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Massive slums have become major features of cities in many low-income and middle-income countries. Here, in the first in a Series of two papers, we discuss why slums are unhealthy places with especially high risks of infection and injury. We show that children are especially vulnerable, and that the combination of malnutrition and recurrent diarrhoea leads to stunted growth and longer-term effects on cognitive development. We find that the scientific literature on slum health is underdeveloped in comparison to urban health, and poverty and health. This shortcoming is important because health is affected by factors arising from the shared physical and social environment, which have effects beyond those of poverty alone. In the second paper we will consider what can be done to improve health and make recommendations for the development of slum health as a field of study.

In the first paper in this Series we assessed theoretical and empirical evidence and concluded that the health of people living in slums is a function not only of poverty but of intimately shared physical and social environments. In this paper we extend the theory of so-called neighbourhood effects. Slums offer high returns on investment because beneficial effects are shared across many people in densely populated neighbourhoods. Neighbourhood effects also help explain how and why the benefits of interventions vary between slum and non-slum spaces and between slums. We build on this spatial concept of slums to argue that, in all low-income and-middle-income countries, census tracts should henceforth be designated slum or non-slum both to inform local policy and as the basis for research surveys that build on censuses. We argue that slum health should be promoted as a topic of enquiry alongside poverty and health.

Background Guidelines for randomised controlled trials (RCTs) recommend reporting relative and absolute measures of effect for binary outcomes while adjusting for covariates. There are a number of different ways covariate-adjusted relative risks and risk differences can be estimated. Objectives Our goal was to identify methods used to estimate covariate-adjusted relative risk and risk differences in RCTs published in high-impact journals with binary outcomes. Other secondary objectives included the identification of how covariates are chosen for adjustment and whether covariate adjustment results in an increase in statistical precision in practice. Methods We included two-arm parallel RCTs published in JAMA , NEJM , Lancet , or the BMJ between January 1, 2018, and March 11, 2023, reporting relative risks or risk differences as a summary measure for a binary primary outcome. The search was conducted in Ovid-MEDLINE. Results Of the 308 RCTs identified, around half (49%; 95% CI: 43–54%) reported a covariate-adjusted relative risk or risk difference. Of these, 82 reported an adjusted relative risk. When the reporting was clear ( n  = 65, 79%), the log-binomial model (used in 65% of studies; 95% CI: 52–76%) and modified Poisson (29%; 95% CI: 19–42%) were most commonly used. Of the 92 studies that reported an adjusted risk difference, when the reporting was clear ( n  = 56, 61%), the binomial model (used in 48% of studies; 95% CI: 35–62%) and marginal standardisation (21%; 95% CI: 12–35%) were the common approaches used. Conclusions Approximately half of the RCTs report either a covariate-adjusted relative risk or risk difference. Many RCTs lack adequate details on the methods used to estimate covariate-adjusted effects. Of those that do report the approaches used, the binomial model, modified Poisson and to a lesser extent marginal standardisation are the approaches used.

Background Experimental studies of wound healing often use survival analysis and time to event outcomes or differences in wound area at a specific time point. However, these methods do not use a potentially large number of observations made over the course of a trial and may be inefficient. A model-based approach can leverage all trial data, but there is little guidance on appropriate models and functional forms to describe wound healing. Methods We derive a general statistical model and review a wide range of plausible mathematical models to describe wound healing. We identify a range of possible derived estimands and their derivation from the models. Using data from a trial of an intervention to promote ulcer healing in patients affected by leprosy that included three measurement methods repeated across the course of the study, we compare the goodness-of-fit of the models using a range of methods and estimate treatment effects and healing rate functions with the best-fitting models. Results Overall, we included 5,581 ulcer measurements of 1,578 unique images from 130 patients. We examined the performance of a range of models. The square root, log square root, and log quadratic models were the best fitting models across all outcome measurement methods. The estimated treatment effects magnitude and sign varied by time post-randomisation, model type, and outcome type, but across all models there was little evidence of effectiveness. The estimated effects were significantly more precise than non-parametric alternatives. For example, estimated differences from the three outcome measurements at 42-days post-randomisation were − 0.01 cm 2 (-0.77, 0.74), -0.44 cm 2 (-1.64, 0.76), and 0.11 cm 2 (-0.87, 1.08) using a non-parametric method versus − 0.03 cm 2 (-0.14, 0.06), 0.06 cm 2 (-0.05, 0.17), and 0.03 cm 2 (-0.07, 0.17) using a square-root model. Conclusions Model-based analyses can dramatically improve the precision of estimates but care must be taken to carefully compare and select the best fitting models. The (log) square-root model is strongly recommended reflecting advice from a century ago.

Autologous blood products like Platelet Rich Plasma (PRP) and Leukocyte and Platelets Rich Fibrin (L-PRF) have been used for many years across many types of skin ulcers. However, the effectiveness of autologous blood products on wound healing is not well established. We evaluated the 'second generation' autologous product- Leukocyte and Platelet- Rich Fibrin (L-PRF). Our trial was undertaken on patients suffering from neuropathic leprosy ulcers at the Anandaban hospital which serves the entire country of Nepal. We conducted a 1:1 (n = 130) individually randomised trial of L-PRF (intervention) vs. normal saline dressing (control) to compare rate of healing and time to complete healing. Rate of healing was estimated using blind assessments of ulcer areas based on three different measurement methods. Time to complete healing was measured by the local unblinded clinicians and by blind assessment of ulcer images. The point estimates for both outcomes were favourable to L-PRF but the effect sizes were small. Unadjusted mean differences (intervention vs control) in mean daily healing rates (cm2) were respectively 0.012 (95% confidence interval 0.001 to 0.023, p = 0.027); 0.016 (0.004 to 0.027, p = 0.008) and 0.005 (-0.005 to 0.016, p = 0.313) across the three measurement methods. Time to complete healing at 42 days yielded Hazard Ratios (unadjusted) of 1.3 (0.8 to 2.1, p = 0.300) assessed by unblinded local clinicians and 1.2 (0.7 to 2.0, p = 0.462) on blind assessment. Any benefit from L-PRF appears insufficient to justify routine use in care of neuropathic ulcers in leprosy. ISRCTN14933421. Date of trial registration: 16 June 2020.

ObjectiveTo assess patient satisfaction (PS) with services and its associated factors across selected primary care facilities in Kono district, Sierra Leone.DesignFacility-based cross-sectional study.SettingFive primary healthcare facilities (Wellbody, Sewafe, Kombayendeh, Gandorhun and Kayima) located in Kono district, Sierra Leone. All five are Community Health Centres (CHCs), with two CHCs benefiting from a comprehensive package of support (5S model) from the non-governmental organisation, Partners In Health (PIH). This support, dubbed as 5S health systems strengthening (HSS) model, will be elaborated in this article. The other three CHCs were not beneficiaries of the 5S model.ParticipantsThe study population comprised all patients and caregivers who attended outpatient services at the selected health facilities. We included adult outpatients over 18 years old and adult caregivers accompanying their children while waiting in the various outpatient departments. This study considered a sample size of 290 and the data were collected from 3rd March to 31st March 2021.OutcomesPS was measured using an 11-item Likert scale questionnaire. The outcome was categorised as good or poor satisfaction level using the median value. Descriptive statistics were applied to assess satisfaction level and multivariable binary logistic regression analysis was applied to identify factors associated with the outcome variable.ResultsOut of the 290 respondents included for analysis, the overall PS level was 63.8% (95% CI 58.1% to 69.0%). Around 69.2% (95% CI 62.1% to 75.4%) of respondents from PIH intervention sites and 53.9% (95% CI 44.1% to 63.4%) from the non-PIH intervention sites had a good satisfaction level. The multivariable binary logistic regression analysis indicated that PIH intervention site status (adjusted OR (AOR)=2.47, 95% CI 1.28 to 4.78), educational status of respondents (AOR=0.53, 95% CI 0.28 to 0.98), distance to health facility (AOR=0.40, 95% CI 0.18 to 0.87) and waiting time to receive care (AOR=0.41, 95% CI 0.22 to 0.76) were the significant factors associated with PS.ConclusionThe overall PS was relatively high andPIH-supported health facilities show better PS than non-PIH health facilities. In addition, patients’ educational status, distance to health facility and waiting time were negatively associated with PS level. The findings suggest that PIH’s model of health systems strengthening with targeted investment on the 5S model can be scaled up and the Ministry of Health could consider implementing this approach for improving the quality of services provided at the primary healthcare facilities.

ObjectiveTo summarise evidence on the effectiveness of Platelet-Rich Plasma (PRP) gel and Leucocyte and Platelet Rich Fibrin (L-PRF) gel as agents promoting ulcer healing compared with the standard wound dressing techniques alone.DesignSystematic review.Eligibility criteriaIndividual patient randomised controlled trials on skin ulcers of all types excluding traumatic lesions.Intervention group: treatment with topical application of L-PRF gel or PRP gel to the wound surface.Control group: treatment with standard skin ulcer care using normal saline, normgel or hydrogel dressings.Information sourcesMedline (Ovid), Excerpta Medica Database (EMBASE), Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Web of Science and manual search of studies from previous systematic reviews and meta-analyses. The papers published from 1946 to 2022 with no restriction on geography and language were included. The last date of the search was performed on 29 August 2022.Data extraction and synthesisIndependent reviewers identified eligible studies, extracted data, assessed risk of bias using V.2 of the Cochrane risk-of-bias tool for randomised trials tool and assessed certainty of evidence by using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach.Main outcome measuresTime to complete healing, proportion healed at a given time and rate of healing.ResultsSeven studies met the inclusion criteria, five using PRP gel and two using L-PRF gel. One study showed a better proportion of complete healing, three reported reduced meantime to complete healing and five showed improved rate of healing per unit of time in the intervention group. The risk of bias was high across all studies with one exception and the GRADE showed very low certainty of evidence.ConclusionThe findings show potential for better outcomes in the intervention; however, the evidence remains inconclusive highlighting a large research gap in ulcer treatment and warrant better-designed clinical trials.PROSPERO registration numberCRD42022352418.

Background There are numerous approaches available to analyse data from cluster randomised trials. These include cluster-level summary methods and individual-level methods accounting for clustering, such as generalised estimating equations and generalised linear mixed models. There has been much methodological work showing that estimates of treatment effects can vary depending on the choice of approach, particularly when estimating odds ratios, essentially because the different approaches target different estimands. Methods In this manuscript, we describe the protocol for a planned re-analysis of data from a large number of cluster randomised trials. Our main objective is to examine empirically whether and how odds ratios estimated using different approaches (for both primary and secondary binary outcomes) vary in cluster randomised trials. We describe the methods that will be used to identify the datasets for inclusion and how they will be analysed and reported. Discussion There have been a number of small comparisons of empirical differences between the different approaches to analysis for CRTs. The systematic approach outlined in this protocol will allow a much deeper understanding of when there are important choices around the model approach and in which settings. This will be of importance given the heightened awareness of the importance of estimands and the specification of statistical analysis plans.

Despite an estimated one billion people around the world living in slums, most surveys of health and well-being do not distinguish between slum and non-slum urban residents. Identifying people who live in slums is important for research purposes and also to enable policymakers, programme managers, donors and non-governmental organisations to better target investments and services to areas of greatest deprivation. However, there is no consensus on what a slum is let alone how slums can be distinguished from non-slum urban precincts. Nor has attention been given to a more fine-grained classification of urban spaces that might go beyond a simple slum/non-slum dichotomy. The purpose of this paper is to provide a conceptual framework to help tackle the related issues of slum definition and classification of the urban landscape. We discuss:The concept of space as an epidemiological variable that results in ‘neighbourhood effects’.The problems of slum area definition when there is no ‘gold standard’.A long-list of variables from which a selection must be made in defining or classifying urban slum spaces.Methods to combine any set of identified variables in an operational slum area definition.Two basic approaches to spatial slum area definitions—top-down (starting with a predefined area which is then classified according to features present in that area) and bottom-up (defining the areal unit based on its features).Different requirements of a slum area definition according to its intended use.Implications for research and future development.

BackgroundPlantar ulcers are a leading complication of leprosy that requires frequent visits to hospital and is associated with stigma. The extent of burden of ulcers in leprosy and its risk factors are scant impeding the development of targeted interventions to prevent and promote healing of ulcers. The aim of this review is to generate evidence on the prevalence of plantar ulcer and its risk factors in leprosy.MethodsDatabases (Medline, Embase, Web of Science, CINAHL, BVS), conference abstracts and reference lists were searched for eligible studies. Studies were included that reported a point prevalence of plantar ulcer and/or its “risk factors” associated with development of ulcers (either causatively or predictively), including individual level, disease related and bio-mechanical factors. We followed PRISMA guidelines for this review. Random-effects meta-analysis was undertaken to estimate the pooled point prevalence of ulcers. Reported risk factors in included studies were narratively synthesised. This review is registered in PROSPERO: CRD42022316726.ResultsOverall, 15 studies (8 for prevalence of ulcer and 7 for risk factors) met the inclusion criteria. The pooled point prevalence of ulcer was 34% (95% CIs: 21%, 46%) and 7% (95% CIs: 4%, 11%) among those with foot anaesthesia and among all people affected by leprosy, respectively. Risk factors for developing ulcers included: unable to feel 10 g of monofilament on sensory testing, pronated/hyper-pronated foot, foot with peak plantar pressure, foot with severe deformities, and those with lower education and the unemployed.ConclusionsThe prevalence of plantar ulceration in leprosy is as high as 34% among those with loss of sensation in the feet. However, the incidence and recurrence rates of ulceration are least reported. The inability to feel 10 g of monofilament appears to be a strong predictor of those at risk of developing ulcers. However, there is a paucity of evidence on identifying those at risk of developing plantar ulcers in leprosy. Prospective studies are needed to estimate the incidence of ulcers. Identifying individuals at risk of ulcers will help design targeted interventions to minimize risk factors, prevent ulcers and promote ulcer healing.