Mapping Health Disparities in 11 High-Income Nations.

Importance: Health 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. Objective: To describe geographic health disparities in 11 high-income countries. Design, Setting, and Participants: In 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 Measures: The 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. Results: There 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 Relevance: In 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.

COVID-19 Pandemic: Did Strict Mobility Restrictions Save Lives and Healthcare Costs in Maharashtra, India?

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.

Wealth Status and Health Insurance Enrollment in India: An Empirical Analysis.

Since 2005, health insurance (HI) coverage in India has significantly increased, largely because of the introduction of government-funded pro-poor insurance programs. As a result, the determinants of HI enrollment and their relative importance may have changed. Using National Family Health Survey (NFHS)-4 data, collected in 2015-2016, and employing a Probit regression model, we re-examine the determinants of household HI enrollment. Then, using a multinomial logistic regression model, we estimate the relative risk ratio for enrollment in different HI schemes. In comparison to the results on the determinants of HI enrollment using the NFHS data collected in 2005-2006, we find a decrease in the wealth gap in public HI enrollment. Nonetheless, disparities in enrollment remain, with some changes in those patterns. Households with low assets have lower enrollments in private and community-based health insurance (CBHI) programs. Households with a higher number of dependents have a higher likelihood of HI enrollment, especially in rural areas. In rural areas, poor Scheduled Caste and Scheduled Tribe households are more likely to be enrolled in public HI than the general Caste households. In urban areas, Muslim households have a lower likelihood of enrollment in any HI. The educational attainment of household heads is positively associated with enrollment in private HI, but it is negatively associated with enrollment in public HI. Since 2005-2006, while HI coverage has improved, disparities across social groups remain.

Explaining Caste-Based Disparities in Enrollment for National Health Insurance Program in India: a Decomposition Analysis.

BACKGROUND: Caste plays a significant role in individual healthcare access and health outcomes in India. Discrimination against low-caste communities contributes to their poverty and poor health outcomes. The Rashtriya Swasthya Bima Yojana (RSBY), a national health insurance program, was created to improve healthcare access for the poor. This study accounts for caste-based disparities in RSBY enrollment in India by decomposing the contributions of relevant factors. METHODS: Using the data from the 2015-2016 round of the National Family Health Survey, we compare RSBY enrollment rates of low-caste and high-caste households. We use a non-linear extension of Oaxaca-Blinder decomposition and estimate two models by pooling coefficients across the comparison groups and all caste groups. Enrollment differentials are decomposed into individual- and household-level characteristics, media access, and state-level fixed effects, allowing 2000 replications and random ordering of variables. RESULTS: The analysis of 480,766 households show that scheduled tribe households have the highest enrollment (18.85%), followed by 14.13% for scheduled caste, 10.67% for other backward caste, and 9.33% for high caste. Household factors, family head's characteristics, media access, and state-level fixed effects account for a 32% to 52% gap in enrollment. More specifically, the enrollment gaps are attributable to differences in wealth status, educational attainment, residence, family size, dependency ratio, media access, and occupational activities of the households. CONCLUSIONS: Weaker socio-economic status of low-caste households explains their high RSBY enrollments.

SARS-CoV-2 RT-PCR positivity in relation to clinical and demographic characteristics in residents of border quarantine centres, Khyber Pakhtunkhwa, Pakistan: a prospective cohort study.

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.

Personalised multicomponent interventions for tobacco dependence management in low socioeconomic populations: a systematic review and meta-analysis.

BACKGROUND: There 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. OBJECTIVE: To assess the effectiveness of multicomponent tobacco dependence interventions for low SES and create a checklist tool examining multicomponent interventions. METHODS: EMBASE 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. RESULTS: Of 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. CONCLUSION: Multicomponent 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 NUMBER: CRD42017076650.

Why health policies should be transnational: A case for East Asia Pacific countries.

This paper argues that health policies should transcend national boundaries yet should not reach the supranational level. Along with multinational global health efforts, such cross-national health policies are essential to leverage joint efforts by countries learning from their peers that experience similar health system challenges. In our analysis, we used World Bank Health, Nutrition, and Population (HNP) data from 1995 to 2014 for East Asia Pacific (EAP) countries to explore health system comparability across member nations. We applied a hierarchical cluster analysis using Ward's method and a squared Euclidean distance approach to classify 24 EAP countries into four relatively stable clusters based on their (dis)similarities over nine selected health expenditure and health system performance related indicators. One-way analysis of variance (ANOVA) was used to assess the discreteness of the formed clusters. Each cluster had unique characteristics based on the included indicators and health system performance of the member countries. We present transnational health policy recommendations for the EAP region based on both our use of robust methodology and the resulting comparative clusters.