Geographic variation in cardiometabolic risk distribution: A cross-sectional study of 256,525 adult residents in the Illawarra-Shoalhaven region of the NSW, Australia.

INTRODUCTION: Metabolic risk factors for cardiovascular disease (CVD) warrant significant public health concern globally. This study aims to utilise the regional database of a major laboratory network to describe the geographic distribution pattern of eight different cardiometabolic risk factors (CMRFs), which in turn can potentially generate hypotheses for future research into locality specific preventive approaches.
METHOD: A cross-sectional design utilising de-identified laboratory data on eight CMRFs including fasting blood sugar level (FBSL); glycated haemoglobin (HbA1c); total cholesterol (TC); high density lipoprotein (HDL); albumin creatinine ratio (ACR); estimated glomerular filtration rate (eGFR); body mass index (BMI); and diabetes mellitus (DM) status was used to undertake descriptive and spatial analyses. CMRF test results were dichotomised into 'higher risk' and 'lower risk' values based on existing risk definitions. Australian Census Statistical Area Level 1 (SA1) were used as the geographic units of analysis, and an Empirical Bayes (EB) approach was used to smooth rates at SA1 level. Choropleth maps demonstrating the distribution of CMRFs rates at SA1 level were produced. Spatial clustering of CMRFs was assessed using Global Moran's I test and Local Indicators of Spatial Autocorrelation (LISA).
RESULTS: A total of 1,132,016 test data derived from 256,525 individuals revealed significant geographic variation in the distribution of 'higher risk' CMRF findings. The populated eastern seaboard of the study region demonstrated the highest rates of CMRFs. Global Moran's I values were significant and positive at SA1 level for all CMRFs. The highest spatial autocorrelation strength was found among obesity rates (0.328), and the lowest for albuminuria (0.028). LISA tests identified significant High-High (HH) and Low-Low (LL) spatial clusters of CMRFs, with LL predominantly in the less populated northern, central and southern regions of the study area.
CONCLUSION: The study describes a range of CMRFs with different distributions in the study region. The results allow generation of hypotheses to test in future research concerning location specific population health approaches.

Introduction

Uncontrolled cardiometabolic risk factors (CMRFs) such as hyperglycaemia, dyslipidaemia, albuminuria, inadequate glomerular filtration, overweight and/or obesity and diabetes can predispose and heighten the risk for cardiovascular disease (CVD).[1-6] Cardiovascular diseases are the leading cause of death worldwide, and the highest absorber of health care expenditure in many developed nations, including Australia.[7-9]

In Australia, CVD remain the single leading cause of death; the largest health problem; and a major economic burden.[10,11] Nine in 10 adult Australians have at least one CVD risk factor, and one in four have three or more risk factors.[11] CVD kills one Australian every 12 minutes and one in six Australians (3.7 million people) are thought to be at risk.[12] In addition, the prevalence of CVD is projected to steeply increase in the coming decades.[11] A deceleration in the rapid growth of this major health care issue is possible only through the prevention and control of CMRFs. The role of CMRFs in the population, over and above individual level factors such as age, are being questioned in regard to discriminatory accuracy for development of CVD.[13] However identification of one or more CMRFs in a person at any age can initiate preventive lifestyle changes which may have significant benefits.[14-18] Similarly, identification of areas with higher rates of CMRFs can potentially trigger further area-level analyses investigating the potential for targeted health service commissioning.[19-21]

Advances in Geographic Information System (GIS) over the last quarter of a century have provided various tools to integrate epidemiological and geographical data.[22-24] Geocoding of risk parameters became feasible with such tools for its area-level analyses, which has facilitated area-level mapping of risk parameters, which has the potential generate hypothesis for regional health care research.[22] Thus integrating risk parameters through GIS has the potential to facilitate area-level health research, [25-28]; however, not without potential pitfalls [29-31]. A limitation of GIS-based mapping is that its outputs may be misleading, especially if maps are not smoothed using appropriate spatial or multilevel analyses.[32-34] However, it is well recognised in the literature that area level community interventions based on GIS approaches have been successful in a number of countries. [19-21,35,36]

There has been a significant increase in the number of epidemiological studies using spatial analytical methods in the last decade, including international studies reporting significant geographic variation in CMRFs at different spatial scales of measurements.[37-45] Hyperglycaemia was the most commonly reported CMRF displaying variation, followed by dyslipidaemia, overweight and/or obesity, and inadequate glomerular filtration.[37] Multiple risk factors were rarely analysed in these studies, though most CMRFs are interrelated and often coexist.[46] In this study, we aim to demonstrate the feasibility of utilising laboratory based routine test data to generate basic distribution maps of eight different CMRFs in regional New South Wales (NSW), Australia. The research questions we address are: (1) what is the geographic distribution pattern of CMRFs in the study area; and (2) is there any significant spatial clustering of CMRFs rates? The research sought to identify area-level patterns in the distribution of CMRFs that could be used to generate hypotheses for future research with the goal of improving health service commissioning in the study region.

Methods

The study adopted a cross-sectional design and was approved by the University of Wollongong (UOW) and Illawarra and Shoalhaven Local Health District (ISLHD) Human Research Ethics Committee (HREC 2017/124).

Setting

The study was undertaken in the Illawarra-Shoalhaven region (ISR) of the NSW, Australia. The ISR region stretches from the immediate south of the metropolitan boarders of Sydney, and extends along the south-eastern coastal belt of NSW—bordered by the Pacific Ocean in the east and the coastal escarpment of the Southern Tablelands in the West. This region encompasses multiple cities, towns and rural areas and includes the four local government areas of Wollongong, Shellharbour, Kiama and Shoalhaven. Overall, the ISR covers a land area of 5615 square kilometres and had an estimated residential population of 369,469 persons at the 2011 Australian Census of Population and Housing, of which 285, 385 (77.24%) were adults (> = 18 years).[47] De-identified data for this study were obtained from the Southern IML Research (SIMLR) Study, a large-scale community-derived cohort of internally-linked and geographically referenced pathology data collected in routine practice by the largest pathology provider servicing the study area. More details on this data source, its access and maintenance are published elsewhere.[48]

Statistical Area level 1 (SA1) was used as the geographic unit of analysis in this study, which was the smallest geographic unit for the release of Census data in 2011.[49] SA1s generally have a population of 200 to 800 persons (400 average), and the ISR includes a total of 980 conterminous SA1s. Fig 1 shows the study area with SA1 units and the major landmarks of the region. Very small and crowded SA1s similar to the areas shown the inset map tend to be more densely populated.

Fig 1

Map of the Illawarra-Shoalhaven region of NSW Australia showing SA1 areas and major landmarks.

Participants and variables

The CMRF test data of the adult residents of ISR between 1 Jan 2012–31 Dec 2017 (6 years) were extracted for analyses from the SIMLR database. Test data were extracted for eight CMRFs: fasting blood sugar level (FBSL); glycated haemoglobin (HbA1c); total cholesterol (TC); high density lipoprotein (HDL); albumin creatinine ratio (ACR); estimated glomerular filtration rate (eGFR); body mass index (BMI) and diabetes mellitus (DM) status. The SIMLR database uses an algorithm to identify DM status based on diagnosis guidelines published by the Royal Australian College of General Practitioners (RACGP) and Diabetes Australia, and methods from the National Health Survey of the Australian Bureau of Statistics (ABS).[50,51] The algorithm identifies DM for HbA1c ≥ 6.5% or FBSL ≥ 7.0 mmol/l within +/- 24 months of HbA1c < 6.5%. The study data included both prevalent and incident DM cases. Study data included only the most recent CMRF test result for each individual. We excluded extreme BMI values <12 and >80 based on cut-off points reported by Cheng (2016), Li (2009) and Littman (2012).[52-54] lists the CMRFs value definitions adopted in this study and their source references.

Statistical and spatial analyses

First, individual-level descriptive analyses of CMRFs were performed. The total number of each CMRF tests and summary statistics of each tests’ results are reported. The summary values for eGFR test results are calculated using the approach for grouped data as eGFR test result values are truncated at >90 in the SIMLR Study data. Test results were dichotomised into ‘higher risk’ and ‘lower risk’ categories based on the CMRF definitions in Table 1.

Table 1

Cardiometabolic risk classification.

‘Higher risk’ CMRFS	Value definition	Adopted from
High FBSL	FBSL ≥7.0 mmol/l	RACGP guidelines[50]
High HbA1c	HbA1c > 7.5%	RACGP guidelines[50]
High TC	TC ≥ 5.5 mmol/l	Australian Health Survey[55]
Low HDL	HDL < 1 mmol/l[56]	National heart foundation of Australia[57]
High ACR	ACR ≥ 30 mcg/L to mg/l	Kidney Health Australia[58]
Low eGFR	eGFR < 60 mL/min/1.73m2	Kidney Health Australia[58]
High BMI	BMI ≥ 30 (Obese)	World Health Organization (WHO)[59]
DM Status	+ve DM test algorithm	RACGP guidelines[50] and AustralianHealth Survey[55]

Second, area-level analyses of CMRFs were undertaken. Within-cohort prevalence of ‘higher risk’ CMRF findings are calculated using the total number of tests within each SA1 as the denominator. The exception were DM cases, which are likely to include most prevalent cases in the study area, so SA1 adult populations aged 18 years and over were used as the denominators (accessed from ABS census 2011 data). Thereafter, an Empirical Bayes (EB) approach was used to smooth all the CMRFs’ raw rates to minimise extreme values arising from small sample sizes. The EB smoothed rates were then imported into GIS software for mapping and spatial statistical analyses.

As individuals with CMRFs are assumed randomly distributed within the study area, the geographic distribution of CMRFs is assumed spatially independent in this study. Global Moran’s I test was used to identify spatial autocorrelation of CMRFs at a 0.05 level of significance. Global Moran's I tests if the geographic distribution of rates is clustered, dispersed or random based.[60] The global Moran’s I also indicates the general strength of spatial autocorrelation in the study area, which theoretically ranges between -1 to +1. Values of I significantly above -1/(N-1) indicate positive spatial autocorrelation, where N is the number of spatial units indexed.[61] When significant spatial autocorrelation was detected, Local Indicator of Spatial Autocorrelation (LISA) spatial statistics were used to identify any clustering of CMRFs.[62] LISA was used to indicate spatial clustering of High-High (HH) or Low-Low (LL) CMRFs rates at SA1-level within the study region. False Discovery Rate (FDR) corrections were applied to LISA tests to correct p-values for multiple testing.

All descriptive statistics and EB smoothing were performed using R version 3.4.4.(R Foundation for Statistical Computing, Vienna, Austria).[63] Mapping and spatial analyses were performed using ArcGIS version 10.4.1(ESRI Inc. Redlands, CA, USA).[64]

Results

The study sample comprised 1,132,016 test results contributed by 256,525 adult individuals residing in the study region. Of the 256,525 individuals, 193,679 (75.5%) had FBSL, 73,885 (28.8%) had HbA1, 194,816 (75.9%) had TC, 182,237 had HDL (71.0%), 50,790 had ACR (19.8%), 244,166 had eGFR (95.2%), and 192,443 had BMI (75.0%) test results. It was estimated 23,704 (9.2%) of persons met the clinical criteria for diabetes. Table 2 provides the summary statistics of CMRF test results.

Table 2

Summary statistics of CMRFs test results.

CMRFs	Tests	Mean	SD	Min	1st Qu	Median	3rd Qu	Max
FBSL	193679	5.6	1.6	0.7	4.9	5.3	5.8	43.9
HbA1c	73885	6.0	1.3	2.6	5.3	5.6	6.4	17.8
TC	194816	5.0	1.1	1.1	4.2	4.9	5.7	39.4
HDL	182237	1.5	1.2	0.1	0.5	1.4	1.8	5.8
ACR	50790	7.4	40.3	0.1	0.4	0.8	2.3	1291.5
eGFR	244166	75.8	13.8	2.0	-	83.2	-	>90.0
BMI	192443	28.4	6.1	12.0	24.1	27.5	31.6	78.1

The CMRF test result values were dichotomised into ‘higher risk’ and ‘lower risk’ categories based on the CMRF definitions in Table 1. The proportion of individuals with ‘higher risk’ CMRFs findings varied considerably between tests. The largest ‘higher risk’ proportions were found for BMI (33.74%) and TC (32.55%), and the lowest for ACR (4.03%). Table 3 provides details on the CMRF test results classification and the identified proportions.

Table 3

Frequency and proportion of ‘higher risk’ results of CMRFs tests.

Cardiometabolic risk	Classification	Tests n (%)*
FBSL		193679 (100)
FBSL ≥7.0 mmol/L	Higher risk	16280(8.4)
FBG < 7.0 mmol/L	Lower risk	177399(91.6)
HbA1c		73885(100)
HbA1c > 7.5%	Higher risk	7927(10.7)
HbA1c ≤ 7.5%	Lower risk	65958(89.3)
TC		194816(100)
TC ≥ 5.5 mmol/L	Higher risk	63422(32.5)
TC < 5.5 mmol/L	Lower risk	131394(67.5)
HDL		182237 (100)
HDL < 1 mmol/l	Higher risk	21261(11.7)
HDL ≥ 1 mmol/l	Lower risk	160976(88.3)
ACR		50790(100)
ACR ≥30 mcg/L to mg/L	Higher risk	2047 (4.1)
ACR <30 mcg/L to mg/L	Lower risk	48743(95.9)
eGFR		244166(100)
eGFR < 60 mL/min/1.73m2	Higher risk	27241(11.2)
eGFR20 ≥ 60 mL/min/1.73m2	Lower risk	216925(88.8)
BMI		192455(100)
BMI ≥ 30 (Obesity)	Higher risk	64832(33.7)
BMI < 30	Lower risk	127511 (66.3)

* The denominators for percentages are the total number of each CMRFs tests

Geographic distribution of cardiometabolic risk factors

Fig 2 shows the geographic distribution of CMRFs at SA1 level in the ISR region with red indicating the highest and blue the lowest rates of risk. SA1s with no test data appear in white. Areas with higher rates of CMRFs were found to be clustering within the study region. The highest rates were found mainly along the populated eastern board of the study region; notably among SA1s around Lake Illawarra, south-east of Berry’s bay, and east of Lake Burill. However, the high TC rates showed a reversed pattern, and higher rates were found in the relatively less populated central and westerly aspects of the study area. HDL rates did not follow this reversed pattern.

Fig 2

Geographic distribution of the proportion of CMRFs within the Illawarra Shoalhaven region of the NSW Australia.

Spatial autocorrelation of CMRFs

The global Moran’s I tests were significant and positive for all CMRFs (Table 4). The highest spatial autocorrelation strength was found among obesity rates (0.328), followed by high FBSL (0.184) and low HDL (0.174). The spatial autocorrelation strength was the lowest for albuminuria (0.028) and low eGFR (0.069).

Table 4

Spatial autocorrelation (Moran’s I) of CMRFs.

CMRFs	Moran's I	z-score	p-value
DM	0.097	27.952	<0.0001
Obesity	0.328	92.086	<0.0001
High FBSL	0.184	51.539	<0.0001
High HbA1c	0.101	28.030	<0.0001
High TC	0.146	41.154	<0.0001
Low HDL	0.174	48.733	<0.0001
Albuminuria	0.028	8.096	<0.0001
Low eGFR	0.069	19.699	<0.0001

LISA tests identified significant spatial clustering of CMRFs in the ISR region. The HH clusters were found mainly along the populated areas of the study region, except for TC. Areas around the immediate surroundings of Lake Illawarra had the most HH clusters, followed by the areas to the south-west of Berry’s Bay and south of Jervis Bay. A few areas around Lake Burrill had HH clusters of DM, TC and eGFR. The LL clusters were mainly around the less populated north, central and south ends of the study area, except for TC. The TC clusters demonstrated a reverse pattern in comparison with all other CMRFs, where HH clusters were mainly around the less populated central and southern ends of the ISR and a few instances in the north-eastern end of the study area. LL clusters of TC were found around the immediate surroundings of Lake Illawarra. Fig 3 illustrates the spatial clustering of CMRFs in the study area.

Fig 3

Local Moran’s I cluster maps showing high-high and low-low spatial associations of CMRFs within the Illawarra Shoalhaven region of the NSW Australia.

Discussion

Place has always been a key element in human health and epidemiology. In the present study, we explored the geographic distribution of eight CMRFs in 980 SA1s in a regional area of NSW, Australia. The study is a first of its kind known to us in providing a comprehensive small area-level profile of a wide range of cardiometabolic risk factors, and provides an example of using population-derived routine laboratory data for area-level research.

Higher rates and clustering of CMRFs were mostly observed along the more densely populated eastern coast line of the study region. Also, some areas were common for multiple risk factors as their distribution pattern frequently converged in these areas, for example areas around Lake Illawarra and south of Jervis Bay. However, not all populated areas were involved in this pattern, and some less populated areas also had higher rates of risk. Spatial analyses revealed significant spatial autocorrelation for all eight CMRFs. Patterns of clustering were different for each CMRFs at the small-area scale used in this study, which provides directions for future research using multilevel analytic methods.[65]

The distribution of high TC values were generally reversed to those distributions of other CMRFs described in this study. The reason for this observation is yet to be explored, but a possible treatment effect is suspected as the lower risk areas were often densely populated areas. It is possible that the people residing in these areas have better access to health care services and more frequently prescribed anti-cholesterol drugs.[66,67] However, not all densely populated areas were involved in this ‘higher risk’ TC distribution pattern and further research is required.

The current study adds to the limited studies from Oceania reporting on geographic variation of CMRFs, and the first from regional Australia. Previous studies from Australia have reported geographic variation of 42% in the odds of being diagnosed with DM among adults living in Sydney.[38] Another study reported geographic variation in glycated haemoglobin (HbA1c) values across 767 Census Collection Districts (CDs) in Adelaide. [44] The study builds on previous research by investigating the distribution of a wide range of CMRFs, which appears to be unique in the literature.

This study must be considered within its limitations. First, the cross-sectional design of the study precludes causal inference. Second, the descriptive analyses performed in this study indicate only significant variations in the geographic distribution of CMRFs, but does not differentiate the individual and/or area-level attributes which might be contributing to this variation.[13] Third, the maps include areas with no test data. Fourth, the study data were obtained from people attending health care services; therefore its point-estimates may not be representative of the general population. Fifth, we cannot exclude the possibility that a higher proportion of positive tests in an area could be due to greater access to pathology services; however exploring this possibility was beyond the scope of the current study.

Future research is required to understand the reasons for the geographic variation reported in this paper. The findings reported in this study suggest hypotheses that will be further explored using appropriate multilevel/hierarchical analyses to differentiate and quantify the individual and area-level contributions to this variation.[65,68-70] Such hierarchical analyses will have the potential to inform development of appropriate area-level health care service policy initiatives. It is important to differentiate the contributions of individual (e.g. age, sex, etc.) and area (e.g. socioeconomic disadvantage, access or proximity to health care services, etc) level attributes to the different patterns of clustering to inform targeted area-level preventive interventions and future health service commissioning decisions to these areas.

In conclusion, area-level descriptive analyses of CMRFs have the potential to highlight inequalities in the geographic distribution of CMRFs. Regional planning for the prevention and management of CMRFs requires information about its epidemiology within specific communities or areas. Centralised approaches of disease prevention and management may not suit regional requirements as the disease pattern in regional areas may differ to those in metropolitan areas and cities. Area specific evidence through regional health care research is important to inform health care service commissioning for area specific decisions and policy developments. This paper demonstrates an initial step in such regional health care research, and a feasible method using population data derived from routine clinical practice.

9 Jul 2019

PONE-D-19-14817

Geographic variance in cardiometabolic risk distribution: a cross sectional study of 256,525 adult residents in the Illawarra-Shoalhaven region of the NSW, Australia.

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Reviewer #1: The authors provided a traditional argumentation on the relevance of CMRFs in CVD. However, the role of traditional risk factors over and above age in the population is being questioned because their low discriminatory accuracy. The low discriminatory accuracy of traditional risk factors is not a mayor problem as far as the treatment of risk factors does not harm. For instance, physical activity, healthy diet, reducing stress and an equitable distribution of resources is always recommended but pharmacological treatment could be questioned. I suggest the authors include a critical perspective in the introduction, rather than the usual “mantra” on the role CMRFs in CVD in the population. Se for instance:

# Merlo J, Mulinari S, Wemrell M, Subramanian SV, Hedblad B. The tyranny of the averages and the indiscriminate use of risk factors in public health: The case of coronary heart disease. SSM Popul Health. 2017;3:684-98.

The above publication refers to many others that may help to understand the idea.

The authors also indicate, “individual level approaches in the past have demonstrated limited success evidenced by its still increasing rates (of CVD), (7–9) area-level community health approaches, in addition, are important”. This is not new, and community level intervention has been launched in many countries since many year. The authors should review this issue and provide some references (to reviews of the literature). However, if you like to quantify the relative “importance” of the areas versus individuals for intervention you should identify the geographical component of the total individual risk variance by applying a multilevel approach. Se for instance

# Merlo J, Asplund K, Lynch J, Rastam L, Dobson A. Population effects on individual systolic blood pressure: a multilevel analysis of the World Health Organization MONICA Project. Am J Epidemiol. 2004;159(12):1168-79.

In addition, a main argument of the authors is that visualizing the geographical distribution of CMRFs has the potential to become a powerful toll for policy makers and health care service planners when planning area-level health care service. Again, this is not new. The use of GIS in health care has a long and well-established tradition. The news could be that the authors have developed an innovative GIS tool in their Region and this is always worthy. I do congratulate the authors. However, the authors should situate their work within this long and well-established GIS tradition.

May central concern, however, is the suitability of the analytical approach in this study. I agree in that “Discerning the distribution pattern of CMRFs in a given area is fundamental to such areal-level targeted approaches”. However, I think this “discerning” is not appropriately done by the use of GIS and spatial analyses. Those approaches are exclusively based on the analysis of area variance but question is when we can consider the area variance as large or important. Statistical significance is not a good criterion. To answer the question you need to discern the share of the total individual variance that is at the area level. For this purpose, you need to perform multilevel models and calculate some measures like the variance partition coefficient (VPC), the area under the curve (AUC) for random effect and even a measure of heterogeneity like the median odds ratio (MOR). Spatial analyses are much better analytical approach than traditional ecological analysis but they are not free of the ecological problems (fallacies, MAUP) and as the ecological analyses, they focus on geographical variation only. The “clustering” in spatial analyses refers to areas not to individuals within areas.

GIS and spatial analysis are fancy and popular because coloured choropleth maps are attractive and easy to understand, and–for decisions makers– the cryptic language and scientific halo of the spatial analyses strength the authority of the information. However, they are ecological analyses with many pitfalls as compared with multilevel analyses. The problem with spatial analysis and GIS is that they may provide misguiding information to decision makers …. That is, just the opposite of what the authors aimed to do…. I refer to other publication for and extended explanation of my critics

# Merlo J, Wagner P, Leckie G. A simple multilevel approach for analysing geographical inequalities in public health reports: The case of municipality differences in obesity. Health Place. 2019;58:102145.

# Merlo J, Viciana-Fernandez FJ, Ramiro-Farinas D, Research Group of Longitudinal Database of Andalusian P. Bringing the individual back to small-area variation studies: a multilevel analysis of all-cause mortality in Andalusia, Spain. Soc Sci Med. 2012;75(8):1477-87.

The authors have a database that seems suitable for multilevel analyses, so my advice is reanalyse the database with this methodology. You can see examples on how to perform the multilevel analyses together with codes and spreadsheets for calculations in previous publications, for instance

# Merlo J, Wagner P, Ghith N, Leckie G. An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy: The Case of Neighbourhoods and Health. PLoS One. 2016;11(4):e0153778.

I also recommended the (free) information provided at the Centre for Multilevel Modelling, Bristol University http://www.bristol.ac.uk/cmm/

You can use the shrunken residuals from the multilevel analysis to create maps but you need to interpret the discriminatory accuracy of those maps by using the VPC and the AUC for instance.

In summary, the authors have had a very worthy initiative by creating a record linkage database in their Region, The GIS and spatial analyses seems well performed but my concern is very fundamental. That is, the spatial GIS analyses are not suitable for your research question and, therefore, the conclusions you provide have not support.

Your results and interpretation may even give misleading information to decision makers, as we do not known the discriminatory accuracy of the maps. To consider targeted area-level preventive interventions and regional health care service planning you need to perform a multilevel analysis as suggested above and considered the idea of proportionate universalism.

Your database seems suitable and I recommend you read the analytical framework proposed above and reanalyse the data accordingly.

Reviewer #2: This manuscript aims to assess spatial clustering patterns of CMRFs in the Illawarra Shoalhaven region of NSW Australia. The methods selected by the authors to do so are well-established ones and the resulting distribution patterns are insightful towards targeted local interventions.

The following may need to be clarified before the manuscript potentially moves forward to publication:

Data source: While the authors note the institutions which own the data & manage access, the link provided seems to be broken (https://www.ihmri.org.au/research-projects/simlr-cohortstudy/) and thus one would be unable to verify pertinent information

Table 2: Summary statistics of ACR & eGFR may need to be re-evaluated; specifically SD value of ACR and 3rd Qu & Max values of eGFR.

Reviewer #3: This paper uses pathology data to present the geographic distributions of a number of cardiovascular disease risk factors. There are problems with the underlying analysis and description of the analysis. These problems are severe enough that they must be addressed prior to a more detailed review.

The denominator used is the paper is unclear and is described differently at multiple points in the paper. The title indicates that this analysis uses 256,385 adult individuals. This value again appears in the results (line 193). However, Table 2 and the methods (line 167) indicate that there are 256,385 tests, not individuals. This is almost certainly the case. The authors report a population of 285,385 adults. The data source is a private pathology service, not a population-level registry or health record. These numbers would then indicate that almost 90% of individuals living in the study area had at least one lab test from this pathology company. Another example of this confusion between number of tests and number of adults is the GFR test. 244,166 tests are reported from 256,525 adults. These values indicate that 85% of the adult population received a GFR test, which almost certainly cannot be correct.

However, if the denominator of the described proportions is the number of tests, then these values cannot be interpreted as the authors have. The authors refer to the calculated measures as “risk”. In epidemiology, this term has a specific meaning that implies using person-time as a denominator. Had the authors used population as a denominator, they would have been calculating prevalence, not risk. However, if (as seems to be true) the authors used the number of tests as the denominator, then these values are simply the proportion of tests meeting a specific criteria. Since these tests were performed on an unhealthy population (as evidenced by 95% having had a kidney function test), these results cannot be generalized to the total population. The results should then be interpreted in light of this.

Overall, the writing in this article is very poor. There are multiple instances of sentences and phrases that are convoluted and have an unclear meaning. I would recommend that an editor and an experienced epidemiologist review this manuscript before publication to clarify the manuscript and to ensure that correct epidemiologic terms are used. One example of this is the last sentence of the introduction. I’m unsure what the authors are trying to convey in that sentence and why that sentence is in the paper. There are multiple, similar examples throughout the paper (e.g. “frequency and proportion of risks” (line 166), “discrete test proportions” (line 194), etc.).

Finally, I question if it is reasonable to assume that individuals in the lab data are equally distributed throughout the study area. Spatial clustering of some results could simply be the result of some areas being over-represented in the data. That is, some locations with a high proportion of positive tests could reflect greater access to the pathology lab in that location.

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28 Aug 2019

Response to reviewers’ comments

We sincerely thank our reviewers for the constructive comments and valuable criticisms, which were of great help in revising the manuscript. Please find following our detailed responses (AC) to the reviewers’ comments (RC) below.

1. Response to reviewer # 1 comments:

RC 1.1 The authors provided a traditional argumentation on the relevance of CMRFs in CVD. However, the role of traditional risk factors over and above age in the population is being questioned because their low discriminatory accuracy. The low discriminatory accuracy of traditional risk factors is not a major problem as far as the treatment of risk factors does not harm. For instance, physical activity, healthy diet, reducing stress and an equitable distribution of resources is always recommended but pharmacological treatment could be questioned. I suggest the authors include a critical perspective in the introduction, rather than the usual “mantra” on the role CMRFs in CVD in the population. See for instance: Merlo J, Mulinari S, Wemrell M, Subramanian SV, Hedblad B. The tyranny of the averages and the indiscriminate use of risk factors in public health: The case of coronary heart disease. SSM Popul Health. 2017;3:684-98. The above publication refers to many others that may help to understand the idea.

AC 1.1 The authors are thankful to for the very constructive suggestion provided by the reviewer. After reading the Reviewer’s comments it was apparent that we had inadvertently over-emphasised considerations of individual- and area-level factors in the introduction, which suggested the aim of the study was to evaluate the importance of area-level factors in health service planning and provisioning. This was not our intent or the aim of the study, which was simply to describe geographic variation in cardiometabolic risk factors (CMRFs) in the study area. In light of this, we have substantially re-written the introduction to better reflect the intent of the research.

Specific changes

1.1.1 In the revised manuscript, the Introduction now includes a critical perspective on inequalities in the geographic distribution CMRFs, and identified their potential to generate hypotheses on the individual- and area-level correlates of this variation. The changes are highlighted in the revised manuscript (with tracked in changes). Page: 4-5, lines 1- 41

1.1.2 We are also grateful to the reviewer in providing a useful source reference, which indicates the limitations of the current study. We have cited this work in the Discussion section under the limitations. Page: 4, line 14

RC 1.2 The authors also indicate, “Individual level approaches in the past have demonstrated limited success evidenced by its still increasing rates (of CVD), (7–9) area-level community health approaches, in addition, are important”. This is not new, and community level intervention has been launched in many countries since many years. The authors should review this issue and provide some references (to reviews of the literature).

AC 1.2 We agree with the reviewer, and have reviewed on this and reported with references on previous reports.

Specific Change

Reported on previous studies and reviews. Page 5, line: 28

RC 1.2.1 However, if you like to quantify the relative “importance” of the areas versus individuals for intervention you should identify the geographical component of the total individual risk variance by applying a multilevel approach. See for instance: Merlo J, Asplund K, Lynch J, Rastam L, Dobson A. Population effects on individual systolic blood pressure: a multilevel analysis of the World Health Organization MONICA Project. Am J Epidemiol. 2004;159(12):1168-79.

AC 1.2.1 As per our previous response, the study was designed for exploratory/descriptive analyses of any significant variation in the geographic distribution of CMRFs, which we have stated more clearly in the introduction section of the revised manuscript as it was ambiguous in the original manuscript.

Specific change

1.2.1 Stated the main intent of study (page 5, lines 37 - 41).

1.2.2 Referenced the article under future research in Discussion. Page13, line 219

RC 1.3 In addition, a main argument of the authors is that visualizing the geographical distribution of CMRFs has the potential to become a powerful toll for policy makers and health care service planners when planning area-level health care service. Again, this is not new. The use of GIS in health care has a long and well-established tradition. The news could be that the authors have developed an innovative GIS tool in their Region and this is always worthy. I do congratulate the authors. However, the authors should situate their work within this long and well-established GIS tradition.

AC 1.3 We thank the reviewer for recognising the potential contribution of this work to health care service policy initiatives. As suggested, we have altered this paragraph to appropriately situate the work within the well-established GIS domain.

Specific change

1.3.1 Repositioned the current study in the literature, and included additional references to reflect the analytical possibilities and limitations of GIS. Page: 5, lines 21–28

RC 1.4 My central concern, however, is the suitability of the analytical approach in this study. I agree in that “Discerning the distribution pattern of CMRFs in a given area is fundamental to such areal-level targeted approaches”. However, I think this “discerning” is not appropriately done by the use of GIS and spatial analyses. Those approaches are exclusively based on the analysis of area variance but question is when we can consider the area variance as large or important. Statistical significance is not a good criterion. To answer the question you need to discern the share of the total individual variance that is at the area level. For this purpose, you need to perform multilevel models and calculate some measures like the variance partition coefficient (VPC), the area under the curve (AUC) for random effect and even a measure of heterogeneity like the median odds ratio (MOR). Spatial analyses are much better analytical approach than traditional ecological analysis but they are not free of the ecological problems (fallacies, MAUP) and as the ecological analyses, they focus on geographical variation only. The “clustering” in spatial analyses refers to areas not to individuals within areas.

AC 1.4 The authors thank the reviewer for their comments and agree regarding the limitations of the analytical methods used in this study. As mentioned in our previous responses, the intent of the paper was not to “discern” the relative importance of individual- and area-level factors, but rather to elucidate the geographic variation in CMRFs. We apologise for the confusion generated in the Introduction, which has been clarified in the revised manuscript.

We would also like to disclose that the study is part of the first author’s PhD program, which is using different analytical methodologies to contrast and compare these data. We acknowledge that the level of evidence drawn out at different stages of analysis will vary; however, the staged nature of the research program precludes publishing all levels of evidence in a single paper. The multilevel analyses suggested by the reviewer are the focus of the next stage of this work, and we are grateful for their advice.

In response to the reviewer’s comments here, we have made clear of the limitations of this study design in the Discussion section. Also, a future research paragraph is added in the discussion section, where we have included and cited the analytical methodologies proposed by the reviewer in our future research based on the current results.

Specific changes

1.4.1 Addressed the limitations of study methodology in Discussion. Page 13, lines 183–185

1.4.2 Included a paragraph on future research directions in Discussion. Page 13, lines 191–199

RC 1.5 GIS and spatial analysis are fancy and popular because coloured choropleth maps are attractive and easy to understand, and–for decisions makers– the cryptic language and scientific halo of the spatial analyses strength the authority of the information. However, they are ecological analyses with many pitfalls as compared with multilevel analyses. The problem with spatial analysis and GIS is that they may provide misguiding information to decision makers…. That is, just the opposite of what the authors aimed to do….

AC 1.5 We agree with the reviewers comments on the potential pitfalls of GIS methods and have now addressed this in the introduction with regards to GIS approaches in public health research. We have also included in the future research directions of this study that “Future research is required to understand the reasons for the geographic variation reported in this paper”

Specific changes

1.5.1 Addressed the pitfalls of GIS methods (page 4, lines 24–26).

1.5.2 Stated the need for further explorations of current findings in Discussion. Page 13, line 191-192

RC 1.6 I refer to other publication for and extended explanation of my critics:

Merlo J, Wagner P, Leckie G. A simple multilevel approach for analysing geographical inequalities in public health reports: The case of municipality differences in obesity. Health Place. 2019;58:102145.

Merlo J, Viciana-Fernandez FJ, Ramiro-Farinas D, Research Group of Longitudinal Database of Andalusian P. Bringing the individual back to small-area variation studies: a multilevel analysis of all-cause mortality in Andalusia, Spain. Soc Sci Med. 2012;75(8):1477-87.

AC 1.6 The authors accept the very helpful references to analytical examples provided. We have included these references into our future research section.

Specific change

1.6.1 Included citations of suggested studies in future research in Discussion. Page 13, line 219

RC 1.7 The authors have a database that seems suitable for multilevel analyses, so my advice is to reanalyse the database with this methodology. You can see examples on how to perform the multilevel analyses together with codes and spreadsheets for calculations in previous publications, for instance: Merlo J, Wagner P, Ghith N, Leckie G. An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy: The Case of Neighbourhoods and Health. PLoS One. 2016;11(4):e0153778.

I also recommended the (free) information provided at the Centre for Multilevel Modelling, Bristol University http://www.bristol.ac.uk/cmm/ You can use the shrunken residuals from the multilevel analysis to create maps but you need to interpret the discriminatory accuracy of those maps by using the VPC and the AUC for instance.

AC 1.7 We agree with the reviewer on the suitability of our database for the suggested analyses, and we are very thankful for the detailed advice provided. As indicated in the previous responses, we intend to undertake these analyses in upcoming stages of our research. The intent of the current was to undertake a descriptive/explorative analysis, which we have now balanced with clear statements on its methodological limitations.

Specific change

1.7.1 Included citations of suggested analyses in future research section (Page 13, line 219).

RC 1.8 In summary, the authors have had a very worthy initiative by creating a record linkage database in their Region, The GIS and spatial analyses seems well performed but my concern is very fundamental. That is, the spatial GIS analyses are not suitable for your research question and, therefore, the conclusions you provide have not support. Your results and interpretation may even give misleading information to decision makers, as we do not know the discriminatory accuracy of the maps. To consider targeted area-level preventive interventions and regional health care service planning you need to perform a multilevel analysis as suggested above and considered the idea of proportionate universalism. Your database seems suitable and I recommend you read the analytical framework proposed above and reanalyse the data accordingly.

AC 1.8 We appreciate the recognition that the ‘GIS and spatial analyses seems well performed’, and the initiative taken in this study. The reviewer’s comments were very helpful to us in clarifying the analytical expectations which we had unintentionally created in the Introduction around the ‘quantification of variance’. The authors are grateful to the reviewer for identifying this ambiguity.

We acknowledge the descriptive approach of our study, and therefore its limitations with regards to ‘discriminatory accuracy’. We have now clearly framed the study within an exploratory/hypothesis generating domain of research, and moderate our claims to direct use in policy making in the revised Introduction and Discussion sections on limitations and future research. The current study will form the basis for further detailed multilevel analyses in the study region.

2. Response to reviewer # 2 comments:

RC 2.1 This manuscript aims to assess spatial clustering patterns of CMRFs in the Illawarra Shoalhaven region of NSW Australia. The methods selected by the authors to do so are well-established ones and the resulting distribution patterns are insightful towards targeted local interventions.

AC 2.1 The authors are thankful to the reviewer for critically evaluating this work, their assessment of the appropriateness of the methods used and potential value of the reported findings for local interventions.

RC 2.2 The following may need to be clarified before the manuscript potentially moves forward to publication:

Data source: While the authors note the institutions which own the data & manage access, the link provided seems to be broken (https://www.ihmri.org.au/research-projects/simlr-cohortstudy/) and thus one would be unable to verify pertinent information.

AC 2.2 We thank the reviewer for identifying the broken link, which appears to have been updated since we submitted the manuscript. We have updated this in the revised manuscript, as follows:

Specific change

Made sure that the link is given/written right the Revised manuscript. The correct link is: https://www.ihmri.org.au/research-projects/simlr-cohort-study/ (page 14, line 243).

RC 2.3 The following may need to be clarified before the manuscript potentially moves forward to publication:

Table 2: Summary statistics of ACR & eGFR may need to be re-evaluated; specifically SD value of ACR and 3rd Qu & Max values of eGFR.

AC 2.3 Thank you raising these potential data errors, which are addressed below and in the manuscript where necessary.

Specific change

We have carefully reviewed the summary statistics values or ACR and eGFR in Table 2:

a) The SD value of the ACR is correct (SD = 40.3); however, there is a typographical error in the Max value of ACR in the original manuscript (Max = 91.5), which we have corrected in the Revised Manuscript (Max = 1291.50). The wider spread of ACR values in our data is the reason for the SD magnitude, which is noew correctly reflected by the updated Max value. Table 2, ACR Max value (corrected).

b) This was an oversight when we were preparing Table 2. The eGFR is truncated in the SIMLR Study database at >90 and assigned a value of 91 to indicate “normal” function. We have recalculated the mean, SD and median using grouped frequency data, and removed Q1 and Q3 for eGFR values in Table 2. Thank you for identifying this oversight. Table 2, eGFR summary values (corrected).

3. Response to reviewer # 3 comments:

RC 3.1 This paper uses pathology data to present the geographic distributions of a number of cardiovascular disease risk factors. There are problems with the underlying analysis and description of the analysis. These problems are severe enough that they must be addressed prior to a more detailed review:

AC 3.1 The authors are thankful to the reviewer 3 for their critical evaluation of this work and helpful suggestions for improvement.

RC 3.1.1 The denominator used is the paper is unclear and is described differently at multiple points in the paper.

a) The title indicates that this analysis uses 256,385 adult individuals.

b) This value again appears in the results (line 193).

c) However, Table 2 indicates that there are 256,385 tests, not individuals.

d) However, the methods (line 167) indicate that there are 256,385 tests, not individuals. This is almost certainly the case.

e) The authors report a population of 285,385 adults. The data source is a private pathology service, not a population-level registry or health record. These numbers would then indicate that almost 90% of individuals living in the study area had at least one lab test from this pathology company.

f) Another example of this confusion between number of tests and number of adults is the GFR test. 244,166 tests are reported from 256,525 adults. These values indicate that 85% of the adult population received a GFR test, which almost certainly cannot be correct.

g) However, if the denominator of the described proportions is the number of tests, then these values cannot be interpreted as the authors have.

AC 3.1.1 Thank you for raising these issues. While we have been consistent throughout the manuscript that the sample giving rise to the tests included in this study is 256,525, we realise we have used denominators inconsistently, which and this has given rise to the confusion. To clarify, the study base for this analysis comprises 256,525 unique individuals who have contributed a total of 1,132,016 test results. We have removed the percentages in Table 2 that were mixing numerator (tests) and denominator (persons) sources, and have included a textual narrative at the beginning of the result sections indicating: (1) the total number of tests (1,132,016); (2) the total number of individuals contributing these tests (256,525); and (3) the number and percentage of individuals with a result for each of the tests included in Table 2. We sincerely apologise for the confusion our inexactness has caused.

Specific actions

3.1.1.1 Add textual narrative to results section (page 9, lines 136–141)

3.1.1.2 Removed percentages from Table 2 (page 9, Table 2: column 2)

3.1.1.4 Updated footnote for Table 3 (page 10, line 150).

RC 3.2 The authors refer to the calculated measures as “risk”. In epidemiology, this term has a specific meaning that implies using person-time as a denominator. Had the authors used population as a denominator, they would have been calculating prevalence, not risk. However, if (as seems to be true) the authors used the number of tests as the denominator, then these values are simply the proportion of tests meeting a specific criteria.

AC 3.2 Thank you for identifying our inexact use of risk, which was not intended in the epidemiological sense but with regards to the “CMRF risk classification” defined in Table 1 (page 7, line 105). We have gone through the manuscript and clarified the use of ‘risk’ in reference to CMRF ‘higher risk’ classification.

Specific action

3.2.1 The usage of word ‘risk’ has been changed to define ‘higher risk’ classification throughout the manuscript. Page 7, line 105; Page 8, line 111, 113; Page 9 line 144,145,146; Page 19 line 149; Page 13 line 199

RC 3.3 Since these tests were performed on an unhealthy population (as evidenced by 95% having had a kidney function test), these results cannot be generalized to the total population. The results should then be interpreted in light of this.

AC 3.3 We agree with the reviewer 3 that these results may not be generalisable, and have noted this possibility as study limitation in the Discussion.

Specific change

3.3.1 Stated in the limitations of the study. Page 13, lines 186 -188

RC 3.4 Overall, the writing in this article is very poor. There are multiple instances of sentences and phrases that are convoluted and have an unclear meaning. I would recommend that an editor and an experienced epidemiologist review this manuscript before publication to clarify the manuscript and to ensure that correct epidemiologic terms are used.

3.4.1 One example of this is the last sentence of the introduction. I’m unsure what the authors are trying to convey in that sentence and why that sentence is in the paper.

3.4.2 There are multiple, similar examples throughout the paper.

a) e.g. “frequency and proportion of risks” (line 166),

b) “discrete test proportions” (line 194), etc.).

AC 3.4 We thank the reviewer for bringing this to our attention, and have sought to address these in the revised manuscript.

Specific changes

3.4.1 Changed the last sentence in the Introduction section (page 5, line 39-41).

3.4.2 We have carefully gone through the revised manuscript and sought to identify and correct similar instances of usages indicated by the reviewer that are convoluted or have an unclear meaning (changes highlighted throughout the revised manuscript).

RC 3.5 Finally, I question if it is reasonable to assume that individuals in the lab data are equally distributed throughout the study area. Spatial clustering of some results could simply be the result of some areas being over-represented in the data. That is, some locations with a high proportion of positive tests could reflect greater access to the pathology lab in that location.

AC 3.5 We thank the reviewer for raising this concern. For commercial in-confidence reasons we are unable to indicate in the manuscript that this laboratory provides >90 of the private pathology services in the study area, and has an organisational commitment to equity of access that is realised through an extensive and diverse collection centre network that provides population coverage for the study area. The laboratory also provides Medicare bulk-billed services where this is indicated on the request form. While we agree with the reviewer that individuals in the laboratory data need not be distributed equally in the study area, this more likely represents the geographic distribution of the population across the study area rather than a testing bias due to greater access. However, we cannot exclude the possibility a that higher proportion of positive tests in an area could be due to greater access to pathology labs. This is beyond the scope of the current study but we thank the reviewer for raising this possibility and have included it as a potential limitation in the Discussion.

Specific changes

3.5 Added this as a limitation in Discussion (Page 13, line 188-190)

Submitted filename: Response to Reviewers.docx

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17 Sep 2019

Geographic variation in cardiometabolic risk distribution: a cross sectional study of 256,525 adult residents in the Illawarra-Shoalhaven region of the NSW, Australia.

PONE-D-19-14817R1

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20 Sep 2019

PONE-D-19-14817R1

Geographic variation in cardiometabolic risk distribution: a cross sectional study of 256,525 adult residents in the Illawarra-Shoalhaven region of the NSW, Australia.

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