Estimating the burden of malaria in Senegal : Bayesian zero-inflated binomial geostatistical modeling of the MIS 2008 data

The Research Center for Human Development in Dakar (CRDH) with the technical assistance of ICF Macro and the National Malaria Control Programme (NMCP) conducted in 2008/2009 the Senegal Malaria Indicator Survey (SMIS), the first nationally representative household survey collecting parasitological data and malaria-related indicators. In this paper, we present spatially explicit parasitaemia risk estimates and number of infected children below 5 years. Geostatistical Zero-Inflated Binomial models (ZIB) were developed to take into account the large number of zero-prevalence survey locations (70%) in the data. Bayesian variable selection methods were incorporated within a geostatistical framework in order to choose the best set of environmental and climatic covariates associated with the parasitaemia risk. Model validation confirmed that the ZIB model had a better predictive ability than the standard Binomial analogue. Markov chain Monte Carlo (MCMC) methods were used for inference. Several insecticide treated nets (ITN) coverage indicators were calculated to assess the effectiveness of interventions. After adjusting for climatic and socio-economic factors, the presence of at least one ITN per every two household members and living in urban areas reduced the odds of parasitaemia by 86% and 81% respectively. Posterior estimates of the ORs related to the wealth index show a decreasing trend with the quintiles. Infection odds appear to be increasing with age. The population-adjusted prevalence ranges from 0.12% in Thille-Boubacar to 13.1% in Dabo. Tambacounda has the highest population-adjusted predicted prevalence (8.08%) whereas the region with the highest estimated number of infected children under the age of 5 years is Kolda (13940). The contemporary map and estimates of malaria burden identify the priority areas for future control interventions and provide baseline information for monitoring and evaluation. Zero-Inflated formulations are more appropriate in modeling sparse geostatistical survey data, expected to arise more frequently as malaria research is focused on eliminatio

Effects of an Extensively Hydrolyzed Formula Supplemented with Two Human Milk Oligosaccharides on Growth, Tolerability, Safety and Infection Risk in Infants with Cow’s Milk Protein Allergy: A Randomized, Multi-Center Trial

This randomized clinical trial (Registration: NCT03085134) assessed if an extensively hydrolyzed formula (EHF) supplemented with two human milk oligosaccharides (HMO) and reduced protein content (2.20 g/100 kcal) supports normal growth in infants with cow’s milk protein allergy (CMPA). Secondary outcomes were gastrointestinal tolerability, safety, and effect on infections. Nonbreastfed infants aged 0–6 months with CMPA were enrolled. Body weight, length, and head circumference were measured monthly for 4 months (primary study endpoint), after 6 months, and at the age of 12 months. Of 200 infants screened, 194 (mean age 3.2 months) were randomized. At the 4-month follow-up, daily weight gain for the test formula was noninferior to the control formula; p < 0.005. There were no significant group differences in anthropometric parameters. Both formulas were safe and well tolerated. Infants in the HMO group had a statistically significant reduction in the frequency of upper respiratory tract infections and a lower incidence of ear infections at 12 months (per protocol analysis). The relative risk of lower respiratory tract and gastrointestinal infections was reduced by 30–40%, but this was not statistically significant due to sample size limitations. In summary, the HMO-supplemented formula supports normal growth in infants with CMPA and suggests a protective effect against respiratory and ear infections in the first year of life

Baseline spatial distribution of malaria prior to an elimination programme in Vanuatu

BACKGROUND: The Ministry of Health in the Republic of Vanuatu has implemented a malaria elimination programme in Tafea Province, the most southern and eastern limit of malaria transmission in the South West Pacific. Tafea Province is comprised of five islands with malaria elimination achieved on one of these islands (Aneityum) in 1998. The current study aimed to establish the baseline distribution of malaria on the most malarious of the province's islands, Tanna Island, to guide the implementation of elimination activities. METHODS: A parasitological survey was conducted in Tafea Province in 2008. On Tanna Island there were 4,716 participants from 220 villages, geo-referenced using a global position system. Spatial autocorrelation in observed prevalence values was assessed using a semivariogram. Backwards step-wise regression analysis was conducted to determine the inclusion of environmental and climatic variables into a prediction model. The Bayesian geostatistical logistic regression model was used to predict malaria risk, and associated uncertainty across the island. RESULTS: Overall, prevalence on Tanna was 1.0% for Plasmodium falciparum (accounting for 32% of infections) and 2.2% for Plasmodium vivax (accounting for 68% of infections). Regression analysis showed significant association with elevation and distance to coastline for P. vivax and P. falciparum, but no significant association with NDVI or TIR. Colinearity was observed between elevation and distance to coastline with the later variable included in the final Bayesian geostatistical model for P. vivax and the former included in the final model for P. falciparum. Model validation statistics revealed that the final Bayesian geostatistical model had good predictive ability. CONCLUSION: Malaria in Tanna Island, Vanuatu, has a focal and predominantly coastal distribution. As Vanuatu refines its elimination strategy, malaria risk maps represent an invaluable resource in the strategic planning of all levels of malaria interventions for the island

Patient's site of first access to health system influences length of delay for tuberculosis treatment in Tajikistan

BACKGROUND: Tajikistan has the highest incidence rate of tuberculosis (TB) in Central Asia. Its health system still bears many features from Soviet times and is under-funded. Affordability is a major barrier to health care. Little is known about health care seeking of TB patients in post-Soviet countries and their delay until the start of TB therapy. The low estimated case detection rate in Tajikistan suggests major problems with access to care and consequently long delays are likely. METHODS: The study investigated extent and determinants of patient and health system delays for TB. A questionnaire was administered to a cohort of TB patients in twelve study districts representing a wide range of conditions found in Tajikistan. Common patterns of health care seeking were analysed. Cox proportional hazards models using eight predictor variables, including characteristics of health services delivery, were built to identify determinants of patient and health system delays. RESULTS: Two-hundred-and-four TB patients were interviewed. A common pattern in treatment-seeking was visiting a specialised TB facility at some stage. Typical delays until start of TB therapy were moderate and did not confirm the expectation of long delays. Median patient, health system and total delays to TB treatment were 21.5, 16 and 52 days, respectively. None of the investigated predictors was significantly associated with patient delay. The type of facility, where patients made their first contact with the health system, was the main determinant of health system delay (p > 0.00005). We show for the first time that patients who had fallen ill and first presented to health care in Russia had the longest delays. Those who first presented to peripheral primary care facilities also had relatively long delays. CONCLUSIONS: While overall delays were moderate, further improvement is needed for different subgroups. An international referral system between Russia and Tajikistan to reduce delays of Tajik migrants who develop active TB in Russia is urgently needed and would benefit both countries. Within Tajikistan, diagnostic pathways for patients in the periphery should be shortened. To achieve this, strengthening of sputum smear examination possibly including collection of sputa at peripheral primary care facilities may be needed

A new world malaria map: Plasmodium falciparum endemicity in 2010

Background: transmission intensity affects almost all aspects of malaria epidemiology and the impact of malaria on human populations. Maps of transmission intensity are necessary to identify populations at different levels of risk and to evaluate objectively options for disease control. To remain relevant operationally, such maps must be updated frequently. Following the first global effort to map Plasmodium falciparum malaria endemicity in 2007, this paper describes the generation of a new world map for the year 2010. This analysis is extended to provide the first global estimates of two other metrics of transmission intensity for P. falciparum that underpin contemporary questions in malaria control: the entomological inoculation rate (PfEIR) and the basic reproductive number (PfR). Methods: annual parasite incidence data for 13,449 administrative units in 43 endemic countries were sourced to define the spatial limits of P. falciparum transmission in 2010 and 22,212 P. falciparum parasite rate (PfPR) surveys were used in a model-based geostatistical (MBG) prediction to create a continuous contemporary surface of malaria endemicity within these limits. A suite of transmission models were developed that link PfPR to PfEIR and PfR and these were fitted to field data. These models were combined with the PfPR map to create new global predictions of PfEIR and PfR. All output maps included measured uncertainty. Results: an estimated 1.13 and 1.44 billion people worldwide were at risk of unstable and stable P. falciparum malaria, respectively. The majority of the endemic world was predicted with a median PfEIR of less than one and a median PfRc of less than two. Values of either metric exceeding 10 were almost exclusive to Africa. The uncertainty described in both PfEIR and PfR was substantial in regions of intense transmission. Conclusions: the year 2010 has a particular significance as an evaluation milestone for malaria global health policy. The maps presented here contribute to a rational basis for control and elimination decisions and can serve as a baseline assessment as the global health community looks ahead to the next series of milestones targeted at 20

Spatial variation and socio-economic determinants of Plasmodium falciparum infection in northeastern Tanzania

<p>Abstract</p> <p>Background</p> <p>Malaria due to <it>Plasmodium falciparum </it>is the leading cause of morbidity and mortality in Tanzania. According to health statistics, malaria accounts for about 30% and 15% of hospital admissions and deaths, respectively. The risk of <it>P. falciparum </it>infection varies across the country. This study describes the spatial variation and socio-economic determinants of <it>P. falciparum </it>infection in northeastern Tanzania.</p> <p>Methods</p> <p>The study was conducted in 14 villages located in highland, lowland and urban areas of Korogwe district. Four cross-sectional malaria surveys involving individuals aged 0-19 years were conducted during short (Nov-Dec) and long (May-Jun) rainy seasons from November 2005 to June 2007. Household socio-economic status (SES) data were collected between Jan-April 2006 and household's geographical positions were collected using hand-held geographical positioning system (GPS) unit. The effects of risk factors were determined using generalized estimating equation and spatial risk of <it>P. falciparum </it>infection was modelled using a kernel (non-parametric) method.</p> <p>Results</p> <p>There was a significant spatial variation of <it>P. falciparum </it>infection, and urban areas were at lower risk. Adjusting for covariates, high risk of <it>P. falciparum </it>infection was identified in rural areas of lowland and highland. Bed net coverage levels were independently associated with reduced risk of <it>P. falciparum </it>by 19.1% (95%CI: 8.9-28.2, p < 0.001) and by 39.3% (95%CI: 28.9-48.2, p < 0.001) in households with low and high coverage, respectively, compared to those without bed nets. Households with moderate and lower SES had risk of infection higher than 60% compared to those with higher SES; while inhabitants of houses built of mud walls were at 15.5% (95%CI: 0.1 - 33.3, p < 0.048) higher risk compared to those living in houses built by bricks. Individuals in houses with thatched roof had an excess risk of 17.3% (95%CI: 4.1 - 32.2, p < 0.009) compared to those living in houses roofed with iron sheet.</p> <p>Conclusions</p> <p>There was high spatial variation of risk of <it>P. falciparum </it>infection and urban area was at the lowest risk. High bed net coverage, better SES and good housing were among the important risk factors associated with low risk of <it>P. falciparum </it>infection.</p

Young and vulnerable: Spatial-temporal trends and risk factors for infant mortality in rural South Africa (Agincourt), 1992-2007

<p>Abstract</p> <p>Background</p> <p>Infant mortality is an important indicator of population health in a country. It is associated with several health determinants, such as maternal health, access to high-quality health care, socioeconomic conditions, and public health policy and practices.</p> <p>Methods</p> <p>A spatial-temporal analysis was performed to assess changes in infant mortality patterns between 1992-2007 and to identify factors associated with infant mortality risk in the Agincourt sub-district, rural northeast South Africa. Period, sex, refugee status, maternal and fertility-related factors, household mortality experience, distance to nearest primary health care facility, and socio-economic status were examined as possible risk factors. All-cause and cause-specific mortality maps were developed to identify high risk areas within the study site. The analysis was carried out by fitting Bayesian hierarchical geostatistical negative binomial autoregressive models using Markov chain Monte Carlo simulation. Simulation-based Bayesian kriging was used to produce maps of all-cause and cause-specific mortality risk.</p> <p>Results</p> <p>Infant mortality increased significantly over the study period, largely due to the impact of the HIV epidemic. There was a high burden of neonatal mortality (especially perinatal) with several hot spots observed in close proximity to health facilities. Significant risk factors for all-cause infant mortality were mother's death in first year (most commonly due to HIV), death of previous sibling and increasing number of household deaths. Being born to a Mozambican mother posed a significant risk for infectious and parasitic deaths, particularly acute diarrhoea and malnutrition.</p> <p>Conclusions</p> <p>This study demonstrates the use of Bayesian geostatistical models in assessing risk factors and producing smooth maps of infant mortality risk in a health and socio-demographic surveillance system. Results showed marked geographical differences in mortality risk across a relatively small area. Prevention of vertical transmission of HIV and survival of mothers during the infants' first year in high prevalence villages needs to be urgently addressed, including expanded antenatal testing, prevention of mother-to-child transmission, and improved access to antiretroviral therapy. There is also need to assess and improve the capacity of district hospitals for emergency obstetric and newborn care. Persisting risk factors, including inadequate provision of clean water and sanitation, are yet to be fully addressed.</p

Bayesian Spatio-Temporal Modeling of Schistosoma japonicum Prevalence Data in the Absence of a Diagnostic ‘Gold’ Standard

Schistosomiasis is a serious public health problem in the People's Republic of China and elsewhere, and mapping of risk areas is important for guiding control interventions. Here, a 10-year surveillance database from Dangtu County in the southeastern part of the People's Republic of China was utilized for modeling the spatial and temporal distribution of infections in relation to environmental features and socioeconomic factors. Disease surveillance was done on the basis of a serological test, and we explicitly considered the imperfect sensitivity and specificity of the test when modeling the ‘true’ infection prevalence of Schistosoma japonicum. We then produced a risk map for S. japonicum transmission, which can assist decision making for local control interventions. Our work emphasizes the importance of accounting for the uncertainty in the diagnosis of schistosomiasis, and the potential of predicting the spatial and temporal distribution of the disease when using a Bayesian modeling framework. Our study can therefore serve as a template for future risk profiling of neglected tropical diseases studies, particularly when exploring spatial and temporal disease patterns in relation to environmental and socioeconomic factors, and how to account for the influence of diagnostic uncertainty