Health, psychosocial, and economic impacts of the COVID-19 pandemic on people with chronic conditions in India: a mixed methods study

Background People with chronic conditions are disproportionately prone to be affected by the COVID-19 pandemic but there are limited data documenting this. We aimed to assess the health, psychosocial and economic impacts of the COVID-19 pandemic on people with chronic conditions in India. Methods Between July 29, to September 12, 2020, we telephonically surveyed adults (n = 2335) with chronic conditions across four sites in India. Data on participants’ demographic, socio-economic status, comorbidities, access to health care, treatment satisfaction, self-care behaviors, employment, and income were collected using pre-tested questionnaires. We performed multivariable logistic regression analysis to examine the factors associated with difficulty in accessing medicines and worsening of diabetes or hypertension symptoms. Further, a diverse sample of 40 participants completed qualitative interviews that focused on eliciting patient’s experiences during the COVID-19 lockdowns and data analyzed using thematic analysis. Results One thousand seven hundred thirty-four individuals completed the survey (response rate = 74%). The mean (SD) age of respondents was 57.8 years (11.3) and 50% were men. During the COVID-19 lockdowns in India, 83% of participants reported difficulty in accessing healthcare, 17% faced difficulties in accessing medicines, 59% reported loss of income, 38% lost jobs, and 28% reduced fruit and vegetable consumption. In the final-adjusted regression model, rural residence (OR, 95%CI: 4.01,2.90–5.53), having diabetes (2.42, 1.81–3.25) and hypertension (1.70,1.27–2.27), and loss of income (2.30,1.62–3.26) were significantly associated with difficulty in accessing medicines. Further, difficulties in accessing medicines (3.67,2.52–5.35), and job loss (1.90,1.25–2.89) were associated with worsening of diabetes or hypertension symptoms. Qualitative data suggest most participants experienced psychosocial distress due to loss of job or income and had difficulties in accessing in-patient services. Conclusion People with chronic conditions, particularly among poor, rural, and marginalized populations, have experienced difficulties in accessing healthcare and been severely affected both socially and financially by the COVID-19 pandemic.

Separate services or integrated services

The chapter takes its position in the consequences of globalization and its disequalizing effects on equity in care. The chapter presents and discusses pros and cons related to separate versus integrated services in mental health care for migrants, including trauma-affected refugees. Throughout the chapter there is a special focus on trauma-affected refugees as a particularly vulnerable group. The chapter gives a brief overview of the historical development behind specialized services for trauma-affected refugees and other migrants, including their relation to documentation of human rights violations, and the variations among such services are described. With the overall aim of avoiding inequalities in mental health care, the chapter then compares specialized versus integrated services. The comparison is done with a focus on diagnostic assessment, access to mental health care, treatment outcomes, and treatment satisfaction. Finally, the role of specialized services is discussed.

How Sanctions Have Impacted Iranian Healthcare Sector: A Brief Review

Many studies have shown the crippling effects of sanctions on the healthcare sector of different countries, including Iran. Long-standing sanctions against Iran escalated recently and severely limited commercial activities with Iran. The devastating consequences of these embargoes have affected all aspects of health care delivery in Iran, limiting the availability of critical medicines and medical devices, and negatively impacting primary health care, treatment of complex diseases including cancer, medical tourism, and medical education and research. The present novel coronavirus disease 2019 (COVID-19) pandemic has uncovered this long-standing crisis in the Iranian health care sector. In this communication, we briefly discuss selected aspects of these sanctions and their impact on the health care system and people of Iran during this critical time.

The vexing relationship between socioeconomic status and health

In a recent issue of this Journal, Politzer, Shmueli, and Avni estimate the economic costs of health disparities due to socioeconomic status (SES) in Israel (Politzer et al., Isr J Health Policy Res 8: 46, 2019). Using three measures of SES, the socioeconomic ranking of localities, individual income, and individual education, Politzer and colleagues estimate welfare loss due to higher mortality, productivity loss due to poorer health, excess health care treatment costs, and excess disability payments for individuals with below median SES relative to those with above median SES. They find the economic costs of health disparities are substantial, adding up to between 1.1 and 3.1 billion USD annually—between 0.7 and 1.6% of Israel’s GDP.This paper is useful and informative. It is, to our knowledge, the first comprehensive quantification of the economic costs stemming from health disparities in Israel. In spite of many social policies designed to level economic opportunity and social welfare generally, by most measures, Israel is among the most unequal in the distribution of income among all OECD countries (Cornfeld and Danieli, Isr Econ Rev 12:51–95, 2015). Politzer and colleagues expose the magnitude and sources of health-related loss that Israel faces because of such inequality and shows how the costs of inequality are borne to some degree by all members of society. This short commentary discusses the complicated relationship between SES and health and puts the findings from Politzer and colleagues in the context of the international literature on the subject.

Theorizing financial extraction: The curious case of telephone profits in the Los Angeles county jails

Existing models of financial extraction in the criminal justice system are applied to the case of profitable telephone contracts in the Los Angeles County jails during the mid-1990s. The case exemplifies instances of a “punishment” model of monetary sanctions, in which profits are derived for crime control purposes, and a “predation” model, in which inmates are seen as potential resources to absolve fiscal crisis and create market opportunities. However, the case contains an additional element: legal demand concerning inmates’ mental health care treatment. It is shown that telephone profits in jails could not have been used for predation or punishment purposes without the exploitation of legal ambiguity around inmate’s healthcare. Broader implications for the study of financial extraction as a constitutive theory – one that is both historical causal explanation and generalizable description – are drawn.

Fierce Autonomy: How Girls and Young Women Impacted by Commercial Sexual Exploitation Perceive Health and Exercise Agency in Health Care Decision-Making

Prior research has examined the high health care needs and vulnerabilities faced by survivors of commercial sexual exploitation (CSE), yet their perspectives are frequently absent. We sought to understand the narratives and views of individuals affected by CSE on their bodies, health, and motivations to seek health care treatment. Twenty-one girls and young women ages 15 to 19 years with self-identified histories of CSE participated in the study. All participants had current or prior involvement in the juvenile justice and/or child welfare systems. Data collection included brief questionnaires, followed by semi-structured individual interviews. The interviews took place between March and July 2017 and were analyzed using iterative and inductive techniques, using the shared decision-making model as a guide. “Fierce Autonomy” emerged as a core theme, depicting how past traumas and absence of control led the girls and young women to exercise agency and reclaim autonomy over decisions affecting their health.

Eliminating biasing signals in lung cancer images for prognosis predictions with deep learning

Deep learning has shown remarkable results for image analysis and is expected to aid individual treatment decisions in health care. Treatment recommendations are predictions with an inherently causal interpretation. To use deep learning for these applications in the setting of observational data, deep learning methods must be made compatible with the required causal assumptions. We present a scenario with real-world medical images (CT-scans of lung cancer) and simulated outcome data. Through the data simulation scheme, the images contain two distinct factors of variation that are associated with survival, but represent a collider (tumor size) and a prognostic factor (tumor heterogeneity), respectively. When a deep network would use all the information available in the image to predict survival, it would condition on the collider and thereby introduce bias in the estimation of the treatment effect. We show that when this collider can be quantified, unbiased individual prognosis predictions are attainable with deep learning. This is achieved by (1) setting a dual task for the network to predict both the outcome and the collider and (2) enforcing a form of linear independence of the activation distributions of the last layer. Our method provides an example of combining deep learning and structural causal models to achieve unbiased individual prognosis predictions. Extensions of machine learning methods for applications to causal questions are required to attain the long-standing goal of personalized medicine supported by artificial intelligence.