Extraction of Unstructured Electronic Health Records to Evaluate Glioblastoma Treatment Patterns.

PURPOSE: Data on lines of therapy (LOTs) for cancer treatment are important for clinical oncology research, but LOTs are not explicitly recorded in electronic health records (EHRs). We present an efficient approach for clinical data abstraction and a flexible algorithm to derive LOTs from EHR-based medication data on patients with glioblastoma multiforme (GBM). METHODS: Nonclinicians were trained to abstract the diagnosis of GBM from EHRs, and their accuracy was compared with abstraction performed by clinicians. The resulting data were used to build a cohort of patients with confirmed GBM diagnosis. An algorithm was developed to derive LOTs using structured medication data, accounting for the addition and discontinuation of therapies and drug class. Descriptive statistics were calculated and time-to-next-treatment (TTNT) analysis was performed using the Kaplan-Meier method. RESULTS: Treating clinicians as the gold standard, nonclinicians abstracted GBM diagnosis with a sensitivity of 0.98, specificity 1.00, positive predictive value 1.00, and negative predictive value 0.90, suggesting that nonclinician abstraction of GBM diagnosis was comparable with clinician abstraction. Of 693 patients with a confirmed diagnosis of GBM, 246 patients contained structured information about the types of medications received. Of them, 165 (67.1%) received a first-line therapy (1L) of temozolomide, and the median TTNT from the start of 1L was 179 days. CONCLUSION: We described a workflow for extracting diagnosis of GBM and LOT from EHR data that combines nonclinician abstraction with algorithmic processing, demonstrating comparable accuracy with clinician abstraction and highlighting the potential for scalable and efficient EHR-based oncology research.

Burn Care Funding in the Era of Price Transparency-Does Verification Signal Bargaining Power?

The Price Transparency Rule of 2021 forced payors and hospitals to publicly disclose negotiated prices to foster competition and reduce cost. Burn care is costly and concentrated at less than 130 centers in the US. We aimed to analyze geographic price variations for inpatient burn care and measure the effects of American Burn Association (ABA) verification status and market concentration on prices. All available commercial rates for 2021-2022 for burn-related Diagnosis Related Groups (DRG) 927, 928, 929, 933, 934, and 935 were merged with hospital-level variables, ABA verification status, and Herfindahl-Hirschman Index (HHI) data. For the DRG 927 (most intensive burn admission) a linear mixed effects model was fit with cost as the outcome and the following variables as covariates: HHI, plan type, safety net status, profit status, verification status, rural status, teaching hospital status. Random intercepts allowed for individual burn centers. There were 170,738 rates published from 1541 unique hospitals. Commercial reimbursement rates for the same DRG varied by a factor of approximately three within hospitals for all DRGs. Similarly, rates across different hospitals varied by a factor of three for all DRGs, with DRG 927 having the most variation. Burn center status was independently associated with higher reimbursement rates adjusting for facility-level factors for all DRGs except for 935. Notably, HHI was the largest predictor of commercial rates (p<0.001). Negotiated prices for inpatient burn care vary widely. ABA-verified centers garner higher rates along with burn centers in more concentrated/monopolistic markets.

Emerging Outlook on Personalized Neuromodulation for Depression: Insights from Tractography-Based Targeting.

BACKGROUND: Deep brain stimulation (DBS) has shown individual promise in treating treatment resistant depression (TRD), but larger-scale trials have been less successful. Here, we create the largest meta-analysis with individual patient data (IPD) to date to explore if the use of tractography enhances the efficacy of DBS for TRD. METHODS: We systematically reviewed 1823 articles, selecting 32 that contributed data from 366 patients. We stratified the IPD based on stimulation target and use of tractography. Utilizing two-way type III Analysis of Variance (ANOVA), Welch Two Sample t-tests, and mixed-effects linear regression models, we evaluated changes in depression severity 9-15 months post-surgery (1-Y) and at last follow-up (LFU) (4 weeks - 8 years) as assessed by depression scales. RESULTS: Tractography was used for medial forebrain bundle (MFB, n=17/32), subcallosal cingulate (SCC, n=39/241), and ventral capsule/ventral striatum (VC/VS, n=3/41) targets; and not used for bed nucleus of stria terminalis (n=11), lateral habenula (n=10), and inferior thalamic peduncle (n=1). Across all patients, tractography significantly improved mean depression scores at 1-Y (p<0.001) and LFU (p=0.009). Within the target cohorts, tractography improved depression scores at 1-Y for both MFB and SCC, though significance was only met at the alpha = 0.1 level (SCC: ß=15.8%, p=0.09; MFB: ß=52.4%, p=0.10). Within the tractography cohort, MFB with tractography patients showed greater improvement than those with SCC with tractography (72.42±7.17% versus 54.78±4.08%) at 1-Y (p=0.044). CONCLUSIONS: Our findings underscore the promise of tractography in DBS for TRD as a methodology for personalization of therapy, supporting its inclusion in future trials.

Natural language processing system for rapid detection and intervention of mental health crisis chat messages.

Patients experiencing mental health crises often seek help through messaging-based platforms, but may face long wait times due to limited message triage capacity. Here we build and deploy a machine-learning-enabled system to improve response times to crisis messages in a large, national telehealth provider network. We train a two-stage natural language processing (NLP) system with key word filtering followed by logistic regression on 721 electronic medical record chat messages, of which 32% are potential crises (suicidal/homicidal ideation, domestic violence, or non-suicidal self-injury). Model performance is evaluated on a retrospective test set (4/1/21-4/1/22, N = 481) and a prospective test set (10/1/22-10/31/22, N = 102,471). In the retrospective test set, the model has an AUC of 0.82 (95% CI: 0.78-0.86), sensitivity of 0.99 (95% CI: 0.96-1.00), and PPV of 0.35 (95% CI: 0.309-0.4). In the prospective test set, the model has an AUC of 0.98 (95% CI: 0.966-0.984), sensitivity of 0.98 (95% CI: 0.96-0.99), and PPV of 0.66 (95% CI: 0.626-0.692). The daily median time from message receipt to crisis specialist triage ranges from 8 to 13 min, compared to 9 h before the deployment of the system. We demonstrate that a NLP-based machine learning model can reliably identify potential crisis chat messages in a telehealth setting. Our system integrates into existing clinical workflows, suggesting that with appropriate training, humans can successfully leverage ML systems to facilitate triage of crisis messages.

Selective prediction for extracting unstructured clinical data.

OBJECTIVE: While there are currently approaches to handle unstructured clinical data, such as manual abstraction and structured proxy variables, these methods may be time-consuming, not scalable, and imprecise. This article aims to determine whether selective prediction, which gives a model the option to abstain from generating a prediction, can improve the accuracy and efficiency of unstructured clinical data abstraction. MATERIALS AND METHODS: We trained selective classifiers (logistic regression, random forest, support vector machine) to extract 5 variables from clinical notes: depression (n = 1563), glioblastoma (GBM, n = 659), rectal adenocarcinoma (DRA, n = 601), and abdominoperineal resection (APR, n = 601) and low anterior resection (LAR, n = 601) of adenocarcinoma. We varied the cost of false positives (FP), false negatives (FN), and abstained notes and measured total misclassification cost. RESULTS: The depression selective classifiers abstained on anywhere from 0% to 97% of notes, and the change in total misclassification cost ranged from -58% to 9%. Selective classifiers abstained on 5%-43% of notes across the GBM and colorectal cancer models. The GBM selective classifier abstained on 43% of notes, which led to improvements in sensitivity (0.94 to 0.96), specificity (0.79 to 0.96), PPV (0.89 to 0.98), and NPV (0.88 to 0.91) when compared to a non-selective classifier and when compared to structured proxy variables. DISCUSSION: We showed that selective classifiers outperformed both non-selective classifiers and structured proxy variables for extracting data from unstructured clinical notes. CONCLUSION: Selective prediction should be considered when abstaining is preferable to making an incorrect prediction.

Critically reading machine learning literature in neurosurgery: a reader's guide and checklist for appraising prediction models.

OBJECTIVE: Machine learning (ML) has become an increasingly popular tool for use in neurosurgical research. The number of publications and interest in the field have recently seen significant expansion in both quantity and complexity. However, this also places a commensurate burden on the general neurosurgical readership to appraise this literature and decide if these algorithms can be effectively translated into practice. To this end, the authors sought to review the burgeoning neurosurgical ML literature and to develop a checklist to help readers critically review and digest this work. METHODS: The authors performed a literature search of recent ML papers in the PubMed database with the terms "neurosurgery" AND "machine learning," with additional modifiers "trauma," "cancer," "pediatric," and "spine" also used to ensure a diverse selection of relevant papers within the field. Papers were reviewed for their ML methodology, including the formulation of the clinical problem, data acquisition, data preprocessing, model development, model validation, model performance, and model deployment. RESULTS: The resulting checklist consists of 14 key questions for critically appraising ML models and development techniques; these are organized according to their timing along the standard ML workflow. In addition, the authors provide an overview of the ML development process, as well as a review of key terms, models, and concepts referenced in the literature. CONCLUSIONS: ML is poised to become an increasingly important part of neurosurgical research and clinical care. The authors hope that dissemination of education on ML techniques will help neurosurgeons to critically review new research better and more effectively integrate this technology into their practices.

Post-traumatic growth in PhD students during the COVID-19 pandemic.

Throughout the COVID-19 pandemic, graduate students have faced increased risk of mental health challenges. Research suggests that experiencing adversity may induce positive psychological changes, called post-traumatic growth (PTG). These changes can include improved relationships with others, perceptions of oneself, and enjoyment of life. Few existing studies have explored this phenomenon among graduate students. This secondary data analysis of a survey conducted in November 2020 among graduate students at a private R1 University in the northeast United States examined graduate students' levels and correlates of PTG during the COVID-19 pandemic. Students had a low level of PTG, with a mean score of 10.31 out of 50. Linear regression models showed significant positive relationships between anxiety and PTG and between a measure of self-reported impact of the pandemic and PTG. Non-White minorities also had significantly greater PTG than White participants. Experiencing more negative impact due to the pandemic and ruminating about the pandemic were correlated with greater PTG. These findings advance research on the patterns of PTG during the COVID-19 pandemic and can inform future studies of graduate students' coping mechanisms and support efforts to promote pandemic recovery and resilience.

Diversifying cardiac intensive care unit models: Successful example of an operating surgeon-led unit.

Objective: The intensivist-led cardiovascular intensive care unit model is the standard of care in cardiac surgery. This study examines whether a cardiovascular intensive care unit model that uses operating cardiac surgeons, cardiothoracic surgery residents, and advanced practice providers is associated with comparable outcomes. Methods: This is a single-institution review of the first 400 cardiac surgery patients admitted to an operating surgeon-led cardiovascular intensive care unit from 2020 to 2022. Inclusion criteria are elective status and operations managed by both cardiovascular intensive care unit models (aortic operations, valve operations, coronary operations, septal myectomy). Patients from the surgeon-led cardiovascular intensive care unit were exact matched by operation type and 1:1 propensity score matched with controls from the traditional cardiovascular intensive care unit using a logistic regression model that included age, sex, preoperative mortality risk, incision type, and use of cardiopulmonary bypass and circulatory arrest. Primary outcome was total postoperative length of stay. Secondary outcomes included postoperative intensive care unit length of stay, 30-day mortality, 30-day Society of Thoracic Surgeons-defined morbidity (permanent stroke, renal failure, cardiac reoperation, prolonged intubation, deep sternal infection), packed red cell transfusions, and vasopressor use. Outcomes between the 2 groups were compared using chi-square, Fisher exact test, or 2-sample t test as appropriate. Results: A total of 400 patients from the surgeon-led cardiovascular intensive care unit (mean age 61.2 ± 12.8 years, 131 female patients [33%], 346 patients [86.5%] with European System for Cardiac Operative Risk Evaluation II <2%) and their matched controls were included. The most common operations across both units were coronary artery bypass grafting (n = 318, 39.8%) and mitral valve repair or replacement (n = 238, 29.8%). Approximately half of the operations were performed via sternotomy (n = 462, 57.8%). There were 3 (0.2%) in-hospital deaths, and 47 patients (5.9%) had a 30-day complication. The total length of stay was significantly shorter for the surgeon-led cardiovascular intensive care unit patients (6.3 vs 7.0 days, P = .028), and intensive care unit length of stay trended in the same direction (2.5 vs 2.9 days, P = .16). Intensive care unit readmission rates, 30-day mortality, and 30-day morbidity were not significantly different between cardiovascular intensive care unit models. The surgeon-led cardiovascular intensive care unit was associated with fewer postoperative red blood cell transfusions in the cardiovascular intensive care unit (P = .002) and decreased vasopressor use (P = .001). Conclusions: In its first 2 years, the surgeon-led cardiovascular intensive care unit demonstrated comparable outcomes to the traditional cardiovascular intensive care unit with significant improvements in total length of stay, postoperative transfusions in the cardiovascular intensive care unit, and vasopressor use. This early success exemplifies how an operating surgeon-led cardiovascular intensive care unit can provide similar outcomes to the standard-of-care model for patients undergoing elective cardiac surgery.

The influence of maternal and paternal education on birth outcomes: an analysis of the Ottawa and Kingston (OaK) birth cohort.

BACKGROUND: Education is considered one of the most robust determinants of health. However, it is unclear whether maternal education and paternal education have differential impacts on perinatal health outcomes. We assess maternal and paternal education differences and their association with adverse birth outcomes in a large birth cohort from Ontario, Canada. METHODS: The OaK Birth Cohort recruited patients from Ontario, Canada, between October 2002 and April 2009. We recruited mothers were recruited between 12 and 20 weeks' gestation and collected both mother and infant data. The final sample size of the cohort was 8,085 participants. We use logistic regression to model the probability of preterm birth (less than 34 and 37 weeks' gestation), small-for-gestational-age (SGA), or stillbirth as a function of maternal and paternal educational attainment. We adjust for household-level income, maternal and paternal race and ethnicity, and compare the strength of the association between maternal and paternal education on outcomes using Wald tests. RESULTS: 7,928 mother-father-offspring triads were available for the current analysis. 75% of mothers and fathers had college or university level education, and 8.7% of mothers experienced preterm delivery. Compared to mothers with college or university education, mothers with a high school education had an odds ratio of 1.37 (95% CI: 1.01-1.87) for SGA. Paternal education was not associated with infant outcomes. Comparing the odds ratios for maternal education and paternal education showed a stronger association than paternal education at the high school level for SGA birth (difference in odds ratio: 1.95, 95% CI: 1.13-3.36, p = .016) among women at least 25 years old. CONCLUSION: Maternal education was associated with SGA, and this effect was more robust than paternal education, but both associations were weaker than previously reported.

Real-world association of HER2/ERBB2 concordance with trastuzumab clinical benefit in advanced esophagogastric cancer.

Aim: To assess concordance between HER2 status measured by traditional methods and ERBB2 amplification measured by next-generation sequencing and its association with first-line trastuzumab clinical benefit in patients with advanced esophagogastric cancer. Methods: Retrospective analysis of HER2/ERBB2 concordance using a deidentified USA-based clinicogenomic database. Clinical outcomes were assessed for patients with HER2+ advanced esophagogastric cancer who received first-line trastuzumab. Results: Overall HER2/ERBB2 concordance was 87.5%. Among patients who received first-line trastuzumab, concordant HER2/ERBB2 was associated with longer time to treatment discontinuation (adjusted hazard ratio [aHR]: 0.63; 95% CI: 0.43-0.90) and overall survival (aHR: 0.51; 95% CI: 0.33-0.79). ERBB2 copy number ≥25 (median) was associated with longer time to treatment discontinuation (aHR: 0.56; 95% CI: 0.35-0.88) and overall survival (aHR: 0.52; 95% CI: 0.30-0.91). Conclusion: HER2/ERBB2 concordance and higher ERBB2 copy number predicted clinical benefit from trastuzumab.

Lay abstract Trastuzumab is a drug that has been shown to prolong survival in some patients with advanced esophagogastric cancer whose tumor expresses a protein biomarker called HER2. There are different methods for assessing whether a patient's tumor expresses HER2, including but not limited to traditional methods such as immunohistochemistry and in situ hybridization and novel methods such as next-generation sequencing, which detects alterations in the gene (ERBB2) that encodes the HER2 protein. In our study, we assessed concordance between HER2 status (HER2-positive or HER2-negative) measured by traditional methods and ERBB2 amplification measured by next-generation sequencing, to determine whether there was an association between concordance and clinical benefit in patients with advanced esophagogastric cancer treated with trastuzumab. Our results suggest that, when HER2 positivity is detected through traditional methods, both ERBB2 concordance (i.e., agreement that a patient's tumor had the biomarker) and a higher ERBB2 copy number (the amount of the ERBB2 gene expressed by the tumor) were associated with longer time to treatment discontinuation and overall survival in patients with advanced esophagogastric cancer treated with first-line trastuzumab.

Precision mapping child undernutrition for nearly 600,000 inhabited census villages in India.

There are emerging opportunities to assess health indicators at truly small areas with increasing availability of data geocoded to micro geographic units and advanced modeling techniques. The utility of such fine-grained data can be fully leveraged if linked to local governance units that are accountable for implementation of programs and interventions. We used data from the 2011 Indian Census for village-level demographic and amenities features and the 2016 Indian Demographic and Health Survey in a bias-corrected semisupervised regression framework to predict child anthropometric failures for all villages in India. Of the total geographic variation in predicted child anthropometric failure estimates, 54.2 to 72.3% were attributed to the village level followed by 20.6 to 39.5% to the state level. The mean predicted stunting was 37.9% (SD: 10.1%; IQR: 31.2 to 44.7%), and substantial variation was found across villages ranging from less than 5% for 691 villages to over 70% in 453 villages. Estimates at the village level can potentially shift the paradigm of policy discussion in India by enabling more informed prioritization and precise targeting. The proposed methodology can be adapted and applied to diverse population health indicators, and in other contexts, to reveal spatial heterogeneity at a finer geographic scale and identify local areas with the greatest needs and with direct implications for actions to take place.

Prevalence of Gestational Diabetes in India by Individual Socioeconomic, Demographic, and Clinical Factors.

Importance: Gestational diabetes is common in pregnancy and is associated with adverse pregnancy and fetal outcomes. Currently, population-based data on the prevalence of gestational diabetes are limited in India. Objective: To provide a comprehensive national assessment of gestational diabetes in India and its socioeconomic, demographic, and geographic associations, using elevated random blood glucose data as a proxy for a gestational diabetes diagnosis. Design, Setting, and Participants: This cross-sectional study analyzed the fourth National Family Health Survey, conducted in India between January 2015 and December 2016. This nationally representative sample comprised 699 686 women 15 to 49 years of age, of whom 32 428 (4.6%) were pregnant. Data were analyzed between July and December 2019 and between July and August 2020. Exposures: Age, body mass index, hypertension, wealth, and social caste were factors potentially associated with gestational diabetes. Main Outcomes and Measures: Gestational diabetes, defined as elevated random blood glucose according to predetermined thresholds (≥200 mg/dL for nonfasting, ≥92 mg/dL for fasting). Results: Of the 31 746 pregnant women with complete data in the study, the mean (SD) age was 24.3 (4.7) years, and the mean (SD) gestational age was 5.1 (2.3) months. The weighted age-adjusted prevalence of gestational diabetes was 1.3% (95% CI, 1.1%-1.5%). The prevalence of gestational diabetes increased with age, from 1.0% (95% CI, 0.5%-1.5%) at age 15 to 19 years to 2.4% (95% CI, 1.0%-3.8%) at age 35 years or older. The age-adjusted prevalence of gestational diabetes was higher among women with a body mass index of 27.5 or greater (1.8%; 95% CI, 1.0%-2.5%) compared with women with a body mass index of less than 18.5 (0.8%; 95% CI, 0.5%-1.1%), among women in the highest wealth quartile (1.7%; 95% CI, 1.1%-2.5%) compared with those in the lowest (0.9%; 95% CI, 0.7%-1.2%), and women in the south (eg, Kerala: 4.5%; 95% CI, 2.4%-6.7%; Telangana: 5.4%; 95% CI, 0.0%-11.0%) compared with the northeast (eg, Assam: 0.23%; 95% CI, 0.0%-0.48%; Mizoram: 0.16%; 95% CI, 0.0%-0.49%). Conclusions and Relevance: In this study, considerable variation was found in the prevalence of gestational diabetes by state, socioeconomic status, and demographic factors. This finding has implications for the method of gestational diabetes screening in low-resource settings in India, especially in areas or among demographic groups with lower prevalence.

Association does not imply prediction: the accuracy of birthweight in predicting child mortality and anthropometric failure.

PURPOSE: Epidemiologic studies often conflate the strength of association with predictive accuracy and build classification models based on arbitrarily selected probability cutoffs without considering the cost of misclassification. We illustrated these common pitfalls by building association, prediction, and classification models using birthweight as an exposure and child mortality and child anthropometric failure as outcomes. METHODS: Nationally representative samples of 188,819 and 164,113 children aged less than 5 years across India were used for our analysis of mortality and anthropometric failure, respectively. We assessed outcomes of neonatal, postneonatal, and child mortality as well as stunting, wasting, and underweight. Birthweight was the main exposure. We used adjusted and unadjusted logistic regression models to evaluate association strength, univariable and multivariable logistic regression models trained on 80% of the data using 10-fold cross-validation to evaluate predictive power, and classification models across a series of possible misclassification cost scenarios to evaluate classification accuracy. RESULTS: Birthweight was strongly associated with five of six outcomes (P < .001), and associations were robust to covariate adjustment. Prediction models evaluated on the test set showed that birthweight was a poor discriminator of all outcomes (area under the curve < 0.62), and that adding birthweight to a multivariable model did not meaningfully improve discrimination. Prediction models for anthropometric failure showed substantially better calibration than prediction models for mortality. Depending on the ratio of false positive (FP) cost to false negative (FN) cost, the probability cutoff that minimized total misclassification cost ranged from 0.116 (cost ratio = 7:93) to 0.706 (cost ratio = 4:1), TPR ranged from 0.999 to 0.004, and PPV ranged from 0.355 to 0.867.. CONCLUSIONS: Although birthweight is strongly associated with mortality and anthropometric failure, it is a poor predictor of child health outcomes, highlighting that strong associations do not imply predictive power. We recommend that (1) future research focus on building predictive models for anthropometric failure given their clinical relevance in diagnosing individual cases, and that (2) studies that build classifiers report performance metrics across a range of cutoffs to account for variation in the cost of FPs and FNs.

Forecasting efforts from prior epidemics and COVID-19 predictions.

Since the onset of the COVID-19 pandemic, countless disease prediction models have emerged, shaping the focus of news media, policymakers, and broader society. We reviewed the accuracy of forecasts made during prior twenty-first century epidemics, namely SARS, H1N1, and Ebola. We found that while disease prediction models were relatively nascent as a research focus during SARS and H1N1, for Ebola, numerous such forecasts were published. We found that forecasts of deaths for Ebola were often far from the eventual reality, with a strong tendency to over predict. Given the societal prominence of these models, it is crucial that their uncertainty be communicated. Otherwise, we will be unaware if we are being falsely lulled into complacency or unjustifiably shocked into action.

Proof of Concept of a Gamified DEvelopmental Assessment on an E-Platform (DEEP) Tool to Measure Cognitive Development in Rural Indian Preschool Children.

Over 250 million children in developing countries are at risk of not achieving their developmental potential, and unlikely to receive timely interventions because existing developmental assessments that help identify children who are faltering are prohibitive for use in low resource contexts. To bridge this "detection gap," we developed a tablet-based, gamified cognitive assessment tool named DEvelopmental assessment on an E-Platform (DEEP), which is feasible for delivery by non-specialists in rural Indian households and acceptable to all end-users. Here we provide proof-of-concept of using a supervised machine learning (ML) approach benchmarked to the Bayley's Scale of Infant and Toddler Development, 3rd Edition (BSID-III) cognitive scale, to predict a child's cognitive development using metrics derived from gameplay on DEEP. Two-hundred children aged 34-40 months recruited from rural Haryana, India were concurrently assessed using DEEP and BSID-III. Seventy percent of the sample was used for training the ML algorithms using a 10-fold cross validation approach and ensemble modeling, while 30% was assigned to the "test" dataset to evaluate the algorithm's accuracy on novel data. Of the 522 features that computationally described children's performance on DEEP, 31 features which together represented all nine games of DEEP were selected in the final model. The predicted DEEP scores were in good agreement (ICC [2,1] > 0.6) and positively correlated (Pearson's r = 0.67) with BSID-cognitive scores, and model performance metrics were highly comparable between the training and test datasets. Importantly, the mean absolute prediction error was less than three points (<10% error) on a possible range of 31 points on the BSID-cognitive scale in both the training and test datasets. Leveraging the power of ML which allows iterative improvements as more diverse data become available for training, DEEP, pending further validation, holds promise to serve as an acceptable and feasible cognitive assessment tool to bridge the detection gap and support optimum child development.

The State of School Infrastructure in the Assembly Constituencies of Rural India: Analysis of 11 Census Indicators from Pre-Primary to Higher Education.

In India, assembly constituencies (ACs), represented by elected officials, are the primary geopolitical units for state-level policy development. However, data on social indicators are traditionally reported and analyzed at the district level, and are rarely available for ACs. Here, we combine village-level data from the 2011 Indian Census and AC shapefiles to systematically derive AC-level estimates for the first time. We apply this methodology to describe the distribution of 11 education infrastructures-ranging from pre-primary school to senior secondary school-across rural villages in 3773 ACs. We found high variability in access to higher education infrastructures and low variability in access to lower education variables. For 40.3% (25th percentile) to 79.7% (75th percentile) of villages in an AC, the nearest government senior secondary school was >5 km away, whereas the nearest government primary school was >5 km away in just 0% (25th percentile) to 1.9% (75th percentile) of villages in an AC. The states of Manipur, Arunachal Pradesh, and Bihar showed the greatest within-state variation in access to education infrastructures. We present a novel analysis of access to education infrastructure to inform AC-level policy, and demonstrate how geospatial and Census data can be leveraged to derive AC-level estimates for any population health and development indicators collected in the Census at the village level.