Quantitative urban classification for malaria epidemiology in sub-Saharan Africa

<p>Abstract</p> <p>Background</p> <p>Although sub-Saharan Africa (SSA) is rapidly urbanizing, the terms used to classify urban ecotypes are poorly defined in the context of malaria epidemiology. Lack of clear definitions may cause misclassification error, which likely decreases the accuracy of continent-wide estimates of malaria burden, limits the generalizability of urban malaria studies, and makes identification of high-risk areas for targeted interventions within cities more difficult. Accordingly, clustering techniques were applied to a set of urbanization- and malaria-related variables in Kisumu, Kenya, to produce a quantitative classification of the urban environment for malaria research.</p> <p>Methods</p> <p>Seven variables with a known or expected relationship with malaria in the context of urbanization were identified and measured at the census enumeration area (EA) level, using three sources: a) the results of a citywide knowledge, attitudes and practices (KAP) survey; b) a high-resolution multispectral satellite image; and c) national census data. Principal components analysis (PCA) was used to identify three factors explaining higher proportions of the combined variance than the original variables. A k-means clustering algorithm was applied to the EA-level factor scores to assign EAs to one of three categories: "urban," "peri-urban," or "semi-rural." The results were compared with classifications derived from two other approaches: a) administrative designation of urban/rural by the census or b) population density thresholds.</p> <p>Results</p> <p>Urban zones resulting from the clustering algorithm were more geographically coherent than those delineated by population density. Clustering distributed population more evenly among zones than either of the other methods and more accurately predicted variation in other variables related to urbanization, but not used for classification.</p> <p>Conclusion</p> <p>Effective urban malaria epidemiology and control would benefit from quantitative methods to identify and characterize urban areas. Cluster analysis techniques were used to classify Kisumu, Kenya, into levels of urbanization in a repeatable and unbiased manner, an approach that should permit more relevant comparisons among and within urban areas. To the extent that these divisions predict meaningful intra-urban differences in malaria epidemiology, they should inform targeted urban malaria interventions in cities across SSA.</p

A census-weighted, spatially-stratified household sampling strategy for urban malaria epidemiology

<p>Abstract</p> <p>Background</p> <p>Urban malaria is likely to become increasingly important as a consequence of the growing proportion of Africans living in cities. A novel sampling strategy was developed for urban areas to generate a sample simultaneously representative of population and inhabited environments. Such a strategy should facilitate analysis of important epidemiological relationships in this ecological context.</p> <p>Methods</p> <p>Census maps and summary data for Kisumu, Kenya, were used to create a pseudo-sampling frame using the geographic coordinates of census-sampled structures. For every enumeration area (EA) designated as urban by the census (n = 535), a sample of structures equal to one-tenth the number of households was selected. In EAs designated as rural (n = 32), a geographically random sample totalling one-tenth the number of households was selected from a grid of points at 100 m intervals. The selected samples were cross-referenced to a geographic information system, and coordinates transferred to handheld global positioning units. Interviewers found the closest eligible household to the sampling point and interviewed the caregiver of a child aged < 10 years. The demographics of the selected sample were compared with results from the Kenya Demographic and Health Survey to assess sample validity. Results were also compared among urban and rural EAs.</p> <p>Results</p> <p>4,336 interviews were completed in 473 of the 567 study area EAs from June 2002 through February 2003. EAs without completed interviews were randomly distributed, and non-response was approximately 2%. Mean distance from the assigned sampling point to the completed interview was 74.6 m, and was significantly less in urban than rural EAs, even when controlling for number of households. The selected sample had significantly more children and females of childbearing age than the general population, and fewer older individuals.</p> <p>Conclusion</p> <p>This method selected a sample that was simultaneously population-representative and inclusive of important environmental variation. The use of a pseudo-sampling frame and pre-programmed handheld GPS units is more efficient and may yield a more complete sample than traditional methods, and is less expensive than complete population enumeration.</p

Significance of Travel to Rural Areas as a Risk Factor for Malarial Anemia in an Urban Setting

Disclaimer: This manuscript was published with the approval of the Director of the Kenya Medical Research Institute. The findings and conclusions in this report are those of the author(s) and do not necessarily represent the views of the Centers for Disease Control and Prevention.The epidemiology of malaria in urban environments is poorly characterized, yet increasingly problematic. We conducted an unmatched case–control study of risk factors for malarial anemia with high parasitemia in urban Kisumu, Kenya, from June 2002 through February 2003. Cases (n = 80) were hospital patients with a hemoglobin level <= 8 g/dL and a Plasmodium parasite density ≥ 10,000/μL. Controls (n = 826) were healthy respondents to a concurrent citywide knowledge, attitude, and practice survey. Children who reported spending at least one night per month in a rural area were especially at risk (35% of cases; odds ratio = 9.3, 95% confidence interval [CI] = 4.4–19.7, P < 0.0001), and use of mosquito coils, bed net ownership, and house construction were non-significant, potentially indicating that malaria exposure during rural travel comprises an important element of risk. Control of severe malaria in an urban setting may be complicated by Plasmodium infections acquired elsewhere. Epidemiologic studies of urban malaria in low transmission settings should take travel history into account.This research was supported by CDC/KEMRI and by the University of Michigan through the Rackham Graduate School, the Center for Research on Ethnicity, Culture and Health, and the Global Health Program.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/91955/1/2010 AJTMH Significance of Travel to Rural Areas as a Risk Factor for Malarial Anemia in an Urban Setting.pd

Environmental, socio-demographic and behavioural determinants of malaria risk in the western Kenyan highlands: a case–control study

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73660/1/j.1365-3156.2009.02370.x.pd

Low usage of government healthcare facilities for acute respiratory infections in guatemala: implications for influenza surveillance

<p>Abstract</p> <p>Background</p> <p>Sentinel surveillance for severe acute respiratory infections in hospitals and influenza-like illness in ambulatory clinics is recommended to assist in global pandemic influenza preparedness. Healthcare utilization patterns will affect the generalizability of data from sentinel sites and the potential to use them to estimate burden of disease. The objective of this study was to measure healthcare utilization patterns in Guatemala to inform the establishment of a sentinel surveillance system for influenza and other respiratory infections, and allow estimation of disease burden.</p> <p>Methods</p> <p>We used a stratified, two-stage cluster survey sample to select 1200 households from the Department of Santa Rosa. Trained interviewers screened household residents for self-reported pneumonia in the last year and influenza-like illness (ILI) in the last month and asked about healthcare utilization for each illness episode.</p> <p>Results</p> <p>We surveyed 1131 (94%) households and 5449 residents between October and December 2006 and identified 323 (6%) cases of pneumonia and 628 (13%) cases of ILI. Treatment for pneumonia outside the home was sought by 92% of the children <5 years old and 73% of the persons aged five years and older. For both children <5 years old (53%) and persons aged five years and older (31%) who reported pneumonia, private clinics were the most frequently reported source of care. For ILI, treatment was sought outside the home by 81% of children <5 years old and 65% of persons aged five years and older. Government ambulatory clinics were the most frequently sought source of care for ILI both for children <5 years old (41%) and persons aged five years and older (36%).</p> <p>Conclusions</p> <p>Sentinel surveillance for influenza and other respiratory infections based in government health facilities in Guatemala will significantly underestimate the burden of disease. Adjustment for healthcare utilization practices will permit more accurate estimation of the incidence of influenza and other respiratory pathogens in the community.</p

Possible Interruption of Malaria Transmission, Highland Kenya, 2007–2008

Annual insecticide spraying and artemisinin combination therapy may stop transmission

\u3cem\u3eRickettsia felis\u3c/em\u3e in \u3cem\u3eCtenocephalides felis\u3c/em\u3e from Guatemala and Costa Rica

Rickettsia felis is an emerging human pathogen associated primarily with the cat flea Ctenocephalides felis. In this study, we investigated the presence of Rickettsia felis in C. felis from Guatemala and Costa Rica. Ctenocephalides felis were collected directly from dogs and cats, and analyzed by polymerase chain reaction for Rickettsia-specific fragments of 17-kDa protein, OmpA, and citrate synthase genes. Rickettsia DNA was detected in 64% (55 of 86) and 58% (47 of 81) of flea pools in Guatemala and Costa Rica, respectively. Sequencing of gltA fragments identified R. felis genotype URRWXCal2 in samples from both countries, and genotype Rf2125 in Costa Rica. This is the first report of R. felis in Guatemala and of genotype Rf2125 in Costa Rica. The extensive presence of this pathogen in countries of Central America stresses the need for increased awareness and diagnosis

Mass drug administration for malaria

Background Studies evaluating mass drug administration (MDA) in malarious areas have shown reductions in malaria immediately following the intervention. However, these effects vary by endemicity and are not sustained. Since the 2013 version of this Cochrane Review on this topic, additional studies have been published. Objectives Primary objectives To assess the sustained effect of MDA with antimalarial drugs on: ‐ the reduction in malaria transmission in moderate‐ to high‐transmission settings; ‐ the interruption of transmission in very low‐ to low‐transmission settings. Secondary objective To summarize the risk of drug‐associated adverse effects following MDA. Search methods We searched several trial registries, citation databases, conference proceedings, and reference lists for relevant articles up to 11 February 2021. We also communicated with researchers to identify additional published and unpublished studies. Selection criteria Randomized controlled trials (RCTs) and non‐randomized studies comparing MDA to no MDA with balanced co‐interventions across study arms and at least two geographically distinct sites per study arm. Data collection and analysis Two review authors independently assessed trials for eligibility and extracted data. We calculated relative risk (RR) and rate ratios with corresponding 95% confidence intervals (CIs) to compare prevalence and incidence, respectively, in MDA compared to no‐MDA groups. We stratified analyses by malaria transmission and by malaria species. For cluster‐randomized controlled trials (cRCTs), we adjusted standard errors using the intracluster correlation coefficient. We assessed the certainty of the evidence using the GRADE approach. For non‐randomized controlled before‐and‐after (CBA) studies, we summarized the data using difference‐in‐differences (DiD) analyses. Main results Thirteen studies met our criteria for inclusion. Ten were cRCTs and three were CBAs. Cluster‐randomized controlled trials Moderate‐ to high‐endemicity areas (prevalence ≥ 10%) We included data from two studies conducted in The Gambia and Zambia. At one to three months after MDA, the Plasmodium falciparum (hereafter, P falciparum) parasitaemia prevalence estimates may be higher compared to control but the CIs included no effect (RR 1.76, 95% CI 0.58 to 5.36; Zambia study; low‐certainty evidence); parasitaemia incidence was probably lower (RR 0.61, 95% CI 0.40 to 0.92; The Gambia study; moderate‐certainty evidence); and confirmed malaria illness incidence may be substantially lower, but the CIs included no effect (rate ratio 0.41, 95% CI 0.04 to 4.42; Zambia study; low‐certainty evidence). At four to six months after MDA, MDA showed little or no effect on P falciparum parasitaemia prevalence (RR 1.18, 95% CI 0.89 to 1.56; The Gambia study; moderate‐certainty evidence) and, no persisting effect was demonstrated with parasitaemia incidence (rate ratio 0.91, 95% CI 0.55 to 1.50; The Gambia study). Very low‐ to low‐endemicityareas (prevalence < 10%) Seven studies from Cambodia, Laos, Myanmar (two studies), Vietnam, Zambia, and Zanzibar evaluated the effects of multiple rounds of MDA on P falciparum. Immediately following MDA (less than one month after MDA), parasitaemia prevalence was reduced (RR 0.12, 95% CI 0.03 to 0.52; one study; low‐certainty evidence). At one to three months after MDA, there was a reduction in both parasitaemia incidence (rate ratio 0.37, 95% CI 0.21 to 0.55; 1 study; moderate‐certainty evidence) and prevalence (RR 0.25, 95% CI 0.15 to 0.41; 7 studies; low‐certainty evidence). For confirmed malaria incidence, absolute rates were low, and it is uncertain whether MDA had an effect on this outcome (rate ratio 0.58, 95% CI 0.12 to 2.73; 2 studies; very low‐certainty evidence). For P falciparum prevalence, the relative differences declined over time, from RR 0.63 (95% CI 0.36 to 1.12; 4 studies) at four to six months after MDA, to RR 0.86 (95% CI 0.55 to 1.36; 5 studies) at 7 to 12 months after MDA. Longer‐term prevalence estimates showed overall low absolute risks, and relative effect estimates of the effect of MDA on prevalence varied from RR 0.82 (95% CI 0.20 to 3.34) at 13 to 18 months after MDA, to RR 1.25 (95% CI 0.25 to 6.31) at 31 to 36 months after MDA in one study. Five studies from Cambodia, Laos, Myanmar (2 studies), and Vietnam evaluated the effect of MDA on Plasmodium vivax (hereafter, P vivax). One month following MDA, P vivax prevalence was lower (RR 0.18, 95% CI 0.08 to 0.40; 1 study; low‐certainty evidence). At one to three months after MDA, there was a reduction in P vivax prevalence (RR 0.15, 95% CI 0.10 to 0.24; 5 studies; low‐certainty evidence). The immediate reduction on P vivax prevalence was not sustained over time, from RR 0.78 (95% CI 0.63 to 0.95; 4 studies) at four to six months after MDA, to RR 1.12 (95% CI 0.94 to 1.32; 5 studies) at 7 to 12 months after MDA. One of the studies in Myanmar provided estimates of longer‐term effects, where overall absolute risks were low, ranging from RR 0.81 (95% CI 0.44 to 1.48) at 13 to 18 months after MDA, to RR 1.20 (95% CI 0.44 to 3.29) at 31 to 36 months after MDA. Non‐randomized studies Three CBA studies were conducted in moderate‐ to high‐transmission areas in Burkina Faso, Kenya, and Nigeria. There was a reduction in P falciparum parasitaemia prevalence in MDA groups compared to control groups during MDA (DiD range: ‐15.8 to ‐61.4 percentage points), but the effect varied at one to three months after MDA (DiD range: 14.9 to ‐41.1 percentage points). Authors' conclusions In moderate‐ to high‐transmission settings, no studies reported important effects on P falciparum parasitaemia prevalence within six months after MDA. In very low‐ to low‐transmission settings, parasitaemia prevalence and incidence were reduced initially for up to three months for both P falciparum and P vivax; longer‐term data did not demonstrate an effect after four months, but absolute risks in both intervention and control groups were low. No studies provided evidence of interruption of malaria transmission

Estimation of the national disease burden of influenza-associated severe acute respiratory illness in Kenya and Guatemala : a novel methodology

Background: Knowing the national disease burden of severe influenza in low-income countries can inform policy decisions around influenza treatment and prevention. We present a novel methodology using locally generated data for estimating this burden. Methods and Findings: This method begins with calculating the hospitalized severe acute respiratory illness (SARI) incidence for children <5 years old and persons ≥5 years old from population-based surveillance in one province. This base rate of SARI is then adjusted for each province based on the prevalence of risk factors and healthcare-seeking behavior. The percentage of SARI with influenza virus detected is determined from provincial-level sentinel surveillance and applied to the adjusted provincial rates of hospitalized SARI. Healthcare-seeking data from healthcare utilization surveys is used to estimate non-hospitalized influenza-associated SARI. Rates of hospitalized and non-hospitalized influenza-associated SARI are applied to census data to calculate the national number of cases. The method was field-tested in Kenya, and validated in Guatemala, using data from August 2009–July 2011. In Kenya (2009 population 38.6 million persons), the annual number of hospitalized influenza-associated SARI cases ranged from 17,129–27,659 for children <5 years old (2.9–4.7 per 1,000 persons) and 6,882–7,836 for persons ≥5 years old (0.21–0.24 per 1,000 persons), depending on year and base rate used. In Guatemala (2011 population 14.7 million persons), the annual number of hospitalized cases of influenza-associated pneumonia ranged from 1,065–2,259 (0.5–1.0 per 1,000 persons) among children <5 years old and 779–2,252 cases (0.1–0.2 per 1,000 persons) for persons ≥5 years old, depending on year and base rate used. In both countries, the number of non-hospitalized influenza-associated cases was several-fold higher than the hospitalized cases. Conclusions: Influenza virus was associated with a substantial amount of severe disease in Kenya and Guatemala. This method can be performed in most low and lower-middle income countries