Characterizing multidimensional poverty in Migori County, Kenya and its association with depression.

INTRODUCTION: Narrow, unidimensional measures of poverty often fail to measure true poverty and inadequately capture its drivers. Multidimensional indices of poverty more accurately capture the diversity of poverty. There is little research regarding the association between multidimensional poverty and depression.
METHODS: A cross-sectional survey was administered in five sub-locations in Migori County, Kenya. A total of 4,765 heads of household were surveyed. Multidimensional poverty indices were used to determine the association of poverty with depression using the Patient Health Questionnaire (PHQ-8) depression screening tool.
RESULTS: Across the geographic areas surveyed, the overall prevalence of household poverty (deprivation headcount) was 19.4%, ranging from a low of 13.6% in Central Kamagambo to a high of 24.6% in North Kamagambo. Overall multidimensional poverty index varied from 0.053 in Central Kamagambo to 0.098 in North Kamagambo. Of the 3,939 participants with depression data available, 481 (12.2%) met the criteria for depression based on a PHQ-8 depression score ≥10. Poverty showed a dose-response association with depression.
CONCLUSIONS: Multidimensional poverty indices can be used to accurately capture poverty in rural Kenya and to characterize differences in poverty across areas. There is a clear association between multidimensional poverty and depressive symptoms, including a dose effect with increasing poverty intensity. This supports the importance of multifaceted poverty policies and interventions to improve wellbeing and reduce depression.

Introduction

Poverty reduction has long been a focus of international development, including the first Millennium Development Goal, which was to eradicate extreme poverty and hunger by 2015 [1]. Subsequently, the first Sustainable Development Goal is to end poverty in all its forms by 2030 [2]. Both of these goals include metrics that utilize unidimensional measures of poverty, specifically the use of $1.25 daily to represent extreme poverty.

Despite ease of conceptualization and widespread use, unidimensional measures of poverty are problematic and have faced significant criticism [3,4]. Specifically, these narrow measures have been criticized for inaccurately measuring true poverty and, more importantly, inadequately capturing its drivers. The Alkire-Foster method of measuring multidimensional poverty offers a more robust measurement system [5]. This method has been applied in a broad range of geographic and cultural contexts across Latin America, Africa, and Asia by the Oxford Poverty and Human Development Initiative. It uses metrics across education, health, and living standards dimensions to create an aggregate measure of multidimensional poverty. It has since been used to create the multidimensional poverty index (MPI) used by the United Nations Development Program (UNDP) and has been adopted by many Demographic and Health Surveys (DHS), including the Kenya DHS [6].

The link between financial challenges and depression has been reported in a wide variety of contexts from rural Nigeria [7] to Hong Kong [8]. This relationship can be bidirectional, as those with depressive disorders have been reported to have lower long-term earnings [9]. In addition, this relationship has shown it can be generational, with childhood poverty being associated with depression in adults [10,11]. Few studies have utilized a multidimensional approach to measure poverty and depression, but studies that have looked at non-monetary measures of poverty have generally found a stronger association with depression than monetary measures. Lacking daily necessities was more strongly associated with depressive symptoms than monetary poverty in a Japanese cohort [12]. Similarly, agricultural wealth was more strongly associated with reduction in depressive symptoms than cash wealth in Haiti [13]. A study in Australia found that 26% of those with multidimensional poverty had self-reported depression or a mood disorder and that those with a mood disorder were nearly seven times more likely to live in multidimensional poverty [14]. We are not aware of any studies in low-income countries that examine the relationship between multidimensional poverty and depression.

Monetary and multidimensional measures of poverty often identify different groups of people as poor [15]. This may reflect differences in abilities, whether at the individual or community level, to translate monetary wealth into resources and improved socioeconomic outcomes. Mental state, and therefore depressive symptoms, are more likely to be influenced by true deprivation than by monetary poverty alone. Based on this, we hypothesized that a multidimensional, deprivation-based metric would more accurately capture the relationship between poverty and depression. Further, the transient nature of both employment and agricultural income has made the quantification of monetary wealth difficult in our population. Multidimensional measures not only more accurately capture poverty but are also easier to implement in this and similar contexts.

This work was conducted as a part of ongoing research and evaluation efforts of the Lwala Community Alliance (Lwala). Lwala is an organization that serves to promote the health and well-being of communities in Migori County, Kenya. Little is known about the burden or prevalence of depression and depressive symptoms in Migori County. This study aimed both to adapt the Alkire-Foster method to Migori County, Kenya and to examine the relationship between multidimensional poverty and depression in this context. We hypothesized that those experiencing multidimensional poverty would be more likely to demonstrate depressive symptoms.

Methods

Ethical considerations

This study was approved by the Ethics and Scientific Review Committee at Amref Health Africa (AMREF-ESRC P452/2018) and the Institutional Review Board at Vanderbilt University Medical Center (#161396). Written informed consent was obtained from all participants. Approval was also obtained from the local area chief and Ministry of Health. Respondents were provided contact information for a mental health counselor if concerns were identified, and relevant referrals were made.

Study design

The design of the Lwala household survey has been described in detail elsewhere [16-18]. Briefly, a cross-sectional survey was administered in five sub-locations in Migori County, Kenya between May 2018 and July 2019. This is part of an ongoing, repeated, cross-sectional survey study to measure public health metrics in the region. Data related to demographics, poverty, and depression were extracted from the larger dataset for the purpose of this analysis.

Survey

The survey contained more than 300 questions across multiple domains and was modeled on several validated tools, including the Kenya DHS (S1 Appendix) [6]. Surveys were administered by trained surveyors using REDCap electronic data capture tools [19,20]. All surveyors were hired from the community and were fluent in English, Dholuo, and Swahili. Survey responses were de-identified, and participants received 50 KES (~$0.50 USD) in cellphone airtime for their time.

Study population

A total of 4,765 heads of household were surveyed. The survey was administered to the female head of household if present and the male head of household if not. Respondents had to be at least 18 years of age. Four thousand and sixty (85.2%) respondents had complete responses needed to calculate the household poverty score. An additional 121 (2.5%) had missing data for responses to depression questions. Given the relatively large proportion of missing data and significant differences between included and excluded respondents, multiple imputation using predictive mean matching and five imputed datasets per analysis were used for regression analyses.

Statistical analysis

We calculated an MPI using an adaptation of the recommendations from the Oxford Poverty and Human Development Initiative (OPHI) [21]. First, the household deprivation score (HDS) was determined for each household by summing the weighted deprivations for the household. Table 1 shows the components of the HDS and the cutoffs for deprivation. Households with an HDS of at least 33.3% were classified as multidimensionally poor, which is a predefined cutoff for the MPI [21]. To calculate MPI, the proportion of households above the poverty threshold (> 33.3% deprivation) in a region was multiplied by that region’s average percentage of deprivations (deprivation intensity). This was adapted from the OPHI methodology to calculate household-level poverty in place of individual-level poverty.

Table 1

Components of Household Deprivation Score (HDS).

Dimension	Deprived if	Weight
Education
Years of education	Respondent has not achieved at least Class 6	1/6
School attendance	Any school-aged child in the household is not attending school	1/6
Health
Nutrition	Any member of the household has been referred to a health facility for malnutrition	1/6
Child mortality	Any child has died in the household in the five-year period preceding the survey	1/6
Living standards
Cooking fuel	The household cooks with firewood, paraffin, or charcoal	1/18
Sanitation	The household has no facility, a traditional pit toilet, shares neighbor’s traditional pit or improved pit latrine, or sanitation is not usable	1/18
Drinking water	The household uses an unprotected well, unprotected spring, or surface water	1/18
Electricity	The household has no electricity or uses firewood or paraffin for lighting	1/18
Housing	The household floor is made of earth or wood	1/18
Assets	The household does not own more than one of the following assets: radio, TV, fridge, cell phone, and bicycle	1/18

Further, the survey contained an eight-question Patient Health Questionnaire depression scale (PHQ-8) that can be used both for individuals and population-based studies [22]. Each question has scores ranging from zero to three, which are summed to give a total score. A cutoff of 10 or higher is considered positive for depression.

Further analyses were performed using logistic regression to determine the relationship of variables with multidimensional poverty. Logistic regressions were performed using inverse probability weighting and robust sandwich estimators. Sensitivity analyses were performed using linear regression and HDS as a continuous variable as well as ordinal regression and PHQ-8 score as an ordinal variable. All analyses were performed using R (version 3.5.3) and the survey library [23].

Results

Surveyed individuals were generally similar across areas surveyed (Table 2). The median age was 30, and 81% were women. About 76% of respondents were married and monogamous, and the majority (54%) had not progressed past primary school. The majority were employed by an employer (59%) while a smaller group were self-employed (16%) or worked in agriculture (23%). Demographics were generally similar across areas. Fewer individuals were employed in agriculture in more urban Central Kamagambo than in other more rural areas.

Table 2

Descriptive statistics.

N(%)	NK N = 228	EK N = 696	CK N = 1,199	SK N = 1,086	Uriri N = 851	Total N = 4,061
Age Median [IQR]	30 [25, 36]	31 [26, 38]	29 [25, 35]	30 [25, 36]	29 [25, 35]	30 [25, 35]
Household Size Median [IQR]	5 [4, 6]	5 [4, 6]	4 [3, 5]	4 [3, 6]	4 [3, 5]	4 [3, 6]
Sex
Male	57 (25.0)	193 (27.7)	166 (13.9)	184 (16.9)	182 (21.4)	782 (19.3)
Female	171 (75.0)	503 (72.3)	1030 (86.1)	902 (83.1)	669 (78.6)	3275 (80.7)
Marital Status
Never Married	4 (1.8)	21 (3.0)	62 (5.2)	52 (4.8)	34 (4.0)	173 (4.3)
Married (monogamous)	178 (78.1)	514 (73.9)	935 (77.9)	847 (77.9)	625 (73.4)	3099 (76.4)
Married (polygamous)	31 (13.6)	125 (17.9)	101 (8.4)	83 (7.6)	112 (13.2)	452 (11.1)
Cohabitating	0 (0.0)	0 (0.0)	9 (0.8)	13 (1.2)	2 (0.2)	24 (0.6)
Divorced/Separated	2 (0.9)	6 (0.9)	27 (2.3)	12 (1.1)	18 (2.1)	65 (1.6)
Widowed	13 (5.7)	29 (4.2)	65 (5.4)	79 (7.3)	60 (7.0)	246 (6.1)
Education Level
No School	7 (3.1)	12 (1.7)	15 (1.3)	11 (1.0)	14 (1.6)	59 (1.4)
1–4 Years	9 (4.0)	33 (4.7)	25 (2.1)	28 (2.6)	16 (1.9)	111 (2.7)
5–8 Years	143 (62.7)	406 (58.3)	478 (39.9)	528 (48.6)	486 (57.1)	2041 (50.3)
9–12 Years	61 (26.8)	188 (27.0)	472 (39.4)	410 (37.8)	273 (32.0)	1404 (34.6)
Some College	8 (3.5)	57 (8.2)	209 (17.4)	109 (10.0)	62 (7.3)	445 (10.9)
Employment
Labor/Employed	132 (57.9)	397 (57.0)	770 (64.4)	687 (63.3)	418 (49.3)	2404 (59.3)
Livestock/Agriculture	54 (23.7)	175 (25.1)	129 (10.8)	255 (23.5)	314 (37.0)	927 (22.9)
Self-employed	38 (16.7)	112 (16.1)	266 (22.2)	134 (12.3)	108 (12.7)	658 (16.2)
Other/Don’t Know	4 (1.6)	12 (1.7)	31 (2.6)	10 (0.9)	8 (0.9)	65 (1.6)

Multidimensional poverty index

Across the geographic areas surveyed, the overall prevalence of household poverty (deprivation headcount) was 19.4%, ranging from a low of 13.6% of households in Central Kamagambo to a high of 24.6% of households in North Kamagambo. However, across all regions, the average poverty intensity was nearly constant. MPI regional scores for the areas surveyed were substantially lower than the most recently available national data from the 2014 Kenya DHS [24]. Table 3 shows the deprivation headcount and average poverty intensity for each area, which are multiplied to obtain the MPI.

Table 3

Multidimensional poverty index.

	MPI (H×A)	Deprivation Headcount (H)*	Average Poverty Intensity (A)**
North Kamagambo	0.098	24.6%	0.398
East Kamagambo	0.087	22.1%	0.391
Central Kamagambo	0.053	13.6%	0.390
South Kamagambo	0.074	19.0%	0.388
Uriri	0.096	24.5%	0.390
2014 Kenya [24]	0.178	38.7%	0.460

*Deprivation Headcount (H) = percent of households > 33.3% deprived per region.

**Average Poverty Intensity (A) = average proportion of deprivations experienced by households in a region.

The domain areas of deprivation were generally similar across surveyed areas (Table 4). Notably, deprivation in sanitation facilities was much lower in North and East Kamagambo than in other areas. Central Kamagambo had lower rates of deprivation in drinking water, electricity, and housing than other areas. Rates of deprivation in living standards dimensions were generally higher than in education and health dimensions.

Table 4

Domain areas of deprivation.

N(%)	NK N = 228	EK N = 696	CK N = 1,199	SK N = 1,086	Uriri N = 851	Total N = 4,061
Education
Years of education	26 (11.4)	71 (10.2)	63 (5.3)	59 (5.4)	69 (8.1)	288 (7.1)
School attendance	40 (17.5)	98 (14.1)	120 (10.0)	85 (7.8)	66 (7.8)	409 (10.1)
Health
Nutrition	6 (2.6)	15 (2.2)	13 (1.1)	12 (1.1)	5 (0.6)	51 (1.3)
Child mortality	8 (3.5)	27 (3.9)	44 (3.7)	24 (2.2)	34 (4.0)	137 (3.4)
Living standards
Cooking fuel	223 (97.8)	658 (94.5)	988 (82.4)	990 (91.2)	827 (97.2)	3,686 (90.8)
Sanitation	7 (3.1)	25 (3.6)	777 (64.8)	869 (80.0)	707 (83.1)	2,385 (58.7)
Drinking water	159 (69.7)	464 (66.7)	451 (37.6)	608 (55.9)	570 (66.9)	2,252 (55.5)
Electricity	88 (38.6)	243 (34.9)	246 (20.5)	301 (27.7)	229 (26.9)	1,107 (27.3)
Housing	145 (63.6)	408 (58.6)	381 (31.8)	650 (59.9)	605 (71.1)	2,189 (53.9)
Assets*	107 (46.9)	282 (40.5)	421 (35.1)	476 (43.8)	340 (39.9)	1,626 (40.1)

NK = North Kamagambo; EK = East Kamagambo; CK = Central Kamagambo; SK = South Kamagambo.

Years of education: respondent has not achieved at least Class 6.

School attendance: any school-aged child in the household is not attending school.

Nutrition: any member of the household has been referred to a health facility for malnutrition.

Child mortality: any child has died in the household in the five-year period preceding the survey.

Cooking fuel: the household cooks with firewood, paraffin, or charcoal.

Sanitation: the household has no facility, a traditional pit toilet, shares neighbor’s traditional pit or improved pit latrine, or sanitation is not usable.

Drinking water: the household uses an unprotected well, unprotected spring, or surface water.

Electricity: the household has no electricity or uses firewood or paraffin for lighting.

Housing: the household floor is made of earth or wood.

Assets: household does not own more than one of the following assets: radio, TV, fridge, cell phone, bicycle.

Poverty and depression

Of the 3,939 participants for which depression data were available, 481 (12.2%) met the criteria for depression based on a PHQ-8 depression score ≥10. In logistic regression stratified by region and adjusted for age, household size, marital status, and income source, for every one-point increase in PHQ-8 score there was an associated increased likelihood of living in multidimensional poverty (OR 1.05, p < 0.001) (Table 5). Additionally, increasing age (OR 1.03, p < 0.001) and household size (OR 1.06, p = 0.046) were also associated with poverty. Similar trends and significances were seen when HDS was treated as a continuous variable using linear regression (Table A in S1 Table).

Table 5

Association with multidimensional poverty.

	OR	95% CI	p-value
PHQ Depression Score	1.053	(1.028, 1.078)	<0.001
Age	1.025	(1.015, 1.036)	<0.001
Household Size	1.063	(1.001, 1.128)	0.046

Increasing severity of HDS showed a dose effect relationship with a positive screen for depressive symptoms when adjusted for age, sex, and education (Table 6). Those classified as being severely poor had a two-fold increased likelihood of screening positive for depression compared to those classified as non-poor. Similar trends were seen when PHQ-8 was treated as an ordinal variable using ordinal regression (Table B in S1 Table).

Table 6

Poverty status and PHQ-8 depressive symptoms.

Household Deprivation Score	OR	95% CI	p-value
Non-poor (0–20% deprived)	Ref.	--	--
Vulnerable (21–33.2% deprived)	1.292	(0.971, 1.719)	0.079
Poor (33.3–50% deprived)	1.459	(1.024, 2.077)	0.036
Severely Poor (>50% deprived)	2.171	(0.959, 4.913)	0.063

Discussion

We have successfully applied the Alkire-Foster method to characterize multidimensional poverty at the sub-county level in Kenya. Some indicators have been substituted based on data availability in our survey, but dimensions and relative weighting were preserved to the extent possible. Of all households surveyed, roughly 19% met the criteria of being multidimensionally poor, reporting deprivations in >33.3% of domains measured. Poverty intensity was generally similar across areas, but prevalence varied. Interestingly, calculated MPI for all our surveyed areas was notably lower than the MPI most recently reported for Kenya based on the 2014 Kenya DHS [24]. This may reflect temporal change in poverty in the region or lower poverty in the region compared to Kenya as a whole. The next iteration of the Kenya DHS, currently in the planning stages, could allow a more direct temporal comparison.

There were several notable differences in relative deprivations across areas. Central Kamagambo showed less household deprivation across many living standards indicators, including drinking water source, electricity, and housing, and had a lower overall MPI than the other regions surveyed. This is not unexpected as Central Kamagambo is substantially more urban than the other areas surveyed. Additionally, both North and East Kamagambo had much lower rates of unimproved latrines in the sanitation indicator (3% vs. 60–85%). Although not directly evaluated by this study, this likely reflects longstanding sanitation programming by the Lwala Community Alliance in those two locations, which has not yet been expanded to other areas. These differences emphasize the need for a broad definition of poverty in order to capture true deprivation, as the domain deprivations across areas are different despite similar overall poverty.

Increasing household size was associated with an increased odds of being classified as multidimensionally poor (OR 1.06 per person, p = 0.046). This may reflect the increased cost that comes with supporting additional individuals and the general dilution of resources. Increasing age of the respondent was also associated with poverty (OR 1.03 per year, p < 0.001). This is less straightforward to interpret but may reflect lower earning potential as heads of household reach more advanced ages and social factors that have led older individuals to be the head of household [25].

The overall rate of depressive symptoms, as defined by PHQ-8 score of 10 or greater, was 12.2%. This is lower than other studies from Migori County, but these studies were conducted in specific sub-groups vulnerable to depression [18,26]. The national prevalence of Major Depressive Disorder in Kenya has been estimated at 5.15% [27]. The higher rate in our study may reflect that the PHQ-8 is a screening tool with high sensitivity that may overestimate prevalence.

We found poverty and depression to be associated in a dose-response relationship, with increasing odds of depressive symptoms in the respondent as their household`s poverty increased in severity. Although it is impossible to know the directionality of this association in a cross-sectional study, this provides important evidence of this association. The relationship between poverty and mental illnesses, including depression, in low- and middle-income countries is well described [28-30]. Most studies have focused on traditional definitions of poverty, including income, assets, and consumption [30,31]. However, income and consumption are less consistently associated with depression than other poverty dimensions [28]. This has led to a change in rhetoric from whether poverty is associated with depression to identifying what dimensions of poverty are associated with depression. For example, a recent study conducted in Ghana explored the relationship of energy poverty and depression in which energy poverty was defined based on the extent of deprivations in four dimensions and six indicators: electricity access, modern cooking fuel access, indoor air pollution, household appliance ownership, ownership of a radio or television, and means of telecommunications (mobile phone) [32]. They found that a deprivation in household appliance ownership had the highest impact on the depression levels of household heads. Our study builds on a very limited literature specifically using multidimensional poverty [12-14].

Limitations

The main limitation of this study is that its cross-sectional nature does not allow for determination of causal relationships or longitudinal analyses. Multiple imputation also had to be used due to missing data. Additionally, survey questions were written in English with interviewers trained on word choices when interviews were conducted in either Dholuo or Swahili. As such, there was potential for participant misunderstanding of concepts or loss of translation for specific wording.

Conclusions

The Alkire-Foster method allows for characterization of a multidimensional poverty index at the sub-county level in rural Kenya. Areas with overall similar poverty rates had differing rates of deprivation across categories, which emphasizes the need for a multidimensional poverty metric to capture the variable nature of poverty. Governments and organizations should account for this when measuring poverty. Further, there is a clear association between multidimensional poverty and depressive symptoms, including a dose effect with increasing poverty intensity. This supports the importance of multifaceted poverty policies and interventions to improve wellbeing and reduce depression.

Survey.

Complete survey as it was administered, although survey was digitized into a tablet-based program.

(DOCX)

Click here for additional data file.

Supplemental tables.

Sensitivity analyses using continuous and ordinal outcomes in place of binary.

(DOCX)

Click here for additional data file.

17 Jun 2021

PONE-D-21-08861

Characterizing multidimensional poverty in Migori County, Kenya and its association with depression

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Reviewer #1: This study investigates the relationship between depression and the multidimensional poverty index (MPI). Using cross-sectional survey data collected in Migori County, Kenya, the study finds a statistically significant association between depression and MPI, supporting the results of the previous studies that show nexus between poverty and depression.

This is an interesting study that explores the association between the multidimensionality of poverty and depression. However, there are several essential issues to be addressed in both theoretical and empirical approaches.

Major Comments:

(1) Theoretical background on the relationship between MPI and depression should be discussed more in detail. As is evident from its definition, there is a strong correlation between MPI and conventional income/consumption poverty. Therefore, it is not surprising that the study finds a strong correlation between MPI and depression without controlling for income/consumption. The causal mechanism of why MPI, not income/consumption, affects depression is vital for interpreting the results. Unless these issues are clarified, the contribution of the study also remains unclear.

(2) There is a large room for improvement in the statistical analysis.

1. The current dependent variable is a binary indicator of depression. However, the original variable is continuous, and the authors discard much of the information when they construct the binary variable. A similar concern is also applied to the MPI. In addition to the current analysis, the authors should test the robustness of their findings by using the original variables as well.

2. The set of the control variables can be extended. For example, the authors can include region fixed effects to control for region-level unobserved heterogeneities. Furthermore, the standard errors should be clustered to control for the correlation within a cluster. Other sensitivity analyses would also be informative to test the robustness of the findings. Currently, the detailed empirical analysis is missing from the manuscript.

3. Since the non-negligible number of households is dropped from the analysis due to missing observations, it would be informative to compare other characteristics between dropped and non-dropped households.

Reviewer #2: Characterizing multidimensional poverty in Migori County, Kenya and its association with depression

The review

Overall, authors attempted to illustrate and put lucid most of the requirements of this work. It is with no qualm that, authors invested a lot of time to produce this work. It has been an interesting moment to review this work which might be used as benchmark for further improvement of the wellbeing within Kenya and other sub-Saharan African countries. Below, there are observations which would improve quality of this work. Some concerns are questions which require answers/ clarifications:

1. Ethical Considerations

-This section provided the process of acquiring permission to conduct this study from different authorities. However, the study did not indicate ethical procedures and their implications in course of executing the study. Given the health related nature of the topic, I feel there was a need to provide detailed ethical procedures and implications. For instance, what happened during the interview when your team realised that a respondent was depressed? What were the ethical measures in place?

2. Survey

-How did you ensure that, the 0.50 USD reimbursement was not a negative or positive aspect of your results?

3 .Statistical Analysis

-Households with HDS of at least 33.3 were classified with multidimensional poverty. While this might be a well calculated, this paper does not provide the reason for that cut off point

4. Descriptive Statistics

-In Table 2: Item 3, there is classification titled gender with male and female option. Does the author intended to present gender or sex. I have a feeling that the data available is for sex classification and not gender.

-In Table 2: Item 5 has classification of schooling level, with the second category having an option of 1-8 years which is primary education in Kenya. This might be a blanket classification as it constitute those who completed primary education and those with some primary education or rather termed as some primary education. It could have been good idea to separate the two and see the changes in the entire results section.

-In Table 2: Item 6: There is classification of employment which puts together Investment/Retired as one group. I am not sure of why the two were put together but I am certain they do not mean the same. One may have investment and also being retired at the same time.

5. Poverty and Depression

- In logistic regression data were adjusted for age, household size, marital status, and income source. However, other social variables such as belief/religion which are crucial for well-being were not included. What was the reason for non-inclusion of some important social variables?

Reviewer #3: The study is about Characterizing multidimensional poverty in Migori County, Kenya and its association with depression. The author has given a good attempt in the analysis; however, the present form of the paper is not eligible for publication and substantial revision is required before get it publish.

Major comment:

• Abstract should be restructured and clearly written

• Full form of PHQ?? should mention in the abstract.

• In the abstract, what is the conclusion and policy suggestion? It should briefly mention here.

• It was well known that there is an association between poverty and depression. What is new in this paper? What is the strength of the paper? This should mention clearly in the introduction section.

• In the results section, Study population-interpretation should be more elaborate.

• In line number 143-145, “MPI regional scores for the areas surveyed were substantially lower than the most recently available national data from the 2014 Kenya DHS [23]- Why low as compared to DHS, any specific reason?

• Under the poverty and depression section, first paragraph, which table supports the odd ratio values? Same in the discussion section. Please check it carefully. Suggest to present the regression table for clear reading and understanding.

• The survey questionnaire administrated for field data collection can be present as a supplementary document.

• Conclusion of the paper should be strengthened and current form of the write up is not impressive.

• What is the policy suggestion from the finding of the paper is missing?

**********

6. 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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16 Aug 2021

PONE-D-21-08861

Dear Editors,

We very much appreciate this thoughtful feedback on our submission and are happy to make modifications accordingly. We have copied the reviewers’ comments below and have given our responses, including any changes made to the manuscript, below each comment. We have also included both a version with tracked changes and a clean version of our manuscript with this submission.

Reviewer Comments

Reviewer #1

This study investigates the relationship between depression and the multidimensional poverty index (MPI). Using cross-sectional survey data collected in Migori County, Kenya, the study finds a statistically significant association between depression and MPI, supporting the results of the previous studies that show nexus between poverty and depression.

This is an interesting study that explores the association between the multidimensionality of poverty and depression. However, there are several essential issues to be addressed in both theoretical and empirical approaches.

Major Comments:

(1) Theoretical background on the relationship between MPI and depression should be discussed more in detail. As is evident from its definition, there is a strong correlation between MPI and conventional income/consumption poverty. Therefore, it is not surprising that the study finds a strong correlation between MPI and depression without controlling for income/consumption. The causal mechanism of why MPI, not income/consumption, affects depression is vital for interpreting the results. Unless these issues are clarified, the contribution of the study also remains unclear.

We agree that this theoretical background is very important to the contextualization of our work. Our previous draft included several citations that found a stronger association of deprivation with depression outcomes than monetary wealth. This forms the basis of our hypothesis that MPI is more strongly associated with depression than monetary poverty alone. Further, we have had difficulty in our population quantifying monetary wealth, which is not an uncommon problem in similar survey-based studies in similar areas. For example, the Kenya Demographic and Health Survey utilizes a deprivation-based metric in place of monetary wealth. We believe that MPI is both more reliably captured and more strongly associates with depression. We have included additional text in the Introduction to specify this.

(2) There is a large room for improvement in the statistical analysis.

1. The current dependent variable is a binary indicator of depression. However, the original variable is continuous, and the authors discard much of the information when they construct the binary variable. A similar concern is also applied to the MPI. In addition to the current analysis, the authors should test the robustness of their findings by using the original variables as well.

We appreciate the very thoughtful statistical suggestions from this reviewer. We agree that utilizing binary outcomes discards information and loses statistical power. We elected to use binary cutoffs (MPI >33.3% and PHQ-8 ≥10) because these are established cutoffs for the metrics and allow for simpler interpretation of results as odds ratios. We have performed additional analyses using the original variables and have added them as supporting information in S2 Tables. The overall trends are similar, which supports the robustness of the results.

2. The set of the control variables can be extended. For example, the authors can include region fixed effects to control for region-level unobserved heterogeneities. Furthermore, the standard errors should be clustered to control for the correlation within a cluster. Other sensitivity analyses would also be informative to test the robustness of the findings. Currently, the detailed empirical analysis is missing from the manuscript.

We have performed additional analyses as detailed above to support the robustness of findings. We have also added additional control variables to the dose-response regression, including age, sex, and education. Our reported model uses robust standard errors that account for the design that stratifies by region. As a sensitivity analyses, we also created a linear mixed effects model accounting for correlation within clusters. The standard errors were similar between the mixed effect and survey design models.

3. Since the non-negligible number of households is dropped from the analysis due to missing observations, it would be informative to compare other characteristics between dropped and non-dropped households.

We appreciate this very valid concern. On analyzing missing data, there were significant differences between those with complete data and those without. Significant differences included region, household size, marital status, and employment. To avoid biases based on this, we have redone the analyses using multiple imputation. The general trends in the results did not change, further supporting the robustness of the findings. We have updated the corresponding tables and text.

Reviewer #2

Characterizing multidimensional poverty in Migori County, Kenya and its association with depression

The review

Overall, authors attempted to illustrate and put lucid most of the requirements of this work. It is with no qualm that, authors invested a lot of time to produce this work. It has been an interesting moment to review this work which might be used as benchmark for further improvement of the wellbeing within Kenya and other sub-Saharan African countries. Below, there are observations which would improve quality of this work. Some concerns are questions which require answers/ clarifications:

1. Ethical Considerations

-This section provided the process of acquiring permission to conduct this study from different authorities. However, the study did not indicate ethical procedures and their implications in course of executing the study. Given the health related nature of the topic, I feel there was a need to provide detailed ethical procedures and implications. For instance, what happened during the interview when your team realised that a respondent was depressed? What were the ethical measures in place?

We agree that follow-up of identified depression is important. When depression was identified during the survey, the respondent was provided contact information for a mental health counselor. We have added text to the Methods to clarify this.

2. Survey

-How did you ensure that, the 0.50 USD reimbursement was not a negative or positive aspect of your results?

Because it is not uncommon for residents within the catchment area to work for $1 per day, we selected 50 KES to avoid coercing potential participants to participate for financial benefit. Similarly, the GNI per capita in Kenya is estimated by the World Bank as $1,340 annually. This works out to approximately $5.36 per working day, which is $0.67 per hour.

3. Statistical Analysis

-Households with HDS of at least 33.3 were classified with multidimensional poverty. While this might be a well calculated, this paper does not provide the reason for that cut off point

We agree that this was unclear in our manuscript. This is a predefined cutoff used in the Multidimensional Poverty Index (see reference 20 of the manuscript). We have added language to the Methods to clarify this.

4. Descriptive Statistics

-In Table 2: Item 3, there is classification titled gender with male and female option. Does the author intended to present gender or sex. I have a feeling that the data available is for sex classification and not gender.

It is correct that we intended to mean sex and not gender. We have changed the table to reflect this.

-In Table 2: Item 5 has classification of schooling level, with the second category having an option of 1-8 years which is primary education in Kenya. This might be a blanket classification as it constitute those who completed primary education and those with some primary education or rather termed as some primary education. It could have been good idea to separate the two and see the changes in the entire results section.

We agree that this is an important distinction. We have updated the table to separate these two groups.

-In Table 2: Item 6: There is classification of employment which puts together Investment/Retired as one group. I am not sure of why the two were put together but I am certain they do not mean the same. One may have investment and also being retired at the same time.

We agree that these employment classifications are quite different. On review of this table, several small categories had very small cell counts. Splitting all of these into separate categories made regressions difficult due to large confidence intervals from small cell counts. We have now grouped the variables here into a larger Other category so that the categories represent those used in later regressions. We believe this makes it easiest for readers to follow the analyses.

5. Poverty and Depression

- In logistic regression data were adjusted for age, household size, marital status, and income source. However, other social variables such as belief/religion which are crucial for well-being were not included. What was the reason for non-inclusion of some important social variables?

We agree that religion has an effect on resilience and depression. We did not include this in our model because nearly all (99.8%) of respondents in our survey population identify with a religion with the vast majority identifying as Catholic or Protestant. Because of this, the odds ratios remain essentially unchanged with the inclusion of a religion variable. We have added other additional adjustment variables (see response above).

Reviewer #3

The study is about Characterizing multidimensional poverty in Migori County, Kenya and its association with depression. The author has given a good attempt in the analysis; however, the present form of the paper is not eligible for publication and substantial revision is required before get it publish.

Major comment:

Abstract should be restructured and clearly written

We have reviewed the abstract and made changes in the hopes of making it more clear. We would be happy to make additional changes if specific portions remain unclear.

Full form of PHQ?? should mention in the abstract.

We have used the PHQ-8, which is the PHQ-9 without the final question regarding suicidal ideation. This question has been omitted due to community concern about the sensitive nature of this question. We have added this to the abstract to clarify.

In the abstract, what is the conclusion and policy suggestion? It should briefly mention here.

We have added text to the end of the abstract to state potential policy implications of this work.

It was well known that there is an association between poverty and depression. What is new in this paper? What is the strength of the paper? This should mention clearly in the introduction section.

Although the association between monetary poverty and depression is established, there have been almost no studies investigating the relationship between multidimensional poverty metrics and depression. In our literature search, only a single study from Australia investigated this relationship (citation 14). Our paper builds substantially on this work by extending it to a rural African setting and establishing a dose-response relationship not previously described. We have added text to the Introduction to make this more clear.

In the results section, Study population-interpretation should be more elaborate.

We have added text to elaborate on the study population.

In line number 143-145, “MPI regional scores for the areas surveyed were substantially lower than the most recently available national data from the 2014 Kenya DHS [23]- Why low as compared to DHS, any specific reason?

We believe this may reflect temporal improvements in poverty in the region, as the last Kenya DHS was in 2014. It may also reflect lower poverty rates in the surveyed areas than in Kenya as a whole. We have added text to the Discussion stating this.

Under the poverty and depression section, first paragraph, which table supports the odd ratio values? Same in the discussion section. Please check it carefully. Suggest to present the regression table for clear reading and understanding.

We had not previously created a table for this regression. We have added this to the paper to allow for easier reading. We have also updated other table numbers appropriately.

The survey questionnaire administrated for field data collection can be present as a supplementary document.

We agree that this will be helpful for readers to better understand the analyses. We have added the survey instrument as supporting information to our submission.

Conclusion of the paper should be strengthened and current form of the write up is not impressive.

We recognize the limitations of our cross-sectional study and have tried not to overstate conclusions. We have added to this section to elaborate further while trying not to overstate the implications of our study.

What is the policy suggestion from the finding of the paper is missing?

As our study was not designed to evaluate specific programs or policies, our ability to provide policy recommendations is relatively limited. However, we believe this work allows two policy conclusions: (1) measurements of poverty should use multidimensional metrics to accurately capture the variable nature of poverty, and (2) programs and policies aimed to combat poverty and depression should account for this variable nature of poverty. We have added text to the Conclusions section to specify this.

Thank you again for your consideration, and we look forward to hearing from you.

Sincerely,

Joseph R. Starnes

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

13 Sep 2021

PONE-D-21-08861R1Characterizing multidimensional poverty in Migori County, Kenya and its association with depressionPLOS ONE

Dear Dr. Joseph R. Starnes,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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PLOS ONE

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Additional Editor Comments (if provided):

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I would ask you to follow the reviewers' comments and suggestions and then please submit the revised version.

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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: (No Response)

Reviewer #3: All comments have been addressed

**********

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #3: Partly

**********

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

Reviewer #1: Yes

Reviewer #3: Yes

**********

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #3: Yes

**********

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: No

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I am glad to confirm that most of my comments are incorporated into the current manuscript.

However, theoretical backgrounds still need further explanation. There are many studies that established the association between poverty and depression. Then, why is it expected that MPI has a stronger association with depression than monetary wealth? What is the difference in the causal mechanism between MPI and monetary poverty?

The contribution comparing to the previous study is also not clear. Especially, the authors mention that Callander et al. (2013) investigated the same issue in the Australian context. Then, what is the advantage of the authors’ study?

Reviewer #3: The Authors have tried to incorporate most of the comments and congratulations for that. However, high-quality English editing is required. The abstract and Introduction part still need to improve in writing before it got to publish. Interpretation of the tables can be improved.

**********

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.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

6 Oct 2021

Reviewer #1

"I am glad to confirm that most of my comments are incorporated into the current manuscript.

However, theoretical backgrounds still need further explanation. There are many studies that established the association between poverty and depression. Then, why is it expected that MPI has a stronger association with depression than monetary wealth? What is the difference in the causal mechanism between MPI and monetary poverty?"

The theoretical background for this association is similar to the theoretical background for the multidimensional poverty index itself. An extensive discussion of this theoretical background is available through the OPHI (https://ophi.org.uk/multidimensional-poverty-measurement-and-analysis-chapter-1/). Simple income measures do not capture true deprivation, which is generally how those living in poverty conceive of their state. These deprivations are also the target of poverty mitigation policies and programs, not simply increased monetary income if this does not translate to less deprivation. Similarly, we believe a measure that actually captures deprivation—which is what is likely to affect mental state—more accurately captures the relationship with depression than a monetary measure which may or may not correlate with deprivation. It is also important to note the seasonality and unpredictability of monetary income in our setting (and similar settings), which makes the implementation of a unidimensional measure of monetary income very difficult and even less reliable. We have added further text to the Introduction to reflect the above.

"The contribution comparing to the previous study is also not clear. Especially, the authors mention that Callander et al. (2013) investigated the same issue in the Australian context. Then, what is the advantage of the authors’ study?"

The Callander study was performed in a high-income country, which is seen in the much lower rate of multidimensional poverty in their study. The rate was just 10% in the Australian context compared with nearly 20% in our low-income context in Kenya. Further, the Australian study utilized self-reported chronic illness or depression in place of our use of the PHQ-8. This is an important difference as many individuals, especially in low-resource settings, may not be formally diagnosed with depression. We feel that this expanded geographic and socioeconomic context as well as use of a validated screening tool allow us to build on the findings of Callander et al. We have added text to the Introduction to emphasize this.

Reviewer #3

"The Authors have tried to incorporate most of the comments and congratulations for that. However, high-quality English editing is required. The abstract and Introduction part still need to improve in writing before it got to publish. Interpretation of the tables can be improved."

We have reviewed and edited the paper accordingly, resulting in several grammatical changes. We believe the paper to be written in grammatically correct English but welcome any specific grammar corrections. We have also added additional text to assist with table interpretation.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

28 Oct 2021

Characterizing multidimensional poverty in Migori County, Kenya and its association with depression

PONE-D-21-08861R2

Dear Dr. Joseph R. Starnes,

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.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Shah Md Atiqul Haq

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Dear authors,

Congratulations!

The paper is accepted now.

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 #3: All comments have been addressed

**********

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #3: Yes

**********

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

Reviewer #1: Yes

Reviewer #3: Yes

**********

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: Yes

**********

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #3: Congratulations to the Authors that they have updated the comments in the revised version of the paper. Best wishes for your paper. Good luck.

**********

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4 Nov 2021

PONE-D-21-08861R2

Characterizing multidimensional poverty in Migori County, Kenya and its association with depression

Dear Dr. Starnes:

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.

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