Causal datasheet for datasets: an evaluation guide for real-world data analysis and data collection design using Bayesian networks

Developing data-driven solutions that address real-world problems requires understanding of these problems’ causes and how their interaction affects the outcome–often with only observational data. Causal Bayesian Networks (BN) have been proposed as a powerful method for discovering and representing the causal relationships from observational data as a Directed Acyclic Graph (DAG). BNs could be especially useful for research in global health in Lower and Middle Income Countries, where there is an increasing abundance of observational data that could be harnessed for policy making, program evaluation, and intervention design. However, BNs have not been widely adopted by global health professionals, and in real-world applications, confidence in the results of BNs generally remains inadequate. This is partially due to the inability to validate against some ground truth, as the true DAG is not available. This is especially problematic if a learned DAG conflicts with pre-existing domain doctrine. Here we conceptualize and demonstrate an idea of a “Causal Datasheet” that could approximate and document BN performance expectations for a given dataset, aiming to provide confidence and sample size requirements to practitioners. To generate results for such a Causal Datasheet, a tool was developed which can generate synthetic Bayesian networks and their associated synthetic datasets to mimic real-world datasets. The results given by well-known structure learning algorithms and a novel implementation of the OrderMCMC method using the Quotient Normalized Maximum Likelihood score were recorded. These results were used to populate the Causal Datasheet, and recommendations could be made dependent on whether expected performance met user-defined thresholds. We present our experience in the creation of Causal Datasheets to aid analysis decisions at different stages of the research process. First, one was deployed to help determine the appropriate sample size of a planned study of sexual and reproductive health in Madhya Pradesh, India. Second, a datasheet was created to estimate the performance of an existing maternal health survey we conducted in Uttar Pradesh, India. Third, we validated generated performance estimates and investigated current limitations on the well-known ALARM dataset. Our experience demonstrates the utility of the Causal Datasheet, which can help global health practitioners gain more confidence when applying BNs

HIV Epidemic Appraisals for Assisting in the Design of Effective Prevention Programmes: Shifting the Paradigm Back to Basics

To design HIV prevention programmes, it is critical to understand the temporal and geographic aspects of the local epidemic and to address the key behaviours that drive HIV transmission. Two methods have been developed to appraise HIV epidemics and guide prevention strategies. The numerical proxy method classifies epidemics based on current HIV prevalence thresholds. The Modes of Transmission (MOT) model estimates the distribution of incidence over one year among risk-groups. Both methods focus on the current state of an epidemic and provide short-term metrics which may not capture the epidemiologic drivers. Through a detailed analysis of country and sub-national data, we explore the limitations of the two traditional methods and propose an alternative approach.We compared outputs of the traditional methods in five countries for which results were published, and applied the numeric and MOT model to India and six districts within India. We discovered three limitations of the current methods for epidemic appraisal: (1) their results failed to identify the key behaviours that drive the epidemic; (2) they were difficult to apply to local epidemics with heterogeneity across district-level administrative units; and (3) the MOT model was highly sensitive to input parameters, many of which required extraction from non-regional sources. We developed an alternative decision-tree framework for HIV epidemic appraisals, based on a qualitative understanding of epidemiologic drivers, and demonstrated its applicability in India. The alternative framework offered a logical algorithm to characterize epidemics; it required minimal but key data.Traditional appraisals that utilize the distribution of prevalent and incident HIV infections in the short-term could misguide prevention priorities and potentially impede efforts to halt the trajectory of the HIV epidemic. An approach that characterizes local transmission dynamics provides a potentially more effective tool with which policy makers can design intervention programmes

Seroprevalence and risk factors of herpes simplex virus type-2 infection among pregnant women in Northeast India

<p>Abstract</p> <p>Background</p> <p>Herpes simplex virus type-2 (HSV-2) is one of the most common sexually transmitted infections that facilitate human immunodeficiency virus (HIV) acquisition by over two fold or more. The development of HSV-2 control methods as a measure to control HIV epidemic in high HSV-2/HIV areas has become a priority. Two out of the six high HIV prevalent states of India are located in the Northeastern region of India. Due to lack of documented HSV-2 studies from this part of the country; there was a need for estimating the seroprevalence and risk factors of HSV-2 infection in this defined population.</p> <p>Methods</p> <p>Pregnant women (n = 1640) aged18 years and above attending antenatal clinics of tertiary referral hospitals in five Northeastern states of India were screened for type specific HSV-2 IgG antibodies. Blood samples were collected from all the participants after conducting interviews. Univariate and multivariate analyses were performed to identify the risk factors associated with HSV-2 seropositivity.</p> <p>Results</p> <p>Overall seroprevalence of HSV-2 infection was 8.7% (142/1640; 95% CI 7.3-10.0) with a highest prevalence of 15.0% (46/307; 95% CI 11.0-19.0) in the state of Arunachal Pradesh. Higher seroprevalence was observed with increasing age (Adj. Odds Ratio [AOR] 1.9 for 22-25 years old, AOR 2.29 for > 29 years old). The risk factors associated with HSV-2 seropositives were multiple sex partners (AOR 2.5, <it>p </it>= 0.04), condom non-user's (AOR 4.7, p <it><</it>0.001), early coitarchal age (age of first intercourse) 'less than 18 years' (AOR 9.6, <it>p = </it>0.04), middle income group (AOR 2.1, <it>p = </it>0.001) compared to low income group and low level of education (AOR 3.7, <it>p = </it>0.02) compared to higher education. HSV-2 seropositivity was higher among Christians (12.6%) compared to Muslims (3.8%). The most frequent clinical symptoms among HSV-2 seropositives were excess vaginal discharge in last one year (53.5%, 76/142) and pelvic pain (26.1%, 37/142). While among subjects with genital ulcers, HSV-2 seroprevalence was 36.8% (7/19).</p> <p>Conclusions</p> <p>Overall seroprevalence of HSV-2 infection among pregnant women of Northeast India is relatively low. The generation of awareness among high risk groups may have played key role to limit the infection. The role of vaccination against HSV-2 in near future and elimination of HSV-2 viral shedding along with genital tract inflammation in high HIV/HSV-2 areas may be an option for initiating successful intervention strategies to reduce the transmission and acquisition of HIV infection in Northeast India.</p

Molecular heterogeneity and CXorf67 alterations in posterior fossa group A (PFA) ependymomas

Of nine ependymoma molecular groups detected by DNA methylation profiling, the posterior fossa type A (PFA) is most prevalent. We used DNA methylation profiling to look for further molecular heterogeneity among 675 PFA ependymomas. Two major subgroups, PFA-1 and PFA-2, and nine minor subtypes were discovered. Transcriptome profiling suggested a distinct histogenesis for PFA-1 and PFA-2, but their clinical parameters were similar. In contrast, PFA subtypes differed with respect to age at diagnosis, gender ratio, outcome, and frequencies of genetic alterations. One subtype, PFA-1c, was enriched for 1q gain and had a relatively poor outcome, while patients with PFA-2c ependymomas showed an overall survival at 5 years of > 90%. Unlike other ependymomas, PFA-2c tumors express high levels of OTX2, a potential biomarker for this ependymoma subtype with a good prognosis. We also discovered recurrent mutations among PFA ependymomas. H3 K27M mutations were present in 4.2%, occurring only in PFA-1 tumors, and missense mutations in an uncharacterized gene, CXorf67, were found in 9.4% of PFA ependymomas, but not in other groups. We detected high levels of wildtype or mutant CXorf67 expression in all PFA subtypes except PFA-1f, which is enriched for H3 K27M mutations. PFA ependymomas are characterized by lack of H3 K27 trimethylation (H3 K27-me3), and we tested the hypothesis that CXorf67 binds to PRC2 and can modulate levels of H3 K27-me3. Immunoprecipitation/mass spectrometry detected EZH2, SUZ12, and EED, core components of the PRC2 complex, bound to CXorf67 in the Daoy cell line, which shows high levels of CXorf67 and no expression of H3 K27-me3. Enforced reduction of CXorf67 in Daoy cells restored H3 K27-me3 levels, while enforced expression of CXorf67 in HEK293T and neural stem cells reduced H3 K27-me3 levels. Our data suggest that heterogeneity among PFA ependymomas could have clinicopathologic utility and that CXorf67 may have a functional role in these tumors

Prevention of Parent to Child Transmission (PPTCT) program data in India: an emerging data set for appraising the HIV epidemic.

BACKGROUND: Evidence based resource allocation and decentralized planning of an effective HIV/AIDS response requires reliable information on levels and trends of HIV at national and sub-national geographic levels. HIV sentinel surveillance data from antenatal clinics (HSS-ANC) has been an important data source to assess the HIV/AIDS epidemic in India, but has a number of limitations. We assess the value of Prevention of Parent to Child Transmission (PPTCT) programme data to appraise the HIV epidemic in India. METHODS/FINDINGS: HIV data from PPTCT sites were compared to HSS-ANC and general population level surveys at various geographic levels in the states of Karnataka, Maharashtra and Andhra Pradesh. Chi-square tests were used to ascertain statistical significance. PPTCT HIV prevalence was significantly lower than HSS-ANC HIV prevalence (0.92% vs. 1.22% in Andhra Pradesh, 0.65% vs. 0.89% in Karnataka, 0.52% vs. 0.60% in Maharashtra, p<0.001 for all three states). In all three states, HIV prevalence from PPTCT centres that were part of the sentinel surveillance was comparable to HSS-ANC prevalence but significantly higher than PPTCT centres that were not part of the sentinel surveillance. HIV prevalence from PPTCT data was comparable to that from general population surveys. In all three states, significant declines in HIV prevalence between 2007 and 2010 were observed with the PPTCT data set. District level analyses of HIV trends and sub-district level analysis of HIV prevalence were possible using the PPTCT and not the HSS-ANC data sets. CONCLUSION: HIV prevalence from PPTCT may be a better proxy for general population prevalence than HSS-ANC. PPTCT data allow for analysis of HIV prevalence and trends at smaller geographic units, which is important for decentralized planning of HIV/AIDS programming. With further improvements to the system, India could replace its HSS-ANC with PPTCT programme data for surveillance

How do psychobehavioural variables shed light on heterogeneity in COVID-19 vaccine acceptance? Evidence from United States general population surveys on a probability panel and social media

Objectives To (1) understand what behaviours, beliefs, demographics and structural factors predict US adults’ intention to get a COVID-19 vaccination, (2) identify segments of the population (‘personas’) who share similar factors predicting vaccination intention, (3) create a ‘typing tool’ to predict which persona people belong to and (4) track changes in the distribution of personas over time and across the USA.Design Three surveys: two on a probability-based household panel (NORC’s AmeriSpeak) and one on Facebook.Setting The first two surveys were conducted in January 2021 and March 2021 when the COVID-19 vaccine had just been made available in the USA. The Facebook survey ran from May 2021 to February 2022.Participants All participants were aged 18+ and living in the USA.Outcome measures In our predictive model, the outcome variable was self-reported vaccination intention (0–10 scale). In our typing tool model, the outcome variable was the five personas identified by our clustering algorithm.Results Only 1% of variation in vaccination intention was explained by demographics, with about 70% explained by psychobehavioural factors. We identified five personas with distinct psychobehavioural profiles: COVID Sceptics (believe at least two COVID-19 conspiracy theories), System Distrusters (believe people of their race/ethnicity do not receive fair healthcare treatment), Cost Anxious (concerns about time and finances), Watchful (prefer to wait and see) and Enthusiasts (want to get vaccinated as soon as possible). The distribution of personas varies at the state level. Over time, we saw an increase in the proportion of personas who are less willing to get vaccinated.Conclusions Psychobehavioural segmentation allows us to identify why people are unvaccinated, not just who is unvaccinated. It can help practitioners tailor the right intervention to the right person at the right time to optimally influence behaviour