Declining Fish and Protein Consumption in Low-Income Countries: Evidence from Demographic Health Surveys

A Thesis Submitted in Partial Fulfillment of theRequirements of the Renée Crown University Honors Program at

Syracuse University

Terynn Young

Candidate for Bachelor of Scienceand Renée Crown University Honors

December 2019

Honors Thesis in Your Major

Thesis Advisor: _______________________ Advisor’s Name and

Title

Thesis Reader: _______________________ Reader’s Name and

Title

Honors Director: _______________________ Dr. Danielle Smith,

Director

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Table of Contents

Executive Summary......................................................................................................4

Background..........................................................................................................................4

Methods..............................................................................................................................4

Results.................................................................................................................................5

Discussion............................................................................................................................5

Abstract.......................................................................................................................6

Background.................................................................................................................7

Methods......................................................................................................................9

Study design........................................................................................................................9

Data sources........................................................................................................................9

Outcome variables.............................................................................................................10

Potential bias.....................................................................................................................10

Statistical methods.............................................................................................................11

Equation 1:..........................................................................................................................................11

Results.......................................................................................................................12

Data available....................................................................................................................12

Meta-analysis results.........................................................................................................12

Discussion..................................................................................................................13

Key results.........................................................................................................................13

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Interpretation....................................................................................................................13

Limitations.........................................................................................................................15

Policy Recommendations...................................................................................................16

Conclusion.........................................................................................................................18

References.................................................................................................................18

Appendix....................................................................................................................24

Figure 1..............................................................................................................................24

Figure 2..............................................................................................................................25

Figure 3..............................................................................................................................25

Figure 4..............................................................................................................................26

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Executive Summary

Background

Fish are a valuable source of protein, specifically for their branched chain amino acids which

contribute to muscle growth (Brestenský, Nitrayová, Patráš, Heger, & Nitray, 2015), and low

levels of saturated fat (Brunner, Jones, Friel, & Bartley, 2009). Ensuring fish availability for

consumption is an important food security issue; however wild fish populations are declining.

Marine resources are greatly depleted due to unsustainable fishing practices (Blasiak, 2015). At

the global scale there was an 11% decline in total fish biomass since 1977 (Venter et al., 2016)

Declining fishery productivity is most harmful in lower income countries (Allison et al., 2009;

Golden, 2016), where fish often make up a larger portion of the population’s protein intake

(Kawarazuka, 2010).

Methods

Using survey data from the Demographic Health Surveys (DHS) program run by US AID, we

compared individual level data of both fish and protein consumption in children under five years

of age across different countries and years. A total of 427 datasets collected in 2005 or later were

available in August 2017 (Figure 1). Each dataset was screened according to the following

inclusion criteria. First, each data set must have included nutrition information. Second, each

country must have had available at least two separate surveys conducted in two separate years so

that the changes within the country could be measured. If more than two datasets from a single

country matched the inclusion criteria, all appropriate datasets were included in the analysis. We

used a logistic regression to measure the odds of a child under 5 years of age eating either fish

4

(primary outcome) or any protein (secondary outcome) in the previous 24 hours. Then the odds

ratio and standard error for each country-pair was analyzed in a meta-analysis to determine

variation over time.

Results

Significant variation in changes in fish consumption was observed across countries included in

the analysis (I-squared = 97.62%). The study found that the average fish consumption has

decreased with an OR of 0.71 (95% CI = 0.52 – 0.97) representing the average change in fish

consumption between time one and time two in the 25 countries included in the analysis. Further,

significant variation in changes in protein consumption was observed across countries included

in the analysis (I-squared = 92.54%). The average change in protein consumption between time

one and time two in the countries included in the analysis is represented by an OR of 0.78 (95%

CI = 0.66 – 0.92)

Discussion

One possible explanation of the observed decline in fish consumption is that the fisheries

themselves are depleted and wild-caught fish are not as readily available. In 2012, 4,714 fisheries

were examined and 68% were below the critical biomass threshold (Worm, 2016). Additionally,

another explanation of this trend could be the expansion of the global fishing industry. As trade

has increased, many fisheries have begun fishing for non-local consumption. For example, in

2004 Morocco, a developing country, was the fourth largest producer of internationally traded

small pelagic fish (Tacon & Metian, 2009). , it is possible that the decline in fish consumption

and production is caused by increased exports of fish to high income countries while the

traditional fisheries ran by lower income countries are overfished and exploited. Some solutions

5

to this could be to create a task force to enforce and monitor the international fishing agreements

implemented by the FAO, to include scientists in managerial decisions within fisheries and in

creating regulations that would affect them, and to closely monitor the production, exportation

and importation of fish products to help support responsible consumption.

Abstract

The primary purpose of this study is to determine whether fish consumption in children under the

age of five within low-income countries is declining, increasing or remaining relatively steady.

Additionally, this study also investigates protein consumption within children as a secondary

outcome. This study uses survey data collected by the Demographic Health Survey Program

funded by USAID. Any country with two years’ worth of child food consumption data

(consumption of animal products and/or fish products) with matching regional data was analyzed

using a logistic regression. The standard error and odds ratios of each country pair were then

analyzed using a meta-analysis to determine by-country variation and variation over time. This

study found that fish consumption has, on average, declined between time one and time two with

an OR of 0.71 (95% CI = 0.52 – 0.97). Additionally, protein consumption has also declined with

the average change being represented by an OR of 0.78 (95% CI = 0.66 – 0.92). These changes

can possibly be explained by the collapse of fisheries across the globe, a shift in global trade of

fish products or by natural changes in dietary preferences. There are some policies that can be

implemented to reverse these trends such as improvements to pre-existing international

conservation agreements, changes to the measures of fishery success and the addition of

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scientists in making managerial decisions within fisheries and in the creation of regulations that

would impact them.

Background

Although the current situation of global food insecurity is driven by access, not lack of

production, it is also true that the world’s population is projected to reach 9 billion people by

2050 and producing the amount of food necessary to feed to that number of people will be

challenging. This will mean not only ensuring sufficient caloric intake in general, but also

ensuring that sufficient protein and other nutritious foods are available, accessible and culturally

satisfying (Godfray et al., 2010). Fish can play a key factor in this regard (Béné et al., 2015).

Fish are a valuable source of protein, specifically for their branched chain amino acids which

contribute to muscle growth (Brestenský, Nitrayová, Patráš, Heger, & Nitray, 2015), and low

levels of saturated fat (Brunner, Jones, Friel, & Bartley, 2009). Beyond providing protein, fish

contains selenium and omega-3 fatty acids, which appear to have a positive effect on cardiac

function. As a result, fish consumption is associated with a host of benefits including: reduced

risk of heart disease and stroke (Kris-Etherton et al., 2002), and benefits to both brain and visual

development in infants (Mahaffey et al., 2011).

Ensuring fish availability for consumption is an important food security issue; however

wild fish populations are declining. Marine resources are greatly depleted due to unsustainable

fishing practices (Blasiak, 2015). At the global scale there was an 11% decline in total fish

biomass since 1977 (Venter et al., 2016). And it is estimated that by 2050, 88% of fishery stocks

will be overfished and below their target biomass (Worm, 2016). Inland fisheries are also

threatened. An estimated 65% of freshwater systems are at high threat of biodiversity loss and

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water security (Vörösmarty et al., 2010). Inland fisheries are already over-exploited in Asia and

Africa (Welcomme et al., 2010) where the inland fishery harvest is more than double what was

previously reported (Fluet-Chouinard, Funge-Smith, & McIntyre, 2018).

Declining fishery productivity is most harmful in lower income countries (Allison et al.,

2009; Golden, 2016), where fish often make up a larger portion of the population’s protein intake

(Kawarazuka, 2010). Declining fishery productivity worldwide harms lower income countries in

two ways. First, declines in fisheries within a lower income country can lead to both food and

economic loss. Fish are the most traded of all food commodities worldwide (Asche, Bellemare,

Roheim, Smith, & Tveteras, 2015). For the poor, fish serve as the “bank in the water”,

representing a cash crop that can be harvested as needed (Béné, Steel, Luadia, & Gordon, 2009).

Second, declines in fishery productivity in richer countries lead to greater exploitation of lower-

income country fisheries by richer countries (Swartz, Rashid Sumaila, Watson, & Pauly, 2010)

as the fishing industry moves onto other types and locations of fisheries when fisheries in high

income countries collapse (Hutchings, 2000; Pauly et al., 1998).

Protein consumption is important for many different reasons. Nutritionally, protein helps

to regulate food intake by facilitating satiety signals (Westerterp-Plantenga, 2003). Specifically,

for children, protein intake can help promote growth and prevent stunting. While both animal-

and vegetable-based proteins both share these health benefits, consumption of animal proteins is

unique in a couple ways. Generally, animal-based protein is a higher quality source of protein for

human ingestion due to its high bioavailability which results from the amino acid structure

(Elmfada & Meyer, 2017). Because of this, lower amounts of animal protein are required to

cover the body’s physiological needs. This is especially important in regard to infants and young

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children because they have significantly higher amino acid requirements (Elmfada & Meyer,

2017).

While it is known that fishery productivity worldwide is threatened (Worm, 2016), it

remains to be seen how these trends may affect fish consumption in lower income countries. In

this article we examine trends in fish and animal-based protein consumption among young

children in lower income countries and particularly how these have changed over the last ten

years.

Methods

Study design

We conducted an individual country-level meta-analysis of multiple nationally

representative surveys over time. Using survey data from the Demographic Health Surveys

(DHS) program run by US AID, we compared individual level data of both fish and protein

consumption in children under five years of age across different countries and years.

Data sources

We utilized publicly available data from nationally representative datasets collected by

the Demographic Health Surveys project. These data are collected primarily for the monitoring

of fertility and child mortality in lower income countries. Beginning in 2005, these surveys

included questions regarding fish consumption among children under 5 years of age.

Specifically, the surveys ask whether or not the child ate different types of food, including fish,

in the previous 24 hours.

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A total of 427 datasets collected in 2005 or later were available in August 2017 (Figure

1). Each dataset was screened according to the following inclusion criteria. First, each data set

must have included nutrition information. Second, each country must have had available at least

two separate surveys conducted in two separate years so that the changes within the country

could be measured. If more than two datasets from a single country matched the inclusion

criteria, all appropriate datasets were included in the analysis and the change between each

different time period was assessed.

Outcome variables

We assessed how fish consumption changed over time as our primary outcome. If

mothers reported giving their young children fish or fish-related food (shellfish, fish paste, etc) in

the previous 24 hours then the children were considered as having consumed fish. As a

secondary outcome we examined children’s consumption of any type of animal-based protein to

determine whether or not mothers reported giving their young children any source of animal-

based protein in the previous 24 hours. We included the following types of foods as any protein:

meat (chicken, beef, fish, etc.), eggs, dairy (yogurt, milk, etc.), and foods specific to a country

such as caterpillars.

Potential bias

Fish stocks and availability often fluctuate with the seasons and in order to properly

measure fish consumption, which has the potential to affect our analysis (Kvamsdal et. al, 2017).

Further, demand for fish fluctuates with seasonality and could lead to either a decrease in fish

availability due to a stress on fisheries, or an increase in consumption, or possibly both (Oliveira

et. al, 2014). We therefore limited our analyses to surveys from the same country in which the

two different surveys collected data during the same months in the same regions. Surveys that

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lacked matching data in terms of months and regions were excluded from the study. However,

after adjusting for seasonality we found that seasonality did not significantly alter the results of

the analysis. From this, in order to increase the sample size, we decided not to adjust for

seasonality in our final analysis. However, it is possible that the months included in some

countries represented a lean season where fish was less available while others represented a time

where fish was plentiful. Additionally, this study did not account for any potential variation

caused by climate change.

Statistical methods

We used a logistic regression to measure the odds of a child under 5 years of age eating

either fish (primary outcome) or any protein (secondary outcome) in the previous 24 hours after

adjusting for factors hypothesized a priori to influence those odds. Specifically we adjusted

regression models for mother’s education level (categorized as no education, some education, or

completed primary or higher), household wealth quintile, child’s age (categorized into years),

location of the household (urban or rural), and region of the country where the survey was

conducted. We pooled together two surveys from the same country but two different time points,

with the measure of time (survey 1 or survey 2) being the exposure of interest and repeated this

process for each of the pair comparisons available in the data. We conducted regression analyses

in Stata version 15.1 and adjusted standard errors for clustering at the survey cluster level. The

following equation represents the paired survey regression analyses. The numerical difference of

years between survey 1 and survey 2 was not included as part of the statistical analysis.

Equation 1: The odds of fish consumption for child i in survey j is estimated where Cij is

a vector of child characteristics and Hij is a vector household characteristics.

y ij∨π ij Binomial (1 , π ij)

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logit ( π ij )=β1Time j+ χC ij+δH ij

Following the paired survey regression analyses, the coefficients and standard errors for

the estimate of survey time were included in a meta-analysis to determine by-country variation

and overall trends across time. Regression models were conducted in Stata version 15.1. Meta-

analyses were conducted using the metafor package (Viechtbauer, 2010) in R version 3.5.2 (R

Core Development Team, 2010).

Results

Data available

In July 2017, there were 427 survey datasets available on the DHS Program website.

Surveys that did not include nutrition information (344) were excluded from the study, leaving

89 surveys available for analysis. Of those 89 surveys, 74 surveys included fish-specific

consumption and all 89 surveys included measures of animal-based protein consumption. After

excluding surveys that were not conducted during the same month and in the same region, there

were 43 protein consumption datasets and 39 fish consumption datasets. A total of 25 countries

were represented in the final datasets. Figure 2 shows the geographical distribution of countries

included in the analysis.

Meta-analysis results

Significant variation in changes in fish consumption was observed across countries

included in the analysis (I-squared = 98.51%). Some countries, such as Zimbabwe, saw

significant increases in fish consumption over time, whereas other countries, such as Sierra

Leone and Zambia, saw significant decreases in fish consumption over time. Figure 3 shows the

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forest plot of the different country-pair comparisons, with an OR of 0.71 (95% CI = 0.52 – 0.97)

being the average change in fish consumption between time one and time two in the countries

included in the analysis.

Significant variation in changes in protein consumption was observed across countries

included in the analysis (I-squared = 96.82%). Some countries, such as Ethiopia, saw significant

increases in protein consumption over time, whereas other countries, such as Lesotho and Ghana,

saw significant decreases in fish consumption over time. Figure 4 shows the forest plot of the

different country-pair comparisons, with an OR of 0.78 (95% CI = 0.66 – 0.92) being the average

change in protein consumption between time one and time two in the countries included in the

analysis.

Discussion

Key results

These analyses suggest that fish consumption has declined significantly in many lower

income countries across the globe in recent years. A high level of variation in the meta-analysis

suggests that the decline in fish consumption is not shared by a number of countries, including

Zimbabwe. It does not appear that the decline in fish consumption is being replaced with

increases in other animal-based foods as consumption of any type of protein has declined. These

results suggest that diets in lower income countries may be decreasing in quality.

Interpretation

Food consumption patterns at the population level are complex, and numerous factors

could be influencing the observed decline in both fish and protein consumption. Herein we

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hypothesize about some, but certainly not all of the potential contributors to what appears to be

declining fish consumption.

The first possible explanation of this decline in fish consumption is that the fisheries

themselves are depleted and wild-caught fish are not as readily available. In 2012, 4,714 fisheries

were examined and 68% were below the critical biomass threshold (Worm, 2016). It could be

possible that, because of the depletion of fish stores, more people could not access or afford to

eat fish. Further, fisheries could be exporting the fish out of the countries being studied. Despite

observed probabilities of individuals eating fish mentioned in this paper, the global per capita

fish consumption in 2016 has increased to above 20 kilograms a year (Food and Agricultural

Organization, 2016). While more fish is being consumed globally, the high-income countries

may be responsible for the increase (Food and Agricultural Organization, 2016).

A second cause of the decline in fish consumption with no replacement in protein could

be a change in diets globally. Historically diets have changed and continue to change. As the

gross national product increases, the proportion of animal proteins in a diet usually increases as

well (Drewnowski & Popkin, 1997). While traditionally more plant-based diets adjust in

response to globalized food trends, the production and trade of food such as pork, eggs, dairy

products and poultry has had major increases, particularly in low income countries (Popkin et al.,

2013). It is possible that while trade of these foods is increasing, the foods were being traded to

countries outside of the ones included in this analysis which may explain the decreased fish and

animal-based food consumption in the countries studied herein.

To build off of this, a third explanation of this trend could be the expansion of the global

fishing industry. As trade has increased, many fisheries have begun fishing for non-local

consumption. For example, in 2004 Morocco, a developing country, was the fourth largest

14

producer of internationally traded small pelagic fish (Tacon & Metian, 2009). Further, about

70% of Namibian fish from fisheries is exported to other countries (Tacon & Metian, 2009).

Overall, middle- and low-income countries accounted for about 53% of the global inland catch in

2008 (Fluet-Chouinard, Funge-Smith & McIntyre, 2017). Regions of the world such as Sub-

Saharan Africa and Latin America were responsible for about 5.65 million and 16.76 million

metric tons of fish provided by capture fisheries in the year 2008 (Ritchie & Roser, 2017). In that

same year, North America produced 5.33 million metric tons of fish while Europe and Central

Asia produced 14.19 million metric tons through capture fisheries (Ritchie & Roser, 2017).

However, in 2008, South America (9.05 kg per person per year), Southern Africa (6.69 kg per

person per year), and Western Africa (15.44 kg per person per year) as well as all other regions

of Africa consumed considerably less fish than Europe (22.3 kg per person per year) and

Northern America (21.66 kg per person per year) (Ritchie & Roser, 2017). While fisheries

provide essential contributions to rural economies and food security, there have been some

alarming reports that suggest that various fisheries may soon collapse due to overfishing (Fluet-

Chouinard, Funge-Smith & McIntyre, 2017). Many fisheries in low-income countries have

reported a reduced abundance and size of fish in addition to changes in the composition of the

fish caught as well (Fluet-Chouinard, Funge-Smith & McIntyre, 2017). From these numbers, it is

possible that the decline in fish consumption and production is caused by increased exports of

fish to high income countries while the traditional fisheries ran by lower income countries are

overfished and exploited.

Limitations

In these analyses we did not account for variation across time, which could play a role in

the availability of different foods. This analysis also relies on the 24-hour recall of mothers.

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Although there are measured inaccuracies of food consumption (Beaton, Wright, Devenish, Do,

& Scott, 2018) (Karvetti & Knuts, 2018), we expect these inaccuracies to be temporally stable

and to therefore not threaten the validity of the analyses presented herein. While 24 hours

represents only a brief time window to measure fish consumption in a single person, utilizing

many survey participants allows for an examination of consumption trends at the population

level.

Policy Recommendations

One group of policies that should be developed in response to global fish consumption

declines are those that regulate, plan and fund the world food economy. Fisheries are often

poorly managed and are overfished due to a lack of scientists involved in regulation and

managerial decisions as well as the lack of guidance on import and export decisions from

international organizations and national organizations/ governments. Measures of success for

fisheries should begin to switch from the size of the catch and employment to sustainability and

growth. Fishery Performance Indicators (FPIs) have been established as a solution to this

problem (Anderson et. al, 2015). FPIs include 68 output metrics and 54 input metrics measured

on a 5-point scale that work to evaluate fishery performance in a holistic way. Some metrics

included in this measure are the degree of over-fishing, illegal landings, data availability and

disease amongst more traditional factors such as the harvest performance, excess capacity and

landing level (Anderson et. al, 2015).

Another policy that would work to ensure sustainable fishing practices is to implement

community management of small-scale fisheries by involving fishers in the management process.

This type of management structure has been tested in a couple experimental settings and has

proven to be an effective management strategy. One study, conducted in the Mamirauá

16

Sustainable Development Reserve in the Amazon in Brazil, recruited farmers to manage

pirarucu. Traditional management strategies such as mark-recapture were not successful due to

costs, labor and the geographic spread of the area resulting in a lack of data regarding the

pirarucu population in the area. Additionally, conservation efforts in the area were failing. The

government implemented a fishing season and minimum length of catch, however, due to a lack

of financial and human resources, these regulations were not adequately enforced (Castello,

Viana, Watkins, Pinedo-Vasquez, & Luzadis, 2009). In the experimental study, researchers

recruited fishers who volunteered to learn a new counting method developed by fishers

themselves and count the pirarucu. The counts were then used to establish the harvest quota for

the following year. Further, the Association of Producers was established from an informal group

created by fishers with the motivation of eliminating commercial intermediaries which decreased

profit margins for the fishers. The original thought was that by increasing profit margins, the

fishers would not need to fish as heavily which would reduce the pressure on the fish population.

Once the Association of Producers was established, they required fishers to formally agree to

follow the regulations laid out by the government and any regulations created by the association

itself (Castello, Viana, Watkins, Pinedo-Vasquez, & Luzadis, 2009). As a result of this

experimental management structure eight years later, the pirarucu population increased by 9-fold

while the harvest quotas increased by 10-fold. The surrounding areas with differing management

structures did not experience the same growth (Castello, Viana, Watkins, Pinedo-Vasquez, &

Luzadis, 2009). Further, the number of fishers involved doubled while their per-capita incomes

increased 8-fold (Castello, Viana, Watkins, Pinedo-Vasquez, & Luzadis, 2009). The success of

this experimental management scheme should encourage other low-income subsistence fishing

communities to implement a similar structure.

17

Further, national policies should focus on establishing regulations surrounding the

amount of fish that can be exported out of the country in order to preserve food and protein

sources locally while still maintaining exports to financially support the industry and local

fisheries. Additionally, and more importantly, countries involved in the international trade of fish

and fish products should formulate policies that are consistent with the sustainable development

of fisheries as evidenced by scientific knowledge (Food and Agricultural Organization, 2009).

Conclusion

The decline in fish consumption in lower income countries will have negative health

consequences for the children who do not get replacement proteins elsewhere. Children with

protein deficiencies in their diet may become malnourished and typically experience wasting or

stunting. If the protein malnourishment continues, children will be at a greater risk of severe and

chronic infections (Müller & Krawinkel, 2005). Further, there are important micronutrients in

fish that children cannot get from many other sources. The omega-3 fatty acids can lower blood

pressure and improve other cardiovascular risk factors, meaning that children who are not eating

fish would be expected to have more heart problems. Further, docosahexaenoic acid or DHA is

present in fish and helps the development of infants’ brains (Torpy, 2006). A lack of fish or

DHA in the diet would therefore lead to poor brain development.

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Appendix

Figure 1

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Figure 2

Figure 3

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Figure 4

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