How prevalent is COVID-19 vaccine hesitancy in low-income and middle-income countries and what are the key drivers of hesitancy? Results from 53 countries.

OBJECTIVES: This study aims to estimate the levels of COVID-19 vaccine hesitancy in 53 low-income and middle-income countries, differences across population groups in hesitancy, and self-reported reasons for being hesitant to take the COVID-19 vaccine. METHODS: This paper presents new evidence on levels and trends of vaccine hesitancy in low-income and middle-income countries based on harmonised high-frequency phone surveys from more than 120 000 respondents in 53 low-income and middle-income countries collected between October 2020 and August 2021. These countries represent a combined 53% of the population of low-income and middle-income countries excluding India and China. RESULTS: On average across countries, one in five adults reported being hesitant to take the COVID-19 vaccine, with the most cited reasons for hesitancy being concerns about the safety of the vaccine, followed by concerns about its efficacy. Between late 2020 and the first half of 2021, there tended to be little change in hesitancy rates in 11 of the 14 countries with available data, while hesitancy increased in Iraq, Malawi and Uzbekistan. COVID-19 vaccine hesitancy was higher among female, younger adults and less educated respondents, after controlling for selected observable characteristics. CONCLUSIONS: Country estimates of vaccine hesitancy from the high-frequency phone surveys are correlated with but lower than those from earlier studies, which often relied on less representative survey samples. The results suggest that vaccine hesitancy in low-income and middle-income countries, while less prevalent than previously thought, will be an important and enduring obstacle to recovery from the pandemic.

A Simple Method for Simulating Dementia Onset and Death within an Existing Demographic Model.

BACKGROUND: Dementia is a common disease that has an impact on both the affected individual and family members who provide caregiving. Simulation models can assist in setting policy that anticipates public health needs by predicting the demand for and availability of care. OBJECTIVE: We developed a relatively simple method for simulating the onset of dementia that can be used in combination with an existing microsimulation model. METHODS: We started with Socsim, a demographic microsimulation model that simulates a population with family kinship networks. We simulated dementia in the Socsim population by simulating the number of individuals diagnosed with dementia in their lifetime and the ages of onset and death from dementia for each of these dementia cases. We then matched dementia cases to the simulated population based on age at death, so for each individual, we simulate whether they develop dementia and, if so, their age at onset. This approach simulates dementia onset but does not alter the demographic model's simulated age of death. RESULTS: We selected model dementia parameters so that the combined Socsim-Dementia model reproduces published dementia prevalence rates and survival times after diagnosis. CONCLUSIONS: Adding simulation of dementia to a kinship network model enables prediction of the availability of family caregivers for people with dementia under a range of different assumptions about future fertility, mortality, and dementia risk. We demonstrated how to add simulation of dementia onset and death to an existing microsimulation model to obtain a method for predicting dementia prevalence in the context of another more detailed model. The approach we developed can be generalized to simulate other progressive health conditions that affect mortality.

Improving Child Welfare Outcomes: Balancing Investments in Prevention and Treatment.

To provide objective analyses about the effects of prevention and treatment programs on child welfare outcomes, RAND researchers built a quantitative model that simulated how children enter and flow through the nation's child welfare system. They then used the model to project how different policy options (preventive services, family preservation treatment efforts, kinship care treatment efforts, and a policy package that combined preventive services and kinship care) would affect a child's pathway through the system, costs, and outcomes in early adulthood. This study is the first attempt to integrate maltreatment risk, detection, pathways through the system, and consequences in a comprehensive quantitative model that can be used to simulate the impact of policy changes. This research suggests that expanding both prevention and treatment is needed to achieve the desired policy objectives: Combining options that intervene at different points in the system and increasing both prevention and treatment generates stronger effects than would any single option. The simulation model identifies ways to increase both targeted prevention and treatment while achieving multiple objectives: reducing maltreatment and the number of children entering the system, improving a child's experience moving through the system, and improving outcomes in young adulthood. These objectives can all be met while also reducing total child welfare system costs. A policy package combining expanded prevention and kinship supports pays for itself: There is a net cost reduction in the range of 3 to 7 percent of total spending (or approximately $5.2 billion to $10.5 billion saved against the current baseline of $155.9 billion) for a cohort of children born over a five-year period.