Study protocol: improving response to malaria in the Amazon through identification of inter-community networks and human mobility in border regions of Ecuador, Peru and Brazil.

INTRODUCTION: Understanding human mobility's role in malaria transmission is critical to successful control and elimination. However, common approaches to measuring mobility are ill-equipped for remote regions such as the Amazon. This study develops a network survey to quantify the effect of community connectivity and mobility on malaria transmission. METHODS: We measure community connectivity across the study area using a respondent driven sampling design among key informants who are at least 18 years of age. 45 initial communities will be selected: 10 in Brazil, 10 in Ecuador and 25 in Peru. Participants will be recruited in each initial node and administered a survey to obtain data on each community's mobility patterns. Survey responses will be ranked and the 2-3 most connected communities will then be selected and surveyed. This process will be repeated for a third round of data collection. Community network matrices will be linked with each country's malaria surveillance system to test the effects of mobility on disease risk. ETHICS AND DISSEMINATION: This study protocol has been approved by the institutional review boards of Duke University (USA), Universidad San Francisco de Quito (Ecuador), Universidad Peruana Cayetano Heredia (Peru) and Universidade Federal Minas Gerais (Brazil). Results will be disseminated in communities by the end of the study.

Network Profile: Improving Response to Malaria in the Amazon through Identification of Inter-Community Networks and Human Mobility in Border Regions of Ecuador, Peru, and Brazil.

Objectives: Understanding human mobility's role on malaria transmission is critical to successful control and elimination. However, common approaches to measuring mobility are ill-equipped for remote regions such as the Amazon. This study develops a network survey to quantify the effect of community connectivity and mobility on malaria transmission. Design: A community-level network survey. Setting: We collect data on community connectivity along three river systems in the Amazon basin: the Pastaza river corridor spanning the Ecuador-Peru border; and the Amazon and Javari river corridors spanning the Brazil-Peru border. Participants: We interviewed key informants in Brazil, Ecuador, and Peru, including from indigenous communities: Shuar, Achuar, Shiwiar, Kichwa, Ticuna, and Yagua. Key informants are at least 18 years of age and are considered community leaders. Primary outcome: Weekly, community-level malaria incidence during the study period. Methods: We measure community connectivity across the study area using a respondent driven sampling design. Forty-five communities were initially selected: 10 in Brazil, 10 in Ecuador, and 25 in Peru. Participants were recruited in each initial node and administered a survey to obtain data on each community's mobility patterns. Survey responses were ranked and the 2-3 most connected communities were then selected and surveyed. This process was repeated for a third round of data collection. Community network matrices will be linked with eadch country's malaria surveillance system to test the effects of mobility on disease risk. Findings: To date, 586 key informants were surveyed from 126 communities along the Pastaza river corridor. Data collection along the Amazon and Javari river corridors is ongoing. Initial results indicate that network sampling is a superior method to delineate migration flows between communities. Conclusions: Our study provides measures of mobility and connectivity in rural settings where traditional approaches are insufficient, and will allow us to understand mobility's effect on malaria transmission.

Gait speed according to cognitive status in older adults: a cross-sectional study in a resource-limited country.

OBJECTIVE: We aimed to investigate the association between gait speed and cognitive status in outpatient older adults from a resource-limited setting in Peru. METHODS: We performed a cross-sectional study including older adults aged ≥60 years attending a geriatrics outpatient clinic between July 2017 and February 2020. Gait speed was measured over a 10-meters distance without considering the first and last meter traveled. Cognitive status was assessed through the Short Portable Mental Status Questionnaire (SPMSQ) and the Mini-Mental State Examination (MMSE). We used a multivariate binomial logistic regression to conduct both an epidemiological and fully adjusted models. RESULTS: We included 519 older adults (mean age: 75 years; IQR = 10), of whom 95 (18.3%) and 151 (31.5%) were cognitively impaired according to the SPMSQ and MMSE, respectively. Gait speed was slower among patients with poorer cognitive status as assessed by both tools (p < 0.001). Malnutrition (PR: 1.74; CI: 1.45-2.08) and functional dependency (PR: 4.35; CI: 2.68-7.08) were associated with a greater prevalence of cognitive impairment according to the SPMSQ, whereas a faster gait speed (PR: 0.27, CI: 0.14-0.52) and longer years of education (PR: 0.83, CI: 0.77-0.88) were associated with a less prevalence. CONCLUSIONS: Slower gait speed was associated with poorer cognitive status in outpatient older adults. Gait speed may be a complementary tool in the cognitive assessment of older adults from resource-limited settings.

Systematic review of diagnostic and prognostic models of chronic kidney disease in low-income and middle-income countries.

OBJECTIVE: To summarise available chronic kidney disease (CKD) diagnostic and prognostic models in low-income and middle-income countries (LMICs). METHOD: Systematic review (Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines). We searched Medline, EMBASE, Global Health (these three through OVID), Scopus and Web of Science from inception to 9 April 2021, 17 April 2021 and 18 April 2021, respectively. We first screened titles and abstracts, and then studied in detail the selected reports; both phases were conducted by two reviewers independently. We followed the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies recommendations and used the Prediction model Risk Of Bias ASsessment Tool for risk of bias assessment. RESULTS: The search retrieved 14 845 results, 11 reports were studied in detail and 9 (n=61 134) were included in the qualitative analysis. The proportion of women in the study population varied between 24.5% and 76.6%, and the mean age ranged between 41.8 and 57.7 years. Prevalence of undiagnosed CKD ranged between 1.1% and 29.7%. Age, diabetes mellitus and sex were the most common predictors in the diagnostic and prognostic models. Outcome definition varied greatly, mostly consisting of urinary albumin-to-creatinine ratio and estimated glomerular filtration rate. The highest performance metric was the negative predictive value. All studies exhibited high risk of bias, and some had methodological limitations. CONCLUSION: There is no strong evidence to support the use of a CKD diagnostic or prognostic model throughout LMIC. The development, validation and implementation of risk scores must be a research and public health priority in LMIC to enhance CKD screening to improve timely diagnosis.

Timed up and go test predicts mortality in older adults in Peru: a population-based cohort study.

BACKGROUND: While there is evidence about stablished risk factors (e.g., raised blood pressure) and higher mortality risk in older population, less has been explored about other functional parameters like the Timed Up and Go test and the Gait Speed in older people at low- and middle-income countries. We aimed to study these mobility tests as predictors of mortality in a population of older people in Peru. METHODS: Population-based prospective cohort study (2013-2020). Random sampling of people aged 60+ years in a community of Lima, Peru. Geriatricians conducted all clinical evaluations and laboratory tests were conducted in the local hospital. Participants were sought in the national vital registration system, and we collated cause (ICD-10) and date of death. We conducted a nested forward multivariate Cox proportional hazard model to identify all potential predictors of all-cause, communicable and non-communicable diseases mortality. RESULTS: At baseline, there were 501 older people (mean age 70.6 and 62.8% were women), complete follow-up information was available from 427 people. Mean follow-up time was 46.5 months (SD = 25.3). In multivariate models, the Timed Up and Go test was associated with higher risk of all-cause mortality (HR = 1.05; 95% CI: 1.02-1.09). For cause-specific mortality, history of heart disease (HR = 2.25; 95% CI: 1.07-4.76) and age in years (HR = 1.05; 95% CI: 1.01-1.09) were predictors of non-communicable diseases mortality. CONCLUSIONS: In addition to established risk factors for mortality in older population, the Timed Up and Go test, a functional parameter, raised as a relevant predictor of all-cause mortality.