HDDS

Aggregation of food groups from the questionnaire to create HDDS


# HDDS_01:HDDS_18 in columns 116:133
dfS <- dfS %>% mutate_at(.vars = 116:133,~ifelse(.=="Yes",1,0)) %>% 
                    rowwise() %>% 
                    mutate(H01.Cereals=HDDS_01,
                           H02.White.tubers.roots=HDDS_02,
                           H03.Vegetables=ifelse((HDDS_03+HDDS_04+HDDS_05)>0,1,0),
                           H04.Fruits=ifelse((HDDS_06+HDDS_07)>0,1,0),
                           H05.Meat=ifelse((HDDS_08+HDDS_09)>0,1,0),
                           H06.Eggs=HDDS_10,
                           H07.Fish=HDDS_11,
                           H08.Legumes.nuts.seeds=ifelse((HDDS_12+HDDS_13)>0,1,0),
                           H09.Milk=HDDS_14,
                           H10.Oils.fats=HDDS_15,
                           H11.Sweets=HDDS_16,
                           H12.Spices.condiments.beverages=ifelse((HDDS_17+HDDS_18)>0,1,0)) %>% 
                    mutate(HDDS12.Tot = H01.Cereals + H02.White.tubers.roots + H03.Vegetables + H04.Fruits +
                             H05.Meat + H06.Eggs + H07.Fish + H08.Legumes.nuts.seeds + H09.Milk + 
                             H10.Oils.fats + H11.Sweets + H12.Spices.condiments.beverages)


HDDS by country (% of Yes)



Food security

dependent variable HDDS12

Survey data - project level

dfS.FS <- dfS %>% mutate(city=factor(city),country=factor(country)) %>%
    mutate(city=paste(country,city,sep=".")) %>% 
  select(country, HDDS12.Tot, meet_food_needs, lacking_cereal:lacking_oil, city, age, gender, educ3, HH_size,
         income_avg, hh_salary, income_food, hh_head_employ, imp_availability:imp_product,
         local_food_interest, local_envfriend:obst_loc_trust, healty_diet,
         nutri_food_interest,nutri_healthy:nustri_doctor,obst_nutri_safe:obst_nutri_friend) %>% 
  filter(gender %in% levels(dfS$gender)[1:2])
dfS.FS <- droplevels(dfS.FS)
dfS.FS.1 <- dfS.FS %>% select(HDDS12.Tot,city,age:obst_nutri_friend)
dfS.FS.1 <- na.omit(dfS.FS.1)
mod.FS.1 <- lm(HDDS12.Tot~.,data=dfS.FS.1)
my.flextb(lm2df(mod.FS.1),title = "HDDS12 - coefficients") %>% autofit()
my.flextb(Anova2df(mod.FS.1),title = "HDDS12 - anova") %>% autofit()
# summary(mod.FS.1)
# Anova(mod.FS.1)
stepwise AIC
mod.FS.1.s <- step(mod.FS.1,trace = 0)
my.flextb(lm2df(mod.FS.1.s),title = "HDDS12 stepwise - coefficients") %>% autofit()
my.flextb(Anova2df(mod.FS.1.s),title = "HDDS12 stepwise - anova") %>% autofit()
stepwise BIC
frm1 <- as.formula(paste(names(dfS.FS.1)[1],"~",paste(names(dfS.FS.1)[-1],collapse = "+"),sep=" "))
mod1 <- lm(frm1,data = dfS.FS.1)
stp2 <- stepCriterion(mod1, direction="forward", criterion="bic",trace = F)
stpBIC <- lm(as.formula(paste("HDDS12.Tot",stp2$final)),data = dfS.FS.1)
my.flextb(lm2df(stpBIC),title = "HDDS12 stepwise BIC - coefficients") %>% autofit()
my.flextb(Anova2df(stpBIC),title = "HDDS12 stepwise BIC - anova") %>% autofit()
# plot_multi_way_importance(importance_frame20, x_measure = "mse_increase", 
#                           y_measure = "node_purity_increase", size_measure = "mean_min_depth", 
#                           no_of_labels = 10,main=NULL)+
#   xlab("mse_increase")+ylab("node_purity_increase")

Survey data - by country

I modelli per paese sono il risultato di una procedura stepwise.

Survey & Experiment data - project level

dfSE <- inner_join(dfS,dfE,by="unique_id")
dfSE.FS <- dfSE %>% 
  mutate(city=factor(city),country=factor(country)) %>%
  mutate(city=paste(country,city,sep=".")) %>% 
  select(country,city, HDDS12.Tot, meet_food_needs, lacking_cereal:lacking_oil, city, age, gender, educ3, HH_size,
         income_avg, hh_salary, income_food, hh_head_employ, imp_availability:imp_product,
         local_food_interest, local_envfriend:obst_loc_trust, healty_diet,
         nutri_food_interest,nutri_healthy:nustri_doctor,obst_nutri_safe:obst_nutri_friend,
         risk,time,PGG1,PGG2,Trust.1:Trust.4,type.Risk,type.Time,type.PGG,type.Trust) %>% 
  mutate(type.Risk=ifelse(type.Risk != "consistent","No","consistent")) %>% 
  mutate(type.Time=ifelse(type.Time != "consistent","No","consistent")) %>% 
  mutate(type.PGG=ifelse(type.PGG != "consistent","No","consistent")) %>% 
  mutate(type.Trust=ifelse(type.Trust != "consistent","No","consistent")) %>% 
  filter(gender %in% levels(dfS$gender)[1:2])
dfSE.FS <- droplevels(dfSE.FS)
Valid responses to experiments
Survey + Risk and Time games - project level
dfSE.FS.1 <- dfSE.FS %>% filter(type.Risk=="consistent" & type.Time=="consistent") %>% select(HDDS12.Tot,city,age:obst_nutri_friend,risk,time)

dfSE.FS.1 <- na.omit(dfSE.FS.1)

modSE.FS.1 <- lm(HDDS12.Tot~.,data=dfSE.FS.1)

tbSE.FS.1 <- lm2df(modSE.FS.1)

tmp <- Anova(modSE.FS.1) %>% as.data.frame() %>% rownames_to_column("variable") %>% 
  select(1,2,3,4,p.value=5) %>% signif.stars() %>% mutate_at(.vars = c(2,4,5),~round(.,digits=5))

my.flextb(tbSE.FS.1,title = "model survey & risk & time - coefficients") %>% autofit()
my.flextb(tmp,title = "model survey & risk & time - Anova") %>% autofit()

dependent variable: Lacking

Lacking variables summarised in a single variable with confirmatory factor analysis

project level
dfS.FSr <- na.omit(dfS.FS)
library(lavaan)
model1 <- 'lack =~ lacking_cereal+lacking_veg+lacking_fruit+lacking_leg+lacking_fish+lacking_oil'
cfa.lack <- cfa(model1,data = dfS.FSr)
dfS.FSr$lacking <- predict(cfa.lack)
hist(dfS.FSr$lacking)

# correlation
round(cor(dfS.FSr[,c("lacking","lacking_cereal","lacking_veg","lacking_fruit","lacking_leg","lacking_fish", "lacking_oil")]),4)
##                lacking lacking_cereal lacking_veg lacking_fruit lacking_leg lacking_fish lacking_oil
## lacking         1.0000         0.6141      0.6832        0.7844      0.5999       0.5645      0.5277
## lacking_cereal  0.6141         1.0000      0.3117        0.2745      0.3603       0.1695      0.3451
## lacking_veg     0.6832         0.3117      1.0000        0.4429      0.2528       0.2443      0.2148
## lacking_fruit   0.7844         0.2745      0.4429        1.0000      0.2949       0.3995      0.2661
## lacking_leg     0.5999         0.3603      0.2528        0.2949      1.0000       0.2558      0.2534
## lacking_fish    0.5645         0.1695      0.2443        0.3995      0.2558       1.0000      0.1823
## lacking_oil     0.5277         0.3451      0.2148        0.2661      0.2534       0.1823      1.0000
stepwise AIC
stepwise BIC
frm1 <- as.formula(paste(names(dfS.FSr.2)[1],"~",paste(names(dfS.FSr.2)[-1],collapse = "+"),sep=" "))
mod1 <- lm(frm1,data = dfS.FSr.2)
stp2 <- stepCriterion(mod1, direction="forward", criterion="bic",trace = F)
stpBIC <- lm(as.formula(paste("lacking",stp2$final)),data = dfS.FSr.2)
my.flextb(lm2df(stpBIC),title = "lacking stepwise BIC - coefficients") %>% autofit()
my.flextb(Anova2df(stpBIC),title = "lacking stepwise BIC - anova") %>% autofit()
by country

lacking - survey + experiment

variables from the experiments are considered one at a time

risk
time
PGG
Trust

dependent variable meet_food_needs

Survey data - project level

stepwise
stepwise BIC
frm1 <- as.formula(paste(names(dfS.FS.3)[1],"~",paste(names(dfS.FS.3)[-1],collapse = "+"),sep=" "))
mod1 <- lm(frm1,data = dfS.FS.3)
stp2 <- stepCriterion(mod1, direction="forward", criterion="bic",trace = F)
stpBIC <- lm(as.formula(paste("meet_food_needs",stp2$final)),data = dfS.FS.3)
my.flextb(lm2df(stpBIC),title = "meet_food_needs stepwise BIC - coefficients") %>% autofit()
my.flextb(Anova2df(stpBIC),title = "meet_food_needs stepwise BIC - anova") %>% autofit()

by country

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Diet Quality Questionnaire (DQQ)

dfSt <- dfS %>% select(unique_id,68:99) %>% 
  mutate_at(.vars = 2:33,~ifelse(.=="Yes",1,0)) %>%
  mutate_at(.vars = 2:33,~ifelse(is.na(.),0,.)) %>% 
  mutate(DQ01 = DQQ_01+DQQ_02+DQQ_03,
         DQ02 = DQQ_04,
         DQ03 = DQQ_21,
         DQ04 = DQQ_25+DQQ_14+DQQ_15,
         DQ05 = DQQ_17+DQQ_18+DQQ_16+DQQ_19+DQQ_20,
         DQ06 = DQQ_13,
         DQ07 = DQQ_061+DQQ_062,
         DQ08 = DQQ_05+DQQ_08,
         DQ09 = DQQ_071+DQQ_072,
         DQ10 = DQQ_09+DQQ_101+DQQ_102,
         DQ11 = DQQ_22+DQQ_24+DQQ_23+DQQ_29,
         DQ12 = DQQ_11+DQQ_12,
         DQ13 = DQQ_27+DQQ_28+DQQ_26)
dfSt$FGDS=rowSums(dfSt[,34:43]>0)
dfSt$MDD.W <- factor(ifelse(dfSt$FGDS>=5,1,0),levels = 0:1,labels = c("No","Yes"))
dfSt <- dfSt %>% mutate_at(.vars = 34:46,~ifelse(.==0,"No","Yes"))

dfSD <- inner_join(dfS,dfSt %>% select(1,34:48),by="unique_id")

dfStA5 <- dfS %>% select(unique_id,68:99) %>% 
  mutate_at(.vars = 2:33,~ifelse(.=="Yes",1,0)) %>%
  mutate_at(.vars = 2:33,~ifelse(is.na(.),0,.)) %>% 
  mutate(DQA501 = DQQ_01+DQQ_02+DQQ_03,
         DQA502 = DQQ_05+DQQ_061+DQQ_062+DQQ_071+DQQ_072,
         DQA503 = DQQ_08+DQQ_09+DQQ_101+DQQ_102,
         DQA504 = DQQ_04+DQQ_21,
         DQA505 = DQQ_13+DQQ_14+DQQ_15+DQQ_17+DQQ_18+DQQ_19+DQQ_20+DQQ_25)
dfStA5$All.5=rowSums(dfStA5[,34:38]>0)


dfStNCD_P <- dfS %>% select(unique_id,68:99) %>% 
  mutate_at(.vars = 2:33,~ifelse(.=="Yes",1,0)) %>%
  mutate_at(.vars = 2:33,~ifelse(is.na(.),0,.)) %>% 
  mutate(DQNCD_P01 = DQQ_02,
         DQNCD_P02 = DQQ_04,
         DQNCD_P03 = DQQ_21,
         DQNCD_P04 = DQQ_05,
         DQNCD_P05 = DQQ_061+DQQ_062,
         DQNCD_P06 = DQQ_071+DQQ_072,
         DQNCD_P07 = DQQ_08,
         DQNCD_P08 = DQQ_09,
         DQNCD_P09 = DQQ_101+DQQ_102)
dfStNCD_P$NCD_P=rowSums(dfStNCD_P[,34:42]>0)

dfStNCD_R <- dfS %>% select(unique_id,68:99) %>% 
  mutate_at(.vars = 2:33,~ifelse(.=="Yes",1,0)) %>%
  mutate_at(.vars = 2:33,~ifelse(is.na(.),0,.)) %>% 
  mutate(DQNCD_R01 = DQQ_28,
         DQNCD_R02 = DQQ_11,
         DQNCD_R03 = DQQ_12,
         DQNCD_R04 = DQQ_16,
         DQNCD_R05 = DQQ_17+DQQ_18,
         DQNCD_R06 = DQQ_24,
         DQNCD_R07 = DQQ_23+DQQ_29,
         DQNCD_R08 = DQQ_22)

dfStNCD_R$NCD_R=rowSums(dfStNCD_R[,34:41]>0)


dfSD2 <- left_join(left_join(
  left_join(dfSD,dfStA5 %>% select(unique_id,All.5),by="unique_id"),
  dfStNCD_P %>% select(unique_id,NCD_P),by="unique_id"),
  dfStNCD_R %>% select(unique_id,NCD_R),by="unique_id") %>% 
  mutate(GDR= NCD_P - NCD_R + 9 ) %>% mutate(All.5=factor(ifelse(All.5==5,1,0),levels = 0:1,labels = c("No","Yes")))

Minimum Dietary Diversity

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1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}</style><table class='cl-1f6d542e'>

Minimum Dietary Diversity for Women of Reproductive Age (MDD-W) by country

.

Values

No

Yes

Sum

count

KE

61

644

705

MO

27

161

188

TN

46

373

419

TZ

42

412

454

UG

181

699

880

Sum

357

2289

2646

row.%

KE

8.7

91.3

100.0

MO

14.4

85.6

100.0

TN

11.0

89.0

100.0

TZ

9.3

90.7

100.0

UG

20.6

79.4

100.0

Sum

13.5

86.5

100.0

col.%

KE

17.1

28.1

26.6

MO

7.6

7.0

7.1

TN

12.9

16.3

15.8

TZ

11.8

18.0

17.2

UG

50.7

30.5

33.3

Sum

100.0

100.0

100.0

stepwise