HIV/AIDS impact on childhood mortality and childhood mortality measurement : from the perspective of Kenyan and Malawian DHS data

Includes abstract.Includes bibliographical references (leaves 79-84).This study has two goals. The first is to assess the consistency of the childhood mortality trends constructed from the direct and the indirect methods of estimation in high HIV prevalence scttings. The second goal is to assess the direct impact of HIV / AIDS on childhood mortality in Kenya and Malawi for the periods 1999 - 2003 and 2000 - 2004 respectively. It is important to understand the impact of HIV on childhood mortality and childhood mortality measurement to ensure that child health planning and cvaluation are correctly informed

Data for the paper on "Early school failure predicts teenage pregnancy and marriage: a large population-based cohort study in Northern Malawi"

The data originate from a demographic surveillance site (DSS) in Karonga district in northern Malawi covering a population of >35,000 individuals from approx. 8000 households since 2002. This is run by MEIRU (Malawi Epidemiology and Intervention Research Unit). Annual individual and household-level socio-demographic and schooling data were combined with data on participants’ sexual behaviour, including age at sexual debut, pregnancy and marriage to examine the relationship between school progression (drop-out and age-for-grade) and sexual debut, pregnancy and marriage

Privacy of study participants in open-access health and demographic surveillance system data : requirements analysis for data anonymization

Background: Data anonymization and sharing have become popular topics for individuals, organizations, and countries worldwide. Open-access sharing of anonymized data containing sensitive information about individuals makes the most sense whenever the utility of the data can be preserved and the risk of disclosure can be kept below acceptable levels. In this case, researchers can use the data without access restrictions and limitations. Objective: This study aimed to highlight the requirements and possible solutions for sharing health surveillance event history data. The challenges lie in the anonymization of multiple event dates and time-varying variables. Methods: A sequential approach that adds noise to event dates is proposed. This approach maintains the event order and preserves the average time between events. In addition, a nosy neighbor distance-based matching approach to estimate the risk is proposed. Regarding the key variables that change over time, such as educational level or occupation, we make 2 proposals: one based on limiting the intermediate statuses of the individual and the other to achieve k-anonymity in subsets of the data. The proposed approaches were applied to the Karonga health and demographic surveillance system (HDSS) core residency data set, which contains longitudinal data from 1995 to the end of 2016 and includes 280,381 events with time-varying socioeconomic variables and demographic information. Results: An anonymized version of the event history data, including longitudinal information on individuals over time, with high data utility, was created. Conclusions: The proposed anonymization of event history data comprising static and time-varying variables applied to HDSS data led to acceptable disclosure risk, preserved utility, and being sharable as public use data. It was found that high utility was achieved, even with the highest level of noise added to the core event dates. The details are important to ensure consistency or credibility. Importantly, the sequential noise addition approach presented in this study does not only maintain the event order recorded in the original data but also maintains the time between events. We proposed an approach that preserves the data utility well but limits the number of response categories for the time-varying variables. Furthermore, using distance-based neighborhood matching, we simulated an attack under a nosy neighbor situation and by using a worst-case scenario where attackers have full information on the original data. We showed that the disclosure risk is very low, even when assuming that the attacker’s database and information are optimal. The HDSS and medical science research communities in low- and middle-income country settings will be the primary beneficiaries of the results and methods presented in this paper; however, the results will be useful for anyone working on anonymizing longitudinal event history data with time-varying variables for the purposes of sharing

Preservation of individuals’ privacy in shared COVID-19 related data

Preprint VersionThis paper provides insight into how restricted data can be incorporated in an open-be-default-by-design digital infrastructure for scientific data. We focus, in particular, on the ethical component of FAIRER (Findable, Accessible, Interoperable, Ethical, and Reproducible) data, and the pseudo-anonymization and anonymization of COVID-19 datasets to protect personally identifiable information (PII). First we consider the need for the customisation of the existing privacy preservation techniques in the context of rapid production, integration, sharing and analysis of COVID-19 data. Second, the methods for the pseudo-anonymization of direct identification variables are discussed. We also discuss different pseudo-IDs of the same person for multi-domain and multi-organization. Essentially, pseudo-anonymization and its encrypted domain specific IDs are used to successfully match data later, if required and permitted, as well as to restore the true ID (and authenticity) in individual cases of a patient's clarification.Third, we discuss application of statistical disclosure control (SDC) techniques to COVID-19 disease data. To assess and limit the risk of re-identification of individual persons in COVID-19 datasets (that are often enriched with other covariates like age, gender, nationality, etc.) to acceptable levels, the risk of successful re-identification by a combination of attribute values must be assessed and controlled. This is done using statistical disclosure control for anonymization of data. Lastly, we discuss the limitations of the proposed techniques and provide general guidelines on using disclosure risks to decide on appropriate modes for data sharing to preserve the privacy of the individuals in the datasets

User requirements elicitation study for ALPHA network data provenance documentation browsing software

The ALPHA Network (Analysing Longitudinal Population-based HIV/AIDS data on Africa) is working on a project to produce a sharable set of harmonised data that combines both population-based and clinic data from the partner studies with funding from the Wellcome Trust. Whilst community-based cohorts and demographic surveillance systems provide a rich source of data, use of the data is often limited because successful analysis requires detailed knowledge of the study's contemporary and historical procedures and of data management practices. To date the ALPHA Network has successfully extracted and harmonised 10 standard data tables from the partner studies. However, these data are still complex and require considerable prior knowledge to use effectively, which in practice means the data can only be used in collaboration with one of the ALPHA staff. This data collection contains qualitative data collected as part of scoping work to establish domain experts’ perspectives on the functionality that a user-friendly metadata browser for ALPHA datasets should provide. It contains transcripts of 10 semi-structured Skype interviews conducted with individual researchers and data managers affiliated to the ALPHA network and the Cohort & Longitudinal Studies Enhancement Resources (CLOSER) project. Interviews explored proposed features of the metadata browser, including: provision for viewing all tasks performed in the process of creating a dataset, browsing the steps in each task, task purpose, related concepts, related code scripts, association between a sub-task and its input data and outputs and provision for viewing data structure

Using HIV-attributable mortality to assess the impact of antiretroviral therapy on adult mortality in rural Tanzania.

BACKGROUND: The Tanzanian national HIV care and treatment programme has provided free antiretroviral therapy (ART) to HIV-positive persons since 2004. ART has been available to participants of the Kisesa open cohort study since 2005, but data to 2007 showed a slow uptake of ART and a modest impact on mortality. Additional data from the 2010 HIV serological survey provide an opportunity to update the estimated impact of ART in this setting. METHODS: The Kisesa Health and Demographic Surveillance Site (HDSS) has collected HIV serological data and demographic data, including verbal autopsy (VA) interviews since 1994. Serological data to the end of 2010 were used to make two estimates of HIV-attributable mortality, the first among HIV positives using the difference in mortality between HIV positives and HIV negatives, and the second in the population using the difference between the observed mortality rate in the whole population and the mortality rate among the HIV negatives. Four time periods (1994-1999, 2000-2004, 2005-2007, and 2008-2010) were used and HIV-attributable mortality estimates were analysed in detail for trends over time. A computer algorithm, InterVA-4, was applied to VA data to estimate the HIV-attributable mortality for the population, and this was compared to the estimates from the serological survey data. RESULTS: Among HIV-positive adults aged 45-59 years, high mortality rates were observed across all time periods in both males and females. In HIV-positive men, the HIV-attributable mortality was 91.6% (95% confidence interval (CI): 84.6%-95.3%) in 2000-2004 and 86.3% (95% CI: 71.1%-93.3%) in 2008-2010, while among women, the HIV-attributable mortality was 87.8% (95% CI: 71.1%-94.3%) in 2000-2004 and 85.8% (95% CI: 59.6%-94.4%) in 2008-2010. In the whole population, using the serological data, the HIV-attributable mortality among men aged 30-44 years decreased from 57.2% (95% CI: 46.9%-65.3%) in 2000-2004 to 36.5% (95% CI: 18.8%-50.1%) in 2008-2010, while among women the corresponding decrease was from 57.3% (95% CI: 49.7%-63.6%) to 38.7% (95% CI: 27.4%-48.2%). The HIV-attributable mortality in the population using estimates from the InterVA model was lower than that from HIV sero-status data in the period prior to ART, but slightly higher once ART became available. DISCUSSION: In the Kisesa HDSS, ART availability corresponds with a decline in adult overall mortality, although not as large as expected. Using InterVA to estimate HIV-attributable mortality showed smaller changes in HIV-related mortality following ART availability than the serological results

WorldFAIR (D7.2) Population health resource library and training package

This project, WorldFAIR – Global Cooperation on FAIR Data Policy and Practice, is funded by the European Commission's WIDERA coordination and support programme under the Grant Agreement no. 101058393. The project consists of 14 work packages, of which work package 7 (WP07) focusses on Population Health. WP07 is led by London School of Hygiene and Tropical Medicine working under the INSPIRE network. The work builds on the delivery of the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) which includes funding by Wellcome (formerly Wellcome Trust) and IDRC Canada. The objective of WP07 is to develop a suite of methods and standards to provide the framework for the Go-FAIR principles for population health data. These standards form the basis of an AI-Ready description of data suitable for use by population health scientists, and understandable across domain and institutional boundaries. The first deliverable (D7.1) identified the Implementation Guide that could be used for population health data, and how it can be developed. This deliverable (D7.2) provides a step-by-step guide as to how to achieve the standards. The deliverable is aimed at population health scientists in low-resource settings, who know their own data and want to make those data FAIR. Population health uses many different tools to collect and manage data. One set of tools includes the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and an OHDSI data analysis workbench that runs on top of it. The OMOP common data model has been used to harmonise and share data, and previous work has shown the tools needed to make OMOP data FAIR. Beyond the data themselves, the results from the analyses conducted on OMOP data can be used as indicators for the success of development goals, including the United Nations Sustainable Development Goals (SDGs). At each stage the tools, data, models and activities need to be described in a way that can be understood by other scientists and by computer search algorithms. This deliverable provides an introduction to the processes involved in making population health data FAIR in a pipeline that spans data collection through data analysis into an SDMX indicators database, and gives seven tutorials on what is needed at each step in this pipeline. It outlines the need to describe the study and the study context, how to use DDI Codebook and DDI Lifecycle with study data and how to use repositories like GitHub to make the metadata available. The next tutorials describe the extract-transform-load (ETL) process for putting the data into an OMOP CDM and the role of JSON-LD in preparing the data for machine searching in Schema.org in line with DDI-CDI. Together these tutorials give an overview of the steps in the OMOP processes which are a pipeline for the data, and how these steps can be performed and documented. Finally the tutorials show how predictive and causal analysis can be conducted and documented using the OMOP CDM and the OHDSI data analysis workbench and how the results can be integrated into an SDMX data cube, which would align with UN standards for SDG indicators. The deliverable does not provide detailed training for each step, but rather introduces the topic and clarifies the practical knowledge and skills that are needed to make this type of health data more FAIR

Does the Spectrum model accurately predict trends in adult mortality? Evaluation of model estimates using empirical data from a rural HIV community cohort study in north-western Tanzania.

Introduction : Spectrum epidemiological models are used by UNAIDS to provide global, regional and national HIV estimates and projections, which are then used for evidence-based health planning for HIV services. However, there are no validations of the Spectrum model against empirical serological and mortality data from populations in sub-Saharan Africa. Methods : Serologic, demographic and verbal autopsy data have been regularly collected among over 30,000 residents in north-western Tanzania since 1994. Five-year age-specific mortality rates (ASMRs) per 1,000 person years and the probability of dying between 15 and 60 years of age (45Q15,) were calculated and compared with the Spectrum model outputs. Mortality trends by HIV status are shown for periods before the introduction of antiretroviral therapy (1994-1999, 2000-2005) and the first 5 years afterwards (2005-2009). Results : Among 30-34 year olds of both sexes, observed ASMRs per 1,000 person years were 13.33 (95% CI: 10.75-16.52) in the period 1994-1999, 11.03 (95% CI: 8.84-13.77) in 2000-2004, and 6.22 (95% CI; 4.75-8.15) in 2005-2009. Among the same age group, the ASMRs estimated by the Spectrum model were 10.55, 11.13 and 8.15 for the periods 1994-1999, 2000-2004 and 2005-2009, respectively. The cohort data, for both sexes combined, showed that the 45Q15 declined from 39% (95% CI: 27-55%) in 1994 to 22% (95% CI: 17-29%) in 2009, whereas the Spectrum model predicted a decline from 43% in 1994 to 37% in 2009. Conclusion : From 1994 to 2009, the observed decrease in ASMRs was steeper in younger age groups than that predicted by the Spectrum model, perhaps because the Spectrum model under-estimated the ASMRs in 30-34 year olds in 1994-99. However, the Spectrum model predicted a greater decrease in 45Q15 mortality than observed in the cohort, although the reasons for this over-estimate are unclear

Open-access for existing LMIC demographic surveillance data using DDI

Open-access for existing LMIC demographic surveillance data using DDI</jats:p

The effect of BCG revaccination on all-cause mortality beyond infancy: 30-year follow-up of a population-based, double-blind, randomised placebo-controlled trial in Malawi.

BACKGROUND: Trials of BCG vaccination to prevent or reduce severity of COVID-19 are taking place in adults, some of whom have been previously vaccinated, but evidence of the beneficial, non-specific effects of BCG come largely from data on mortality in infants and young children, and from in-vitro and animal studies, after a first BCG vaccination. We assess all-cause mortality following a large BCG revaccination trial in Malawi. METHODS: The Karonga Prevention trial was a population-based, double-blind, randomised controlled in Karonga District, northern Malawi, that enrolled participants between January, 1986, and November, 1989. The trial compared BCG (Glaxo-strain) revaccination versus placebo to prevent tuberculosis and leprosy. 46 889 individuals aged 3 months to 75 years were randomly assigned to receive BCG revaccination (n=23 528) or placebo (n=23 361). Here we report mortality since vaccination as recorded during active follow-up in northern areas of the district in 1991-94, and in a demographic surveillance follow-up in the southern area in 2002-18. 7389 individuals who received BCG (n=3746) or placebo (n=3643) lived in the northern follow-up areas, and 5616 individuals who received BCG (n=2798) or placebo (n=2818) lived in the southern area. Year of death or leaving the area were recorded for those not found. We used survival analysis to estimate all-cause mortality. FINDINGS: Follow-up information was available for 3709 (99·0%) BCG recipients and 3612 (99·1%) placebo recipients in the northern areas, and 2449 (87·5%) BCG recipients and 2413 (85·6%) placebo recipients in the southern area. There was no difference in mortality between the BCG and placebo groups in either area, overall or by age group or sex. In the northern area, there were 129 deaths per 19 694 person-years at risk in the BCG group (6·6 deaths per 1000 person-years at risk [95% CI 5·5-7·8]) versus 133 deaths per 19 111 person-years at risk in the placebo group (7·0 deaths per 1000 person-years at risk [95% CI 5·9-8·2]; HR 0·94 [95% CI 0·74-1·20]; p=0·62). In the southern area, there were 241 deaths per 38 399 person-years at risk in the BCG group (6·3 deaths per 1000 person-years at risk [95% CI 5·5-7·1]) versus 230 deaths per 38 676 person-years at risk in the placebo group (5·9 deaths per 1000 person-years at risk [95% CI 5·2-6·8]; HR 1·06 [95% CI 0·88-1·27]; p=0·54). INTERPRETATION: We found little evidence of any beneficial effect of BCG revaccination on all-cause mortality. The high proportion of deaths attributable to non-infectious causes beyond infancy, and the long time interval since BCG for most deaths, might obscure any benefits. FUNDING: British Leprosy Relief Association (LEPRA); Wellcome Trust