Dataset Information

Measuring health related quality of life for dengue patients in Iquitos, Peru.

ABSTRACT: Previous studies measuring the health-related quality of life (HRQoL) of individuals with dengue focused on treatment seeking populations. However, the vast majority of global dengue cases are unlikely to be detected by health systems. Representative measurements of HRQoL should therefore include patients with disease not likely to trigger treatment-seeking behavior. This study based in Iquitos, Peru used the Quality of Wellbeing Scale-Self Administered, a survey that enquires about not only physical health, but also psychological health, self-care, mobility, and usual social activities, and rates HRQoL between 0 (death) and 1 (optimum function), to evaluate the impact of dengue on HRQoL. In order to enroll treatment and non treatment-seeking participants, three modalities of participant recruitment were used. In addition to clinic and community-based febrile surveillance, a contact-cluster methodology was also employed to identify infected individuals less likely to seek treatment. We measured changes in HRQoL and identified common areas of health impairment in 73 virologically confirmed dengue cases at 3 time points during the participant's illness; the early-acute (days 0-6 post symptom onset), late-acute (days 7-20), and convalescent illness phases (days 21 +). Participants reported HRQoL related impairments at significantly higher frequency during the early-acute versus convalescent illness phase (Fisher's exact: P<0.01). There was substantial heterogeneity in scores during each illness phase with median scores in the early-acute, late-acute and convalescent phases of 0.56 (IQR: 0.41-0.64), 0.70 (IQR: 0.57-0.94), and 1 (IQR: 0.80-1.00), respectively. In all illness phases participants recruited in clinics had on average the lowest HRQoL scores where as those recruited in the contact clusters had the highest. Only 1 individual who was recruited in the contact-clusters had no reduction in HRQoL score during their illness. These data illustrate that dengue should be considered as a disease that may have significant implications for not only physical health but also psychological health and social functioning. The impact of dengue on the HRQoL of non-treatment-seeking individuals, although lower than the impact among treatment-seeking individuals, is not necessarily trivial.

SUBMITTER: Elson WH

PROVIDER: S-EPMC7413550 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Measuring health related quality of life for dengue patients in Iquitos, Peru.

Elson William H WH Riley-Powell Amy R AR Morrison Amy C AC Gotlieb Esther E EE Groessl Erik J EJ Cordova Jhonny J JJ Rios J Esther JE Quiroz W Lorena WL Vizcarra Alfonso S AS Reiner Robert C RC Barker Christopher M CM Vazquez-Prokopec Gonzalo M GM Scott Thomas W TW Rothman Alan L AL Elder John P JP Paz-Soldan Valerie A VA

PLoS neglected tropical diseases 20200728 7

Previous studies measuring the health-related quality of life (HRQoL) of individuals with dengue focused on treatment seeking populations. However, the vast majority of global dengue cases are unlikely to be detected by health systems. Representative measurements of HRQoL should therefore include patients with disease not likely to trigger treatment-seeking behavior. This study based in Iquitos, Peru used the Quality of Wellbeing Scale-Self Administered, a survey that enquires about not only phy ...[more]

PMID: 32722709

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Project description:IntroductionLong-term disease surveillance data provide a basis for studying drivers of pathogen transmission dynamics. Dengue is a mosquito-borne disease caused by four distinct, but related, viruses (DENV-1-4) that potentially affect over half the world's population. Dengue incidence varies seasonally and on longer time scales, presumably driven by the interaction of climate and host susceptibility. Precise understanding of dengue dynamics is constrained, however, by the relative paucity of laboratory-confirmed longitudinal data.MethodsWe studied 10 years (2000-2010) of laboratory-confirmed, clinic-based surveillance data collected in Iquitos, Peru. We characterized inter and intra-annual patterns of dengue dynamics on a weekly time scale using wavelet analysis. We explored the relationships of case counts to climatic variables with cross-correlation maps on annual and trimester bases.FindingsTransmission was dominated by single serotypes, first DENV-3 (2001-2007) then DENV-4 (2008-2010). After 2003, incidence fluctuated inter-annually with outbreaks usually occurring between October and April. We detected a strong positive autocorrelation in case counts at a lag of ∼ 70 weeks, indicating a shift in the timing of peak incidence year-to-year. All climatic variables showed modest seasonality and correlated weakly with the number of reported dengue cases across a range of time lags. Cases were reduced after citywide insecticide fumigation if conducted early in the transmission season.ConclusionsDengue case counts peaked seasonally despite limited intra-annual variation in climate conditions. Contrary to expectations for this mosquito-borne disease, no climatic variable considered exhibited a strong relationship with transmission. Vector control operations did, however, appear to have a significant impact on transmission some years. Our results indicate that a complicated interplay of factors underlie DENV transmission in contexts such as Iquitos.

Project description:BackgroundPredictive models can serve as early warning systems and can be used to forecast future risk of various infectious diseases. Conventionally, regression and time series models are used to forecast dengue incidence, using dengue surveillance (e.g., case counts) and weather data. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) methods are designed to work with a large number of predictors and thus offer an appealing alternative. Here, we compared the performance of ML algorithms with that of regression models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contribution of dengue surveillance and weather data on the predictive power of these models.MethodsWe developed ML, regression, and time series models to forecast weekly dengue case counts and outbreaks in Iquitos, Peru; San Juan, Puerto Rico; and Singapore from 1990-2016. Forecasts were generated using available weekly dengue surveillance, and weather data. We evaluated the agreement between model forecasts and actual dengue observations using Mean Absolute Error and Matthew's Correlation Coefficient (MCC).ResultsFor near term predictions of weekly case counts and when using surveillance data, ML models had 21% and 33% less error than regression and time series models respectively. However, using weather data only, ML models did not demonstrate a practical advantage. When forecasting weekly dengue outbreaks 12 weeks in advance, ML models achieved a maximum MCC of 0.61.ConclusionsOur results identified 2 scenarios when ML models are advantageous over regression model: 1) predicting dengue weekly case counts 4 weeks ahead when dengue surveillance data are available and 2) predicting weekly dengue outbreaks 12 weeks ahead when dengue surveillance data are unavailable. Given the advantages of ML models, dengue early warning systems may be improved by the inclusion of these models.

Project description:BackgroundStudies have shown that women aging with HIV have significantly lower health-related quality of life (HRQoL) compared to women without HIV. However, no studies have examined this issue in Latin America and the Caribbean. We aimed to explore HRQoL measured by the 36-Item Short Form Health Survey (SF-36) among women aging with and without HIV in Peru.Materials and methodsWe conducted a cross-sectional study at a large HIV-clinic in Peru. Outcomes of the SF-36 were evaluated, exploring the relationship between physical activity (International Physical Activity Questionnaire), sociodemographic factors (ethnicity, alcohol/tobacco use, age, BMI) and clinical data (AIDS progression, treatment duration, CD4+ cell count and viral load, years since HIV diagnosis) with HRQoL using regression analysis. Statistical significance was set with a two-tailed p-value <0.05.ResultsWe enrolled 427 women (175 HIV-infected) with mean age of 54±8 years. From the SF-36 individual domains: physical functioning, role limitations due to physical and emotional health, and emotional wellbeing were significantly lower for HIV-infected women. Summary component scores were lower for the HIV-subset for both physical (45.8 vs 47.3) and mental (45.1 vs 45.8) components, although they did not achieve statistical significance. Regression analysis of the HIV-infected women revealed that the physical component score was significantly associated with physical activity, ethnicity, and chronic comorbidities while the mental component was significantly associated with physical activity, employment, and CD4+ cell count.ConclusionIn our study, HIV-infected women scored lower in both physical and mental component scores. Important determinants for each component included CD4+ cell count as an assessment of HIV severity for the mental component, and ethnicity, reflecting socio-cultural factors, for the physical component. These results reveal the importance of a holistic approach to addressing HRQoL in this population. Better understanding of these factors will help shape future policies and interventions to improve HRQoL of women aging with HIV.

Project description:BackgroundChild maltreatment causes substantial morbidity and mortality in the U.S. Morbidity associated with child maltreatment can reduce health-related quality of life. Accurately measuring the reduction in quality of life associated with child maltreatment is essential to the economic evaluation of educational programs and interventions to reduce the incidence of child maltreatment. The objective of this study was to review the literature for existing approaches and instruments for measuring quality-of-life for child maltreatment outcomes.MethodsWe reviewed the current literature to identify current approaches to valuing child maltreatment outcomes for economic evaluations. We also reviewed available preference-based generic QOL instruments (EQ-5D, HUI, QWB, SF-6D) for appropriateness in measuring change in quality of life due to child maltreatment.ResultsWe did not identify any studies that directly evaluated quality-of-life in maltreated children. We identified 4 studies that evaluated quality of life for adult survivors of child maltreatment and 8 studies that measured quality-of-life for pediatric injury not related to child maltreatment. No study reported quality-of-life values for children younger than age 3. Currently available preference-based QOL instruments (EQ-5D, HUI, QWB, SF-6D) have been developed primarily for adults with the exception of the Health Utilities Index. These instruments do not include many of the domains identified as being important in capturing changes in quality of life for child maltreatment, such as potential for growth and development or psychological sequelae specific to maltreatment.ConclusionRecommendations for valuing preference-based quality-of-life for child maltreatment will vary by developmental level and type of maltreatment. In the short-term, available multi-attribute utility instruments should be considered in the context of the type of child maltreatment being measured. However, if relevant domains are not included in existing instruments or if valuing health for children less than 6 years of age, direct valuation with a proxy respondent is recommended. The choice of a proxy respondent is not clear in the case of child maltreatment since the parent may not be a suitable proxy. Adult survivors should be considered as appropriate proxies. Longer-term research should focus on identifying the key domains for measuring child health and the development of preference-based quality-of-life instruments that are appropriate for valuing child maltreatment outcomes.

Dataset Information

Measuring health related quality of life for dengue patients in Iquitos, Peru.

Publications

Measuring health related quality of life for dengue patients in Iquitos, Peru.

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