Objective: This study has two-fold objectives: First, to test the global convergence hypothesis in the progress of child stunting across 174 countries over the period 1990-2015; Second, to identify factors determining the process of convergence or divergence. Design: The study design comprises macro-level cross-country analyses. Our empirical strategy uses parametric convergence models such as absolute and conditional β-convergence models, while non-parametric convergence models such as Kernel density plots serve as robustness checks. Setting: The study uses a global setting comprising child stunting information from 174 countries. Participants: The participants for this study are 174 countries. The information on child stunting prevalence for most countries is available from the UNICEF-WHO-WB Joint Child Malnutrition Estimates Expanded Database (April-2019), while national-level surveys are used for those countries where UNICEF-WHO-WB Database is not available. The data for socioeconomic variables are taken from the World Bank’s data bank (1990-2015). Results: Findings from the absolute β-convergence model estimates show that progress in child stunting has diverged over the entire period (1990-2015). However, the speed of divergence has reduced for the recent period (2010-15). The conditional β-convergence model estimates show that cross-country heterogeneity in GDP per capita, poverty, and health care expenditure are significant factors explaining divergence in child stunting. Conclusions: For replacing current divergence with convergence in child stunting worldwide, the study demonstrates the critical role of economic factors and public spending on health care to reduce child stunting, particularly in countries where progress is slow.

Emergency situations typically lead to a plethora of public attention on social media platforms like ‘Twitter’. Twitter provides a unique opportunity for public health researchers to analyze untampered information shared during a disease outbreak. Considering the ongoing public health emergency, we conducted a study investigating the public reaction to COVID-19 pandemic around the world using in-depth thematic analysis of Twitter data. A dataset of 212846 tweets was retrieved over a period of seven days (from April 13, 2020, to April 19, 2020) via Twitter Application Programme Interface (API). The following five keywords were used to collect the tweets: “coronavirus”, “covid-19”, “corona”, “covid”, “covid19”. After data filtering and cleaning 6348 tweets were randomly selected for in-depth thematic analysis. Thematic analysis was done manually using a two-level coding guide. A total of six main themes emerged from the analysis: ‘sentiments and feelings’, ‘Information’, ‘General Discussion’, ‘Politics’, ‘Food’, and ‘Sarcasm or humor’. The aforementioned themes were divided into 26 sub-themes. The results of the thematic analysis show that 30.1% of the tweets were regarding ‘sentiments and feelings’, 15.6% were regarding ‘politics’, and 6.5% were related to ‘sarcasm or humor’. The present study is the first study that has analyzed the public response to COVID-19 on Twitter. The study demonstrates that social media platforms (like Twitter) can be used to conduct infodemiological studies related to public health emergencies like the COVID-19 pandemic. We believe that the results of this study will be of potential interest to policymakers, health authorities, stakeholders, and public health and social science researchers. KEYWORDS:COVID-19, Twitter, Social Media, Coronavirus, Lockdown, Pandemic

Background The cost of maternity care is seen as the barrier in utilizing maternity care, resulting in high maternal deaths. This study focuses on the distress financing and its coping mechanisms associated with maternity care expenditure in India so that corrective measures can be taken to reduce the burden of maternity care. Methods This study used the National Sample Survey (NSS) data conducted in 20,014–15 (71st round of NSS) and 2017–18(75th round of NSS). We define distress financing as use of formal borrowing, borrowing from friends or family or sale of asser to finance maternity care. Percentage of pregnant/delivered females using distress financing were calculated.. The present study also used multinomial logistic regression with 95% to understand the impact of socio-economic variables on distress financing and concentration index to measure the inequality in maternity care expenditure. Results This study found that the maternity care expenditure has decreased from the INR. 9379 in 2014–15 to INR. 7835 in 2017–18. The percentage of households using distress financing is higher among the poorest (13.2%). Almost 14% of the SC households experience distress financing. Among EAG + A states, particularly in Madhya Pradesh and Uttarakhand, the percentage of households are which experience a high level of distress financing increased from 8.9 to 18.3 and 0.7 to 8.1 from 2014–15 to 2017–18 respectively. The study finds that more urban households (37%) utilized insurance than rural households (26%). Among EAG + A states, 67.9 percent of households were dependent upon household savings, and it was 63.6 percent in the non-EAG states. The households with a high burden of maternity care expenditure were at higher risk of borrowing money to finance the cost of maternity as compared to use of savings/income for the same (relative risk (RR) (R: 2.59; P < 0.01; 95% CI: 2.15–3.13). Mothers belonging to the SC caste were at significantly higher risk (RR: 1.43; P < 0.1; 95% CI: 1.07–1.91). of using borrowings as compared to the use of income/savings. Mothers with college education were 50% more likely to use health insurance as compared to those with primary education. Conclusions The study found that even though many programs for maternity care services are there, the maternity care expenditure, particularly the delivery care expenses, is very high in many states. The study recommends that India should increase subsidized maternity care facilities to decrease catastrophic maternity expenditure among households.

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