components of nutrition governance are also important.
Together, such components can bring about the desired
change [37]. Governance is also important for showing how
a country is committed to accelerating nutrition actions
[38,39]. It is a measure of the country’s responsiveness to
varied threats of nutrition, both undernutrition and obesity
[38]. In this study, MICs had stronger nutrition governance
compared to LICs. Results of this study further showed that
strong nutrition governance was more likely to be associated
with improved magnitudes of undernutrition and
showed a direction towards improving magnitudes of overweight,
after controlling for other confounding variables.
Results of this study should be discussed in light of the
following potential limitations. First, this study reviewed
data whose sources might have used different methods and
tools for collection. For example, we used nutrition status
data, which is reported by the WHO, but originated from
national sources, DHS, and other UN organizations. Although
this could have lead to over- or under-estimation,
these are the best estimates available and have been
adopted globally. Second, to examine the changes of nutrition
statuses over time, we used data for as many different
years and different countries as we could get. Such analyses
may have been more accurate if data for the same
time intervals and years was available. To mitigate the effect
of time differences, we controlled for the years in the
regression analyses. We also collected data of GDP per
capita and school enrolment that corresponded to the
years of nutrition status data collection.We controlled nutrition
policy and governance by matching them with
years of anthropometric data. Third, only a few countries
had data on nutrition governance. This could reduce the
power of our results. Fourth, a few countries had data on
nutrition governance, which was also our important independent
variable. This might lead into under estimation of
the association between it and nutrition statuses, or limit
generalizability into countries with no such data. However,
for the available data, we controlled important
confounders to find independent association with nutrition
statuses. Fifth, we used GINA database to collect data
on nutrition policy. This database uses available policies in
the country or ones that are provided upon request, in
native languages. This may result in missing some policies
or updates thereof. Sixth, while the problem of nutrition
transition affects populations across age groups, we focused
on child population. This was mainly because of the
lack of nutrition data among adults in LAMICs and unclear
nutrition policy to address such problems.
components of nutrition governance are also important.
Together, such components can bring about the desired
change [37]. Governance is also important for showing how
a country is committed to accelerating nutrition actions
[38,39]. It is a measure of the country’s responsiveness to
varied threats of nutrition, both undernutrition and obesity
[38]. In this study, MICs had stronger nutrition governance
compared to LICs. Results of this study further showed that
strong nutrition governance was more likely to be associated
with improved magnitudes of undernutrition and
showed a direction towards improving magnitudes of overweight,
after controlling for other confounding variables.
Results of this study should be discussed in light of the
following potential limitations. First, this study reviewed
data whose sources might have used different methods and
tools for collection. For example, we used nutrition status
data, which is reported by the WHO, but originated from
national sources, DHS, and other UN organizations. Although
this could have lead to over- or under-estimation,
these are the best estimates available and have been
adopted globally. Second, to examine the changes of nutrition
statuses over time, we used data for as many different
years and different countries as we could get. Such analyses
may have been more accurate if data for the same
time intervals and years was available. To mitigate the effect
of time differences, we controlled for the years in the
regression analyses. We also collected data of GDP per
capita and school enrolment that corresponded to the
years of nutrition status data collection.We controlled nutrition
policy and governance by matching them with
years of anthropometric data. Third, only a few countries
had data on nutrition governance. This could reduce the
power of our results. Fourth, a few countries had data on
nutrition governance, which was also our important independent
variable. This might lead into under estimation of
the association between it and nutrition statuses, or limit
generalizability into countries with no such data. However,
for the available data, we controlled important
confounders to find independent association with nutrition
statuses. Fifth, we used GINA database to collect data
on nutrition policy. This database uses available policies in
the country or ones that are provided upon request, in
native languages. This may result in missing some policies
or updates thereof. Sixth, while the problem of nutrition
transition affects populations across age groups, we focused
on child population. This was mainly because of the
lack of nutrition data among adults in LAMICs and unclear
nutrition policy to address such problems.
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