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Your search keyword '"Catherine M. Sebuguzi"' showing total 4 results
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4 results on '"Catherine M. Sebuguzi"'

1. Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019

Author

Simon P. Kigozi, Ruth N. Kigozi, Catherine M. Sebuguzi, Jorge Cano, Damian Rutazaana, Jimmy Opigo, Teun Bousema, Adoke Yeka, Anne Gasasira, Benn Sartorius, and Rachel L. Pullan

Subjects
Uganda, Malaria, Incidence, Relative risk, Routine surveillance, HMIS, Public aspects of medicine, RA1-1270
Abstract

Abstract Background As global progress to reduce malaria transmission continues, it is increasingly important to track changes in malaria incidence rather than prevalence. Risk estimates for Africa have largely underutilized available health management information systems (HMIS) data to monitor trends. This study uses national HMIS data, together with environmental and geographical data, to assess spatial-temporal patterns of malaria incidence at facility catchment level in Uganda, over a recent 5-year period. Methods Data reported by 3446 health facilities in Uganda, between July 2015 and September 2019, was analysed. To assess the geographic accessibility of the health facilities network, AccessMod was employed to determine a three-hour cost-distance catchment around each facility. Using confirmed malaria cases and total catchment population by facility, an ecological Bayesian conditional autoregressive spatial-temporal Poisson model was fitted to generate monthly posterior incidence rate estimates, adjusted for caregiver education, rainfall, land surface temperature, night-time light (an indicator of urbanicity), and vegetation index. Results An estimated 38.8 million (95% Credible Interval [CI]: 37.9–40.9) confirmed cases of malaria occurred over the period, with a national mean monthly incidence rate of 20.4 (95% CI: 19.9–21.5) cases per 1000, ranging from 8.9 (95% CI: 8.7–9.4) to 36.6 (95% CI: 35.7–38.5) across the study period. Strong seasonality was observed, with June–July experiencing highest peaks and February–March the lowest peaks. There was also considerable geographic heterogeneity in incidence, with health facility catchment relative risk during peak transmission months ranging from 0 to 50.5 (95% CI: 49.0–50.8) times higher than national average. Both districts and health facility catchments showed significant positive spatial autocorrelation; health facility catchments had global Moran’s I = 0.3 (p

Published
2020
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2. Rapid shifts in the age-specific burden of malaria following successful control interventions in four regions of Uganda

Author

Simon P. Kigozi, Ruth N. Kigozi, Adrienne Epstein, Arthur Mpimbaza, Asadu Sserwanga, Adoke Yeka, Joaniter I. Nankabirwa, Katherine Halliday, Rachel L. Pullan, Damian Rutazaana, Catherine M. Sebuguzi, Jimmy Opigo, Moses R. Kamya, Sarah G. Staedke, Grant Dorsey, Bryan Greenhouse, and Isabel Rodriguez-Barraquer

Subjects
Malaria, Routine surveillance, Age distribution, Reduced transmission, Burden shift, Arctic medicine. Tropical medicine, RC955-962, Infectious and parasitic diseases, RC109-216
Abstract

Abstract Background Malaria control using long-lasting insecticidal nets (LLINs) and indoor residual spraying of insecticide (IRS) has been associated with reduced transmission throughout Africa. However, the impact of transmission reduction on the age distribution of malaria cases remains unclear. Methods Over a 10-year period (January 2009 to July 2018), outpatient surveillance data from four health facilities in Uganda were used to estimate the impact of control interventions on temporal changes in the age distribution of malaria cases using multinomial regression. Interventions included mass distribution of LLINs at all sites and IRS at two sites. Results Overall, 896,550 patient visits were included in the study; 211,632 aged 15 years. Over time, the age distribution of patients not suspected of malaria and those malaria negative either declined or remained the same across all sites. In contrast, the age distribution of suspected and confirmed malaria cases increased across all four sites. In the two LLINs-only sites, the proportion of malaria cases in 15 years increased from 40 to 61% and 29 to 39%, respectively. In the sites receiving LLINs plus IRS, these proportions increased from 19 to 44% and 18 to 31%, respectively. Conclusions These findings demonstrate a shift in the burden of malaria from younger to older individuals following implementation of successful control interventions, which has important implications for malaria prevention, surveillance, case management and control strategies.

Published
2020
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3. Associations between environmental covariates and temporal changes in malaria incidence in high transmission settings of Uganda: a distributed lag nonlinear analysis

Author

Isobel Routledge, Catherine M. Sebuguzi, Moses R. Kamya, Damian Rutazaana, Steven M. Kiwuwa, Jaffer Okiring, Emmanuel V. Kamya, Ronald Wesonga, Joaniter I. Nankabirwa, Joan N. Kalyango, Grant Dorsey, Adrienne Epstein, Gloria Adobea Odei Obeng-Amoako, and Jane Frances Namuganga

Subjects
Distributed lag, medicine.medical_specialty, Temporal, Normalized Difference Vegetation Index, Environmental, law.invention, law, parasitic diseases, Epidemiology, medicine, Humans, Effect, Uganda, Epidemics, Disease burden, DLNM, business.industry, Incidence, Incidence (epidemiology), Temperature, Public Health, Environmental and Occupational Health, medicine.disease, Malaria, Transmission (mechanics), population characteristics, Public aspects of medicine, RA1-1270, Biostatistics, business, Covariates, Research Article, Demography
Abstract

Background Environmental factors such as temperature, rainfall, and vegetation cover play a critical role in malaria transmission. However, quantifying the relationships between environmental factors and measures of disease burden relevant for public health can be complex as effects are often non-linear and subject to temporal lags between when changes in environmental factors lead to changes in malaria incidence. The study investigated the effect of environmental covariates on malaria incidence in high transmission settings of Uganda. Methods This study leveraged data from seven malaria reference centres (MRCs) located in high transmission settings of Uganda over a 24-month period. Estimates of monthly malaria incidence (MI) were derived from MRCs’ catchment areas. Environmental data including monthly temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed lag nonlinear model was used to investigate the effect of environmental covariates on malaria incidence. Results Overall, the median (range) monthly temperature was 30 °C (26–47), rainfall 133.0 mm (3.0–247), NDVI 0.66 (0.24–0.80) and MI was 790 per 1000 person-years (73–3973). Temperature of 35 °C was significantly associated with malaria incidence compared to the median observed temperature (30 °C) at month lag 2 (IRR: 2.00, 95% CI: 1.42–2.83) and the increased cumulative IRR of malaria at month lags 1–4, with the highest cumulative IRR of 8.16 (95% CI: 3.41–20.26) at lag-month 4. Rainfall of 200 mm significantly increased IRR of malaria compared to the median observed rainfall (133 mm) at lag-month 0 (IRR: 1.24, 95% CI: 1.01–1.52) and the increased cumulative IRR of malaria at month lags 1–4, with the highest cumulative IRR of 1.99(95% CI: 1.22–2.27) at lag-month 4. Average NVDI of 0.72 significantly increased the cumulative IRR of malaria compared to the median observed NDVI (0.66) at month lags 2–4, with the highest cumulative IRR of 1.57(95% CI: 1.09–2.25) at lag-month 4. Conclusions In high-malaria transmission settings, high values of environmental covariates were associated with increased cumulative IRR of malaria, with IRR peaks at variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.

Published
2021
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4. Practical Implications of a Relationship between Health Management Information System and Community Cohort–Based Malaria Incidence Rates

Author

Moses R. Kamya, Emanuele Giorgi, Emmanuel Arinaitwe, Joaniter I. Nankabirwa, Arthur Mpimbaza, Catherine M. Sebuguzi, Sarah G. Staedke, Teun Bousema, Ruth Kigozi, Rachel L. Pullan, Simon P. Kigozi, and Grant Dorsey

Subjects
Male, Endemic Diseases, 030231 tropical medicine, Decision Making, Health informatics, Cohort Studies, 03 medical and health sciences, Health Information Systems, All institutes and research themes of the Radboud University Medical Center, 0302 clinical medicine, Health facility, Virology, Environmental health, Ambulatory Care, Medicine, Humans, Uganda, Child, Health policy, business.industry, Incidence (epidemiology), Data Collection, Health Policy, Incidence, Infant, Articles, medicine.disease, Malaria, lnfectious Diseases and Global Health Radboud Institute for Health Sciences [Radboudumc 4], Infectious Diseases, Child, Preschool, Cohort, Communicable Disease Control, Epidemiological Monitoring, Population Health Management, Parasitology, Female, business, Cohort study
Abstract

Global malaria burden is reducing with effective control interventions, and surveillance is vital to maintain progress. Health management information system (HMIS) data provide a powerful surveillance tool; however, its estimates of burden need to be better understood for effectiveness. We aimed to investigate the relationship between HMIS and cohort incidence rates and identify sources of bias in HMIS-based incidence. Malaria incidence was estimated using HMIS data from 15 health facilities in three subcounties in Uganda. This was compared with a gold standard of representative cohort studies conducted in children aged 0.5 to < 11 years, followed concurrently in these sites. Between October 2011 and September 2014, 153,079 children were captured through HMISs and 995 followed up through enhanced community cohorts in Walukuba, Kihihi, and Nagongera subcounties. Although HMISs substantially underestimated malaria incidence in all sites compared with data from the cohort studies, there was a strong linear relationship between these rates in the lower transmission settings (Walukuba and Kihihi), but not the lowest HMIS performance highest transmission site (Nagongera), with calendar year as a significant modifier. Although health facility accessibility, availability, and recording completeness were associated with HMIS incidence, they were not significantly associated with bias in estimates from any site. Health management information systems still require improvements; however, their strong predictive power of unbiased malaria burden when improved highlights the important role they could play as a cost-effective tool for monitoring trends and estimating impact of control interventions. This has important implications for malaria control in low-resource, high-burden countries.

Published
2020

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