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  • 1
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    Online Resource
    Multimorbidity in Latin America and the Caribbean: a systematic review and meta-analysis
    BMJ ; 2021
    In:  BMJ Open Vol. 11, No. 7 ( 2021-07), p. e050409-
    Details
    In: BMJ Open, BMJ, Vol. 11, No. 7 ( 2021-07), p. e050409-
    Abstract: To estimate the pooled prevalence of multimorbidity (≥2 non-communicable diseases in the same individual) among adults of the general population of Latin American and the Caribbean (LAC). Design Systematic review and meta-analysis. Data sources MEDLINE, Embase, Global Health, Scopus and LILACS up to 1 July 2020. Eligibility criteria for selecting studies The outcome was the prevalence of multimorbidity. Reports were selected whether they enrolled adult individuals (age ≥18 years) from the general population. Data extraction and synthesis Reviewers extracted relevant data and assessed risk of bias independently. A random-effects meta-analysis was conducted to report pooled prevalence estimates of multimorbidity; pooled estimates by pre-specified subgroups (eg, national studies) were also pursued. Results From 5830 results, we selected 28 reports, mostly from Brazil and 16 were based on a nationally representative sample. From the 28 selected reports, 26 were further included in the meta-analysis revealing a pooled multimorbidity prevalence of 43% (95% CI: 35% to 51%; I 2 : 99.9%). When only reports with a nationally representative sample were combined, the pooled prevalence was 37% (95% CI: 27% to 47%; I 2 : 99.9%). When the ascertainment of multimorbidity was based on self-reports alone, the pooled prevalence was 40% (95% CI: 31% to 48%; I 2 : 99.9%); this raised to 52% (95% CI: 33% to 70%; I 2 : 99.9%) for reports including self-reported and objective diagnosis. Conclusions Our results complement and advance those from global efforts by incorporating much more reports from LAC. We revealed a larger presence of multimorbidity in LAC than previously reported. PROSPERO registration number CRD42020196177.
    Type of Medium: Online Resource
    ISSN: 2044-6055 , 2044-6055
    URL: Article
    DOI: 10.1136/bmjopen-2021-050409
    Language: English
    Publisher: BMJ
    Publication Date: 2021
    detail.hit.zdb_id: 2599832-8
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  • 2
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    Migration, urbanisation and mortality: 5-year longitudinal analysis of the PERU MIGRANT study
    BMJ ; 2015
    In:  Journal of Epidemiology and Community Health Vol. 69, No. 7 ( 2015-07), p. 715-718
    Details
    In: Journal of Epidemiology and Community Health, BMJ, Vol. 69, No. 7 ( 2015-07), p. 715-718
    Type of Medium: Online Resource
    ISSN: 0143-005X , 1470-2738
    URL: Article
    DOI: 10.1136/jech-2015-205657
    Language: English
    Publisher: BMJ
    Publication Date: 2015
    detail.hit.zdb_id: 2015405-7
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  • 3
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    Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol
    BMJ ; 2020
    In:  BMJ Open Vol. 10, No. 5 ( 2020-05), p. e035983-
    Details
    In: BMJ Open, BMJ, Vol. 10, No. 5 ( 2020-05), p. e035983-
    Abstract: Machine learning (ML) has been used in bio-medical research, and recently in clinical and public health research. However, much of the available evidence comes from high-income countries, where different health profiles challenge the application of this research to low/middle-income countries (LMICs). It is largely unknown what ML applications are available for LMICs that can support and advance clinical medicine and public health. We aim to address this gap by conducting a scoping review of health-related ML applications in LMICs. Methods and analysis This scoping review will follow the methodology proposed by Levac et al . The search strategy is informed by recent systematic reviews of ML health-related applications. We will search Embase, Medline and Global Health (through Ovid), Cochrane and Google Scholar; we will present the date of our searches in the final review. Titles and abstracts will be screened by two reviewers independently; selected reports will be studied by two reviewers independently. Reports will be included if they are primary research where data have been analysed, ML techniques have been used on data from LMICs and they aimed to improve health-related outcomes. We will synthesise the information following evidence mapping recommendations. Ethics and dissemination The review will provide a comprehensive list of health-related ML applications in LMICs. The results will be disseminated through scientific publications. We also plan to launch a website where ML models can be hosted so that researchers, policymakers and the general public can readily access them.
    Type of Medium: Online Resource
    ISSN: 2044-6055 , 2044-6055
    URL: Article
    DOI: 10.1136/bmjopen-2019-035983
    Language: English
    Publisher: BMJ
    Publication Date: 2020
    detail.hit.zdb_id: 2599832-8
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  • 4
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    Street images classification according to COVID-19 risk in Lima, Peru: a convolutional neural networks feasibility analysis
    BMJ ; 2022
    In:  BMJ Open Vol. 12, No. 9 ( 2022-09), p. e063411-
    Details
    In: BMJ Open, BMJ, Vol. 12, No. 9 ( 2022-09), p. e063411-
    Abstract: During the COVID-19 pandemic, convolutional neural networks (CNNs) have been used in clinical medicine (eg, X-rays classification). Whether CNNs could inform the epidemiology of COVID-19 classifying street images according to COVID-19 risk is unknown, yet it could pinpoint high-risk places and relevant features of the built environment. In a feasibility study, we trained CNNs to classify the area surrounding bus stops (Lima, Peru) into moderate or extreme COVID-19 risk. Design CNN analysis based on images from bus stops and the surrounding area. We used transfer learning and updated the output layer of five CNNs: NASNetLarge, InceptionResNetV2, Xception, ResNet152V2 and ResNet101V2. We chose the best performing CNN, which was further tuned. We used GradCam to understand the classification process. Setting Bus stops from Lima, Peru. We used five images per bus stop. Primary and secondary outcome measures Bus stop images were classified according to COVID-19 risk into two labels: moderate or extreme. Results NASNetLarge outperformed the other CNNs except in the recall metric for the moderate label and in the precision metric for the extreme label; the ResNet152V2 performed better in these two metrics (85% vs 76% and 63% vs 60%, respectively). The NASNetLarge was further tuned. The best recall (75%) and F1 score (65%) for the extreme label were reached with data augmentation techniques. Areas close to buildings or with people were often classified as extreme risk. Conclusions This feasibility study showed that CNNs have the potential to classify street images according to levels of COVID-19 risk. In addition to applications in clinical medicine, CNNs and street images could advance the epidemiology of COVID-19 at the population level.
    Type of Medium: Online Resource
    ISSN: 2044-6055 , 2044-6055
    URL: Article
    URL: Article
    DOI: 10.1136/bmjopen-2022-063411
    DOI: 10.1136/bmjopen-2022-063411.supp1
    Language: English
    Publisher: BMJ
    Publication Date: 2022
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  • 5
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    Systematic review of diagnostic and prognostic models of chronic kidney disease in low-income and middle-income countries
    BMJ ; 2022
    In:  BMJ Open Vol. 12, No. 3 ( 2022-03), p. e058921-
    Details
    In: BMJ Open, BMJ, Vol. 12, No. 3 ( 2022-03), p. e058921-
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2044-6055 , 2044-6055
    URL: Article
    URL: Article
    DOI: 10.1136/bmjopen-2021-058921
    DOI: 10.1136/bmjopen-2021-058921.supp1
    Language: English
    Publisher: BMJ
    Publication Date: 2022
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  • 6
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    Trends in the prevalence and treatment of depressive symptoms in Peru: a population-based study
    BMJ ; 2020
    In:  BMJ Open Vol. 10, No. 7 ( 2020-07), p. e036777-
    Details
    In: BMJ Open, BMJ, Vol. 10, No. 7 ( 2020-07), p. e036777-
    Abstract: This study aimed to estimate the trends in the prevalence and treatment of depressive symptoms using nationally representative surveys in Peru from 2014 to 2018. Design A secondary analysis was conducted using five nationally representative surveys carried out consecutively in the years between 2014 and 2018. Setting The study was conducted in Peru. Participants Individuals, men and women, aged ≥15 years who participated in the selected surveys. Sampling was probabilistic using a two-stage approach. Main outcome measures Two versions of the Patient Health Questionnaire (PHQ-9) that focused on the presence of depressive symptoms were administered (one in the last 2 weeks and other in the last year). Scores ≥15 were used as the cut-off point in both versions of the PHQ-9 to define the presence of depressive symptoms. Also, the treatment rate was based on the proportion of individuals who had experienced depressive symptoms in the last year and who had self-reported having received specific treatment for these symptoms. The age-standardised prevalence was estimated. Results A total of 161 061 participants were included. There was no evidence of a change in age-standardised prevalence rates of depressive symptoms at the 2 weeks prior to the point of data collection (2.6% in 2014 to 2.3% in 2018), or in the last year (6.3% in 2014 to 6.2% in 2018). Furthermore, no change was found in the proportion of depressive cases treated in the last year (14.6% in 2014 to 14.4% in 2018). Rural areas and individuals with low-level of wealth had lower proportion of depressive cases treated. Conclusions No changes in trends of rates of depressive symptoms or in the proportion of depressive cases treated were observed. This suggests the need to reduce the treatment gap considering social determinants associated with inequality in access to adequate therapy.
    Type of Medium: Online Resource
    ISSN: 2044-6055 , 2044-6055
    URL: Article
    DOI: 10.1136/bmjopen-2020-036777
    Language: English
    Publisher: BMJ
    Publication Date: 2020
    detail.hit.zdb_id: 2599832-8
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  • 7
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    Impact of urbanisation and altitude on the incidence of, and risk factors for, hypertension
    BMJ ; 2017
    In:  Heart Vol. 103, No. 11 ( 2017-06), p. 827-833
    Details
    In: Heart, BMJ, Vol. 103, No. 11 ( 2017-06), p. 827-833
    Type of Medium: Online Resource
    ISSN: 1355-6037 , 1468-201X
    URL: Article
    URL: Article
    DOI: 10.1136/heartjnl-2016-310347
    DOI: 10.1136/annrheumdis-2016-210131.supp1
    Language: English
    Publisher: BMJ
    Publication Date: 2017
    detail.hit.zdb_id: 2378689-9
    detail.hit.zdb_id: 1475501-4
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  • 8
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    FINDRISC in Latin America: a systematic review of diagnosis and prognosis models
    BMJ ; 2020
    In:  BMJ Open Diabetes Research & Care Vol. 8, No. 1 ( 2020-04), p. e001169-
    Details
    In: BMJ Open Diabetes Research & Care, BMJ, Vol. 8, No. 1 ( 2020-04), p. e001169-
    Abstract: This review aimed to assess whether the FINDRISC, a risk score for type 2 diabetes mellitus (T2DM), has been externally validated in Latin America and the Caribbean (LAC). We conducted a systematic review following the CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) framework. Reports were included if they validated or re-estimated the FINDRISC in population-based samples, health facilities or administrative data. Reports were excluded if they only studied patients or at-risk individuals. The search was conducted in Medline, Embase, Global Health, Scopus and LILACS. Risk of bias was assessed with the PROBAST (Prediction model Risk of Bias ASsessment Tool) tool. From 1582 titles and abstracts, 4 (n=7502) reports were included for qualitative summary. All reports were from South America; there were slightly more women, and the mean age ranged from 29.5 to 49.7 years. Undiagnosed T2DM prevalence ranged from 2.6% to 5.1%. None of the studies conducted an independent external validation of the FINDRISC; conversely, they used the same (or very similar) predictors to fit a new model. None of the studies reported calibration metrics. The area under the receiver operating curve was consistently above 65.0%. All studies had high risk of bias. There has not been any external validation of the FINDRISC model in LAC. Selected reports re-estimated the FINDRISC, although they have several methodological limitations. There is a need for big data to develop—or improve—T2DM diagnostic and prognostic models in LAC. This could benefit T2DM screening and early diagnosis.
    Type of Medium: Online Resource
    ISSN: 2052-4897
    URL: Article
    DOI: 10.1136/bmjdrc-2019-001169
    Language: English
    Publisher: BMJ
    Publication Date: 2020
    detail.hit.zdb_id: 2732918-5
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  • 9
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    Mortality attributable to type 2 diabetes mellitus in Latin America and the Caribbean: a comparative risk assessment analysis
    BMJ ; 2022
    In:  BMJ Open Diabetes Research & Care Vol. 10, No. 1 ( 2022-02), p. e002673-
    Details
    In: BMJ Open Diabetes Research & Care, BMJ, Vol. 10, No. 1 ( 2022-02), p. e002673-
    Abstract: We quantified the proportion and the absolute number of deaths attributable to type 2 diabetes mellitus (T2DM) in Latin America and the Caribbean (LAC) using an estimation approach. Research design and methods We combined T2DM prevalence estimates from the NCD Risk Factor Collaboration, relative risks between T2DM and all-cause mortality from a meta-analysis of cohorts in LAC, and death rates from the Global Burden of Disease Study 2019. We estimated population-attributable fractions (PAFs) and computed the absolute number of attributable deaths in 1990 and 2019 by multiplying the PAFs by the total deaths in each country, year, sex, and 5-year age group. Results Between 1985 and 2014 in LAC, the proportion of all-cause mortality attributable to T2DM increased from 12.2% to 16.9% in men and from 14.5% to 19.3% in women. In 2019, the absolute number of deaths attributable to T2DM was 349 787 in men and 330 414 in women. The highest death rates (deaths per 100 000 people) in 2019 were in Saint Kitts and Nevis (325 in men, 229 in women), Guyana (313 in men, 272 in women), and Haiti (269 in men, 265 in women). Conclusions A substantial burden of all deaths is attributed to T2DM in LAC. To decrease the mortality attributable to T2DM in LAC, policies are needed to strengthen early diagnosis and management, along with the prevention of complications.
    Type of Medium: Online Resource
    ISSN: 2052-4897
    URL: Article
    DOI: 10.1136/bmjdrc-2021-002673
    Language: English
    Publisher: BMJ
    Publication Date: 2022
    detail.hit.zdb_id: 2732918-5
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  • 10
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    Clusters of people with type 2 diabetes in the general population: unsupervised machine learning approach using national surveys in Latin America and the Caribbean
    BMJ ; 2021
    In:  BMJ Open Diabetes Research & Care Vol. 9, No. 1 ( 2021-01), p. e001889-
    Details
    In: BMJ Open Diabetes Research & Care, BMJ, Vol. 9, No. 1 ( 2021-01), p. e001889-
    Abstract: We aimed to identify clusters of people with type 2 diabetes mellitus (T2DM) and to assess whether the frequency of these clusters was consistent across selected countries in Latin America and the Caribbean (LAC). Research design and methods We analyzed 13 population-based national surveys in nine countries (n=8361). We used k-means to develop a clustering model; predictors were age, sex, body mass index (BMI), waist circumference (WC), systolic/diastolic blood pressure (SBP/DBP), and T2DM family history. The training data set included all surveys, and the clusters were then predicted in each country-year data set. We used Euclidean distance, elbow and silhouette plots to select the optimal number of clusters and described each cluster according to the underlying predictors (mean and proportions). Results The optimal number of clusters was 4. Cluster 0 grouped more men and those with the highest mean SBP/DBP. Cluster 1 had the highest mean BMI and WC, as well as the largest proportion of T2DM family history. We observed the smallest values of all predictors in cluster 2. Cluster 3 had the highest mean age. When we reflected the four clusters in each country-year data set, a different distribution was observed. For example, cluster 3 was the most frequent in the training data set, and so it was in 7 out of 13 other country-year data sets. Conclusions Using unsupervised machine learning algorithms, it was possible to cluster people with T2DM from the general population in LAC; clusters showed unique profiles that could be used to identify the underlying characteristics of the T2DM population in LAC.
    Type of Medium: Online Resource
    ISSN: 2052-4897
    URL: Article
    DOI: 10.1136/bmjdrc-2020-001889
    Language: English
    Publisher: BMJ
    Publication Date: 2021
    detail.hit.zdb_id: 2732918-5
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