Prevalence and factors associated with anemia among pregnant women in East Lombok district during the COVID-19 pandemic

doi:10.21203/rs.3.rs-3121262/v1

Download PDF

Research Article

Prevalence and factors associated with anemia among pregnant women in East Lombok district during the COVID-19 pandemic

https://doi.org/10.21203/rs.3.rs-3121262/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Maternal anemia is a major public health problem that has detrimental effects on pregnancy and birth outcomes. The threat of food insecurity and nutritional deficiencies is growing as a result of the COVID-19 pandemic. The aim of this study was to determine the prevalence and risk factors of anemia among pregnant women in East Lombok during the pandemic.

Methods

This community-based cross-sectional study is part of an observational cohort study entitled “UKRI-GCRF Action Against Stunting Hub (AASH)” in rural areas of East Lombok, Indonesia, from August 2021 to February 2022. Data were collected from women (18-40 years) in their third trimester pregnancy (N=446). Data collection included hemoglobin, mid-upper arm circumference, intestinal helminth infections, and structured questionnaires for sociodemographic, dietary diversity, pregnancy-related, reduced coping strategy index and COVID-19-related variables. The association between anemia and its determinants was analyzed using binary logistic regression model.

Results

Anemia was found in 40.8% of pregnant women. During the pandemic, 74.7% and 28.9% of women reported a decrease in household income and food expenditure whereas 47% of them reported having medium-to-high coping strategies against the pandemic. Higher RCSI scores (≥5) was associated with food insecurity, decrease in household income, older age (>35 years), low education level, multiparity, receiving government assistance targeted for poor families, but with contraceptive use. In logistic regression, anemia was significantly associated with chronic energy deficiency (CED, AOR=1.92; 95%CI: 1.06-3.48), inadequate dietary diversity (AOR=1.58; 95%CI: 1.02-2.45), and non-use of contraception (AOR=1.58; 95%CI: 1.02-2.45).

Conclusions

CED, inadequate dietary diversity, and non-use of contraception were major contributors to anemia in pregnancy in the area. The findings highlight the importance of improving anemia control program among pregnant women in rural areas like East Lombok and improving policy to strengthen their resilience to natural phenomenon such as COVID-19.

Anemia

Pregnant women

Chronic energy deficiency

Contraceptive use

Dietary diversity

COVID-19 pandemic

Maternal anemia remains a major public health problem in most developing countries [1], including Indonesia [2]. Anemia can have adverse impacts on both maternal and child health, resulting in premature delivery, intrauterine growth restriction (IUGR), low birth weight (LBW), and perinatal mortality [3, 4]. The Indonesia Basic Health Survey reported that 37.1% of pregnant women were anemic in 2013 [5], which increased to 48.9% in 2018 [6]. In some provinces such as West Nusa Tenggara Province the prevalence was much higher i.e. 56.5% in 2013 [7]. These figures on anemia prevalence exceed 40% i.e. which WHO considers as a severe public health significance [2].

Reducing anemia has been designated as WHO's second global nutrition target, aiming for a 50% reduction in anemia among women of reproductive age by 2025 [8]. Nonetheless, progress in eliminating anemia from 2012 to 2019 was considerably slow, at less than half the rate of other nutrition, child, and maternal health indicators. In addition, the disruption to the health system and decreased access to food caused by the COVID-19 pandemic will likely impede the roadmap to achieving the Sustainable Development Goals (SDGs) in maternal and child health among low and middle-income countries [9].

The COVID-19 pandemic and lockdowns have impacted household income and food security [10], affecting dietary practices and potentially contributing to long-term diet-related health consequences [11]. Many people were unable to purchase adequate and nutritious food as a result of unexpected increases in unemployment, income shocks, lower availability of food in local markets, and higher costs for a wide variety of food groups caused by the pandemic [12]. To the best of our knowledge, there are no data on community-based studies that assess the risk factors of anemia among pregnant women during the COVID-19 pandemic. Therefore, this study aims to determine the prevalence and risk factors associated with anemia among pregnant women in East Lombok, Indonesia during the COVID-19 pandemic.

Study design and population

This cross-sectional study is part of an observational cohort study in East Lombok, entitled “UKRI − GCRF Action Against Stunting Hub (AASH).” East Lombok is a district located in the Lombok Island, West Nusa Tenggara Province (also known as Nusa Tenggara Barat or NTB), Indonesia. It is the most populated district in NTB, with an estimated population of 1,200,612 in 2019. The administrative area of the district consists of 21 sub-districts and 254 villages. There are 32 public health centers, three hospitals, a few private medical clinics, and over 1,000 active integrated healthcare service posts (referred as Posyandu in Indonesian) [7].

The data collection in this study was carried out from August 2021 to February 2022. Our study participants were pregnant women in their third trimester, aged between 18 and 40 years old, domiciled in East Lombok and of Sasaknese ethnicity. Pregnant women who were expecting multiple births and had consanguinity were excluded.

Sampling procedures

The sample size was determined using the single population proportion formula [13], with a 95% Confidence Intervals (CI), 5% margin of error, and anemia prevalence (49.7%) based on a previous community-based study in Indonesia [14]. A minimum sample size of 422 pregnant women was required after assuming a 10% non-response rate and a design effect of 1.0 (since identical or homogeneous samples are expected in the large observational cohort study.

Aikmel, Lenek, Sikur, and Sakra sub-districts were purposively chosen from a total of twenty-one sub-districts in East Lombok and 39 villages in these sub-districts were included in the study. In the main study, which will be reported separately, the pregnant women were assigned into intervention arm and to the control arm [15]. The current cross-sectional study only included participants from the control arm.

Data Collection

Structured questionnaire

Enumerators were recruited from local nutrition academies and were trained for data collection. Criteria for enumerators included the ability to speak local Sasak language. Enumerators were trained for electronic data collection using tablets (CommCare).

In addition to the background characteristics, data collection was done for variables relevant to the COVID-19 pandemic, including changes in household income and food expenditure, and coping strategy. The questionnaire was adapted to Indonesian and piloted prior to data collection. During the interviews, the enumerators were instructed to directly translate the questionnaire's content into the local Sasak language in order to make the questionnaire more understandable for mothers who could not speak Indonesian.

Food security and coping strategies assessment

The US Household Food Security Survey Module (US-HFSSM) which consists of 18 question items, was used to determine the household food security status. Respondents are asked about their household food access, availability, and utilization, in the preceding 12 months due to a lack of financial resources to purchase food. The affirmative response (i.e., “yes”; “often”; “sometimes”; “almost every month”; and/or “some months but not every month”) to each question item earning a one-point score. The category of marginal to high food security (score 0–2) is referred to as food secure, while the category of low to very low food security (score ≥ 3) is referred to as food insecure [16].

The Reduced Coping Strategy Index (RCSI) is a validated assessment of behavioral strategies that an individual might employ to cope with not having enough food in the preceding seven days. RCSI includes five behaviors, and respondents were asked how many days (between 0 and 7) in the previous week they engaged in each strategy, which is then multiplied by its severity weight [17]. The overall RCSI score is divided into two main categories: Low coping (0–4) and moderate to high coping strategies (≥ 5) [18].

Wealth index calculation

The principal component analysis (PCA) based on the USAID dataset of the 2017 Indonesia Demographic and Health Surveys (DHS) Program was used to calculate the wealth index in the current study [19]. The household wealth index is constructed using several variables, which included housing amenities, household assets, and mode of transportation. If the household has or owns the variables, it was classified as "1," and if not, it was classified as "0." It was then multiplied by the component score coefficient obtained from the dataset. The results of each variable were added to determine the overall scores. Finally, the household wealth index scores were divided into three equal classes (terciles) for the purposes of classification: poor, middle, and rich [20].

Dietary diversity assessment

Dietary diversity was obtained from 24-h dietary recall with a four-pass multistage interviewing technique [21]. This method was employed since it is less prone to recall bias, and is less burdensome for respondents. Each study participant was asked to list and describe the ingredients of all food and beverages consumed the day and night prior to the survey. The results were then coded and categorized into 10 food groups, including (1) starchy staples (e.g., grains, white roots, tubers, and plantations); (2) pulses (beans and peas); (3) nuts and seeds; (4) milk and dairy products; (5) meat, fish, and poultry; (6) eggs; (7) dark green leafy vegetables; (8) vitamin A-rich fruits and vegetables; (9) other vegetables; (10) other fruits. Participants received a score of 1 if they consumed at least 15 grams of each group; otherwise, they received a score of 0. The dietary diversity score of 10 food groups was calculated by adding all of the food group values and then classified as inadequate (< 5 food groups per day) or adequate (≥ 5 food groups per day) [22].

Complete Blood Count and Kato-Katz examination

Venous blood sample was withdrawn by experienced phlebotomists. Hemoglobin (Hb) was determined using an automated hematology analyzer for complete blood count (CBC). Fresh stool samples were collected and assessed for the presence of intestinal helminths using the microscopic Kato Katz method [23]. The results were classified as helminth infection if they contained ova for roundworm (Ascaris lumbricoides), whipworm (Trichuris trichiura), tapeworm (Taenia saginata, Hymenolepis nana), or blood flukes (Schistosoma mansoni). Blood and stool samples were kept in the cold chain (below 5°C) and delivered within 4 hours after collection to the main laboratory at the Faculty of Medicine, Universitas Mataram and UNRAM Hospital.

Anthropometry measurement

Anthropometry standardization procedures were done to ensure enumerators met the required precision and accuracy. Mid-Upper Arm-Circumference (MUAC) was measured by using ergonomic circumference measuring tape (SECA 201) to determine the nutritional status of pregnant women. MUAC was measured on the upper arm, halfway between the olecranon acromion and olecranon processes by using flexible and non-stretchable tape to the nearest 0.1 cm.

Statistical Analysis

The overall statistical data analysis in this study is performed using SPSS version 20.0 software. Pregnant women's characteristics were summarized using frequencies and percentages and presented using descriptive analysis. To ascertain if the continuous variables were normally distributed, the Kolmogorov-Smirnov test was performed. The frequency distribution of pregnant women stratified by anemia status was assessed using Chi-square test (for comparison when appropriate). Logistic regression analysis was used to determine the odds ratio (OR) of each significant risk factor of anemia. If any variables have a p-value of 0.25 in bivariate analysis, they will be included in the binary logistic regression model (multivariate). All statistics were performed using 2-sided tests, statistical significance was considered at a p-value of less than 0.05, and OR with 95% confidence intervals (CI) were presented.

Anemia was found in 40.8% of pregnant women in the third trimester. One third of study participants were young mothers aged < 25 years and 1 in 2 mothers had a low education level of primary to middle school. Most mothers were housewives with 3 to 5 members living in the same household and majority of them were passive smokers. Approximately 10.5% of pregnant women had chronic energy deficiency (CED, indicated by MUAC < 23.5 cm) and 6.5% were infected with intestinal helminth, most of which was Trichuris trichiura (see Table 1).

Table 1

Socio-economic characteristics and nutritional status of pregnant women in East Lombok district (N = 446)
Variables		n	(%)
Age of pregnant women (years) ^a		28.0 (10.0)
	< 25	134	30.0
	25–29	121	27.1
	30–34	123	27.6
	≥ 35	68	15.2
Level of education	Primary to middle school	222	49.8
Level of education	Secondary school	178	39.9
	College and above	46	10.3
Employment status	Unemployed	328	73.5
Employment status	Employed	118	26.5
Occupation	Housewife/not working/ student	328	73.5
	Merchant or traders	42	9.4
	Agricultural-related work	31	7.0
	Professional work	23	5.2
	Private work	22	4.9
Household wealth index (tercile) ^c	Poor	150	33.6
	Middle	150	33.6
	Rich	146	32.7
Family size (person)	1–2	120	26.9
Family size (person)	3–5	304	68.2
	6 and above	22	4.9
Smoking status	No	84	18.8
	Yes	362	81.2
Hemoglobin concentration (g/dl) ^a		11.2 ± 1.3
Anemia status	Anemic	182	40.8
	Non-Anemic	264	59.2
Mid Upper Arm Circumference or MUAC (cm) ^a		27.4 ± 13.0
	≥ 23.5 (Normal)	399	89.5
	< 23.5 (CED)	47	10.5
Intestinal helminth infection	Absent (No)	417	93.5
Intestinal helminth infection	Present (Yes)	29	6.5
Types of helminth infection	Trichuris trichiura	27	6.1
Types of helminth infection	Taenia saginata	2	0.4
	Hymenolepis nana	2	0.4
	Ascaris lumbricoides	1	0.2
	Schistosoma mansoni	1	0.2
Food security status	Food secure (0–2)	313	70.2
	Food insecure (≥ 3)	133	29.8
Food taboos and/or aversions	No	146	32.7
	Yes	300	67.3
Appetite status	Decreased	67	15.0
	No change	105	23.5
	Increased	274	61.4
Dietary Diversity Score ^b		6.0 (3.0)
MDD-W	Adequate (≥ 5)	320	71.7
	Inadequate (< 5)	126	28.3
Use of contraceptives	Never / Not used (N)	173	38.8
	Ever Used (Y)	273	61.2
Planned and desired pregnancy	No	69	15.5
	Yes	377	84.5
Gravidity	Nulligravida (0)	113	25.3
	Primigravida (1)	152	34.1
	Multigravida (≥ 2)	181	40.6
Number of ANC Visits	< 6	85	19.1
	≥ 6	361	80.9
ANC Visits (according to WHO guideline)	< 8 (Inadequate)	369	82.7
	≥ 8 (Adequate)	77	17.3
IFAS consumption (adherence)	Not consumed	124	27.8
	< 30 tablets/month	279	62.6
	≥ 30 tablets/month	43	9.6
Deworming medication	No	446	100
Animal rearing ownership	Poultry	173	38.8
	Livestock	36	8.1
	Fish pond	14	3.1
Government assistance	Yes, both cash and non-cash	75	16.8
	Yes, only cash	102	22.9
	Yes, only non-cash	54	12.1
	No	215	48.2
Changes in household income	No change or increased	113	25.3
	Decreased	333	74.7
Changes in household food expenditure	Decreased	129	28.9
	No change	135	30.3
	Increased	182	40.8
RCSI ^b		4.0 (8.0)
	Low coping (0–4)	237	53.1
	Medium to high coping (≥ 5)	209	46.9
^a Mean ± SD ^b Median (IQR) ^c Poor if the wealth index score ranges from − 174.470 to − 0.281; middle if the score range from − 0.281 to − 0.039; rich if the score range from − 0.040 to 0.430 Abbreviations. CED: Chronic energy deficiency; MDD-W: Minimum dietary diversity for women; ANC: Antenatal care; IFAS: Iron-folic acid supplementation; RCSI: Reduced coping strategy index

The proportion of food insecurity, food taboos, and inadequate dietary diversity were 29.8%, 67.3%, and 28.3%, respectively. Plant source foods (PSFs) and animal source foods (ASFs) were acquired mostly through cash purchases or as gifts from friends or relatives. More than half of the participants reported an increase in appetite status during the third trimester of pregnancy compared to the first trimester. About 39% of study participants had never or had not used any contraceptive method prior to their current pregnancy, 15.5% had unplanned pregnancies (self-perceived), and 40.6% were multigravida. Approximately 19.1% of women had insufficient ANC visits, with a total of fewer than 6 visits throughout pregnancy. Most of them did not adhere to the IFAS consumption guidelines, with less than 30 tablets consumed per month (62.6%). None of the women reported consuming deworming medication during their current pregnancy (see Table 1).

More than half of pregnant women received government assistance programs during the COVID-19 pandemic, in the form of cash and/or non-cash aid. Three-quarters of participants reported a decrease in household income, while 28.9% reported a decrease in household food expenditure. Nearly half of them reported medium to high coping strategies, indicating food insecurity (Table 1). The three most common strategies used by pregnant women, as shown in Fig. 1, were relying on less preferred and less expensive foods (65.9%), limiting adult intake to allow young children to eat (30.5%), and borrowing food or relying on help from friends or relatives (29.8%).

Our findings demonstrated that older women above 35 years (COR = 2.73; 95% CI: 1.50, 4.98), with low levels of education (COR = 2.64; 95% CI: 1.34, 5.22), multiparous (COR = 2.82; 95% CI: 1.73, 4.63), experienced decrease in household income during pandemic (COR = 2.31; 95% CI: 1.47, 3.63), and food insecurity (COR = 1.52; 95% CI: 1.01, 2.28) were more likely to employ medium to high coping strategies (RCSI ≥ 5). Furthermore, pregnant women who did not obtain any government assistance (COR = 0.53; 95% CI: 0.31, 0.90) were less likely to have higher RCSI scores. However, women who had ever used contraceptives prior to their current pregnancy (COR = 1.86; 95% CI: 1.26, 2.74) were associated with higher RCSI score.

In the bivariate analysis as shown in Table 2, the factors significantly associated with medium to high food coping strategies during the COVID-19 pandemic were the older age of mothers (aged ≥ 35 years, COR = 2.73; 95% CI: 1.50, 4.98), attained primary to middle school (COR = 2.64; 95% CI: 1.34, 5.22), ever-used contraceptives (COR = 1.86; 95% CI: 1.26, 2.74), were multiparous (COR = 2.82; 95% CI: 1.73, 4.63), food insecure (COR = 1.52; 95% CI: 1.01, 2.28), and experienced a decrease in household income during the pandemic (COR = 2.31; 95% CI: 1.47, 3.63). Otherwise, pregnant women who did not receive government assistance are less likely to employ medium-to-high coping strategies (COR = 0.53; 95% CI: 0.31, 0.90) compared with their counterparts (Table 2).

Table 2

Factors associated with medium to high food coping strategies during the COVID-19 pandemic among pregnant women (N = 446)
Variables		Medium to high coping (RCSI score ≥ 5)		COR (95% CI)	p-value
Variables		Yes, n (%)	No, n (%)	COR (95% CI)	p-value
Anemia status
	Non-anemic (≥ 11 mg/dl)	126 (47.7%)	138 (52.3%)	1
	Anemic (< 11 mg/dl)	83 (45.6%)	99 (54.4%)	0.92 (0.63, 1.34)	0.659
MUAC
	≥ 23.5 cm (normal)	178 (46.6%)	204 (53.4%)	1
	< 23.5 cm (CED)	31 (48.4%)	33 (51.6%)	1.08 (0.63, 1.83)	0.785
Intestinal helminth infection
	Absent (No)	195 (46.8%)	222 (53.2%)	1
	Present (Yes)	14 (48.3%)	15 (51.7%)	1.06 (0.50, 2.26)	0.875
Age of pregnant women
	< 25	46 (34.3%)	88 (65.7%)	1
	25–29	60 (49.6%)	61 (50.4%)	1.88 (1.14, 3.12)	0.014*
	30–34	63 (51.2%)	60 (48.8%)	2.01 (1.22, 3.32)	0.006*
	≥ 35	40 (58.8%)	28 (41.2%)	2.73 (1.50, 4.98)	0.001*
Level of education
	Primary to middle school	119 (53.6%)	103 (46.4%)	2.64 (1.34, 5.22)	0.005*
	Secondary school	76 (42.7%)	102 (57.3%)	1.70 (0.85, 3.41)	0.133
	College and above	14 (30.4%)	32 (69.6%)	1
Employment status
	Unemployed	155 (47.3%)	173 (52.7%)	1.06 (0.70, 1.62)	0.780
	Employed	54 (45.8%)	64 (54.2%)	1
Household wealth index
	Poor	75 (50.0%)	75 (50.0%)	1.43 (0.91, 2.27)	0.125
	Middle	74 (49.3%)	76 (50.7%)	1.40 (0.88, 2.21)	0.155
	Rich	60 (41.1%)	86 (58.9%)	1
Family size
	1–2	48 (40.0%)	72 (60.0%)	1
	3–5	149 (49.0%)	155 (51.0%)	1.44 (0.94, 2.21)	0.094
	6 and above	12 (54.5%)	10 (45.5%)	1.80 (0.72, 4.50)	0.208
Smoking status
	No	37 (44.0%)	47 (56.0%)	1
	Yes	172 (47.5%)	190 (52.5%)	1.15 (0.71, 1.85)	0.566
Use of contraceptives
	Never/ not used (N)	65 (37.6%)	108 (62.4%)	1
	Ever used (Y)	144 (52.7%)	129 (47.3%)	1.86 (1.26, 2.74)	0.002*
Planned and Desired Pregnancy
	No	34 (49.3%)	35 (50.7%)	1.12 (0.67, 1.87)	0.662
	Yes	175 (46.4%)	202 (53.6%)	1
Parity	Nulliparous (0)	36 (31.9%)	77 (68.1%)	1
	Primiparous (1)	70 (46.1%)	82 (53.9%)	1.83 (1.10, 3.04)	0.020*
	Multiparous (≥ 2)	103 (56.9%)	78 (43.1%)	2.82 (1.73, 4.63)	< 0.001*
Number of ANC Visits
	< 6	47 (55.3%)	38 (44.7%)	1.52 (0.95, 2.44)	0.085
	≥ 6	162 (44.9%)	199 (55.1%)	1
IFAS consumption (adherence)
	Not consumed	58 (46.8%)	66 (53.2%)	0.70 (0.35, 1.40)	0.308
	< 30 tablets/month	127 (45.5%)	152 (54.5%)	0.66 (0.35, 1.26)	0.210
	≥ 30 tablets/month	24 (55.8%)	19 (44.2%)	1
Food security status
	Food secure	137 (43.8%)	176 (56.2%)	1
	Food insecure	72 (54.1%)	61 (45.9%)	1.52 (1.01, 2.28)	0.045*
Food taboos and aversions
	No	64 (43.8%)	82 (56.2%)	1
	Yes	145 (48.3%)	155 (51.7%)	1.20 (0.81, 1.78)	0.372
Status of the appetite
	Decreased	32 (47.8%)	35 (52.2%)	1.00 (0.59, 1.70)	0.994
	No change	46 (43.8%)	59 (56.2%)	0.85 (0.54, 1.34)	0.851
	Increased	131 (47.8%)	143 (52.2%)	1
MDD-W	Adequate (≥ 5)	152 (47.5%)	168 (52.5%)	1
	Inadequate (< 5)	57 (45.2%)	69 (54.8%)	0.91 (0.60, 1.38)	0.667
Government assistance
	Yes, both cash and non-cash	44 (58.7%)	31 (41.3%)	1
	Yes, only cash	46 (45.1%)	56 (54.9%)	0.58 (0.32, 1.06)	0.075
	Yes, only non-cash	27 (50.0%)	27 (50.0%)	0.71 (0.35, 1.43)	0.330
	No	92 (42.8%)	123 (57.2%)	0.53 (0.31, 0.90)	0.019*
Changes in household income
	No change or increased	36 (31.9%)	77 (68.1%)	1
	Decreased	173 (52.0%)	160 (48.0%)	2.31 (1.47, 3.63)	< 0.001*
Changes in household food expenditure
	Decreased	62 (48.1%)	67 (51.9%)	1
	No change	53 (39.3%)	82 (60.7%)	0.70 (0.43, 1.14)	0.150
	Increased	94 (51.6%)	88 (48.4%)	1.15 (0.74, 1.81)	0.533
1 set as a reference, * Significant at p-value < 0.05. Abbreviations. COR: Crude odds ratio; CI: Confidence interval

The factors associated with anemia in pregnant women were determined using a logistic regression model (Table 3). In bivariate analyses, pregnant women under the age of 25 year were 1.8 times more likely than other age groups to develop anemia (COR = 1.83; 95% CI: 1.00, 3.32). Pregnant women with 3 to 5 family members were less likely to have anemia than women with 1 to 2 family members and more than 6 family members (COR = 0.57; 95% CI: 0.37, 0.87). Additionally, women who were nulligravida were more likely to have anemia (COR = 1.90; 95% CI: 1.18, 3.07) than multigravida or primigravida.

Table 3

Factors associated with anemia among pregnant women in East Lombok district (N = 446)
Variables		Anemia Status		COR (95% CI)	AOR (95% CI) ^a
		Yes	No
		n (%)	n (%)
MUAC
	≥ 23.5 cm (Normal)	145 (38.0)	237 (62.0)	1	1
	< 23.5 cm (CED)	37 (57.8)	27 (42.2)	2.24 (1.31, 3.83)*	1.92 (1.06, 3.48)*
Intestinal helminth infection
	Absent (No)	168 (40.3)	249 (59.7)	1
	Present (Yes)	14 (48.3)	15 (51.7)	1.38 (0.65, 2.94)	−
Age of pregnant women
	< 25	69 (51.5)	65 (48.5)	1.83 (1.00, 3.32)*	1.27 (0.54, 2.95)
	25–29	48 (39.7)	73 (60.3)	1.13 (0.61, 2.09)	1.01 (0.50, 2.02)
	30–34	40 (32.5)	83 (67.5)	0.83 (0.45, 1.54)	0.79 (0.41, 1.52)
	≥ 35	25 (36.8)	43 (63.2)	1	1
Level of education
	Primary to middle school	82 (36.9)	140 (63.1)	0.70 (0.37, 1.32)	−
	Secondary school	79 (44.4)	99 (55.6)	0.95 (0.50, 1.82)	−
	College and above	21 (45.7)	25 (54.3)	1	−
Employment status
	Unemployed	137 (41.8)	191 (58.2)	1.16 (0.76, 1.79)	−
	Employed	45 (38.1)	73 (61.9)	1	−
Household wealth index
	Poor	69 (46.0)	81 (54.0)	1.54 (0.97, 2.46)	1.23 (0.74, 2.04)
	Middle	61 (40.7)	89 (59.3)	1.24 (0.77, 1.98)	1.08 (0.65, 1.79)
	Rich	52 (35.6)	94 (64.4)	1	1
Family size
	1–2	60 (50.0)	60 (50.0)	1	1
	3–5	110 (36.2)	194 (63.8)	0.57 (0.37, 0.87)*	0.96 (0.54, 1.71)
	6 and above	12 (54.5)	10 (45.5)	1.20 (0.48, 2.99)	1.89 (0.70, 5.12)
Smoking status
	No	38 (45.2)	46 (54.8)	1	−
	Yes	144 (39.8)	218 (60.2)	0.80 (0.50, 1.29)	−
Food security status
	Food secure	132 (42.2)	181 (57.8)	1	−
	Food insecure	50 (37.6)	83 (62.4)	0.83 (0.55, 1.25)	−
Food taboos and aversions
	No	58 (39.7)	88 (60.3)	1	−
	Yes	124 (41.3)	176 (58.7)	1.07 (0.71, 1.60)	−
Appetite status
	Decreased	24 (35.8)	43 (64.2)	1	1
	No change	36 (34.3)	69 (65.7)	0.94 (0.49, 1.78)	0.83 (0.42, 1.62)
	Increased	122 (44.5)	152 (55.5)	1.44 (0.83, 2.50)	1.21 (0.68, 2.17)
MDD-W
	Adequate (≥ 5)	119 (37.2)	201 (62.8)	1	1
	Inadequate (< 5)	63 (50.0)	63 (50.0)	1.69 (1.11, 2.56)*	1.58 (1.02, 2.45)*
Use of contraceptives
	Never/ Not used (N)	86 (49.7)	87 (50.3)	1.82 (1.23, 2.69)*	1.58 (1.02, 2.45)*
	Ever used (Y)	96 (35.2)	177 (64.8)	1	1
Planned and desired pregnancy
	No	30 (43.5)	39 (56.5)	1.14 (0.68, 1.91)	−
	Yes	152 (40.3)	225 (59.7)	1	−
Gravidity
	Nulligravida (0)	59 (52.2)	54 (47.8)	1.90 (1.18, 3.07)*	0.92 (0.42, 2.02)
	Primigravida (1)	57 (37.5)	95 (62.5)	1.05 (0.67, 1.63)	0.83 (0.49, 1.41)
	Multigravida (≥ 2)	66 (36.5)	115 (36.5)	1	1
Number of ANC visits
	< 6	35 (41.2)	50 (58.8)	1.02 (0.63, 1.65)	−
	≥ 6	147 (40.7)	214 (59.3)	1	−
IFAS consumption (adherence)
	Not consumed	52 (41.9)	72 (58.1)	1.11 (0.54, 2.24)	−
	< 30 tablets/month	113 (40.5)	166 (59.5)	1.04 (0.54, 2.01)	−
	≥ 30 tablets/month	17 (39.5)	26 (60.5)	1	−
Government assistance
	Yes, both cash and non-cash	26 (34.7)	49 (65.3)	1	1
	Yes, only cash	41 (40.2)	61 (59.8)	1.27 (0.68, 2.35)	1.23 (0.64, 2.37)
	Yes, only non-cash	18 (33.3)	36 (66.7)	0.94 (0.45, 1.97)	0.74 (0.34, 1.64)
	No	97 (45.1)	118 (54.9)	1.55 (0.90, 2.68)	1.10 (0.59, 2.05)
Changes in household income
	No change or increased	51 (45.1)	62 (54.9)	1	−
	Decreased	131 (39.3)	202 (60.7)	0.79 (0.51, 1.21)	−
Changes in household food expenditure
	Decreased	46 (35.7)	83 (64.3)	1	1
	No Change	60 (44.4)	75 (55.6)	1.44 (0.88, 2.37)	1.44 (0.85, 2.45)
	Increased	76 (41.8)	106 (58.2)	1.29 (0.81, 2.06)	1.53 (0.93, 2.51)
RCSI
	Low coping (0–4)	99 (41.8)	138 (58.2)	1	−
	Medium to high coping (≥ 5)	83 (39.7)	126 (60.3)	0.92 (0.63, 1.34)	−
1 set as a reference, * Significant at p-value < 0.05. Only variables with p < 0.25 were retained in the final multivariate analysis model ^a Adjusted for MUAC, age, household wealth index, family size, appetite status, MDD-W, use of contraceptives, gravidity, government assistance, and changes in household food expenditure. Abbreviations. COR: Crude odds ratio; AOR: Adjusted odds ratio; CI: Confidence interval

Chronic energy deficiency, non-use of contraceptive method, and inadequate dietary diversity were significant predictors of anemia (p < 0.05) in pregnant women after controlling for other confounders in multivariate analysis. Pregnant women with CED, as indicated by a MUAC of less than 23.5 cm, were 1.9 times more likely to have anemia in the third trimester (AOR = 1.92; 95% CI: 1.06, 3.48). Women who had never or had not used contraceptives prior to their current pregnancy were nearly 1.6 times as likely to have anemia (AOR = 1.58; 95% CI: 1.02, 2.45). Furthermore, inadequate dietary diversity, increased the risk of anemia in pregnant women by 1.6 times (AOR = 1.58; 95% CI: 1.02, 2.45) (see Table 3).

Maternal anemia remains a major problem in rural area of East Lombok, Indonesia. During this COVID-19 pandemic decrease in household income and food expenditure were observed, along with medium-to-high coping strategies in half of the households. Chronic energy deficiency, inadequate dietary diversity, and non-use of contraception were significant determinants of anemia among pregnant mothers in the district.

The current study showed that the prevalence of maternal anemia was 40.8%, and is classified as severe public health issue by the WHO [2]. The proportion of anemia demonstrated by this study is lower than the national prevalence (48.9%) [6], yet higher than the global average of 36.5% [24]. While anemia prevalence remains high, this figure suggests reduction over the past two decades since study conducted in 2001 amongst pregnant mothers in the third trimester in Lombok which showed anemia prevalence of 60% [25].

The proportion of CED in our study was 10.5%, which was lower than the national average of 17.3% and the sub-national rate in East Lombok of 20.2%. In terms of the high proportion of WRA with CED, our study area i.e., NTB Province ranks eighth out of 34 provinces in Indonesia, indicating that maternal malnutrition remains a major public health concern [26]. Our study found that pregnant women with CED were 1.8 times more likely to develop anemia. This was in agreement with a case-control study conducted in West Ethiopia that reported higher odds of anemia among pregnant women with a MUAC of less than 23 cm (AOR = 3.42; 95% CI: 2.07, 5.63) [27]. Malnutrition during pregnancy may be caused by poor dietary intake [4], low pre-pregnancy BMI (< 18.5 kg/m²) [28], accompanied by infections and infestations, which is common in LMICs [29]. In our study we found that worm infestation was relatively low therefore the CED was likely due to inadequate nutrient intake rather than infection.

The present study found that 28% of the pregnant women had inadequate dietary diversity which was associated with a higher odd of anemia, and this is concurrent with the findings in developing countries [46]. The typical diets of pregnant women in rural areas of East Lombok should also be underlined, which consist of rice, legumes (e.g., tempeh, tofu), and locally grown vegetables or fruits. Such predominantly plant-based diets are poor sources of bioavailable iron, in addition to the high content of iron-absorption inhibitors (e.g., phytate and polyphenols) [30]. The consumption of meat, poultry, and fish which are protein-dense foods rich in heme iron [31], was reported to be low among pregnant women with low sociodemographic and economic status [32]. Correspondingly, animal source foods (ASFs) are often consumed at special family or public gatherings (e.g., ceremony, Eid al-Adha festival, etc) since it is regarded as a delight rather than a pivotal component of the regular family diet in predominantly rural areas [33]. This may have been due to ASFs are costly and unaffordable, especially for those with low socioeconomic status [31]. Accordingly, we found that more than one-third of participants owned livestock (e.g., poultry, cows, goats, buffalo, and/or fish ponds). However, ownership of these farm assets did not translate into consumption, given that ASFs consumption among pregnant women remained low i.e. 30% of them did not consume animal protein in the past 24 hours. The findings of our study indicate the importance of health and nutrition education aimed at mothers in order to promote dietary diversity and adequate dietary intake [34].

In our study, women who utilized contraception prior to pregnancy have a lower risk of anemia, which is in line with previous research [27]. Contraception use is critical for maternal and child survival since it assists in optimal birth spacing and the prevention of unintended pregnancies [35]. According to the 2017 Indonesia DHS, contraceptive use (all methods) has only slightly increased from 62% in 2012 to 64% in 2017 [36]. Approximately 40% study participants had not used any kind of contraception prior to their current pregnancy, which could be explained by young and first-time mothers. In Indonesia, the trend for modern contraceptive use, among married women increased with age (most users are aged 35–39), multiparity (3–4 live births), and a quintile lower middle-class wealth [36]. Additionally, data from East Lombok statistics office showed that contraceptive users decrease with wealth i.e. 78%, 76% and 65% in bottom 40, middle 40 and top 20 percentile, respectively [37]. Half (52%) of our study subjects received government poverty alleviation programs which consist of conditional cash transfers and/or food aid. During the COVID-19 pandemic, government of Indonesia under Program Keluarga Harapan (PKH Program) provided conditional cash money for poor families with pregnant mothers and/or children 0 to 6 months [38]. Another program called Bantuan Pangan Non Tunai (BPNT Program) provided non-cash support for poor families in the form of rice, egg, poultry, meat and vegetables. These programs were also likely to have protected mothers from lower socioeconomics for better access to basic healthcare facilities, including antenatal care [39]. This is explained by more than three-quarters of women completed a minimum of six antenatal care visits throughout pregnancy, especially in the midst of the COVID-19 pandemic, as recommended by the Ministry of Health Indonesia (i.e., 2, 1 and 3 times in first, second and third trimesters, respectively) [40].

Most women in our study reported a substantial decrease in household income, increase in food expenditure and approximately half of them had moderate to high food coping strategies as a result of the COVID-19 pandemic. Previous studies in developing countries discovered that respondents experienced significantly soaring food prices and decreased income as a result of the economic downturn during the pandemic [41, 42]. Poor households that spend a large portion of their income on food and have limited coping strategies are vulnerable to food insecurity and are negatively impacted by rising food prices, which will likely affect the quantity and quality of food since their purchasing power has declined [42, 43]. Our hypothesis is that pregnant women with medium to high coping strategies which indicated food insecurity are more likely to be anemic. The study findings, however, found no relationship between these variables. Meanwhile, our data showed that higher RCSI, while associated with lower maternal education and lower wealth index, was also associated with higher contraceptive users which was protective against anemia based on the multivariate analysis.

This study is community-based study which, to the best of our knowledge, is the first study in in Indonesia to investigate the association between anemia and risk factors in pregnant women during the COVID-19 pandemics. Most of the previous studies were institutional-based research that investigated pregnancy outcomes in women infected with SARS-CoV-2. Nonetheless, our study included solely pregnant women in the third trimester and Sasaknese ethnicity (as of the main observational cohort study), hence these results may not be generalizable to women in other trimesters of pregnancies and other areas. In addition to hemoglobin, assessment of other micronutrients to determine iron, B12, and/or folate-deficiency anemia would be beneficial to supplement our findings. As previous study in the same Sasaknese ethnicity reported over 70% of under two having iron-lowering allele in TMPRSS6 [44] contribution of the genetic factor as risk factor of anemia and iron-deficiency anemia in pregnant mothers needs to be further explored. Finally, due to the cross-sectional nature of the study, the authors were unable to establish a causal relationship between the independent variables and anemia.

The prevalence of anemia among pregnant women in East Lombok during COVID-19 pandemics was of public health significance and was associated with chronic energy deficiency, inadequate dietary diversity, and non-use of contraceptives. Despite decreased income, increased food expenditure and increased RCSI, these were not found to be significant risk factors for anemia, potentially due to the existing cash and non-cash support for the poor families. The findings from our study highlight the importance of improving anemia control program among pregnant women in rural areas like East Lombok as well as improving policy to strengthen their resilience to natural phenomenon such as COVID-19.

ANC

Antenatal care

ASF

Animal source foods

CED

Chronic energy deficiency

DMPA

Depot-medroxyprogesterone acetate

IFAS

Iron-folic acid supplementation

IYCF

Infant and young child feeding

MUAC

Mid-upper arm circumference

RCSI

Reduced coping strategy index

PSF

Plant source foods

UKRI-GCRF

United Kingdom Research and Innovation – Global Challenges Research Fund

WASH

Water, sanitation and hygiene

WRA

Women of reproductive age.

Acknowledgments

The East Lombok district administration is acknowledged for their support and permission to conduct this study. We would like to thank the Faculty of Medicine, Universitas Mataram, and RS UNRAM for providing a laboratory for blood and stool assessment. Our thanks go to all enumerators, field supervisors, and field officers for their hardship, commitment, and persistence. Last but not least, we would like to thank the study participants who volunteered to take part in the study.

Authors’ contributions

UF and MKH conceptualized and designed the study. UF obtained funding. UF, MKH, and DNC supervised the study. UF, MKH, NLZ, and ASA conducted the analyses and interpretation of the results. ASA drafted the first article. UF, MKH, DNC, and NLZ revised the draft critically for important intellectual content. All authors received multiple drafts of the manuscript, read, and approve the final version.

Funding

The study received support from Action Against Stunting Hub which is funded by United Kingdom Research and Innovation Global Challenges Research Fund (UKRI–GCRF), Grant Number: MR/S01313X/1. The funder had no role in the study design, data collection and analysis, manuscript preparation, or decision for publication.

Availability of data and materials

There is no public access to all data generated or analyzed in this research to preserve the privacy of the identities of our study subjects. The dataset that supports the findings is available to the corresponding author upon reasonable request.

Ethics approval and consent to participate

The study was approved by the Research Ethical Review Committee of the Faculty of Medicine, Universitas Indonesia (Reference Number: ND-914/UN2.F1/ETIK/PPM.00.02/2021). All individuals provided written informed consent before participating in the study (or a witnessed thumbprint for those who were unable to provide a signature). Prior to the interview, the enumerators fully explained the study's objectives and those who agreed to participate signed a written informed consent form. The participants were also informed that any data obtained from them would be kept anonymous and confidential through the use of codes (instead of any personal identifiers) and would be used only for the purpose of the study. The study participants can withdraw their participation at any time of the study period without penalty.

Consent for publication

Not applicable.

Competing interests

The authors declare no conflict of interest.

Author details

¹Department of Nutrition Science, Faculty of Medicine, Universitas Indonesia − Dr. Cipto Mangunkusumo General Hospital, Jakarta, Indonesia

²Southeast Asian Ministers of Education Organization Regional Centre for Food and Nutrition (SEAMEO RECFON), Pusat Kajian Gizi Regional Universitas Indonesia, Jakarta, Indonesia

³Sinergi Qalbu Fikri, Depok, Indonesia

Varghese R, Roy M. Coresidence with mother-in-law and maternal anemia in rural India. Soc Sci Med. 2019;226:37–46.
World Health Organization (WHO). Haemoglobin Concentrations for the Diagnosis of Anaemia and Assessment of Severity inside VMNIS | Vitamin and Mineral Nutrition Information System [Internet]. 2011 [cited 2021 Jul 6]. Available from: https://www.who.int/vmnis/indicators/haemoglobin.pdf.
Tan J, He G, Qi Y, Yang H, Xiong Y, Liu C, Wang W, Zou K, Lee AH, Sun X, Liu X. Prevalence of anemia and iron deficiency anemia in Chinese pregnant women (IRON WOMEN): a national cross-sectional survey. BMC Pregnancy Childbirth. 2020;20(1):670.
Ayensu J, Annan R, Lutterodt H, Edusei A, Peng LS. Prevalence of anaemia and low intake of dietary nutrients in pregnant women living in rural and urban areas in the Ashanti region of Ghana. PLoS ONE. 2020;15(1):e0226026.
Indonesia Ministry of Health. Riset Kesehatan Dasar (RISKESDAS) [Internet]. 2013 [cited 2021 Jul 11]. Available from: https://pusdatin.kemkes.go.id/resources/download/general/Hasil%20Riskesdas%202013.pdf.
Indonesia Ministry of Health. Hasil Utama RISKESDAS 2018 [Internet]. 2018 [cited 2021 Jul 11]. Available from: https://kesmas.kemkes.go.id/assets/upload/dir_519d41d8cd98f00/files/Hasil-riskesdas-484 2018_1274.pdf.
NTB Province Health Office (PHO). GEN2025 - DINAS KESEHATAN PROVINSI NTB [Internet]. 2017 [cited 2021 Dec 22]. Available from: https://dinkes.ntbprov.go.id/gen2025/.
World Health Organization. Global nutrition targets 2025: anaemia policy brief [Internet]. 2014 [cited 2023 May 17]. Available from: https://www.who.int/publications-detail-redirect/WHO-NMH-NHD-14.4.
Roberton T, Carter ED, Chou VB, Stegmuller AR, Jackson BD, Tam Y, Sawadogo-Lewis T, Walker N. Early estimates of the indirect effects of the COVID-19 pandemic on maternal and child mortality in low-income and middle-income countries: a modelling study. Lancet Glob Health. 2020;8(7):e901–8.
Ceballos F, Hernandez MA, Paz C. Short-term impacts of COVID‐19 on food security and nutrition in rural Guatemala: Phone‐based farm household survey evidence. Agric Econ. 2021;52(3):477–94.
Sidor A, Rzymski P. Dietary Choices and Habits during COVID-19 Lockdown: Experience from Poland. Nutrients. 2020;12(6):1657.
Poskute AS, Nzesi A, Geliebter A. Changes in food intake during the COVID-19 pandemic in New York City. Appetite. 2021;163:105191.
Shrestha V, Paudel R, Sunuwar DR, Lyman ALT, Manohar S, Amatya A. Factors associated with dietary diversity among pregnant women in the western hill region of Nepal: A community based cross-sectional study. PLoS ONE. 2021;16(4):e0247085.
University of Sydney. Effectiveness of an integrated programme to reduce maternal and child malnutrition in Indonesia: cross-sectional impact evaluation report [Internet]. Global Alliance for Improved Nutrition (GAIN). 2017 [cited 2021 Dec 22]. Available from: https://www.gainhealth.org/resources/reports-and-publications/effectiveness-integrated-programme-reduce-maternal-and-child.
Action Against Stunting Hub (AASH). Alleviating child undernutrition, globally [Internet]. 2020 [cited 2021 Dec 7]. Available from: https://actionagainststunting.org/.
United States Department of Agriculture (USDA). U.S. Household Food Security Survey Module: Three-Stage Design, with Screeners. 2012 [cited 2021 Nov 21]; Available from: https://www.ers.usda.gov/media/8271/hh2012.pdf.
Food and Agriculture Organization (FAO). The Coping Strategies Index: A tool for rapidly measuring food security and the impact of food aid programmes in emergencies. 2003 [cited 2021 Dec 17]; Available from: http://www.fao.org/3/ae513e/ae513e.pdf.
Maxwell D, Caldwell R. The Coping Strategies Index: A tool for rapid measurement of household food security and the impact of food aid programs in humanitarian emergencies. Field Methods Manual, Second Edition. 2008 [cited 2021 Dec 17]; Available from: https://documents.wfp.org/stellent/groups/public/documents/manual_guide_proced/wfp211058.pdf.
U.S. Agency for International Development (USAID). The Demographic and Health Surveys (DHS) Program - Indonesia: Standard DHS. [Internet]. 2017 [cited 2022 Nov 12]. Available from: https://dhsprogram.com/methodology/survey/survey-display-522.cfm.
Lebso M, Anato A, Loha E. Prevalence of anemia and associated factors among pregnant women in Southern Ethiopia: A community based cross-sectional study. PLoS ONE. 2017;12(12).
Htet MK, Fahmida U, Do TT, Dibley MJ, Ferguson E. The use of tablet-based multiple-pass 24-hour dietary recall application (MP24diet) to collect dietary intake of children under two years old in the prospective cohort study in Indonesia. Nutrients. 2019;11(12).
Food and Agriculture Organization (FAO). Minimum Dietary Diversity for Women (MDD-W). An updated guide to measurement - from collection to action [Internet]. FAO. ; 2021 [cited 2021 Jul 8]. Available from: http://www.fao.org/documents/card/en/c/cb3434en.
Keller L, Patel C, Welsche S, Schindler T, Hürlimann E, Keiser J. Performance of the Kato-Katz method and real time polymerase chain reaction for the diagnosis of soil-transmitted helminthiasis in the framework of a randomised controlled trial: treatment efficacy and day-to-day variation. Parasit Vectors. 2020;13(1):517.
Aryanto E, Sugiarto AD, Darmawan PH, Pande NPYA. Gambaran anemia pada kehamilan trimester III di bagian obstetri dan ginekologi RSUD Waikabubak, Nusa Tenggara Timur periode 2019–2020. Intisari Sains Medis. 2021;12(2):463–7.
Prihatini S, Jahari AB, Sebayang S, Iswidahni I. Food Consumption and Anaemia Status on Pregnant Women Sample Study SUMMIT (The Supplementation with Multiple Micronutrients Intervention Trial) in Lombok. Penelitian Gizi dan Makanan (PGM) [Internet]. 2009 [cited 2023 Jun 19];32(1):37–44. Available from: https://www.neliti.com/id/publications/157099/gambaran-konsumsi-makanan-dan-status-anemia-ibu-hamil-sampel-penelitian-summit-t.
Mofid LS, Casapía M, Montresor A, Rahme E, Marquis GS, Vercruysse J, Allen LH, Blouin B, Razuri H, Pezo L, Gyorkos TW. Maternal postpartum deworming and infant milk intake: Secondary outcomes from a trial. Matern Child Nutr. 2021;17(4).
Teshome AA, Berra WG, Hiruy AF. Modern Contraceptive Methods Predict Hemoglobin Levels Among Women of Childbearing Age from DHS 2016. Open Access J Contracept. 2022;13:1–8.
Lin L, Wei Y, Zhu W, Wang C, Su R, Feng H, Yang H. Prevalence, risk factors and associated adverse pregnancy outcomes of anaemia in Chinese pregnant women: A multicentre retrospective study. BMC Pregnancy Childbirth. 2018;18(1).
Tesema GA, Worku MG, Tessema ZT, Teshale AB, Alem AZ, Yeshaw Y, Alamneh TS, Liyew AM. Prevalence and determinants of severity levels of anemia among children aged 6–59 months in sub-Saharan Africa: A multilevel ordinal logistic regression analysis. PLoS ONE. 2021;16(4):e0249978.
Seyoum Y, Humblot C, Nicolas G, Thomas M, Baye K. Iron deficiency and anemia in adolescent girls consuming predominantly plant-based diets in rural Ethiopia. Sci Rep. 2019;9(1).
Gibore NS, Ngowi AF, Munyogwa MJ, Ali MM. Dietary Habits Associated with Anemia in Pregnant Women Attending Antenatal Care Services. Curr Dev Nutr. 2021;5(1).
Fite MB, Tura AK, Yadeta TA, Oljira L, Roba KT. Consumption of animal source food and associated factors among pregnant women in eastern Ethiopia: A community-based study. PLoS ONE. 2022;17(6 June).
Tulu BD, Atomssa EM, Mengist HM. Determinants of anemia among pregnant women attending antenatal care in Horo Guduru Wollega Zone, West Ethiopia: Unmatched case-control study. PLoS ONE. 2019;14(10):e0224514.
Yadav UK, Ghimire P, Amatya A, Lamichhane A. Factors Associated with Anemia among Pregnant Women of Underprivileged Ethnic Groups Attending Antenatal Care at Provincial Level Hospital of Province 2, Nepal. Anemia. 2021;2021:1–9.
Rwabilimbo MM, Elewonibi BR, Yussuf MH, Robert M, Msuya SE, Mahande MJ. Initiation of postpartum modern contraceptive methods: Evidence from Tanzania demographic and health survey. PLoS ONE. 2021;16(3):e0249017.
Indonesia National Population and Family Planning Board. Indonesia Demographic and Health Survey [Internet]. 2018 [cited 2023 May 21]. Available from: https://kbsulteng.com/slims/index.php?p=show_detail&id=234
Indonesia Central Bureau of Statistics. Profil Kesehatan Ibu dan Anak 2020 [Internet]. 2020 [cited 2023 Jun 18]. Available from: https://www.bps.go.id/publication.html?Publikasi%5BtahunJudul%5D=2020&Publikasi%5BkataKunci%5D=Susenas&Publikasi%5BcekJudul%5D=0&yt0=Tampilkan.
Ministry of Social Affairs Republic of Indonesia. The Family Hope Program Program Keluarga Harapan (PKH) [Internet]. 2016 [cited 2022 Oct 4]. Available from: https://kemensos.go.id/program-keluarga-harapan-pkh#:~:text=Sebagai%20upaya%20percepatan%20penanggulangan%20kemiskinan,Pemerintah%20Indonesia%20telah%20melaksanakan%20PKH.
Ministry of Social Affairs Republic of Indonesia. Bantuan Pangan Non Tunai (BPNT) [Internet]. 2019 [cited 2022 Jul 7]. Available from: https://pusdatin.kemensos.go.id/bantuan-pangan-non-tunai-bpnt.
Indonesia Ministry of Health. Pedoman Pelayanan Antenatal, Persalinan, Nifas dan Bayi Baru Lahir di Era Adaptasi Kebiasaan Baru. Revisi 2. [Internet]. 2020 [cited 2023 Jan 1]. Available from: https://covid19.go.id/storage/app/media/Materi%20Edukasi/2020/Oktober/revisi-2-a5-pedoman-pelayanan-antenatal-persalinan-nifas-dan-bbl-di-era-adaptasi-kebiasaan-baru.pdf.
Kundu S, Banna MH al, Sayeed A, Sultana MS, Brazendale K, Harris J, Mandal M, Jahan I, Abid MT, Khan MSI. Determinants of household food security and dietary diversity during the COVID-19 pandemic in Bangladesh. Public Health Nutr. 2021;24(5):1079–87.
Angeles-Agdeppa I, Javier CA, Duante CA, Maniego ML. v. Impacts of COVID-19 Pandemic on Household Food Security and Access to Social Protection Programs in the Philippines: Findings From a Telephone Rapid Nutrition Assessment Survey. Food Nutr Bull. 2022;43(2):213–31.
Brinkman HJ, de Pee S, Sanogo I, Subran L, Bloem MW. High Food Prices and the Global Financial Crisis Have Reduced Access to Nutritious Food and Worsened Nutritional Status and Health. J Nutr. 2010;140(1):153S–61.
Shinta D, Asmarinah, Adhiyanto C, Htet MK, Fahmida U. The association of tmprss6 gene polymorphism and iron intake with iron status among under-two-year-old children in Lombok, Indonesia. Nutrients. 2019;11(4).

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Prevalence and factors associated with anemia among pregnant women in East Lombok district during the COVID-19 pandemic

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Background

Methods

Study design and population

Sampling procedures

Data Collection

Structured questionnaire

Food security and coping strategies assessment

Wealth index calculation

Dietary diversity assessment

Complete Blood Count and Kato-Katz examination

Anthropometry measurement

Statistical Analysis

Results

Discussion

Conclusions

Abbreviations

Declarations

Acknowledgments

Authors’ contributions

Funding

Availability of data and materials

Ethics approval and consent to participate

Consent for publication

Competing interests

Author details

References

Additional Declarations

Status:

Version 1