Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.
Why is infant mortality higher in the US than in Europe? American Economic Journal: Economic Policy [RePEc] Published May 2016
First up we have not one but two papers on infant health. The poor health outcomes in the United States relative to its healthcare expenditure have long been noted. In this paper the authors delve into the question of why the infant mortality rate is so much higher in the US compared to countries with a similar GDP per capita. In the US there are approximately 3 more deaths per 1,000 live births than in Scandinavian countries. However, these figures are derived from aggregate statistics and don’t reveal what’s going on underneath. This paper uses micro-data from the US and a number of European countries including Finland, Austria, and the UK, which enables the construction of comparable samples of infants from each country. Once birth weight is taken into account the disparity between the US and European nations shrinks but does not disappear, it is still around 1.5 to 2 deaths per 1,000 higher. Perhaps the most interesting finding is that most of the remaining disparity is accounted for by post-neonatal mortality (1-12 months post birth) rather than neonatal mortality (death within one month); geographical variation in income does not explain these differences. These results are revealing for policy since much of the focus of policy to improve infant mortality rate is on health at birth despite the majority of the variation in mortality rates being during the post-neonatal period. As we have seen with evidence on the 7-day NHS policy, once aggregate statistics are examined in more depth, the initially ‘obvious’ policy choices can be revealed to be sub-optimal.
Do cash transfers improve birth outcomes? Evidence from matched vital statistics, program and social security data. American Economic Journal: Economic Policy [RePEc] Published May 2016
The previous paper showed that most of the disparity in US and European infant mortality rates is due to variations in post-neonatal mortality. This suggests that resources may be better devoted to the post-neonatal period than to improving infant health at birth, at least in the US. Nevertheless, babies born to families from a poorer background are more likely to be of lower birth weight, have birth complications, and have poor health in later life. This may suggest conditional cash transfers to pregnant women could reduce poor health at birth. However, in the aggregate the effects of these transfers on infant health are ambiguous since this could lead to more women of low incomes choosing to have children and may lead parents to increase harmful consumption. This paper analyses the effects of a social assistance program consisting predominantly of cash transfers in Uruguay on the incidence of low birth weight (<2,500g) births using a large, detailed dataset. They find that participation in the program led to a reduction in the incidence of low birth weight live births of around 15%. Mothers in the program showed increased weight gain, reduction in labour supply, and a reduction in smoking, all potentially contributing to infant health. However, in the context of the wider literature on the effect of economic conditions on infant health at birth, there is no solid consensus. For example, at the state level in the US, increases to the unemployment rate also lead to reductions in low birth weight births. Reconciling all these results to better understand how to target policy to improving maternal and infant health is therefore complex. Something we’ll come back to in a later post…
Measuring and forecasting quality in English hospitals. JRSS A Published 14th May 2016
The accurate measurement of hospital quality is a long standing methodological question. Identification of poorly performing hospitals, measuring the effect of quality improvement interventions, and planning healthcare policy all rely on measuring hospital quality. Perhaps one of the most widely used measures is the standardised mortality ratio frequently employed by groups such as Dr. Foster; however, as we have discussed previously in other contexts these methods are widely criticised. The best method of measuring quality is to identify preventable harms and deaths through case note review or other means, but this is time consuming and costly, and there is a need to measure quality using routine data for the aforementioned reasons. This paper applies a method previously proposed elsewhere to examine hospital quality in England. Briefly, hospital fixed effects are estimated from a regression controlling for patient characteristics for four different outcome measures, these fixed effects are assumed to be correlated with an underlying latent quality variable, which is then estimated. A further ‘smoothing’ step is conducted that allows for correlation between the previous and next year’s quality variable; this step also permits prediction of future quality. This method reduces the problem of low signal to noise ratio present in many hospital quality statistics. However, a true validation requires comparison with information from case note review (such as this one), which, while being a costly piece of research, may have important implications to understanding how routine data can be used to identify and improve hospital quality.