The world is not on track to achieve the goals for immunization coverage and equity described by the World Health Organization’s Global Vaccine Action Plan. Many countries struggle to increase coverage of routine vaccination, and there is little evidence about how to do so effectively. In India in 2016, only 62% of children had received a full course of basic vaccines. In response, in 2017-18 the government implemented Intensified Mission Indradhanush (IMI), a nationwide effort to improve coverage and equity using a campaign-style strategy. Campaign-style approaches to routine vaccine delivery like IMI, sometimes called ‘periodic intensification of routine immunization’ (PIRI), are widely used, but there is little robust evidence on their effectiveness. We conducted a quasi-experimental evaluation of IMI using routine data on vaccine doses delivered, comparing districts participating and not participating in IMI. Our sample included all districts that could be merged with India’s 2016 Demographic and Health Surveys data and had available data for the full study period. We used controlled interrupted time-series analysis to estimate the impact of IMI during the 4-month implementation period and in subsequent months. This method assumes that, if IMI had not occurred, vaccination trends would have changed in the same way in the participating and not participating districts. We found that, during implementation, IMI increased delivery of 13 infant vaccines, with a median effect of 10.6% (95% confidence interval 5.1% to 16.5%). We did not find evidence of a sustained effect during the 8 months after implementation ended. Over the 12 months from the beginning of implementation, we estimated reductions in the number of under-immunized children that were large but not statistically significant, ranging from 3.9% (-6.9% to 13.7%) to 35.7% (-7.5% to 77.4%) for different vaccines. The largest effects were for the first doses of vaccines against diphtheria-tetanus-pertussis and polio: IMI reached approximately one-third of children who would otherwise not have received these vaccines. This suggests that PIRI can be successful in increasing routine immunization coverage, particularly for early infant vaccines, but other approaches may be needed for sustained coverage improvements.

[This corrects the article DOI: 10.1371/journal.pone.0201723.].

Multimorbidity (the presence of two or more non-communicable diseases) is a major growing challenge for many low-income and middle-income countries (LMICs). Yet, its effects on health care costs and financial burden for patients have not been adequately studied. This study investigates the effect of multimorbidity across the different percentiles of healthcare utilisation and out-of-pocket expenditure (OOPE).

Demand for neonatal care in the United Kingdom (UK) has increased in recent years. This care is provided by neonatal services, which are chronically saturated due to years of budget austerity in the UK. The aim of this paper is to investigate the possible impact of increasing resources to these services to improve their operational performance and alleviate the pressure they are facing. To achieve this aim, a system dynamics (SD) simulation model was built and validated in a UK neonatal unit. The SD model was used initially to evaluate the impact of increasing resources on the unit performance and the results showed that this policy will have a limited effect on performance. The model was then extended to predict the effect of reducing the length of stay (LoS) in conjunction with increasing resources. These joint interventions will have a positive impact on the unit performance if LoS is reduced for all care categories and resources are slightly increased. Results’ implications and SD’s modelling usefulness to guide decision making in complex health settings are discussed.

Mathematical modeling has played a prominent and necessary role in the current coronavirus disease 2019 (COVID-19) pandemic, with an increasing number of models being developed to track and project the spread of the disease, as well as major decisions being made based on the results of these studies. A proliferation of models, often diverging widely in their projections, has been accompanied by criticism of the validity of modeled analyses and uncertainty as to when and to what extent results can be trusted. Drawing on examples from COVID-19 and other infectious diseases of global importance, we review key limitations of mathematical modeling as a tool for interpreting empirical data and informing individual and public decision making. We present several approaches that have been used to strengthen the validity of inferences drawn from these analyses, approaches that will enable better decision making in the current COVID-19 crisis and beyond.