Frontiers in epidemiology最新文献

Introduction: Limited research has examined the economic impact of vision difficulty (VD) and dementia on older adults and their caregivers. We aimed to determine whether older adults with VD and/or dementia, and their caregivers, face more economic hardships than their counterparts without VD or dementia.

Methods: We used cross-sectional data from the 2015 National Health and Aging Trends Study (NHATS), a population-based survey of Medicare beneficiaries, linked to their family/unpaid caregivers from the National Study of Caregiving (NSOC). Regression models characterized the association of VD (self-report), dementia (survey and cognitive assessments), and co-occurring VD and dementia with debt, receiving financial help from relatives, government-based Supplemental Nutrition Assistance Program (SNAP), other food assistance, utility assistance, and caregiver financial difficulty.

Results: The NHATS sample included 6,879 community-dwelling older adults (5670 no VD/dementia, 494 VD-alone, 512 dementia-alone, 203 co-occurring VD and dementia). Adults with VD and dementia had higher odds of receiving SNAP benefits (OR = 2.6, 95%CI = 1.4-4.8) and other food assistance (OR = 4.1, 95%CI = 1.8-9.1) than adults without VD/dementia, while no differences were noted for debt, financial help, and utility assistance. Adults with VD-alone had higher odds of debt (OR = 2.1, 95%CI = 1.3-3.2), receiving financial help (OR = 1.7, 95%CI = 1.1-2.5) and other food assistance (OR = 2.7, 95%CI = 1.7-4.3); while adults with dementia-alone had higher odds of debt (OR = 2.8, 95%CI = 1.4-5.5). The NSOC sample included 1,759 caregivers (995 caring for adults without VD/dementia, 223 for VD-alone, 368 for dementia-alone, and 173 for co-occurring VD and dementia). Compared to caregivers of older adults without VD/dementia, caregivers of adults with VD and dementia had higher odds of financial difficulty (OR = 3.0, 95%CI = 1.7-5.3) while caregivers of adults with VD-alone or dementia-alone did not.

Discussion: While older adults with VD- or dementia-alone experienced increased economic hardships, disparities in food assistance were amplified among older adults with co-occurring disease. Caregivers of adults with co-occurring disease experienced more financial difficulty than caregivers of adults with a single or no disease. This study highlights the need for interventions across clinical and social services to support the economic wellbeing of our aging population and their caregivers.

To accelerate malaria elimination in the Southern African region by 2030, it is essential to prevent cross-border malaria transmission. However, countries within the region are highly interconnected due to human migration that aids in the movement of the parasite across geographical borders. It is therefore important to better understand Plasmodium falciparum transmission dynamics in the region, and identify major parasite source and sink populations, as well as cross-border blocks of high parasite connectivity. We performed a meta-analysis using collated parasite allelic data generated by microsatellite genotyping of malaria parasites from Namibia, Eswatini, South Africa, and Mozambique (N = 5,314). The overall number of unique alleles was significantly higher (P ≤ 0.01) in Namibia (mean A = 17.3 ± 1.46) compared to South Africa (mean A = 12.2 ± 1.22) and Eswatini (mean A = 13.3 ± 1.27, P ≤ 0.05), whilst the level of heterozygosity was not significantly different between countries. The proportion of polyclonal infections was highest for Namibia (77%), and lowest for Mozambique (64%). A was significant population structure was detected between parasites from the four countries, and patterns of gene flow showed that Mozambique was the major source area and Eswatini the major sink area of parasites between the countries. This study showed strong signals of parasite population structure and genetic connectivity between malaria parasite populations across national borders. This calls for strengthening the harmonization of malaria control and elimination efforts between countries in the southern African region. This data also proves its potential utility as an additional surveillance tool for malaria surveillance on both a national and regional level for the identification of imported cases and/or outbreaks, as well as monitoring for the potential spread of anti-malarial drug resistance as countries work towards malaria elimination.

Introduction: Efforts to reduce the stigma associated with mental illness have intensified over the past 30 years with a particular focus on improving public attitudes. Difficult economic circumstances can be harmful to intergroup relations, but little is known about whether there is a relationship between socioeconomic conditions and attitudes towards people with mental illnesses.

Methods: Random effects logistic regression modelling was employed to explore the relationship between individual financial circumstances, contextual socioeconomic factors and difficulty speaking to a person with a significant mental illness across European countries.

Results: Lower GDP per capita and higher income inequality at the country level, alongside individual financial difficulties, were each associated with a greater likelihood of reporting difficulty speaking to a person with a significant mental illness.

Discussion: Micro and macro-economic factors are associated with public attitudes towards people with mental illness across Europe. With prolonged economic instability predicted over the coming years in Europe it is important that these findings are taken into consideration when designing mental health and social policies, in order to safeguard the progress that has been made in reducing mental health stigma to date.

Background: Chronic kidney disease is defined as a reduction in glomerular filtration rate below 60 ml/min per 1.73 m² and presence of albuminuria over a period of time. Globally, 10%-15% of populations are affected by chronic kidney disease. Studies conducted in Jimma, Addis Ababa, and the Tigray region were conducted on a single chronic disease and did not include human immune viruses. In addition, there has been no such study conducted in the Amhara region. Therefore, the aim of this study was to determine the magnitude and associated factors of chronic kidney disease among chronic patients who are followed up at an outpatient department.

Methods: An institutional-based cross-sectional study of 480 chronic patients was conducted at Dessie Referral Hospital, Dessie, Ethiopia between 15 March and 16 April 2020. Data were entered into Epidata and exported to SPSS version 25 for analysis. Binary logistic regression models were performed to identify factors associated with chronic kidney disease. The variables with a p-value ≤0.25 were considered to be a candidate for multivariable logistic regression. A p-value ≤0.05 was considered a statistically significant association.

Results: The magnitude of chronic kidney disease among the study participants was 21.3%. Variables such as hypertension [adjusted odds ratio (AOR): 2.6, 95% CI: 1.58-4.27], use of non-steroidal anti-inflammatory drugs (AOR: 2.4, 95% CI: 1.41-3.97), smoking (AOR: 4.4, 95% CI: 2.65-7.34), routine physical activity (AOR: 0.6, 95% CI: 0.35-0.94), and obesity (AOR: 3.0, 95% CI: 1.76-5.05) were significantly associated with the chronic kidney disease.

Conclusion: This study found that the magnitude of chronic kidney disease in the study area was high. Hypertension, use of non-steroidal anti-inflammatory drugs, smoking, routine physical activity, and obesity were found to be significant factors for chronic kidney disease.

Objectives: This study aims to investigate the prevalence of undiagnosed cardiovascular risk factors in patients with ischaemic heart disease (IHD).

Methods: We assessed the prevalence of previously undiagnosed cardiovascular risk factors, including elevated lipoprotein(a) [Lp(a)], among consenting patients with IHD who were admitted to hospital. Clinical information, including dietary history, from patients with newly diagnosed IHD and known IHD were compared.

Results: Of the 555 patients, 82.3% were males and 48.5% of Chinese ethnicity. Overall, 13.3% were newly diagnosed with hypertension, 14.8% with hypercholesterolemia, and 5% with type 2 diabetes (T2DM). Patients with newly diagnosed IHD, compared to those with known IHD, had a higher prevalence of new diagnoses of hypercholesterolemia (29.1% vs. 2.0%, p < 0.001), hypertension (24.5% vs. 3.4%, p < 0.001) and T2DM (7.3% vs. 3.1%, p = 0.023). Active smoking was prevalent in 28.3% of patients, and higher in newly diagnosed IHD (34.1% vs. 23.2%, p = 0.005). Elevated Lp(a) of ≥120 nmol/L was detected in 15.6% of all patients, none of whom were previously diagnosed. Dietary habits of >50% of patients in both groups did not meet national recommendations for fruits, vegetables, wholegrain and oily fish intake. However, patients with known IHD had a more regular omega-3 supplement intake (23.4% vs. 10.3%, p = 0.024).

Conclusion: Increased detection efforts is necessary to diagnose chronic metabolic diseases (hypertension, hypercholesterolemia, T2DM) especially among patients at high risk for IHD. Cardiovascular risk factors, in particular elevated Lp(a), smoking, and suboptimal dietary intake in patients with IHD deserve further attention.

Data that is collected at the individual-level from mobile phones is typically aggregated to the population-level for privacy reasons. If we are interested in answering questions regarding the mean, or working with groups appropriately modeled by a continuum, then this data is immediately informative. However, coupling such data regarding a population to a model that requires information at the individual-level raises a number of complexities. This is the case if we aim to characterize human mobility and simulate the spatial and geographical spread of a disease by dealing in discrete, absolute numbers. In this work, we highlight the hurdles faced and outline how they can be overcome to effectively leverage the specific dataset: Google COVID-19 Aggregated Mobility Research Dataset (GAMRD). Using a case study of Western Australia, which has many sparsely populated regions with incomplete data, we firstly demonstrate how to overcome these challenges to approximate absolute flow of people around a transport network from the aggregated data. Overlaying this evolving mobility network with a compartmental model for disease that incorporated vaccination status we run simulations and draw meaningful conclusions about the spread of COVID-19 throughout the state without de-anonymizing the data. We can see that towns in the Pilbara region are highly vulnerable to an outbreak originating in Perth. Further, we show that regional restrictions on travel are not enough to stop the spread of the virus from reaching regional Western Australia. The methods explained in this paper can be therefore used to analyze disease outbreaks in similarly sparse populations. We demonstrate that using this data appropriately can be used to inform public health policies and have an impact in pandemic responses.

Introduction: Previous studies have sought to identify risk factors for malnutrition in populations of schoolchildren, depending on traditional logistic regression methods. However, holistic machine learning (ML) approaches are emerging that may provide a more comprehensive analysis of risk factors.

Methods: This study employed feature selection and association rule learning ML methods in conjunction with logistic regression on epidemiological survey data from 1,036 Ethiopian school children. Our first analysis used the entire dataset and then we reran this analysis on age, residence, and sex population subsets.

Results: Both logistic regression and ML methods identified older childhood age as a significant risk factor, while females and vaccinated individuals showed reduced odds of stunting. Our machine learning analyses provided additional insights into the data, as feature selection identified that age, school latrine cleanliness, large family size, and nail trimming habits were significant risk factors for stunting, underweight, and thinness. Association rule learning revealed an association between co-occurring hygiene and socio-economical variables with malnutrition that was otherwise missed using traditional statistical methods.

Discussion: Our analysis supports the benefit of integrating feature selection methods, association rules learning techniques, and logistic regression to identify comprehensive risk factors associated with malnutrition in young children.

Introduction: Brucellosis is endemic in Uganda and is a major cause of production losses in livestock. Early detection and quantification of the disease is vital for its control and eradication. The aim of this study was to assess the sero-prevalence and factors associated with anti-Brucella antibodies in slaughtered livestock.

Materials and methods: Sera from 886 cattle, 925 small ruminants, and 900 pigs were collected from regional abattoirs in Northern, Eastern and Central Uganda. To estimate sero-prevalence, sera were serially tested using a combination of the Rose Bengal Test (RBT) and Native Hapten (NH) immunoprecipitation test. True sero-prevalence was estimated using the Rogan-Gladden estimator considering the sensitivity and specificity of the NH immunoprecipitation test. Multivariable logistic regression was used to assess factors associated with seropositivity for anti-Brucella antibodies.

Results and discussion: Small ruminants showed the highest seroprevalence (6.7%, 95% CI = 4.2-7.1) followed by cattle (3.8%, 95% CI = 2.4-4.9) and pigs (2.8%, 95% CI = 1.1-2.9). Seropositivity for anti-Brucella antibodies was associated with region of origin (OR = 4.6,95%CI=1.49-17.75, p = 0.013) for cattle; sex (OR = 2.90, 95% C = 1.5-6.34, p = 0.004), age (OR=4.04, 95% CI = 1.07-8.52, p = 0.006) and species (OR = 2.53, 95% CI = 1.08-6.98, p = 0.048) for small ruminants; and finally sex for pigs (OR = 2.88, 95% CI = 1.07-8.52, p = 0.041). Progressive control interventions must include both cattle and small ruminants since they play a bigger role in the maintenance and dissemination of Brucella. The interventions should adopt a risk-based approach with regions at higher risk being given top priority. Bacteriological and molecular studies should be undertaken to clarify the role of pigs and the goat-cattle cross infections in the epidemiological cycle of brucellosis in Uganda.

Greece opened its points of entry on July 1, 2020, with specific guidelines for travellers arriving by sea, air or land. The aim of this article is to examine the effect of tourism on the long term course of the Coronavirus Disease 2019 (COVID-19) pandemic during the pre-vaccination era (June to December 2020) on the popular Greek island of Crete. To achieve this, a cross-sectional serosurvey, repeated at monthly intervals, was conducted to compare the seroprevalence in Crete with seroprevalence in the mainland of Greece. Crete welcomed nearly 2,000,000 travellers during the 2020 summer season. Left-over serum samples were collected and obtained from public and private laboratories located in Greece, including the island of Crete. These samples were tested for the presence of anti-SARS-CoV-2 IgG antibodies. A total of 55,938 samples were collected, 3,785 of which originated from Crete. In Crete, the seroprevalence ranged between 0% (June 2020) and 2.58% (December 2020), while the corresponding seroprevalence in Greece was 0.19% and 10.75%, respectively. We identified 4.16 times lower seropositivity in Crete (2.58%) in comparison with the mainland of Greece (10.75%) during December 2020. Moreover, the monthly infection fatality rate (IFR) in Crete was calculated at 0.09%, compared with 0.21% in mainland Greece for December 2020. The island of Crete presented more than four times lower seroprevalence than the mainland of Greece, despite being a highly attractive tourist destination. This evidence supports the idea that tourism may not have affected the long term course of the COVID-19 pandemic in Greece. However, due to contradicting results from previous studies, further investigation is needed.

Background: A subset of patients experience persisting symptoms after an acute COVID-19 infection, referred to as "post COVID-19 condition". This cross-sectional study aimed to compare symptoms, health-related quality of life (HRQoL), fatigue, mental well-being, and determinants of diminished HRQoL, between patients with post COVID-19 condition categorized by time since acute infection.

Methods: We performed an online survey and analyzed responses of 10,194 adult respondents with a confirmed or suspected COVID-19 infection, who experienced persisting symptoms ≥3 months after the initial infection. The most debilitating symptoms and health outcomes were studied separately for respondents 3-6, 7-9, 10-12, 13-18, 19-24, and >24 months after acute infection.

Results: At each time period, fatigue, sensory-processing problems, and concentration problems were the most debilitating symptoms reported by respondents, although the proportion of respondents who reported these symptoms differed significantly between time periods. Respondents 3-6 months post-acute infection had the lowest HRQoL (median EQ-5D utility score: 0.59), the highest fatigue level (median score: 110.0) and the highest proportion with a likely depressive disorder (32.4%), whereas respondents 13-18 months post-infection had the highest HRQoL (0.65), the lowest fatigue level (106.0), and the second lowest proportion with a likely depressive disorder (25.0%) (p = 0.000-0.007). Compared to those 13-18 and 19-24 months post-infection, respondents >24 months post-infection had a slightly lower HRQoL (0.60), lower fatigue level (108.0), and lower proportion with a likely depressive disorder (29.2%), although only the differences in HRQoL were statistically significant (p = 0.001-0.010). Younger age, female gender, lower level of education, not having paid work before COVID-19, comorbidity, and not being vaccinated, seemed to be associated with lower HRQoL.

Conclusion: Regardless of time since infection, respondents considered fatigue, sensory processing problems and concentration problems the most debilitating symptoms. They experienced a low HRQoL and severe fatigue, even more than two years after acute COVID-19 infection. Respondents 3-6 months post-infection had the worst health outcomes, whereas respondents 13-18 months post-infection had the best outcomes, indicating that, at least for a subgroup of patients, health status may improve over time.