Large household reduces dementia mortality: A cross-sectional data analysis of 183 populations.

BACKGROUND: Large households/families may create more happiness and offer more comprehensive healthcare among the members. We correlated household size to dementia mortality rate at population level for analysing its protecting role against dementia mortality.
METHODS: This is a retrospective cross-sectional study. Dementia specific mortality rates of the 183 member states of World Health Organization were calculated and matched with the respective country data on household size, Gross Domestic Product (GDP), urban population and ageing. Scatter plots were produced to explore and visualize the correlations between household size and dementia mortality rates. Pearson's and nonparametric correlations were used to evaluate the strength and direction of the associations between household size and all other variables. Partial correlation of Pearson's approach was used to identify that household size protects against dementia regardless of the competing effects from ageing, GDP and urbanization. Multiple regression was used to identify significant predictors of dementia mortality.
RESULTS: Household size was in a negative and moderately strong correlation (r = -0.6034, p < 0.001) with dementia mortality. This relationship was confirmed in both Pearson r (r = - 0.524, p<0.001) and nonparametric (rho  =  -0.579, p < 0.001) analyses. When we controlled for the contribution of ageing, socio-economic status and urban lifestyle in partial correlation analysis, large household was still in inverse and significant correlation to dementia mortality (r = -0.331, p <0.001). This suggested that, statistically, large household protect against dementia mortality regardless of the contributing effects of ageing, socio-economic status and urban lifestyle. Stepwise multiple regression analysis selected large household as the variable having the greatest contribution to dementia mortality with R2 = 0.263 while ageing was placed second increasing R2 to 0.259. GDP and urbanization were removed as having no statistically significant influence on dementia mortality.
CONCLUSIONS: While acknowledging ageing, urban lifestyle and greater GDP associated with dementia mortality, this study suggested that, at population level, household size was another risk factor for dementia mortality. As part of dementia prevention, healthcare practitioners should encourage people to increase their positive interactions with persons from their neighbourhood or other fields where large household/family size is hard to achieve.

1. Introduction

Dementia is an umbrella neurological syndrome resulting from more than 100 brain disorders, most common of which include Alzheimer’s disease, vascular diseases, Lewy bodies, frontotemporal disease etc. [1, 2]. Worldwide, it is estimated that 5–8% of general population aged 60 and over have been diagnosed with dementia in past years [2]. It has become one of the most common causes of dependency and disability among the elderly [1, 2].

Dementia produces an increasing societal burden resulting in a total economic costs of US$ 818 billion (1.1% of the world’s gross domestic product) in 2016 [2], which has been considered as the major challenging health issue in 21st century [3, 4]. Many health professionals do not follow the regulations or facility policies about human rights and freedom, and still consider physical or chemical restraints as the inevitable approach to control patients’ behavioural symptoms and prevent the disruption of life-sustaining therapies [2]. Furthermore, dementia patients and their families are subject to prejudice, and their life quality is affected [1].

Since 1906 when Alzheimer’s disease has been associated with dementia, enormous investment has been allocated for studying dementia prevention and treatment [5, 6]. Ageing and family history are associated with genetic background which are easily recognized as the irreversible risk factors for dementia [2, 7, 8]. The environmental and behavioural risk factors, such as sedentary lifestyle, lower socioeconomic status (SES) and unhealthy diet have been circumstantially postulated as the risk factors for dementia [7, 9–12]. Studies have consistently revealed that negative psychological functioning, such as depressive symptoms [13] and neuroticism [14] are the risk factors for dementia development. However, to date, the aetiology and pathology of dementia are still not well understood and there is still no treatment currently available to cure dementia or to reverse its progressive course [2]. A statistical study of the prevalence of dementia, or its ultimate result–mortality, globally and in countries grouped by their socioeconomic characteristics may shed light on the role environmental factors play in aetiology of this condition.

Human species had lived in small hunter-gatherer groups for millions of years before they started to live in large scale societies some ten thousand years ago [15]. During the hunting and gathering period, humans have well adapted to cooperative breeding [16, 17], and then evolved alloparental care [18]. Therefore, human’s millions of years of adaptation suggest that biological foundations of human love have genetically shaped humans for flourishing in small communities [19, 20]. However, in the last few hundreds of years, human societies were industrialized quickly. The rapid industrialization has made most people grow up in just core families with few siblings, which is different from how humans had adapted for flourishing. Such discrepancies or mismatches have been associated with mental health in human population [21, 22].

It is well researched that positive psychological well-being has been implicated in health across adulthood [23]. Household creates a social environment which is salient to maintain health for the co-residential members. On a daily basis, the individual members encounter this environment, play their social role and enjoy the social relations [24]. Moreover, studies also showed that large household offers the residents the subjective happiness [25] leading to low risk for residents to develop various cancers [26], for instance female breast cancer [27, 28] and ovarian cancer [29]. Subjective happiness was associated with mental health significantly stronger than with physical health in people with disabilities [30] and hospital patients [31]. A recent study revealed that greater household size has the protecting role against children developing mental health disorders [32].

The above considerations directed us to try to identify whether smaller household could serve as a risk factor for people to develop and die of dementia. Therefore, in this study, we assessed, from a global perspective, whether large household has the inhibitory role in lowering the risk for the residents to die of dementia using empirical population level data obtained from international organizations. This approach has been already used by our team in a number of studies on various other aspects of human health [26, 28, 29, 33–40].

2. Materials and methods

2.1 Study design

This is a retrospective cross-sectional study using data already reported by international organisations. In epidemiology this type of study is considered an “ecological analysis”

2.2 Data sources

The population level data were collected for this ecological study. A whole set of data was attached (S1 Data).

1. Population specific dementia mortality rate (DMR, per 100,000) was calculated as the dependent variable.

The comprehensive and comparable assessment of country specific number of deaths due to “Alzheimer disease and other dementias (GHE Code 950, Line 140)” and the country specific total population (ISO-3 Code, Line 10) were provided by the WHO Global Health Estimates [41, 42].

DMR estimates are integrated by the team of WHO Global Health Estimates considering the latest available mortality and cause distributions reported by each country and most recent information from various WHO programs for causes of public health importance. This increases the comparability of DMR between countries. WHO website published the description of data, methods and cause categories in a Technical Paper [43]. Table 4.1 and figures 1.1, 4.1, 9.1 in this document explain in detail how the number of deaths due to Alzheimer disease and other dementias estimates were obtained, while its Section 10 refers to quantitative uncertainty ranges that are available as part of the comprehensive GHE 2016 estimates on the WHO website. Though it is difficult to repeat here all careful considerations of the WHO team involved in obtaining as reliable as possible estimates it may be mentioned that no estimates were provided for WHO member countries whose populations were less than 90,000 in 2016. Thus, data for these countries are missing. Obviously, the estimates are not a 100% reliable data, but they are the best information available. The formula for dementia mortality calculation is below:

The United Nations and its agencies define the countries and territories in different ways. In order to avoid this conflict, both the country and territory are called “population” in the study.

2. The population specific household size was extracted from the United Nations Booklet as the predicting/independent variable [44]. In this document data sources are described as follows (Page 31): “The database comprises estimates of household size and composition obtained through analysis of microdata from the following data sources: Demographic and Health Surveys (dhsprogram.com), European Union Labour Force Surveys () and microdata samples maintained by Minnesota Population Center, Integrated Public Use Microdata Series, International: Version 6.4 [dataset]. Minneapolis, MN: University of Minnesota, 2015. . Selected estimates of the average household size and headship rates were obtained through secondary sources, including the Demographic Yearbook of the United Nations ( products/dyb/dyb_Household/dyb_household.htm) and published reports of censuses.”

The household is a fundamental socio-economic unit in human societies. It consists of one individual or a group of people, regardless of whether there are any kinship ties, living together for sharing food, shelter and other daily life essentials. Therefore, household refers to people living together in a housing unit who may or may not be family members.

Household relations are usually characterized with family relationships because they are invested with the powerful norms, histories, and emotions which originated from family [23, 45–47]. Therefore, in this study family and household are used interchangeably.

Considering the majority of dementia patients are cared for at home which is called “informal” care, household size may represent the level of care which the patients can receive.

3. The WHO published life expectancy at 60 (Life e(60)) was selected as a potential confounder [48].

Ageing has been a well-known significant risk predictor of dementia. Most of the studies include 65 years age as the start of ageing for reporting the prevalence and incidence of dementia. However, the United Nations generally use 60+ years to refer to the older population, and it takes years for dementia associated symptoms and signs to appear because development of most types of dementia is slow and progressive [49, 50]. Therefore, in this study, Life expectancy at age 60 years (e(60)) was considered as the indicator of ageing.

4. The World Bank published data on Gross Domestic Product (GDP) and urbanization were also included as the potential confounder [51]. The World Bank website provides detailed information explaining the validity and reliability of such data provided (http://opendatatoolkit.worldbank.org/en/supply.html in its section of Supply and Quality of Data).

GDP was expressed in per capita purchasing power parity (PPP in current international $) in 2010. SES has been associated with prevalence and mortality rates of dementia, and also with regional variation of dementia prevalence. GDP PPP was included as the confounding factor because it relates to the levels of healthcare service which affects the mortality rate. GDP may also affect quality of information reported to international organisations.

Urbanization was measured with the percentage of total population living in urban communities in 2010. Urbanization represents the demographic trend in which more and more population has become concentrated in urban communities. It entails air pollution, consumption of food with few nutritional benefits, but energy dense, high levels of salt, fat, sugar and alcohol. Urbanization is also associated with less physical exercise, obesity and overweight. Therefore, urban living has been considered as a complex risk factor for chronic diseases [52].

The international organizations, the United Nations, WHO and the World Bank have monitored and published the data on the population specific economic status, health risk factors and nutrition and diet intake for decades. These data have been assisting government policy-makers and funders to track and investigate priorities of health research and development based on public health needs, while ensuring that funds and resources are used to meet the priorities. Their data have been more and more used in academic area for identifying the relationships between population level health and their risk factors. For example, recently they were used to examine the relationships between nutrients and obesity [35, 53–55], diabetes [33, 56–58], and the relationship between relaxed natural selection and obesity [36, 37]; type 1 diabetes [33] and cancers [28, 30, 36] respectively.

2.3 Data selection

In order to capture as many populations as we could to increase the sample size for this study, we kept the full list of 183 WHO member states (populations) which have the data available for dementia mortality rate calculations. Details of the way these data were produced are described in the WHO Technical Paper [43]. This paper specifies that: “… most recent vital registration (VR) data for all countries submitting VR data to the WHO Mortality Database (WHO MDB), where the VR data meets certain criteria for completeness and quality; … “were used together with careful estimates for other countries. [

Greater household size not only shows its beneficial effects on protecting residents from developing dementia, but also offers its supporting or caring role in reducing the risk for patient to die from dementia. The dementia mortality rate could manifest the level of beneficial effect for dementia patient during their whole life span.

Population specific household size, urbanization life expectancy and GDP PPP were matched for those states with dementia mortality rate data. We obtained most recent population specific dementia mortality rates (N = 183) through calculation, household size (N = 170), GDP PPP (N = 178), Urbanization (N = 183) and ageing (N = 183) through extraction. Each population was considered as an individual research subject in the analysis. Therefore, numbers of populations included in analyses of relationships between variables may differ somewhat because all information was not uniformly available for all states.

All the aforementioned data were freely available from the websites of the UN agencies.

2.4 Data analysis

Scatter plots were produced in Excel (Microsoft® 2016) to explore and visualize the correlations between household size and dementia mortality rates. Scatter plots also allowed us to assess data quality and distributions of the variables.

Prior to correlation/regression analyses all data were log-transformed (ln) in order to reduce non-homoscedasticity of their distributions and possible curvilinearity of regressions. To assess the relationships between household size and dementia mortality rate in different data analysis models, the analysis proceeded in four (4) steps.

1. Pearson’s and nonparametric correlations (Spearman’s rho) were used to evaluate the strength and direction of the associations between household size and all other variables, including independent variables and competing variables.

2. Partial correlation of Pearson’s moment-product approach was used to assess the relationship between household size and dementia mortality rate while we controlled for ageing, GDP PPP and urbanization which have been commonly considered as the contributing factors of dementia.

We alternated the four variables (DMR, ageing, GDP PPP and urbanization) as the independent predictor to explore its relationship to DMR while keeping all the other three variables statistically constant. This allowed us to analyse and compare the levels of correlations between DMR and four potential risk factor while controlling for the other three variables [28, 39]. Subsequently, we alternately controlled for each variable as the potential confounder for analysing if and how much it could explain the correlation between DMR and each of the three variables.

Fisher’s r-to-z transformation was performed to test significance of differences between correlation coefficients.

3. Standard multiple linear regression (enter and stepwise) was performed to visualize the relation between DMR and each predicting factor and identify the most significant predictor(s) of DMR respectively. In order to explore if household size can partially explain why ageing, GDP PPP and urbanization were correlated with DMR, the multiple linear regression analyses were performed to determine the correlations between DMR incidence and the risk factors in two models, i.e. with and without incorporating household size as one of the predicting variables.

4. In order to demonstrate that correlation universally exists between household size and DMR regardless of these factors, populations were grouped for correlation analyses. The exploration into different correlations between household size and DMR also allowed us to compare the different levels of correlations in different country groupings. The criteria for grouping countries used the World Bank income classifications [59], WHO regions [60], countries sharing specific characteristics like geography, culture, development role or socio-economic status, like Asia Cooperation Dialogue (ACD) [61], Asia-Pacific Economic Cooperation (APEC) [62], the Arab World [62], Latin America and the Caribbean (LAC) [63], Southern African Development Community (SADC) [64] and Organization for Economic Co-operation and Development (OECD) [62]. All the population listings are sourced from their official websites for matching with the list of populations with DMR.

Pearson’s, non-parametric Spearman’s rho correlations, partial correlation and multiple linear regression (enter and stepwise) were computed with SPSS v. 27 (SPSS Inc., Chicago Il USA). The significance was reported when p-value was <0.05, but the significance levels of p < 0.01 and p<0.001 were also reported. Regression analysis criteria were set at probability of F to enter ≤ 0.05 and probability of F to remove ≥ 0.10. The raw data were used for scatter plots in Excel® 2016.

3. Results

Fig 1 shows the relationship between household size and DMR. The relationship is negative along a power curve with moderately strong negative correlation (r = -0.6678, p < 0.001). Subsequent parametric analyses of log-transformed data and nonparametric analyses confirmed the relationship between DMR variables and the household size.

Fig 1

The relationship between household size and dementia mortality rate.

Table 1 presents relationships between all the variables (dependent and independent) in Pearson r (above the diagonal) and nonparametric (below the diagonal) analyses. Worldwide (n = 169), Spearman’s rank correlation showed that household size was in significant negative correlation to DMR (r  =  -0.579, p < 0.001). This strength and direction of relationship were similar and observed in Pearson’s r household size and SMR variables (r = - 0.524, p<0.001). Worldwide, non-parametric analysis showed that DMR was associated with ageing (r = 0.533, p<0.001), GDP PPP (r = 0.497, p<0.001) and urbanization (r = 0.436, p<0.001). These strengths and directions of the relationship were observed in the Pearson analysis as well (Table 1).

Table 1

Data descriptives and bivariate correlations (Pearson, above diagonal & non-parametric below diagonal) between all variables.

	Household size	Dementia Mortality Rate (Per 100,000)	Ageing (Life expectancy at age 60 years)	GDP PPP (US$ (2010)	Urbanization (Urban population percentage)
Average	4.1	23.0	19.5	16,324.3	55.4
Median	3.9	12.2	19.0	9,715.0	55.5
Standard deviation	1.4	25.0	3.1	18,351.3	22.8
Minimum	2.1	0.1	13.0	646.9	10.6
Maximum	8.3	175.1	26.0	122,609.4	100.0
CORRELATIONS
Household size	1	-0.524***	-0.682***	-0.628***	-0.522***
N	169	169	169	165	169
Dementia Mortality	-0.579***	1	0.458***	0.375***	0.318***
N	169	183	183	177	183
Ageing	-0.689***	0.533***	1	0.760***	0.592***
N	169	183	183	177	183
GDP PPP	-0.648***	0.497***	0.760***	1	0.747***
N	165	177	177	177	177
Urbanization	-0.564***	0.436***	0.650***	0.791***	1
N	169	183	183	177	183

Significance level: * p<0.05

** p< 0.01

***p< 0.001.

Data sources & definitions: Household size (the United Nations): the number of persons who make common provision of food, shelter and other essentials for living. Dementia Mortality Rate (World Health Organization): Calculated from Global Health Estimates. Ageing (the United Nations) measured with the Life Expectancy at 60 years old. Per capita GDP PPP (the World Bank): the per capita purchasing power parity (PPP) value of all final goods and services produced within a country in a given year. Urbanization (the World Bank): the percentage of population living in urban area.

Table 2 shows the relationship between DMR and household size, ageing, GDP PPP and urbanization examined by controlling for the other three variables in a partial correlation analysis. Household size was the only independent variable to have significant correlation (r = −0.332, p < 0.001) with DMR independent of the other three variables (Table 2). Neither GDP nor urbanization showed a correlation with DMR independent of the other three variables despite the fact that each of them (GDP and urbanization) had a significant correlation to DMR in simple bivariate analyses. This suggests that household size was the independent risk factor for DMR. This suggestion was proved true in the subsequent Stepwise linear regression analyses (Table 3).

Table 2

Comparison of partial correlation coefficients between dementia mortality rate and each variable when the other three variables are controlled for.

Variables	Household size, aging, GDP PPP and urbanization were alternated as the predicting variable for calculating its relationship with dementia mortality rate while the other three independent variables were kept statistically constant. All data logarithmically transformed.
	Dementia Mortality			Dementia Mortality			Dementia Mortality			Dementia Mortality
	R	P	df	r	p	df	R	p	df	r	p	df
Household size	-0.322**	<0.01	159	-	-	-	-	-	-	-	-	-
Ageing	-	-	-	0.154	0.051	159	-	-	-	-	-	-
GDP PPP	-	-	-	-	-	-	-0.020	0.798	159	-	-	-
Urbanization	-	-	-	-	-	-	-	-	-	0.018	0.821	159

Significance level

** p< 0.01.

Data sources & definitions: Household size (the United Nations): the number of persons who make common provision of food, shelter and other essentials for living. Dementia Mortality Rate (World Health Organization): Calculated from Global Health Estimates. Ageing (the United Nations), measured with the Life Expectancy at 60 years old. Per capita GDP PPP (the World Bank): the per capita purchasing power parity (PPP) value of all final goods and services produced within a country in a given year. Urbanization (the World Bank): the percentage of population living in urban area.

Table 3

Multiple linear regression showing predicting effects of independent variables and identify the significant predictors of dementia mortality.

Enter
	Dementia Mortality
	Household size excluded, adjusted R2 = 0.196				Household size included, adjusted R2 = 0.282
Variable		Beta		Sig.	Beta	Sig.
Household Size		-		-	-0.357	0.001
Ageing		0.400		0.001	0.236	0.036
GDP PPP		0.043		0.735	0.063	0.624
Urbanization		0.039		0.704	-0.076	0.474
Stepwise
	Dementia Mortality
	Household size excluded				Household size included
Model	Variable		Adjusted R2		Variable	Adjusted R2
1	Ageing		0.203		Household Size	0.263
2	-		-		Ageing	0.289
3	-		-		-	-
4	-		-		-	-
5	-		-		-	-

Significance level: * p<0.05

** p< 0.01

***p< 0.001.

Data sources & definitions: Household size (the United Nations): the number of persons who make common provision of food, shelter and other essentials for living. Dementia Mortality Rate (World Health Organization): Calculated from Global Health Estimates. Ageing (the United Nations) measured with the Life Expectancy at 60 years old. Per capita GDP PPP (the World Bank): the per capita purchasing power parity (PPP) value of all final goods and services produced within a country in a given year. Urbanization (the World Bank): the percentage of population living in urban area.

Table 3 shows if and how much household size explained the correlations of the other three variables to DMR respectively. In the enter model, when household size was not considered as one of the independent variables, ageing was the only significant predictor of DMR (β = 0.357, p < 0.001). However, when household size was incorporated as an independent variable, it ranked as the strongest predictor of DMR (β = - 0.362, p < 0.001). Ageing was still a significant predictor of DMR (β = 0.226, p < 0.05), but the correlation strength has significantly decreased (z = 1.67, p<0.05). Both GDP and urbanization barely showed correlations with DMR regardless of household size inclusion.

Table 3 also shows that when household size was not included as one of the independent variables, ageing (R2 = 0.203) was the only variable included as the significant predictor of DMR. However, when household size was included as an independent variable, household was selected as the variable having the greatest influence on DMR with R2 = 0.263, while ageing was placed second increasing R2 to 0.289. The other variables (GDP PPP and urbanization) were removed by the analysis as having no statistically significant influence on DMR.

Table 4 presents that, in general, household size is negatively associated with DMR in different country groupings. The highlight of these relationships was that household size was constantly in negative correlation to DMR. Small sample sizes in some categories of countries make differences between correlation coefficients found for them statistically insignificant and thus it is difficult to discern differences among those categories in the strength of correlations. However, in all categories, correlations are negative and often significantly different from zero. In economically developed country groupings, such as in the World Bank High income economics, WHO European Region and OECD all correlations are significant and moderately strong. Lower values of correlation coefficients in some groupings, especially those of lower income categories in the World Bank income classifications, may be a result of less variability in variables correlated when total ranges of their variation are artificially curtailed by categorisation. Averages of household size and dementia mortality in variously categorised countries show a tendency of larger household size to be associated with smaller dementia mortality.

Table 4

Correlation of dementia mortality to household size in different country groupings.

Partial correlations are calculated when GDP, Urbanisation and Ageing are kept statistically constant.

	Correlation coefficients				Means (standard dev.)
	Pearson	Non-parametric	Partial	n	Household size	Dementia mortality
Country groupings
Worldwide	-0.524***	-0.579***	-0.322**	169	4.08(1.42)	22.95(25.00)
World Bank income classifications
Low income	-0.287	-0.274	-0.159	32	5.32(1.11)	8.40(4.15)
Low middle income	-0.271	-0.221	0.008	44	4.68(1.06)	13.47(10.12)
Upper middle income	-0.091	-0.127	-0.071	48	3.92(0.99)	21.20(16.68)
High income	-0.623***	-0.651***	-0.460***	48	2.96(1.20)	42.48(35.58)
WHO Regions
African Region (AFRO)	-0.351**	-0.437**	-0.192	42	5.01(1.19)	9.54(6.18)
Eastern Mediterranean Region (EMRO)	-0.414	-0.681**	0.027	17	5.54(1.27)	17.22(14.69)
European Region (EURO)	-0.519***	-0.548***	-0.101	52	3.02(1.04)	40.94(34.71)
Pan-American Region (PARO)	-0.436**	-0.340	-0.386	29	3.61(0.64)	18.74(18.77)
South-East Asia Region (SEARO)	-0.589*	-0.823***	-0.831*	12	4.26(0.85)	21.67(14.11)
Western Pacific Region (WPRO)	-0.300	-0.472	-0.406	19	4.28(1.16)	19.39(16.41)
Countries grouped based on various factors
Asia Cooperation Dialogue (ACD)	-0.322	-0.600***	-0.338	30	4.68(1.38)	17.33(14.20)
Asia-Pacific Economic Coop. (APEC)	-0.511*	-0.663**	-0.633*	18	3.39(0.90)	31.13(23.27)
Arab World	-0.372	-0.544*	-0.018	18	5.63(1.09)	17.04(14.47)
Latin America and the Caribbean (LAC)	-0.206	-0.182	-0.258	27	3.70(0.56)	15.09(12.15)
Southern African Development Community (SADC)	-0.563*	-0.652*	-0.482	14	4.39(0.70)	10.32(8.00)
Organisation for Economic Co-operation and Development (OECD)	-0.447**	-0.497**	-0.098	35	2.54(0.45)	54.98(36.09)

Significance level

* p<0.05

** p< 0.01

***p< 0.001.

Data sources & definitions: Household size (the United Nations): the number of persons who make common provision of food, shelter and other essentials for living. Dementia Mortality Rate (World Health Organization): Calculated from Global Health Estimates. Ageing (the United Nations) measured with the Life Expectancy at 60 years old. Per capita GDP PPP (the World Bank): the per capita purchasing power parity (PPP) value of all final goods and services produced within a country in a given year. Urbanization (the World Bank): the percentage of population living in urban area.

4. Discussion

The worldwide trend of increased DMR may have multiple aetiologies, which may act through multiple mechanisms. This study not only suggested that household size may be a major factor for dementia mortality at the population level, but also showed that household size was a determining risk factor overriding risk factors such as ageing, SES and urbanization. This study also revealed that the predicting effect of household size on dementia mortality was independent of the effects of other common risk factors, such as ageing, socio-economic status and urbanization.

Household relations are usually invested with the powerful norms, histories, and emotions that characterize family relationships [24]. From a life course perspective, they are related to the dementia risks in the essential pathways which are grounded on biological, psychological and social contributions [65]. In the industrialized societies, especially in the developed countries, most people grew up in core families with very limited number of playmates and little interaction with neighbours. This is different from how humans had adapted for flourishing through early cooperative breeding [16, 17], and then evolved alloparental care [18], both of which laid the biological foundations of human love which may be heritable generation by generation [19]. The mismatch between the way we live now and how our ancestors did has been postulated as the risk factor for mental disorders in young generation [21, 22].

The lack of cure for dementia, and of accurate diagnoses of specific causal factors, has made it difficult to target preventative interventions [66]. It is well established that appropriate psychological, social support and physical care have been the key strategies for the healthcare for dementia patients [67]. Studies have revealed that, psychologically, family life increases levels of purpose in life [68, 69], which may reduce by 30% risk of dementia [70, 71]. Large household may offer the residents more social engagement in life which is protective against dementia [72, 73].

Compiled 2,200 years ago, Huangdi Neijing has been the fundamental doctrinal source of Asian medicine. It illustrates how emotions are associated with the visceral organs which are in charge of five qi’s (translation: gas; meaning: emotions): happiness, anger, sadness, worry and fear. Among these five qi (emotions), only happiness makes the gas smooth [74], which keeps people healthy. In Western medicine, hundreds of years’ exploration of the manifestation of emotions through physiological responses (mind—body interaction) [75-77] has suggested that the formation of mental experiences (emotions) is closely associated with bodily responses [78, 79].

Alzheimer’s disease is the most common cause of dementia. The most prevalent hypothesis about this pathology is that it occurs when the beta-amyloid (toxic protein) begins to clump around neurons in the brain. The neurons degenerate, which leads to a decrease in synaptic plasticity and ultimately to cognitive decline. Oxytocin has been considered chemical responsible for targeting the removal or reduction of beta-amyloid or improvement of the cognitive ability of patients. An animal model study showed that toxic beta-amyloid damaged synaptic plasticity in mice’s brains, but this beta-amyloid-induced impairment of hippocampal synaptic plasticity in mice was reversed by oxytocin in mice [80]. Interestingly, this study also revealed that oxytocin did not improve brain’s synaptic plasticity in mice’s brains if oxytocin was the only treatment [80]. This may imply that oxytocin was produced as an auto-immune response to the neuron blockage by toxic beta-amyloid in mice’s brains. Similarly, another animal model study found that oxytocin strengthened social memory [81] and improved spatial memory [82] when mice were in their motherhood. In human studies, it has been reported that oxytocin selectively strengthened participants’ memory for social stimuli depending on the participants’ social contexts and individual attachment styles [82, 83].

Oxytocin has been implicated in many aspects of social functioning. The therapeutic effects of oxytocin have not only been explored in dementia prevention and treatment, but also have targeted the treatment of diseases for other aberrant social behaviour related disorders [84, 85], such as autism spectrum disorder [86-89], posttraumatic stress disorder [90, 91], schizophrenia [92, 93], and anxiety disorders [94, 95].

The nature of vascular dementia (VaD) is strongly associated with stroke [96, 97]. To date, there are few therapeutic options to protect cognitive decline arising from cerebrovascular diseases [98, 99]. Worldwide, prevention of strokes and management of post-stroke symptoms have been considered approaches to reduce the vascular dementia initiation [100]. Based on a number of studies in human and animal models, a systematic review conducted by Gutkowska and colleagues concluded that oxytocin has multiple roles in protecting cardiovascular system [101], which prevents VaD onset and might be the candidate treatment for VaD [99].

Frontotemporal dementia (FTD) is an umbrella term for a group of uncommon neurological diseases due to progressive damage to the frontal and/or temporal lobes of the brain. Empathy loss is one of hallmark symptoms of FTD [85, 88]. Oxytocin is an important mediator of social behaviour, potentially enhancing empathy and prosocial behaviours [89]. Finger and colleagues reported that intranasal oxytocin improved the behavioural symptoms in FTD, including levels of apathy and expressions of empathy [89]. And the benefit or efficacy was associated with dosage and time related [89, 91]. This beneficial effect of oxytocin also improved emotional expression processing [93, 95], empathy [102], anxiety [103] and cooperative behaviour [104] in healthy adults and autism patients. Therefore, the mechanism of upregulated oxytocin mediation of empathy and behavioural deficits have been postulated as a potential treatment approach in FTD [89].

Large household promotes more interpersonal interactions between household members that may offer mind-body interaction which offers biological protection against dementia through the therapeutic effects of oxytocin [105-109]. At the same time, positive psychosocial well-being produced by large household may exert a beneficial slow-down on dementia development [70]. Family members who receive more family support may feel comprehensive positive psychological well-being [70, 110–112]. However, a greater meaning (purpose) of life may be the most important psychological resource to lower dementia risk [70, 110]. Sutin and colleagues explored the protective effects of psychological functioning (life satisfaction, optimism, mastery, purpose in life, positive affect) on preventing high risk population from developing dementia [70]. The results revealed that people from large household showed more psychological well-being leading to lower risk of dementia onset [70]. Interestingly, purpose in life explained 30% dementia risk and this protecting role was independent of the competing effects of other multiple risk factors, such as chronic disease and low physical activities, genetic risk, psychological distress and socioeconomic status [70].

Household residents may interact with each other more often to create life satisfaction [25, 113]. They may also share healthcare knowledge, encourage each other to establish healthy lifestyle and utilize health care services in an effective way [114-116]. People with positive psychological well-being tend to practise healthy lifestyle, have more knowledge of health risk factors and attend regular physical examination [27]. The protecting role of such positive psychological well-being has been postulated to decrease the risk in the development of breast cancer and general cancers [27, 36, 117, 118].

Large household is even more important for pre-dementia patient, especially the young onset dementia. Generally, young onset dementia present non-specific signs and symptoms at the early onset in young patient, but they are progressing and irreversible [119]. With household residents’ observations and encouragement, the atypical dementia symptoms can be noticed by co-residential members, and accordingly they can have the examination in time, and proper treatment subsequently.

Additionally, from the perspective of evolution, a population with large household offers more chance to survive the natural selection. This produces the opportunity to have portion of a population with less fitness, for instance, dementia, removed through greater mortality rate without disturbing population’s essential activities [33, 36, 37, 120–123]. In other words, the genetic background of dementia in such population may be more often eliminated from the population with large household without affecting population as a whole. Therefore, population with large household may have less genes/mutations of dementia to incur high mortality rate of dementia. Furthermore, a population with large household means less birth control and high total fertility rate which allows more biological variation in fertility [124]. A portion of this additional variation, however small, provides the opportunity for the natural selection [124].

Interestingly, large household has shown its consistent and significant, but inverse correlations to dementia mortality rates in the developed world. Several unique phenomena in the developed world can explain this interesting relationship: 1) The fertility rate keeps falling down leading to small household/family size. 2) The cultural doctrines, especially individualism and independence, have reduced the interactions between people living in the developed world. 3) Importantly, dementia care delivery in the developed world is primarily through individual home support service, because dementia patients live with small family/household, or through nursing homes [125]. While in the developing world people live with big family/household for most of their life and they would receive the “informal” healthcare from family members and/or household residents [126], instead of the “formal” care through nursing homes. The fact that people live in bigger households in developing countries is a combined result of the economic situation and social customs. With lower incomes and less availability of housing people are more likely to share accommodation while social customs demand respect and care for elderly people, who, in the situation of low incomes cannot afford to have living arrangements independent of members of younger generations. In developed countries higher incomes, greater availability of accommodation, higher mobility and individualistic lifestyles of adults, together with well-resourced retirement situations of older persons allow individuals or couples to have separate households. In this situation, social interactions with other individuals become sporadic personal choices rather than a daily necessity.

5. Strengths and limitations in this study

Due to the inherent uncertainties of the worldwide estimates provided by the WHO and the World Bank, our results must be seen as showing relations between published estimates, not necessarily precisely measured actual phenomena. The relations, however, are the closest available approximation of actual situation.

Little work has been done on the dementia epidemiology studies, which may be due to extremely low onset rate making data collection difficult. For example, worldwide, mortality rate of dementia is only 26.61 per 100,000, and this presence is not noticeable. Therefore, the low prevalence rate of dementia would require unaffordable sample size for identifying small household as the potential risk factor for dementia in individual based epidemiological or laboratory approaches. Ecological studies are based on aggregated quantitative data zooming in the rare presence of dementia 100,000 times, which makes dementia presence noticeable for analysing the potential effects of dementia risk-modifying factors at population level. This also suggests the necessity of engaging ecological study into the epidemiology studies of rare chronic diseases such as dementia and cancer [28, 29, 36] and Type 1 diabetes [33].

Due to the nature of the cross-sectional data, a couple of intrinsic limitations should be mentioned. Firstly, the results in this study only showed the relationship between household size and dementia mortality rate as correlational, instead of causal. Secondly, the results based on the ecological approach in this work are subject to the “ecological fallacy”. Therefore, the protective role of large household size may not always hold true for each individual to predict their dementia specific death risk. Thirdly, dementia mortality has been associated with multiple risk factors which could have confounded the relationship between large household/family size and dementia mortality rate in this study. These factors include, but are not limited to, hypertension, obesity, head injury and hearing loss in middle life, and smoking, depression, diabetes and social isolation in later life. However, these factors have not been well established for explaining dementia mortality. Also considering unavailability of the data on these factors at population level, we cannot include them as the potential confounders for ruling out their competition with the detrimental effect of small household/family size on dementia mortality in this study. Finally, the data employed in this study might be crude. The WHO, United Nations and World Bank may have made some random errors arising from the methodologies used for collecting and aggregating the data.

Regardless of the strength and limitations of the data quality, we have showed that countries with large household size had lower dementia mortality rate in different data analysis models. The findings in this study may have shed light for further research into the subject with exposure based longitudinal cohort studies at population level. Accordingly, this may lead to far reaching public health implications in dementia and its prevention.

6. Conclusions

Independent of ageing, urban lifestyle and low socio-economic status, large household has been shown to have significant protective role against dementia mortality in this study. This forward-thinking study approach will help shed light on the further study into the protective role of positive psychological well-being against dementia. As part of dementia prevention, when large household/family size is impossible to achieve, healthcare practitioners should encourage people to increase their positive interactions with people from their neighbourhood or other fields.

S1 A whole set of data for this study.

(XLSX)

Click here for additional data file.

17 Sep 2021

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Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #2: Yes

Reviewer #3: Yes

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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: This a fascinating and worthwhile article.

Databases

Please report the assessment of the data compilers of the original documents and any technical reports on how the data on household size was collected and the reliability and validity of the measurement of household size.

Similarly please comment in detail how the diagnosis of dementia death was made in the various countries you reported and any reliability and validity data. Was it based on samples and if so what was the sample size?

What proportion of individuals and what are the numbers of individuals who were diagnosed with dementia in the countries you assessed? Some less developed countries have fewer people in the relevant age groups so the confidence intervals if based on samples would be wider.

Please comment in detail on Table 4 and differences between countries for which you have an explanation.

My comments are directed to strengthening your paper.

Reviewer #2: Well done!

The thematic is very important and we know that is a lack of evidence on this particular subject related to Large households/families to prevent dementia.

I have some considerations to the article that should be clarified

Abstract: Please mention the type of study design such as cross-sectional/correlation design. The method section should not have any of your results as in your last paragraph of method section.

SES: This abbreviation did not mention before.

Introduction:

1- It is easy to read, written in a professional way, and has plenty of information.

2- Many unnecessary conjunctions between paragraphs.

3- typos before reference (20, 21) in in small communities.

4- Please can you add a paragraph regarding country grouping (low, middle, and high-income country). do you have statistics on the dementia rate?

Methodology:

1- Study design?

2- The data source is not clear, need to clarify how it was done?

3- The sample size is not well described - Power analyses?

Results:

1- SMR variables???

2- Table 3. 0.000 replace it with 0.001, please add R change.

3- All abbreviations in all Tables should be mention as a footnote. Mainly Table 4

Discussion:

1-The therapeutic effects of Oxytocin. Why you added. Is your study investigate the level of Oxytocin? You discussed previous studies regarding Oxytocin and its effect on dementia (VaD, FTD...) If not I suggest removing this or a better explain why you mention that in the first paragraph.

2- You mention this statement: Similarly, five (5) studies conducted by Lambert and co-workers also identified the

independent relationship between the meaning of life and family support among your people [116]. I returned to this paper and I read it carefully, Unfortunately, it is about the use of social media in healthcare settings not as you mention.

3- Can you add the year after you mention the authors such as Stutin and colleagues (??).

4- Finally, the last paragraph regarding developed countries before the limitation section. I like the explanation, but to add more strength can you add more explanation between developing countries (Low- and middle-income) and developed countries.

5- Good luck.

Reviewer #3: 1) Please consider reversing the order of x-axis values so that it shows a negative association between household size and dementia mortality rates as reported in text.

2) Please provide a table for the descriptive statistics of the variables used for the study: basic measures of central tendency and variability for the whole countries and by different country categorizations.

3) The correlation between household size and DMR reported on page 7 is -.6034, but the correlation for “worldwide” reported in Table 4 is -.524. Please clarify the difference between these two numbers.

4) The main research question is to assess whether large household has the inhibitory role in lowering the risk for the residents “to develop dementia”. However, the dependent variable of the study is DMR, which is different from “development of dementia”. Please clarify why DMR, not some other measures more closely measuring the development of dementia. Perhaps, Alzheimer's disease morbidity can be explored together to draw a bigger picture but not sure about data availability.

5) The top part of Table 2 and the bottom part of Table 2 provide the same numbers. And the numbers on the first line of page 8 do not exactly match the numbers in Table 2. FYI, ageing is not significant at the .05 level of significance since the p-value reported in Table 2 is .051, which is >.050. Please double-check.

6) Table 4 reports valuable information about the variation in the association between household size and DMR. But it seems that the negative association between household size and DMR is only observed among high income countries (-.623, n=43), which is contradictory to the main finding of the study (Table 3). The results for countries grouped based on various factors somewhat agree with this result. Please discuss potential sources of the discrepancy.

7) Also wondering if the negative association between household size and DMR controlling for ageing, GDP, and urbanization, is still observed for those countries with high income. It would be very informative if the regression results with all variables (household size, ageing, GDP, and urbanization) can be reported for each country group, especially based on World Bank income classification, as well.

8) It is unclear why using three different categorizations for countries. Other than World Bank one, it seems that the other categorizations provide more heterogenous grouping than homogenous.

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Reviewer #1: Yes: Roger E. Thomas

Reviewer #2: No

Reviewer #3: No

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7 Oct 2021

Please see the uploaded document, titled 1. PLOS ONE 1 to 1 Response.

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25 Oct 2021

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If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Wen-Wei Sung, M.D., Ph.D.

Academic Editor

PLOS ONE

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 #2: 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: No

Reviewer #2: Yes

Reviewer #3: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: 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 #2: 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 #2: Yes

Reviewer #3: Yes

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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: As this is a data base study the vaildity and reliability of the data are key The authors have not provided any new data on the reliability and validity of their data bases

Page 2 They provide a reference to the WHO mortality rate, WHO Technical Paper [ref 43) but do not provide key data from that report.

Page 5 Please comment on data validity and reliability and missing data

Page 6 You refer to the World Bank published data on GDP and urbanisation but do not explain in detail the potential confounders you mention

Page 6. "certain criteria for completeness and quality" Please provide details and how this affects your study

Page 12 You wrote "Complied 2,200 years ago Huangdi Neijing" ... What are the data for the reliability and validity of the following statements?

"Large household promotes more mind-body interaction which offers biological protection" What is the evidence for this unsupported unsubstantiated statement.

Pleased read your manuscript carefully and remove any unsubstantiated statements, especially to be found in the Discussion section.

Reviewer #2: All my comments have been answered prperly. I would like to take this chance to congrat the authors for their valuable effort. Great job. Best of my luck.

Reviewer #3: Thank you for considering my comments to revise the manuscript. The manuscript has substantially improved.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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15 Nov 2021

Please see detailed response in the attached document titled, 1 to 1 Response to Reviewer 1 comments.

Submitted filename: 1 to 1 Response to Reviewer 1 comments.docx

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6 Dec 2021

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If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

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Journal Requirements:

Additional Editor Comments:

According to the review report of reviewer #1, previous revision did not meet the criteria for publication. Therefore, I added more reviewers for evaluation and the decision was made base on total of five reviewers. In this major revision, please carefully answer all questions from reviwer #1 and #6 and submit revised MS for further consideration.

Reviewers' comments:

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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.

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Reviewer #7: All comments have been addressed

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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: No

Reviewer #4: Yes

Reviewer #5: Yes

Reviewer #6: Yes

Reviewer #7: (No Response)

**********

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

Reviewer #1: No

Reviewer #4: Yes

Reviewer #5: Yes

Reviewer #6: Yes

Reviewer #7: (No Response)

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Reviewer #1: No

Reviewer #4: Yes

Reviewer #5: Yes

Reviewer #6: No

Reviewer #7: (No Response)

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Reviewer #1: Yes

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Reviewer #5: Yes

Reviewer #6: Yes

Reviewer #7: (No Response)

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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: Thank you for your updated version.

As I mentioned with your first submission the assessment of the risk of bias of the databases is crucial to your study. There are minimal changes in your manuscript. You have not assessed the risk of bias in your databases, which would have required identifying key documents about the methods they used and their assessments of reliability from the database constructors.

You have not considered all the possible confounders.

You do not assess the implications of your data. For example you define aging as life expectancy at 60 and found the median is 19 years and the SD = 3.1 years. This implies a median age at death of 79, which would imply your population is heavily influenced by wealthier countries. The SD at 3.1 is small so can you draw conclusions based on this small SD?

Similarly you found median household size was 3.9 with an SD = 1.4 Can you draw conclusions with such a small SD?

I suggested that you remove undocumented and unsubstantiated statements but they remain.

You have found an important idea but have not proven your hypothesis with data of sufficient low risk of bias.

Reviewer #4: The author responses well for the rebuttal and makes a great change for the revised manuscript. I have no further comment or suggestion for this manuscript.

Reviewer #5: This ecological study showed that household size was an independent factor associated with dementia mortality at the population level, which provided evidence that social interaction may improve the prognosis of dementia.

The authors do their best to reduce any possible biases. I have no issue to highlight.

Reviewer #6: The manuscript is well-written; however, I would like to clarify some of the points regarding the study:

Major issues:

1, In the introduction, the authors mentioned, “…., The above considerations directed us to try to identify possible contributing factors for dementia from the evolutionary perspective.” However, the current study seemed to focus on “whether smaller household could serve as a risk factor for people dying of dementia”, instead of “whether smaller household could serve as a risk factor for dementia”. Though the authors had avoided using the sentence “developing dementia” in the revised version, the quoted sentence might be confusing for readers, for the two aforementioned concepts are not exactly same.

I would recommend the authors provide a clearer explanation in the manuscript regarding the rationale of utilizing DMR manifesting the development of dementia.

2, Frailty bias could exist and should be addressed:

Severe adverse statuses or comorbidities, such as cancer or organ transplantation could massively affect mortality, influencing risk for death occurrence. Could you please comment why the issue was not considered as one of the variables in the current study?

Minor issues:

1, For reference 43, according to the formal name of the document, I think the correct title of this technical paper should be “WHO methods and data sources for country-level causes of death 2000-2016”.

(According to the WHO technical paper, retrieved from: https://www.who.int/healthinfo/global_burden_disease/GlobalCOD_method_2000-2016.pdf)

2, For information related to dementia mortality, the dataset utilized by the authors was based on Global Health Estimates 2016; however, currently the GHE report has been updated to 2019 version. Hence it might be difficult for the readers to directly find the dataset you have retrieved. Therefore, I would recommend the authors provide the exact website address to make readers more easily to access the key data identifying the number of dementia death and other information.

Reviewer #7: (No Response)

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11 Dec 2021

Please refer to the document, titled 1 to 1 response to review comments

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26 Dec 2021

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Reviewer #1: The authors have made minimal changes. This is a retrospective databases study. Therefore, assertions of causal relationships cannot be made.

1. The authors persist is making predictive statements:

Abstract:

"Large households/families create more positive psychological well-being"

"Large household was an independent predictor."

Text

"large household protects against dementia mortality."

large household "most significant predictor of DMR."

2. The section on oxytocin.

In this section the authors assert that oxytocin is the mediator molecule between their assertions and refer to "a stream of studies..." Could the authors please provide detailed analyses of studies with serum levels and differences in serum levels of oxytocin with different household sizes and rates of interaction (which again would be correlational relationships and not causal) or delete this section on oxytocin. Neurobiologists would ask for better data than are presented here.

3. Data elements.

The authors used DMR, "ageing" GDP and urbanisation as their variables. Could the authors please list potential known confounders (and potential confounders unknown in their database and control variables) which could affect their results.

Reviewer #6: The authors have fully addressed all raised issues, and I have no further major concerns.

However, it should be noticed that in the updated reference 41 (namely, the dataset of GHE utilized by the author), the website is revamped and it seems the original dataset is no longer available.

Hence, I think this should be stated in the manuscript to remind the readers that the version 2016 is no longer publicly accessible.

**********

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Reviewer #1: No

Reviewer #6: No

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5 Jan 2022

Please refer to the attachment titled 1 to 1 response_ 02012022.

This allows us to maintain the formatting of the response to help the editor and reviewers to read. However. we are pasting it below:

PONE-D-21-26012R3

Large household reduces dementia mortality: A cross-sectional data analysis of 183 populations

PLOS ONE

__________________________________________________________________________________

6. Review Comments to the Author

Reviewer #1: The authors have made minimal changes. This is a retrospective databases study. Therefore, assertions of causal relationships cannot be made.

Authors: This had been listed as one of the study limitations (first).

1. The authors persist is making predictive statements:

Abstract: Thanks a lot for this comment.

The predictive statements have been toned down or deleted from the manuscript.

"Large households/families create more positive psychological well-being"

Authors: We stated that “Large households/families create more positive psychological well-being—-" in the Background of the Abstract. Now it is amended as:

Large households/families may create more happiness and offer more comprehensive healthcare between the members. We analyse the protecting role against dementia mortality through examining the relationship between household/family size and dementia mortality rate at population level.

This paragraph has been elaborated in the following paragraph copied from the manuscript:

It is well researched that positive psychological well-being has been implicated in health across adulthood [1]. Household creates a social environment which is salient to maintain health for the co-residential members. On a daily basis, the individual members encounter this environment, play their social role and enjoy the social relations [2]. Moreover, studies also showed that large household offers the residents the subjective happiness [3] leading to low risk for residents to develop various cancers [4], for instance female breast cancer [5, 6] and ovarian cancer [7]. Subjective happiness was associated with mental health significantly stronger than with physical health in people with disabilities [8] and hospital patients [9]. A recent study revealed that greater household size has the protecting role against children developing mental health disorders [10].

"Large household was an independent predictor."

Authors: This sentence was included in the Result of the Abstract below:

Regardless of the contribution of ageing, socio-economic status and urban lifestyle to dementia mortality, large household was an independent predictor of dementia mortality (r = −0.331, p <0.001) in partial correlation analysis.

Thanks for this comment. We found that we did not state this properly. We meant to say,

Small (NOT large) household was an independent predictor/risk factor for dementia mortality [independent of ageing, socio-economic status and urban lifestyle]. We apologize for this confusion.

Now it has been rewritten and included in the Abstract in consideration of the context:

When we controlled for the contribution of ageing, socio-economic status and urban lifestyle in partial correlation analysis, large household was still in inverse and significant correlation to dementia mortality (r = −0.331, p <0.001). This suggested that, statistically, large household protect against dementia mortality regardless of the contributing effects of ageing, socio-economic status and urban lifestyle.

Text

"large household protects against dementia mortality."

Authors: This sentence was in the Conclusion section of the Abstract:

Independent of ageing, urbanization and GDP, large household protects against dementia mortality.

Now it has been toned down:

While acknowledging that ageing, urban lifestyle and greater GDP are associated with dementia mortality, this study suggested that, at population level, small household size was another risk factor for dementia mortality.

large household "most significant predictor of DMR."

Authors: We stated that "most significant predictor of DMR." because of our data analysis results. In this study, standard multiple linear regression analysis was performed. This analysis model regressed multiple variables while simultaneously removing those that were not important/significant, but at the same time those most significant (important) predictors were maintained in order of their influencing effects. For instance, in Table 3 - Stepwise model, when household size was included as one of the predicting variables (four in total: household size, ageing, urban lifestyle and GDP), it became the most significant/important risk factor for dementia mortality rate. We described this in the Result:

However, when household size was included as an independent variable, household was selected as the variable having the greatest influence on DMR with R2 = 0.263, while ageing was placed second increasing R2 to 0.289.

Before this, this analysis model was introduced in 2.4 Data analysis of the manuscript:

3. Standard multiple linear regression (enter and stepwise) was performed to visualize the correlation between DMR and each predicting factor and identify the most significant predictor(s) of DMR respectively.

2. The section on oxytocin.

In this section the authors assert that oxytocin is the mediator molecule between their assertions and refer to "a stream of studies..." Could the authors please provide detailed analyses of studies with serum levels and differences in serum levels of oxytocin with different household sizes and rates of interaction (which again would be correlational relationships and not causal) or delete this section on oxytocin. Neurobiologists would ask for better data than are presented here.

Authors: The paragraph discussing the mediating effects of oxytocin has been removed.

Much appreciated for providing this important comment from the perspective of neurobiologist. This makes this study reporting and discussion more rigorous. A stream of studies did introduce the therapeutic role of oxytocin on different diseases, but it seems that there is not enough substantiated scientific support, at least at the molecular level. The following two paragraphs have been deleted from the Discussion:

Oxytocin is a hormone and a neurotransmitter that is associated with social bonding, such as empathy, trust, sexual activity, group bonding and relationship-building [11]. A stream of studies in the last decade reported that oxytocin release is not only associated with giving birth [12] and lactation [13], but also with daily interactions between non-kin household residents and/or family members, such as spouses [14-16], mother and children [17], father and children [18] and co-residential household members [19-21]. Oxytocin can keep family members and household residents happy and loyal to each other [22, 23], which may bring more positive psychological well-being to the family members. Regardless of cultural backgrounds [3], people from large household, especially from the same family have more life satisfaction [3, 24] which may lead to more oxytocin production within the hypothalamo-pituitary magnocellular systems. A self-reinforcing cycle is formed between more household interactions and more oxytocin production [11].

Oxytocin has been implicated in many aspects of social functioning. The therapeutic effects of oxytocin have not only been explored in dementia prevention and treatment, but also have targeted the treatment of diseases for other aberrant social behaviour related disorders [25], such as autism spectrum disorder [26, 27], posttraumatic stress disorder [28], schizophrenia [29], and anxiety disorders [30].

3. Data elements.

The authors used DMR, "ageing" GDP and urbanisation as their variables. Could the authors please list potential known confounders (and potential confounders unknown in their database and control variables) which could affect their results.

Authors: In statistics-based epidemiology studies, those best-established contributing variables are included in data analysis models as the existing detrimental or beneficial factors for the studied health challenge. These factors may compete the studied (independent) variable for predicting the specific health challenge (dependent variable). As the health effects of these factors have not been examined in the clinical trials, the relationship between the health challenge between each of the best-established variables is potential or correlational (not causal). During the study result reporting and discussing, those best- established variables are generally called potential (due to their effects not confirmed in clinical trials) confounders (due to their competition with the specific studied independent variable).

In this study, our literature review showed that ageing, GDP and urban lifestyle are well established/known risk factors. However, the aim of this study is to advance small household/family size may be another risk factor for the increase of dementia mortality rate. In other words, small household/family size may exert some detrimental effects on dementia mortality except those contributed by ageing, GDP and urban lifestyle. We achieved this goal through data analyses in partial correlation when we statistically controlled for the three potential confounders (ageing, GDP and urban lifestyle), and in enter linear regression when we included all the four variables (household size, ageing, GDP and urban lifestyle).

Some factors could be the risk factors as well, such as hypertension, obesity, head injuries and hearing loss in middle life, and smoking, depression, diabetes and social isolation in later life. However, due to data availability and/or low levels of establishment as risk factors for dementia mortality in previous studies. We could not include them as the potential confounders in our data analyses. This question has led us to list an additional study limitation:

Thirdly, dementia mortality has been associated with multiple risk factors which could confound the relationship between large household/family size and dementia mortality rate in this study. These factors include, but not limited to, hypertension, obesity, head injury and hearing loss in middle life, and smoking, depression, diabetes and social isolation in later life. However, these factors have not been well established for explaining dementia mortality. Also considering unavailability of the data on these factors at population level, we cannot include them as the potential confounders for ruling out their competition with the detrimental effect of small household/family size on dementia mortality in this study.

Sorry, we did not collect and keep the data of the variables which were not considered as the major risk factors for dementia mortality rate.

Reviewer #6: The authors have fully addressed all raised issues, and I have no further major concerns.

However, it should be noticed that in the updated reference 41 (namely, the dataset of GHE utilized by the author), the website is revamped and it seems the original dataset is no longer available.

Hence, I think this should be stated in the manuscript to remind the readers that the version 2016 is no longer publicly accessible.

Authors: Thanks for bringing this important comment again.

With the assistance of Dr Cherian Varghese, the Coordinator, Management of Noncommunicable Diseases (MND), the hyperlink of the data source has been obtained:

https://www.who.int/healthinfo/global_burden_disease/GHE2016_Deaths_WBInc_2000_2016.xls

It has been included in the References of the manuscript.

References:

1. Howell RT, Kern ML, Lyubomirsky S: Health benefits: Meta-analytically determining the impact of well-being on objective health outcomes. Health Psychology Review 2007, 1(1):83-136.

2. Hughes ME, Waite LJ: Health in household context: Living arrangements and health in late middle age. Journal of health and social behavior 2002, 43(1):1.

3. Nan H, Ni MY, Lee PH, Tam WW, Lam TH, Leung GM, McDowell I: Psychometric evaluation of the Chinese version of the Subjective Happiness Scale: evidence from the Hong Kong FAMILY Cohort. International journal of behavioral medicine 2014, 21(4):646-652.

4. You W, Rühli FJ, Henneberg RJ, Henneberg M: Greater family size is associated with less cancer risk: an ecological analysis of 178 countries. BMC cancer 2018, 18(1):924.

5. Bai A, Li H, Huang Y, Liu X, Gao Y, Wang P, Dai H, Song F, Hao X, Chen K: A survey of overall life satisfaction and its association with breast diseases in Chinese women. Cancer medicine 2016, 5(1):111-119.

6. You W, Symonds I, Rühli FJ, Henneberg M: Decreasing birth rate determining worldwide incidence and regional variation of female breast Cancer. Advances in Breast Cancer Research 2018, 7(01):1-14.

7. You W, Symonds I, Henneberg M: Low fertility may be a significant determinant of ovarian cancer worldwide: an ecological analysis of cross-sectional data from 182 countries. Journal of ovarian research 2018, 11(1):1-9.

8. Van Campen C, Iedema J: Are persons with physical disabilities who participate in society healthier and happier? Structural equation modelling of objective participation and subjective well-being. Quality of Life Research 2007, 16(4):635.

9. Mukuria C, Brazier J: Valuing the EQ-5D and the SF-6D health states using subjective well-being: a secondary analysis of patient data. Social Science & Medicine 2013, 77:97-105.

10. Grinde B, Tambs K: Effect of household size on mental problems in children: results from the Norwegian Mother and Child Cohort study. BMC psychology 2016, 4(1):31.

11. Magon N, Kalra S: The orgasmic history of oxytocin: Love, lust, and labor. Indian journal of endocrinology and metabolism 2011, 15(Suppl3):S156.

12. Takayanagi Y, Yoshida M, Bielsky IF, Ross HE, Kawamata M, Onaka T, Yanagisawa T, Kimura T, Matzuk MM, Young LJ: Pervasive social deficits, but normal parturition, in oxytocin receptor-deficient mice. Proceedings of the National Academy of Sciences of the United States of America 2005, 102(44):16096-16101.

13. White‐Traut R, Watanabe K, Pournajafi‐Nazarloo H, Schwertz D, Bell A, Carter CS: Detection of salivary oxytocin levels in lactating women. Developmental psychobiology 2009, 51(4):367-373.

14. Carmichael MS, Humbert R, Dixen J, Palmisano G, Greenleaf W, Davidson JM: Plasma oxytocin increases in the human sexual response. The Journal of Clinical Endocrinology & Metabolism 1987, 64(1):27-31.

15. Carmichael MS, Warburton VL, Dixen J, Davidson JM: Relationships among cardiovascular, muscular, and oxytocin responses during human sexual activity. Archives of sexual behavior 1994, 23(1):59-79.

16. Gordon Jr G, Burch RL, Platek SM: Does semen have antidepressant properties? Archives of Sexual Behavior 2002, 31(3):289-293.

17. Kendrick KM: The neurobiology of social bonds. Journal of neuroendocrinology 2004, 16(12):1007-1008.

18. Weisman O, Zagoory-Sharon O, Feldman R: Oxytocin administration to parent enhances infant physiological and behavioral readiness for social engagement. Biological psychiatry 2012, 72(12):982-989.

19. MacDonald K, MacDonald TM: The peptide that binds: a systematic review of oxytocin and its prosocial effects in humans. Harvard review of psychiatry 2010, 18(1):1-21.

20. Van IJzendoorn MH, Bakermans-Kranenburg MJ: A sniff of trust: meta-analysis of the effects of intranasal oxytocin administration on face recognition, trust to in-group, and trust to out-group. Psychoneuroendocrinology 2012, 37(3):438-443.

21. Wudarczyk OA, Earp BD, Guastella A, Savulescu J: Could intranasal oxytocin be used to enhance relationships? Research imperatives, clinical policy, and ethical considerations. Current opinion in psychiatry 2013, 26(5):474.

22. Insel TR, Hulihan TJ: A gender-specific mechanism for pair bonding: oxytocin and partner preference formation in monogamous voles. Behavioral neuroscience 1995, 109(4):782.

23. Young LJ, Murphy Young AZ, Hammock EA: Anatomy and neurochemistry of the pair bond. Journal of Comparative Neurology 2005, 493(1):51-57.

24. Angeles L: Children and life satisfaction. Journal of happiness Studies 2010, 11(4):523-538.

25. Fineberg SK, Ross DA: Oxytocin and the social brain. Biological psychiatry 2017, 81(3):e19.

26. Parker KJ, Oztan O, Libove RA, Sumiyoshi RD, Jackson LP, Karhson DS, Summers JE, Hinman KE, Motonaga KS, Phillips JM: Intranasal oxytocin treatment for social deficits and biomarkers of response in children with autism. Proceedings of the National Academy of Sciences 2017, 114(30):8119-8124.

27. Alvares GA, Quintana DS, Whitehouse AJ: Beyond the hype and hope: critical considerations for intranasal oxytocin research in autism spectrum disorder. Autism Research 2017, 10(1):25-41.

28. Knobloch HS, Charlet A, Hoffmann LC, Eliava M, Khrulev S, Cetin AH, Osten P, Schwarz MK, Seeburg PH, Stoop R: Evoked axonal oxytocin release in the central amygdala attenuates fear response. Neuron 2012, 73(3):553-566.

29. Oya K, Matsuda Y, Matsunaga S, Kishi T, Iwata N: Efficacy and safety of oxytocin augmentation therapy for schizophrenia: an updated systematic review and meta-analysis of randomized, placebo-controlled trials. European archives of psychiatry and clinical neuroscience 2016, 266(5):439-450.

30. Dodhia S, Hosanagar A, Fitzgerald DA, Labuschagne I, Wood AG, Nathan PJ, Phan KL: Modulation of resting-state amygdala-frontal functional connectivity by oxytocin in generalized social anxiety disorder. Neuropsychopharmacology 2014, 39(9):2061-2069.

Submitted filename: 1 to 1 response_ 02012022.docx

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17 Jan 2022

Large household reduces dementia mortality: A cross-sectional data analysis of 183 populations

PONE-D-21-26012R4

Dear Dr. Wenpeng You,

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27 Jan 2022

PONE-D-21-26012R4

Large household reduces dementia mortality: A cross-sectional data analysis of 183 populations

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