Impact of lung cancer screening on stage migration and mortality among the national Veterans Health Administration population with lung cancer.

BACKGROUND: Despite randomized trials demonstrating a mortality benefit to low-dose computed tomography screening to detect lung cancer, uptake of lung cancer screening (LCS) has been slow, and the benefits of screening remain unclear in clinical practice. METHODS: This study aimed to assess the impact of screening among patients in the Veterans Health Administration (VA) health care system diagnosed with lung cancer between 2011 and 2018. Lung cancer stage at diagnosis, lung cancer-specific survival, and overall survival between patients with cancer who did and did not receive screening before diagnosis were evaluated. We used Cox regression modeling and inverse propensity weighting analyses with lead time bias adjustment to correlate LCS exposure with patient outcomes. RESULTS: Of 57,919 individuals diagnosed with lung cancer in the VA system between 2011 and 2018, 2167 (3.9%) underwent screening before diagnosis. Patients with screening had higher rates of stage I diagnoses (52% vs. 27%; p ≤ .0001) compared to those who had no screening. Screened patients had improved 5-year overall survival rates (50.2% vs. 27.9%) and 5-year lung cancer-specific survival (59.0% vs. 29.7%) compared to unscreened patients. Among screening-eligible patients who underwent National Comprehensive Cancer Network guideline-concordant treatment, screening resulted in substantial reductions in all-cause mortality (adjusted hazard ratio [aHR], 0.79; 95% confidence interval [CI], 0.67-0.92; p = .003) and lung-specific mortality (aHR, 0.61; 95% CI, 0.50-0.74; p < .001). CONCLUSIONS: While LCS uptake remains limited, screening was associated with earlier stage diagnoses and improved survival. This large national study corroborates the value of LCS in clinical practice; efforts to widely adopt this vital intervention are needed.

Leveraging data science and machine learning for urban climate adaptation in two major African cities: a HE2AT Center study protocol.

INTRODUCTION: African cities, particularly Abidjan and Johannesburg, face challenges of rapid urban growth, informality and strained health services, compounded by increasing temperatures due to climate change. This study aims to understand the complexities of heat-related health impacts in these cities. The objectives are: (1) mapping intraurban heat risk and exposure using health, socioeconomic, climate and satellite imagery data; (2) creating a stratified heat-health forecast model to predict adverse health outcomes; and (3) establishing an early warning system for timely heatwave alerts. The ultimate goal is to foster climate-resilient African cities, protecting disproportionately affected populations from heat hazards. METHODS AND ANALYSIS: The research will acquire health-related datasets from eligible adult clinical trials or cohort studies conducted in Johannesburg and Abidjan between 2000 and 2022. Additional data will be collected, including socioeconomic, climate datasets and satellite imagery. These resources will aid in mapping heat hazards and quantifying heat-health exposure, the extent of elevated risk and morbidity. Outcomes will be determined using advanced data analysis methods, including statistical evaluation, machine learning and deep learning techniques. ETHICS AND DISSEMINATION: The study has been approved by the Wits Human Research Ethics Committee (reference no: 220606). Data management will follow approved procedures. The results will be disseminated through workshops, community forums, conferences and publications. Data deposition and curation plans will be established in line with ethical and safety considerations.

Legalization of Cannabis Does Not Reduce Opioid Prescribing in Patients With Inflammatory Bowel Disease: A Difference-in-Difference Analysis.

INTRODUCTION: Cannabis may provide inflammatory bowel disease (IBD) patients with an alternative to opioids for pain. METHODS: We conducted a difference-in-difference analysis using MarketScan. Changes over time in rates of opioid prescribing were compared in states with legalized cannabis to those without. RESULTS: We identified 6,240 patients with IBD in states with legalized cannabis and 79,272 patients with IBD in states without legalized cannabis. The rate of opioid prescribing decreased over time in both groups and were not significantly different (attributed differential = 0.34, confidence interval -13.02 to 13.70, P = 0.96). DISCUSSION: Opioid prescribing decreased from 2009 to 2016 among patients with IBD in both states with legalized and state without legalized cannabis, similar to what has been observed nationally across a variety of diseases. Cannabis legalization was not associated with a lower rate of opioid prescribing for patients with IBD.

Pan-cancer molecular signatures connecting aspartate transaminase (AST) to cancer prognosis, metabolic and immune signatures.

Background: Serum aspartate transaminase (sAST) level is used routinely in conjunction with other clinical assays to assess liver health and disease. Increasing evidence suggests that sAST is associated with all-cause mortality and has prognostic value in several cancers, including gastrointestinal and urothelial cancers. Here, we undertake a systems approach to unravel molecular connections between AST and cancer prognosis, metabolism, and immune signatures at the transcriptomic and proteomic levels. Methods: We mined public gene expression data across multiple normal and cancerous tissues using the Genotype Tissue Expression (GTEX) resource and The Cancer Genome Atlas (TCGA) to assess the expression of genes encoding AST isoenzymes (GOT1 and GOT2) and their association with disease prognosis and immune infiltration signatures across multiple tumors. We examined the associations between AST and previously reported pan-cancer molecular subtypes characterized by distinct metabolic and immune signatures. We analyzed human protein-protein interaction networks for interactions between GOT1 and GOT2 with cancer-associated proteins. Using public databases and protein-protein interaction networks, we determined whether the subset of proteins that interact with AST (GOT1 and GOT2 interactomes) are enriched with proteins associated with specific diseases, miRNAs and transcription factors. Results: We show that AST transcript isoforms (GOT1 and GOT2) are expressed across a wide range of normal tissues. AST isoforms are upregulated in tumors of the breast, lung, uterus, and thymus relative to normal tissues but downregulated in tumors of the liver, colon, brain, kidney and skeletal sarcomas. At the proteomic level, we find that the expression of AST is associated with distinct pan-cancer molecular subtypes with an enrichment of specific metabolic and immune signatures. Based on human protein-protein interaction data, AST physically interacts with multiple proteins involved in tumor initiation, suppression, progression, and treatment. We find enrichments in the AST interactomes for proteins associated with liver and lung cancer and dermatologic diseases. At the regulatory level, the GOT1 interactome is enriched with the targets of cancer-associated miRNAs, specifically mir34a - a promising cancer therapeutic, while the GOT2 interactome is enriched with proteins that interact with cancer-associated transcription factors. Conclusions: Our findings suggest that perturbations in the levels of AST within specific tissues reflect pathophysiological changes beyond tissue damage and have implications for cancer metabolism, immune infiltration, prognosis, and treatment personalization.

Loneliness and Depressive Symptoms Are High Among Older Adults With Digestive Disease and Associated With Lower Perceived Health.

BACKGROUND & AIMS: Current approaches to managing digestive disease in older adults fail to consider the psychosocial factors contributing to a person's health. We aimed to compare the frequency of loneliness, depression, and social isolation in older adults with and without a digestive disease and to quantify their association with poor health. METHODS: We conducted an analysis of Health and Retirement Study data from 2008 to 2016, a nationally representative panel study of participants 50 years and older and their spouses. Bivariate analyses examined differences in loneliness, depression, and social isolation among patients with and without a digestive disease. We also examined the relationship between these factors and health. RESULTS: We identified 3979 (56.0%) respondents with and 3131 (44.0%) without a digestive disease. Overall, 60.4% and 55.6% of respondents with and without a digestive disease reported loneliness (P < .001), 12.7% and 7.5% reported severe depression (P < .001), and 8.9% and 8.7% reported social isolation (P = NS), respectively. After adjusting for covariates, those with a digestive disease were more likely to report poor or fair health than those without a digestive disease (odds ratio [OR], 1.25; 95% CI, 1.11-1.41). Among patients with a digestive disease, loneliness (OR, 1.43; 95% CI, 1.22-1.69) and moderate and severe depression (OR, 2.93; 95% CI, 2.48-3.47; and OR, 8.96; 95% CI, 6.91-11.63, respectively) were associated with greater odds of poor or fair health. CONCLUSIONS: Older adults with a digestive disease were more likely than those without a digestive disease to endorse loneliness and moderate to severe depression and these conditions are associated with poor or fair health. Gastroenterologists should feel empowered to screen patients for depression and loneliness symptoms and establish care pathways for mental health treatment.

Profiles of Web-based Portal Users with Inflammatory Bowel Disease.

BACKGROUND: Web-based portals can enhance communication between patients and providers to support IBD self-management and improve care. We aimed to identify portal use patterns of patients with inflammatory bowel disease (IBD) to inform future web portal-based interventions and portal design. METHODS: Patients with IBD receiving care at the University of Michigan between 2012 and 2021 were identified. Meta-data from electronic logs of each patient's most recent year of portal use were abstracted. Portal engagement was characterized in terms of intensity (ie, frequency of use); comprehensiveness (ie, number of portal functions used); and duration (ie, quarters per year of portal use). We used k-means clustering, a machine-learning technique, to identify groupings of portal users defined in terms of engagement features. RESULTS: We found 5605 patients with IBD who had accessed their portal account at least once. The average age was 41.2 years (SD 16.7), 3035 (54.2%) were female, and 2214 (39.5%) received immune-targeted therapies. We identified 3 patterns of portal engagement: (1) low intensity users (29.5%); (2) moderate intensity, comprehensive, and sustained users (63.3%); and (3) high intensity, comprehensive, sustained users (7.2%). Patients with more intense, comprehensive, and sustained use of the portal were older, female, with more comorbidities, and were more likely to receive immune-targeted therapies. CONCLUSION: Understanding distinct patterns of portal use can inform portal-based interventions and portal design. Patient portals may be particularly helpful in delivering assistance to those with comorbidities and those receiving immune-targeted therapies-many of whom demonstrate more intense, comprehensive, and sustained portal use.

Inflammatory bowel disease patients have varying patterns of web-based portal engagement that can be characterized into distinct groupings. Portals-based interventions may be particularly helpful for those with comorbidities or receiving immune-targeted therapies­many of whom demonstrate more intense, comprehensive, and sustained use.

Pharmacogenetic testing and monitoring of complete blood counts among Veterans newly prescribed thiopurine treatments: a retrospective cohort study.

Pharmacogenetic (PGx) testing before initiation of thiopurine treatment and CBC monitoring post-initiation helps avoid adverse events and ensure patient safety. This study aims to evaluate trends in PGx testing and CBC monitoring among Veterans prescribed azathioprine, thioguanine, or mercaptopurine to demonstrate VA's efforts to improve medication safety after an adverse event. To assess testing patterns, we used VA electronic health report data to identify 20,524 Veterans who first began thiopurine treatment between January 1, 2010, to December 31, 2021. Aggregate monthly counts of thiopurine prescriptions and associated lab tests were tabulated, and the trend in the proportion of patients tested was analyzed using the Mann-Kendall test. The proportion of patients undergoing PGx testing rose from 30.0% in 2010 to 47.5% in late 2014 (July-December). However, PGx testing and overall testing only increased slightly after the sentinel event, and orders levelled off over time at slightly lower levels than before the sentinel event. Very little change was seen in the overall proportion of individuals receiving any testing across all patients with new prescriptions from the time of the sentinel event in 2014 to the end of 2021. A large portion of patients prescribed thiopurine drugs did not receive testing that could help prevent the development of potential adverse events, leading to a predominantly reactive approach. Increased PGx testing may result in a more proactive approach to the prevention of adverse events due to genetic interaction.

Incorporation of quantitative imaging data using artificial intelligence improves risk prediction in veterans with liver disease.

BACKGROUND AND AIMS: Utilization of electronic health records data to derive predictive indexes such as the electronic Child-Turcotte-Pugh (eCTP) Score can have significant utility in health care delivery. Within the records, CT scans contain phenotypic data which have significant prognostic value. However, data extractions have not traditionally been applied to imaging data. In this study, we used artificial intelligence to automate biomarker extraction from CT scans and examined the value of these features in improving risk prediction in patients with liver disease. APPROACH AND RESULTS: Using a regional liver disease cohort from the Veterans Health System, we retrieved administrative, laboratory, and clinical data for Veterans who had CT scans performed for any clinical indication between 2008 and 2014. Imaging biomarkers were automatically derived using the analytic morphomics platform. In all, 4614 patients were included. We found that the eCTP Score had a Concordance index of 0.64 for the prediction of overall mortality while the imaging-based model alone or with eCTP Score performed significantly better [Concordance index of 0.72 and 0.73 ( p <0.001)]. For the subset of patients without hepatic decompensation at baseline (n=4452), the Concordance index for predicting future decompensation was 0.67, 0.79, and 0.80 for eCTP Score, imaging alone, or combined, respectively. CONCLUSIONS: This proof of concept demonstrates that the potential of utilizing automated extraction of imaging features within CT scans either alone or in conjunction with classic health data can improve risk prediction in patients with chronic liver disease.

Racial Disparity in Esophageal Squamous Cell Carcinoma Treatment and Survival in the United States.

INTRODUCTION: Esophageal squamous cell carcinoma (ESCC) has a higher incidence and prevalence than esophageal adenocarcinoma among Black individuals in the United States. Black individuals have lower ESCC survival. These racial disparities have not been thoroughly investigated. We examined the disparity in treatment and survival stratified by ESCC stage at diagnosis. METHODS: The Surveillance, Epidemiology, and End Results database was queried to identify patients with ESCC between 2000 and 2019. The identified cohort was divided into subgroups by race. Patient and cancer characteristics, treatment received, and survival rates were compared across the racial subgroups. RESULTS: A total of 23,768 patients with ESCC were identified. Compared with White individuals, Black individuals were younger and had more distant disease during diagnosis (distant disease: 26.7% vs 23.8%, P < 0.001). Black individuals had lower age-standardized 5-year survival for localized (survival % [95% confidence interval]: 19.3% [16-22.8] vs 27.6% [25.1-30.2]), regional (14.3% [12-16.7] vs 21.1% [19.6-22.7]), and distant (2.9% [1.9-4.1] vs 6.5% [5.5-7.5]) disease. Black individuals were less likely to receive chemotherapy (54.7% vs 57.5%, P = 0.001), radiation (58.5% vs 60.4%, P = 0.03), and surgery (11.4% vs 16.3%, P < 0.0001). DISCUSSION: Black individuals with ESCC have a lower survival rate than White individuals. This could be related to presenting at a later stage but also disparities in which treatments they receive even among individuals with the same stage of disease. To what extent these disparities in receipt of treatment is due to structural racism, social determinants of health, implicit bias, or patient preferences deserves further study.

Promoting data harmonization to evaluate vaccine hesitancy in LMICs: approach and applications.

BACKGROUND: Factors influencing the health of populations are subjects of interdisciplinary study. However, datasets relevant to public health often lack interdisciplinary breath. It is difficult to combine data on health outcomes with datasets on potentially important contextual factors, like political violence or development, due to incompatible levels of geographic support; differing data formats and structures; differences in sampling procedures and wording; and the stability of temporal trends. We present a computational package to combine spatially misaligned datasets, and provide an illustrative analysis of multi-dimensional factors in health outcomes. METHODS: We rely on a new software toolkit, Sub-National Geospatial Data Archive (SUNGEO), to combine data across disciplinary domains and demonstrate a use case on vaccine hesitancy in Low and Middle-Income Countries (LMICs). We use data from the World Bank's High Frequency Phone Surveys (HFPS) from Kenya, Indonesia, and Malawi. We curate and combine these surveys with data on political violence, elections, economic development, and other contextual factors, using SUNGEO. We then develop a stochastic model to analyze the integrated data and evaluate 1) the stability of vaccination preferences in all three countries over time, and 2) the association between local contextual factors and vaccination preferences. RESULTS: In all three countries, vaccine-acceptance is more persistent than vaccine-hesitancy from round to round: the long-run probability of staying vaccine-acceptant (hesitant) was 0.96 (0.65) in Indonesia, 0.89 (0.21) in Kenya, and 0.76 (0.40) in Malawi. However, vaccine acceptance was significantly less durable in areas exposed to political violence, with percentage point differences (ppd) in vaccine acceptance of -10 (Indonesia), -5 (Kenya), and -64 (Malawi). In Indonesia and Kenya, although not Malawi, vaccine acceptance was also significantly less durable in locations without competitive elections (-19 and -6 ppd, respectively) and in locations with more limited transportation infrastructure (-11 and -8 ppd). CONCLUSION: With SUNGEO, researchers can combine spatially misaligned and incompatible datasets. As an illustrative example, we find that vaccination hesitancy is correlated with political violence, electoral uncompetitiveness and limited access to public goods, consistent with past results that vaccination hesitancy is associated with government distrust.

Association between antenatal corticosteroid treatment and severe adverse events in pregnant women.

BACKGROUND: Antenatal corticosteroids are considered the standard of care for pregnant women at risk for preterm birth, but studies examining their potential risks are scarce. We aimed to estimate the associations of antenatal corticosteroids with three severe adverse events: sepsis, heart failure, and gastrointestinal bleeding, in pregnant women. METHODS: Of 2,157,321 pregnant women, 52,119 at 24 weeks 0/7 days to 36 weeks 6/7 days of gestation were included in this self-controlled case series study during the study period of 2009-2018. We estimated incidence rates of three severe adverse events: sepsis, heart failure, and gastrointestinal bleeding. Conditional Poisson regression was used to calculate incidence rate ratios (IRRs) for comparing incidence rates of the adverse events in each post-treatment period compared to those during the baseline period among pregnant women exposed to a single course of antenatal corticosteroid treatment. RESULTS: Among 52,119 eligible participants who received antenatal corticosteroid treatment, the estimated incidence rates per 1000 person-years were 0.76 (95% confidence interval (CI): 0.69-0.83) for sepsis, 0.31 (95% CI: 0.27-0.36) for heart failure, and 11.57 (95% CI: 11.27-11.87) for gastrointestinal bleeding. The IRRs at 5 ~ 60 days after administration of antenatal corticosteroids were 5.91 (95% CI: 3.10-11.30) for sepsis and 4.45 (95% CI: 2.63-7.55) for heart failure, and 1.26 (95% CI: 1.02-1.55) for gastrointestinal bleeding; and the IRRs for days 61 ~ 180 were 2.00 (95% CI: 1.01-3.96) for sepsis, 3.65 (95% CI: 2.14-6.22) for heart failure, and 1.81 (95% CI: 1.56-2.10) for gastrointestinal bleeding. CONCLUSIONS: This nationwide population-based study suggests that a single course of antenatal corticosteroids is significantly associated with a 1.3- to 5.9-fold increased risk of sepsis, heart failure, and gastrointestinal bleeding in pregnant women. Maternal health considerations, including recommendations for adverse event monitoring, should be included in future guidelines for antenatal corticosteroid treatment.

Prevalence and prescribing patterns of oral corticosteroids in the United States, Taiwan, and Denmark, 2009-2018.

Oral corticosteroids (OCS) are commonly prescribed for acute, self-limited conditions, despite studies demonstrating toxicity. Studies evaluating longitudinal OCS prescribing in the general population are scarce and do not compare use across countries. This study investigated and compared OCS prescription patterns from 2009 to 2018 in the general populations of the United States, Taiwan, and Denmark. This international population-based longitudinal cohort study used nationwide claims databases (United States: Optum Clinformatics Data Mart; de-identified; Taiwan: National Health Insurance Research Database; and Denmark: National Prescription and Patient Registries/Danish National Patient Registry) to evaluate OCS prescribing. We classified annual OCS duration as short-term (1-29 days), medium-term (30-89 days), or long-term (≥90 days). Longitudinal change in annual prevalence of OCS use and physician prescribing patterns were reported. Among 54,630,437 participants, average annual percentage of overall OCS use was 6.8% in the United States, 17.5% in Taiwan, and 2.2% in Denmark during 2009-2018. Prevalence of OCS prescribing increased at an average annual rate of 0.1%-0.17%, mainly driven by short-term prescribing to healthy adults. One-quarter to one-fifth of OCS prescribing was associated with a diagnosis of respiratory infection. Family practice and internal medicine physicians were among the highest OCS prescribers across countries and durations. Age- and sex-stratified trends mirrored unstratified trends. This study provides real-world evidence of an ongoing steady increase in OCS use in the general populations of the United States, Taiwan, and Denmark. This increase is largely driven by short-term OCS prescribing to healthy adults, a practice previously viewed as safe but recently shown to incur substantial population-level risk.

Use of mobile technology to identify behavioral mechanisms linked to mental health outcomes in Kenya: protocol for development and validation of a predictive model.

OBJECTIVE: This study proposes to identify and validate weighted sensor stream signatures that predict near-term risk of a major depressive episode and future mood among healthcare workers in Kenya. APPROACH: The study will deploy a mobile application (app) platform and use novel data science analytic approaches (Artificial Intelligence and Machine Learning) to identifying predictors of mental health disorders among 500 randomly sampled healthcare workers from five healthcare facilities in Nairobi, Kenya. EXPECTATION: This study will lay the basis for creating agile and scalable systems for rapid diagnostics that could inform precise interventions for mitigating depression and ensure a healthy, resilient healthcare workforce to develop sustainable economic growth in Kenya, East Africa, and ultimately neighboring countries in sub-Saharan Africa. This protocol paper provides an opportunity to share the planned study implementation methods and approaches. CONCLUSION: A mobile technology platform that is scalable and can be used to understand and improve mental health outcomes is of critical importance.

Predicting Incident Adenocarcinoma of the Esophagus or Gastric Cardia Using Machine Learning of Electronic Health Records.

BACKGROUND & AIMS: Tools that can automatically predict incident esophageal adenocarcinoma (EAC) and gastric cardia adenocarcinoma (GCA) using electronic health records to guide screening decisions are needed. METHODS: The Veterans Health Administration (VHA) Corporate Data Warehouse was accessed to identify Veterans with 1 or more encounters between 2005 and 2018. Patients diagnosed with EAC (n = 8430) or GCA (n = 2965) were identified in the VHA Central Cancer Registry and compared with 10,256,887 controls. Predictors included demographic characteristics, prescriptions, laboratory results, and diagnoses between 1 and 5 years before the index date. The Kettles Esophageal and Cardia Adenocarcinoma predictioN (K-ECAN) tool was developed and internally validated using simple random sampling imputation and extreme gradient boosting, a machine learning method. Training was performed in 50% of the data, preliminary validation in 25% of the data, and final testing in 25% of the data. RESULTS: K-ECAN was well-calibrated and had better discrimination (area under the receiver operating characteristic curve [AuROC], 0.77) than previously validated models, such as the Nord-Trøndelag Health Study (AuROC, 0.68) and Kunzmann model (AuROC, 0.64), or published guidelines. Using only data from between 3 and 5 years before index diminished its accuracy slightly (AuROC, 0.75). Undersampling men to simulate a non-VHA population, AUCs of the Nord-Trøndelag Health Study and Kunzmann model improved, but K-ECAN was still the most accurate (AuROC, 0.85). Although gastroesophageal reflux disease was strongly associated with EAC, it contributed only a small proportion of gain in information for prediction. CONCLUSIONS: K-ECAN is a novel, internally validated tool predicting incident EAC and GCA using electronic health records data. Further work is needed to validate K-ECAN outside VHA and to assess how best to implement it within electronic health records.

Coronavirus pandemic in the South Asia region: Health policy and economy trade-off.

Background: The South Asian Association for Regional Cooperation (SAARC) covers Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. We conducted a comparative analysis of the trade-off between the health policies for the prevention of COVID-19 spread and the impact of these policies on the economies and livelihoods of the South Asia populations. Methods: We analyzed COVID-19 data on epidemiology, public health and health policy, health system capacity, and macroeconomic indicators from January 2020 to March 2021 to determine temporal trends by conducting joinpoint regression analysis using average weekly percent change (AWPC). Results: Bangladesh had the highest statistically significant AWPC for new COVID-19 cases (17.0; 95% CI = 7.7-27.1, P < 0.001), followed by the Maldives (12.9; 95% CI = 5.3-21.0, P < 0.001) and India (10.0; 95% CI = 8.4-11.5, P < 0.001). The AWPC for COVID-19 deaths was significant for India (6.5; 95% CI = 4.3-8.9, P < 0.001) and Bangladesh (6.1; 95% CI = 3.7-8.5, P < 0.001). Nepal (55.79%), and India (34.91%) had the second- and third-highest increase in unemployment, while Afghanistan (6.83%) and Pakistan (16.83%) had the lowest. The rate of change of real GDP had the highest decrease for Maldives (557.51%), and India (297.03%); Pakistan (46.46%) and Bangladesh (70.80%), however, had the lowest decrease. The government response stringency index for Pakistan had a see-saw pattern with a sharp decline followed by an increase in the government health policy restrictions that approximated the test-positivity trend. Conclusions: Unlike developed economies, the South Asian developing countries experienced a trade-off between health policy and their economies during the COVID-19 pandemic. South Asian countries (Nepal and India), with extended periods of lockdowns and a mismatch between temporal trends of government response stringency index and the test-positivity or disease incidence, had higher adverse economic effects, unemployment, and burden of COVID-19. Pakistan demonstrated targeted lockdowns with a rapid see-saw pattern of government health policy response that approximated the test-positivity trend and resulted in lesser adverse economic effects, unemployment, and burden of COVID-19.