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Annotated food purchase receipts were collected for a four-week period by the primary household shopper. Receipt food source and foods items were classified into specific categories, and food quantities in ounces were recorded by research staff. For home sources, a limited number of food/beverage categories were recorded. For eating out sources, all food/beverage items were recorded. Median monthly per person dollars spent and per person ounces purchased were computed. Food sources and food categories were examined by household income tertile.

Household per person monthly dollars spent was significantly higher among middle and high income households for premium chain grocery stores, and among high income households for wholesale stores and specialty food stores. No significant income differences were observed for per person dollars spent at discount chain grocery stores, small grocery stores, convenience stores, gas stations or nonfood stores.

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Table 2 shows the household monthly per person dollars spent for each food category by household income group. Dollars spent at home sources (left panel) and eating out sources (right panel) are presented. Compared with lower income households, home and eating out fruit and vegetable purchases (dollars per person), home purchases for snacks and sweets, and eating out entrees and side order purchases were significantly higher among high income households.

Cambodia has a population of about 16.5 million and a monthly substantial income of United States dollar (USD) 137 (1643 per year) as of 2019 [2]. Table 1 provides an overview of the status of dialysis in Cambodia in that year.

Myanmar is a large country in mainland Southeast Asia that is almost twice the size of Japan but with slightly less than half the population (53.6 million) and a monthly substantial income is about USD 119 (1422 per year) as of 2021 [4]. Table 2 provides an overview of dialysis care in Myanmar. Around 2016 to 2017, the government began fully covering the cost of once-weekly dialysis sessions at public hospitals. This is a major advancement considering that everyone other than the military had to pay completely out of pocket for dialysis care when I first visited Myanmar in 2013. However, due to the limited number of dialysis machines available, in reality patients often never get the chance to receive that once-weekly dialysis session to which they are entitled, forcing many poorer patients to choose conservative therapy and die. Patients who need dialysis more frequently than once a week can do so at a private hospital. A dialysis session costs USD 45 in total, but the government covers just USD10, so only those patients who can afford to pay the USD 35 balance are able to receive dialysis. CAPD costs USD 400, all of which must be paid for out of pocket. Dialyzers are reused up to 8 times, a dialysis session lasts 4 h, and the number of sessions per week depends on how much the patient can afford. For the educational lecture of the 64th annual meeting of JSDT, the author requested that the NIPRO Co. Ltd., medical equipment distributor in Myanmar conducted an interview survey with local physicians. They reported that 5% of patients were receiving dialysis 3 times a week, 70% twice a week, and 25% once a week. The estimated number of patients on hemodialysis is 4000. All the numbers presented so far have been rough estimates, but the number of patients on CAPD is exactly 66 (as of June 2019). No statistical surveys on renal failure have been conducted in Myanmar to date. The numbers presented should be considered rough estimates reflecting the status of dialysis care in 2019.

Laos, whose capital is Vientiane, is a country about two-thirds the size of Japan and a population one-seventeenth that of Japan, at 7.12 million. It is also a lower-middle income country, where citizens have an average monthly income of USD 223 (2670 per year) [8].

Mongolia covers a large expanse of territory with a land area of 1,564,116 km2, roughly 4 times that of Japan, but has a much smaller population, at only 3.35 million. The monthly substantial income of USD 346 (4151 per year) as of 2020 [11].

Literature on SES measurement distinguishes between wealth, or accumulated financial resources, and income, a measure of shorter-term access to capital [2]. Researchers have identified challenges in collecting income data, particularly in low-income settings, due to monthly fluctuations, informal work, and reporting biases [6]. Recent empirical work has drawn attention to the approach of supplementing or replacing information on income with direct measures of wealth, such as household assets [7]. Perhaps the most widespread approach to direct measurement of household wealth is that used by the Demographic and Health Surveys (DHS), implemented in more than 90 countries since 1984 [8]. Using nationally representative data from India, Filmer and Pritchett [7] created an index based on household ownership of assets and housing materials to serve as a proxy for wealth. The resulting index was internally valid and coherent, and robust to the choice of assets. Using additional data sets from Indonesia, Nepal, and Pakistan, they further argued that a composite asset index is as reliable as data on household consumption and is less subject to measurement error [7]. Their statistical approach, using principal components analysis (PCA), has since been adapted to create a household wealth index in each DHS dataset [8]. Concerns about this approach include its over-representation of urban settings, and its failure to distinguish between the poorest of the poor, particularly in rural areas [9]. Furthermore, this approach requires lengthy surveys of household assets. Several studies have found that rapid wealth appraisals requiring as few as four survey questions perform as well as the DHS wealth index in categorizing households [10] and predicting mortality [11].

We then examined associations between each wealth measurement approach and monthly household income. We converted household income to USD using January 1, 2010 exchange rates. Given the expected association between household wealth and income, these analyses provided evidence of the construct validity of each approach to measuring household wealth [22]. Based on the cumulative evidence from these analyses, we selected one approach to measuring household wealth.

Vaccines have contributed to substantial reductions of morbidity and mortality from vaccine-preventable diseases, mainly in children [1]. Still, in 2019, vaccine hesitancy is listed by the World Health Organization (WHO) as one of the top ten threats to world health [2]. And so, vaccine compliance remains inconsistent and is therefore a growing concern [3]. Hesitancy can be defined as any manifestation between full acceptance of vaccines, and outright refusal of all vaccines [4] Kumar et al., claim that vaccine hesitancy can be caused by a complex interaction between socio-demographic factors (age, race, education, income); immune-specific characteristics (immunization requirements, vaccine-efficacy, vaccine safety), and sociological factors such as norms and attitudes. Hesitancy can be impacted by lack of information, access to misinformation, and is undermined by distrust of medical and government sources, which are sometimes seen as overplaying the risk or severity of disease and underplaying adverse side effects of vaccines [3, 5]. Since the relationship between on-line communication and vaccination hesitancy is complex and indirect, we need more than self-reports to assess it. To that end, we applied specialized machine learning tools that thus far had only been scantly applied to health research.

We use a Bayesian Poisson framework to estimate both the expected deaths (for all countries and all months) in the absence of the pandemic, and the ACM for those countries with no such data during the pandemic. In addition to the data on ACM, we gathered information on specific variables with spatiotemporal variations considered to be associated with changes in excess mortality over the course of the pandemic. These variables are chosen based on the strengths of the associations and availability across locations for the duration under study. We consider several that are assumed to change by month such as the COVID-19 death rate, the COVID-19 test positivity rate, aggregate containment measures (combining lockdown restrictions and closures) and average national temperature, together with others that are fixed over the period of study including a high-income country binary indicator, historic cardiovascular disease death rates and historic diabetes prevalence rates. A log-linear regression model on these variables, also within the Bayesian Poisson framework, is used to predict mortality levels in the locations without adequate reporting of mortality during the pandemic. For a handful of countries, instead of covariates, their subnational observed deaths are used to predict the national deaths using multinomial models that assume the relationships estimated between pre-pandemic subnational and national mortality persist into the pandemic. Finally, the reported or distributions of predicted deaths, conditional on data availability, together with the derived expected death distributions, are used to estimate monthly excess deaths in all locations for the years 2020 and 2021.

Our approach, differs from those of the other two global endeavours of the Institute for Health Metrics and Evaluation (IHME)49 and The Economist50. We have used a very conventional statistical modelling approach in which a parametric model is fitted using Bayesian inferential machinery, and with the models for different data types being consistent with each other to make the country by country results directly comparable to each other. As an example, if the mortality in subnational regions are Poisson random variables, then the sum (the mortality in the country) is also Poisson. Further, given the total mortality in a country the subnational counts follow a multinomial distribution. Our framework exploits these relationships when we formulate models for the situation in which we have subnational data only. Similarly, our annual model (for countries with such data only) is consistent with the monthly models we use for the majority of the countries. The IHME approach is unprincipled and not transparent and corresponds to a number of steps being bolted together, without a coherent model tying them together. Rather than using a direct count model based on a Poisson framework, the IHME approach models the log of the excess rate as a function of covariates, without any weighting, so that the population sizes of the different countries do not feed into the uncertainty calculation. A fundamental problem with the overall approach is that the uncertainty intervals are constructed in a non-standard and ad hoc way, so that the confidence intervals, in particular, will not be accurate representations of the true uncertainty. The Economist approach models the excess rate with a flexible tree-based machine learning technique, gradient boosting. The approach is clearly described and uses a resampling technique, the bootstrap, to form interval estimates, but there is no theory to support the use of the bootstrap with boosting, and so again, the uncertainty intervals should be viewed sceptically. A full description and critique of the alternative methods are available in Knutson et al.15. In the Supplementary information, we provide a comparison between point and interval country estimates obtained by the methods of the WHO, IHME and The Economist. be457b7860