Rumi Chunara
Rumi Chunara
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Associate Professor of Biostatistics
Associate Professor of Computer Science and Engineering, Tandon
Director of Center for Health Data Science
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Professional overview
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The overarching goal of Dr. Rumi Chunara's research is to develop computational and statistical approaches for acquiring, integrating and using data to improve population-level public health. She focuses on the design and development of data mining and machine learning methods to address challenges related to data and goals of public health, as well as fairness and ethics in the design and use of data and algorithms embedded in social systems.
At NYU, Dr. Chunara also leads the Chunara Lab, which develops computational and statistical methods across data mining, natural language processing, spatio-temporal analyses and machine learning, to study population health. Previously, she was a Postdoctoral Fellow and Instructor at HealthMap and the Children's Hospital Informatics Program at Harvard Medical School. She completed her PhD at the Harvard-MIT Division of Health Sciences and Technology and BSc at Caltech.
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Education
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BS, Electrical Engineering (Honors), CaltechMS, Electrical Engineering and Computer Science, MITPhD, Medical and Electrical Engineering, MIT (Harvard-MIT Division of Health Sciences and Technology)
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Honors and awards
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Max Planck Sabbatical Award (2021)speaker at NSF Computer and Information Science and Engineering Directorate Career Proposal Writing Workshop (2020)Invited tutorial on Public Health and Machine Learning at ACM Conference on Health, Inference and Learning (2020)Keynote at Human Computation and Crowdsourcing (2019)Invited Speaker at Expert Group Meeting at United Nations Population Fund, Advances in Mobile Technologies for Data Collection Panel (2019)Keynote at ''Mapping the Equity Dimensions of Artificial Intelligence in Public Health'', University of Toronto (2019)Facebook Research Award (2019)Gates Foundation Grand Challenges Exploration Award (2019)NSF CAREER Award (2019)MIT Technology Review Top 35 Innovators Under 35 (2014)MIT Presidential Fellow (2004)
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Areas of research and study
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Health DisparitiesMachine learningSocial ComputingSocial Determinants of Health
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Publications
Publications
Mitigating Urban-Rural Disparities in Contrastive Representation Learning with Satellite Imagery
AbstractZhang*, M., myname{}, Chunara, R., & Chunara, R. (n.d.).Publication year
2024Volume
7Page(s)
1723--1734Abstract~Mitigating Urban-Rural Disparities in Contrastive Representation Learning with Satellite Imagery
AbstractZhang, M., Chunara, R., & Chunara, R. (n.d.).Publication year
2024Volume
7Page(s)
1723--1734Abstract~Social determinants of health : the need for data science methods and capacity
AbstractChunara, R., Chunara, R., Gjonaj, J., Immaculate, E., Wanga, I., Alaro, J., Scott-Sheldon, L. A., Mangeni, J., Mwangi, A., Vedanthan, R., & Hogan, J. (n.d.).Publication year
2024Journal title
The Lancet Digital HealthVolume
6Issue
4Page(s)
e235-e237Abstract~The impact of Monsoon weather on food security in Pakistan
AbstractConte, K. R., Chunara, R., & Chunara, R. (n.d.). (1st ed.).Publication year
2024Volume
2024Abstract~Understanding colorectal cancer screening message preferences of black Americans: Results from a crowdsourced wiki survey
AbstractKing, A., Chen, T., Wicke, R., Tong, C., Margolin, D., Chunara, R., Chunara, R., Kanrar, R., Nettleton, D., & Niederdeppe, J. (n.d.).Publication year
2024Abstract~Understanding Disparities in Post Hoc Machine Learning Explanation
AbstractMhasawade*, V., Rahman*, S., Haskell-Craig*, Z., myname, Chunara, R., & Chunara, R. (n.d.).Publication year
2024Page(s)
2374--2388Abstract~Understanding Disparities in Post Hoc Machine Learning Explanation
AbstractMhasawade, V., Rahman, S., Haskell-Craig, Z., Chunara, R., & Chunara, R. (n.d.).Publication year
2024Page(s)
2374-2388AbstractPrevious work has highlighted that existing post-hoc explanation methods exhibit disparities in explanation fidelity (across "race"and "gender"as sensitive attributes), and while a large body of work focuses on mitigating these issues at the explanation metric level, the role of the data generating process and black box model in relation to explanation disparities remains largely unexplored. Accordingly, through both simulations as well as experiments on a real-world dataset, we specifically assess challenges to explanation disparities that originate from properties of the data: limited sample size, covariate shift, concept shift, omitted variable bias, and challenges based on model properties: inclusion of the sensitive attribute and appropriate functional form. Through controlled simulation analyses, our study demonstrates that increased covariate shift, concept shift, and omission of covariates increase explanation disparities, with the effect pronounced higher for neural network models that are better able to capture the underlying functional form in comparison to linear models. We also observe consistent findings regarding the effect of concept shift and omitted variable bias on explanation disparities in the Adult income dataset. Overall, results indicate that disparities in model explanations can also depend on data and model properties. Based on this systematic investigation, we provide recommendations for the design of explanation methods that mitigate undesirable disparities.Utilizing big data without domain knowledge impacts public health decision-making
AbstractZhang, M., Rahman, S., Mhasawade, V., Chunara, R., & Chunara, R. (n.d.).Publication year
2024Journal title
Proceedings of the National Academy of Sciences of the United States of AmericaVolume
121Issue
39AbstractNew data sources and AI methods for extracting information are increasingly abundant and relevant to decision-making across societal applications. A notable example is street view imagery, available in over 100 countries, and purported to inform built environment interventions (e.g., adding sidewalks) for community health outcomes. However, biases can arise when decision-making does not account for data robustness or relies on spurious correlations. To investigate this risk, we analyzed 2.02 million Google Street View (GSV) images alongside health, demographic, and socioeconomic data from New York City. Findings demonstrate robustness challenges; built environment characteristics inferred from GSV labels at the intracity level often do not align with ground truth. Moreover, as average individual-level behavior of physical inactivity significantly mediates the impact of built environment features by census tract, intervention on features measured by GSV would be misestimated without proper model specification and consideration of this mediation mechanism. Using a causal framework accounting for these mediators, we determined that intervening by improving 10% of samples in the two lowest tertiles of physical inactivity would lead to a 4.17 (95% CI 3.84–4.55) or 17.2 (95% CI 14.4–21.3) times greater decrease in the prevalence of obesity or diabetes, respectively, compared to the same proportional intervention on the number of crosswalks by census tract. This study highlights critical issues of robustness and model specification in using emergent data sources, showing the data may not measure what is intended, and ignoring mediators can result in biased intervention effect estimates.A Brief Tutorial on Sample Size Calculations for Fairness Audits
AbstractSingh, H., Xia, F., Kim, M.-O., Pirracchio, R., Chunara, R., Chunara, R., & Feng, J. (n.d.).Publication year
2023Abstract~Area-based determinants of outreach vaccination for reaching vulnerable populations : A cross-sectional study in Pakistan
AbstractChen, X., Porter, A., Rehman, N. A., Morris, S. K., Saif, U., Chunara, R., & Chunara, R. (n.d.).Publication year
2023Journal title
PLOS global public healthVolume
3Issue
9 SeptemberAbstractThe objective of this study is to gain a comparative understanding of spatial determinants for outreach and clinic vaccination, which is critical for operationalizing efforts and breaking down structural biases; particularly relevant in countries where resources are low, and sub-region variance is high. Leveraging a massive effort to digitize public system reporting by Lady and Community Health Workers (CHWs) with geo-located data on over 4 million public-sector vaccinations from September 2017 through 2019, understanding health service operations in relation to vulnerable spatial determinants were made feasible. Location and type of vaccinations (clinic or outreach) were compared to regional spatial attributes where they were performed. Important spatial attributes were assessed using three modeling approaches (ridge regression, gradient boosting, and a generalized additive model). Consistent predictors for outreach, clinic, and proportion of third dose pentavalent vaccinations by region were identified. Of all Penta-3 vaccination records, 86.3% were performed by outreach efforts. At the tehsil level (fourth-order administrative unit), controlling for child population, population density, proportion of population in urban areas, distance to cities, average maternal education, and other relevant factors, increased poverty was significantly associated with more in-clinic vaccinations (β = 0.077), and lower proportion of outreach vaccinations by region (β = -0.083). Analyses at the union council level (fifth-administrative unit) showed consistent results for the differential importance of poverty for outreach versus clinic vaccination. Relevant predictors for each type of vaccination (outreach vs. in-clinic) show how design of outreach vaccination can effectively augment vaccination efforts beyond healthcare services through clinics. As Pakistan is third among countries with the most unvaccinated and under-vaccinated children, understanding barriers and factors associated with vaccination can be demonstrative for other national and sub-national regions facing challenges and also inform guidelines on supporting CHWs in health systems.Area-based determinants of outreach vaccination for reaching vulnerable populations: A cross-sectional study in Pakistan
AbstractChen, X., Porter, A., Abdur, R. N., Morris, S. K., Saif, U., Chunara, R., & Chunara, R. (n.d.).Publication year
2023Journal title
PLOS Global Public HealthVolume
3Issue
9Page(s)
e0001703Abstract~Cohort profile : a large EHR-based cohort with linked pharmacy refill and neighbourhood social determinants of health data to assess heart failure medication adherence
AbstractAdhikari, S., Mukhyopadhyay, A., Kolzoff, S., Li, X., Nadel, T., Fitchett, C., Chunara, R., Chunara, R., Dodson, J., Kronish, I., & Blecker, S. B. (n.d.).Publication year
2023Journal title
BMJ openVolume
13Issue
12AbstractPurpose Clinic-based or community-based interventions can improve adherence to guideline-directed medication therapies (GDMTs) among patients with heart failure (HF). However, opportunities for such interventions are frequently missed, as providers may be unable to recognise risk patterns for medication non-adherence. Machine learning algorithms can help in identifying patients with high likelihood of non-adherence. While a number of multilevel factors influence adherence, prior models predicting non-adherence have been limited by data availability. We have established an electronic health record (EHR)-based cohort with comprehensive data elements from multiple sources to improve on existing models. We linked EHR data with pharmacy refill data for real-time incorporation of prescription fills and with social determinants data to incorporate neighbourhood factors. Participants Patients seen at a large health system in New York City (NYC), who were >18 years old with diagnosis of HF or reduced ejection fraction (Disparate Effect Of Missing Mediators On Transportability of Causal Effects
AbstractMhasawade, V., Chunara, R., & Chunara, R. (n.d.).Publication year
2023AbstractTransported mediation effects provide an avenue to understand how upstream interventions (such as improved neighborhood conditions like green spaces) would work differently when applied to different populations as a result of factors that mediate the effects. However, when mediators are missing in the population where the effect is to be transported, these estimates could be biased. We study this issue of missing mediators, motivated by challenges in public health, wherein mediators can be missing, not at random. We propose a sensitivity analysis framework that quantifies the impact of missing mediator data on transported mediation effects. This framework enables us to identify the settings under which the conditional transported mediation effect is rendered insignificant for the subgroup with missing mediator data. Specifically, we provide the bounds on the transported mediation effect as a function of missingness. We then apply the framework to longitudinal data from the Moving to Opportunity Study, a large-scale housing voucher experiment, to quantify the effect of missing mediators on transport effect estimates of voucher receipt, an upstream intervention on living location, in childhood on subsequent risk of mental health or substance use disorder mediated through parental health across sites. Our findings provide a tangible understanding of how much missing data can be withstood for unbiased effect estimates. [Journal_ref: ]Generalization in Healthcare AI: Evaluation of a Clinical Large Language Model
AbstractRahman, S., Jiang, L. Y., Gabriel, S., Aphinyanaphongs, Y., Oermann, E. K., Chunara, R., & Chunara, R. (n.d.).Publication year
2023AbstractAdvances in large language models (LLMs) provide new opportunities in healthcare for improved patient care, clinical decision-making, and enhancement of physician and administrator workflows. However, the potential of these models importantly depends on their ability to generalize effectively across clinical environments and populations, a challenge often underestimated in early development. To better understand reasons for these challenges and inform mitigation approaches, we evaluated ClinicLLM, an LLM trained on [HOSPITAL]'s clinical notes, analyzing its performance on 30-day all-cause readmission prediction focusing on variability across hospitals and patient characteristics. We found poorer generalization particularly in hospitals with fewer samples, among patients with government and unspecified insurance, the elderly, and those with high comorbidities. To understand reasons for lack of generalization, we investigated sample sizes for fine-tuning, note content (number of words per note), patient characteristics (comorbidity level, age, insurance type, borough), and health system aspects (hospital, all-cause 30-day readmission, and mortality rates). We used descriptive statistics and supervised classification to identify features. We found that, along with sample size, patient age, number of comorbidities, and the number of words in notes are all important factors related to generalization. Finally, we compared local fine-tuning (hospital specific), instance-based augmented fine-tuning and cluster-based fine-tuning for improving generalization. Among these, local fine-tuning proved most effective, increasing AUC by 0.25% to 11.74% (most helpful in settings with limited data). Overall, this study provides new insights for enhancing the deployment of large language models in the societally important domain of healthcare, and improving their performance for broader populations. [Journal_ref: ]Global prevalence and content of information about alcohol use as a cancer risk factor on Twitter
AbstractKing, A. J., Dunbar, N. M., Margolin, D., Chunara, R., Chunara, R., Tong, C., Jih-Vieira, L., Matsen, C. B., & Niederdeppe, J. (n.d.).Publication year
2023Journal title
Preventive MedicineVolume
177AbstractObjectives: Alcohol use is a major risk factor for several forms of cancer, though many people have limited knowledge of this link. Public health communicators and cancer advocates desire to increase awareness of this link with the long-term goal of reducing cancer burden. The current study is the first to examine the prevalence and content of information about alcohol use as a cancer risk on social media internationally. Methods: We used a three-phase process (hashtag search, dictionary-based auto-identification of content, and human coding of content) to identify and evaluate information from Twitter posts between January 2019 and December 2021. Results: Our hashtag search retrieved a large set of cancer-related tweets (N = 1,122,397). The automatic search process using an alcohol dictionary identified a small number of messages about cancer that also mentioned alcohol (n = 9061, 0.8%), a number that got small after adjusting for human coded estimates of the dictionary precision (n = 5927, 0.5%). When cancer-related messages also mentioned alcohol, 82% (n = 1003 of 1225 examined through human coding) indicated alcohol use as a risk factor. Coding found rare instances of problematic information (e.g., promotion of alcohol, misinformation) in messages about alcohol use and cancer. Conclusions: Few social media messages about cancer types that can be linked to alcohol mention alcohol as a cancer risk factor. If public health communicators and cancer advocates want to increase knowledge and understanding of alcohol use as a cancer risk factor, efforts will need to be made on social media and through other communication platforms to increase exposure to this information over time.Global prevalence and content of information about alcohol use as a cancer risk factor on Twitter
AbstractKing, A. J., Dunbar, N. M., Margolin, D., Chunara, R., Chunara, R., Tong, C., Jih-Vieira, L., Matsen, C. B., & Niederdeppe, J. (n.d.).Publication year
2023Journal title
Preventive MedicineVolume
177Page(s)
107728Abstract~Impact on Public Health Decision Making by Utilizing Big Data Without Domain Knowledge
AbstractZhang, M., Rahman, S., Mhasawade, V., Chunara, R., & Chunara, R. (n.d.).Publication year
2023AbstractNew data sources, and artificial intelligence (AI) methods to extract information from them are becoming plentiful, and relevant to decision making in many societal applications. An important example is street view imagery, available in over 100 countries, and considered for applications such as assessing built environment aspects in relation to community health outcomes. Relevant to such uses, important examples of bias in the use of AI are evident when decision-making based on data fails to account for the robustness of the data, or predictions are based on spurious correlations. To study this risk, we utilize 2.02 million GSV images along with health, demographic, and socioeconomic data from New York City. Initially, we demonstrate that built environment characteristics inferred from GSV labels at the intra-city level may exhibit inadequate alignment with the ground truth. We also find that the average individual-level behavior of physical inactivity significantly mediates the impact of built environment features by census tract, as measured through GSV. Finally, using a causal framework which accounts for these mediators of environmental impacts on health, we find that altering 10% of samples in the two lowest tertiles would result in a 4.17 (95% CI 3.84 to 4.55) or 17.2 (95% CI 14.4 to 21.3) times bigger decrease on the prevalence of obesity or diabetes, than the same proportional intervention on the number of crosswalks by census tract. This work illustrates important issues of robustness and model specification for informing effective allocation of interventions using new data sources. [Journal_ref: ]Is there a need for graduate-level programmes in health data science? A perspective from Pakistan
AbstractHoodbhoy, Z., Chunara, R., Chunara, R., Waljee, A., AbuBakr, A., & Samad, Z. (n.d.).Publication year
2023Journal title
The Lancet Global HealthVolume
11Issue
1Page(s)
e23-e25Abstract~Making sense of social media data about colorectal cancer screening
AbstractKing, A. J., Margolin, D., Tong, C., Chunara, R., Chunara, R., & Niederdeppe, J. (n.d.).Publication year
2023Journal title
Journal of the American College of RadiologyAbstract~Measures of Disparity and their Efficient Estimation
AbstractSingh, H., Chunara, R., & Chunara, R. (n.d.).Publication year
2023Page(s)
927-938AbstractQuantifying disparities, that is differences in outcomes among population groups, is an important task in public health, economics, and increasingly in machine learning. In this work, we study the question of how to collect data to measure disparities. The field of survey statistics provides extensive guidance on sample sizes necessary to accurately estimate quantities such as averages. However, there is limited guidance for estimating disparities. We consider a broad class of disparity metrics including those used in machine learning for measuring fairness of model outputs. For each metric, we derive the number of samples to be collected per group that increases the precision of disparity estimates given a fixed data collection budget. We also provide sample size calculations for hypothesis tests that check for significant disparities. Our methods can be used to determine sample sizes for fairness evaluations. We validate the methods on two nationwide surveys, used for understanding population-level attributes like employment and health, and a prediction model. Absent a priori information on the groups, we find that equally sampling the groups typically performs well.Methodological Improvements in Social Vulnerability Index Construction Reinforce Role of Wealth Across International Contexts
AbstractPaul, R., Reid, S., Vieira, C. C., Wolfe, C., Zhange, Y., Zhao, Y., Chunara, R., & Chunara, R. (n.d.).Publication year
2023Journal title
MPIDR Working PapersAbstract~National cervical cancer burden estimation through systematic review and analysis of publicly available data in Pakistan
AbstractChughtai, N., Perveen, K., Gillani, S. R., Abbas, A., Chunara, R., Chunara, R., Manji, A. A., Karani, S., Noorali, A. A., Zakaria, M., Shamsi, U., Chishti, U., Khan, A. A., Soofi, S., Pervez, S., & Samad, Z. (n.d.).Publication year
2023Journal title
BMC public healthVolume
23Issue
1AbstractBackground: Cervical cancer is a major cause of cancer-related deaths among women worldwide. Paucity of data on cervical cancer burden in countries like Pakistan hamper requisite resource allocation. Objective: To estimate the burden of cervical cancer in Pakistan using available data sources. Methods: We performed a systematic review to identify relevant data on Pakistan between 1995 to 2022. Study data identified through the systematic review that provided enough information to allow age specific incidence rates and age standardized incidence rates (ASIR) calculations for cervical cancer were merged. Population at risk estimates were derived and adjusted for important variables in the care-seeking pathway. The calculated ASIRs were applied to 2020 population estimates to estimate the number of cervical cancer cases in Pakistan. Results: A total of 13 studies reported ASIRs for cervical cancer for Pakistan. Among the studies selected, the Karachi Cancer Registry reported the highest disease burden estimates for all reported time periods: 1995–1997 ASIR = 6.81, 1998–2002 ASIR = 7.47, and 2017–2019 ASIR = 6.02 per 100,000 women. Using data from Karachi, Punjab and Pakistan Atomic Energy Cancer Registries from 2015–2019, we derived an unadjusted ASIR for cervical cancer of 4.16 per 100,000 women (95% UI 3.28, 5.28). Varying model assumptions produced adjusted ASIRs ranging from 5.2 to 8.4 per 100,000 women. We derived an adjusted ASIR of 7.60, (95% UI 5.98, 10.01) and estimated 6166 (95% UI 4833, 8305) new cases of cervical cancer per year. Conclusion: The estimated cervical cancer burden in Pakistan is higher than the WHO target. Estimates are sensitive to health seeking behavior, and appropriate physician diagnostic intervention, factors that are relevant to the case of cervical cancer, a stigmatized disease in a low-lower middle income country setting. These estimates make the case for approaching cervical cancer elimination through a multi-pronged strategy.Neighborhood-Level Socioeconomic Status and Prescription Fill Patterns Among Patients With Heart Failure
AbstractMukhopadhyay, A., Blecker, S., Li, X., Kronish, I. M., Chunara, R., Chunara, R., Zheng, Y., Lawrence, S., Dodson, J. A., Kozloff, S., & Adhikari, S. (n.d.).Publication year
2023Journal title
JAMA network openVolume
6Issue
12Page(s)
e2347519AbstractImportance: Medication nonadherence is common among patients with heart failure with reduced ejection fraction (HFrEF) and can lead to increased hospitalization and mortality. Patients living in socioeconomically disadvantaged areas may be at greater risk for medication nonadherence due to barriers such as lower access to transportation or pharmacies. Objective: To examine the association between neighborhood-level socioeconomic status (nSES) and medication nonadherence among patients with HFrEF and to assess the mediating roles of access to transportation, walkability, and pharmacy density. Design, Setting, and Participants: This retrospective cohort study was conducted between June 30, 2020, and December 31, 2021, at a large health system based primarily in New York City and surrounding areas. Adult patients with a diagnosis of HF, reduced EF on echocardiogram, and a prescription of at least 1 guideline-directed medical therapy (GDMT) for HFrEF were included. Exposure: Patient addresses were geocoded, and nSES was calculated using the Agency for Healthcare Research and Quality SES index, which combines census-tract level measures of poverty, rent burden, unemployment, crowding, home value, and education, with higher values indicating higher nSES. Main Outcomes and Measures: Medication nonadherence was obtained through linkage of health record prescription data with pharmacy fill data and was defined as proportion of days covered (PDC) of less than 80% over 6 months, averaged across GDMT medications. Results: Among 6247 patients, the mean (SD) age was 73 (14) years, and majority were male (4340 [69.5%]). There were 1011 (16.2%) Black participants, 735 (11.8%) Hispanic/Latinx participants, and 3929 (62.9%) White participants. Patients in lower nSES areas had higher rates of nonadherence, ranging from 51.7% in the lowest quartile (731 of 1086 participants) to 40.0% in the highest quartile (563 of 1086 participants) (P < .001). In adjusted analysis, patients living in the lower 2 nSES quartiles had significantly higher odds of nonadherence when compared with patients living in the highest nSES quartile (quartile 1: odds ratio [OR], 1.57 [95% CI, 1.35-1.83]; quartile 2: OR, 1.35 [95% CI, 1.16-1.56]). No mediation by access to transportation and pharmacy density was found, but a small amount of mediation by neighborhood walkability was observed. Conclusions and Relevance: In this retrospective cohort study of patients with HFrEF, living in a lower nSES area was associated with higher rates of GDMT nonadherence. These findings highlight the importance of considering neighborhood-level disparities when developing approaches to improve medication adherence.Prevalence of familial hypercholesterolemia in a country-wide laboratory network in Pakistan : 10-year data from 988, 306 patients
AbstractFarhad, A., Noorali, A. A., Tajuddin, S., Khan, S. D., Ali, M., Chunara, R., Chunara, R., Khan, A. H., Zafar, A., Merchant, A., Bokhari, S. S., Virani, S. S., & Samad, Z. (n.d.).Publication year
2023Journal title
Progress in Cardiovascular DiseasesVolume
79Page(s)
19-27AbstractIntroduction: Familial hypercholesterolemia (FH) is a modifiable risk factor for premature coronary heart disease but is poorly diagnosed and treated. We leveraged a large laboratory network in Pakistan to study the prevalence, gender and geographic distribution of FH. Methodology: Data were curated from the Aga Khan University Hospital clinical laboratories, which comprises of 289 laboratories and collection points spread over 94 districts. Clinically ordered lipid profiles from 1st January 2009 to 30th June 2018 were included and data on 1,542,281 LDL-C values was extracted. We used the Make Early Diagnosis to Prevent Early Death (MEDPED) criteria to classify patients as FH and reported data on patients with low-density liporotein -cholesterol (LDL-C) ≥ 190 mg/dL. FH cases were also examined by their spatial distribution. Results: After applying exclusions, the final sample included 988,306 unique individuals, of which 24,273 individuals (1:40) had LDL-C values of ≥190 mg/dL. Based on the MEDPED criteria, 2416 individuals (1:409) had FH. FH prevalence was highest in individuals 10–19 years (1:40) and decreased as the patient age increased. Among individuals ≥40 years, the prevalence of FH was higher for females compared with males (1:755 vs 1:1037, p < 0.001). Median LDL-C for the overall population was 112 mg/dL (IQR = 88-136 mg/dL). The highest prevalence after removing outliers was observed in Rajan Pur district (1.23% [0.70–2.10%]) in Punjab province, followed by Mardan (1.18% [0.80–1.70%]) in Khyber Pakhtunkhwa province, and Okara (0.99% [0.50–1.80%]) in Punjab province. Conclusion: There is high prevalence of actionable LDL-C values in lipid samples across a large network of laboratories in Pakistan. Variable FH prevalence across geographic locations in Pakistan may need to be explored at the population level for intervention and management of contributory factors. Efforts at early diagnosis and treatment of FH are urgently needed.Quantifying greenspace using deep learning in Karachi, Pakistan
AbstractZhang, M., Arshad, H., Abbas, M., Jehanzeb, H., Tahir, I., Hassan, J., Chunara, R., Chunara, R., & others. (n.d.).Publication year
2023Abstract~