Resource extraction projects and health: evidence from cross-national and national data sources

Implementation of resource extraction projects often triggers a series of complex environmental and social-ecological changes. These changes may include alterations in land use (i.e., from forestry and vegetation to infrastructure and mining), an increase in construction activities (new buildings such as houses, schools and hospitals), population increase (more people, more road traffic), urbanization, movement and installation of heavy machinery, increases in employment and business opportunities and household resettlements. These changes can positively or negatively affect health of the population living within mining areas and beyond. For instance, one common and most visible contribution of resource extraction projects is the impact on income generation. This has been widely studied in the economic literature, showing both positive and negative effects between natural resources activities and income generation. Positively, governments can benefit from the generated resource rents and royalties. Individually, people can earn income from employment and business opportunities. The revenue generated can help governments to re-invest in other sectors, including health, education and infrastructure. Negatively, the sharp increase in economic development in one sector can hamper growth in other sectors causing what is known as the Dutch disease. The presence of resource-income dependency can as well fuel local conflicts, political instability, weak institutions and corruption, and ultimately result in a slow development process causing the resource curse. One major aspect of resource extraction projects which is often under-represented is its implication on health. Health is influenced both directly and indirectly through activities involved in resource extraction projects. Evidence suggests that resource extraction projects can positively or negatively affect health and well-being of the population therein. This directly relates to the Sustainable Development Goal (SDG) number 3 (SDG3) of the SDGs 2030 agenda. SDG3 aims to ensure healthy lives and promote well-being for all at all ages. Health has a central place in SDG3, and it is also central to the three dimensions of sustainable development: environment, society and economy. Resource extract projects can act on determinants of health and ultimately contribute to improve lives and well-being. An increase in income can promote access to better care, construction of health care post and hospitals can contribute to improving healthcare delivery, constructions of water points can improve the availability of clean water, and lastly but not least, the provision of health education can contribute to knowledge and disease prevention. On the other hand, resource extraction projects can cause environmental disruption linked to air, water and land pollution. This can further result in disease outcomes. Combustion activities associated with the extraction process can result in the presence of small particulate matter (PM2.5) in the atmosphere and further lead to respiratory and cardiovascular diseases. Toxic substances often used in the extraction process can leak into the environment and result in cancer diseases. The presence of both positive and negative health outcomes in resource extraction areas present an opportunity to systematically study the contribution of resource extraction projects to health outcomes. This PhD thesis embarked on this particular opportunity and studied the association between resource extraction projects and population health indicators in three layered perspectives: global, national and subnational

Investigating health impacts of natural resource extraction projects in Burkina Faso, Ghana, Mozambique, and Tanzania: protocol for a mixed methods study

Natural resource extraction projects offer both opportunities and risks for sustainable development and health in host communities. Often, however, the health of the community suffers. Health impact assessment (HIA) can mitigate the risks and promote the benefits of development but is not routinely done in the developing regions that could benefit the most.; Our study aims to investigate health and health determinants in regions affected by extractive industries in Burkina Faso, Ghana, Mozambique, and Tanzania. The evidence generated in our study will inform a policy dialogue on how HIA can be promoted as a regulatory approach as part of the larger research initiative called the HIA4SD (Health impact assessment for sustainable development) project.; The study is a concurrent triangulation, mixed methods, multi-stage, multi-focus project that specifically addresses the topics of governance and policy, social determinants of health, health economics, health systems, maternal and child health, morbidity and mortality, and environmental determinants, as well as the associated health outcomes in natural resource extraction project settings across four countries. To investigate each of these health topics, the project will (1) use existing population-level databases to quantify incidence of disease and other health outcomes and determinants over time using time series analysis; (2) conduct two quantitative surveys on mortality and cost of disease in producer regions; and (3) collect primary qualitative data using focus groups and key informant interviews describing community perceptions of the impacts of extraction projects on health and partnership arrangements between the projects and local and national governance. Differences in health outcomes and health determinants between districts with and without an extraction project will be analyzed using matched geographical analyses in quasi-Poisson regression models and binomial regression models. Costs to the health system and to the households from diseases found to be associated with projects in each country will be estimated retrospectively.; Fieldwork for the study began in February 2019 and concluded in February 2020. At the time of submission, qualitative data collection had been completed in all four study countries. In Burkina Faso, 36 focus group discussions and 74 key informant interviews were conducted in three sites. In Ghana, 34 focus group discussions and 64 key informant interviews were conducted in three sites. In Mozambique, 75 focus group discussions and 103 key informant interviews were conducted in four sites. In Tanzania, 36 focus group discussions and 84 key informant interviews were conducted in three sites. Quantitative data extraction and collection is ongoing in all four study countries. Ethical approval for the study was received in all four study countries prior to beginning the fieldwork. Data analyses are underway and results are expected to be published in 2020 and 2021.; Disentangling the complex interactions of resource extraction projects with their host communities requires an integrative approach drawing on many methodologies under the HIA umbrella. By using complementary data sources to address the question of population health in project areas from several angles, bias and missing data will be reduced, generating high-quality evidence to aid countries in moving toward sustainable development.; DERR1-10.2196/17138

The Status and Risk Factors of Brucellosis in Smallholder Dairy Cattle in Selected Regions of Tanzania

Bovine brucellosis is a bacterial zoonoses caused by Brucella abortus. We conducted a cross-sectional study to determine brucellosis seroprevalence and risk factors among smallholder dairy cattle across six regions in Tanzania. We sampled 2048 dairy cattle on 1374 farms between July 2019 and October 2020. Sera were tested for the presence of anti-Brucella antibodies using a competitive enzyme-linked immunosorbent assay. Seroprevalence was calculated at different administrative scales, and spatial tests were used to detect disease hotspots. A generalized mixed-effects regression model was built to explore the relationships among Brucella serostatus, animals, and farm management factors. Seroprevalence was 2.39% (49/2048 cattle, 95% CI 1.7-3.1) across the study area and the Njombe Region represented the highest percentage with 15.5% (95% CI 11.0-22.0). Moreover, hotspots were detected in the Njombe and Kilimanjaro Regions. Mixed-effects models showed that having goats (OR 3.02, 95% C 1.22-7.46) and abortion history (OR 4.91, 95% CI 1.43-16.9) were significant risk factors for brucellosis. Education of dairy farmers regarding the clinical signs, transmission routes, and control measures for brucellosis is advised. A One Health approach is required to study the role of small ruminants in cattle brucellosis and the status of brucellosis in dairy farmers in the Njombe and Kilimanjaro Regions

The Status and Risk Factors of Brucellosis in Smallholder Dairy Cattle in Selected Regions of Tanzania

This research article was published by MPDI, 2023Bovine brucellosis is a bacterial zoonoses caused by Brucella abortus. We conducted a cross-sectional study to determine brucellosis seroprevalence and risk factors among smallholder dairy cattle across six regions in Tanzania. We sampled 2048 dairy cattle on 1374 farms between July 2019 and October 2020. Sera were tested for the presence of anti-Brucella antibodies using a competitive enzyme-linked immunosorbent assay. Seroprevalence was calculated at different administrative scales, and spatial tests were used to detect disease hotspots. A generalized mixed-effects regression model was built to explore the relationships among Brucella serostatus, animals, and farm management factors. Seroprevalence was 2.39% (49/2048 cattle, 95% CI 1.7–3.1) across the study area and the Njombe Region represented the highest percentage with 15.5% (95% CI 11.0–22.0). Moreover, hotspots were detected in the Njombe and Kilimanjaro Regions. Mixed-effects models showed that having goats (OR 3.02, 95% C 1.22–7.46) and abortion history (OR 4.91, 95% CI 1.43–16.9) were significant risk factors for brucellosis. Education of dairy farmers regarding the clinical signs, transmission routes, and control measures for brucellosis is advised. A One Health approach is required to study the role of small ruminants in cattle brucellosis and the status of brucellosis in dairy farmers in the Njombe and Kilimanjaro Regions

Brucella Species Circulating in Smallholder Dairy Cattle in Tanzania

This research article was published by Pathogens ,Volume 13, 2024Brucellosis is a zoonosis caused by bacteria of the genus Brucella, which results in economic losses relating to livestock and threatens public health. A cross-sectional study was conducted to determine the molecular prevalence of Brucella species in smallholder dairy cattle in six regions of Tanzania from July 2019 to October 2020. Dairy cattle (n = 2048) were sampled from 1371 farms. DNA extracted from blood and vaginal swabs was tested for Brucella using qPCR targeting the IS711 gene and positives were tested for the alkB marker for B. abortus and BMEI1172 marker for B. melitensis. The molecular prevalence was 3.5% (95% CI: 2.8–4.4) with the highest prevalence 8.1% (95% CI: 4.6–13.0) in Njombe region. B. melitensis was the predominant species detected (66.2%). Further studies are recommended to understand the source of B. melitensis and its implications for veterinary public health. Livestock keepers should be informed of the risks and biosecurity practices to reduce the introduction and control of Brucella. Cattle and small ruminant vaccination programs could be implemented to control brucellosis in high-risk populations in the country

Estimating causes of death where there is no medical certification: evolution and state of the art of verbal autopsy

Over the past 70 years, significant advances have been made in determining the causes of death in populations not served by official medical certification of cause at the time of death using a technique known as Verbal Autopsy (VA). VA involves an interview of the family or caregivers of the deceased after a suitable bereavement interval about the circumstances, signs and symptoms of the deceased in the period leading to death. The VA interview data are then interpreted by physicians or, more recently, computer algorithms, to assign a probable cause of death. VA was originally developed and applied in field research settings. This paper traces the evolution of VA methods with special emphasis on the World Health Organization's (WHO)'s efforts to standardize VA instruments and methods for expanded use in routine health information and vital statistics systems in low- and middle-income countries (LMICs). These advances in VA methods are culminating this year with the release of the 2022 WHO Standard Verbal Autopsy (VA) Toolkit. This paper highlights the many contributions the late Professor Peter Byass made to the current VA standards and methods, most notably, the development of InterVA, the most commonly used automated computer algorithm for interpreting data collected in the WHO standard instruments, and the capacity building in low- and middle-income countries (LMICs) that he promoted. This paper also provides an overview of the methods used to improve the current WHO VA standards, a catalogue of the changes and improvements in the instruments, and a mapping of current applications of the WHO VA standard approach in LMICs. It also provides access to tools and guidance needed for VA implementation in Civil Registration and Vital Statistics Systems at scale

Seroprevalence and risk factors for Q-fever (Coxiella burnetii) exposure in smallholder dairy cattle in Tanzania

Q fever is a zoonotic disease, resulting from infection with Coxiella burnetii. Infection in cattle can cause abortion and infertility, however, there is little epidemiological information regarding the disease in dairy cattle in Tanzania. Between July 2019 and October 2020, a serosurvey was conducted in six high dairy producing regions of Tanzania. Cattle sera were tested for antibodies to C. burnetii using an indirect enzyme-linked immunosorbent assay. A mixed effect logistic regression model identified risk factors associated with C. burnetii seropositivity. A total of 79 out of 2049 dairy cattle tested positive with an overall seroprevalence of 3.9% (95% CI 3.06–4.78) across the six regions with the highest seroprevalence in Tanga region (8.21%, 95% CI 6.0–10.89). Risk factors associated with seropositivity included: extensive feeding management (OR 2.77, 95% CI 1.25–3.77), and low precipitation below 1000 mm (OR 2.76, 95% 1.37–7.21). The disease seroprevalence is relatively low in the high dairy cattle producing regions of Tanzania. Due to the zoonotic potential of the disease, future efforts should employ a “One Health” approach to understand the epidemiology, and for interdisciplinary control to reduce the impacts on animal and human health

Estimating causes of death where there is no medical certification: evolution and state of the art of verbal autopsy.

Over the past 70 years, significant advances have been made in determining the causes of death in populations not served by official medical certification of cause at the time of death using a technique known as Verbal Autopsy (VA). VA involves an interview of the family or caregivers of the deceased after a suitable bereavement interval about the circumstances, signs and symptoms of the deceased in the period leading to death. The VA interview data are then interpreted by physicians or, more recently, computer algorithms, to assign a probable cause of death. VA was originally developed and applied in field research settings. This paper traces the evolution of VA methods with special emphasis on the World Health Organization's (WHO)'s efforts to standardize VA instruments and methods for expanded use in routine health information and vital statistics systems in low- and middle-income countries (LMICs). These advances in VA methods are culminating this year with the release of the 2022 WHO Standard Verbal Autopsy (VA) Toolkit. This paper highlights the many contributions the late Professor Peter Byass made to the current VA standards and methods, most notably, the development of InterVA, the most commonly used automated computer algorithm for interpreting data collected in the WHO standard instruments, and the capacity building in low- and middle-income countries (LMICs) that he promoted. This paper also provides an overview of the methods used to improve the current WHO VA standards, a catalogue of the changes and improvements in the instruments, and a mapping of current applications of the WHO VA standard approach in LMICs. It also provides access to tools and guidance needed for VA implementation in Civil Registration and Vital Statistics Systems at scale

COVID-19 Preventive Practices, Psychological Distress, and Reported Barriers to Healthcare Access during the Pandemic among Adult Community Members in Sub-Saharan Africa: A Phone Survey

The COVID-19 pandemic has had serious negative health and economic impacts in sub-Saharan Africa. Continuous monitoring of these impacts is crucial to formulate interventions to minimize the consequences of COVID-19. This study surveyed 2,829 adults in urban and rural sites among five sub-Saharan African countries: Burkina Faso, Ethiopia, Nigeria, Tanzania, and Ghana. Participants completed a mobile phone survey that assessed self-reported sociodemographics, COVID-19 preventive practices, psychological distress, and barriers to healthcare access. A modified Poisson regression model was used to estimate adjusted prevalence ratios (aPRs) and 95% CIs to investigate potential factors related to psychological distress and barriers to reduced healthcare access. At least 15.6% of adults reported experiencing any psychological distress in the previous 2 weeks, and 10.5% reported that at least one essential healthcare service was difficult to access 2 years into the pandemic. The majority of participants reported using several COVID-19 preventive methods, with varying proportions across the sites. Participants in the urban site of Ouagadougou, Burkina Faso (aPR: 2.29; 95% CI: 1.74–3.03) and in the rural site of Kintampo, Ghana (aPR: 1.68; 95% CI: 1.21–2.34) had a higher likelihood of experiencing any psychological distress compared with those in the rural area of Nouna, Burkina Faso. Loss of employment due to COVID-19 (aPR: 1.77; 95% CI: 1.47–2.11) was also associated with an increased prevalence of psychological distress. The number of children under 5 years in the household (aPR: 1.23; 95% CI: 1.14–1.33) and participant self-reported psychological distress (aPR: 1.83; 95% CI: 1.48–2.27) were associated with an increased prevalence of reporting barriers to accessing health services, whereas wage employment (aPR: 0.67; 95% CI: 0.49–0.90) was associated with decreased prevalence of reporting barriers to accessing health services. Overall, we found a high prevalence of psychological distress and interruptions in access to healthcare services 2 years into the pandemic across five sub-Saharan African countries. Increased effort and attention should be given to addressing the negative impacts of COVID-19 on psychological distress. An equitable and collaborative approach to new and existing preventive measures for COVID-19 is crucial to limit the consequences of COVID-19 on the health of adults in sub-Saharan Africa

On Parameter Variation in Mathematical Modeling of Infectious Diseases: Malaria

Mathematical modeling of malaria can be dated back to 1911 from Ross SIR population compartment models and followed by a major extension done by MacDonald in 1957. Recently, there have been a number of modifications to suit different types of scenarios. Of the challenges that faces this type of modeling is the availability, and variations of parameter values. Differences in the type of the parasite involved in the infection, variation of mosquito species, environmental variability and changes in the ecological system, they all bring about disparity in the parameter variation of malaria modeling. It has been discovered that [Koella 1991], the amount of variability in transmission parameters strongly affects the outcome of control measures and that predictions of the outcome can be misleading. This thesis addresses parameter variation in malaria models using ordinary differential equations, (ODE)