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ImportanceHealth care delivery faces a myriad of challenges globally with well-documented health inequities based on geographic location. Yet, researchers and policy makers have a limited understanding of the frequency of geographic health disparities.ObjectiveTo describe geographic health disparities in 11 high-income countries.Design, Setting, and ParticipantsIn this survey study, we analyzed results from the 2020 Commonwealth Fund International Health Policy (IHP) Survey—a nationally representative, self-reported, and cross-sectional survey of adults from Australia, Canada, France, Germany, the Netherlands, New Zealand, Norway, Sweden, Switzerland, the UK, and the US. Eligible adults older than age 18 years were included by random sampling. Survey data were compared for the association of area type (rural or urban) with 10 health indicators across 3 domains: health status and socioeconomic risk factors, affordability of care, and access to care. Logistic regression was used to determine the associations between countries with area type for each factor, controlling for individuals’ age and sex.Main Outcomes and MeasuresThe main outcomes were geographic health disparities as measured by differences in respondents living in urban and rural settings in 10 health indicators across 3 domains.ResultsThere were 22 402 survey respondents (12 804 female [57.2%]), with a 14% to 49% response rate depending on the country. Across the 11 countries and 10 health indicators and 3 domains (health status and socioeconomic risk factors, affordability of care, access to care), there were 21 occurrences of geographic health disparities; 13 of those in which rural residence was a protective factor and 8 of those where rural residence was a risk factor. The mean (SD) number of geographic health disparities in the countries was 1.9 (1.7). The US had statistically significant geographic health disparities in 5 of 10 indicators, the most of any country, while Canada, Norway, and the Netherlands had no statistically significant geographic health disparities. The indicators with the most occurrences of geographic health disparities were in the access to care domain.Conclusions and RelevanceIn this survey study of 11 high-income nations, health disparities across 10 indicators were identified. Differences in number of disparities reported by country suggest that health policy and decision makers in the US should look to Canada, Norway, and the Netherlands to improve geographic-based health equity.

BackgroundThere remains a disproportionally high tobacco smoking rate in low-income populations. Multicomponent tobacco dependence interventions in theory are effective. However, which intervention components are necessary to include for low socioeconomic status (SES) populations is still unknown.ObjectiveTo assess the effectiveness of multicomponent tobacco dependence interventions for low SES and create a checklist tool examining multicomponent interventions.MethodsEMBASE and MEDLINE databases were searched to identify randomised controlled trials (RCTs) published with the primary outcome of tobacco smoking cessation measured at 6 months or post intervention. RCTs that evaluated tobacco dependence management interventions (for reduction or cessation) in low SES (experience of housing insecurity, poverty, low income, unemployment, mental health challenges, illicit substance use and/or food insecurity) were included. Two authors independently abstracted data. Random effects meta-analysis and post hoc sensitivity analysis were performed.ResultsOf the 33 included studies, the number of intervention components ranged from 1 to 6, with smoking quit rates varying between 1% and 36.6%. Meta-analysis revealed that both the 6-month and 12-month outcome timepoints, multicomponent interventions were successful in achieving higher smoking quit rates than the control (OR 1.64, 95% Cl 1.41 to 1.91; OR 1.74, 95% Cl 1.30 to 2.33). Evidence of low heterogeneity in the effect size was observed at 6-month (I2=26%) and moderate heterogeneity at 12-month (I2=56%) outcomes.ConclusionMulticomponent tobacco dependence interventions should focus on inclusion of social support, frequency and duration of components. Employing community-based participatory-action research approach is essential to addressing underlying psychosocioeconomic-structural factors, in addition to the proven combination pharmacotherapies.PROSPERO registration numberCRD42017076650.

Introduction: Maharashtra, India, remained a hotspot during the COVID-19 pandemic. After the initial complete lockdown, the state slowly relaxed restrictions. We aim to estimate the lockdown’s impact on COVID-19 cases and associated healthcare costs. Methods: Using daily case data for 84 days (9 March–31 May 2020), we modeled the epidemic’s trajectory and predicted new cases for different phases of lockdown. We fitted log-linear models to estimate the growth rate, basic (R0), daily reproduction number (Re), and case doubling time. Based on pre-restriction and Phase 1 R0, we predicted new cases for the rest of the restriction phases, and we compared them with the actual number of cases during each phase. Furthermore, using the published and gray literature, we estimated the costs and savings of implementing these restrictions for the projected period, and we performed a sensitivity analysis. Results: The estimated median R0 during the different phases was 1.14 (95% CI: 0.85, 1.45) for pre-lockdown, 1.67 (95% CI: 1.50, 1.82) for phase 1 (strict mobility restrictions), 1.24 (95% CI: 1.12, 1.35) for phase 2 (extension of phase 1 with no restrictions on agricultural and essential services), 1.12 (95% CI: 1.01, 1.23) for phase 3 (extension of phase 2 with mobility relaxations in areas with few infections), and 1.05 (95% CI: 0.99, 1.123) for phase 4 (implementation of localized lockdowns in high-case-load areas with fewer restrictions on other areas), respectively. The corresponding doubling time rate for cases (in days) was 17.78 (95% CI: 5.61, −15.19), 3.87 (95% CI: 3.15, 5.00), 10.37 (95% CI: 7.10, 19.30), 20.31 (95% CI: 10.70, 212.50), and 45.56 (95% CI: 20.50, –204.52). For the projected period, the cases could have reached 631,819 without the lockdown, as the actual reported number of cases was 64,975. From a healthcare perspective, the estimated total value of averted cases was INR 194.73 billion (USD 2.60 billion), resulting in net cost savings of 84.05%. The Incremental Cost-Effectiveness Ratio (ICER) per Quality Adjusted Life Year (QALY) for implementing the lockdown, rather than observing the natural course of the pandemic, was INR 33,812.15 (USD 450.83). Conclusion: Maharashtra’s early public health response delayed the pandemic and averted new cases and deaths during the first wave of the pandemic. However, we recommend that such restrictions be carefully used while considering the local socio-economic realities in countries like India.

Background As international travellers were the primary source of sever acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, border checkpoints became an important tool to isolate cases. We determined the period prevalence and SARS-CoV-2 reverse transcription polymerase chain reaction positivity in relation to clinical and demographic characteristics in healthy travellers quarantined at the Pakistan–Afghanistan border. Methods The study was conducted from 15 to 25 April 2020. Period prevalence was calculated and the association between positivity and individuals’ age, sex and occupation were assessed using χ2 and Mantel–Haenszel tests. Logistic regression was used to calculate adjusted odds ratios (ORs) for each age group. Time-to-event (TTE) analysis was conducted to check the difference in positivity among various groups. Results In a total of 708 individuals, 71 tested positive (10%). Compared with those ≤20 y of age, the sex- and occupation-adjusted odds of testing positive were less among the older age group (41–60 y; OR 0.26, p=0.008). Taxi drivers had higher odds of testing positive (OR 4.08, p<0.001). Kaplan–Meier curves and hazard ratios (0.32, p<0.01) showed that the positivity period differed significantly across the pre-symptomatic vs asymptomatic group (26 vs 14 d). Conclusions The cases who were likely to acquire infection through occupational exposure largely remained asymptomatic. For effective control of transmission and the emergence of new variants, testing capacities should be revamped with effective isolation measures.