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Research Article

Recent quantitative research on determinants of health in high income countries: A scoping review

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium

Roles Conceptualization, Data curation, Funding acquisition, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing

Vladimira Varbanova,
Philippe Beutels

Published: September 17, 2020
https://doi.org/10.1371/journal.pone.0239031
Peer Review
Reader Comments

Identifying determinants of health and understanding their role in health production constitutes an important research theme. We aimed to document the state of recent multi-country research on this theme in the literature.

We followed the PRISMA-ScR guidelines to systematically identify, triage and review literature (January 2013—July 2019). We searched for studies that performed cross-national statistical analyses aiming to evaluate the impact of one or more aggregate level determinants on one or more general population health outcomes in high-income countries. To assess in which combinations and to what extent individual (or thematically linked) determinants had been studied together, we performed multidimensional scaling and cluster analysis.

Sixty studies were selected, out of an original yield of 3686. Life-expectancy and overall mortality were the most widely used population health indicators, while determinants came from the areas of healthcare, culture, politics, socio-economics, environment, labor, fertility, demographics, life-style, and psychology. The family of regression models was the predominant statistical approach. Results from our multidimensional scaling showed that a relatively tight core of determinants have received much attention, as main covariates of interest or controls, whereas the majority of other determinants were studied in very limited contexts. We consider findings from these studies regarding the importance of any given health determinant inconclusive at present. Across a multitude of model specifications, different country samples, and varying time periods, effects fluctuated between statistically significant and not significant, and between beneficial and detrimental to health.

Conclusions

We conclude that efforts to understand the underlying mechanisms of population health are far from settled, and the present state of research on the topic leaves much to be desired. It is essential that future research considers multiple factors simultaneously and takes advantage of more sophisticated methodology with regards to quantifying health as well as analyzing determinants’ influence.

Citation: Varbanova V, Beutels P (2020) Recent quantitative research on determinants of health in high income countries: A scoping review. PLoS ONE 15(9): e0239031. https://doi.org/10.1371/journal.pone.0239031

Editor: Amir Radfar, University of Central Florida, UNITED STATES

Received: November 14, 2019; Accepted: August 28, 2020; Published: September 17, 2020

Copyright: © 2020 Varbanova, Beutels. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This study (and VV) is funded by the Research Foundation Flanders ( https://www.fwo.be/en/ ), FWO project number G0D5917N, award obtained by PB. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Identifying the key drivers of population health is a core subject in public health and health economics research. Between-country comparative research on the topic is challenging. In order to be relevant for policy, it requires disentangling different interrelated drivers of “good health”, each having different degrees of importance in different contexts.

“Good health”–physical and psychological, subjective and objective–can be defined and measured using a variety of approaches, depending on which aspect of health is the focus. A major distinction can be made between health measurements at the individual level or some aggregate level, such as a neighborhood, a region or a country. In view of this, a great diversity of specific research topics exists on the drivers of what constitutes individual or aggregate “good health”, including those focusing on health inequalities, the gender gap in longevity, and regional mortality and longevity differences.

The current scoping review focuses on determinants of population health. Stated as such, this topic is quite broad. Indeed, we are interested in the very general question of what methods have been used to make the most of increasingly available region or country-specific databases to understand the drivers of population health through inter-country comparisons. Existing reviews indicate that researchers thus far tend to adopt a narrower focus. Usually, attention is given to only one health outcome at a time, with further geographical and/or population [ 1 , 2 ] restrictions. In some cases, the impact of one or more interventions is at the core of the review [ 3 – 7 ], while in others it is the relationship between health and just one particular predictor, e.g., income inequality, access to healthcare, government mechanisms [ 8 – 13 ]. Some relatively recent reviews on the subject of social determinants of health [ 4 – 6 , 14 – 17 ] have considered a number of indicators potentially influencing health as opposed to a single one. One review defines “social determinants” as “the social, economic, and political conditions that influence the health of individuals and populations” [ 17 ] while another refers even more broadly to “the factors apart from medical care” [ 15 ].

In the present work, we aimed to be more inclusive, setting no limitations on the nature of possible health correlates, as well as making use of a multitude of commonly accepted measures of general population health. The goal of this scoping review was to document the state of the art in the recent published literature on determinants of population health, with a particular focus on the types of determinants selected and the methodology used. In doing so, we also report the main characteristics of the results these studies found. The materials collected in this review are intended to inform our (and potentially other researchers’) future analyses on this topic. Since the production of health is subject to the law of diminishing marginal returns, we focused our review on those studies that included countries where a high standard of wealth has been achieved for some time, i.e., high-income countries belonging to the Organisation for Economic Co-operation and Development (OECD) or Europe. Adding similar reviews for other country income groups is of limited interest to the research we plan to do in this area.

In view of its focus on data and methods, rather than results, a formal protocol was not registered prior to undertaking this review, but the procedure followed the guidelines of the PRISMA statement for scoping reviews [ 18 ].

We focused on multi-country studies investigating the potential associations between any aggregate level (region/city/country) determinant and general measures of population health (e.g., life expectancy, mortality rate).

Within the query itself, we listed well-established population health indicators as well as the six world regions, as defined by the World Health Organization (WHO). We searched only in the publications’ titles in order to keep the number of hits manageable, and the ratio of broadly relevant abstracts over all abstracts in the order of magnitude of 10% (based on a series of time-focused trial runs). The search strategy was developed iteratively between the two authors and is presented in S1 Appendix . The search was performed by VV in PubMed and Web of Science on the 16 th of July, 2019, without any language restrictions, and with a start date set to the 1 st of January, 2013, as we were interested in the latest developments in this area of research.

Eligibility criteria

Records obtained via the search methods described above were screened independently by the two authors. Consistency between inclusion/exclusion decisions was approximately 90% and the 43 instances where uncertainty existed were judged through discussion. Articles were included subject to meeting the following requirements: (a) the paper was a full published report of an original empirical study investigating the impact of at least one aggregate level (city/region/country) factor on at least one health indicator (or self-reported health) of the general population (the only admissible “sub-populations” were those based on gender and/or age); (b) the study employed statistical techniques (calculating correlations, at the very least) and was not purely descriptive or theoretical in nature; (c) the analysis involved at least two countries or at least two regions or cities (or another aggregate level) in at least two different countries; (d) the health outcome was not differentiated according to some socio-economic factor and thus studied in terms of inequality (with the exception of gender and age differentiations); (e) mortality, in case it was one of the health indicators under investigation, was strictly “total” or “all-cause” (no cause-specific or determinant-attributable mortality).

Data extraction

The following pieces of information were extracted in an Excel table from the full text of each eligible study (primarily by VV, consulting with PB in case of doubt): health outcome(s), determinants, statistical methodology, level of analysis, results, type of data, data sources, time period, countries. The evidence is synthesized according to these extracted data (often directly reflected in the section headings), using a narrative form accompanied by a “summary-of-findings” table and a graph.

Search and selection

The initial yield contained 4583 records, reduced to 3686 after removal of duplicates ( Fig 1 ). Based on title and abstract screening, 3271 records were excluded because they focused on specific medical condition(s) or specific populations (based on morbidity or some other factor), dealt with intervention effectiveness, with theoretical or non-health related issues, or with animals or plants. Of the remaining 415 papers, roughly half were disqualified upon full-text consideration, mostly due to using an outcome not of interest to us (e.g., health inequality), measuring and analyzing determinants and outcomes exclusively at the individual level, performing analyses one country at a time, employing indices that are a mixture of both health indicators and health determinants, or not utilizing potential health determinants at all. After this second stage of the screening process, 202 papers were deemed eligible for inclusion. This group was further dichotomized according to level of economic development of the countries or regions under study, using membership of the OECD or Europe as a reference “cut-off” point. Sixty papers were judged to include high-income countries, and the remaining 142 included either low- or middle-income countries or a mix of both these levels of development. The rest of this report outlines findings in relation to high-income countries only, reflecting our own primary research interests. Nonetheless, we chose to report our search yield for the other income groups for two reasons. First, to gauge the relative interest in applied published research for these different income levels; and second, to enable other researchers with a focus on determinants of health in other countries to use the extraction we made here.

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https://doi.org/10.1371/journal.pone.0239031.g001

Health outcomes

The most frequent population health indicator, life expectancy (LE), was present in 24 of the 60 studies. Apart from “life expectancy at birth” (representing the average life-span a newborn is expected to have if current mortality rates remain constant), also called “period LE” by some [ 19 , 20 ], we encountered as well LE at 40 years of age [ 21 ], at 60 [ 22 ], and at 65 [ 21 , 23 , 24 ]. In two papers, the age-specificity of life expectancy (be it at birth or another age) was not stated [ 25 , 26 ].

Some studies considered male and female LE separately [ 21 , 24 , 25 , 27 – 33 ]. This consideration was also often observed with the second most commonly used health index [ 28 – 30 , 34 – 38 ]–termed “total”, or “overall”, or “all-cause”, mortality rate (MR)–included in 22 of the 60 studies. In addition to gender, this index was also sometimes broken down according to age group [ 30 , 39 , 40 ], as well as gender-age group [ 38 ].

While the majority of studies under review here focused on a single health indicator, 23 out of the 60 studies made use of multiple outcomes, although these outcomes were always considered one at a time, and sometimes not all of them fell within the scope of our review. An easily discernable group of indices that typically went together [ 25 , 37 , 41 ] was that of neonatal (deaths occurring within 28 days postpartum), perinatal (fetal or early neonatal / first-7-days deaths), and post-neonatal (deaths between the 29 th day and completion of one year of life) mortality. More often than not, these indices were also accompanied by “stand-alone” indicators, such as infant mortality (deaths within the first year of life; our third most common index found in 16 of the 60 studies), maternal mortality (deaths during pregnancy or within 42 days of termination of pregnancy), and child mortality rates. Child mortality has conventionally been defined as mortality within the first 5 years of life, thus often also called “under-5 mortality”. Nonetheless, Pritchard & Wallace used the term “child mortality” to denote deaths of children younger than 14 years [ 42 ].

As previously stated, inclusion criteria did allow for self-reported health status to be used as a general measure of population health. Within our final selection of studies, seven utilized some form of subjective health as an outcome variable [ 25 , 43 – 48 ]. Additionally, the Health Human Development Index [ 49 ], healthy life expectancy [ 50 ], old-age survival [ 51 ], potential years of life lost [ 52 ], and disability-adjusted life expectancy [ 25 ] were also used.

We note that while in most cases the indicators mentioned above (and/or the covariates considered, see below) were taken in their absolute or logarithmic form, as a—typically annual—number, sometimes they were used in the form of differences, change rates, averages over a given time period, or even z-scores of rankings [ 19 , 22 , 40 , 42 , 44 , 53 – 57 ].

Regions, countries, and populations

Despite our decision to confine this review to high-income countries, some variation in the countries and regions studied was still present. Selection seemed to be most often conditioned on the European Union, or the European continent more generally, and the Organisation of Economic Co-operation and Development (OECD), though, typically, not all member nations–based on the instances where these were also explicitly listed—were included in a given study. Some of the stated reasons for omitting certain nations included data unavailability [ 30 , 45 , 54 ] or inconsistency [ 20 , 58 ], Gross Domestic Product (GDP) too low [ 40 ], differences in economic development and political stability with the rest of the sampled countries [ 59 ], and national population too small [ 24 , 40 ]. On the other hand, the rationales for selecting a group of countries included having similar above-average infant mortality [ 60 ], similar healthcare systems [ 23 ], and being randomly drawn from a social spending category [ 61 ]. Some researchers were interested explicitly in a specific geographical region, such as Eastern Europe [ 50 ], Central and Eastern Europe [ 48 , 60 ], the Visegrad (V4) group [ 62 ], or the Asia/Pacific area [ 32 ]. In certain instances, national regions or cities, rather than countries, constituted the units of investigation instead [ 31 , 51 , 56 , 62 – 66 ]. In two particular cases, a mix of countries and cities was used [ 35 , 57 ]. In another two [ 28 , 29 ], due to the long time periods under study, some of the included countries no longer exist. Finally, besides “European” and “OECD”, the terms “developed”, “Western”, and “industrialized” were also used to describe the group of selected nations [ 30 , 42 , 52 , 53 , 67 ].

As stated above, it was the health status of the general population that we were interested in, and during screening we made a concerted effort to exclude research using data based on a more narrowly defined group of individuals. All studies included in this review adhere to this general rule, albeit with two caveats. First, as cities (even neighborhoods) were the unit of analysis in three of the studies that made the selection [ 56 , 64 , 65 ], the populations under investigation there can be more accurately described as general urban , instead of just general. Second, oftentimes health indicators were stratified based on gender and/or age, therefore we also admitted one study that, due to its specific research question, focused on men and women of early retirement age [ 35 ] and another that considered adult males only [ 68 ].

Data types and sources

A great diversity of sources was utilized for data collection purposes. The accessible reference databases of the OECD ( https://www.oecd.org/ ), WHO ( https://www.who.int/ ), World Bank ( https://www.worldbank.org/ ), United Nations ( https://www.un.org/en/ ), and Eurostat ( https://ec.europa.eu/eurostat ) were among the top choices. The other international databases included Human Mortality [ 30 , 39 , 50 ], Transparency International [ 40 , 48 , 50 ], Quality of Government [ 28 , 69 ], World Income Inequality [ 30 ], International Labor Organization [ 41 ], International Monetary Fund [ 70 ]. A number of national databases were referred to as well, for example the US Bureau of Statistics [ 42 , 53 ], Korean Statistical Information Services [ 67 ], Statistics Canada [ 67 ], Australian Bureau of Statistics [ 67 ], and Health New Zealand Tobacco control and Health New Zealand Food and Nutrition [ 19 ]. Well-known surveys, such as the World Values Survey [ 25 , 55 ], the European Social Survey [ 25 , 39 , 44 ], the Eurobarometer [ 46 , 56 ], the European Value Survey [ 25 ], and the European Statistics of Income and Living Condition Survey [ 43 , 47 , 70 ] were used as data sources, too. Finally, in some cases [ 25 , 28 , 29 , 35 , 36 , 41 , 69 ], built-for-purpose datasets from previous studies were re-used.

In most of the studies, the level of the data (and analysis) was national. The exceptions were six papers that dealt with Nomenclature of Territorial Units of Statistics (NUTS2) regions [ 31 , 62 , 63 , 66 ], otherwise defined areas [ 51 ] or cities [ 56 ], and seven others that were multilevel designs and utilized both country- and region-level data [ 57 ], individual- and city- or country-level [ 35 ], individual- and country-level [ 44 , 45 , 48 ], individual- and neighborhood-level [ 64 ], and city-region- (NUTS3) and country-level data [ 65 ]. Parallel to that, the data type was predominantly longitudinal, with only a few studies using purely cross-sectional data [ 25 , 33 , 43 , 45 – 48 , 50 , 62 , 67 , 68 , 71 , 72 ], albeit in four of those [ 43 , 48 , 68 , 72 ] two separate points in time were taken (thus resulting in a kind of “double cross-section”), while in another the averages across survey waves were used [ 56 ].

In studies using longitudinal data, the length of the covered time periods varied greatly. Although this was almost always less than 40 years, in one study it covered the entire 20 th century [ 29 ]. Longitudinal data, typically in the form of annual records, was sometimes transformed before usage. For example, some researchers considered data points at 5- [ 34 , 36 , 49 ] or 10-year [ 27 , 29 , 35 ] intervals instead of the traditional 1, or took averages over 3-year periods [ 42 , 53 , 73 ]. In one study concerned with the effect of the Great Recession all data were in a “recession minus expansion change in trends”-form [ 57 ]. Furthermore, there were a few instances where two different time periods were compared to each other [ 42 , 53 ] or when data was divided into 2 to 4 (possibly overlapping) periods which were then analyzed separately [ 24 , 26 , 28 , 29 , 31 , 65 ]. Lastly, owing to data availability issues, discrepancies between the time points or periods of data on the different variables were occasionally observed [ 22 , 35 , 42 , 53 – 55 , 63 ].

Health determinants

Together with other essential details, Table 1 lists the health correlates considered in the selected studies. Several general categories for these correlates can be discerned, including health care, political stability, socio-economics, demographics, psychology, environment, fertility, life-style, culture, labor. All of these, directly or implicitly, have been recognized as holding importance for population health by existing theoretical models of (social) determinants of health [ 74 – 77 ].

https://doi.org/10.1371/journal.pone.0239031.t001

It is worth noting that in a few studies there was just a single aggregate-level covariate investigated in relation to a health outcome of interest to us. In one instance, this was life satisfaction [ 44 ], in another–welfare system typology [ 45 ], but also gender inequality [ 33 ], austerity level [ 70 , 78 ], and deprivation [ 51 ]. Most often though, attention went exclusively to GDP [ 27 , 29 , 46 , 57 , 65 , 71 ]. It was often the case that research had a more particular focus. Among others, minimum wages [ 79 ], hospital payment schemes [ 23 ], cigarette prices [ 63 ], social expenditure [ 20 ], residents’ dissatisfaction [ 56 ], income inequality [ 30 , 69 ], and work leave [ 41 , 58 ] took center stage. Whenever variables outside of these specific areas were also included, they were usually identified as confounders or controls, moderators or mediators.

We visualized the combinations in which the different determinants have been studied in Fig 2 , which was obtained via multidimensional scaling and a subsequent cluster analysis (details outlined in S2 Appendix ). It depicts the spatial positioning of each determinant relative to all others, based on the number of times the effects of each pair of determinants have been studied simultaneously. When interpreting Fig 2 , one should keep in mind that determinants marked with an asterisk represent, in fact, collectives of variables.

Groups of determinants are marked by asterisks (see S1 Table in S1 Appendix ). Diminishing color intensity reflects a decrease in the total number of “connections” for a given determinant. Noteworthy pairwise “connections” are emphasized via lines (solid-dashed-dotted indicates decreasing frequency). Grey contour lines encircle groups of variables that were identified via cluster analysis. Abbreviations: age = population age distribution, associations = membership in associations, AT-index = atherogenic-thrombogenic index, BR = birth rate, CAPB = Cyclically Adjusted Primary Balance, civilian-labor = civilian labor force, C-section = Cesarean delivery rate, credit-info = depth of credit information, dissatisf = residents’ dissatisfaction, distrib.orient = distributional orientation, EDU = education, eHealth = eHealth index at GP-level, exch.rate = exchange rate, fat = fat consumption, GDP = gross domestic product, GFCF = Gross Fixed Capital Formation/Creation, GH-gas = greenhouse gas, GII = gender inequality index, gov = governance index, gov.revenue = government revenues, HC-coverage = healthcare coverage, HE = health(care) expenditure, HHconsump = household consumption, hosp.beds = hospital beds, hosp.payment = hospital payment scheme, hosp.stay = length of hospital stay, IDI = ICT development index, inc.ineq = income inequality, industry-labor = industrial labor force, infant-sex = infant sex ratio, labor-product = labor production, LBW = low birth weight, leave = work leave, life-satisf = life satisfaction, M-age = maternal age, marginal-tax = marginal tax rate, MDs = physicians, mult.preg = multiple pregnancy, NHS = Nation Health System, NO = nitrous oxide emissions, PM10 = particulate matter (PM10) emissions, pop = population size, pop.density = population density, pre-term = pre-term birth rate, prison = prison population, researchE = research&development expenditure, school.ref = compulsory schooling reform, smoke-free = smoke-free places, SO = sulfur oxide emissions, soc.E = social expenditure, soc.workers = social workers, sugar = sugar consumption, terror = terrorism, union = union density, UR = unemployment rate, urban = urbanization, veg-fr = vegetable-and-fruit consumption, welfare = welfare regime, Wwater = wastewater treatment.

https://doi.org/10.1371/journal.pone.0239031.g002

Distances between determinants in Fig 2 are indicative of determinants’ “connectedness” with each other. While the statistical procedure called for higher dimensionality of the model, for demonstration purposes we show here a two-dimensional solution. This simplification unfortunately comes with a caveat. To use the factor smoking as an example, it would appear it stands at a much greater distance from GDP than it does from alcohol. In reality however, smoking was considered together with alcohol consumption [ 21 , 25 , 26 , 52 , 68 ] in just as many studies as it was with GDP [ 21 , 25 , 26 , 52 , 59 ], five. To aid with respect to this apparent shortcoming, we have emphasized the strongest pairwise links. Solid lines connect GDP with health expenditure (HE), unemployment rate (UR), and education (EDU), indicating that the effect of GDP on health, taking into account the effects of the other three determinants as well, was evaluated in between 12 to 16 studies of the 60 included in this review. Tracing the dashed lines, we can also tell that GDP appeared jointly with income inequality, and HE together with either EDU or UR, in anywhere between 8 to 10 of our selected studies. Finally, some weaker but still worth-mentioning “connections” between variables are displayed as well via the dotted lines.

The fact that all notable pairwise “connections” are concentrated within a relatively small region of the plot may be interpreted as low overall “connectedness” among the health indicators studied. GDP is the most widely investigated determinant in relation to general population health. Its total number of “connections” is disproportionately high (159) compared to its runner-up–HE (with 113 “connections”), and then subsequently EDU (with 90) and UR (with 86). In fact, all of these determinants could be thought of as outliers, given that none of the remaining factors have a total count of pairings above 52. This decrease in individual determinants’ overall “connectedness” can be tracked on the graph via the change of color intensity as we move outwards from the symbolic center of GDP and its closest “co-determinants”, to finally reach the other extreme of the ten indicators (welfare regime, household consumption, compulsory school reform, life satisfaction, government revenues, literacy, research expenditure, multiple pregnancy, Cyclically Adjusted Primary Balance, and residents’ dissatisfaction; in white) the effects on health of which were only studied in isolation.

Lastly, we point to the few small but stable clusters of covariates encircled by the grey bubbles on Fig 2 . These groups of determinants were identified as “close” by both statistical procedures used for the production of the graph (see details in S2 Appendix ).

Statistical methodology

There was great variation in the level of statistical detail reported. Some authors provided too vague a description of their analytical approach, necessitating some inference in this section.

The issue of missing data is a challenging reality in this field of research, but few of the studies under review (12/60) explain how they dealt with it. Among the ones that do, three general approaches to handling missingness can be identified, listed in increasing level of sophistication: case-wise deletion, i.e., removal of countries from the sample [ 20 , 45 , 48 , 58 , 59 ], (linear) interpolation [ 28 , 30 , 34 , 58 , 59 , 63 ], and multiple imputation [ 26 , 41 , 52 ].

Correlations, Pearson, Spearman, or unspecified, were the only technique applied with respect to the health outcomes of interest in eight analyses [ 33 , 42 – 44 , 46 , 53 , 57 , 61 ]. Among the more advanced statistical methods, the family of regression models proved to be, by and large, predominant. Before examining this closer, we note the techniques that were, in a way, “unique” within this selection of studies: meta-analyses were performed (random and fixed effects, respectively) on the reduced form and 2-sample two stage least squares (2SLS) estimations done within countries [ 39 ]; difference-in-difference (DiD) analysis was applied in one case [ 23 ]; dynamic time-series methods, among which co-integration, impulse-response function (IRF), and panel vector autoregressive (VAR) modeling, were utilized in one study [ 80 ]; longitudinal generalized estimating equation (GEE) models were developed on two occasions [ 70 , 78 ]; hierarchical Bayesian spatial models [ 51 ] and special autoregressive regression [ 62 ] were also implemented.

Purely cross-sectional data analyses were performed in eight studies [ 25 , 45 , 47 , 50 , 55 , 56 , 67 , 71 ]. These consisted of linear regression (assumed ordinary least squares (OLS)), generalized least squares (GLS) regression, and multilevel analyses. However, six other studies that used longitudinal data in fact had a cross-sectional design, through which they applied regression at multiple time-points separately [ 27 , 29 , 36 , 48 , 68 , 72 ].

Apart from these “multi-point cross-sectional studies”, some other simplistic approaches to longitudinal data analysis were found, involving calculating and regressing 3-year averages of both the response and the predictor variables [ 54 ], taking the average of a few data-points (i.e., survey waves) [ 56 ] or using difference scores over 10-year [ 19 , 29 ] or unspecified time intervals [ 40 , 55 ].

Moving further in the direction of more sensible longitudinal data usage, we turn to the methods widely known among (health) economists as “panel data analysis” or “panel regression”. Most often seen were models with fixed effects for country/region and sometimes also time-point (occasionally including a country-specific trend as well), with robust standard errors for the parameter estimates to take into account correlations among clustered observations [ 20 , 21 , 24 , 28 , 30 , 32 , 34 , 37 , 38 , 41 , 52 , 59 , 60 , 63 , 66 , 69 , 73 , 79 , 81 , 82 ]. The Hausman test [ 83 ] was sometimes mentioned as the tool used to decide between fixed and random effects [ 26 , 49 , 63 , 66 , 73 , 82 ]. A few studies considered the latter more appropriate for their particular analyses, with some further specifying that (feasible) GLS estimation was employed [ 26 , 34 , 49 , 58 , 60 , 73 ]. Apart from these two types of models, the first differences method was encountered once as well [ 31 ]. Across all, the error terms were sometimes assumed to come from a first-order autoregressive process (AR(1)), i.e., they were allowed to be serially correlated [ 20 , 30 , 38 , 58 – 60 , 73 ], and lags of (typically) predictor variables were included in the model specification, too [ 20 , 21 , 37 , 38 , 48 , 69 , 81 ]. Lastly, a somewhat different approach to longitudinal data analysis was undertaken in four studies [ 22 , 35 , 48 , 65 ] in which multilevel–linear or Poisson–models were developed.

Regardless of the exact techniques used, most studies included in this review presented multiple model applications within their main analysis. None attempted to formally compare models in order to identify the “best”, even if goodness-of-fit statistics were occasionally reported. As indicated above, many studies investigated women’s and men’s health separately [ 19 , 21 , 22 , 27 – 29 , 31 , 33 , 35 , 36 , 38 , 39 , 45 , 50 , 51 , 64 , 65 , 69 , 82 ], and covariates were often tested one at a time, including other covariates only incrementally [ 20 , 25 , 28 , 36 , 40 , 50 , 55 , 67 , 73 ]. Furthermore, there were a few instances where analyses within countries were performed as well [ 32 , 39 , 51 ] or where the full time period of interest was divided into a few sub-periods [ 24 , 26 , 28 , 31 ]. There were also cases where different statistical techniques were applied in parallel [ 29 , 55 , 60 , 66 , 69 , 73 , 82 ], sometimes as a form of sensitivity analysis [ 24 , 26 , 30 , 58 , 73 ]. However, the most common approach to sensitivity analysis was to re-run models with somewhat different samples [ 39 , 50 , 59 , 67 , 69 , 80 , 82 ]. Other strategies included different categorization of variables or adding (more/other) controls [ 21 , 23 , 25 , 28 , 37 , 50 , 63 , 69 ], using an alternative main covariate measure [ 59 , 82 ], including lags for predictors or outcomes [ 28 , 30 , 58 , 63 , 65 , 79 ], using weights [ 24 , 67 ] or alternative data sources [ 37 , 69 ], or using non-imputed data [ 41 ].

As the methods and not the findings are the main focus of the current review, and because generic checklists cannot discern the underlying quality in this application field (see also below), we opted to pool all reported findings together, regardless of individual study characteristics or particular outcome(s) used, and speak generally of positive and negative effects on health. For this summary we have adopted the 0.05-significance level and only considered results from multivariate analyses. Strictly birth-related factors are omitted since these potentially only relate to the group of infant mortality indicators and not to any of the other general population health measures.

Starting with the determinants most often studied, higher GDP levels [ 21 , 26 , 27 , 29 , 30 , 32 , 43 , 48 , 52 , 58 , 60 , 66 , 67 , 73 , 79 , 81 , 82 ], higher health [ 21 , 37 , 47 , 49 , 52 , 58 , 59 , 68 , 72 , 82 ] and social [ 20 , 21 , 26 , 38 , 79 ] expenditures, higher education [ 26 , 39 , 52 , 62 , 72 , 73 ], lower unemployment [ 60 , 61 , 66 ], and lower income inequality [ 30 , 42 , 53 , 55 , 73 ] were found to be significantly associated with better population health on a number of occasions. In addition to that, there was also some evidence that democracy [ 36 ] and freedom [ 50 ], higher work compensation [ 43 , 79 ], distributional orientation [ 54 ], cigarette prices [ 63 ], gross national income [ 22 , 72 ], labor productivity [ 26 ], exchange rates [ 32 ], marginal tax rates [ 79 ], vaccination rates [ 52 ], total fertility [ 59 , 66 ], fruit and vegetable [ 68 ], fat [ 52 ] and sugar consumption [ 52 ], as well as bigger depth of credit information [ 22 ] and percentage of civilian labor force [ 79 ], longer work leaves [ 41 , 58 ], more physicians [ 37 , 52 , 72 ], nurses [ 72 ], and hospital beds [ 79 , 82 ], and also membership in associations, perceived corruption and societal trust [ 48 ] were beneficial to health. Higher nitrous oxide (NO) levels [ 52 ], longer average hospital stay [ 48 ], deprivation [ 51 ], dissatisfaction with healthcare and the social environment [ 56 ], corruption [ 40 , 50 ], smoking [ 19 , 26 , 52 , 68 ], alcohol consumption [ 26 , 52 , 68 ] and illegal drug use [ 68 ], poverty [ 64 ], higher percentage of industrial workers [ 26 ], Gross Fixed Capital creation [ 66 ] and older population [ 38 , 66 , 79 ], gender inequality [ 22 ], and fertility [ 26 , 66 ] were detrimental.

It is important to point out that the above-mentioned effects could not be considered stable either across or within studies. Very often, statistical significance of a given covariate fluctuated between the different model specifications tried out within the same study [ 20 , 49 , 59 , 66 , 68 , 69 , 73 , 80 , 82 ], testifying to the importance of control variables and multivariate research (i.e., analyzing multiple independent variables simultaneously) in general. Furthermore, conflicting results were observed even with regards to the “core” determinants given special attention, so to speak, throughout this text. Thus, some studies reported negative effects of health expenditure [ 32 , 82 ], social expenditure [ 58 ], GDP [ 49 , 66 ], and education [ 82 ], and positive effects of income inequality [ 82 ] and unemployment [ 24 , 31 , 32 , 52 , 66 , 68 ]. Interestingly, one study [ 34 ] differentiated between temporary and long-term effects of GDP and unemployment, alluding to possibly much greater complexity of the association with health. It is also worth noting that some gender differences were found, with determinants being more influential for males than for females, or only having statistically significant effects for male health [ 19 , 21 , 28 , 34 , 36 , 37 , 39 , 64 , 65 , 69 ].

The purpose of this scoping review was to examine recent quantitative work on the topic of multi-country analyses of determinants of population health in high-income countries.

Measuring population health via relatively simple mortality-based indicators still seems to be the state of the art. What is more, these indicators are routinely considered one at a time, instead of, for example, employing existing statistical procedures to devise a more general, composite, index of population health, or using some of the established indices, such as disability-adjusted life expectancy (DALE) or quality-adjusted life expectancy (QALE). Although strong arguments for their wider use were already voiced decades ago [ 84 ], such summary measures surface only rarely in this research field.

On a related note, the greater data availability and accessibility that we enjoy today does not automatically equate to data quality. Nonetheless, this is routinely assumed in aggregate level studies. We almost never encountered a discussion on the topic. The non-mundane issue of data missingness, too, goes largely underappreciated. With all recent methodological advancements in this area [ 85 – 88 ], there is no excuse for ignorance; and still, too few of the reviewed studies tackled the matter in any adequate fashion.

Much optimism can be gained considering the abundance of different determinants that have attracted researchers’ attention in relation to population health. We took on a visual approach with regards to these determinants and presented a graph that links spatial distances between determinants with frequencies of being studies together. To facilitate interpretation, we grouped some variables, which resulted in some loss of finer detail. Nevertheless, the graph is helpful in exemplifying how many effects continue to be studied in a very limited context, if any. Since in reality no factor acts in isolation, this oversimplification practice threatens to render the whole exercise meaningless from the outset. The importance of multivariate analysis cannot be stressed enough. While there is no “best method” to be recommended and appropriate techniques vary according to the specifics of the research question and the characteristics of the data at hand [ 89 – 93 ], in the future, in addition to abandoning simplistic univariate approaches, we hope to see a shift from the currently dominating fixed effects to the more flexible random/mixed effects models [ 94 ], as well as wider application of more sophisticated methods, such as principle component regression, partial least squares, covariance structure models (e.g., structural equations), canonical correlations, time-series, and generalized estimating equations.

Finally, there are some limitations of the current scoping review. We searched the two main databases for published research in medical and non-medical sciences (PubMed and Web of Science) since 2013, thus potentially excluding publications and reports that are not indexed in these databases, as well as older indexed publications. These choices were guided by our interest in the most recent (i.e., the current state-of-the-art) and arguably the highest-quality research (i.e., peer-reviewed articles, primarily in indexed non-predatory journals). Furthermore, despite holding a critical stance with regards to some aspects of how determinants-of-health research is currently conducted, we opted out of formally assessing the quality of the individual studies included. The reason for that is two-fold. On the one hand, we are unaware of the existence of a formal and standard tool for quality assessment of ecological designs. And on the other, we consider trying to score the quality of these diverse studies (in terms of regional setting, specific topic, outcome indices, and methodology) undesirable and misleading, particularly since we would sometimes have been rating the quality of only a (small) part of the original studies—the part that was relevant to our review’s goal.

Our aim was to investigate the current state of research on the very broad and general topic of population health, specifically, the way it has been examined in a multi-country context. We learned that data treatment and analytical approach were, in the majority of these recent studies, ill-equipped or insufficiently transparent to provide clarity regarding the underlying mechanisms of population health in high-income countries. Whether due to methodological shortcomings or the inherent complexity of the topic, research so far fails to provide any definitive answers. It is our sincere belief that with the application of more advanced analytical techniques this continuous quest could come to fruition sooner.

Supporting information

S1 checklist. preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (prisma-scr) checklist..

https://doi.org/10.1371/journal.pone.0239031.s001

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https://doi.org/10.1371/journal.pone.0239031.s002

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https://doi.org/10.1371/journal.pone.0239031.s003

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Quantitative research

Affiliation.

1 Faculty of Health and Social Care, University of Hull, Hull, England.
PMID: 25828021
DOI: 10.7748/ns.29.31.44.e8681

This article describes the basic tenets of quantitative research. The concepts of dependent and independent variables are addressed and the concept of measurement and its associated issues, such as error, reliability and validity, are explored. Experiments and surveys – the principal research designs in quantitative research – are described and key features explained. The importance of the double-blind randomised controlled trial is emphasised, alongside the importance of longitudinal surveys, as opposed to cross-sectional surveys. Essential features of data storage are covered, with an emphasis on safe, anonymous storage. Finally, the article explores the analysis of quantitative data, considering what may be analysed and the main uses of statistics in analysis.

Keywords: Experiments; measurement; nursing research; quantitative research; reliability; surveys; validity.

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Writing Quantitative Research Studies

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First Online: 13 January 2019
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journal article about quantitative research

Ankur Singh 2 ,
Adyya Gupta 3 &
Karen G. Peres 4

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Summarizing quantitative data and its effective presentation and discussion can be challenging for students and researchers. This chapter provides a framework for adequately reporting findings from quantitative analysis in a research study for those contemplating to write a research paper. The rationale underpinning the reporting methods to maintain the credibility and integrity of quantitative studies is outlined. Commonly used terminologies in empirical studies are defined and discussed with suitable examples. Key elements that build consistency between different sections (background, methods, results, and the discussion) of a research study using quantitative methods in a journal article are explicated. Specifically, recommended standard guidelines for randomized controlled trials and observational studies for reporting and discussion of findings from quantitative studies are elaborated. Key aspects of methodology that include describing the study population, sampling strategy, data collection methods, measurements/variables, and statistical analysis which informs the quality of a study from the reviewer’s perspective are described. Effective use of references in the methods section to strengthen the rationale behind specific statistical techniques and choice of measures has been highlighted with examples. Identifying ways in which data can be most succinctly and effectively summarized in tables and graphs according to their suitability and purpose of information is also detailed in this chapter. Strategies to present and discuss the quantitative findings in a structured discussion section are also provided. Overall, the chapter provides the readers with a comprehensive set of tools to identify key strategies to be considered when reporting quantitative research.

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Ankur Singh

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Singh, A., Gupta, A., Peres, K.G. (2019). Writing Quantitative Research Studies. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_117

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v.21(6); 2018 Dec

Public and patient involvement in quantitative health research: A statistical perspective

Ailish hannigan.

1 Public and Patient Involvement Research Unit, Graduate Entry Medical School, University of Limerick, Limerick, Ireland

2 Health Research Institute, University of Limerick, Limerick, Ireland

The majority of studies included in recent reviews of impact for public and patient involvement (PPI) in health research had a qualitative design. PPI in solely quantitative designs is underexplored, particularly its impact on statistical analysis. Statisticians in practice have a long history of working in both consultative (indirect) and collaborative (direct) roles in health research, yet their perspective on PPI in quantitative health research has never been explicitly examined.

To explore the potential and challenges of PPI from a statistical perspective at distinct stages of quantitative research, that is sampling, measurement and statistical analysis, distinguishing between indirect and direct PPI.

Conclusions

Statistical analysis is underpinned by having a representative sample, and a collaborative or direct approach to PPI may help achieve that by supporting access to and increasing participation of under‐represented groups in the population. Acknowledging and valuing the role of lay knowledge of the context in statistical analysis and in deciding what variables to measure may support collective learning and advance scientific understanding, as evidenced by the use of participatory modelling in other disciplines. A recurring issue for quantitative researchers, which reflects quantitative sampling methods, is the selection and required number of PPI contributors, and this requires further methodological development. Direct approaches to PPI in quantitative health research may potentially increase its impact, but the facilitation and partnership skills required may require further training for all stakeholders, including statisticians.

1. BACKGROUND

Public and patient involvement (PPI) in health research has been defined as research being carried out “with” or “by” members of the public rather than “to,” “about” or “for” them. 1 PPI covers a diverse range of approaches from “one off” information gathering to sustained partnerships. Tritter's conceptual framework for PPI distinguished between indirect involvement where information is gathered from patients and the public, but they do not have the power to make final decisions and direct involvement where patients and the public take part in the decision‐making. 2

A bibliometric review of the literature reported strong growth in the number of published empirical health research studies with public involvement. 3 In a systematic review of the impact of PPI on health and social care research, Brett et al 4 reported positive impacts at all stages of research from planning and undertaking the study to analysis, dissemination and implementation. The design of the majority of empirical research studies included in both reviews was qualitative (70% of studies in Brett. et al 4 and 73% in Boote et al 3 ). More significant tensions have been reported in community‐academic partnerships that use quantitative methods rather than solely qualitative methods, for example tensions with the community about having and recruiting to a “no intervention” comparison group. 5 Particular challenges for PPI have been reported in the most structured and regulated of quantitative designs, that is a randomized controlled trial (RCT), where there is little opportunity for flexibility once the trial has started 6 and Boote et al 3 concluded that researchers may find it easier to involve the public in qualitative rather than quantitative research.

If the full potential of PPI for health research is to be realized, its potential and challenges in quantitative research require more exploration, particularly the features of quantitative research which are different from qualitative research, for example, sampling, measurement and statistical analysis. Statisticians in practice have a long history of working with a variety of stakeholders in health research and have examined the difference between an indirect or consulting role for the statistician and a more direct, collaborative role, 7 yet their perspective has never been explicitly explored in health research with PPI. The objective of this study therefore was to critically reflect on the potential and challenges for PPI at distinct stages of quantitative research from a statistical perspective, distinguishing between direct and indirect approaches to PPI. 2

2. SAMPLE SIZE AND SELECTION

Quantitative research usually aims to provide precise, unbiased estimates of parameters of interest for the entire population which requires a large, randomly selected sample. Brett et al 4 reported a positive impact of PPI on recruitment in studies, but the representativeness of the sample is as important in quantitative research as sample size. Studies have shown that even when accrual targets have been met, the sample may not be fully representative of the population of interest. In cancer clinical trials, for example, those with health insurance and from higher socio‐economic backgrounds can be over‐represented, while older patients, ethnic minorities and so‐called hard‐to‐reach groups (often with higher cancer mortality rates) are under‐represented. 8 This limits the ability to generalize the results of the trials to all those with cancer. There is evidence that a direct approach to PPI with sustained partnerships between community leaders, primary care providers and clinical trial researchers can be effective in increasing awareness and participation of under‐represented groups in cancer clinical trials 9 , 10 and therefore help to achieve the goal of a population‐representative sample.

Collecting representative health data for some groups in the population may only be possible with their involvement. Marin et al 11 reports on the challenges of identifying an appropriate sampling frame for a health survey of Aboriginal adults in Southern Australia. Access to information identifying Aboriginal dwellings was not publically available, making it difficult to randomly select participants for large population household surveys. Trying to overcome this challenge involved reaching agreement on the process of research for Aboriginal adults with their local communities. An 8‐month consultation process was undertaken with representatives from multiple locations including Aboriginal owned lands in one region; however, it was ultimately agreed that it was culturally inappropriate for the research team to survey this region. The study demonstrated the opportunities for PPI in quantitative research with a representative sample of randomly chosen Aboriginal adults (excluding those resident in one region) ultimately achieved but also the challenges for PPI. The direct approach to involvement in this study, after a lengthy consultation process, resulted in a decision not to carry out the planned sampling and data collection in one region with implications for generalization of results and overall sample size.

Of course, given the importance of representativeness in quantitative research, there may be particular challenges for statisticians and quantitative researchers in accepting the term patient or public representative with some suggesting PPI contributor as a more appropriate term. 6 PPI representative may suggest to a quantitative researcher that an individual patient or member of the public is typical of an often diverse population, yet there is evidence that the opportunities and capacity to be involved as PPI contributors vary by level of education, income, cognitive skills and cultural background. 12 Dudley et al carried out a qualitative study of the impact of PPI in RCTs with patients and researchers from a cohort of RCTs. 6 The types of roles of PPI contributors described by researchers involved in the RCTs were grouped into oversight, managerial and responsive roles. Responsive PPI was described as informal and impromptu with researchers approaching multiple “responsive” PPI contributors as difficulties arose, for example advising on patient information sheets and follow‐up of patients. It was reported that contributions from responsive roles may carry more weight with the researchers in RCTs because it allowed access to a more diverse range of contributors who researchers saw as more “representative” of the target population.

3. MEASUREMENT

Measurement of quantitative data involves decisions about what to measure, how to measure it and how often to measure it with these decisions typically made by the research team. Without the involvement of patients and the public, however, important outcomes for people living with a condition have been missed or overlooked, for example fatigue for people with rheumatoid arthritis 13 or the long‐term effects of therapy for children with asthma. 14

Core outcome sets (COS) are a minimum set of agreed important outcomes to be measured in research on particular illnesses, conditions or treatments to ensure important outcomes are consistently reported and allow the results from multiple studies to be easily combined and compared. Young reported on workshops to explore what principles, methods and strategies that COS developers may need to consider when seeking patient input into the development of a COS. 15 The importance of distinguishing between an indirect role for patients in COS development where patients respond to a consensus survey or a direct role where patients are partners in planning, running and disseminating a COS study was highlighted by delegates in the workshops. While all delegates agreed that participation by patients should be meaningful and on an equal footing with other stakeholders, there was considerable uncertainty on how to achieve this, for example how many patients are needed in the COS development process or what proportion of patients relative to other stakeholders should be included. This raises the issue again of the number and selection of PPI contributors for quantitative researchers, and it was concluded that methodological work was needed to understand the COS development process from the perspective of patients and how the process may be improved for them.

Important considerations in longitudinal research are the number and timing of repeated measurements. From a statistical perspective, measurements on the same subject at different times are almost always correlated, with measurements taken close together in time being more highly correlated than measurements taken far apart in time. Unequal spacing of observation times may be more computationally challenging in statistical analysis of repeated measurements and missing data within subjects over time can be particularly challenging depending on the amount, cause and pattern of missing data. 16 There are therefore important statistical considerations to be taken into account in the design of longitudinal studies but these have to be balanced with input from PPI contributors on appropriate timing and frequency of data collection for potential participants.

Lucas et al reported on how European birth cohorts are engaging and consulting with young birth cohort members. 17 Of the 84 individual cohorts identified, only eight had a mechanism for consulting with parents and three a mechanism for consulting with young people themselves (usually “one off” consultations). Very varied follow‐up rates were reported from 13% to 84% more than 10 years after enrolment for individual data rounds of the birth cohorts. 17 Being motivated to continue to participate may be influenced by whether a participant believes the study is interesting, important, or relevant to them. 18 One of the key strategies for retention in the Australian Aboriginal Birth Cohort study was partnerships with community members with local knowledge who were involved in all phases of the follow‐up. 19 Retention rates of 86% at 11‐year follow‐up and 72% at 18‐year follow‐up were reported which demonstrates the potential of a direct approach to PPI. Ethical approval for the study involved an Aboriginal Ethical Sub‐committee which had the power of veto and a staged consent was used where participants had the right to refuse individual procedures at each wave. As with all missing data, this has implications for the statistical analysis yet only 10% of participants in this study chose to opt out of different assessments at follow‐up.

3.1. Statistical analysis

A report on the impact of PPI found that it had a positive impact at all stages of qualitative research including data analysis but that there was little evidence of its impact on quantitative data analysis. 20 It was concluded this lack of evidence may reflect a lack of involvement rather than an evidence gap. Booth et al 3 also suggested that the public may be more comfortable with interpreting interview and focus group data compared with numeric data. Low levels of numerical and statistical literacy in the general population may contribute to this.

Statistical analysis involves describing the data using appropriate graphical and numerical summaries (descriptive statistics) and using more advanced statistical methods to draw inferences about the population using the data from a sample (statistical inference). Choosing appropriate methods for statistical inference, testing the underlying assumptions and checking the adequacy of the models produced requires advanced statistical training and implementing them typically involves the use of statistical software or programming. Statisticians bring this expertise to quantitative health research and while it is important that the chosen methods are adequately communicated to all stakeholders, replicating this type of expertise in PPI contributors seems like an inefficient use of resources for PPI.

Quantitative data are, however, “not just numbers, they are numbers with a context” 21 and most practising statisticians agree that knowledge of the context is needed to carry out even a purely technical role effectively. 22 While many associate statistical analysis with objectivity, in practice, statisticians routinely use “subjective” external information to guide, for example the decision on what is a meaningful effect size; whether an outlier is an error in data entry or represents an unusual but meaningful observation; and potential issues with measurement of variables and confounding. 23 Gelman and Hennin argue that we should move beyond the discussion of objectivity and subjectivity in statistics and “replace each of them with broader collections of attributes, with objectivity replaced by transparency, consensus, impartiality and correspondence to observable reality, and subjectivity replaced by awareness of multiple perspectives and context dependence.” 23 This debate within statistics is relevant for PPI where the perceived objectivity and standardization of statistical analysis can be used as a reason for lack of involvement.

External information and context are particularly important in statistical modelling where statisticians are often faced with many potential predictors of an outcome. The “best” way of selecting a multivariable model is still unresolved from a statistical perspective, and it is generally agreed that subject matter knowledge, when available, should guide model building. 24 Even when the potential predictors are known, understanding the causal pathways of exposure on an outcome is challenging where the effect of a variable on the outcome can be direct or indirect. Christiaens et al 25 used a causal diagram to visualize the relationship between pain acceptance and personal control of women in labour and the use of pain medication during labour. Their analysis accounted for the maternal care context of the country where the women were giving birth and other characteristics such as age of the woman and duration of labour. The choice of these characteristics was underpinned by a literature review but women who have given birth also have expert knowledge on why they use pain relief and how other variables such as their personal beliefs and social context might influence that decision. 26

Collaborative or participatory modelling is an approach to scientific modelling in areas such as natural resource management which involves all stakeholders in the model building process. Participants can suggest characteristics for inclusion in the model and how they may impact on the outcome. Causal diagrams are then used to create a shared view across stakeholders. 27 Rockman et al 28 concluded, in the context of marine policy, that “participatory modelling has the potential to facilitate and structure discussions between scientists and stakeholders about uncertainties and the quality of the knowledge base. It can also contribute to collective learning, increase legitimacy and advance scientific understanding.”

There is emerging evidence that the importance of PPI in the development and application of modelling in health research is being recognized. Van Voorn 29 discussed the benefits and risks of PPI in health economic modelling of cost‐effectiveness of new drugs and treatment strategies, with public and patients described as the missing stakeholder group in the modelling process. The potential benefits included the expertise that patients could bring to the process, a greater understanding and possible acceptance by patients of the results of the models and improved model validation. The risks included potential patient bias and the increased resources required for training. The number and selection of patients to contribute to the process was also discussed with a suggestion to include patients “who were able to take a neutral view” and “at least five patients that differ significantly in their background,” again highlighting the focus of quantitative researchers on bias and sample size. The role for this type of participatory modelling in informing debate on public health problems is increasingly being recognized, drawing on the experience of its use in other areas where optimal use of limited resources is required to address complex problems in society. 30

4. CONCLUSIONS

Statistical analysis of quantitative data is underpinned by having a representative sample, and there is evidence that a direct approach to PPI can help achieve that by supporting access to and increasing participation of under‐represented groups in the population. The direct approach has also demonstrated its potential in the retention of those recruited over time, thus reducing bias caused by missing data in longitudinal studies. At all stages of statistical analysis, a statistician continuously refers back to the context of the data collected. 22 Lay knowledge of PPI contributors has an important role in providing this context, and there is evidence from other disciplines of the benefits of including this knowledge in analysis to support collective learning and advance scientific understanding.

The direct approach to PPI where patients and the public have the power to make decisions also brings challenges and the statistician needs to be able to clearly communicate the impact of each decision on the scientific rigour and validity of sampling, measurement and analysis to all stakeholders. Decisions made on participation impact on generalizability. Participatory modelling requires facilitation and partnership skills which may require further training for all stakeholders, including statisticians.

The direct and indirect role for PPI contributors mirrors what happens for statisticians in practice. Statisticians can have a consultative role, that is answering a specific statistical question or a collaborative role where a statistician works with others as equal partners to create new knowledge, with professional organizations for statisticians providing guidance and mentorship on moving from consulting to collaboration to leadership roles. 7 , 31 Statisticians therefore bring very relevant experience and understanding for PPI contributors on the ladder of participation in health research. Further exploration is required on the impact of direct compared to indirect involvement in quantitative research, drawing on the evidence base for community‐based participatory research in quantitative designs 9 and the framework for participatory health research and epidemiology. 32 , 33

CONFLICT OF INTERESTS

No conflict of interests.

ACKNOWLEDGEMENTS

Prof. Anne MacFarlane, Public and Patient Involvement Research Unit, University of Limerick, for discussion of ideas and comments on drafts.

Hannigan A. Public and patient involvement in quantitative health research: A statistical perspective . Health Expect . 2018; 21 :939–943. 10.1111/hex.12800 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

The top 10 journal articles

This year, APA’s 89 journals published more than 4,000 articles. Here are the most downloaded to date.

By Lea Winerman

December 2018, Vol 49, No. 11

Print version: page 36

1: Journal Article Reporting Standards for Qualitative Research in Psychology

This American Psychologist open-access article lays out—for the first time—journal article reporting standards for qualitative research in psychology (Levitt, H.M., et al., Vol. 73, No. 1). The voluntary guidelines are designed to help authors communicate their work clearly, accurately and transparently. Developed by a working group of the APA Publications and Communications Board, the new standards describe what should be included in a qualitative research report, as well as in qualitative meta-analyses and mixed-methods research reports. They cover a range of qualitative traditions, methods and reporting styles. The article presents these standards and their rationale, details the ways they differ from quantitative research reporting standards and describes how they can be used by authors as well as by reviewers and editors. DOI: 10.1037/amp0000151

2: The Relationship Between Frequency of Instagram Use, Exposure to Idealized Images, and Psychological Well-Being in Women

Frequent use of the social media photo-sharing app Instagram could contribute to negative psychological outcomes in women, suggests this study in Psychology of Popular Media Culture (Sherlock, M., & Wagstaff, D.L., advance online publication). Researchers surveyed 119 women, ages 18 to 35, about their Instagram use, mental health outcomes and self-perceptions. On average, more Instagram use was correlated with more depressive symptoms, lower self-esteem, more general and physical appearance anxiety, and more body dissatisfaction. In a follow-up experiment, the researchers showed women beauty, fitness or travel images from Instagram. Participants who saw the beauty and fitness images rated their own attractiveness lower than a control group that saw no images. DOI: 10.1037/ppm0000182

3: Journal Article Reporting Standards for Quantitative Research in Psychology

This open-access article in American Psychologist lays out new journal article reporting standards for quantitative research in APA journals (Appelbaum, M., et al., Vol. 73, No. 1). The new standards are voluntary guidelines for authors and reviewers, developed by a task force of APA’s Publications and Communications Board. The recommendations include dividing the hypotheses, analyses and conclusions sections into primary, secondary and exploratory groupings to enhance understanding and reproducibility. The standards also offer modules for authors reporting on N-of-1 designs, replications, clinical trials, longitudinal studies and observational studies, structural equation modeling and Bayesian analysis. DOI: 10.1037/amp0000191

4: The Effects of Sleep Deprivation on Item and Associative Recognition Memory

Sleep deprivation degrades different kinds of memory in the same way, finds this study in the Journal of Experimental Psychology: Learning, Memory, and Cognition (Ratcliff, R., & Van Dongen, H., Vol. 44, No. 2). Researchers assigned 26 participants to either a sleep-deprivation group or a control group. Before and after 57 hours of sleep deprivation, the participants did two memory tests in which they were shown word pairs and asked to recognize whether a word was on the pairs list (item recognition) or whether two words were studied in the same pair (associative recognition). Using a diffusion decision model, they found that sleep deprivation, unlike aging-related memory decline, reduced the quality of the information stored in memory for both tests to the same degree. DOI: 10.1037/xlm0000452

5: Do the Associations of Parenting Styles with Behavior Problems and Academic Achievement Vary by Culture?

Children with authoritative (high-warmth, high-control) parents have fewer behavior problems and better academic achievement compared with children of authoritarian (low-warmth, high-control) parents, and that association generally holds up across different countries and cultural groups, finds this meta-analysis in Cultural Diversity & Ethnic Minority Psychology (Pinquart, M., & Kauser, R., Vol. 24, No. 1). Researchers analyzed the results of 428 studies of parenting styles, with data on nearly 350,000 children from 52 countries. They found more similarities than differences in children’s responses to different parenting styles across ethnic groups and geographic regions. Authoritative parenting was associated with at least one positive outcome and authoritarian parenting was associated with at least one negative outcome in all regions. Overall, the association between parenting style and child outcomes was weaker in countries with more individualistic cultures. DOI: 10.1037/cdp0000149

6: Social Media Behavior, Toxic Masculinity and Depression

Men who adhere to standards of "toxic masculinity" are more likely to engage in negative behaviors on social media and are also more likely to suffer from depression, and these variables are intertwined in nuanced ways, according to a study in Psychology of Men & Masculinity (Parent, M.C., et al., advance online publication). In an online survey with 402 men, ages 18 to 74, researchers measured three areas: participants’ beliefs in toxic masculinity (sexism, heterosexism and competitiveness); their symptoms of depression; and their social media behavior, such as how often they posted positive or negative comments about things they saw online. Overall, the researchers found that men who endorsed "toxic masculinity" ideals reported more negative online behaviors and that negative online behaviors were associated with depression. DOI: 10.1037/men0000156

7: Prevention of Relapse in Major Depressive Disorder With Either Mindfulness-Based Cognitive Therapy or Cognitive Therapy

Mindfulness-based cognitive therapy (MBCT) and cognitive therapy (CT) are equally effective ways to prevent patients from relapsing into depression, finds this article in the Journal of Consulting and Clinical Psychology (Farb, N., et al., Vol. 86, No. 2). In the randomized trial, registered at ClinicalTrials.gov , 166 patients in remission from major depressive disorder were assigned to an eight-week session of either MBCT or CT. Researchers then followed the patients for two years, checking in on their depression symptoms every three months. Overall, relapse rates did not differ between the two treatment groups (18 out of 84 patients in the CT group and 18 out of 82 in the MBCT group), nor did the average time to relapse. DOI: 10.1037/ccp0000266

8: What Do Undergraduates Learn About Human Intelligence?

Many psychology textbooks contain inaccurate and incomplete information about intelligence, finds this analysis in the open-access, open-data journal Archives of Scientific Psychology (Warne, R.T., et al., Vol. 6, No. 1). By examining 29 of the most popular introductory psychology textbooks, researchers found that 79.3 percent contained inaccurate statements in their sections about intelligence and 79.3 percent contained logical fallacies. The five most commonly taught topics were IQ (93.1 percent), Gardner’s multiple intelligences (93.1 percent), Spearman’s g (93.1 percent), Sternberg’s triarchic theory (89.7 percent) and how intelligence is measured (82.8 percent), but few texts discussed the relative lack of empirical evidence for some of these theories. The authors note the limitations of the study, including the choice of standards for accuracy and the inherent subjectivity required for some of the data collection process. DOI: 10.1037/arc0000038

9: Bullying Victimization and Student Engagement in Schools

Students at schools with less bullying and more positive atmospheres are more engaged with their schoolwork and school communities, finds this study in School Psychology Quarterly (Yang, C., et al., Vol. 33, No. 1). Researchers surveyed nearly 26,000 Delaware public school students in fourth through 12th grade about how often they had been the victims of bullying, as well as their perceptions of their schools’ climate, including teacher-student relationships, student-student relationships, fairness of rules, clarity of expectations, school safety and respect for diversity. Students also took a survey that assessed their levels of emotional and cognitive-behavioral engagement in their schools, including how happy they felt at school and how committed they were to their schoolwork. After controlling for student and school demographic factors including gender, race/ethnicity and socioeconomic status, a positive school climate was associated with higher student engagement across all grades. DOI: 10.1037/spq0000250

10: Emotion Regulation Therapy for Generalized Anxiety Disorder With and Without Co-Occurring Depression

Emotion regulation therapy (ERT) is an effective treatment for generalized anxiety disorder, with or without co-occurring major depression, finds this study in the Journal of Consulting and Clinical Psychology (Mennin, D.S., et al., Vol. 86, No. 3). ERT uses principles from cognitive-behavioral therapy and affect science to teach patients to identify, accept and manage their emotions and to use this awareness to guide their thinking and behavior. Researchers assigned 53 patients with anxiety (23 of whom also had depression) to be treated with ERT or to be part of a control group awaiting treatment. After 20 weeks, patients in the treatment group showed statistically and clinically significant improvements in anxiety and depression symptoms—including functional impairment, quality of life, worry and rumination—compared with the control group. DOI: 10.1037/ccp0000289

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All Quantitative research articles

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Quantitative Research Design (JARS–Quant)

The current JARS–Quant standards, released in 2018, expand and revise the types of research methodologies covered in the original JARS, which were published in 2008.

JARS–Quant include guidance for manuscripts that report

Primary quantitative research
Experimental designs
Nonexperimental designs

Special designs

Analytic methods, meta-analyses.

In addition, JARS–Quant now divides hypotheses, analyses, and conclusions into primary, secondary, and exploratory groups. This should enhance the readability and replicability of the research.

Providing the information specified in JARS–Quant should become routine and minimally burdensome, thereby increasing the transparency of reporting in psychological research.

For more information on how the revised standards were created, read Journal Article Reporting Standards for Quantitative Research in Psychology .

General quantitative reporting standards

Quantitative Design Reporting Standards (JARS-Quant) (PDF, 130KB) Information recommended for inclusion in manuscripts that report new data collections regardless of research design

Experimental and nonexperimental designs

Experimental Designs (PDF, 109KB) Reporting standards for studies with an experimental manipulation
Random Assignment (PDF, 101KB) Reporting standards for studies using random assignment
Nonrandom Assignment (PDF, 92KB) Reporting standards for studies using nonrandom assignment
Clinical Trials (PDF, 106KB) Reporting standards for studies involving clinical trials
Nonexperimental Designs (PDF, 103KB) Reporting standards for studies using no experimental manipulation
Longitudinal Studies (PDF, 103KB) Reporting standards for longitudinal studies
N -of-1 Studies (PDF, 102KB) Reporting standards for N -of-1 studies
Replication Studies (PDF, 95KB) Reporting standards for replication studies
Structural Equation Modeling (PDF, 111KB) Reporting standards for studies using structural equation modeling
Bayesian Statistics (PDF, 104KB) Reporting standards for studies using Bayesian techniques
Quantitative Meta-Analysis Reporting Standards (PDF, 116KB) Information recommended for inclusion in manuscripts that report quantitative meta-analyses
Qualitative design standards
Mixed methods standards
Race, Ethnicity, and Culture standards

Return to Journal Article Reporting Standards homepage

Jars resources

History of APA’s journal article reporting standards
APA Style JARS supplemental glossary
Supplemental resource on the ethic of transparency in JARS
Frequently asked questions
JARS-Quant Decision Flowchart (PDF, 98KB)
JARS-Quant Participant Flowchart (PDF, 98KB)

Jars articles

Jars –Quant article
Jars –Qual / Mixed article
Jars – rec executive summary

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Email an APA Style Expert if you have questions, feedback, or suggestions for modules to be included in future JARS updates.

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Qualitative vs Quantitative Research

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Qualitative vs quantitative research.

13 min read You’ll use both quantitative and qualitative research methods to gather survey data. What are they exactly, and how can you best use them to gain the most accurate insights?

What is qualitative research?

Qualitative research is all about language, expression, body language and other forms of human communication . That covers words, meanings and understanding. Qualitative research is used to describe WHY. Why do people feel the way they do, why do they act in a certain way, what opinions do they have and what motivates them?

Qualitative data is used to understand phenomena – things that happen, situations that exist, and most importantly the meanings associated with them. It can help add a ‘why’ element to factual, objective data.

Qualitative research gives breadth, depth and context to questions, although its linguistic subtleties and subjectivity can mean that results are trickier to analyse than quantitative data.

This qualitative data is called unstructured data by researchers. This is because it has not traditionally had the type of structure that can be processed by computers, until today. It has, until recently at least, been exclusively accessible to human brains. And although our brains are highly sophisticated, they have limited processing power. What can help analyse this structured data to assist computers and the human brain?

Discover the 2023 Market Research Trends Transforming the industry

What is quantitative research?

Quantitative data refers to numerical information. Quantitative research gathers information that can be counted, measured, or rated numerically – AKA quantitative data. Scores, measurements, financial records, temperature charts and receipts or ledgers are all examples of quantitative data.

Quantitative data is often structured data, because it follows a consistent, predictable pattern that computers and calculating devices are able to process with ease. Humans can process it too, although we are now able to pass it over to machines to process on our behalf. This is partly what has made quantitative data so important historically, and why quantitative data – sometimes called ‘hard data’ – has dominated over qualitative data in fields like business, finance and economics.

It’s easy to ‘crunch the numbers’ of quantitative data and produce results visually in graphs, tables and on data analysis dashboards. Thanks to today’s abundance and accessibility of processing power, combined with our ability to store huge amounts of information, quantitative data has fuelled the Big Data phenomenon, putting quantitative methods and vast amounts of quantitative data at our fingertips.

As we’ve indicated, quantitative and qualitative data are entirely different and mutually exclusive categories. Here are a few of the differences between them.

1. Data collection

Data collection methods for quantitative data and qualitative data vary, but there are also some places where they overlap.

Qualitative data collection methods	Quantitative data collection methods
Gathered from focus groups, in-depth interviews, case studies, expert opinion, observation, audio recordings, and can also be collected using surveys.	Gathered from surveys, questionnaires, polls, or from secondary sources like census data, reports, records and historical business data.
Uses and open text survey questions	Intended to be as close to objective as possible. Understands the ‘human touch’ only through quantifying the OE data that only this type of research can code.

2. Data analysis

Quantitative data suits statistical analysis techniques like linear regression, T-tests and ANOVA. These are quite easy to automate, and large quantities of quantitative data can be analyzed quickly.

Analyzing qualitative data needs a higher degree of human judgement, since unlike quantitative data, non numerical data of a subjective nature has certain characteristics that inferential statistics can’t perceive. Working at a human scale has historically meant that qualitative data is lower in volume – although it can be richer in insights.

Qualitative data analysis	Quantitative data analysis
Results are categorised, summarised and interpreted using human language and perception, as well as logical reasoning	Results are analysed mathematically and statistically, without recourse to intuition or personal experience.
Fewer respondents needed, each providing more detail	Many respondents needed to achieve a representative result

3. Strengths and weaknesses

When weighing up qualitative vs quantitative research, it’s largely a matter of choosing the method appropriate to your research goals. If you’re in the position of having to choose one method over another, it’s worth knowing the strengths and limitations of each, so that you know what to expect from your results.

Qualitative approach	Quantitative approach
Can be used to help formulate a theory to be researched by describing a present phenomenon	Can be used to test and confirm a formulated theory
Results typically expressed as text, in a report, presentation or journal article	Results expressed as numbers, tables and graphs, relying on numerical data to tell a story.
Less suitable for scientific research	More suitable for scientific research and compatible with most standard statistical analysis methods
Harder to replicate, since no two people are the same	Easy to replicate, since what is countable can be counted again
Less suitable for sensitive data: respondents may be biased or too familiar with the pro	Ideal for sensitive data as it can be anonymized and secured

Qualitative vs quantitative – the role of research questions

How do you know whether you need qualitative or quantitative research techniques? By finding out what kind of data you’re going to be collecting.

You’ll do this as you develop your research question, one of the first steps to any research program. It’s a single sentence that sums up the purpose of your research, who you’re going to gather data from, and what results you’re looking for.

As you formulate your question, you’ll get a sense of the sort of answer you’re working towards, and whether it will be expressed in numerical data or qualitative data.

For example, your research question might be “How often does a poor customer experience cause shoppers to abandon their shopping carts?” – this is a quantitative topic, as you’re looking for numerical values.

Or it might be “What is the emotional impact of a poor customer experience on regular customers in our supermarket?” This is a qualitative topic, concerned with thoughts and feelings and answered in personal, subjective ways that vary between respondents.

Here’s how to evaluate your research question and decide which method to use:

Qualitative research:

Use this if your goal is to understand something – experiences, problems, ideas.

For example, you may want to understand how poor experiences in a supermarket make your customers feel. You might carry out this research through focus groups or in depth interviews (IDI’s). For a larger scale research method you could start by surveying supermarket loyalty card holders, asking open text questions, like “How would you describe your experience today?” or “What could be improved about your experience?” This research will provide context and understanding that quantitative research will not.

Quantitative research:

Use this if your goal is to test or confirm a hypothesis, or to study cause and effect relationships. For example, you want to find out what percentage of your returning customers are happy with the customer experience at your store. You can collect data to answer this via a survey.

For example, you could recruit 1,000 loyalty card holders as participants, asking them, “On a scale of 1-5, how happy are you with our store?” You can then make simple mathematical calculations to find the average score. The larger sample size will help make sure your results aren’t skewed by anomalous data or outliers, so you can draw conclusions with confidence.

Qualitative and quantitative research combined?

Do you always have to choose between qualitative or quantitative data?

Qualitative vs quantitative cluster chart

In some cases you can get the best of both worlds by combining both quantitative and qualitative data.You could use pre quantitative data to understand the landscape of your research. Here you can gain insights around a topic and propose a hypothesis. Then adopt a quantitative research method to test it out. Here you’ll discover where to focus your survey appropriately or to pre-test your survey, to ensure your questions are understood as you intended. Finally, using a round of qualitative research methods to bring your insights and story to life. This mixed methods approach is becoming increasingly popular with businesses who are looking for in depth insights.

For example, in the supermarket scenario we’ve described, you could start out with a qualitative data collection phase where you use focus groups and conduct interviews with customers. You might find suggestions in your qualitative data that customers would like to be able to buy children’s clothes in the store.

In response, the supermarket might pilot a children’s clothing range. Targeted quantitative research could then reveal whether or not those stores selling children’s clothes achieve higher customer satisfaction scores and a rise in profits for clothing.

Together, qualitative and quantitative data, combined with statistical analysis, have provided important insights about customer experience, and have proven the effectiveness of a solution to business problems.

Qualitative vs quantitative question types

As we’ve noted, surveys are one of the data collection methods suitable for both quantitative and qualitative research. Depending on the types of questions you choose to include, you can generate qualitative and quantitative data. Here we have summarized some of the survey question types you can use for each purpose.

Qualitative data survey questions

There are fewer survey question options for collecting qualitative data, since they all essentially do the same thing – provide the respondent with space to enter information in their own words. Qualitative research is not typically done with surveys alone, and researchers may use a mix of qualitative methods. As well as a survey, they might conduct in depth interviews, use observational studies or hold focus groups.

Open text ‘Other’ box (can be used with multiple choice questions)

Text box (space for short written answer)

What is your favourite item on our drinks menu

Essay box (space for longer, more detailed written answers)

Tell us about your last visit to the café

Quantitative data survey questions

These questions will yield quantitative data – i.e. a numerical value.

Net Promoter Score (NPS)

On a scale of 1-10, how likely are you to recommend our café to other people?

Likert Scale

How would you rate the service in our café? Very dissatisfied to Very satisfied

Radio buttons (respondents choose just one option)

Which drink do you buy most often? Coffee, Tea, Hot Chocolate, Cola, Squash

Check boxes (respondents can choose multiple options)

On which days do you visit the cafe? Mon-Saturday

Sliding scale

Star rating

Please rate the following aspects of our café: Service, Quality of food, Seating comfort, Location

Analysing data (quantitative or qualitative) using technology

We are currently at an exciting point in the history of qualitative analysis. Digital analysis and other methods that were formerly exclusively used for quantitative data are now used for interpreting non numerical data too.

Artificial intelligence programs can now be used to analyse open text, and turn qualitative data into structured and semi structured quantitative data that relates to qualitative data topics such as emotion and sentiment, opinion and experience.

Research that in the past would have meant qualitative researchers conducting time-intensive studies using analysis methods like thematic analysis can now be done in a very short space of time. This not only saves time and money, but opens up qualitative data analysis to a much wider range of businesses and organisations.

The most advanced tools can even be used for real-time statistical analysis, forecasting and prediction, making them a powerful asset for businesses.

Qualitative or quantitative – which is better for data analysis?

Historically, quantitative data was much easier to analyse than qualitative data. But as we’ve seen, modern technology is helping qualitative analysis to catch up, making it quicker and less labor-intensive than before.

That means the choice between qualitative and quantitative studies no longer needs to factor in ease of analysis, provided you have the right tools at your disposal. With an integrated platform like Qualtrics, which incorporates data collection, data cleaning, data coding and a powerful suite of analysis tools for both qualitative and quantitative data, you have a wide range of options at your fingertips.

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Related resources

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Methodology

What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

What is the demographic makeup of Singapore in 2020?
How has the average temperature changed globally over the last century?
Does environmental pollution affect the prevalence of honey bees?
Does working from home increase productivity for people with long commutes?

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

In descriptive research , you simply seek an overall summary of your study variables.
In correlational research , you investigate relationships between your study variables.
In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Quantitative research methods
Research method	How to use	Example
	Control or manipulate an to measure its effect on a dependent variable.	To test whether an intervention can reduce procrastination in college students, you give equal-sized groups either a procrastination intervention or a comparable task. You compare self-ratings of procrastination behaviors between the groups after the intervention.
	Ask questions of a group of people in-person, over-the-phone or online.	You distribute with rating scales to first-year international college students to investigate their experiences of culture shock.
(Systematic) observation	Identify a behavior or occurrence of interest and monitor it in its natural setting.	To study college classroom participation, you sit in on classes to observe them, counting and recording the prevalence of active and passive behaviors by students from different backgrounds.
Secondary research	Collect data that has been gathered for other purposes e.g., national surveys or historical records.	To assess whether attitudes towards climate change have changed since the 1980s, you collect relevant questionnaire data from widely available .

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Chi square goodness of fit test
Degrees of freedom
Null hypothesis
Discourse analysis
Control groups
Mixed methods research
Non-probability sampling
Inclusion and exclusion criteria

Research bias

Rosenthal effect
Implicit bias
Cognitive bias
Selection bias
Negativity bias
Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions).
Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Published: 12 June 2024

Identifying therapeutic target genes for migraine by systematic druggable genome-wide Mendelian randomization

Chengcheng Zhang 1 ,
Yiwei He 2 &

The Journal of Headache and Pain volume 25 , Article number: 100 ( 2024 ) Cite this article

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Currently, the treatment and prevention of migraine remain highly challenging. Mendelian randomization (MR) has been widely used to explore novel therapeutic targets. Therefore, we performed a systematic druggable genome-wide MR to explore the potential therapeutic targets for migraine.

We obtained data on druggable genes and screened for genes within brain expression quantitative trait locis (eQTLs) and blood eQTLs, which were then subjected to two-sample MR analysis and colocalization analysis with migraine genome-wide association studies data to identify genes highly associated with migraine. In addition, phenome-wide research, enrichment analysis, protein network construction, drug prediction, and molecular docking were performed to provide valuable guidance for the development of more effective and targeted therapeutic drugs.

We identified 21 druggable genes significantly associated with migraine (BRPF3, CBFB, CDK4, CHD4, DDIT4, EP300, EPHA5, FGFRL1, FXN, HMGCR, HVCN1, KCNK5, MRGPRE, NLGN2, NR1D1, PLXNB1, TGFB1, TGFB3, THRA, TLN1 and TP53), two of which were significant in both blood and brain (HMGCR and TGFB3). The results of phenome-wide research showed that HMGCR was highly correlated with low-density lipoprotein, and TGFB3 was primarily associated with insulin-like growth factor 1 levels.

Conclusions

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Migraine is a prevalent chronic disease characterized by recurring headaches that are typically unilateral and throbbing, ranging from moderate to severe intensity, and often accompanied by nausea, vomiting, sensitivity to light, among other symptoms [ 1 ]. Migraine is recognized as the second most disabling condition globally, creating substantial challenges for those affected and also placing a considerable strain on society overall [ 2 ]. Genetic factors play a substantial role in migraine, with its heritability estimated to be as high as 57% [ 3 ].

Currently, the treatment and prevention of migraine remain highly challenging. Although new drugs (e.g. targeting the calcitonin gene-related peptide, namely CGRP) have been developed, offering significant benefits to migraine sufferers, there are still many issues, such as side effects and less than ideal response rates [ 4 ]. Therefore, it is necessary to continue exploring potential therapeutic targets for migraine treatment. Integrating genetics into drug development may provide a novel approach. While genome-wide association studies (GWAS) are very effective in identifying single nucleotide polymorphisms (SNPs) associated with the risk of migraine [ 5 ], the GWAS method does not clearly and directly identify the causative genes or drive drug development without substantial downstream analyses [ 6 , 7 ].

Mendelian randomization (MR) is a method that utilizes genetic variation as instrumental variables (IVs) to uncover a causal connection between an exposure and an outcome [ 8 ]. MR analysis has been widely applied to discover new therapeutic targets by integrating summarized data from disease GWAS and expression quantitative trait loci (eQTL) studies [ 9 ]. The eQTLs found in the genomic regions of druggable genes are always considered as proxies, since the expression levels of gene can be seen as a form of lifelong exposure. Therefore, we performed a systematic druggable genome-wide MR to explore the potential therapeutic targets for migraine. First, we obtained data on druggable genes and screened for genes within brain eQTLs and blood eQTLs, which were then subjected to two-sample MR analysis with migraine GWAS data to identify genes highly associated with migraine. Subsequently, we conducted colocalization analysis to ensure the robustness of our results. For significant genes both in blood and brain, the phenome-wide research was conducted to explore the relationship between shared potential therapeutic targets and other characteristics. In addition, enrichment analysis, protein network construction, drug prediction, and molecular docking were performed for all significant genes to provide valuable guidance for the development of more effective and targeted therapeutic drugs.

The overview of this study is presented in Fig. 1 .

Overview of this study design. DGIdb: Drug-Gene Interaction Database; eQTL: expression quantitative trait loci; GWAS: genome-wide association studies; PheWAS: Phenome-wide association study; PPI: protein–protein interaction; DSigDB: Drug Signatures Database

Druggable genes

Druggable genes were sourced from the Drug-Gene Interaction Database (DGIdb, https://www.dgidb.org/ ) [ 10 ] and a comprehensive review [ 11 ]. The DGIdb offers insights into drug-gene interactions and the potential for druggability. We accessed the 'Categories Data' from DGIdb, which was updated in February 2022. Additionally, we utilized a list of druggable genes provided in a review authored by Finan et al. [ 11 ]. By consolidating druggable genes from two sources, a broader range of druggable genes can be obtained, which have already been applied in previous study [ 12 ].

eQTL datasets

The blood eQTL dataset was sourced from eQTLGen ( https://eqtlgen.org/ ) [ 13 ], which provided cis-eQTLs for 16,987 genes derived from 31,684 blood samples collected from healthy individuals of European ancestry (Table 1 ). We acquired cis-eQTL results that were fully significant (with a false discovery rate (FDR) less than 0.05) along with information on allele frequencies. We obtained the brain eQTL data from the PsychENCODE consortia ( http://resource.psychencode.org ) [ 14 ], encompassing 1,387 samples from the prefrontal cortex, primarily of European descent (Table 1 ). We downloaded all significant eQTLs (with FDR less than 0.05) for genes that exhibited an expression level greater than 0.1 fragments per kilobase per million mapped fragments in at least 10 samples, along with complete SNP information.

Migraine GWAS dataset

In this study, the summary statistics data for migraine were obtained from a meta-analysis of GWAS conducted by the International Headache Genetics Consortium (IHGC) in 2022 [ 5 ]. To address privacy concerns related to participants in the 23andMe cohort, the GWAS summary statistics data used in this study did not include samples from the 23andMe cohort. The summary data comprised 589,356 individuals of European ancestry, with 48,975 cases and 540,381 controls (Table 1 ).

Mendelian randomization analysis

MR analyses were conducted using the 'TwoSampleMR' package (version 0.5.7) [ 15 ] in R. We chose the eQTLs of the drug genome as the exposure data. For constructing IVs, SNPs with a FDR below 0.05 and located within ± 100 kb of the transcriptional start site (TSS) of each gene were selected. These SNPs were subsequently clumped at an r 2 less than 0.001 using European samples from the 1000 Genomes Project [ 16 ]. The R package 'phenoscanner' [ 17 ] (version 1.0) was employed to identify phenotypes related to the IVs. Additionally, we excluded SNPs that were directly associated with migraine and the trait directly linked to migraine, namely headache. We harmonised and conducted MR analyses on the filtered SNPs. When only one SNP was available for analysis, we use the Wald ratio method to perform MR estimation. When multiple SNPs were available, MR analysis was performed using the inverse-variance weighted (IVW) method with random effects [ 18 ]. We used Cochran's Q test to assess heterogeneity among the individual causal effects of the SNPs [ 19 ]. Additionally, MR Egger's intercept was utilized to evaluate SNP pleiotropy [ 20 ]. P -values were adjusted by FDR, and 0.05 was considered as the significant threshold. Additionally, we selected target genes associated with commonly used medications for migraine and compared their MR results with those of significantly druggable genes.

Colocalization analysis

Sometimes, a single SNP is located in the regions of two or more genes. In such cases, its impact on a disease (here, migraine) is influenced by a mix of different genes. Colocalization analysis was used to confirm the potential shared causal genetic variations in physical location between migraine and eQTLs. We separately filtered SNPs located within ± 100 kb from each migraine risk gene's TSS from migraine GWAS data, blood eQTL data, and brain eQTL data. The probability that a given SNP is associated with migraine is denoted as P1, the probability that a given SNP is a significant eQTL is denoted as P2, and the probability that a given SNP is both associated with migraine and is an eQTL result is denoted as P12. All probabilities were set to default values (P1 = 1 × 10 −4 , P2 = 1 × 10 −4 , and P12 = 1 × 10 −5 ) [ 21 ]. We used posterior probabilities (PP) to quantify the support for all hypotheses, which are identified as PPH0 through PPH4: PPH0, not associated with any trait; PPH1, related to gene expression but not associated with migraine risk; PPH2, associated with migraine risk but not related to gene expression; PPH3, associated with both migraine risk and gene expression, with clear causal variation; and PPH4, associated with both migraine risk and gene expression, with a common causal variant. Given the limited capacity of colocalization analysis, we restricted our subsequent analyses to genes where PPH4 was greater than or equal to 0.75. Colocalization analysis was conducted using the R package 'coloc' (version 5.2.3).

Phenome-wide association analysis

We used the IEU OpenGWAS Project ( https://gwas.mrcieu.ac.uk/phewas/ ) [ 15 ] to obtain the phenome-wide association study (PheWAS) data of SNPs corresponding to druggable genes that were significant in both blood and brain following colocalization analysis.

Enrichment analysis

To explore the functionals' characteristics and biological relevance of predetermined prospective druggable genes, the R package 'clusterProfiler' (version 4.10.1) [ 22 ] was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment studies. GO includes three terms: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). KEGG pathways can provide information about metabolic pathways.

Protein–protein interaction network construction

The protein–protein interaction (PPI) networks can visually display the relationships between protein interactions of significant druggable genes. We constructed PPI networks using the STRING ( https://string-db.org/ ) s' with a confidence score threshold of 0.4 as the minimum required interaction score, while all other parameters were maintained at their default settings [ 23 ].

Candidate drug prediction

Drug Signatures Database (DSigDB, http://dsigdb.tanlab.org/DSigDBv1.0/ ) [ 24 ] is a sizable database with 22,527 gene sets and 17,389 unique compounds spanning 19,531 genes. We uploaded previously identified significant druggable genes to DSigDB to predict candidate drugs and evaluate the pharmacological activity of target genes.

Molecular docking

We conducted molecular docking to assess the binding energies and interaction patterns between candidate drugs and their targets. By identifying ligands that exhibit high binding affinity and beneficial interaction patterns, we are able to prioritize drug targets for additional experimental validation and refine the design of prospective candidate drugs. Drug structural data were sourced from the PubChem Compound Database ( https://pubchem.ncbi.nlm.nih.gov/ ) [ 25 ] and downloaded in SDF format, then converted to pdb format using OpenBabel 2.4.1. Protein structural data were downloaded from the Protein Data Bank (PDB, http://www.rcsb.org/ ). The top five important drugs and the proteins encoded by the respective target genes were subjected to molecular docking using the computerized protein–ligand docking software AutoDock 4.2.6 ( http://autodock.scripps.edu/ ) [ 26 ], and the results were visualized using PyMol 3.0.2 ( https://www.pymol.org/ ). The final structures of six proteins and four drugs were obtained.

Druggable genome

We obtained 3,953 druggable genes from the DGIdb (Table S1). Additionally, we acquired 4,463 druggable genes from previous reviews (Table S2) [ 11 ]. After integrating the data, we obtained 5,883 unique druggable genes named by the Human Genome Organisation Gene Nomenclature Committee for subsequent analysis (Table S3).

Candidate druggable genes

After intersecting eQTLs from blood and brain tissue with druggable genes respectively, the blood eQTLs contained 3,460 gene symbols, while the brain eQTLs had 2,624 gene symbols. We performed MR analysis and identified 24 significant genes associated with migraine from blood and 10 from brain tissue (Figs. 2 and 3 ). Among them, two genes, HMGCR and TGFB3, reached significance in both blood (HMGCR OR 1.38 and TGFB3 OR 0.88) and brain tissues (HMGCR OR 2.02 and TGFB3 OR 0.73). Detailed results for the significant IVs and full results of MR are available in the Table S4-S6.

Forest plot of 24 significant genes associated with migraine from blood

Forest plot of 10 significant genes associated with migraine from brain

We selected target genes associated with commonly used medications for migraine as comparisons for our study results [ 27 ]. These include CGRP-related gene (CALCB, CALCRL, RAMP1 and RAMP3), genes related to 5-hydroxytryptamine (5-HT) receptors targeted by ergot alkaloids, triptans, and ditans (HTR1B, HTR1D, HTR1F), γ-aminobutyric acid (GABA) receptor-related genes targeted by topiramate (GABRA1), calcium ion channel-related genes targeted by flunarizine (CACNA1H, CACNA1I, CALM1), and genes related to β-adrenoceptor targeted by propranolol (ADRB1, ADRB2). Among these genes (Fig. 4 ), CALM1 showed significant association with migraine in blood eQTL, but it lost significance after FDR correction (OR 0.92, P = 0.039, FDR-P = 0.455). In brain eQTL, CALCB and RAMP3 showed correlation with migraine, and after FDR correction, CALCB still maintained significance (CALCB: OR 0.68, P = 0.0001, FDR-P = 0.029; RAMP3: OR 1.16, P = 0.031, FDR-P = 0.425).

Forest plot of 13 genes associated with commonly used medications for migraine from blood and brain

The results indicated that, of the previously identified 24 significant genes from blood, 17 had a PPH4 greater than 0.75. Among the 10 significant genes from brain, 6 had a PPH4 greater than 0.75. HMGCR and TGFB3 showed significant colocalization results in both blood and brain tissues (Table 2 , Table 3 and Table S7).

Due to the presence of the blood–brain barrier, compared to various components in the blood and other organs, brain tissue is more difficult to be affected by the action of drugs [ 28 ]. Therefore, we used the IEU OpenGWAS Project to obtain the PheWAS results of SNPs corresponding to HMGCR and TGFB3 from blood, rather than from brain tissue. The results showed that HMGCR was highly correlated with low-density lipoprotein (LDL), and TGFB3 was primarily associated with the level of insulin-like growth factor 1 (IGF1). The complete results are available in the Table S8-S9.

Through GO analysis of 21 potential targets, we found that these targets are primarily involved in BP such as regulation of protein secretion (GO: 0050708), response to hypoxia (GO: 0001666), negative regulation of carbohydrate metabolic processes (GO: 0045912), and the intrinsic apoptotic signaling pathway in response to DNA damage by p53 class mediator (GO: 0042771). The main MF include transcription coregulator binding (GO: 0001221) and chromatin DNA binding (GO: 0031490, Fig. 5 ). To explore the potential therapeutic pathways of migraine-associated significant druggable genes, KEGG analysis indicates that the target genes were primarily enriched in pathways such as Human T-cell leukemia virus 1 infection (hsa05166) and the Cell cycle (hsa04110, Fig. 6 ).

GO enrichment results for three terms

KEGG enrichment results

We loaded 21 drug target genes into the STRING database to create a PPI network. The results, shown in Fig. 7 , displayed protein interaction pathways consisting of 21 nodes and 22 edges.

PPI network built with STRING

We used DSigDB to predict potentially effective intervention drugs and listed the top 10 potential intervention drugs based on the adjusted P -values (Table 4 ). The results indicated that butyric acid (butyric acid CTD 00007353) and clofibrate (clofibrate CTD 00005684) were the two most significant drugs, connected respectively to TGFB1, TGFB3, EP300, TP53 and TGFB1, CDK4, HMGCR, TP53. Additionally, arsenenous acid (Arsenenous acid CTD 00000922) and dexamethasone (dexamethasone CTD 00005779) were associated with most of the significant druggable genes.

We used AutoDock 4.2.6 to analyze the binding sites and interactions between the top 5 candidate drugs and the proteins encoded by the corresponding genes, generating the binding energy for each interaction. We obtained 14 effective docking results between the proteins and drugs (Table 5 ). Docking amino acid residues and hydrogen bond lengths are shown in Fig. 8 . Among these, the binding between CDK4 and andrographolide exhibited the lowest binding energy (-7.11 kcal/mol), indicating stable binding.

Molecular docking results of available proteins and drugs. a TGFB1 docking butyric acid, b TGFB1 docking clofibrate, c TGFB1 docking Sorafenib, d TGFB1 docking Andrographolide, e TGFB3 docking butyric acid, f EP300 docking butyric acid, g TP53 docking butyric acid, h CDK4 docking clofibrate, i CDK4 docking Sorafenib, j CDK4 docking Andrographolide, k HMGCR docking clofibrate, l TP53 docking clofibrate, m TP53 docking Sorafenib, n TP53 docking Andrographolide

This study integrated existing druggable gene targets with migraine GWAS data through MR and colocalization analysis, identifying 21 druggable genes significantly associated with migraine (BRPF3, CBFB, CDK4, CHD4, DDIT4, EP300, EPHA5, FGFRL1, FXN, HMGCR, HVCN1, KCNK5, MRGPRE, NLGN2, NR1D1, PLXNB1, TGFB1, TGFB3, THRA, TLN1 and TP53). To further illustrate the potential pleiotropy and drug side effects of significant druggable genes, we conducted a phenome-wide research of two SNPs associated with two druggable genes of interest (HMGCR and TGFB3). Additionally, we performed enrichment analysis and constructed PPI network for these 21 significant genes to understand the biological significance and interaction mechanisms of these drug targets. Finally, drug prediction and molecular docking were conducted to further validate the pharmaceutical value of these significant druggable genes.

The association between HMGCR and migraine has been supported by multiple prior studies. One study indicated that migraine has significant shared signals with certain lipoprotein subgroups at the HMGCR locus [ 29 ]. Hong et al. found that HMGCR genotypes associated with higher LDL cholesterol levels are linked to an increased risk of migraine [ 30 ]. Statins inhibit the activity of HMG-CoA reductase, which is encoded by the HMGCR gene, to exert their lipid-lowering effects and have been widely used in the prevention and treatment of coronary heart disease and ischemic stroke. Previous clinical research has shown that simvastatin combined with vitamin D can effectively prevent episodic migraines in adults [ 31 ]. Additionally, HMGCR may also be involved in immune modulation, with studies suggesting that migraine patients experience neuroinflammation due to activation of the trigeminal-vascular system, leading to peripheral and central sensitization of pain and triggering migraine attacks [ 32 , 33 ]. HMGCR inhibitors can suppress the production of inflammatory mediators and cytokines, thus reducing inflammatory responses [ 34 ]. We speculate that the role of HMGCR in regulating inflammation and immunity may have influenced the drug prediction results generated by DSigDB, which based on Gene Set Enrichment Analysis (GSEA) [ 24 , 35 , 36 ], diluting the role of HMGCR in regulating lipid metabolism. Therefore, statins did not appear in the predicted list of candidate drugs.

TGFB1 and TGFB3 encodes different secreted ligands of the transforming growth factor-beta (TGF-β) superfamily of proteins, namely TGF-β1 and TGF-β3. TGF-β is a pleiotropic cytokine closely associated with immunity and inflammation [ 37 ]. Research indicated that TGF-β3 can inhibit B cell proliferation and antibody production by suppressing the phosphorylation of NF-κB, thus exerting its anti-inflammatory effects [ 38 ]. The activation of the classical NF-κB pathway is a key mechanism that upregulates pro-inflammatory cytokines, promoting central sensitization and leading to the onset of chronic migraine [ 39 ]. A previous clinical study indicated that the serum levels of TGF-β1 are significantly elevated in migraine patients [ 40 ]. Ishizaki et al. found that TGF-β1 levels in the platelet poor plasma of migraine patients are significantly increased during headache-free intervals [ 41 ]. Bø et al. discovered that during acute migraine attacks, the levels of TGF-β1 in cerebrospinal fluid are significantly higher compared to the control group [ 42 ]. Although some studies consider TGF-β1 to be an anti-inflammatory cytokine [ 43 ], based on previous research and the results of this study, we believe that TGFB1 and its encoded protein, TGF-β1, are associated with an increased risk of migraine. The pleiotropic effects of TGF-β1 on inflammation may depend on concentration and environment [ 44 ]. In addition, we found an association between TGFB3 and IGF1 in our phenome-wide research. A previous MR study showed that increased levels of IGF1 are causally associated with decreased migraine risk [ 45 ]. Recent experimental results suggest that the miR-653-3p/IGF1 axis regulating the AKT/TRPV1 signaling pathway may be a potential pathogenic mechanism for migraine [ 46 ]. The beneficial effects of TGF-β3 and IGF1 on migraine may be associated with the regulation of gene expression in different microenvironments to promote the transition of microglial cells from M1 (pathogenic) to M2 (protective) phenotypes [ 47 ].

Among the 13 genes targeted by some commonly used migraine treatment drugs, the MR results for 3 genes were significant in blood or brain eQTL. Although only one gene remained significant after FDR correction, this still demonstrates that the significant genes newly identified in this study are reliable and have potential as drug targets to some extent. The lack of significance in certain drug target genes may be related to the insufficient sample size of the migraine GWAS data included in our study. It would be meaningful to validate the results of this study with more large-sample GWAS data available in the future.

In this study, DSigDB predicted 10 potential drugs for migraine, but current clinical research is mainly focused on melatonin and dexamethasone. ClinicalTrials ( https://clinicaltrials.gov/ ) has registered multiple studies on the efficacy of melatonin and dexamethasone for migraine. Many research findings differ differently and controversially. A published clinical study on acute treatment of pediatric migraine showed that both low and high doses of melatonin contributed to pain relief [ 48 ]. The consensus published by the Brazilian Headache Society in 2022 lists melatonin as a recommended medication for preventing episodic migraine (Class II; Level C) [ 49 ]. However, study indicated that bedtime administration of sustained-release melatonin did not lead to a reduction in migraine attack frequency compared to placebo [ 50 ]. Dexamethasone has shown good efficacy for severe acute migraine attacks [ 51 ]. The 2016 guidelines for the emergency treatment of acute migraines in adults, issued by the American Headache Society, mention that dexamethasone should be administered to prevent the recurrence of migraine (Should offer—Level B) [ 52 ]. But study suggested that dexamethasone does not reduce migraine recurrence [ 53 ].

An animal study has shown that clofibrate can improve oxidative stress and neuroinflammation caused by the exaggerated production of lipid peroxidation products [ 54 ]. Clofibrate can activate peroxisome-proliferator-activated receptors (PPAR) α, inhibit the activation of the NF-κB signaling pathway and the production of interleukin (IL)-6, exerting an anti-inflammatory effect [ 55 , 56 ]. Additionally, a recent animal study indicated the upregulation of astrocytic activation and glial fibrillary acidic protein (GFAP) expression in the trigeminal nucleus caudalis (TNC) in migraine mice model induced by recurrent dural infusion of inflammatory soup (IS). This was accompanied by the release of various cytokines, increased neuronal excitability, and promotion of central sensitization processes [ 57 ]. Clofibrate can reduce the activation of astrocytes and the expression of GFAP, thereby inhibiting neuroinflammation [ 54 ]. Andrographolide is a major bioactive constituent of Andrographis paniculata, has broad effects on various inflammatory and neurological disorders [ 58 , 59 , 60 ]. Although we did not find any migraine clinical trials related to clofibrate and andrographolide on PubMed and ClinicalTrials, we believe that the prospects for using clofibrate and andrographolide in the treatment of migraine are quite promising. We hope to see more research on the association of clofibrate and andrographolide with migraine in the future.

Our study has several advantages. First, we provided compelling genetic evidence about migraine drug targets using MR, utilizing the largest publicly available GWAS data to date. Additionally, colocalization analysis helps reduce false negatives and false positives to ensure the robustness of the results. Enrichment analysis and PPI illustrate the functional characteristics and regulatory relationships of these targets genes, providing potential avenues for migraine drug development. The drug predictions demonstrate the medicinal potential of these genes, and high binding activity from molecular docking indicates the strong potential of these genes as drug targets. Our research conducts a comprehensive evaluation from identifying migraine-related druggable genes to drug binding properties, proposing migraine drug targets with compelling evidence.

This study also includes several notable limitations. Firstly, the number of eQTL IVs in MR is limited, with most not exceeding three SNPs, which restricts the credibility of the MR results. Additionally, while MR offers valuable insights into causality, it assumes a linear connection between low-dose drug exposure and the exposure-outcome relationship, which may not fully replicate real-world clinical trials that typically assess high doses of drugs in a short timeframe. Therefore, MR results may not accurately reflect the effect sizes observed in actual clinical settings, nor fully predict the impacts of drugs. Secondly, the generalizability of this study is limited by its primary inclusion of individuals of European descent. Extrapolating the findings to individuals of other genetic ancestry populations requires further research and validation to ensure broader applicability. Thirdly, the study focuses mainly on cis-eQTLs and their relationship with migraine, potentially overlooking other regulatory and environmental factors that contribute to the complexity of the disease. Fourthly, while enrichment analysis is valuable, it has inherent limitations as it relies on predefined gene sets or pathways, which may not encompass all possible biological mechanisms or interactions. A lack of significant enrichment does not necessarily mean there is no biological relevance, and researchers should interpret results cautiously. Fifth, the accuracy of molecular docking analysis largely depends on the quality of the protein structures and ligands. While this method identified potential drug targets, it does not guarantee their efficacy in clinical settings. Subsequent experimental validation and clinical trials are necessary to confirm the therapeutic potential of the identified targets. Moreover, we only investigated the side effects of 2 significant druggable genes. The effects of drugs on targets are very broad, and many off-target effects cannot be explored through MR, requiring further basic and clinical trials to gain a more comprehensive understanding. Finally, the clinical relevance of our study results needs further validation; the lack of clinical data related to our study is a significant limitation.

This study utilized MR and colocalization analysis to identify 21 potential drug targets for migraine, two of which were significant in both blood and brain. These findings provide promising leads for more effective migraine treatments, potentially reducing drug development costs. The study contributes valuably to the field, highlighting the importance of these druggable genes significantly associated with migraine. Further clinical trials on drugs targeting these genes are necessary in the future.

Availability of data and materials

The Migraine GWAS dataset provided by Hautakangas et al. can be obtained by contacting International Headache Genetics Consortium [ 5 ]. Other data can be obtained from the original literature and websites.

Abbreviations

Mendelian randomization

Expression quantitative trait loci

Genome-wide association studies

Calcitonin gene-related peptide

Single nucleotide polymorphisms

Instrumental variables

Drug-Gene Interaction Database

False discovery rate

International Headache Genetics Consortium

Transcriptional start site

Inverse-variance weighted

5-Hydroxytryptamine

γ-Aminobutyric acid

Posterior probabilities

Phenome-wide association study

Gene Ontology

Kyoto Encyclopedia of Genes and Genomes

Biological process

Molecular function

Cellular component

Protein–protein interaction

Drug Signatures Database

Protein Data Bank

Low-density lipoprotein

Gene Set Enrichment Analysis

Insulin-like growth factor 1

Transforming growth factor-beta

Peroxisome-proliferator-activated receptors

Interleukin

Glial fibrillary acidic protein

Trigeminal nucleus caudalis

Inflammatory soup

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Acknowledgements

The authors sincerely thank related investigators for sharing the statistics included in this study.

This study was funded by China National Natural Science Foundation (82374575, 82074179), Beijing Natural Science Foundation (7232270), Outstanding Young Talents Program of Capital Medial University (B2207), Capital’s Funds for Health Improvement and Research (CFH2024-2–2235).

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LL contributed to the study conception and design. CCZ and YWH performed the statistical analysis. CCZ drafted the manuscript. All authors commented on previous versions of the manuscript. All authors contributed to the article and approved the submitted version.

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Zhang, C., He, Y. & Liu, L. Identifying therapeutic target genes for migraine by systematic druggable genome-wide Mendelian randomization. J Headache Pain 25 , 100 (2024). https://doi.org/10.1186/s10194-024-01805-3

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The Psychological Assessment: A New Prospective

| Published: June 21, 2024

Psychological assessment of the behaviour. Psychology is the scientific study of behaviour but human behaviour is a very complex system It has many variables. The definition of psychology has widely changed during past decades and the current trends of research are going on in a new concept. Now psychology is defined as the study of mind and behaviour, whereas mind is the outcome of the brain. The brain is working on two concepts, one is structure and the other functions. Now psychology is well defined as neurobehavioral studies. Now the time has come to study behaviour in relation to the mind. The mind is the outcome of the brain i.e. working brain. In neuroscience the brain is moreover defined in the form of consciousness and electrical impulses. But psychological research is limited to behaviour only, and needs a Standard Operating Procedure in conducting psychological testing. The objective is to find a similar procedure/ protocol for all psychological testing. The outcome of this research is limited to the study of behaviour and psychological research and development.

Psychological , Behavioural , Neurobehavioural , Mind , Psychological Testing , Standard Operating Procedure

This is an Open Access Research distributed under the terms of the Creative Commons Attribution License (www.creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any Medium, provided the original work is properly cited.

Received: May 22, 2024; Revision Received: June 16, 2024; Accepted: June 21, 2024

Dr. Pramod Kumar @ [email protected]

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Recent quantitative research on determinants of health in high income countries: A scoping review

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Public and patient involvement in quantitative health research: A statistical perspective

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Rationalising reasoning

Analysing analogies

Flipped chemistry revisited

International Conference on Education in Chemistry, 2014

Moles and titrations

Quantitative Research Design (JARS–Quant)

Special designs

General quantitative reporting standards

Experimental and nonexperimental designs

Jars resources

Jars articles

Questions / feedback

APA resources

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