Ocular fungal infections

[extracted from abstract] Fungal infections of the eye continue to be an important cause of ocular morbidity and loss of vision, particularly in the developing world [1]. These infections have increased in recent decades due to broad-spectrum antibiotic use, the growing number of patients undergoing procedures that lead to immunosuppression, postoperative infection, trauma, and prolonged corticosteroid use [2]. Ocular fungal infections are categorized by the anatomical location of the infection. These infections can occur around the eye (ocular adnexa), or in the eye, including the anterior and posterior segments of the eye [3]. Major pathogenic fungi of the eye include Aspergillus, Candida spp., Cryptococcus species, and Coccidioides spp., Fusarium, Penicillium, Pseudallescheria, dimorphic fungi as Histoplasma capsulatum, Blastomyces dermatitidis, Sporothrix spp., and Coccidioides spp. (C.immitis and C. posadasii) [3,4]. The diagnosis of ocular fungal infections can be difficult because of non-specific clinical manifestations. However, in recent years it has been improved by laboratory and diagnostic techniques, and the recognition of the clinical signs of ocular fungal infections [4]. This has increased the frequency of correct diagnosis and prevalence of these diseases. Because of this, it is important to maintain to knowledge of new developments in the diagnosis and management of infectious diseases of the eye. In this setting, in this Special Issue, articles have been published describing novel findings and reviews on the epidemiology, diagnosis, and treatment of ocular fungal infections, with a special focus on infections in ocular adnexa, endophthalmitis, keratitis, and ocular sporotrichosis.Campus Lima Centr

Características epidemiológicas de la esporotricosis pediátrica en el foco hiperendémico de Abancay, Perú

El documento digital no refiere asesorPublicación a texto completo no autorizada por el autorDescribir la incidencia y factores de riesgo de la esporotricosis pediátrica en la zona hiperendémica de Abancay, Perú e identificar qué especies de Sporothrix caracterizadas por biología molecular se asociaron comúnmente con esta micosis. Se realizó estudio retrospectivo de pacientes pediátricos de 0 a 14 años, diagnosticados con esporotricosis en la provincia de Abancay de 2004 a 2015, para estimar las tasas de incidencia media, anual, y estratificada por edad (por 100,000 niños ≤14 años) y tipo de esporotricosis (linfocutánea y fija), y los factores de riesgo de estos pacientes. También se identificó las especies de Sporothrix aisladas de estos pacientes por reacción en cadena de la polimerasa (PCR; biología molecular) utilizando el gen de la Calmodulina (CAL). De un total de 280 casos pediátricos identificados, el 54,3% eran varones y la mediana de edad fue 6 años. La tasa de incidencia media fue de 60.3 casos por 100,000 niños ≤14 años entre 2004 y 2015, y fue mayor entre los niños de 5 a 9 años de edad. La incidencia de esporotricosis linfocutánea fue mayor que la del tipo fija (39.4 vs. 20.8 /100,000 niños ≤14 años). Comparado con los casos ocurridos entre 2010–2015, la mayoría de casos ocurridos entre 2004–2009 habitaban en viviendas de adobe (77.8% vs. 58.4%; P<0.001) con plantas espinosas (44.3% vs. 26.0%; P<0.005), utilizaban zapato canasta (43.8% vs. 29.9%; P<0.033) y tuvieron lesiones contaminadas con tierra (59.6% vs. 46.8%; P<0.05). En el análisis molecular (gen CAL) quince aislados de estos pacientes (9 linfocutáneos y 6 fijos) presentaron 98 a 99% de identidad de secuencias de nucleótidos con el Sporothrix schenckii (sensu stricto). La incidencia de esporotricosis en niños de Abancay aumenta con la edad. La esporotricosis linfocutánea fue el tipo más común con una incidencia de casi el doble a la del tipo cutáneo fija. La infección parecía adquirirse debido a las condiciones deficientes de las viviendas e higiene. S. schenckii (sensu stricto) fue la especie predominante y responsable de la esporotricosis linfocutánea y fija en esta serie de casos.Tesi

Sex differences in the incidence, mortality, and fatality of COVID-19 in Peru

Background:There is a worrying lack of epidemiological data on the sex differential in COVID-19 infection and death rates between the regions of Peru. Methods: Using cases and death data from the national population-based surveillance system of Peru, we estimated incidence, mortality and fatality, stratified by sex, age and geographic distribution (per 100,000 habitants) from March 16 to November 27, 2020. At the same time, we calculated the risk of COVID-19 death. Results: During the study period, 961894 cases and 35913 deaths were reported in Peru. Men had a twofold higher risk of COVID-19 death within the overall population of Peru (odds ratio (OR), 2.11; confidence interval (CI) 95%; 2.06–2.16; p<0.00001), as well as 20 regions of Peru, compared to women (p<0.05). There were variations in incidence, mortality and fatality rates stratified by sex, age, and region. The incidence rate was higher among men than among women (3079 vs. 2819 per 100,000 habitants, respectively). The mortality rate was two times higher in males than in females (153 vs. 68 per 100,000 habitants, respectively). The mortality rates increased with age, and were high in men 60 years of age or older. The fatality rate was two times higher in men than in women (4.96% vs. 2.41%, respectively), and was high in men 50 years of age or older. Conclusions: These findings show the higher incidence, mortality and fatality rates among men than among women from Peru. These rates vary widely by region, and men are at greater risk of COVID-19 death. In addition, the mortality and fatality rates increased with age, and were most predominant in men 50 years of age or older.Campus Lima Centr

Characteristics of Respiratory Syncytial Virus versus Influenza infection in hospitalized patients of Peru: a retrospective observational study

Respiratory syncytial virus (RSV) and influenza infections are important causes of respiratory illness associated with hospitalizations in children in Peru; however, comparisons of RSV and influenza hospitalization across all age groups are not available in Peru. Therefore, we conducted an observational, retrospective study between May 2015 and October 2021 using hospitalization from RSV and influenza infection data obtained from SUSALUD (open data) in Peru to compare the baseline characteristics of sex, age, region, and infection type. For the study, 2696 RSV-infected and 1563 influenza-infected hospitalized patients from different age groups were included. Most hospitalizations from RSV infection and the influenza virus occurred in children <5 years of age (86.1% vs. 32.2%, respectively). Compared with influenza infection, RSV infection was less likely to occur in individuals ≥5 years of age (adjusted odds ratio (aOR) = 0.07; 95% confidence interval (CI), 0.06–0.08; p < 0.0001; compared to <5 years of age), and more likely to occur in highlands (aOR = 1.75; 95% CI, 1.46–2.07; p < 0.0001, compared to coast region), and jungle region (aOR = 1.75; 95% CI, 1.27–2.41; p = 0.001, compared to coast region). Among the respiratory complications, RSV pneumonia was less likely to occur between different age groups (aOR = 0.29; 95% CI, 0.22–0.37; p < 0.0001, compared to <5 years of age), compared with influenza pneumonia. These findings on the RSV-hospitalization and its complications are helpful for health services planning and may increase awareness of the Peruvian population’s RSV and influenza disease burden.Campus Lima Centr

Analysis of excess all-cause mortality and COVID-19 mortality in Peru: Observational study

During the COVID-19 pandemic, an excess of all-cause mortality has been recorded in several countries, including Peru. Most excess deaths were likely attributable to COVID-19. In this study, we compared the excess all-cause mortality and COVID-19 mortality in 25 Peruvian regions to determine whether most of the excess deaths in 2020 were attributable to COVID-19. Excess deaths were calculated as the difference between the number of observed deaths from all causes during the COVID-19 pandemic (in 2020) and the number of expected deaths in 2020 based on a historical from recent years (2017–2019). Death data were retrieved from the Sistema Informatico Nacional de Defunciones (SINADEF) at the Ministry of Health of Peru from January 2017 to December 2020. Population counts were obtained from projections from Peru’s Instituto Nacional de Estadística e Informática (INEI). All-cause excess mortality and COVID-19 mortality were calculated by region per 100,000 population. Spearman’s test and linear and multiple regression models were used to estimate the correlation between excess all-cause mortality and COVID-19 mortality per 100,000 population. Excess all-cause death rates varied widely among regions (range: 115.1 to 519.8 per 100,000 population), and COVID-19 mortality ranged between 83.8 and 464.6 per 100,000 population. There was a correlation between the all-cause excess mortality and COVID-19 mortality (r = 0.90; p = 0.00001; y = 0.8729x + 90.808; R 2 = 0.84). Adjusted for confounding factors (mean age in the region, gender balance, and number of intensive care unit (ICU) beds), the all cause excess mortality rate was correlated with COVID-19 mortality rate (β = 0.921; p = 0.0001). These findings suggest that most of the excess deaths in Peru are related to COVID-19. Therefore, these findings can help decision-makers to understand the high COVID-19 mortality rates in Peru.Campus Lima Centr

COVID-19, non-communicable diseases, and behavioral factors in the Peruvian population ≥ 15 years: an ecological study during the first and second year of the COVID-19 pandemic

A range of health-related and behavioral risk factors are associated with COVID-19 incidence and mortality. In the present study, we assess the association between incidence, mortality, and case fatality rate due to COVID-19 and the prevalence of hypertension, obesity, overweight, tobacco and alcohol use in the Peruvian population aged ≥15 years during the first and second year of the COVID-19 pandemic. In this ecological study, we used the prevalence rates of hypertension, overweight, obesity, tobacco, and alcohol use obtained from the Encuesta Demográfica y de Salud Familiar (ENDES) 2020 and 2021. We estimated the crude incidence and mortality rates (per 100,000 habitants) and case fatality rate (%) of COVID-19 in 25 Peruvian regions using data from the Peruvian Ministry of Health that were accurate as of 31 December 2021. Spearman correlation and lineal regression analysis was applied to assess the correlations between the study variables as well as multivariable regression analysis adjusted by confounding factors affecting the incidence and mortality rate and case fatality rate of COVID-19. In 2020, adjusted by confounding factors, the prevalence rate of obesity (β = 0.582; p = 0.037) was found to be associated with the COVID-19 mortality rate (per 100,000 habitants). There was also an association between obesity and the COVID-19 case fatality rate (β = 0.993; p = 0.014). In 2021, the prevalence of obesity was also found to be associated with the COVID-19 mortality rate (β = 0.713; p = 0.028); however, adjusted by confounding factors, including COVID-19 vaccination coverage rates, no association was found between the obesity prevalence and the COVID-19 mortality rate (β = 0.031; p = 0.895). In summary, Peruvian regions with higher obesity prevalence rates had higher COVID-19 mortality and case fatality rates during the first year of the COVID-19 pandemic. However, adjusted by the COVID-19 vaccination coverage, no association between the obesity prevalence rate and the COVID-19 mortality rate was found during the second year of the COVID-19 pandemic.Campus Lima Centr

Excess all-cause deaths stratified by sex and age in Peru: a time series analysis during the COVID-19 pandemic

Background In this study, we estimated excess all-cause deaths and excess death rates during the COVID-19 pandemic in 25 Peruvian regions, stratified by sex and age group. Design Cross-sectional study. Setting Twenty-five Peruvian regions with complete mortality data. Participants Annual all-cause official mortality data set from SINADEF (Sistema Informático Nacional de Defunciones) at the Ministry of Health of Peru for 2017– 2020, disaggregated by age and sex. Main outcome measures Excess deaths and excess death rates (observed deaths vs expected deaths) in 2020 by sex and age (0–29, 30–39, 40 49, 50–59, 60–69, 70–79 and ≥80 years) were estimated using P-score. The ORs for excess mortality were summarised with a randomeffects meta-analysis. Results In the period between January and December 2020, we estimated an excess of 68 608 (117%) deaths in men and 34 742 (69%) deaths in women, corresponding to an excess death rate of 424 per 100 000 men and 211 per 100 000women compared with the expected mortality rate. The number of excess deaths increased with age and was higher in men aged 60–69 years (217%) compared with women (121%). Men between the ages of 40 and 79 years experienced twice the rate of excess deaths compared with the expected rate. In eight regions, excess deaths were higher than 100% in men, and in seven regions excess deaths were higher than 70% in women. Men in eight regions and women in one region had two times increased odds of excess death than the expected mortality. There were differences in excess mortality according to temporal distribution by epidemiological week. Conclusion Approximately 100 000 excess all-cause deaths occurred in 2020 in Peru. Age-stratified excess death rates were higher in men than in women. There was strong excess in geographical and temporal mortality patterns according to region.Campus Lima Centr

Association between obesity and COVID-19 mortality in Peru: an ecological study

There is a gap in the epidemiological data on obesity and COVID-19 mortality in low and middle-income countries worst affected by the COVID-19 pandemic, including Peru. In this ecological study, we explored the association between body mass index (BMI), the prevalence of overweight and obesity, and the COVID-19 mortality rates in 25 Peruvian regions, adjusted for confounding factors (mean age in the region, mean income, gender balance and number of Intensive Care Unit (ICU) beds) using multiple linear regression. We retrieved secondary region-level data on the BMI average and prevalence rates of overweight and obesity in individuals aged ≥ 15 years old, from the Peruvian National Demographics and Health Survey (ENDES 2020). COVID-19 death statistics were obtained from the National System of Deaths (SINADEF) from the Peruvian Ministry of Health and were accurate as of 3 June 2021. COVID-19 mortality rates (per 100,000 habitants) were calculated among those aged ≥ 15 years old. During the study period, a total of 190,046 COVID-19 deaths were registered in individuals aged ≥ 15 years in 25 Peruvian regions. There was association between the BMI (r = 0.74; p = 0.00001) and obesity (r = 0.76; p = 0.00001), and the COVID-19 mortality rate. Adjusted for confounding factors, only the prevalence rate of obesity was associated with COVID-19 mortality rate (β = 0.585; p = 0.033). These findings suggest that as obesity prevalence increases, the COVID-19 mortality rates increase in the Peruvian population ≥ 15 years. These findings can help to elucidate the high COVID-19 mortality rates in Peru.Campus Lima Centr

Liberia adherence and loss-to-follow-up in HIV and AIDS care and treatment: A retrospective cohort of adolescents and adults from 2016–2019

Background Antiretroviral therapy (ART) is a lifesaving intervention for people living with HIV infection, reducing morbidity and mortality; it is likewise essential to reducing transmission. The “Treat all” strategy recommended by the World Health Organization has dramatically increased ART eligibility and improved access. However, retaining patients on ART has been a major challenge for many national programs in low- and middle-income settings, despite actionable local policies and ambitious targets. To estimate retention of patients along the HIV care cascade in Liberia, and identify factors associated with loss-to-follow-up (LTFU), death, and suboptimal treatment adherence, we conducted a nationwide retrospective cohort study utilizing facility and patient-level records. Patients aged ≥15 years, from 28 facilities who were first registered in HIV care from January 2016 –December 2017 were included. We used Cox proportional hazard models to explore associations between demographic and clinical factors and the outcomes of LTFU and death, and a multinomial logistic regression model to investigate factors associated with suboptimal treatment adherence. Among the 4185 records assessed, 27.4% (n = 1145) were males and the median age of the cohort was 37 (IQR: 30–45) years. At 24 months of follow-up, 41.8% (n = 1751) of patients were LTFU, 6.6% (n = 278) died, 0.5% (n = 21) stopped treatment, 3% (n = 127) transferred to another facility and 47.9% (n = 2008) were retained in care and treatment. The incidence of LTFU was 46.0 (95% CI: 40.8–51.6) per 100 person-years. Relative to patients at WHO clinical stage I at first treatment visit, patients at WHO clinical stage III [adjusted hazard ratio (aHR) 1.59, 95%CI: 1.21–2.09; p <0.001] or IV (aHR 2.41, 95%CI: 1.51–3.84; p <0.001) had increased risk of LTFU; whereas at registration, age category 35–44 (aHR 0.65, 95%CI: 0.44–0.98, p = 0.038) and 45 years and older (aHR 0.60, 95%CI: 0.39–0.93, p = 0.021) had a decreased risk. For death, patients assessed with WHO clinical stage II (aHR 2.35, 95%CI: 1.53–3.61, p<0.001), III (aHR 2.55, 95%CI: 1.75–3.71, p<0.001), and IV (aHR 4.21, 95%CI: 2.57–6.89, p<0.001) had an increased risk, while non-pregnant females (aHR 0.68, 95%CI: 0.51–0.92, p = 0.011) and pregnant females (aHR 0.42, 95%CI: 0.20–0.90, p = 0.026) had a decreased risk when compared to males. Suboptimal adherence was strongly associated with the experience of drug side effects–average adherence [adjusted odds ratio (aOR) 1.45, 95% CI: 1.06–1.99, p = 0.02) and poor adherence (aOR 1.75, 95%CI: 1.11–2.76, p = 0.016), and attending rural facility decreased the odds of average adherence (aOR 0.01, 95%CI: 0.01–0.03, p<0.001) and poor adherence (aOR 0.001, 95%CI: 0.0004–0.003, p<0.001). Loss-to-follow-up and poor adherence remain major challenges to achieving viral suppression targets in Liberia. Over two-fifths of patients engaged with the national HIV program are being lost to follow-up within 2 years of beginning care and treatment. WHO clinical stage III and IV were associated with LTFU while WHO clinical stage II, III and IV were associated with death. Suboptimal adherence was further associated with experience of drug side effects. Active support and close monitoring of patients who have signs of clinical progression and/or drug side effects could improve patient outcomes

Liberia adherence and loss-to-follow-up in HIV and AIDS care and treatment: A retrospective cohort of adolescents and adults from 2016–2019

Background Antiretroviral therapy (ART) is a lifesaving intervention for people living with HIV infection, reducing morbidity and mortality; it is likewise essential to reducing transmission. The “Treat all” strategy recommended by the World Health Organization has dramatically increased ART eligibility and improved access. However, retaining patients on ART has been a major challenge for many national programs in low- and middle-income settings, despite actionable local policies and ambitious targets. To estimate retention of patients along the HIV care cascade in Liberia, and identify factors associated with loss-to-follow-up (LTFU), death, and suboptimal treatment adherence, we conducted a nationwide retrospective cohort study utilizing facility and patient-level records. Patients aged ≥15 years, from 28 facilities who were first registered in HIV care from January 2016 –December 2017 were included. We used Cox proportional hazard models to explore associations between demographic and clinical factors and the outcomes of LTFU and death, and a multinomial logistic regression model to investigate factors associated with suboptimal treatment adherence. Among the 4185 records assessed, 27.4% (n = 1145) were males and the median age of the cohort was 37 (IQR: 30–45) years. At 24 months of follow-up, 41.8% (n = 1751) of patients were LTFU, 6.6% (n = 278) died, 0.5% (n = 21) stopped treatment, 3% (n = 127) transferred to another facility and 47.9% (n = 2008) were retained in care and treatment. The incidence of LTFU was 46.0 (95% CI: 40.8–51.6) per 100 person-years. Relative to patients at WHO clinical stage I at first treatment visit, patients at WHO clinical stage III [adjusted hazard ratio (aHR) 1.59, 95%CI: 1.21–2.09; p <0.001] or IV (aHR 2.41, 95%CI: 1.51–3.84; p <0.001) had increased risk of LTFU; whereas at registration, age category 35–44 (aHR 0.65, 95%CI: 0.44–0.98, p = 0.038) and 45 years and older (aHR 0.60, 95%CI: 0.39–0.93, p = 0.021) had a decreased risk. For death, patients assessed with WHO clinical stage II (aHR 2.35, 95%CI: 1.53–3.61, p<0.001), III (aHR 2.55, 95%CI: 1.75–3.71, p<0.001), and IV (aHR 4.21, 95%CI: 2.57–6.89, p<0.001) had an increased risk, while non-pregnant females (aHR 0.68, 95%CI: 0.51–0.92, p = 0.011) and pregnant females (aHR 0.42, 95%CI: 0.20–0.90, p = 0.026) had a decreased risk when compared to males. Suboptimal adherence was strongly associated with the experience of drug side effects–average adherence [adjusted odds ratio (aOR) 1.45, 95% CI: 1.06–1.99, p = 0.02) and poor adherence (aOR 1.75, 95%CI: 1.11–2.76, p = 0.016), and attending rural facility decreased the odds of average adherence (aOR 0.01, 95%CI: 0.01–0.03, p<0.001) and poor adherence (aOR 0.001, 95%CI: 0.0004–0.003, p<0.001). Loss-to-follow-up and poor adherence remain major challenges to achieving viral suppression targets in Liberia. Over two-fifths of patients engaged with the national HIV program are being lost to follow-up within 2 years of beginning care and treatment. WHO clinical stage III and IV were associated with LTFU while WHO clinical stage II, III and IV were associated with death. Suboptimal adherence was further associated with experience of drug side effects. Active support and close monitoring of patients who have signs of clinical progression and/or drug side effects could improve patient outcomes