, large daily influx of travelers and population density provide the ideal conditions for dengue to transmit. Little work has, however, quantified the peaks of dengue outbreaks, when health systems are likely to be most stretched. Nor have methods been developed to infer differences in exogenous factors which lead to the rise and fall of dengue case counts across extreme and non-extreme periods. In this paper, we developed time varying extreme mixture (tvEM) methods to account for the temporal dependence of dengue case counts across extreme and non-extreme periods. This approach permits inference of differences in climatic forcing across non-extreme and extreme periods of dengue case counts, quantification of their temporal dependence as well as estimation of thresholds with associated uncertainties to determine dengue case count extremities. Using tvEM, we found no evidence that weather affects dengue case counts in the near term for non-extreme periods, but that it has non-linear and mixed signals in influencing dengue through tvEM parameters in the extreme periods. Using the most appropriate tvEM specification, we found that a threshold at the 70th (95% credible interval 41.1, 83.8) quantile is optimal, with extreme events of 526.6, 1052.2 and 1183.6 weekly case counts expected at return periods of 5, 50 and 75 years. Weather parameters at a 1% scaled increase was found to decrease the long-run expected case counts, but larger increases would lead to a drastic expected rise from the baseline correspondingly. The tvEM approach can provide valuable inference on the extremes of time series, which in the case of infectious disease notifications, allows public health officials to understand the likely scale of outbreaks in the long run. Dengue is an arbovirus affecting populations across much of the globe. Frequent outbreaks occur, especially in equatorial cities such as Singapore, where the year-round tropical climate, large daily influx of travelers and population density provide the ideal conditions for dengue transmission. Little work has however quantified the peaks of dengue outbreaks, when health systems are likely to be most stretched. Nor have methods been developed to infer differences in exogenous factors which lead to the rise and fall of dengue case counts across extreme and non extreme periods. In this paper, we developed time varying extreme mixture (tvEM) methods to account for the temporal dependence of dengue case counts across extreme and non-extreme periods. tvEM is able to infer differences in climatic forcing across non-extreme and extreme periods of dengue case counts, their temporal dependence as well as estimate suitable thresholds with associated uncertainties to determine dengue case count extremities. Using tvEM, we found no evidence that weather affects dengue case counts in the near term for non extreme periods, but has non-linear and mixed signals in influencing dengue through tvEM parameters in the extreme periods. Using the most appropriate tvEM specification, we found that a high percentile threshold is estimated, with dengue outbreak events far larger than currently observed to be expected in 5, 50 and 75 years. Weather parameters at a 1% scaled increase was found to decrease the long-run expected case counts, but larger increases would lead to a drastic expected rise from the baseline correspondingly. tvEM can provide valuable inference on the extremes of time series, which in the case of infectious disease data, allows public health officials to understand factors and the likely scale of infectious disease outbreaks in the long run.
Collapse
Affiliation(s)
- Jue Tao Lim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- * E-mail:
| | - Yiting Han
- School of Pharmacy, Fudan University, Shanghai, China
| | - Borame Sue Lee Dickens
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Lee Ching Ng
- Environmental Health Institute, National Environmental Agency, Singapore
| | - Alex R. Cook
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| |
Collapse
|
16
|
Kost GJ. Geospatial Hotspots Need Point-of-Care Strategies to Stop Highly Infectious Outbreaks. Arch Pathol Lab Med 2020; 144:1166-1190. [PMID: 32298139 DOI: 10.5858/arpa.2020-0172-ra] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/13/2020] [Indexed: 11/06/2022]
Abstract
CONTEXT.— Point-of-care testing (POCT), diagnostic testing at or near the site of patient care, is inherently spatial, that is, performed at points of need, and also intrinsically temporal, because it produces fast actionable results. Outbreaks generate geospatial "hotspots." POC strategies help control hotspots, detect spread, and speed treatment of highly infectious diseases. OBJECTIVES.— To stop outbreaks, accelerate detection, facilitate emergency response for epidemics, mobilize public health practitioners, enhance community resilience, and improve crisis standards of care. DATA SOURCES.— PubMed, World-Wide Web, newsprint, and others were searched until Coronavirus infectious disease-19 was declared a pandemic, the United States, a national emergency, and Europe, the epicenter. Coverage comprised interviews in Asia, email to/from Wuhan, papers, articles, chapters, documents, maps, flowcharts, schematics, and geospatial-associated concepts. EndNote X9.1 (Clarivate Analytics) consolidated literature as abstracts, ULRs, and PDFs, recovering 136 hotspot articles. More than 500 geospatial science articles were assessed for relevance to POCT. CONCLUSIONS.— POCT can interrupt spirals of dysfunction and delay by enhancing disease detection, decision-making, contagion containment, and safe spacing, thereby softening outbreak surges and diminishing risk before human, economic, and cultural losses mount. POCT results identify where infected individuals spread Coronavirus infectious disease-19, when delays cause death, and how to deploy resources. Results in national cloud databases help optimize outbreak control, mitigation, emergency response, and community resilience. The Coronavirus infectious disease-19 pandemic demonstrates unequivocally that governments must support POCT and multidisciplinary healthcare personnel must learn its principles, then adopt POC geospatial strategies, so that onsite diagnostic testing can ramp up to meet needs in times of crisis.
Collapse
Affiliation(s)
- Gerald J Kost
- From the POCT•CTR (Point-of-care Testing Center for Teaching and Research), University of California, Davis
| |
Collapse
|
17
|
Alegana VA, Okiro EA, Snow RW. Routine data for malaria morbidity estimation in Africa: challenges and prospects. BMC Med 2020; 18:121. [PMID: 32487080 PMCID: PMC7268363 DOI: 10.1186/s12916-020-01593-y] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 04/14/2020] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND The burden of malaria in sub-Saharan Africa remains challenging to measure relying on epidemiological modelling to evaluate the impact of investments and providing an in-depth analysis of progress and trends in malaria response globally. In malaria-endemic countries of Africa, there is increasing use of routine surveillance data to define national strategic targets, estimate malaria case burdens and measure control progress to identify financing priorities. Existing research focuses mainly on the strengths of these data with less emphasis on existing challenges and opportunities presented. CONCLUSION Here we define the current imperfections common to routine malaria morbidity data at national levels and offer prospects into their future use to reflect changing disease burdens.
Collapse
Affiliation(s)
- Victor A Alegana
- Population Health Unit, Kenya Medical Research Institute - Wellcome Trust Research Programme, P.O. Box 43640, Nairobi, 00100, Kenya.
- Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK.
- Faculty of Science and Technology, Lancaster University, Lancaster, LAI 4YW, UK.
| | - Emelda A Okiro
- Population Health Unit, Kenya Medical Research Institute - Wellcome Trust Research Programme, P.O. Box 43640, Nairobi, 00100, Kenya
| | - Robert W Snow
- Population Health Unit, Kenya Medical Research Institute - Wellcome Trust Research Programme, P.O. Box 43640, Nairobi, 00100, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, OX3 7LJ, UK
| |
Collapse
|
18
|
Kamau A, Mogeni P, Okiro EA, Snow RW, Bejon P. A systematic review of changing malaria disease burden in sub-Saharan Africa since 2000: comparing model predictions and empirical observations. BMC Med 2020; 18:94. [PMID: 32345315 PMCID: PMC7189714 DOI: 10.1186/s12916-020-01559-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 03/16/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The most widely used measures of declining burden of malaria across sub-Saharan Africa are predictions from geospatial models. These models apply spatiotemporal autocorrelations and covariates to parasite prevalence data and then use a function of parasite prevalence to predict clinical malaria incidence. We attempted to assess whether trends in malaria cases, based on local surveillance, were similar to those captured by Malaria Atlas Project (MAP) incidence surfaces. METHODS We undertook a systematic review (PROSPERO International Prospective Register of Systematic Reviews; ID = CRD42019116834) to identify empirical data on clinical malaria in Africa since 2000, where reports covered at least 5 continuous years. The trends in empirical data were then compared with the trends of time-space matched clinical malaria incidence from MAP using the Spearman rank correlation. The correlations (rho) between changes in empirically observed and modelled estimates of clinical malaria were displayed by forest plots and examined by meta-regression. RESULTS Sixty-seven articles met our inclusion criteria representing 124 sites from 24 African countries. The single most important factor explaining the correlation between empirical observations and modelled predictions was the slope of empirically observed data over time (rho = - 0.989; 95% CI - 0.998, - 0.939; p < 0.001), i.e. steeper declines were associated with a stronger correlation between empirical observations and modelled predictions. Factors such as quality of study, reported measure of malaria and endemicity were only slightly predictive of such correlations. CONCLUSIONS In many locations, both local surveillance data and modelled estimates showed declines in malaria burden and hence similar trends. However, there was a weak association between individual surveillance datasets and the modelled predictions where stalling in progress or resurgence of malaria burden was empirically observed. Surveillance data were patchy, indicating a need for improved surveillance to strengthen both empiric reporting and modelled predictions.
Collapse
Affiliation(s)
- Alice Kamau
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya. .,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK.
| | | | | | - Robert W Snow
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya.,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Philip Bejon
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya.,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| |
Collapse
|
19
|
Nawa M, Halwindi H, Hangoma P. Modelling malaria reduction in a highly endemic country: Evidence from household survey, climate, and program data in Zambia. J Public Health Afr 2020; 11:1096. [PMID: 33209231 PMCID: PMC7649733 DOI: 10.4081/jphia.2020.1096] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 10/17/2019] [Indexed: 12/03/2022] Open
Abstract
Substantial efforts have seen the reduction in malaria prevalence from 33% in 2006 to 19.4% in 2015 in Zambia. Many studies have used effect measures, such as odds ratios, of malaria interventions without combining this information with coverage levels of the interventions to assess how malaria prevalence would change if these interventions were scaled up. We contribute to filling this gap by combining intervention coverage information with marginal predictions to model the extent to which key interventions can bring down malaria in Zambia. We used logistic regression models and derived marginal effects using repeated cross-sectional survey data from the Malaria Indicator Survey (MIS) datasets for Zambia collected in 2010, 2012 and 2015. Average monthly temperature and rainfall data were obtained from climate explorer a satellite-generated database. We then conducted a counterfactual analysis using the estimated marginal effects and various hypothetical levels of intervention coverage to assess how different levels of coverage would affect malaria prevalence. Increasing IRS and ITNs from the 2015 levels of coverage of 28.9% and 58.9% respectively to at least 80% and rising standard housing to 20% from the 13.4% in 2015 may bring malaria prevalence down to below 15%. If the percentage of modern houses were increased further to 90%, malaria prevalence might decrease to 10%. Other than ITN and IRS, streamlining and increasing of the percentage of standard houses in malaria fight would augment and bring malaria down to the levels needed for focal malaria elimination. The effects of ITNs, IRS and Standard housing were pronounced in high than low epidemiological areas.
Collapse
Affiliation(s)
| | - Hikabasa Halwindi
- Department of Community and Family Medicine, University of Zambia, School of Public Health, Lusaka, Zambia
| | | |
Collapse
|
20
|
Tropical Wetland (TropWet) Mapping Tool: The Automatic Detection of Open and Vegetated Waterbodies in Google Earth Engine for Tropical Wetlands. REMOTE SENSING 2020. [DOI: 10.3390/rs12071182] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Knowledge of the location and extent of surface water and inundated vegetation is vital for a range of applications including flood risk management, biodiversity monitoring, quantifying greenhouse gas emissions, and mapping water-borne disease risk. Here, we present a new tool, TropWet, which enables users of all abilities to map wetlands in herbaceous dominated regions based on simple unmixing of optical Landsat satellite imagery in the Google Earth Engine. The results demonstrate transferability throughout the African continent with a high degree of accuracy (mean 91% accuracy, st. dev 2.6%, n = 10,800). TropWet demonstrated considerable improvements over existing globally available surface water datasets for mapping the extent of important wetlands like the Okavango, Botswana. TropWet was able to provide frequency inundation maps as an indicator of malarial mosquito aquatic habitat extent and persistence in Barotseland, Zambia. TropWet was able to map flood extent comparable to operational flood risk mapping products in the Zambezi Region, Namibia. Finally, TropWet was able to quantify the effects of the El Niño/Southern Oscillation (ENSO) events on the extent of photosynthetic vegetation and wetland extent across Southern Africa. These examples demonstrate the potential for TropWet to provide policy makers with crucial information to help make national, regional, or continental scale decisions regarding wetland conservation, flood/disease hazard mapping, or mitigation against the impacts of ENSO.
Collapse
|
21
|
Rouamba T, Samadoulougou S, Tinto H, Alegana VA, Kirakoya-Samadoulougou F. Severe-malaria infection and its outcomes among pregnant women in Burkina Faso health-districts: Hierarchical Bayesian space-time models applied to routinely-collected data from 2013 to 2018. Spat Spatiotemporal Epidemiol 2020; 33:100333. [PMID: 32370941 PMCID: PMC7613547 DOI: 10.1016/j.sste.2020.100333] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 09/15/2019] [Accepted: 12/27/2019] [Indexed: 11/12/2022]
Abstract
Fine-scale hotspots detection is crucial for optimum delivery of essential health-services for reducing severe malaria in pregnancy (MiP) and death cases in Burkina Faso. This study used hierarchical-Bayesian Spatio-temporal modeling to explore space-time patterns and pinpoint health-districts with an exceedance probability of severe MiP incidence and fatality rate. Study also assessed effect of health-district service delivery (readiness) on severe-MiP outcomes. Severe-MiP fatality rate declined considerably while its incidence rate remained unchanged between January-2013 and December-2018. Severe-MiP cases persisted throughout the year with peaks between August and November. These peaks increased 2.5-fold the fatality rate. Furthermore, severe-MiP fatality was higher in health-districts classified as low-readiness (IRR = 2.469, 95%CrI: 1.632–3.738). However, the fatality rate decreased significantly with proper coverage with three doses for intermittent-preventive-treatment with sulphadoxine-pyrimethamine. Severe-MiP burden was heterogeneous spatially and temporally. The study suggested that health-programs should increase health-districts readiness and optimize resource allocation in high burden areas and months.
Collapse
Affiliation(s)
- Toussaint Rouamba
- Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santé, 42, Avenue Kumda-Yonre, Centre National de la Recherche Scientifique et Technologique, 11 BP 218 Ouaga CMS 11, Ouagadougou, Burkina Faso; Centre d'Epidémiologie, Biostatistique et Recherche Clinique, Ecole de Santé Publique, Université Libre de Bruxelles (ULB), Route de Lennik, 808 B-1070, Bruxelles, Belgique.
| | - Sekou Samadoulougou
- Evaluation Platform on Obesity Prevention, Quebec Heart and Lung Institute Research Center, Quebec City, QC G1V 4G5, Canada; Centre for Research on Planning and Development (CRAD), Laval University, Quebec, G1V 0A6, Canada.
| | - Halidou Tinto
- Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santé, 42, Avenue Kumda-Yonre, Centre National de la Recherche Scientifique et Technologique, 11 BP 218 Ouaga CMS 11, Ouagadougou, Burkina Faso
| | - Victor A Alegana
- Population Health Theme, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya; Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Fati Kirakoya-Samadoulougou
- Centre d'Epidémiologie, Biostatistique et Recherche Clinique, Ecole de Santé Publique, Université Libre de Bruxelles (ULB), Route de Lennik, 808 B-1070, Bruxelles, Belgique.
| |
Collapse
|
22
|
Alegana VA, Khazenzi C, Akech SO, Snow RW. Estimating hospital catchments from in-patient admission records: a spatial statistical approach applied to malaria. Sci Rep 2020; 10:1324. [PMID: 31992809 PMCID: PMC6987150 DOI: 10.1038/s41598-020-58284-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 01/07/2020] [Indexed: 01/20/2023] Open
Abstract
Admission records are seldom used in sub-Saharan Africa to delineate hospital catchments for the spatial description of hospitalised disease events. We set out to investigate spatial hospital accessibility for severe malarial anaemia (SMA) and cerebral malaria (CM). Malaria admissions for children between 1 month and 14 years old were identified from prospective clinical surveillance data recorded routinely at four referral hospitals covering two complete years between December 2015 to November 2016 and November 2017 to October 2018. These were linked to census enumeration areas (EAs) with an age-structured population. A novel mathematical-statistical framework that included EAs with zero observations was used to predict hospital catchment for malaria admissions adjusting for spatial distance. From 5766 malaria admissions, 5486 (95.14%) were linked to specific EA address, of which 272 (5%) were classified as cerebral malaria while 1001 (10%) were severe malaria anaemia. Further, results suggest a marked geographic catchment of malaria admission around the four sentinel hospitals although the extent varied. The relative rate-ratio of hospitalisation was highest at <1-hour travel time for SMA and CM although this was lower outside the predicted hospital catchments. Delineation of catchments is important for planning emergency care delivery and in the use of hospital data to define epidemiological disease burdens. Further hospital and community-based studies on treatment-seeking pathways to hospitals for severe disease would improve our understanding of catchments.
Collapse
Affiliation(s)
- Victor A Alegana
- Kenya Medical Research Institute - Wellcome Trust Research Programme, P.O. Box, 43640-00100, Nairobi, Kenya.
- Geography and Environmental Science, University of Southampton, SO17 1BJ, Southampton, UK.
- Faculty of Science and Technology, Lancaster University, LA1 4YR, Lancaster, UK.
| | - Cynthia Khazenzi
- Kenya Medical Research Institute - Wellcome Trust Research Programme, P.O. Box, 43640-00100, Nairobi, Kenya
| | - Samuel O Akech
- Kenya Medical Research Institute - Wellcome Trust Research Programme, P.O. Box, 43640-00100, Nairobi, Kenya
| | - Robert W Snow
- Kenya Medical Research Institute - Wellcome Trust Research Programme, P.O. Box, 43640-00100, Nairobi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, OX3 7LJ, Oxford, UK
| |
Collapse
|
23
|
Effect of Free Healthcare Policy for Children under Five Years Old on the Incidence of Reported Malaria Cases in Burkina Faso by Bayesian Modelling: "Not only the Ears but also the Head of the Hippopotamus". INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17020417. [PMID: 31936308 PMCID: PMC7014427 DOI: 10.3390/ijerph17020417] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Revised: 12/24/2019] [Accepted: 01/03/2020] [Indexed: 02/02/2023]
Abstract
Burkina Faso has recently implemented an additional strategy, the free healthcare policy, to further improve maternal and child health. This policy targets children under five who bear the brunt of the malaria scourge. The effects of the free-of-charge healthcare were previously assessed in women but not in children. The present study aims at filling this gap by assessing the effect of this policy in children under five with a focus on the induced spatial and temporal changes in malaria morbidity. We used a Bayesian spatiotemporal negative binomial model to investigate the space–time variation in malaria incidence in relation to the implementation of the policy. The analysis relied on malaria routine surveillance data extracted from the national health data repository and spanning the period from January 2013 to December 2018. The model was adjusted for meteorological and contextual confounders. We found that the number of presumed and confirmed malaria cases per 1000 children per month increased between 2013 and 2018. We further found that the implementation of the free healthcare policy was significantly associated with a two-fold increase in the number of tested and confirmed malaria cases compared with the period before the policy rollout. This effect was, however, heterogeneous across the health districts. We attributed the rise in malaria incidence following the policy rollout to an increased use of health services combined with an increased availability of rapid tests and a higher compliance to the “test and treat” policy. The observed heterogeneity in the policy effect was attributed to parallel control interventions, some of which were rolled out at different paces and scales. Our findings call for a sustained and reinforced effort to test all suspected cases so that, alongside an improved case treatment, the true picture of the malaria scourge in children under five emerges clearly (see the hippopotamus almost entirely).
Collapse
|
24
|
Kost GJ. Geospatial Science and Point-of-Care Testing: Creating Solutions for Population Access, Emergencies, Outbreaks, and Disasters. Front Public Health 2019; 7:329. [PMID: 32039125 PMCID: PMC6988819 DOI: 10.3389/fpubh.2019.00329] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 10/24/2019] [Indexed: 12/22/2022] Open
Abstract
Objectives: (a) To understand how to integrate geospatial concepts when implementing point-of-care testing (POCT); (b) to facilitate emergency, outbreak, and disaster preparedness and emergency management in healthcare small-world networks; (c) to enhance community resilience by using POCT in tandem with geographic information systems (GISs) and other geospatial tools; and (d) to advance crisis standards of care at points of need, adaptable and scalable for public health practice in limited-resource countries and other global settings. Content: Visual logistics help integrate and synthesize POCT and geospatial concepts. The resulting geospatial solutions presented here comprise: (1) small-world networks and regional topography; (2) space-time transformation, hubs, and asset mapping; (3) spatial and geospatial care paths™; (4) GIS-POCT; (5) isolation laboratories, diagnostics isolators, and mobile laboratories for highly infectious diseases; (6) alternate care facilities; (7) roaming POCT—airborne, ambulances, space, and wearables; (8) connected and wireless POCT outside hospitals; (9) unmanned aerial vehicles; (10) geospatial practice—demographic care unit resource scoring, geographic risk assessment, and national POCT policy and guidelines; (11) the hybrid laboratory; and (12) point-of-careology. Value: Small-world networks and their connectivity facilitate efficient and effective placement of POCT for optimal response, rescue, diagnosis, and treatment. Spatial care paths™ speed transport from primary encounters to referral centers bypassing topographic bottlenecks, process gaps, and time-consuming interruptions. Regional GISs position POCT close to where patients live to facilitate rapid triage, decrease therapeutic turnaround time, and conserve economic resources. Geospatial care paths™ encompass demographic and population access features. Timeliness creates value during acute illness, complex crises, and unexpected disasters. Isolation laboratories equipped with POCT help stop outbreaks and safely support critically ill patients with highly infectious diseases. POCT-enabled spatial grids can map sentinel cases and establish geographic limits of epidemics for ring vaccination. Impact: Geospatial solutions generate inherently optimal and logical placement of POCT conceptually, physically, and temporally as a means to improve crisis response and spatial resilience. If public health professionals, geospatial scientists, and POCT specialists join forces, new collaborative teamwork can create faster response and higher impact during disasters, complex crises, outbreaks, and epidemics, as well as more efficient primary, urgent, and emergency community care.
Collapse
Affiliation(s)
- Gerald J Kost
- Point-of-Care Testing Center for Teaching and Research (POCT·CTR™), University of California, Davis, Davis, CA, United States.,Knowledge Optimization®, Davis, CA, United States
| |
Collapse
|
25
|
Xie B, Jiao J, An Z, Zheng Y, Li Z. Deciphering the stroke-built environment nexus in transitional cities: Conceptual framework, empirical evidence, and implications for proactive planning intervention. CITIES (LONDON, ENGLAND) 2019; 94:116-128. [PMID: 38239895 PMCID: PMC10795972 DOI: 10.1016/j.cities.2019.05.035] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2024]
Abstract
Adverse lifestyle-associated health outcomes, and stroke in particular, have been aggravated in transitional countries under high-speed urbanisation. Against this backdrop, deciphering the nexus between built environments (BEs) and lifestyle-associated health outcomes is of importance for crafting proactive interventions. The existing literature on this topic, however, fails to sufficiently capture the multiplicity of health-related BEs and, in turn, the complexity of such a nexus, largely challenging the applicability of established frameworks and the reliability of relevant findings. Looking at the case of stroke in Wuhan, China, this research aims to flesh out the understanding of the nexus between multidimensional BEs and lifestyle-associated health outcomes in transitional cities, with regards to conceptual framework and empirical evidence. To this end, we clarified stroke-related BE elements and integrated them into one conceptual framework. We then visualised stroke risk and examined its BE determinants using the Bayesian conditional autoregressive model. The visualisation results showed that stroke risks exhibited significant clustering in the high-density urban core. The statistical analysis found that, after the data were controlled for sociodemographic characteristics, net population density and building density were positively associated with stroke risk. In contrast, an abundance of public parks and institutional land use and access to medical care facilities have presented negative correlations with stroke risk, regardless of urban density. Our research reveals that compact urban developments might not be a silver bullet for health promotion in transitional cities, calling for an urgent need to scrutinise their applicability. To offset the adverse effects of increasingly dense urban environments, more efforts should also be made to provide better access to the identified salubrious resources. Furthermore, we argue that the establishment of comprehensive conceptual frameworks that connect BEs and lifestyle-associated health outcomes deserves to be highlighted in further research, planning intervention schemes, and health impact assessment projects.
Collapse
Affiliation(s)
- Bo Xie
- School of Urban Design, Wuhan University, Wuhan 430072, China
| | - Junfeng Jiao
- School of Architecture, The University of Texas at Austin, 310 Inner Campus Drive, Austin, TX 78712, United States of America
| | - Zihao An
- Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, United Kingdom
| | - Yiling Zheng
- School of Urban Design, Wuhan University, Wuhan 430072, China
| | - Zhigang Li
- School of Urban Design, Wuhan University, Wuhan 430072, China
| |
Collapse
|
26
|
Lee EH, Miller RH, Masuoka P, Schiffman E, Wanduragala DM, DeFraites R, Dunlop SJ, Stauffer WM, Hickey PW. Predicting Risk of Imported Disease with Demographics: Geospatial Analysis of Imported Malaria in Minnesota, 2010-2014. Am J Trop Med Hyg 2019; 99:978-986. [PMID: 30062987 PMCID: PMC6159573 DOI: 10.4269/ajtmh.18-0357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Although immigrants who visit friends and relatives (VFRs) account for most of the travel-acquired malaria cases in the United States, there is limited evidence on community-level risk factors and best practices for prevention appropriate for various VFR groups. Using 2010–2014 malaria case reports, sociodemographic census data, and health services data, we explored and mapped community-level characteristics to understand who is at risk and where imported malaria infections occur in Minnesota. We examined associations with malaria incidence using Poisson and negative binomial regression. Overall, mean incidence was 0.4 cases per 1,000 sub-Saharan African (SSA)–born in communities reporting malaria, with cases concentrated in two areas of Minneapolis–St. Paul. We found moderate and positive associations between imported malaria and counts of SSA- and Asian-born populations, respectively. Our findings may inform future studies to understand the knowledge, attitudes, and practices of VFR travelers and facilitate and focus intervention strategies to reduce imported malaria in the United States.
Collapse
Affiliation(s)
- Elizabeth H Lee
- The Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Robin H Miller
- The Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Penny Masuoka
- The Henry M Jackson Foundation, Bethesda, Maryland.,The Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | | | | | - Robert DeFraites
- The Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Stephen J Dunlop
- University of Minnesota, Minneapolis, Minnesota.,Hennepin County Medical Center, Minneapolis, Minnesota
| | | | - Patrick W Hickey
- The Uniformed Services University of the Health Sciences, Bethesda, Maryland
| |
Collapse
|
27
|
Davis JK, Gebrehiwot T, Worku M, Awoke W, Mihretie A, Nekorchuk D, Wimberly MC. A genetic algorithm for identifying spatially-varying environmental drivers in a malaria time series model. ENVIRONMENTAL MODELLING & SOFTWARE : WITH ENVIRONMENT DATA NEWS 2019; 119:275-284. [PMID: 33814961 PMCID: PMC8018598 DOI: 10.1016/j.envsoft.2019.06.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Time series models of malaria cases can be applied to forecast epidemics and support proactive interventions. Mosquito life history and parasite development are sensitive to environmental factors such as temperature and precipitation, and these variables are often used as predictors in malaria models. However, malaria-environment relationships can vary with ecological and social context. We used a genetic algorithm to optimize a spatiotemporal malaria model by aggregating locations into clusters with similar environmental sensitivities. We tested the algorithm in the Amhara Region of Ethiopia using seven years of weekly Plasmodium falciparum data from 47 districts and remotely-sensed land surface temperature, precipitation, and spectral indices as predictors. The best model identified six clusters, and the districts in each cluster had distinctive responses to the environmental predictors. We conclude that spatial stratification can improve the fit of environmentally-driven disease models, and genetic algorithms provide a practical and effective approach for identifying these clusters.
Collapse
Affiliation(s)
- Justin K. Davis
- Dept. of Geography and Environmental Sustainability, University of Oklahoma, Norman OK, United States
| | | | | | - Worku Awoke
- School of Public Health, Bahir Dar University, Bahir Dar, Ethiopia
| | - Abere Mihretie
- Health, Development, and Anti-Malaria Association, Addis Ababa, Ethiopia
| | - Dawn Nekorchuk
- Dept. of Geography and Environmental Sustainability, University of Oklahoma, Norman OK, United States
| | - Michael C. Wimberly
- Dept. of Geography and Environmental Sustainability, University of Oklahoma, Norman OK, United States
| |
Collapse
|
28
|
Haiyambo DH, Uusiku P, Mumbengegwi D, Pernica JM, Bock R, Malleret B, Rénia L, Greco B, Quaye IK. Molecular detection of P. vivax and P. ovale foci of infection in asymptomatic and symptomatic children in Northern Namibia. PLoS Negl Trop Dis 2019; 13:e0007290. [PMID: 31042707 PMCID: PMC6513099 DOI: 10.1371/journal.pntd.0007290] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2018] [Revised: 05/13/2019] [Accepted: 03/11/2019] [Indexed: 11/19/2022] Open
Abstract
Background Knowledge of the foci of Plasmodium species infections is critical for a country with an elimination agenda. Namibia is targeting malaria elimination by 2020. To support decision making regarding targeted intervention, we examined for the first time, the foci of Plasmodium species infections and regional prevalence in northern Namibia, using nested and quantitative polymerase chain reaction (PCR) methods. Methods We used cross-sectional multi-staged sampling to select 952 children below 9 years old from schools and clinics in seven districts in northern Namibia, to assess the presence of Plasmodium species. Results The median participant age was 6 years (25–75%ile 4–8 y). Participants had a median hemoglobin of 12.0 g/dL (25–75%ile 11.1–12.7 g/dL), although 21% of the cohort was anemic, with anemia being severer in the younger population (p<0.002). Most of children with Plasmodium infection were asymptomatic (63.4%), presenting a challenge for elimination. The respective parasite prevalence for Plasmodium falciparum (Pf), Plasmodium vivax (Pv) and Plasmodium ovale curtisi (Po) were (4.41%, 0.84% and 0.31%); with Kavango East and West (10.4%, 6.19%) and Ohangwena (4.5%) having the most prevalence. Pv was localized in Ohangwena, Omusati and Oshana, while Po was found in Kavango. All children with Pv/Pf coinfections in Ohangwena, had previously visited Angola, affirming that perennial migrations are risks for importation of Plasmodium species. The mean hemoglobin was lower in those with Plasmodium infection compared to those without (0.96 g/dL less, 95%CI 0.40–1.52 g/dL less, p = 0.0009) indicating that quasi-endemicity exists in the low transmission setting. Conclusions We conclude that Pv and Po species are present in northern Namibia. Additionally, the higher number of asymptomatic infections present challenges to the efforts at elimination for the country. Careful planning, coordination with neighboring Angola and execution of targeted active intervention, will be required for a successful elimination agenda. Namibia is a member of the SADC elimination 8 (E8) group with a target to eliminate malaria by 2020. This target stems from years of aggressive interventional strategies that has led to significant reductions in morbidity and mortality. The focus of this strategy is mainly on Plasmodium falciparum as the primary parasite species. Foci of transmission is found in the northern border with Angola and Zambia, which also carries the highest population density. Recently as part of the elimination efforts to predict areas likely to have rebound epidemics, three regions Ohangwena, Kavango and Zambezi were identified. In order to affirm these findings and decision-making process for intervention, we assessed the parasite prevalence in 7 northern regional sites for four Plasmodium species. We identified Pv and Po curtisi parasites in Omusati, Ohangwena and Kavango, as well as a significant number of asymptomatic Pf and Pv infections, part of which may be due to importation from neighboring Angola. As Namibia is targeting elimination by 2020, careful thought and planning will be required to reach the goal.
Collapse
Affiliation(s)
- Daniel H. Haiyambo
- Department of Biochemistry and Microbiology, University of Namibia School of Medicine, Windhoek, Namibia
| | - Petrina Uusiku
- National Vector Borne Disease Control Program, Ministry of Health and Social Services, Windhoek, Namibia
| | - Davies Mumbengegwi
- Multidisciplinary Research Center, University of Namibia, Windhoek, Namibia
| | - Jeff M. Pernica
- Division of Infectious Disease, Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
| | - Ronnie Bock
- Department of Biology, University of Namibia, Windhoek, Namibia
| | - Benoit Malleret
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Biopolis, Singapore
| | - Laurent Rénia
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Biopolis, Singapore
| | - Beatrice Greco
- Research and Development Access, Global Health Institute, Merck KGaA, Darmstadt, Germany
| | - Isaac K. Quaye
- Department of Biochemistry and Microbiology, University of Namibia School of Medicine, Windhoek, Namibia
- * E-mail: ,
| |
Collapse
|
29
|
Umer MF, Zofeen S, Majeed A, Hu W, Qi X, Zhuang G. Effects of Socio-Environmental Factors on Malaria Infection in Pakistan: A Bayesian Spatial Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E1365. [PMID: 30995744 PMCID: PMC6517989 DOI: 10.3390/ijerph16081365] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 04/07/2019] [Accepted: 04/13/2019] [Indexed: 12/04/2022]
Abstract
The role of socio-environmental factors in shaping malaria dynamics is complex and inconsistent. Effects of socio-environmental factors on malaria in Pakistan at district level were examined. Annual malaria cases data were obtained from Directorate of Malaria Control Program, Pakistan. Meteorological data were supplied by Pakistan Meteorological Department. A major limitation was the use of yearly, rather than monthly/weekly malaria data in this study. Population data, socio-economic data and education score data were downloaded from internet. Bayesian conditional autoregressive model was used to find the statistical association of socio-environmental factors with malaria in Pakistan. From 136/146 districts in Pakistan, >750,000 confirmed malaria cases were included, over a three years' period (2013-2015). Socioeconomic status ((posterior mean value -3.965, (2.5% quintile, -6.297%), (97.5% quintile, -1.754%)) and human population density (-7.41 × 10-4, -0.001406%, -1.05 × 10-4 %) were inversely related, while minimum temperature (0.1398, 0.05275%, 0.2145%) was directly proportional to malaria in Pakistan during the study period. Spatial random effect maps presented that moderate relative risk (RR, 0.75 to 1.24) and high RR (1.25 to 1.99) clusters were scattered throughout the country, outnumbering the ones' with low RR (0.23 to 0.74). Socio-environmental variables influence annual malaria incidence in Pakistan and needs further evaluation.
Collapse
Affiliation(s)
- Muhammad Farooq Umer
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an 710061, China.
| | - Shumaila Zofeen
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an 710061, China.
| | - Abdul Majeed
- Directorate of Malaria Control Program, Islamabad 44000, Pakistan.
| | - Wenbiao Hu
- School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, QLD 4059, Australia.
| | - Xin Qi
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an 710061, China.
- Global Health Institute, Xi'an Jiaotong University Health Science Center, Xi'an 710061, China.
| | - Guihua Zhuang
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an 710061, China.
| |
Collapse
|
30
|
Ssempiira J, Kissa J, Nambuusi B, Kyozira C, Rutazaana D, Mukooyo E, Opigo J, Makumbi F, Kasasa S, Vounatsou P. The effect of case management and vector-control interventions on space-time patterns of malaria incidence in Uganda. Malar J 2018; 17:162. [PMID: 29650005 PMCID: PMC5898071 DOI: 10.1186/s12936-018-2312-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 04/06/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Electronic reporting of routine health facility data in Uganda began with the adoption of the District Health Information Software System version 2 (DHIS2) in 2011. This has improved health facility reporting and overall data quality. In this study, the effects of case management with artemisinin-based combination therapy (ACT) and vector control interventions on space-time patterns of disease incidence were determined using DHIS2 data reported during 2013-2016. METHODS Bayesian spatio-temporal negative binomial models were fitted on district-aggregated monthly malaria cases, reported by two age groups, defined by a cut-off age of 5 years. The effects of interventions were adjusted for socio-economic and climatic factors. Spatial and temporal correlations were taken into account by assuming a conditional autoregressive and a first-order autoregressive AR(1) process on district and monthly specific random effects, respectively. Fourier trigonometric functions were incorporated in the models to take into account seasonal fluctuations in malaria transmission. RESULTS The temporal variation in incidence was similar in both age groups and depicted a steady decline up to February 2014, followed by an increase from March 2015 onwards. The trends were characterized by a strong bi-annual seasonal pattern with two peaks during May-July and September-December. Average monthly incidence in children < 5 years declined from 74.7 cases (95% CI 72.4-77.1) in 2013 to 49.4 (95% CI 42.9-55.8) per 1000 in 2015 and followed by an increase in 2016 of up to 51.3 (95% CI 42.9-55.8). In individuals ≥ 5 years, a decline in incidence from 2013 to 2015 was followed by an increase in 2016. A 100% increase in insecticide-treated nets (ITN) coverage was associated with a decline in incidence by 44% (95% BCI 28-59%). Similarly, a 100% increase in ACT coverage reduces incidence by 28% (95% BCI 11-45%) and 25% (95% BCI 20-28%) in children < 5 years and individuals ≥ 5 years, respectively. The ITN effect was not statistically important in older individuals. The space-time patterns of malaria incidence in children < 5 are similar to those of parasitaemia risk predicted from the malaria indicator survey of 2014-15. CONCLUSION The decline in malaria incidence highlights the effectiveness of vector-control interventions and case management with ACT in Uganda. This calls for optimizing and sustaining interventions to achieve universal coverage and curb reverses in malaria decline.
Collapse
Affiliation(s)
- Julius Ssempiira
- Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051, Basel, Switzerland.,University of Basel, Petersplatz 1, 4001, Basel, Switzerland.,Makerere University School of Public Health, New Mulago Hospital Complex, P.O Box 7072, Kampala, Uganda
| | - John Kissa
- Uganda Ministry of Health, Plot 6 Lourdel Road, P.O. Box 7272, Nakasero, Kampala, Uganda
| | - Betty Nambuusi
- Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051, Basel, Switzerland.,University of Basel, Petersplatz 1, 4001, Basel, Switzerland.,Makerere University School of Public Health, New Mulago Hospital Complex, P.O Box 7072, Kampala, Uganda
| | - Carol Kyozira
- Uganda Ministry of Health, Plot 6 Lourdel Road, P.O. Box 7272, Nakasero, Kampala, Uganda
| | - Damian Rutazaana
- Uganda Ministry of Health, Plot 6 Lourdel Road, P.O. Box 7272, Nakasero, Kampala, Uganda
| | - Eddie Mukooyo
- Uganda Ministry of Health, Plot 6 Lourdel Road, P.O. Box 7272, Nakasero, Kampala, Uganda
| | - Jimmy Opigo
- Uganda Ministry of Health, Plot 6 Lourdel Road, P.O. Box 7272, Nakasero, Kampala, Uganda
| | - Fredrick Makumbi
- Makerere University School of Public Health, New Mulago Hospital Complex, P.O Box 7072, Kampala, Uganda
| | - Simon Kasasa
- Makerere University School of Public Health, New Mulago Hospital Complex, P.O Box 7072, Kampala, Uganda
| | - Penelope Vounatsou
- Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051, Basel, Switzerland. .,University of Basel, Petersplatz 1, 4001, Basel, Switzerland.
| |
Collapse
|
31
|
How Socio-Environmental Factors Are Associated with Japanese Encephalitis in Shaanxi, China-A Bayesian Spatial Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:ijerph15040608. [PMID: 29584661 PMCID: PMC5923650 DOI: 10.3390/ijerph15040608] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2018] [Revised: 03/21/2018] [Accepted: 03/22/2018] [Indexed: 12/14/2022]
Abstract
Evidence indicated that socio-environmental factors were associated with occurrence of Japanese encephalitis (JE). This study explored the association of climate and socioeconomic factors with JE (2006–2014) in Shaanxi, China. JE data at the county level in Shaanxi were supplied by Shaanxi Center for Disease Control and Prevention. Population and socioeconomic data were obtained from the China Population Census in 2010 and statistical yearbooks. Meteorological data were acquired from the China Meteorological Administration. A Bayesian conditional autoregressive model was used to examine the association of meteorological and socioeconomic factors with JE. A total of 1197 JE cases were included in this study. Urbanization rate was inversely associated with JE incidence during the whole study period. Meteorological variables were significantly associated with JE incidence between 2012 and 2014. The excessive precipitation at lag of 1–2 months in the north of Shaanxi in June 2013 had an impact on the increase of local JE incidence. The spatial residual variations indicated that the whole study area had more stable risk (0.80–1.19 across all the counties) between 2012 and 2014 than earlier years. Public health interventions need to be implemented to reduce JE incidence, especially in rural areas and after extreme weather.
Collapse
|
32
|
Gunda R, Chimbari MJ, Shamu S, Sartorius B, Mukaratirwa S. Malaria incidence trends and their association with climatic variables in rural Gwanda, Zimbabwe, 2005-2015. Malar J 2017; 16:393. [PMID: 28964255 PMCID: PMC5622423 DOI: 10.1186/s12936-017-2036-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 09/19/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Malaria is a public health problem in Zimbabwe. Although many studies have indicated that climate change may influence the distribution of malaria, there is paucity of information on its trends and association with climatic variables in Zimbabwe. To address this shortfall, the trends of malaria incidence and its interaction with climatic variables in rural Gwanda, Zimbabwe for the period January 2005 to April 2015 was assessed. METHODS Retrospective data analysis of reported cases of malaria in three selected Gwanda district rural wards (Buvuma, Ntalale and Selonga) was carried out. Data on malaria cases was collected from the district health information system and ward clinics while data on precipitation and temperature were obtained from the climate hazards group infrared precipitation with station data (CHIRPS) database and the moderate resolution imaging spectro-radiometer (MODIS) satellite data, respectively. Distributed lag non-linear models (DLNLM) were used to determine the temporal lagged association between monthly malaria incidence and monthly climatic variables. RESULTS There were 246 confirmed malaria cases in the three wards with a mean incidence of 0.16/1000 population/month. The majority of malaria cases (95%) occurred in the > 5 years age category. The results showed no correlation between trends of clinical malaria (unconfirmed) and confirmed malaria cases in all the three study wards. There was a significant association between malaria incidence and the climatic variables in Buvuma and Selonga wards at specific lag periods. In Ntalale ward, only precipitation (1- and 3-month lag) and mean temperature (1- and 2-month lag) were significantly associated with incidence at specific lag periods (p < 0.05). DLNM results suggest a key risk period in current month, based on key climatic conditions in the 1-4 month period prior. CONCLUSIONS As the period of high malaria risk is associated with precipitation and temperature at 1-4 month prior in a seasonal cycle, intensifying malaria control activities over this period will likely contribute to lowering the seasonal malaria incidence.
Collapse
Affiliation(s)
- Resign Gunda
- School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa.
| | - Moses John Chimbari
- College of Health Sciences, University of KwaZulu-Natal, Howard Campus, Durban, South Africa
| | - Shepherd Shamu
- Department of Community Medicine, University of Zimbabwe, Harare, Zimbabwe
| | - Benn Sartorius
- Discipline of Public Health Medicine, School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa
| | - Samson Mukaratirwa
- School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa
| |
Collapse
|
33
|
Ouma PO, Agutu NO, Snow RW, Noor AM. Univariate and multivariate spatial models of health facility utilisation for childhood fevers in an area on the coast of Kenya. Int J Health Geogr 2017; 16:34. [PMID: 28923070 PMCID: PMC5604359 DOI: 10.1186/s12942-017-0107-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 09/11/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Precise quantification of health service utilisation is important for the estimation of disease burden and allocation of health resources. Current approaches to mapping health facility utilisation rely on spatial accessibility alone as the predictor. However, other spatially varying social, demographic and economic factors may affect the use of health services. The exclusion of these factors can lead to the inaccurate estimation of health facility utilisation. Here, we compare the accuracy of a univariate spatial model, developed only from estimated travel time, to a multivariate model that also includes relevant social, demographic and economic factors. METHODS A theoretical surface of travel time to the nearest public health facility was developed. These were assigned to each child reported to have had fever in the Kenya demographic and health survey of 2014 (KDHS 2014). The relationship of child treatment seeking for fever with travel time, household and individual factors from the KDHS2014 were determined using multilevel mixed modelling. Bayesian information criterion (BIC) and likelihood ratio test (LRT) tests were carried out to measure how selected factors improve parsimony and goodness of fit of the time model. Using the mixed model, a univariate spatial model of health facility utilisation was fitted using travel time as the predictor. The mixed model was also used to compute a multivariate spatial model of utilisation, using travel time and modelled surfaces of selected household and individual factors as predictors. The univariate and multivariate spatial models were then compared using the receiver operating area under the curve (AUC) and a percent correct prediction (PCP) test. RESULTS The best fitting multivariate model had travel time, household wealth index and number of children in household as the predictors. These factors reduced BIC of the time model from 4008 to 2959, a change which was confirmed by the LRT test. Although there was a high correlation of the two modelled probability surfaces (Adj R 2 = 88%), the multivariate model had better AUC compared to the univariate model; 0.83 versus 0.73 and PCP 0.61 versus 0.45 values. CONCLUSION Our study shows that a model that uses travel time, as well as household and individual-level socio-demographic factors, results in a more accurate estimation of use of health facilities for the treatment of childhood fever, compared to one that relies on only travel time.
Collapse
Affiliation(s)
- Paul O Ouma
- Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya. .,Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya.
| | - Nathan O Agutu
- Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
| | - Robert W Snow
- Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya.,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Abdisalan M Noor
- Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya.,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| |
Collapse
|
34
|
Ashton RA, Bennett A, Yukich J, Bhattarai A, Keating J, Eisele TP. Methodological Considerations for Use of Routine Health Information System Data to Evaluate Malaria Program Impact in an Era of Declining Malaria Transmission. Am J Trop Med Hyg 2017; 97:46-57. [PMID: 28990915 PMCID: PMC5619932 DOI: 10.4269/ajtmh.16-0734] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Accepted: 10/24/2016] [Indexed: 12/01/2022] Open
Abstract
Coverage of malaria control interventions is increasing dramatically across endemic countries. Evaluating the impact of malaria control programs and specific interventions on health indicators is essential to enable countries to select the most effective and appropriate combination of tools to accelerate progress or proceed toward malaria elimination. When key malaria interventions have been proven effective under controlled settings, further evaluations of the impact of the intervention using randomized approaches may not be appropriate or ethical. Alternatives to randomized controlled trials are therefore required for rigorous evaluation under conditions of routine program delivery. Routine health management information system (HMIS) data are a potentially rich source of data for impact evaluation, but have been underused in impact evaluation due to concerns over internal validity, completeness, and potential bias in estimates of program or intervention impact. A range of methodologies were identified that have been used for impact evaluations with malaria outcome indicators generated from HMIS data. Methods used to maximize internal validity of HMIS data are presented, together with recommendations on reducing bias in impact estimates. Interrupted time series and dose-response analyses are proposed as the strongest quasi-experimental impact evaluation designs for analysis of malaria outcome indicators from routine HMIS data. Interrupted time series analysis compares the outcome trend and level before and after the introduction of an intervention, set of interventions or program. The dose-response national platform approach explores associations between intervention coverage or program intensity and the outcome at a subnational (district or health facility catchment) level.
Collapse
Affiliation(s)
- Ruth A. Ashton
- Center for Applied Malaria Research and Evaluation, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana
| | - Adam Bennett
- Malaria Elimination Initiative, Global Health Group, University of California San Francisco, San Francisco, California
| | - Joshua Yukich
- Center for Applied Malaria Research and Evaluation, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana
| | - Achuyt Bhattarai
- President's Malaria Initiative, Malaria Branch, Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Joseph Keating
- Center for Applied Malaria Research and Evaluation, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana
| | - Thomas P. Eisele
- Center for Applied Malaria Research and Evaluation, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana
| |
Collapse
|
35
|
Kumar M, Gotz D, Nutley T, Smith JB. Research gaps in routine health information system design barriers to data quality and use in low- and middle-income countries: A literature review. Int J Health Plann Manage 2017; 33:e1-e9. [PMID: 28766742 DOI: 10.1002/hpm.2447] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 07/06/2017] [Indexed: 11/06/2022] Open
Abstract
Despite the potential impact of health information system (HIS) design barriers on health data quality and use and, ultimately, health outcomes in low- and middle-income countries (LMICs), no comprehensive literature review has been conducted to study them in this context. We therefore conducted a formal literature review to understand system design barriers to data quality and use in LMICs and to identify any major research gaps related understanding how system design affects data use. We conducted an electronic search across 4 scientific databases-PubMed, Web of Science, Embase, and Global Health-and consulted a data use expert. Following a systematic inclusion and exclusion process, 316 publications (316 abstracts and 18 full papers) were included in the review. We found a paucity of scientific publications that explicitly describe system design factors that hamper data quality or data use for decision making. Although user involvement, work flow, human-computer interactions, and user experience are critical aspects of system design, our findings suggest that these issues are not discussed or conceptualized in the literature. Findings also showed that individual training efforts focus primarily on imparting data analysis skills. The adverse impact of HIS design barriers on data integrity and health system performance may be even bigger in LMICs than elsewhere, leading to errors in population health management and clinical care. We argue for integrating systems thinking into HIS strengthening efforts to reduce the HIS design-user reality gap.
Collapse
Affiliation(s)
- Manish Kumar
- MEASURE Evaluation, Carolina Population Center, University of North Carolina at Chapel Hill, North Carolina, USA.,Carolina Health Informatics Program, University of North Carolina at Chapel Hill, North Carolina, USA
| | - David Gotz
- Carolina Health Informatics Program, University of North Carolina at Chapel Hill, North Carolina, USA.,School of Information and Library Science, University of North Carolina at Chapel Hill, North Carolina, USA
| | - Tara Nutley
- MEASURE Evaluation, Palladium Group, Chapel Hill, North Carolina, USA
| | - Jason B Smith
- MEASURE Evaluation, Carolina Population Center, University of North Carolina at Chapel Hill, North Carolina, USA
| |
Collapse
|
36
|
Adegboye OA, Adegboye M. Spatially Correlated Time Series and Ecological Niche Analysis of Cutaneous Leishmaniasis in Afghanistan. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2017; 14:ijerph14030309. [PMID: 28304356 PMCID: PMC5369145 DOI: 10.3390/ijerph14030309] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Accepted: 03/03/2017] [Indexed: 12/26/2022]
Abstract
Leishmaniasis is the third most common vector-borne disease and a very important protozoan infection. Cutaneous leishmaniasis is one of the most common types of leishmaniasis infectious diseases with up to 1.2 million occurrences of new cases each year worldwide. A dynamic transmission multivariate time series model was applied to the data to account for overdispersion and evaluate the effects of three environmental layers as well as seasonality in the data. Furthermore, ecological niche modeling was used to study the geographically suitable conditions for cutaneous leishmaniasis using temperature, precipitation and altitude as environmental layers, together with the leishmaniasis presence data. A retrospective analysis of the cutaneous leishmaniasis spatial data in Afghanistan between 2003 and 2009 indicates a steady increase from 2003 to 2007, a small decrease in 2008, and then another increase in 2009. An upward trend and regularly repeating patterns of highs and lows were observed related to the months of the year, which suggests seasonality effect in the data. Two peaks were observed in the disease occurrence—January to March and September to December—which coincide with the cold period. Ecological niche modelling indicates that precipitation has the greatest contribution to the potential distribution of leishmaniasis.
Collapse
Affiliation(s)
- Oyelola A Adegboye
- Department of Mathematics, Physics and Statistics, Qatar University, 2713 Doha, Qatar.
| | - Majeed Adegboye
- Department of Information Technology, American University of Nigeria, 640001 Yola, Nigeria.
| |
Collapse
|
37
|
Smith JL, Auala J, Haindongo E, Uusiku P, Gosling R, Kleinschmidt I, Mumbengegwi D, Sturrock HJW. Malaria risk in young male travellers but local transmission persists: a case-control study in low transmission Namibia. Malar J 2017; 16:70. [PMID: 28187770 PMCID: PMC5303241 DOI: 10.1186/s12936-017-1719-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Accepted: 02/03/2017] [Indexed: 11/24/2022] Open
Abstract
Background A key component of malaria elimination campaigns is the identification and targeting of high risk populations. To characterize high risk populations in north central Namibia, a prospective health facility-based case–control study was conducted from December 2012–July 2014. Cases (n = 107) were all patients presenting to any of the 46 health clinics located in the study districts with a confirmed Plasmodium infection by multi-species rapid diagnostic test (RDT). Population controls (n = 679) for each district were RDT negative individuals residing within a household that was randomly selected from a census listing using a two-stage sampling procedure. Demographic, travel, socio-economic, behavioural, climate and vegetation data were also collected. Spatial patterns of malaria risk were analysed. Multivariate logistic regression was used to identify risk factors for malaria. Results Malaria risk was observed to cluster along the border with Angola, and travel patterns among cases were comparatively restricted to northern Namibia and Angola. Travel to Angola was associated with excessive risk of malaria in males (OR 43.58 95% CI 2.12–896), but there was no corresponding risk associated with travel by females. This is the first study to reveal that gender can modify the effect of travel on risk of malaria. Amongst non-travellers, male gender was also associated with a higher risk of malaria compared with females (OR 1.95 95% CI 1.25–3.04). Other strong risk factors were sleeping away from the household the previous night, lower socioeconomic status, living in an area with moderate vegetation around their house, experiencing moderate rainfall in the month prior to diagnosis and living <15 km from the Angolan border. Conclusions These findings highlight the critical need to target malaria interventions to young male travellers, who have a disproportionate risk of malaria in northern Namibia, to coordinate cross-border regional malaria prevention initiatives and to scale up coverage of prevention measures such as indoor residual spraying and long-lasting insecticide nets in high risk areas if malaria elimination is to be realized. Electronic supplementary material The online version of this article (doi:10.1186/s12936-017-1719-x) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Jennifer L Smith
- Malaria Elimination Initiative, Global Health Group, University of California, San Francisco, CA, USA.
| | - Joyce Auala
- Multidisciplinary Research Center, University of Namibia, Windhoek, Namibia
| | - Erastus Haindongo
- Multidisciplinary Research Center, University of Namibia, Windhoek, Namibia
| | - Petrina Uusiku
- National Vector-Borne Disease Control Programme, Ministry of Health and Social Services, Windhoek, Namibia
| | - Roly Gosling
- Malaria Elimination Initiative, Global Health Group, University of California, San Francisco, CA, USA
| | - Immo Kleinschmidt
- MRC Tropical Epidemiology Group, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Davis Mumbengegwi
- Multidisciplinary Research Center, University of Namibia, Windhoek, Namibia
| | - Hugh J W Sturrock
- Malaria Elimination Initiative, Global Health Group, University of California, San Francisco, CA, USA
| |
Collapse
|
38
|
Buckee CO, Tatem AJ, Metcalf CJE. Seasonal Population Movements and the Surveillance and Control of Infectious Diseases. Trends Parasitol 2016; 33:10-20. [PMID: 27865741 DOI: 10.1016/j.pt.2016.10.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Revised: 10/08/2016] [Accepted: 10/19/2016] [Indexed: 10/20/2022]
Abstract
National policies designed to control infectious diseases should allocate resources for interventions based on regional estimates of disease burden from surveillance systems. For many infectious diseases, however, there is pronounced seasonal variation in incidence. Policy-makers must routinely manage a public health response to these seasonal fluctuations with limited understanding of their underlying causes. Two complementary and poorly described drivers of seasonal disease incidence are the mobility and aggregation of human populations, which spark outbreaks and sustain transmission, respectively, and may both exhibit distinct seasonal variations. Here we highlight the key challenges that seasonal migration creates when monitoring and controlling infectious diseases. We discuss the potential of new data sources in accounting for seasonal population movements in dynamic risk mapping strategies.
Collapse
Affiliation(s)
- Caroline O Buckee
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA; Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, USA.
| | - Andrew J Tatem
- Flowminder Foundation, Stockholm, Sweden; WorldPop, Department of Geography and Environment, University of Southampton, Southampton, UK
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, USA; Office of Population Research, Woodrow Wilson School, Princeton University, Princeton, USA
| |
Collapse
|
39
|
Mapping Malaria Risk in Low Transmission Settings: Challenges and Opportunities. Trends Parasitol 2016; 32:635-645. [PMID: 27238200 DOI: 10.1016/j.pt.2016.05.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2016] [Revised: 04/29/2016] [Accepted: 05/02/2016] [Indexed: 11/24/2022]
Abstract
As malaria transmission declines, it becomes increasingly focal and prone to outbreaks. Understanding and predicting patterns of transmission risk becomes an important component of an effective elimination campaign, allowing limited resources for control and elimination to be targeted cost-effectively. Malaria risk mapping in low transmission settings is associated with some unique challenges. This article reviews the main challenges and opportunities related to risk mapping in low transmission areas including recent advancements in risk mapping low transmission malaria, relevant metrics, and statistical approaches and risk mapping in post-elimination settings.
Collapse
|
40
|
Advances in mapping malaria for elimination: fine resolution modelling of Plasmodium falciparum incidence. Sci Rep 2016; 6:29628. [PMID: 27405532 PMCID: PMC4942778 DOI: 10.1038/srep29628] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Accepted: 06/22/2016] [Indexed: 10/31/2022] Open
Abstract
The long-term goal of the global effort to tackle malaria is national and regional elimination and eventually eradication. Fine scale multi-temporal mapping in low malaria transmission settings remains a challenge and the World Health Organisation propose use of surveillance in elimination settings. Here, we show how malaria incidence can be modelled at a fine spatial and temporal resolution from health facility data to help focus surveillance and control to population not attending health facilities. Using Namibia as a case study, we predicted the incidence of malaria, via a Bayesian spatio-temporal model, at a fine spatial resolution from parasitologically confirmed malaria cases and incorporated metrics on healthcare use as well as measures of uncertainty associated with incidence predictions. We then combined the incidence estimates with population maps to estimate clinical burdens and show the benefits of such mapping to identifying areas and seasons that can be targeted for improved surveillance and interventions. Fine spatial resolution maps produced using this approach were then used to target resources to specific local populations, and to specific months of the season. This remote targeting can be especially effective where the population distribution is sparse and further surveillance can be limited to specific local areas.
Collapse
|
41
|
Ebhuoma O, Gebreslasie M. Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2016; 13:ijerph13060584. [PMID: 27314369 PMCID: PMC4924041 DOI: 10.3390/ijerph13060584] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Revised: 06/02/2016] [Accepted: 06/08/2016] [Indexed: 11/16/2022]
Abstract
Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of Knowledge(SM) databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably.
Collapse
Affiliation(s)
- Osadolor Ebhuoma
- School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4000, South Africa.
| | - Michael Gebreslasie
- School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4000, South Africa.
| |
Collapse
|
42
|
Identifying Malaria Transmission Foci for Elimination Using Human Mobility Data. PLoS Comput Biol 2016; 12:e1004846. [PMID: 27043913 PMCID: PMC4820264 DOI: 10.1371/journal.pcbi.1004846] [Citation(s) in RCA: 94] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Accepted: 03/03/2016] [Indexed: 11/30/2022] Open
Abstract
Humans move frequently and tend to carry parasites among areas with endemic malaria and into areas where local transmission is unsustainable. Human-mediated parasite mobility can thus sustain parasite populations in areas where they would otherwise be absent. Data describing human mobility and malaria epidemiology can help classify landscapes into parasite demographic sources and sinks, ecological concepts that have parallels in malaria control discussions of transmission foci. By linking transmission to parasite flow, it is possible to stratify landscapes for malaria control and elimination, as sources are disproportionately important to the regional persistence of malaria parasites. Here, we identify putative malaria sources and sinks for pre-elimination Namibia using malaria parasite rate (PR) maps and call data records from mobile phones, using a steady-state analysis of a malaria transmission model to infer where infections most likely occurred. We also examined how the landscape of transmission and burden changed from the pre-elimination setting by comparing the location and extent of predicted pre-elimination transmission foci with modeled incidence for 2009. This comparison suggests that while transmission was spatially focal pre-elimination, the spatial distribution of cases changed as burden declined. The changing spatial distribution of burden could be due to importation, with cases focused around importation hotspots, or due to heterogeneous application of elimination effort. While this framework is an important step towards understanding progressive changes in malaria distribution and the role of subnational transmission dynamics in a policy-relevant way, future work should account for international parasite movement, utilize real time surveillance data, and relax the steady state assumption required by the presented model. For countries considering pursuing malaria elimination, understanding where malaria transmission occurs is crucial for intervention planning. By identifying the areas that act as sources of malaria parasites, elimination programs can target efforts to end local transmission and achieve nationwide elimination. Mapping parasite sources requires a modeling framework that integrates malaria burden and human movement information, however, as human mobility facilitates parasite spread and drives source-sink disease dynamics. In this study, we present a mathematical model that can be used to identify areas with self-sustaining malaria transmission when analyzed at equilibrium. We demonstrate how this method can inform elimination planning for countries with stable low transmission using data from Namibia. The maps of sources and sinks created using this method can be used to direct policy and target areas with self-sustaining malaria transmission in countries with stable transmission. Finally, we compare the predicted extent of transmission foci with more recent maps of incidence, to determine whether local transmission likely retreated into focal areas and the potential importance of importation.
Collapse
|
43
|
Chitunhu S, Musenge E. Spatial and socio-economic effects on malaria morbidity in children under 5 years in Malawi in 2012. Spat Spatiotemporal Epidemiol 2015; 16:21-33. [PMID: 26919752 DOI: 10.1016/j.sste.2015.11.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Revised: 10/22/2015] [Accepted: 11/04/2015] [Indexed: 11/19/2022]
Abstract
BACKGROUND Malaria is a major health challenge in sub-Saharan Africa with children under 5 being most vulnerable. Identifying regions of greater malarial burden is vital in targeting interventions. METHODS This study analysed malaria morbidity using data from the Malawi 2012 Malaria Indicator Survey that were obtained from Demographic and Health Survey (DHS) program website. These data captured malaria related information on children under 5. Poisson regression was done to determine associations between outcome (number of children under 5 with malaria in household) and explanatory variables. A Bayesian smoothing approach was employed to adjust for spatial random effects on child related variables. RESULTS There were 1878 households in 140 clusters. The number of children under five was 1900. Spatially structured effects accounted for more than 90% of random effects as these had a mean of 1.32 (95% Credible Interval (CI)=0.37, 2.50) whilst spatially unstructured had a mean of 0.10 (CI=9.0 × 10(-4), 0.38). Spatially adjusted significant variables were; type of place of residence (urban or rural) [posterior odds ratio (POR)=2.06; CI= 1.27, 3.34], not owning land [POR=1.77; CI=1.19, 2.64], not staying in a slum [POR=0.52; CI=0.33, 0.83] and enhanced vegetation index [POR=0.02; CI=0.00, 1.08]. A trend was observed on usage of insecticide treated mosquito nets [POR=0.80; CI=0.63, 1.03]. CONCLUSION This study showed that malaria is a disease of poverty. Enhanced vegetation index was an important factor in malaria morbidity. The central region was identified as the area with greatest disease burden.
Collapse
Affiliation(s)
- Simangaliso Chitunhu
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, 27 St Andrews' Road, Parktown, Johannesburg 2193, South Africa.
| | - Eustasius Musenge
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, 27 St Andrews' Road, Parktown, Johannesburg 2193, South Africa.
| |
Collapse
|
44
|
Jia P, Sankoh O, Tatem AJ. Mapping the environmental and socioeconomic coverage of the INDEPTH international health and demographic surveillance system network. Health Place 2015; 36:88-96. [DOI: 10.1016/j.healthplace.2015.09.009] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2015] [Revised: 09/18/2015] [Accepted: 09/27/2015] [Indexed: 01/20/2023]
|
45
|
Smith Gueye C, Gerigk M, Newby G, Lourenco C, Uusiku P, Liu J. Namibia's path toward malaria elimination: a case study of malaria strategies and costs along the northern border. BMC Public Health 2014; 14:1190. [PMID: 25409682 PMCID: PMC4255954 DOI: 10.1186/1471-2458-14-1190] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2014] [Accepted: 11/10/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Low malaria transmission in Namibia suggests that elimination is possible, but the risk of imported malaria from Angola remains a challenge. This case study reviews the early transition of a program shift from malaria control to elimination in three northern regions of Namibia that comprise the Trans-Kunene Malaria Initiative (TKMI): Kunene, Omusati, and Ohangwena. METHODS Thirty-four key informant interviews were conducted and epidemiological and intervention data were assembled for 1995 to 2013. Malaria expenditure records were collected for each region for 2009, 2010, and 2011, representing the start of the transition from control to elimination. Interviews and expenditure data were analyzed across activity and expenditure type. RESULTS Incidence has declined in all regions since 2004; cases are concentrated in the border zone. Expenditures in the three study regions have declined, from an average of $6.10 per person at risk per year in 2009 to an average of $3.61 in 2011. The proportion of spending allocated for diagnosis and treatment declined while that for vector control increased. Indoor residual spraying is the main intervention, but coverage varies, related to acceptability, mobility, accessibility, insecticide stockouts and staff shortages. Bed net distribution was scaled up beginning in 2005, assisted by NGO partners in later years, but coverage was highly variable. Distribution of rapid diagnostic tests in 2005 resulted in more accurate diagnosis and can help explain the large decline in cases beginning in 2006; however, challenges in personnel training and supervision remained during the expenditure study period of 2009 to 2011. CONCLUSIONS In addition to allocating sufficient human resources to vector control activities, developing a greater emphasis on surveillance will be central to the ongoing program shift from control to elimination, particularly in light of the malaria importation challenges experienced in the northern border regions. While overall program resources may continue on a downward trajectory, the program will be well positioned to actively eliminate the remaining foci of malaria if greater resources are allocated toward surveillance efforts.
Collapse
Affiliation(s)
- Cara Smith Gueye
- />UCSF Global Health Group, San Francisco, CA USA
- />UCSF Global Health Sciences, 550 16th Street, 3rd Floor, UCSF Mail Stop 1224, San Francisco, CA 94158 USA
| | | | | | - Chris Lourenco
- />UCSF Global Health Group, San Francisco, CA USA
- />Clinton Health Access Initiative, Boston, MA USA
| | - Petrina Uusiku
- />Namibia National Vector-borne Diseases Control Programme, Windhoek, Namibia
| | - Jenny Liu
- />UCSF Global Health Group, San Francisco, CA USA
| |
Collapse
|
46
|
Alegana VA, Wright JA, Nahzat SM, Butt W, Sediqi AW, Habib N, Snow RW, Atkinson PM, Noor AM. Modelling the incidence of Plasmodium vivax and Plasmodium falciparum malaria in Afghanistan 2006-2009. PLoS One 2014; 9:e102304. [PMID: 25033452 PMCID: PMC4102516 DOI: 10.1371/journal.pone.0102304] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2014] [Accepted: 06/16/2014] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Identifying areas that support high malaria risks and where populations lack access to health care is central to reducing the burden in Afghanistan. This study investigated the incidence of Plasmodium vivax and Plasmodium falciparum using routine data to help focus malaria interventions. METHODS To estimate incidence, the study modelled utilisation of the public health sector using fever treatment data from the 2012 national Malaria Indicator Survey. A probabilistic measure of attendance was applied to population density metrics to define the proportion of the population within catchment of a public health facility. Malaria data were used in a Bayesian spatio-temporal conditional-autoregressive model with ecological or environmental covariates, to examine the spatial and temporal variation of incidence. FINDINGS From the analysis of healthcare utilisation, over 80% of the population was within 2 hours' travel of the nearest public health facility, while 64.4% were within 30 minutes' travel. The mean incidence of P. vivax in 2009 was 5.4 (95% Crl 3.2-9.2) cases per 1000 population compared to 1.2 (95% Crl 0.4-2.9) cases per 1000 population for P. falciparum. P. vivax peaked in August while P. falciparum peaked in November. 32% of the estimated 30.5 million people lived in regions where annual incidence was at least 1 case per 1,000 population of P. vivax; 23.7% of the population lived in areas where annual P. falciparum case incidence was at least 1 per 1000. CONCLUSION This study showed how routine data can be combined with household survey data to model malaria incidence. The incidence of both P. vivax and P. falciparum in Afghanistan remain low but the co-distribution of both parasites and the lag in their peak season provides challenges to malaria control in Afghanistan. Future improved case definition to determine levels of imported risks may be useful for the elimination ambitions in Afghanistan.
Collapse
Affiliation(s)
- Victor A. Alegana
- Spatial Health Metrics Group, Department of Public Health, KEMRI-Wellcome Trust, Nairobi, Kenya
- Centre for Geographical Health Research, Geography and Environment, University of Southampton, Highfield Southampton, United Kingdom
| | - Jim A. Wright
- Centre for Geographical Health Research, Geography and Environment, University of Southampton, Highfield Southampton, United Kingdom
| | - Sami M. Nahzat
- National Malaria and Leishmaniasis Control Programme, Ministry of Public Health, Kabul, Afghanistan
| | - Waqar Butt
- Malaria and Leishmaniasis, WHO Office, Kabul, Afghanistan
| | - Amad W. Sediqi
- National Malaria and Leishmaniasis Control Programme, Ministry of Public Health, Kabul, Afghanistan
| | - Naeem Habib
- Malaria and Leishmaniasis, WHO Office, Kabul, Afghanistan
| | - Robert W. Snow
- Spatial Health Metrics Group, Department of Public Health, KEMRI-Wellcome Trust, Nairobi, Kenya
- Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| | - Peter M. Atkinson
- Centre for Geographical Health Research, Geography and Environment, University of Southampton, Highfield Southampton, United Kingdom
| | - Abdisalan M. Noor
- Spatial Health Metrics Group, Department of Public Health, KEMRI-Wellcome Trust, Nairobi, Kenya
- Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| |
Collapse
|