Household food security access and dietary diversity amidst COVID-19 pandemic in rural Nepal; an evidence from rapid assessment

Dirghayu K C; Namuna Shrestha; Rachana Shrestha; Dev Ram Sunuwar; Anil Poudyal

doi:10.1371/journal.pone.0293514

. 2023 Nov 1;18(11):e0293514. doi: 10.1371/journal.pone.0293514

Household food security access and dietary diversity amidst COVID-19 pandemic in rural Nepal; an evidence from rapid assessment

Dirghayu K C ^1,^*, Namuna Shrestha ¹, Rachana Shrestha ^2,³, Dev Ram Sunuwar ⁴, Anil Poudyal ¹

Editor: George N Chidimbah Munthali⁵

PMCID: PMC10619797 PMID: 37910536

Abstract

Background

The COVID-19 pandemic led to surging concerns about food insecurity status throughout the world. In response to global and national concerns on food and nutrition security, this study aimed to examine the prevalence and determining factors of household food insecurity and dietary diversity among people from selected rural municipalities of Lalitpur district, Nepal.

Methods

A community-based cross-sectional study was conducted among 432 households. Pretested structured questionnaires were used to collect socio-demographic characteristics of the participants, household income; influence of COVID-19 on their income and livelihood, household’s access to food and dietary diversity. Food insecurity was measured using the Household Food Insecurity Access Scale (HFIAS) and the Household Dietary Diversity Score (HDDS). A multivariable analysis was done using binary logistic regression model following a bivariate analysis to assess the association between the dependent and independent variables.

Results

More than one-third (36%) of the households reported some form of food insecurity. The overall mean score for Household Dietary Diversity Score (HDDS) was 6.0 (±1.1). Multivariable logistic regression analysis showed that participants aged 41–64 years (aOR = 0.35, 95% CI: 0.21–0.59), those over 64 years (aOR = 0.22, 95% CI: 0.07–0.66), as well as those in service occupation (aOR = 0.22, 95% CI: 0.06–0.77) were associated with lower odds of HFIAS. Conversely, Participants belonging to a disadvantaged ethnic group (aOR = 2.73, 95% CI: 1.23–6.07), having no education (aOR = 3.70, 95% CI: 1.16–11.71) or primary education (aOR = 3.67, 95% CI: 1.23–9.89), and those suffering from chronic illness (aOR = 3.12, 95% CI: 1.53–6.35) were associated with higher odds of HFIAS. As for HDDS, participants aged 41–64 years (aOR = 0.48, 95% CI: 0.28–0.83) were associated with lower odds of HDDS, while participants having no education (aOR = 10.05, 95% CI: 4.05–24.01) were associated with significantly higher odds of HDDS.

Conclusion

Owing to the pandemic, our study showed a substantial prevalence of food insecurity among diverse community residing in rural outskirts of Kathmandu Valley, particularly among disadvantaged ethnic group and people with lower level of education. Interventions targeting these particular groups may help in improving HFIAS and HDDS among them during emergencies.

Background

The World Health Organization (WHO) declared COVID-19 a Public Health Emergency of International Concern (PHEIC) on 30th January 2020.The global pandemic, which began on March 11^th, shut down the world immediately [1]. As suggested by the Food Agriculture Organization (FAO) of the United Nations, food insecurity occurs when there is an abrupt disruption in the availability, access, utilization, and stability of food [2]. The continuation of lockdown as the pandemic impeded all the pillars of food security led to incensed food insecurity globally together with various adverse public health consequences [3, 4]. Worldwide, several studies have been conducted targeting to evaluate the impact of the COVID-19 pandemic on food insecurity [5–8]. Evermore, evidence from a systematic review suggests that the COVID-19 pandemic had detrimental effect on food insecurity and diet quality [9, 10]. According to the recent World Food Program (WFP) estimates in 2020, over 280 million people worldwide are at a risk of acute food insecurity [2] along with 97 million suffering from chronic food insecurity indicating an alarming rate in comparison to pre-pandemic years [11].

Prior to the COVID-19 pandemic, 690 million individuals worldwide consumed fewer calories than required, which undoubtedly is a public health concern of utmost importance in terms of food consumption, dietary diversity and relevant food insecurity [12]. According to Nepal Demographic Health Survey (NDHS) 2016, in Nepal, 48% households were food-secure whereas, 20% of households were subjected to mild food-insecurity, 22% faced moderate food-insecurity, and 10% suffered severely [13]. Nepal being a least developed nation has always struggled to produce an adequate supply of food for its citizens [14] and has scuffled with its existing downfall of nutritional scenario. The addition of the unprecedented threat of the surging pandemic, has accorded nutrition and food security as a top priority by the Government of Nepal [15]. The adverse health consequences of the COVID-19 together with the measures taken by the government to stem it’s transmission including restrictions on movement and operation of business and markets have resulted in social and economic meltdown further exacerbating food security issues across the country [16, 17]. The pandemic crisis has affected the livelihoods of Nepalese households, with 1 out of 10 households reporting a loss of livelihood whereas reduction in income was reported by 3 out of 10 households jeopardizing the economic scenario of not only an individual but also the whole nation [18].

As defined by FAO, “minimum dietary diversity” for the household as the consumption of food from at least four food groups of the 12 specified food groups through 24 hours period preceding the survey day [19]. Additionally, household food security and dietary diversity are linked together considering dietary diversity as another key indicator of a household’s ability to provide enough as well as diverse food to achieve good nutrition. This in turn serves as a measure of the nutritional quality of the diet to ensure adequate consumption of essential nutrients. Household’s Dietary Diversity Scores (HDDS) are considered a proxy indicator to measure the economic ability of a household in accessing diverse food groups in a recommended period and an overall indicator of food security [19]. Dietary diversity and adequacy were found to have been significantly influenced by multiple socio-cultural as well as economic beliefs and taboos existing in Nepalese society [20]. Even more existing evidence suggests that a large proportion of Nepalese households also faced food insecurity and poor dietary diversity [16].

Based on a recent report from World Bank, Nepal’s rural population accounts for almost four-fifths of the total population [21]. According to NDHS data 2016, nearly half (48%) of Nepal’s households are food secure whereby urban households are more likely to be food secure (58%) compared to rural households (39%) [13]. This is triggered by the fact that most agricultural activities performed in rural areas lack agricultural innovation, inadequate market access and are located in remote areas with less productivity [22]. These figures are likely to increase with the continuance of disruption in agricultural production, supply, and labor market following measures taken to combat the pandemic crisis. Despite the government’s palliative approach at multiple levels, a severe impact on food security among the Nepalese population was observed sequential of the pandemic [18]. However, there still exist inadequacies of evidence that can highlight the impact of COVID-19 on food security and dietary diversity in such rural context as the majority of the population resides there within.

This led to an assessment of the influence of the COVID-19 pandemic on household food security and their dietary diversity among rural communities of Nepal. This study is expected to offer critical evidence on different factors that determine the household food insecurity as well as their dietary diversity during COVID-19 pandemic or other emergencies of such nature and may support the government in effectively planning and designing policies and strategies for combating household food insecurity during imminent emergencies of this nature.

Methodology

Study design, study setting, and sample size

A community-based cross-sectional study was conducted between 3^rd January and 3^rd March, 2021among people residing in rural municipalities of Lalitpur district, Nepal. Lalitpur district comprises of one metropolitan city, two urban municipalities, and three rural municipalities [23]. The COVID-19 crisis triggered multi-dimensional social and economic impacts which stretched beyond the primary health crisis. The study focused on the current scenario of food security and nutritional adequacy of the households residing in rural municipalities of Lalitpur district, Nepal. A total sample size of 432 was estimated based on the single proportional formula n = Z²pq/d²; taking 23% proportion rate [18] with α level of significance at 5%. In the formula, Z = standard normal deviation and equaled 1.96 at α level of significance; p is the prevalence of the outcome of interest which was set at 0.5 considering the prevalence of household food insecurity, q = 1-p; design effect of 1.5 and both the margin of error (d) and non-response rate were set at 5% each.

Sampling technique

We purposively selected Lalitpur district which consisted of three rural municipalities (Gaupalika) (lower administrative unit of Nepal) namely Bagmati, Konjyosom, and Mahankal. A gaupalika or rural municipality is one of the administrative divisions of Nepal and is a sub-unit within the district [23]. A rural municipality in Nepal is usually further divided into nine administrative units called wards which are the lowest administrative units [22]. The total required samples (432) were equally divided across the three rural municipalities (144 in each rural municipality). From each municipality we further selected four wards using simple random sampling, such that we had to enroll 28 samples from each ward making a total of 144 samples in each of the rural municipalities. Within each ward, we calculated the sampling interval by dividing the total households in that particular ward by the required sample size within the ward i.e. 28. Following which we then started with a first household randomly and then consecutively selected household as per the interval for that particular ward to get our required number. In each household, the head of the household was interviewed. In case of the unavailability of the household head, the member who is 18 years and above was interviewed. For the purpose of conducting interviews, research assistants were selected based on their academic background and their experience in conducting such data collection measures including quantitative techniques. Adding to this, careful consideration was given to the situational context with all relevant safety precaution guidelines being strictly maintained and followed throughout the period of data collection.

Data collection procedure

Four trained research assistants were selected and tasked to collect information on the socio-demographic characteristics of the participants; the effect of COVID-19 on their income and livelihood, household’s access to food and dietary diversity. A pretested structured questionnaire was used for the interviews. Questionnaires were translated from English to Nepali. Nepali version of the questionnaire was pretested among 10% of the study sample (n = 44) in neighboring areas before the tools were used for data collection.

Socio-demographic characteristics

The socio-economic variables were used to assess the effect of COVID-19 on Household Food Insecurity Access Scale (HFIAS) and HDDS which comprised of age, gender, ethnicity, level of education, occupation, monthly income, family type, job loss, income loss, and the support they have been receiving from the government and concerned stakeholders throughout the entirety of the pandemic. Basically, Chhetri and Brahmin were classed as an advantaged ethnic group whereas participants of household other than them were grouped as disadvantaged ethic community. Presence of major physical and mental disabilities among the members of household selected for this study was assessed by asking participants on any such known health adversities among the members of their household. Furthermore, presence of chronic illness in the members of household was assessed using “Yes” or “No” question. Also, job losses as well as income loss due to COVID-19 were assessed to highlight its impact on rural people. However, to account for the support provided this study only entails a broader approach in assessing the support received by the participants from various sources including government, non-governmental organizations (NGOs), political parties, social workers and doesn’t reflect individual effort.

Household Food Insecurity Access Scale

Household food insecurity access was measured using guidelines of Food and Agriculture Organization-Food And Nutrition Technical Assistance (FAO-FANTA) adopting HIFAS tool. Household food insecurity access was then illustrated using the indicator of Household Food Insecurity Access Prevalence (HFIAP) Status whereby household food insecurity access was assessed into three categories including food security, mild-to-moderate food insecurity, and severe food insecurity using HFIAS indicator guide [5, 24].

Household Dietary Diversity Score

The HDDS were collected using a 24-hour dietary recall developed by FAO and the FANTA Project [19]. A total of 12 food groups were included in HDDS. The total dietary diversity score (DDS) ranges from 0 to 12. Food groups consumed during the previous 24 hours by the households were given a point score yielding a maximum total DDS of 12 points if his/her responses were positive to all food groups. Further, the HDDS scores were categorized as low DDS with 0–3 food groups, moderate DDS with 4–6 food groups, and high DDS with 7–12 food groups consumed by the members of that particular households during those reference periods [19, 24].

Data analysis and management

Data compilation, checking, and coding were carried out following the data collection. Data were systematically coded and entered into Epi Data 3.1. The entered data was exported to Statistical Package for Social Sciences (SPSS) Version 20 and checked for its consistency. All analysis was finally performed using SPSS version 20. Descriptive statistics (frequency, mean and standard deviation) were presented in a frequency table. Inferential statistics such as chi-square test was applied to test the significance of the association between independent and dependent variables. For each outcome variable and independent variable, using a binary logistic regression model, a bivariate analysis was performed to assess the association between independent and outcome variables. P-values <0.05 were considered statistically significant. Based on the findings of bivariate analyses, the model for multivariable analysis was decided using all those with significant associations in the bivariate analyses. In order to account for potential confounders, independent variables that had a p-value less than 0.2 were also included in the multivariable analysis. To prevent statistical bias in the multivariable logistic regression model, we examined multi co-linearity among the independent variables using variation inflation factors (VIF). We used "10" as a cut-off value for the maximum level of VIF. Results are presented as crude odds ratio (cOR) and adjusted odds ratio (aOR) with 95% confidence intervals (CIs).

Ethical approval

Ethical approval was obtained from the Ethical Review Board (ERB) of the Nepal Health Research Council (NHRC; Ref: 2556). Participants were detailed about the study following which both verbal and written consent from the participants was taken before conducting the survey. In case of participants with no education, verbal consent followed by thumbprint was collected as an approval for the enrollment in the study. Also, the participant’s dignity was maintained by giving the right to reject or discontinue the research study at any time.

Results

Socio-demographic characteristics of the participants

Table 1 depicts the socio-demographic characteristic of study households in relation to food insecurity access and household dietary diversity score. Socio-demographic characteristics of the households interviewed showed the mean age (SD) of the participants to be 45 years. More than two-thirds (64%) of the total households were Brahmin followed by slightly more than one-fourth households (26.2%) of Janajati. The majority of the households were found to be Hindu (78.7%). Two-thirds of the interviewed participants were found to have attended school with one-fifth of them being able to generally read and write only. Agriculture was found to be the major source of income for almost four-fifths of the interviewed households. Only 6.3% of the participating households reported having major physical and mental disabilities whereas 13.4% of them reported the presence of chronic diseases in their household (Table 1).

Table 1. Socio demographic characteristics and COVID related factors by household food security status and household dietary diversity status of the participants.

Variables	Total n (%)	HFIAS		p-value¹	HDDS		p-value¹
	Total n (%)	Food secure n (%)	Food insecure n (%)		High	Low/medium
		63.9 (95% CI: 59.2–68.3)	36.1 (95% CI: 31.7–40.8)		36.8 (95% CI: 32.4–41.5)	63.2 (95%CI: 58.5–67.6)
Socio demographic characteristics
Respondent Age category				0.001^*			0.939
18–40	146 (33.8)	77 (27.9)	69 (44.2)		53 (33.3)	93 (34.1)
41–64	261 (60.4)	185 (67.0)	76 (48.7)		96 (60.4)	165 (60.4)
>64	25 (5.8)	14 (5.1)	11 (7.1)		10 (6.3)	15 (5.5)
Ethnicity				<0.001^*			0.042^*
Advantaged ethnic group	313 (72.5)	225 (81.5)	88 (56.4)		123 (77.4)	190 (69.6)
disadvantaged ethnic group	119 (27.5)	51 (18.5)	68 (43.6)		36 (22.6)	83 (30.4)
Religion				<0.001^*			0.095
Hindu	340 (78.7)	101 (64.7)	239 (86.6)		132 (83.1)	208 (76.2)
Non Hindu	92 (21.3)	55 (35.3)	37 (13.4)		27 (16.9)	65 (23.8)
Family type				0.853			0.943
Nuclear	183 (42.4)	116 (42.1)	67 (42.9)		67 (42.1)	116 (42.5)
Extended	249 (57.6)	160 (57.9)	89 (57.1)		92 (57.9)	157 (57.5)
Education				0.002^*			<0.001^*
No education	240 (55.6)	144 (52.2)	96 (61.5)		51 (32.1)	189 (69.2)
Primary	146 (33.8)	92 (33.3)	54 (34.6)		79 (49.7)	67 (24.5)
Higher secondary or above	46 (10.6)	40 (14.5)	6 (3.9)		29 (18.2)	17 (6.2)
Occupation				<0.001^*			<0.001^*
Agriculture	343 (79.4)	199 (72.1)	144 (92.3)		111 (69.8)	132 (84.9)
Service	43 (9.9)	38 (13.8)	5 (3.2)		27 (16.9)	16 (5.8)
Business	46 (10.6)	39 (14.1)	7 (4.5)		21 (13.2)	25 (9.3)
Household head				0.432			0.420
Male	399 (92.4)	257 (93.1)	142 (91.1)		149 (93.7)	250 (91.6)
Female	33 (7.6)	19 (6.8)	14 (8.9)		10 (6.3)	23 (8.4)
Source of income				<0.001^*			0.146
Agriculture	334 (77.3)	199 (72.1)	135 (86.5)		115 (72.3)	219 (80.2)
Business	54 (12.5)	47 (17.1)	7 (4.5)		23 (14,5)	31 (11.4)
Service	44 (10.2)	30 (10.8)	14 (9.0)		21 (13.2)	23 (8.4)
Chronic illness				0.001^*			0.292
No	374 (86.6)	250 (90.6)	124 (79.5)		149 (93.7)	248 (90.8)
Yes	58 (13.4)	26 (9.4)	32 (20.5)		10 (6.3)	25 (9.2)
COVID related factors
Labor migrant abroad				0.333			0.292
No	397 (91.9)	251 (90.9)	146 (93.6)		149 (93.7)	248 (90.8)
Yes	35 (8.1)	25 (9.1)	10 (6.4)		10 (6.3)	15 (9.2)
Labor migrant returned before COVID				0.302			1.000
No	428 (99.1)	272 (98.5)	156 (100)		158 (99.4)	270 (98.9)
Yes	4 (0.9)	4 (1.5)	0		1 (0.6)	3 (1.1)
Remittance during COVID				1.000			0.337
No	422 (97.7)	269 (97.5)	153 (98.1)		157 (98.7)	265 (97.1)
Yes	10 (2.3)	7 (2.5)	3 (1.9)		2 (1.3)	8 (2.9)
Job loss due to COVID				1.000			1.000
No	428 (99.0)	273 (98.9)	155 (99.4)		158 (99.4)	270 (98.9)
Yes	4 (1.0)	3 (1.1)	1 (0.6)		1 (0.6)	3 (1.1)
Income loss due to COVID				1.000			1.000
No	430 (99.5)	275 (99.6)	155 (99.4)		158 (99.4)	272 (99.6)
Yes	2 (0.5)	1 (0.4)	1 (0.6)		1 (0.6)	1 (0.4)
Government support				0.034			0.311
No	327 (75.7)	218 (78.9)	109 (69.9)		116 (72.9)	211 (77.3)
Yes	105 (24.3)	58 (21.1)	47 (30.1)		43 (27.1)	62 (22.7)
COVID support				0.638			0.858
No	293 (67.8)	185 (67.1)	108 (69.2)		107 (67.3)	186 (68.1)
Yes	139 (32.7)	91 (32.9)	48 (30.8)		52 (32.7)	87 (31.9)

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¹ Chi square test or Fischer exact test

*statistically significant at p<0.05; HFIAS: Household Food Insecurity Access Scale; HDDS: Household dietary diversity score

Status of household’s food security access

Results of the assessment of household’s food security status revealed that more than three-fifth [64% (95% CI: 59.2–68.3)]of the households reported being food secure followed by more than one-third [36.1% (95% CI: 31.7–40.8)] being food insecure whereby, nearly one-fourth being mildly food insecure, exactly one-tenth being moderately food insecure, and only 3% being severely food insecure, respectively (Fig 1).

Our findings further revealed that among those who were food insecure, 44.2% of the household’s respondents were of age category18-40 years and 49% of them were of age 41–64 years respectively. Similarly in terms of ethnicity of food insecure households, more than half (56%) of the advantaged ethnic groups were found food insecure followed by nearly 44% food insecurity amongst household from disadvantaged community. Likewise, 61.5% of the households with no parental education were food insecure followed-up by 34.6% with parents having primary education. Our results also revealed that the source of income was also one of the detrimental factors for food security. Household with agriculture as their primary source of income were found food secure compared to their counterpart household with business and service as their major source of income.

The HDDS also followed similar pattern. It showed 36.8% (95% CI: 32.4–41.5) of the households to have high dietary diversity score, whereas 63.2% (95%CI: 58.5–67.6) had low to medium dietary diversity score. Among the participants consuming low/medium dietary diversity, 69.6% were from advantaged ethnic group and remaining 30.4% were from disadvantaged ethnic group. Parental education also found to be significantly associated with HDDS. While taking low/Medium dietary diversity score into account, those with no education accounted for 69.2% of the households whereas, those with primary education and higher secondary/higher education accounted for 24.5%, and 6.2% of the households respectively.

Association of socio-demographic and COVID related variables with household dietary diversity and household food insecurity score

Household having participants aged 41–64 years had a 65% lower odds of HFIAS (aOR = 0.35, 95% CI: 0.21–0.59) and 52% lower odds of having low/medium dietary diversity (aOR = 0.48, 95% CI: 0.28–0.83) compared to those household having participants aged 18–40 years. By ethnicity, the disadvantaged ethnic group had nearly three times higher odds of HFIAS (aOR = 2.73, 95% CI: 1.23–6.07). compared to advantaged ethnic groups. Participants who reported having no education were associated with increased scores on both the HFIAS (aOR = 3.70, 95% CI: 1.16–11.71) and HDDS (aOR = 10.05, 95% CI: 4.05–24.91) compared to the head of a household having higher secondary and above. Also, households having patients suffering from chronic illness (aOR = 3.12, 95% CI: 1.53–6.35) were associated with higher odds of HFIAS. Regarding the COVID-related characteristics, in an unadjusted analysis, households reported receiving government support during the COVID-19 pandemic had a significantly positive association with HFIAS score (cOR = 1.62, 95% CI: 1.03–2.53) compared to those who did not receive government support during COVID-19 pandemic (Table 2).

Table 2. Factors associated with household food security and insecurity status and household dietary diversity status of the participants.

Variables	HFIAS		HDDS
Variables	cOR (95% CI)	aOR (95% CI)²	cOR (95% CI)	aOR (95% CI)²
Socio demographic characteristics
Respondent Age category
18–40	Ref	Ref	Ref	Ref
41–64	0.45 (0.30–0.69)^***	0.35 (0.21–0.59)^***	0.97 (0.64–1.49)	0.48 (0.28–0.83)^**
>64	0.87 (0.37–2.05)	0.22 (0.07–0.66)^**	0.85 (0.35–2.03)	0.33 (0.11–1.03)
Ethnicity
Advantaged ethnic group	Ref	Ref	Ref	Ref
disadvantaged ethnic group	3.40 (2.19–5.28)^***	2.73 (1.23–6.07)*	1.49 (1.17–2.34)^*	1.27 (0.56–2.84)
Religion
Hindu	Ref	Ref	Ref	Ref
Non Hindu	3.51 (2.18–5.66)^***	2.00 (0.84–4.73)	1.52 (0.92–2.51)	1.02 (0.42–2.45)
Family type
Nuclear	Ref	Ref	Ref	Ref
Extended	0.96 (0.64–1.43)	0.95 (0.59–1.55)	0.98 (0.66–1.46)	0.91 (0.57–1.45
Education
No education	4.44 (1.81–10.88)^**	3.70 (1.16–11.71)^*	6.32 (3.22–12.40)^***	10.05 (4.05–24.91)^***
Primary	3.91 (1.55–9.83)^**	3.67 (1.23–9.89)^*	1.44 (0.73–2.85)	1.68 (0.76–3.73)
Higher secondary or above	ref	Ref	Ref	Ref
Occupation
Agriculture	Ref	Ref	Ref	Ref
Service	0.18 (0.06–0.47)^***	0.22 (0.06–0.77)^*	0.28 (0.14–0.54)^***	0.70 (0.26–1.84)
Business	0.24 (0.10–0.57)^***	0.47 (0.13–1.87)	0.56 (0.30–1.06)	0.78 (0.25–2.39)
Household head
Male	Ref	Ref	Ref	Ref
Female	1.33 (0.64–2.74)	1.03 (0.42–2.51)	1.37 (0.63–2.95)	0.93 (0.37–2.32)
Primary source of income
Agriculture	Ref	Ref	Ref	Ref
Business	0.21 (0.09–0.50)^***	0.31 (0.08–1.10)	0.70 (0.39–1.27)	1.00 (0.35–2.86)
Service	0.68 (0.35–1.34)	1.37 (0.52–3.52)	0.57 (0.29–0.97)^*	1.32 (0.54–3.22)
Chronic illness
No	Ref	Ref	Ref	Ref
Yes	2.48 (1.41–4.34)^**	3.12 (1.53–6.35)^**	0.84 (0.38–1.81)	0.64 (0.32–1.28)
COVID related factors
Labor migrant abroad
No	Ref	Ref	Ref	Ref
Yes	0.68 (0.32–1.47)	0.47 (0.17–1.27)	1.50 (0.70–3.21)	1.36 (0.53–3.47)
Labor migrant returned before COVID
No	Ref		Ref	Ref
Yes	0.68 (0.32–1.47)		2.36 (0.49–11.30)	1.23 (0.09–16.09)
Remittance during COVID
No	Ref	Ref	Ref	Ref
Yes	0.75 (0.19–2.95)	1.44 (0.24–8.55)	1.75 (0.18–17.02)	3.45 (0.43–27.18)
Job loss due to COVID
No	Ref	Ref	Ref	Ref
Yes	0.58 (0.06–5.69)	1.04 (0.06–16.77)	1.75 (0.18–17.02)	2.34 (0.16–33.29)
Income loss due to COVID
No	Ref	Ref	Ref	Ref
Yes	1.77 (0.11–28.56)	0.61 (0.02–18.75)	0.47 (0.04–4.59)	0.15 (0.03–6.42)
Government support
No	Ref	Ref	Ref	Ref
Yes	1.62 (1.03–2.53)^*	1.47 (0.84–2.56)	1.30 (0.66–2.55)	0.72 (0.40–1.31)
COVID support
No	Ref	Ref	Ref	Ref
Yes	0.90 (0.59–1.37)	0.72 (0.43–1.19)	0.83 (0.47–1.48)	1.10 (0.68–1.78)
HDDS
High	Ref	Ref
Low and medium	1.15 (0.47–0.76)	0.81 (0.48–1.35)
HFIAS
Food secure			Ref	Ref
Mildly food secure			0.98 (0.31–3.09)	0.92 (0.24–3.53)
Moderately food secure			1.12 (0.71–1.77)	0.78 (0.44–1.37)
Severely food secure			1.41 (0.64–3.09)	0.80 (0.32–1.96)

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*p<0.05

**p<0.01

***p<0.001; cOR = crude odds ratios for unadjusted model; aOR = adjusted odds ratios for adjusted model; CI: confidence interval; Ref: reference category; ²Single model was run for adjusting the variables p<0.2 in the unadjusted model

Discussion

With the COVID-19 pandemic emergence, it is clear that not only the multiple health aspects of people around the world have been affected but also escalated the level of their food insecurity [7, 25–28]. Although, healthy and balanced food intake is crucial concerns regarding the surging food insecurity are arising. There exists a lack of evidence regarding household food insecurity and the nutritional adequacy of the people living in rural communities during the emergency context of the pandemic. To bridge the existing gap, this study highlights the prevalence of household food insecurity, dietary diversity, and associated factors during the COVID-19 lockdown among rural households of Lalitpur district, Nepal.

Our study demonstrated the high prevalence of food insecurity with 36% of the study participants experiencing some degree of food insecurity which is 13% higher compared to the national average reported by previous study conducted during COVID among households of all seven provinces of the country [18]. This finding is consistent with a study from the Pakistan whereby 36.3% participants were facing some degree of food insecurity [29]. COVID validates its impact on food security with reported 32.4% households assessed to have significant food insecurity status [30]. Also, our study found that households having patients suffering from chronic illness were associated with higher odds of HFIAS. This is supported by the evidence generated from multiple studies around the world especially linking with the presence of diabetes and markers of other chronic diseases [31–34]. This might be because of the fact that food insecure household have increased dependency on inexpensive, highly palatable foods that are energy dense leading to the development of chronic conditions [35].

In addition, the effects of COVID-19 on household food security status were observed in a research conducted in Bangladesh which illustrated the rise of food insecurity from 45% to 61% [36]. Aligning with the evidence generated from the researches worldwide, our result pinpoints the further aggravated scenario of food insecurity among households which can be attributed to COVID-19 and immediate cautionary practices adopted for prevention [10, 36]. Throughout COVID-19, food insecurity among low-income and disadvantaged families in Nepal significantly affected their health and well-being [8], whereby7.4% households reported on adopting negative livelihood coping strategies to address food shortage [18]. These observed adverse scenario might have been influenced by the nationwide lockdown and subsequent restriction of socioeconomic activities all over the country [36].

Also, the results obtained from our study are consistent with the results obtained from various researches conducted targeting similar objectives [3, 6, 27]. Results of this study showed that the participants who reported having no education were associated with increased scores on both the HFIAS and HDDS compared to the head of a household with an educational level of having higher secondary and above. Similarly, study conducted in Iran highlighted that the participants with the knowledge about nutrition from health professionals and other sources had greater HDDS and HFIAS compared to the non-educated group which our study completely disagrees with [37]. Even the previously mentioned study conducted by the same researchers within similar settings concluded inconsistent results whereby education was found to have influential role in having better food insecurity access scores [5].

Our result showed age as the predictor factor as participants aged 41–64 years were less likely to have household food insecurity and consumed diverse diet respectively. However, contrasting results were concluded by researches conducted during the context of COVID [38, 39]. This clearly contradict the statement made by the research conducted by Abdullah et al. whereby household with older participants as the head of the family were found to have high food insecurity [40]. Fascinatingly enough, this study revealed that the respondent’s household who reported receiving support (of any form; either money or ration) during the COVID-19 pandemic had a significantly positive association with HFIAS score compared to those who did not receive any form of support from the government and concerned stakeholders. Despite the high household food insecurity, livelihood as well as income loss reported by WFP [17], the support provided whether cash or food material, was a one-time thing whereas on the other hand pandemic scenario and the lockdown lasted for an extensive period of nearly two and a half years [41]. These factors must have played crucial role in deliberately pushing vulnerable people not involved in any form of agricultural activities to the edge.

Despite the concerning high levels of food insecurity, household dietary diversity score (HDDS>4) was low/medium for 63% of households in the study population and high for 36.8% of households. No such association was observed which implied the COVID-19 related impact namely job loss, and income loss on HDDS which wasn’t the case in various similar studies [42, 43]. This might be contributed by the involvement in agricultural activities of the majority of participants residing in that particular region regardless of other activities contributing to their income generation.

Our study had some strength. At first, this survey evaluated the effect of COVID-19 pandemic on households’ food insecurity and dietary diversity in people residing in rural areas of Lalitpur district Nepal using a household survey through face-to-face interviews. This in turn gave us a clear cut view of a large number of socioeconomic characteristics associated with food insecurity access and dietary diversity during the emergencies. Incorporating dietary diversity of rural residents of Nepal as one of the significant objective along with the large sample size included is also the strength of this study. Also, our study is expected to serve as a piece of evidence to improve nutritional status of people residing in rural community by intervening with the food security and dietary diversity during the emergencies of such scale. With that being mentioned, this study has not beyond some limitations. Firstly, as this study was conducted in three rural municipalities of one district of Nepal so may not be generalized to every rural settings of this country. Also, the study design implicated precludes any possible relationship between the predictors and the outcome (food insecurity, dietary diversity). Moreover, the dietary diversity score is calculated solely being based on 24-hour food consumption and was entirely based on subjective perceptions and hence could be subjected to recall bias.

Conclusion

This study showed substantial prevalence of household food insecurity among people residing in rural areas in the vicinity of Kathmandu valley, Nepal; mainly among the disadvantaged ethnic groups and people having lower educational level during the COVID-19 pandemic highlighting serious public health concerns. Despite the raised level of food insecurity, dietary diversity was found satisfactory as almost every household was found to have at least low to medium dietary diversity during per day course of their meal. This reflects a need of attention on food insecurity from the concerned stakeholders targeting the disadvantaged ethnic group and people having lower educational level. Timely and tailored response from the government focusing on these particular groups of people might help in improving HFIAS and HDDS among the people living in similar settings during emergency situation.

Supporting information

S1 Fig. Conceptual framework on household food security access and dietary diversity amidst COVID-19 pandemic.

(TIF)

Click here for additional data file.^{(502.1KB, tif)}

Acknowledgments

First and foremost authors would like to thank Public Health Promotion and Development Organization for their technical assistance throughout the study period required for the successful completion of this study. Also, the authors would like to express their gratitude to the participants of this study who provided the information related to this study and helped to make our work successful. Without this, the study would not have been possible.

Data Availability

All the relevant data are within the paper.

Funding Statement

This research was funded by Nepal Health Research Council. However, the funding organization had no role in study design, data collection, and analysis, decision to publish or preparation of this manuscript.

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PLoS One. doi: 10.1371/journal.pone.0293514.r001

Decision Letter 0

Md Nazirul Islam Sarker

24 Nov 2022

PONE-D-22-20978Title-Household Food Security access and Dietary Diversity amidst COVID-19 Pandemic in rural Nepal; an evidence from rapid assessmentPLOS ONE

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Reviewer #1: This paper provides a cross-sectional analysis on food security prevalence and associated household factors in purposively selected 'rural' subdistricts in Lalitpur district, Nepal during one of the COVID-19 lockdown periods in 2021. The broader objective of the study - to provide a snapshot of food security, household dietary diversity and associated factors during a pandemic, while important, was not done full justice in the preparation of the manuscript and its results. The presentation of the study design, results and discussion while mostly defensible have not been adequately presented as such. My suggestion is that a full and detailed revision be completed prior to consideration for publication. I hope the comments provided below may provide some helpful input on how to possibly clarify the detailing of the study and results for a reader.

Overall, there are issues of grammatical errors and language clarity and the paper would benefit from an editorial review.

Abstract: The study design is described as "analytical cross-sectional", what do the authors mean by analytical? This is not common utilized terminology to describe study design. Next, what are determining factors? Please clarify. In the methods section, please make mention of what models were used. For the results, reported AORs should come at the end of the sentence for readability and accuracy: "Multiple regression showed the disadvantaged ethnic group (AOR=2.73, 95% CI: (1.23-6.07) who did not attend the formal education (AOR= 3.70, 95% CI: 1.16-11.71) had significant higher odds of household food insecurity. Likewise, participants with no formal education (AOR=, 95% CI: 10.05 (4.05-24.91) were more likely to have household food insecurity." What are ref groups? What is the HFS score? The conclusion speaks of exacerbation but no basis of this conclusion.Als says dietary diversity to acceptable but no average for study was provided.

Data access: Where is the data for this study made available?

Line 69-70 - what is the % increase?

Line 73 - what is relevant food insecurity? Unclear.

Line 73: Mentions that 4.6 million people were food insecure - when? If the argument being made is that food insecurity is likely worse, would be helpful to have more of a timeline setup. Recommend changing suffer to experience based on how food security is assessed.

Line 78-79 - If you choose to make mention of province 2, would include language to indicate what makes province 2 important i.e. that you're speaking of within country variation where some provinces are worse off than others. Otherwise, for a wider audience, province 2 has little relevance. Overall, I recommend giving a little more contextual explanation as it relates to social groups (lots of mention of disadvantaged groups, etc), sampling units.

Corona virus disease and social cultural are not commonly used terms to describe the virus and socio-cultural factors, respectively.

Unclear what the sentence that runs on into lines 83-84 means.

Line 98-99 - there are other factors that affect food insecurity in rural areas and if we are talking about access and availability it is not just related to a dependence of rain but instead general low ag productivity, a lack of ag innovations, inadequate market access and so forth.

Methods: Why did the authors decide on Lalitpur as a study site given the interest in rural areas that might be best studied elsewhere in the country? How many urban vs rural municipalities in Lalitpur are there? What was the rationale for Bagmati province? Please provide clearly the primary sampling unit and detail the systematic random sampling method more elaborately. What are quantitative techniques? Please provide explanation of what gaupalikas are, what was the inclusion criteria was for the study and who specifically in the household was interviewed (if household head, provide definition of household head). I recommended this last part as throughout the methods, results and discussion, the authors weave in and out of household versus individual level language.

For the analysis of factors related to food insecurity and household dietary diversity, what informed the choice of the factors used in the analysis? What theoretical or conceptual underpinning did the authors have to include the factors they did in the analysis?

Line 145: How was family members use to assess impact of COVID 19 The use of the word 'impact' may ve too strong of language given what the authors are referring to. There are discrepancies between the what data is reported to be collected during the interviews and what comes into the reporting further down the line in the paper (household size, etc). In line 159-165 explain household consumption vs individual because as it reads now, it is not clear.

The authors report using semi-structured interviews - what was the unstructured portion of the interview? What were the type of questions were asked?

Line 188: add specifics, participants were heads of hh

Tables not formatted in a reader friendly format on page 17. Most of the tables to would benefit from some major reformatting.

Lines 193-194: What is the sig of the caste and religious groupings - would encourage more detail to be provided on this to help with interpretation. In the methods section, the assessment of medical history and disabilities not included in methods - line 193- 194

What is driving the age categorization cut offs?

Results: The results need to be more clearly explained with more frequent mention of reference groups all the results being discussed are compared to.

Discussion:

There is very little discussion on any other food security data from Nepal during the COVID-19 pandemic nor contextual information about what characteristics of your study areas might be feeding into the results you are seeing. This is not a representative sample of Lalitpur nor a typical rural area of Nepal - if the authors disagree, would urge them to present their argument of why clearly when they describe the strengths of the study .

Line 274: Would be very careful about using causal language in a cross-sectional paper of non-representative population.

Reviewer #2: I appreciate all authors for this study. Although the study's research is quite unique, the authors should provide more related existing literature. Furthermore, the author didn`t clear properly why and this study is very relevant and significant and what is the policy implications? Please address this issue properly. The author(s) can also use a table to present a review of the literature. Determine the research gap in previous literature and how this work differs from previous efforts. Mention the gap in research. However, the methodology is clear and good.

The author can focus to create link how the results on this study fit in with the results from previous studies. The conclusion section is so weak, can provide summary of findings in this part. Author should discuss about research strength and limitations if have in this section. Lastly, I strongly suggest to give a paragraph about policy recommendations based on findings.

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PLoS One. 2023 Nov 1;18(11):e0293514. doi: 10.1371/journal.pone.0293514.r002

Author response to Decision Letter 0

16 Jan 2023

15 January 2023

The Academic Editor,

PLOS ONE

Subject: Resubmission of the research article entitled “Household Food Security access and Dietary Diversity amidst COVID-19 Pandemic in rural Nepal; an evidence from rapid assessment" (Manuscript ID: PONE-D-22-20978)

Dear Editor and Reviewers,

It is our great pleasure to submit our response to the reviewers comments on our manuscript entitled "Household Food Security access and Dietary Diversity amidst COVID-19 Pandemic in rural Nepal; an evidence from rapid assessment” to PLOS ONE. We have tried our best to address comments from both the Reviewers and editor. Thank you for your concern regarding these issues. Here, we have highlighted our point-by-point comments and the accompanying adjustments to the manuscript. We would be looking forward to your response and any further correction to be made to enrich as well as improve the quality of this paper.

We look forward to your kind consideration,

On behalf of all co-authors

Corresponding author

Dirghayu KC

Public Health Promotion and Development Organization

Response to #Reviewer 1

Reviewer #1: This paper provides a cross-sectional analysis on food security prevalence and associated household factors in purposively selected 'rural' sub districts in Lalitpur district, Nepal during one of the COVID-19 lockdown periods in 2021. The broader objective of the study - to provide a snapshot of food security, household dietary diversity and associated factors during a pandemic, while important, was not done full justice in the preparation of the manuscript and its results. The presentation of the study design, results and discussion while mostly defensible have not been adequately presented as such. My suggestion is that a full and detailed revision be completed prior to consideration for publication. I hope the comments provided below may provide some helpful input on how to possibly clarify the detailing of the study and results for a reader.

Overall, there are issues of grammatical errors and language clarity and the paper would benefit from an editorial review.

Response: Thank you for your constructive feedbacks. Authors really appreciate the effort of reviewer in reviewing this study. The whole manuscript has been checked and corrected for grammatical errors as well as language clarity.

Abstract: The study design is described as "analytical cross-sectional", what do the authors mean by analytical? This is not common utilized terminology to describe study design. Next, what are determining factors? Please clarify. In the methods section, please make mention of what models were used. For the results, reported AORs should come at the end of the sentence for readability and accuracy: "Multiple regression showed the disadvantaged ethnic group (AOR=2.73, 95% CI: (1.23-6.07) who did not attend the formal education (AOR= 3.70, 95% CI: 1.16-11.71) had significant higher odds of household food insecurity. Likewise, participants with no formal education (AOR=, 95% CI: 10.05 (4.05-24.91) were more likely to have household food insecurity." What are ref groups? What is the HFS score? The conclusion speaks of exacerbation but no basis of this conclusion. Also says dietary diversity to acceptable but no average for study was provided.

Response: Thank you for your suggestions. The word ‘analytical’ has been removed from the term ‘analytical cross-sectional’ and defined as cross-sectional descriptive study in the revised manuscript. By determining factors, we mean to say the factors that are found to influence household food security status and dietary diversity score. For example, in our study we found that ethnicity, education and age were the factors found to influence household food security status. We performed bivariate and multivariate analyses. Mention of the models used during our statistical analysis has been made in the method section both in the abstract and the main body of the manuscript. We have explained with further clarity about how variables were chosen for the model. The suggested changes for reporting AOR have been made. The reference groups have been denoted as “Ref” in Table 3 which was used to compute the odds ratio in the regression model. To give example, for disadvantaged ethnic group, advantaged ethnic group are the reference group. We have calculated the proportion of people who are food secure and unsecured as64% and 36% respectively. The conclusion in the abstract has been revised according to the findings of the study and we have modified the limitation section such that the readers are aware of what the study limitations were. We have also adjusted the manuscript further as suggested by the reviewer. As recommended, the whole result section has been rearranged and represented.

Mention of the models used during our statistical analysis has been made in the method section both in the abstract and the main body of the manuscript. We have explained with further clarity about how variables were chosen for the model. We have modified the conclusion to match the findings of the study, and also have modified the limitation section such that the readers are aware of what the study limitations were. Reference group has been mentioned in results wherever necessary for clarity.

Data access: Where is the data for this study made available?

Response: After publication of this study, data will be made available by authors upon request.

Line 69-70 - what is the % increase?

Response: Authors would like to thank reviewer for pointing out this issue. The whole sentence has been rephrased as well as the information provided in the revised manuscript. Reference to the statement has also been adjusted subsequently.

Line 73 - what is relevant food insecurity? Unclear.

Response: Thank you for your comment. The word ‘relevant’ was extra in the sentence and has been removed from the sentence in the revised version of the manuscript to make it clearer.

Line 73: Mentions that 4.6 million people were food insecure - when? If the argument made is that food insecurity is likely worse, would be helpful to have more of a timeline setup. Recommend changing suffers to experience based on how food security is assessed.

Response: Thank you for your comment. Data representing the scenario of food insecurity in Nepal have been revised and presented along with the year. Changes suggested by the reviewers have been addressed in the revised manuscript.

Response: Thank you for your comments and constructive suggestions. However, authors felt that this sentence bears less resemblance in this paragraph. Hence this sentence has been removed completely.

Corona virus disease and social cultural are not commonly used terms to describe the virus and socio-cultural factors, respectively.

Response: Thank you for pointing this out. The word mentioned by the reviewer has been changed into much commonly used term. The word ‘Corona virus disease’ has been changed into just ‘Corona virus’ and the word the word ‘social cultural’ has been changed into ‘socio-cultural’ in the revised manuscript.

Unclear what the sentence that runs on into lines 83-84 means.

Response: This sentence has been paraphrased into simple and clear ones. Thank you.

Response: Thank you for your comment. A sentence has added there to highlight the context pointed out by the reviewer and presented as per the reviewer’s suggestions.

Methods: Why did the authors decide on Lalitpur as a study site given the interest in rural areas that might be best studied elsewhere in the country? How many urban vs rural municipalities in Lalitpur are there? What was the rationale for Bagmati province? Please provide clearly the primary sampling unit and detail the systematic random sampling method more elaborately. What are quantitative techniques? Please provide explanation of what gaupalikas are, what was the inclusion criteria were for the study and who specifically in the household was interviewed (if household head provide definition of household head). I recommended this last part as throughout the methods, results and discussion, the authors weave in and out of household versus individual level language.

Response: There are a total of six municipalities in Lalitpur district. Of these, there is one metropolitan city, two of them are urban municipalities and remaining three are rural municipalities. Rural municipalities of Lalitpur are; Konjyoson rural municipality, Bagmati rural municipality and Mahankal rural municipality. Being near to the capital city of Nepal, rural municipalities of Lalitpur are often neglected and have the similar socio-economic conditions as other rural municipalities of the country. Because of this scenario, authors of this study decided to select these study sites. In addition, there was limitation on budget, the authors carried out this study with a small grant that was provided by the Nepal Health Research Council. Hence, choosing the rural areas in the vicinity of Kathmandu valley provided nearly the similar picture of distant rural areas, served the purpose of the study. We have removed “Bagmati Province” as the study is more relevant to rural settings within the district and similar context. We have revisited the sampling technique part of the methodology section to elaborate more on this part. By quantitative techniques we mean to say those having experience in quantitative data collection methods such as household survey using questionnaire through face to face interview. But since we felt this much elaboration about research assistant may not be required, we have deleted that part. A gaupalika or rural municipality is one of the administrative division of Nepal and is a sub-unit within the district. A rural municipality within the district is considered as the primary sampling unit. Samples were equally divided into three rural municipalities (144 in each). Within each of the three rural municipality, 4 wards (lowest administrative unit) were selected as secondary sampling unit followed by 28 households in each of those wards. Detail description has been added to the manuscript. Within the household, particularly the head of the household and in case of unavailability of the household head, the member of the family who is 18 years and above is available comprised our study population. Head of household is the member of household who is managing household activities and takes the decisions as well as responsibility in all household related matters.

Author would like to thank the reviewer in pinpointing such details. With regard to the comment of weaving in and out of the household versus individual level, the whole method section is being checked for the language and information flow. As suggested by the reviewer, the changes have been made in the revised manuscript.

Response: Thank you for your concern. Based on our literature review on household food security and dietary diversity during COVID-19 conducted prior to the study, we made choices of those factors used in the analysis. Based on the same literature review, authors of this study devised an outline including factor influencing household food insecurity and dietary diversity. Details on conceptual framework developed by the authors during proposal development of this particular study has been added in the revised manuscript and submitted as a supplementary file 1. The authors, however, did not use any particular theoretical framework to base the current analysis.

Line 145: How was family members use to assess impact of COVID 19 The use of the word 'impact' may ve too strong of language given what the authors are referring to. There are discrepancies between the what data is reported to be collected during the interviews and what comes into the reporting further down the line in the paper (household size, etc).In line 159-165 explain household consumption vs individual because as it reads now, it is not clear.

Response: Thank you for your comments. As it is already known that family type effect household food security and Dietary diversity, this has been considered one of the variables of interest to see how the size of family effects HFS and DDS of household’s being survey during COVID-19.The words ‘family members’ has been replaced with family type. The word ‘impact’ has been replaced with ‘effect’. The whole method and results section of this manuscript is checked and changes have been made to omit the existing discrepancies. The lines from 159-165 were checked for its consistent flow of information.

The authors report using semi-structured interviews - what was the unstructured portion of the interview? What were the type of questions were asked?

Response: Authors would like to thank reviewer for comments and pointing out this mistake in our manuscript. Our interview questionnaire was completely structured. Changes have been made wherever there was mention of ‘semi-structured interview’ with ‘structured interview’ within the manuscript.

Line 188: add specifics, participants were heads of hh

Response: Thank you for your comments. Among the participants, there were 324 heads of households. This information has been mentioned in the revised manuscript as well.

Tables not formatted in a reader friendly format on page 17. Most of the tables to would benefit from some major reformatting.

Response: Thank you for your comment. Reformatting of table has been done by the authors to make it more reader friendly taking consideration of journal requirement of PLOS ONE.

Response: Thank you for your comments. Caste and religious grouping performed within our study was meant to reflect the exact scenario of food security and nutritional adequacy among multiple ethnic groups residing in such rural settings. As it is a well established fact that there exists a barrier among certain caste and religious groups in accessing public services and resources in the past. In reference to these, authors of this study decided on caste and religious groupings and see whether these existed or not during the emergencies. Details on assessment of medical history and disability have been added in the socio-economic characteristics of the participant in the revised manuscript.

What is driving the age categorization cut offs?

Response: Thank you for your comments. Since our study participants primarily targeted the household head, we assumed that they may be over 40 and have defined the cut offs accordingly, and wanted to have one category for above 40 years but leaving the elderly(65 and above) in separate category, while younger population that is up to 40 years age was kept in one category. Age categorization cut-off was basically driven by the person’s role in the household and his/her involvement as well as knowledge in overall household activities of that particular household.

Results: The results need to be more clearly explained with more frequent mention of reference groups all the results being discussed are compared to.

Response: Thank you for your comments. All the results obtained in this study have been presented with the reference groups.

Discussion:

Response: Thank you for your comments. Authors really do acknowledge the concern of this particular reviewer. However, there were only limited article available in the field of food security and dietary diversity in Nepal during COVID-19 that we could get hold of. Further to this, there might be paucity of evidence targeting food security and dietary diversity among households of rural skirts during the pandemic. Because of these reasons, authors were bound to present limited contextual information on food security data during COVID-19. Few changes have been made and additional information on the effect of COVID-19 on livelihood of Nepalese population has been included in the discussion section of both revised manuscript and manuscript with track change.

Authors of this study agree to this particular reviewer on the fact that this study isn’t a representative sample of Lalitpur, however it may not be true that this study does not represent the typical rural area of Nepal. The rural areas of Lalitpur are truly rural and are similar to majority of the rural areas of Nepal though they seem to be near the capital city. Municipalities here in Nepal are divided into urban and rural municipalities based on multiple developmental features available there, and population that resides there within. Although, this study couldn’t represent every rural area of Nepal, it definitely provides us with the overview on the situational context of most of the rural parts of this country. All these details have been added in the revised manuscript.

Line 274: Would be very careful about using causal language in a cross-sectional paper of non-representative population.

Response: Thank you for your valuable suggestion. We have checked the write up and replaced with a better alternative to the words reflecting causal language.

Response: Authors of this study would like to thank the reviewer for comments and constructive feedbacks. We have addressed and incorporated all the suggestions made by the reviewer. We have specifically made changes in the conclusion section and all other important areas that have been suggested in the revised manuscript.

Thank you.

Attachment

Submitted filename: Response to reviewers.docx

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PLoS One. doi: 10.1371/journal.pone.0293514.r003

Decision Letter 1

George N Chidimbah Munthali

24 Aug 2023

PONE-D-22-20978R1Title-Household Food Security access and Dietary Diversity amidst COVID-19 Pandemic in rural Nepal; an evidence from rapid assessmentPLOS ONE

Dear Dr. K.C.,

Please submit your revised manuscript by Oct 08 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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George Vousden

Staff Editor

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Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: No

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Commend the authors for going through a full revision of the manuscript. This paper's focus remains important and valuable. My main concern that remains is the use of causal language and language use to depict results which is not always consistent and thus confusing for the reader.

Assuming revised manuscript starts from page 43.

Page 43, Line 21: Please say 'COVID-19 pandemic' for specficity.

Line 34: Think you mean to say Household Dietary Diversity Score (HDDS)...score is missing.

Lines 34-36: Tenses used are not consistent - "whereas age and education are the predictors of HDDS", all other results are presented in past tense which makes sense. Also, when we say some is a predictor, there need some indication of directionality. Isn't the intent to say that age, ethnicity, educ, occupation are predictors of household food insecurity?

Lines 105-107: Here and in the abstract to there is still alot of use of causal language such as 'effects'. This is a cross-sectional and observational study. Any the analytic methods leveraged are not able to discuss any form of causal inference. Would temper language to just say that this is study examines the influence of the COVID-19 pandemic on household food security and household dietary diversity among rural communities in Nepal. Lines 109-113 again speak of impact.

Results section: The reporting of the results move back/ forth between discussing households as the unit of measurement to individual to families. Would encourage authors to provide some directionality when discussing associations or just state the ORs (lines 228-232).

Lines 238 + para that follows - Odds ratio do no reflect risk. The reflect odds. This section weaves in and our of reporting risk/odds.

Lines 289 - HFIAS is an indicator for food insecurity and HDDS is for dietary diversity, would simplify language to say thus say 'Despite the concerning high levels of food insecurity, household dietary diversity (HDDS>4) was low/medium for 63% of households in the study population and high for 36.8% of households.

Would recommend strengthening the strength/limitations section.

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

**********

PLoS One. 2023 Nov 1;18(11):e0293514. doi: 10.1371/journal.pone.0293514.r004

Author response to Decision Letter 1

3 Oct 2023

Journals requirements

Author’s response: We have checked all the included reference article used in this manuscript from the reference list. We couldn’t find a retracted article in our reference list and were published online in health journals. However, we hereby want to notify PLOS ONE team that we have updated the whole reference section in our updated manuscript. To meet the journals requirement we have made no significant changes has been made up to reference number 18, however from reference number 19-43 the references have been corrected, and updated which has been accompanied in our manuscript with track change and revised manuscript.

Pont by point response to #Reviewer 1

Reviewer #1: Page 43, Line 21: Please say 'COVID-19' pandemic for specificity.

Author’s response: Thank you for your suggestion. We have made the suggested changes.

Reviewer #1: Line 34: Think you mean to say Household Dietary Diversity Score (HDDS)...score is missing.

Author’s response: Thank you for your suggestion. We have made changes as per the reviewer’s suggestion.

Reviewer #1: Lines 34-36: Tenses used are not consistent - "whereas age and education are the predictors of HDDS", all other results are presented in past tense which makes sense. Also, when we say some is a predictor, there need some indication of directionality. Isn't the intent to say that age, ethnicity, educ, occupation are predictors of household food insecurity?

Author’s response: Thank you for your critical observation. We must have overlooked this during our initial revision. Tenses in the results section of abstract were adjusted as per the reviewer’s suggestion. Yes, we intend to present age, ethnicity, education, and occupation as the predictors of household food insecurity access. Amendment has been done in our revised manuscript following the suggestions of the reviewer.

Reviewer #1: Lines 105-107: Here and in the abstract to there is still a lot of use of causal language such as 'effects'. This is a cross-sectional and observational study. Any the analytic methods leveraged are not able to discuss any form of causal inference. Would temper language to just say that this is study examines the influence of the COVID-19 pandemic on household food security and household dietary diversity among rural communities in Nepal. Lines 109-113 again speak of impact.

Author’s response: Thank you for your constructive feedbacks. We have adjusted the use of language such as “effects” and “impacts” as per the reviewer’s suggestion.

Reviewer #1: Results section: The reporting of the results move back/ forth between discussing households as the unit of measurement to individual to families. Would encourage authors to provide some directionality when discussing associations or just state the ORs (lines 228-232).

Lines 238 + para that follows - Odds ratio do not reflect risk. The reflect odds. This section weaves in and out of reporting risk/odds.

Author’s response: Thank you for your comments and suggestions. As this study measures household food insecurity and dietary diversity among the households and the presented results are based on household as well as the participants. Some of our results needed to be presented based on the households characteristics whereas some needed to be presented based on the participants of the study. Hence, we have adjusted reporting of the results focusing on the households and individual participants interviewed wherever needed. We intended to present the socio-economic characteristics of our study participants in the line 228-232 based on the chi-square results obtained, their further association and ORs are discussed in the paragraph that follows whereby results of our bivariate and multi-variate analysis have been presented (line 239-248 of our tack change manuscript). We have adjusted our result section as per the reviewer’s suggestion and presented with reporting of odds wherever reviewer have pointed the need of reflecting odds.

Reviewer #1: Lines 289 - HFIAS is an indicator for food insecurity and HDDS is for dietary diversity, would simplify language to say thus say 'Despite the concerning high levels of food insecurity, household dietary diversity (HDDS>4) was low/medium for 63% of households in the study population and high for 36.8% of households.

Author’s response: Thank you for your suggestion and directionality. We have adopted and modified line 289 exactly as per the reviewer’s suggestion.

Reviewer #1: Would recommend strengthening the strength/limitations section.

Author’s response: Thank you for your constructive comments. A line has been added to strengthen our strength/limitation section as per the reviewer’s suggestion.

Attachment

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PLoS One. doi: 10.1371/journal.pone.0293514.r005

Decision Letter 2

George N Chidimbah Munthali

16 Oct 2023

Household food security access and dietary diversity amidst COVID-19 pandemic in rural Nepal; an evidence from rapid assessment

PONE-D-22-20978R2

Dear Dr. K.C.,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

George N Chidimbah Munthali

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0293514.r006

Acceptance letter

George N Chidimbah Munthali

23 Oct 2023

PONE-D-22-20978R2

Household food security access and dietary diversity amidst COVID-19 pandemic in rural Nepal; an evidence from rapid assessment

Dear Dr. K.C.:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Mr George N Chidimbah Munthali

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Conceptual framework on household food security access and dietary diversity amidst COVID-19 pandemic.

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Data Availability Statement

All the relevant data are within the paper.

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PERMALINK

Household food security access and dietary diversity amidst COVID-19 pandemic in rural Nepal; an evidence from rapid assessment

Dirghayu K C

Namuna Shrestha

Rachana Shrestha

Dev Ram Sunuwar

Anil Poudyal

Roles

Abstract

Background

Methods

Results

Conclusion

Background

Methodology

Study design, study setting, and sample size

Sampling technique

Data collection procedure

Socio-demographic characteristics

Household Food Insecurity Access Scale

Household Dietary Diversity Score

Data analysis and management

Ethical approval

Results

Socio-demographic characteristics of the participants

Table 1. Socio demographic characteristics and COVID related factors by household food security status and household dietary diversity status of the participants.

Status of household’s food security access

Fig 1. Household’s food security status.

Association of socio-demographic and COVID related variables with household dietary diversity and household food insecurity score

Table 2. Factors associated with household food security and insecurity status and household dietary diversity status of the participants.

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Md Nazirul Islam Sarker

Roles

Author response to Decision Letter 0

Decision Letter 1

George N Chidimbah Munthali

Roles

Author response to Decision Letter 1

Decision Letter 2

George N Chidimbah Munthali

Roles

Acceptance letter

George N Chidimbah Munthali

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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