Experiences and coping strategies of women caring for their husbands with cancer at the Cancer Diseases Hospital in Lusaka, Zambia: a descriptive phenomenological approach.

The Cancer Diseases Hospital (CDH) 2019 annual report revealed an upsurge in the number of new cancer patients accessing services from 35 patients in 2006 to 3,008 in 2019. This study explored the experiences and coping strategies of women caring for their husbands with cancer attending the CDH. A phenomenological research design was used with stratified purposeful sampling. Data were collected using an interview schedule and analysed using thematic analysis. The women's challenges included mobility difficulties and hospital admissions/problems; socio-economic problems, psychological and emotional distress; and caregiving liability and spiritual anguish. The benefits that female spouses experienced during caring for their loved ones included knowledge about cancer and infection prevention, a strong marital relationship, tolerance and perseverance, resilience and hope and good relationship with other caregivers. The women's needs included financial support, physical needs, psychosocial counselling, caregiver accommodation, time off from caregiving, information needs and sexual intimacy and contact. Their coping strategies included spiritual support from spiritual carers, prayer and meditation, music and storytelling, social support and a good marital relationship. The findings demonstrate that wives of patients with cancer experience many challenges in their caring journey. Nurses must anticipate and/or intervene as part of their nursing practice to reduce the negative impact on female caretakers in this situation. Hospital standard operating procedures must be developed to put both the patients and their caregivers at the centre of oncology nursing care, particularly in settings with limited allied professional support, e.g., psychologists. Caretaker coping strategies highlighted in this study must be made available for both the patients and their wives, e.g., linking wives to trained spiritual carers upon their husband's admission to the hospital, to aid a smooth caregiving experience.

Joint spatial modelling of common morbidities of childhood fever and diarrhoea in Malawi.

Availability of geo-referenced data has increased applications of spatially explicit models to understand important health problems in developing countries. This study aims to investigate joint and disease-specific spatial clusters of fever and diarrhoea at a highly disaggregate level, while simultaneously estimating the influence of other covariates. Using the 2000 Malawi DHS, a logistic model was fitted with spatial random effects partitioned into shared and specific effects. Results indicated that the shared area-specific effects were persistently high in the central and southern regions. Fever-specific effects were high along the lakeshore areas of the country, while diarrhoea-specific effects were excessive in the central region and south-eastern zones of the country. The prevalence of fever and diarrhoea was also associated with individual, familial and community risk factors. Our findings present an opportunity for an integrated disease control approach for reducing childhood morbidity and mortality.

Using the hierarchical ordinal regression model to analyse the intensity of urinary schistosomiasis infection in school children in Lusaka Province, Zambia.

BACKGROUND: Urinary schistosomiasis has been a major public health problem in Zambia for many years. However, the disease profile may vary in different locale due to the changing ecosystem that contributes to the risk of acquiring the disease. The objective of this study was to quantify risk factors associated with the intensity of urinary schistosomiasis infection in school children in Lusaka Province, Zambia, in order to better understand local transmission. METHODS: Data were obtained from 1 912 school children, in 20 communities, in the districts of Luangwa and Kafue in Lusaka Province. Both individual- and community-level covariates were incorporated into an ordinal logistic regression model to predict the probability of an infection being a certain intensity in a three-category outcome response: 0 = no infection, 1 = light infection, and 2 = moderate/heavy infection. Random effects were introduced to capture unobserved heterogeneity. RESULTS: Overall, the risk of urinary schistosomiasis was strongly associated with age, altitude at which the child lived, and sex. Weak associations were observed with the normalized difference vegetation index, maximum temperature, and snail abundance. Detailed analysis indicated that the association between infection intensities and age and altitude were category-specific. Particularly, infection intensity was lower in children aged between 5 and 9 years compared to those aged 10 to 15 years (OR = 0.72, 95% CI = 0.51-0.99). However, the age-specific risk changed at different levels of infection, such that when comparing children with light infection to those who were not infected, age was associated with a lower odds (category 1 vs category 0: OR = 0.71, 95% CI: 0.50-0.99), yet such a relation was not significant when considering children who were moderately or heavily infected compared to those with a light or no infection (category 2 vs category 0: OR = 0.96, 95% CI: 0.45-1.64). Overall, we observed that children living in the valley were less likely to acquire urinary schistosomiasis compared to those living in plateau areas (OR = 0.48, 95% CI: 0.16-0.71). However, category-specific effects showed no significant association in category 1 (light infection), whereas in category 2 (moderate/high infection), the risk was still significantly lower for those living in the valley compared to those living in plateau areas (OR = 0.18, 95% CI: 0.04-0.75). CONCLUSIONS: This study demonstrates the importance of understanding the dynamics and heterogeneity of infection in control efforts, and further suggests that apart from the well-researched factors of Schistosoma intensity, various other factors influence transmission. Control programmes need to take into consideration the varying infection intensities of the disease so that effective interventions can be designed.

Survival of people on antiretroviral treatment in Zambia: a retrospective cohort analysis of HIV clients on ART.

INTRODUCTION: Provision of free anti-retroviral therapy in Zambia started in June 2004. There were only 15,000 people on treatment as at December that year, mainly due to lack of access. This number rose to 580,000 people as at December 2013. The general objective of this study was to determine survival of people on ART and to examine associated predictors for survival. METHODS: The study included ART patients enrolled between the year 2002 and 2013 (n=10,395) in 285 health facilities in Zambia. Patient files were analyzed retrospectively. The study used Kaplan Meier and Cox-proportional hazard models to describe the relationship between lost to follow up and age, sex, baseline CD4 cell count and weight. RESULTS: Results showed that lost to follow up accounted for 90% of the clients that had dropped out, while 10% was to deaths. Low baseline CD4 count (p-value 0.001, HR 0.9994, (95% CI 0.9993, 0.9996) at initiation was associated with lost to follow up together with weight at initiation (p-value 0.031, HR 0.9987 at 95% CI (0.9975, 0.9998)) of ART. CONCLUSION: This study has demonstrated that lost to follow up is a substantial contributing factor to drop outs among HIV patients on treatment. Strengthening of community treatment supporters especially immediate family members in emphasizing to the client the need to continue treatment is necessary. The health facility could do more in emphasizing the importance of treatment especially in the initial stages. Further, in order to reduce opportunistic infections and probable deaths during treatment, cotrimoxazole prophylaxis should be maintained so as to raise the CD4 levels. Improved nutritional assessment and counseling to boost the nutritional status of the clients throughout should be encouraged.

Zero adjusted models with applications to analysing helminths count data.

BACKGROUND: It is common in public health and epidemiology that the outcome of interest is counts of events occurrence. Analysing these data using classical linear models is mostly inappropriate, even after transformation of outcome variables due to overdispersion. Zero-adjusted mixture count models such as zero-inflated and hurdle count models are applied to count data when over-dispersion and excess zeros exist. Main objective of the current paper is to apply such models to analyse risk factors associated with human helminths (S. haematobium) particularly in a case where there's a high proportion of zero counts. METHODS: The data were collected during a community-based randomised control trial assessing the impact of mass drug administration (MDA) with praziquantel in Malawi, and a school-based cross sectional epidemiology survey in Zambia. Count data models including traditional (Poisson and negative binomial) models, zero modified models (zero inflated Poisson and zero inflated negative binomial) and hurdle models (Poisson logit hurdle and negative binomial logit hurdle) were fitted and compared. RESULTS: Using Akaike information criteria (AIC), the negative binomial logit hurdle (NBLH) and zero inflated negative binomial (ZINB) showed best performance in both datasets. With regards to zero count capturing, these models performed better than other models. CONCLUSION: This paper showed that zero modified NBLH and ZINB models are more appropriate methods for the analysis of data with excess zeros. The choice between the hurdle and zero-inflated models should be based on the aim and endpoints of the study.

Spatially explicit Schistosoma infection risk in eastern Africa using Bayesian geostatistical modelling.

Schistosomiasis remains one of the most prevalent parasitic diseases in the tropics and subtropics, but current statistics are outdated due to demographic and ecological transformations and ongoing control efforts. Reliable risk estimates are important to plan and evaluate interventions in a spatially explicit and cost-effective manner. We analysed a large ensemble of georeferenced survey data derived from an open-access neglected tropical diseases database to create smooth empirical prevalence maps for Schistosoma mansoni and Schistosoma haematobium for a total of 13 countries of eastern Africa. Bayesian geostatistical models based on climatic and other environmental data were used to account for potential spatial clustering in spatially structured exposures. Geostatistical variable selection was employed to reduce the set of covariates. Alignment factors were implemented to combine surveys on different age-groups and to acquire separate estimates for individuals aged ≤20 years and entire communities. Prevalence estimates were combined with population statistics to obtain country-specific numbers of Schistosoma infections. We estimate that 122 million individuals in eastern Africa are currently infected with either S. mansoni, or S. haematobium, or both species concurrently. Country-specific population-adjusted prevalence estimates range between 12.9% (Uganda) and 34.5% (Mozambique) for S. mansoni and between 11.9% (Djibouti) and 40.9% (Mozambique) for S. haematobium. Our models revealed that infection risk in Burundi, Eritrea, Ethiopia, Kenya, Rwanda, Somalia and Sudan might be considerably higher than previously reported, while in Mozambique and Tanzania, the risk might be lower than current estimates suggest. Our empirical, large-scale, high-resolution infection risk estimates for S. mansoni and S. haematobium in eastern Africa can guide future control interventions and provide a benchmark for subsequent monitoring and evaluation activities.

Large-scale determinants of intestinal schistosomiasis and intermediate host snail distribution across Africa: does climate matter?

The geographical ranges of most species, including many infectious disease agents and their vectors and intermediate hosts, are assumed to be constrained by climatic tolerances, mainly temperature. It has been suggested that global warming will cause an expansion of the areas potentially suitable for infectious disease transmission. However, the transmission of infectious diseases is governed by a myriad of ecological, economic, evolutionary and social factors. Hence, a deeper understanding of the total disease system (pathogens, vectors and hosts) and its drivers is important for predicting responses to climate change. Here, we combine a growing degree day model for Schistosoma mansoni with species distribution models for the intermediate host snail (Biomphalaria spp.) to investigate large-scale environmental determinants of the distribution of the African S. mansoni-Biomphalaria system and potential impacts of climatic changes. Snail species distribution models included several combinations of climatic and habitat-related predictors; the latter divided into "natural" and "human-impacted" habitat variables to measure anthropogenic influence. The predictive performance of the combined snail-parasite model was evaluated against a comprehensive compilation of historical S. mansoni parasitological survey records, and then examined for two climate change scenarios of increasing severity for 2080. Future projections indicate that while the potential S. mansoni transmission area expands, the snail ranges are more likely to contract and/or move into cooler areas in the south and east. Importantly, we also note that even though climate per se matters, the impact of humans on habitat play a crucial role in determining the distribution of the intermediate host snails in Africa. Thus, a future contraction in the geographical range size of the intermediate host snails caused by climatic changes does not necessarily translate into a decrease or zero-sum change in human schistosomiasis prevalence.

DNA 'barcoding' of Schistosoma mansoni across sub-Saharan Africa supports substantial within locality diversity and geographical separation of genotypes.

Schistosoma mansoni is a widespread human helminth and causes intestinal schistosomiasis in 54 countries, mainly across Africa but also in Madagascar, the Arabian Peninsula and the neotropics. The geographical range of this parasite relies on the distribution of certain species of freshwater pulmonate snails of the genus Biomphalaria. Whilst S. mansoni is known to exhibit high population diversity the true extent of this diversity is still to be fully elucidated as sampling of this taxon progressively accrues. Here a DNA 'barcoding' approach is taken using sequence analysis of a 450bp region within the mitochondrial cox1 gene to assess the genetic diversity within a large number of S. mansoni larval stages collected from their natural human hosts across sub-Saharan Africa. Five hundred and sixty one individual parasite samples were examined from 22 localities and 14 countries. Considerable within-species diversity was found with 120 unique haplotypes splitting geographically into five discrete lineages. The highest diversity was found in East Africa with samples forming three of the five lineages. Less diversity was found in the Far and Central Western regions of Africa with haplotypes from the New World showing a close affinity to the Far Western African S. mansoni populations supporting the hypothesis of a colonisation of South America via the West African slave trade. The data are discussed in relation to parasite diversity and disease epidemiology.

Genetic diversity within Schistosoma haematobium: DNA barcoding reveals two distinct groups.

BACKGROUND: Schistosomiasis in one of the most prevalent parasitic diseases, affecting millions of people and animals in developing countries. Amongst the human-infective species S. haematobium is one of the most widespread causing urogenital schistosomiasis, a major human health problem across Africa, however in terms of research this human pathogen has been severely neglected. METHODOLOGY/PRINCIPAL FINDINGS: To elucidate the genetic diversity of Schistosoma haematobium, a DNA 'barcoding' study was performed on parasite material collected from 41 localities representing 18 countries across Africa and the Indian Ocean Islands. Surprisingly low sequence variation was found within the mitochondrial cytochrome oxidase subunit I (cox1) and the NADH-dehydrogenase subunit 1 snad1). The 61 haplotypes found within 1978 individual samples split into two distinct groups; one (Group 1) that is predominately made up of parasites from the African mainland and the other (Group 2) that is made up of samples exclusively from the Indian Ocean Islands and the neighbouring African coastal regions. Within Group 1 there was a dominance of one particular haplotype (H1) representing 1574 (80%) of the samples analyzed. Population genetic diversity increased in samples collected from the East African coastal regions and the data suggest that there has been movement of parasites between these areas and the Indian Ocean Islands. CONCLUSIONS/SIGNIFICANCE: The high occurrence of the haplotype (H1) suggests that at some point in the recent evolutionary history of S. haematobium in Africa the population may have passed through a genetic 'bottleneck' followed by a population expansion. This study provides novel and extremely interesting insights into the population genetics of S. haematobium on a large geographic scale, which may have consequence for control and monitoring of urogenital schistosomiasis.

Toward an open-access global database for mapping, control, and surveillance of neglected tropical diseases.

BACKGROUND: After many years of general neglect, interest has grown and efforts came under way for the mapping, control, surveillance, and eventual elimination of neglected tropical diseases (NTDs). Disease risk estimates are a key feature to target control interventions, and serve as a benchmark for monitoring and evaluation. What is currently missing is a georeferenced global database for NTDs providing open-access to the available survey data that is constantly updated and can be utilized by researchers and disease control managers to support other relevant stakeholders. We describe the steps taken toward the development of such a database that can be employed for spatial disease risk modeling and control of NTDs. METHODOLOGY: With an emphasis on schistosomiasis in Africa, we systematically searched the literature (peer-reviewed journals and 'grey literature'), contacted Ministries of Health and research institutions in schistosomiasis-endemic countries for location-specific prevalence data and survey details (e.g., study population, year of survey and diagnostic techniques). The data were extracted, georeferenced, and stored in a MySQL database with a web interface allowing free database access and data management. PRINCIPAL FINDINGS: At the beginning of 2011, our database contained more than 12,000 georeferenced schistosomiasis survey locations from 35 African countries available under http://www.gntd.org. Currently, the database is expanded to a global repository, including a host of other NTDs, e.g. soil-transmitted helminthiasis and leishmaniasis. CONCLUSIONS: An open-access, spatially explicit NTD database offers unique opportunities for disease risk modeling, targeting control interventions, disease monitoring, and surveillance. Moreover, it allows for detailed geostatistical analyses of disease distribution in space and time. With an initial focus on schistosomiasis in Africa, we demonstrate the proof-of-concept that the establishment and running of a global NTD database is feasible and should be expanded without delay.

Virtual globes and geospatial health: the potential of new tools in the management and control of vector-borne diseases.

The rapidly growing field of three-dimensional software modeling of the Earth holds promise for applications in the geospatial health sciences. Easy-to-use, intuitive virtual globe technologies such as Google Earth enable scientists around the world to share their data and research results in a visually attractive and readily understandable fashion without the need for highly sophisticated geographical information systems (GIS) or much technical assistance. This paper discusses the utility of the rapid and simultaneous visualization of how the agents of parasitic diseases are distributed, as well as that of their vectors and/or intermediate hosts together with other spatially-explicit information. The resulting better understanding of the epidemiology of infectious diseases, and the multidimensional environment in which they occur, are highlighted. In particular, the value of Google Earth, and its web-based pendant Google Maps, are reviewed from a public health view point, combining results from literature searches and experiences gained thus far from a multidisciplinary project aimed at optimizing schistosomiasis control and transmission surveillance in sub-Saharan Africa. Although the basic analytical capabilities of virtual globe applications are limited, we conclude that they have considerable potential in the support and promotion of the geospatial health sciences as a userfriendly, straightforward GIS tool for the improvement of data collation, visualization and exploration. The potential of these systems for data sharing and broad dissemination of scientific research and results is emphasized.

The epidemiology and small-scale spatial heterogeneity of urinary schistosomiasis in Lusaka province, Zambia.

In line with the aims of the "National Bilharzia Control Programme" and the "School Health and Nutrition Programme" in Zambia, a study on urinary schistosomiasis was conducted in 20 primary schools of Lusaka province to further our understanding of the epidemiology of the infection, and to enhance spatial targeting of control. We investigated risk factors associated with urinary schistosomiasis, and examined small-scale spatial heterogeneity in prevalence, using data collected from 1,912 schoolchildren, 6 to 15-year-old, recruited from 20 schools in Kafue and Luangwa districts. The risk factors identified included geographical location, altitude, normalized difference vegetation index (NDVI), maximum temperature, age, sex of the child and intermediate host snail abundance. Three logistic regression models were fitted assuming different random effects to allow for spatial structuring. The mean prevalence rate was 9.6%, with significance difference between young and older children (odds ratio (OR) = 0.71; 95% confidence interval (CI) = 0.51-0.96). The risk of infection was related to intermediate host snail abundance (OR = 1.03; 95% CI = 1.00-1.05) and vegetation cover (OR = 1.04; 95% CI = 1.00-1.07). Schools located either on the plateau and the valley also differed in prevalence and intensity of infection for moderate infection to none (OR = 1.64; 95% CI = 1.36- 1.96). The overall predictive performance of the spatial random effects model was higher than the ordinary logistic regression. In addition, evidence of heterogeneity of the infection risk was found at the micro-geographical level. A sound understanding of small-scale heterogeneity, caused by spatial aggregation of schoolchildren, is important to inform health planners for implementing control schistosomiasis interventions.