INSPIRE datahub: a pan-African integrated suite of services for harmonising longitudinal population health data using OHDSI tools.

Introduction: Population health data integration remains a critical challenge in low- and middle-income countries (LMIC), hindering the generation of actionable insights to inform policy and decision-making. This paper proposes a pan-African, Findable, Accessible, Interoperable, and Reusable (FAIR) research architecture and infrastructure named the INSPIRE datahub. This cloud-based Platform-as-a-Service (PaaS) and on-premises setup aims to enhance the discovery, integration, and analysis of clinical, population-based surveys, and other health data sources. Methods: The INSPIRE datahub, part of the Implementation Network for Sharing Population Information from Research Entities (INSPIRE), employs the Observational Health Data Sciences and Informatics (OHDSI) open-source stack of tools and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to harmonise data from African longitudinal population studies. Operating on Microsoft Azure and Amazon Web Services cloud platforms, and on on-premises servers, the architecture offers adaptability and scalability for other cloud providers and technology infrastructure. The OHDSI-based tools enable a comprehensive suite of services for data pipeline development, profiling, mapping, extraction, transformation, loading, documentation, anonymization, and analysis. Results: The INSPIRE datahub's "On-ramp" services facilitate the integration of data and metadata from diverse sources into the OMOP CDM. The datahub supports the implementation of OMOP CDM across data producers, harmonizing source data semantically with standard vocabularies and structurally conforming to OMOP table structures. Leveraging OHDSI tools, the datahub performs quality assessment and analysis of the transformed data. It ensures FAIR data by establishing metadata flows, capturing provenance throughout the ETL processes, and providing accessible metadata for potential users. The ETL provenance is documented in a machine- and human-readable Implementation Guide (IG), enhancing transparency and usability. Conclusion: The pan-African INSPIRE datahub presents a scalable and systematic solution for integrating health data in LMICs. By adhering to FAIR principles and leveraging established standards like OMOP CDM, this architecture addresses the current gap in generating evidence to support policy and decision-making for improving the well-being of LMIC populations. The federated research network provisions allow data producers to maintain control over their data, fostering collaboration while respecting data privacy and security concerns. A use-case demonstrated the pipeline using OHDSI and other open-source tools.

Enabling data sharing and utilization for African population health data using OHDSI tools with an OMOP-common data model.

The COVID-19 pandemic has spurred the use of AI and DS innovations in data collection and aggregation. Extensive data on many aspects of the COVID-19 has been collected and used to optimize public health response to the pandemic and to manage the recovery of patients in Sub-Saharan Africa. However, there is no standard mechanism for collecting, documenting and disseminating COVID-19 related data or metadata, which makes the use and reuse a challenge. INSPIRE utilizes the Observational Medical Outcomes Partnership (OMOP) as the Common Data Model (CDM) implemented in the cloud as a Platform as a Service (PaaS) for COVID-19 data. The INSPIRE PaaS for COVID-19 data leverages the cloud gateway for both individual research organizations and for data networks. Individual research institutions may choose to use the PaaS to access the FAIR data management, data analysis and data sharing capabilities which come with the OMOP CDM. Network data hubs may be interested in harmonizing data across localities using the CDM conditioned by the data ownership and data sharing agreements available under OMOP's federated model. The INSPIRE platform for evaluation of COVID-19 Harmonized data (PEACH) harmonizes data from Kenya and Malawi. Data sharing platforms must remain trusted digital spaces that protect human rights and foster citizens' participation is vital in an era where information overload from the internet exists. The channel for sharing data between localities is included in the PaaS and is based on data sharing agreements provided by the data producer. This allows the data producers to retain control over how their data are used, which can be further protected through the use of the federated CDM. Federated regional OMOP-CDM are based on the PaaS instances and analysis workbenches in INSPIRE-PEACH with harmonized analysis powered by the AI technologies in OMOP. These AI technologies can be used to discover and evaluate pathways that COVID-19 cohorts take through public health interventions and treatments. By using both the data mapping and terminology mapping, we construct ETLs that populate the data and/or metadata elements of the CDM, making the hub both a central model and a distributed model.

Cancer causing chewing habits and related oral lesions - A comparative study among females of socio-culturally different areas of West Bengal.

Background: Smokeless tobacco, as well as areca-nut both, causes cancerous and precancerous lesions of the oral cavity. The traditional value of India, as well as West Bengal do not allow females for smoking, but there is no such disapproval for using smokeless tobacco or areca nut. Geographically and socio-culturally, the Northern part of West Bengal differs from its Southern part. Aims: This study aimed to assess the prevalence of different chewing habits, habit products, and habit-related different oral lesions among females in two socio-culturally different areas of West Bengal. Methods: A total of 222 women from areas of North Bengal and 173 women from areas of South Bengal aged 15 years and above were selected. A face-to-face interview was conducted using a structured questionnaire. An oral cavity examination was done to rule out any oral mucosal alterations caused by cancer-causing habits. Statistical Analysis: Chi-square test or Fischer's exact tests were used to compare unpaired proportions as appropriate. Results: In areas of the northern part of Bengal, 42.34% of females were chewers, and in areas of the southern part of Bengal 18.50% of females were chewers. Younger female chewers were more from areas of the northern part of Bengal. Women, residing in different areas of the northern part of Bengal had more habit-related oral lesions, compared to the southern part of Bengal. Conclusion: Special attention should be given to increasing awareness regarding chewing habit-related health hazards among females, especially in areas of North Bengal.

Differences of oral cancer in men and women of West Bengal, India.

INTRODUCTION: Oral cancer constitutes a major public health problem in South East Asia, as it causes profound social and economic consequences for people in this area. Nowadays, pattern of tobacco and alcohol use among females is changing and at the same time incidence of oral squamous cell carcinoma (SCC) among females is increasing. OBJECTIVES: The aim of the study was to evaluate the possible differences between male and female patients suffering from oral SCC. MATERIALS AND METHODS: One hundred and sixteen patients with oral SCC, who were diagnosed between 2017 and 2018, were evaluated retrospectively. Special attention was paid to tobacco and alcohol use, as well as to patients without the risk factors. Data obtained were entered into a Microsoft Excel Spreadsheet and then were analyzed by SPSS 24.0. RESULTS: The men: women ratio was 2.5:1. Mean age for diagnosis of oral cancer in men was 57.5 and mean age for diagnosis of oral cancer in women was 46.33. Majority of men oral cancer patients had smoking habits (61.45%) and majority of women oral cancer patients had chewing habits (66.67%). Tongue was the most common site for women oral cancer patients and palate, retromolar area and tonsillar area were the most common site for men oral cancer patients who never used any habit products. CONCLUSION: Compared to earlier studies women gets diagnosis of oral cancer earlier than men in our study. Gutkha use in men and Gudaku use in women were associated with oral cancer in early age. Further studies should be performed in women tongue cancer patients and men patients with cancer of maxillary area, retromolar and tonsillar area without risk factors to find etiology.

Smokeless tobacco use and related oral mucosal changes in Bengali Women.

BACKGROUND: Tobacco use is called the single most cause of preventable cause of death all over the world. The various study confirmed that smokeless tobacco use is directly related to oral cancer and pre-cancer. The prevalence of smokeless tobacco use varies widely in different countries and states based on age group, gender, with varied socioeconomic, cultural and educational backgrounds. CONTEXT: Bengali female population. AIM: Explore the pattern of smokeless tobacco use and oral mucosal changes caused by it. METHODS: 155 women aged 15 years and above were selected. Face-to-face interview was conducted using a structured questionnaire. Data were summarized and statistically, analysis was done. STATISTICAL ANALYSIS USED: Chi-square test and univariate logistic regression done. RESULTS: The prevalence of current smokeless tobacco use was found to be 18.7%. On univariate logistic regression, it was found that there was a significant association between smokeless tobacco use and less educated females, odds ratio 0.4209 (0.1855-0.9550) family income less than 10,000, odds ratio 3.9773 (1.3047-12.1242), and oral changes odds ratio 0.2693 (0.1027-0.7061). CONCLUSIONS: Health care providers, as well as social workers, should give all efforts to bring the women from behind the curtain and educate them about the hazards of smokeless tobacco use.

Validating a GPS-based approach to detect health facility visits against maternal response to prompted recall survey.

INTRODUCTION: Common approaches to measure health behaviors rely on participant responses and are subject to bias. Technology-based alternatives, particularly using GPS, address these biases while opening new channels for research. This study describes the development and implementation of a GPS-based approach to detect health facility visits in rural Pune district, India. METHODS: Participants were mothers of under-five year old children within the Vadu Demographic Surveillance area. Participants received GPS-enabled smartphones pre-installed with a location-aware application to continuously record and transmit participant location data to a central server. Data were analyzed to identify health facility visits according to a parameter-based approach, optimal thresholds of which were calibrated through a simulation exercise. Lists of GPS-detected health facility visits were generated at each of six follow-up home visits and reviewed with participants through prompted recall survey, confirming visits which were correctly identified. Detected visits were analyzed using logistic regression to explore factors associated with the identification of false positive GPS-detected visits. RESULTS: We enrolled 200 participants and completed 1098 follow-up visits over the six-month study period. Prompted recall surveys were completed for 694 follow-up visits with one or more GPS-detected health facility visits. While the approach performed well during calibration (positive predictive value (PPV) 78%), performance was poor when applied to participant data. Only 440 of 22 251 detected visits were confirmed (PPV 2%). False positives increased as participants spent more time in areas of high health facility density (odds ratio (OR) = 2.29, 95% confidence interval (CI) = 1.62-3.25). Visits detected at facilities other than hospitals and clinics were also more likely to be false positives (OR = 2.78, 95% CI = 1.65-4.67) as were visits detected to facilities nearby participant homes, with the likelihood decreasing as distance increased (OR = 0.89, 95% CI = 0.82-0.97). Visit duration was not associated with confirmation status. CONCLUSIONS: The optimal parameter combination for health facility visits simulated by field workers substantially overestimated health visits from participant GPS data. This study provides useful insights into the challenges in detecting health facility visits where providers are numerous, highly clustered within urban centers and located near residential areas of the population which they serve.

Determinants and patterns of care-seeking for childhood illness in rural Pune District, India.

BACKGROUND: An estimated 1.2 million children under five years of age die each year in India, with pneumonia and diarrhea among the leading causes. Increasing care-seeking is important to reduce mortality and morbidity from these causes. This paper explores the determinants and patterns of care-seeking for childhood illness in rural Pune district, India. METHODS: Mothers having at least one child <5 years from the study area of the Vadu Health and Demographic Surveillance System were enrolled in a prospective cohort study. Household sociodemographic information was collected through a baseline questionnaire administered at enrollment. Participants were visited up to six times between July 2015 and February 2016 to collect information on recent childhood acute illness and associated care-seeking behavior. Multivariate logistic regression explored the associations between care-seeking and child, participant, and household characteristics. RESULTS: We enrolled 743 mothers with 1066 eligible children, completing 2585 follow-up interviews (90% completion). Overall acute illness prevalence in children was 26% with care sought from a health facility during 71% of episodes. Multivariable logistic regression showed care-seeking was associated with the number of reported symptoms (Odds ratio (OR) = 2.4, 95% confidence interval (CI) = 1.5-3.9) and household insurance coverage (OR = 2.2, 95% CI = 1.1-4.3). We observed an interaction between the associations of illness severity and maternal employment on care-seeking. Somewhat-to-very severe illness was associated with increased care-seeking among both employed (OR = 5.0, 95% CI = 2.2-11.1) and currently unemployed mothers (OR = 7.0, 95% CI = 3.9-12.6). Maternal employment was associated with reduced care-seeking for non-severe illness (OR = 0.3, 95% CI = 0.1-0.7), but not associated with care-seeking for somewhat-to-very severe illness. Child sex was not associated with care-seeking. CONCLUSIONS: This study demonstrates the importance of illness characteristics in determining facility-based care-seeking while also suggesting that maternal employment resulted in decreased care-seeking among non-severe illness episodes. The nature of the association between maternal employment and care-seeking is unclear and should be explored through additional studies. Similarly, the absence of male bias in care-seeking should be examined to assess for potential bias at other stages in the management of childhood illness.

Demographic surveillance over 12 years helps elicit determinants of low birth weights in India.

BACKGROUND: Low birth weight is an important predictor of maternal and child health. Birth weight is likely to be affected by maternal health, socioeconomic status and quality of health care facilities. OBJECTIVE: To assess trends in the birth weight, the proportion of low birth weight, maternal factors and health care facilities for delivery in villages of Western Maharashtra from the year 2004 to 2016 and to analyze factors associated with low birth weight for total birth data of 2004-2016. METHODS: Data collected for 19244 births from 22 villages in Vadu Health and Demographic Surveillance System (HDSS), Pune, Maharashtra, India from the year 2004 to 2016 were used for this analysis. RESULTS: There was an overall increase in the annual mean birth weight from 2640.12 gram [95% CI 2602.21-2686.84] in the year 2004 to 2781.19 gram [95% CI 2749.49-2797.95] in the year 2016. There was no secular trend to show increase or decrease in the proportion of low weight at birth. Increasing maternal age (>18 years) compounded with better education, reduced parity and increasing number of institutional deliveries were significant trends observed during the past decade. Low birth weight was found to be associated with female gender, first birth order, poor maternal education and occupation as cultivation. CONCLUSION: Changes in maternal age, education, occupation, and increased institutionalized deliveries contributed in to increasing birth weights in rural Maharashtra. Female gender, first birth order, poor maternal education and occupation of cultivation are associated with increased risk of low birth weight.

Concordance between GPS-based smartphone app for continuous location tracking and mother's recall of care-seeking for child illness in India.

BACKGROUND: Traditionally, health care-seeking behaviour for child illness is assessed through population-based national demographic and health surveys. GPS-based technologies are increasingly used in human behavioural research including tracking human mobility and spatial behaviour. This paper assesses how well a care-seeking event to a health care facility for child illness, as recalled by the mother in a survey setting using questions sourced from Demographic and Health Surveys, concurs with one that is identified by TrackCare, a GPS-based location-aware smartphone application. METHODS: Mothers residing in the Vadu HDSS area in Pune district, India having at least one young child were randomly assigned to receive a GPS-enabled smartphone with a pre-installed TrackCare app configured to record the device location data at one-minute intervals over a 6-month period. Spatio-temporal parameters were derived from the location data and used to detect a care-seeking event to any of the health care facilities in the area. Mothers were asked to recall a child illness and if, where and when care was sought, using a questionnaire during monthly visits over a 6-month period. Concordance between the mother's recall and the TrackCare app to identify a care-seeking event was estimated according to percent positive agreement. RESULTS: Mean concordance for a care-seeking event between the two methods (mother's recall and TrackCare location data) ranged up to 45%, was significantly higher (P-value <0.001) for care-seeking at a hospital as compared to a clinic and for a health care facility in the private sector compared to that in the public sector. Overall, the proportion of disagreement for a care-seeking event not detected by TrackCare but reported by mother ranged up to 77% and was significantly higher (P-value <0.001) compared to those not reported by mother but detected by TrackCare. CONCLUSIONS: Given the uncertainty and limitations in use of continuous location tracking data in a field setting and the complexity of classifying human activity patterns, additional research is needed before continuous location tracking can serve as a gold standard substitute for other methods to determine health care-seeking behaviour. Future performance may be improved by incorporating other smartphone-based sensors, such as Wi-Fi and Bluetooth, to obtain more precise location estimates in areas where GPS signal is weakest.

Assessing the reactivity to mobile phones and repeated surveys on reported care-seeking for common childhood illnesses in rural India.

BACKGROUND: Traditionally, health care-seeking for child illness is assessed through population-based and nationally representative demographic and health surveys (DHS) that are conducted once every five to seven years and are based on maternal recall. These maternal reports are subject to recall bias. Mobile phones (with the use of GPS technology) have the potential to constantly track movements of phone owners and provide high quality and more accurate data at a population level in low and middle income countries (LMICs) to assess the validity of maternal recall. We provided a group of mothers with smartphones installed with a location-aware application and visited them monthly to administer a survey questionnaire on care-seeking for diarrhoea, fever and cough with fever. This paper assesses for any reactivity to smartphones or repeated study contacts for measuring care-seeking and if this resulted in change in health care provider preference. METHODS: We enrolled 749 mothers from rural areas of Pune district in Maharashtra, India and randomly allocated them to one of three groups - a longitudinal phone group, a longitudinal control group and a cross-sectional control group. We collected baseline information from mothers, including individual and household demographic and socio-economic characteristics and care-seeking preferences for child illness. We followed up both longitudinal groups monthly and each cross-sectional sub-group once over a period of 6 months. At each follow up, we administered questions identical to those in the National Family Health Survey (NFHS) questionnaire to determine an episode of diarrhoea, fever or cough within the last 15 days, care seeking for the same, and the type of provider. The data were analysed using the χ2 test or Fisher Exact Test for categorical variables, or with the Kruskall-Wallis non-parametric test for continuous variables (due to the non-normal nature of the data). Multivariable joint models of group and visit time were analysed with logistic regression methods. RESULTS: All three groups were similar in their socio-demographic characteristics at baseline. We did not observe any significant difference in care seeking for diarrhoea, fever or cough with fever between groups. Also, we did not observe any significant difference in proportion of children seeking care from the private sector. CONCLUSIONS: We did not observe any reactivity in this study due to the presence of the phone (Hawthorne effect) or repeated study visits. The study also shows the potential of using GPS enabled smartphones to enrich DHS surveys in LMICs like India. However, further studies need to be conducted in other population groups before the findings can be generalised.

The INDEPTH Data Repository: An International Resource for Longitudinal Population and Health Data From Health and Demographic Surveillance Systems.

The International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH) is a global network of research centers that conduct longitudinal health and demographic evaluation of populations in low- and middle-income countries (LMICs) currently in 52 health and demographic surveillance system (HDSS) field sites situated in sub-Saharan Africa (14 countries), Asia (India, Bangladesh, Thailand, Vietnam, and Indonesia), and Oceania (Papua New Guinea). Through this network of HDSS field sites, INDEPTH is capable of producing reliable longitudinal data about the lives of people in the research communities as well as how development policies and programs affect those lives. The aim of the INDEPTH Data Repository is to enable INDEPTH member centers and associated researchers to contribute and share fully documented, high-quality datasets with the scientific community and health policy makers.

Apoptosis in cancer--an update.

Apoptosis is programmed cell death which is essential for development and survival of living organisms. It is a sequentially regulated suicidal programme where cells activate certain enzymes which dissolute their own nuclear component and various protein component of nucleus and cytoplasm. Disturbance of this regulatory pathway may lead to various diseases like autoimmune diseases, neurodegenerative diseases and cancers. The potential mechanisms of apoptosis and its role in cancer are discussed. The ability of apoptosis to modulate the life or death of a cell is also recognized for its immense therapeutic potential. Understanding the mechanisms from this review will give us better insight to the pathogenesis of various diseases including cancer and will open new horizons to therapeutic approaches.