CHAPTER 4 Aims, objectives, and methodology
4.4 Methodology
4.4.2 Objective 2: The changing patterns of access to service and mortality of ESRD
4.4.2 Objective 2: The changing patterns of access to service and mortality of ESRD patients overtime
Method of objective 2: Age-period-cohort analysis based on administrative data of the National Health Security Office
This method focuses on the longitudinal assessment of the renal replacement therapy programme (RRT) in terms of access to the programme and mortality.
Access to the RRT programme was represented by registration for the programme. Mortality is one of the indicators used to measure population health outcomes. In this study, mortality referred to all‐cause mortality,
assessed in both groups of patients who were maintaining RRT and overall end‐
stage renal disease (ESRD) patients.
4.4.2.1 Justification of using the age-period-cohort analysis Regarding changing patterns of birth, morbidity, and mortality, it is important to account for three factors: age, period, and cohort. Age effects are defined as variations associated with biological and social processes of aging. They represent development of changes across individuals’ life times. Period effects are defined as variations over time periods that affect everyone equally,
regardless of age or birth cohort. These variations can be an immediate change of any event such as economic crisis, endemic, and health care treatment or intervention. Cohort effects can be characterised by changes that individuals experienced in early life. Members of the same cohort group age together and share similar historical and social events at the same ages (Yang and Land 2008). For these reasons, cohort effects are sometimes referred as longitudinal effects and period effects are known as cross‐sectional effects (Clayton and Schifflers 1987).
Conventional models are solely based on assessment of age, yet ignoring effects of cohort and period could result in misinterpretation of results. This is because such effects can be a mixed consequence of various time‐related factors. To give an example, older age groups and older generations experience higher
mortality, and consequently, dominate the overall death rate and mask real
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events occurring in recent age groups and cohorts. Age period cohort analysis is one approach that allows us to separate the effects of age, period, and cohort through a statistical model and to understand why disease trends change over time by taking account of such three variables (Glenn 2005).
4.4.2.2 Data source
This method used four databases. Three databases were obtained from the NHSO and were collected from health care facilities all over the country. They were: inpatient, outpatient, and renal replacement therapy (hemodialysis and peritoneal dialysis). The NHSO has audit procedures to ensure quality as well as prevent duplicates of the claims data at both central and regional NHSO. The last database used in this study was mortality data which was obtained from the Ministry of Interior’s civil registration system. All four databases keep
information at the individual level and can be linked together by using the 13‐
digit citizen identification number. These numbers will be encoded before handing to any third party.
i. Inpatient database
The inpatient database provides individuals’ profiles (such as name, date of birth, address, occupation, and so on) and individuals’ health information while they are hospitalised (such as diagnoses, care given, medications, laboratory tests, hospital code, and admission/discharge date). The inpatient database consisting of 18 sub‐files was well designed; its structure has rarely been changed since the beginning
of the UCS. A patient’s diagnosis is the compulsory field since the NHSO has to calculate the relative weight for the diagnosis related group (DRG) payment.
ii. Outpatient database
The outpatient database contains patient‐level information including:
individuals’ profile and their ambulatory visits, such as outpatient, accident/emergency, prevention/promotion programmes, referral, and
medications. Because of this diversity, the structure and details of the database
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have been adjusted several times and became quite stable from 2012. Currently the outpatient database consists of 21 sub‐files. It is noted here that outpatient data used in this study was from the period 2008 to 2013, while data from the inpatient database was taken from 2005 to 2013.
iii. RRT database
Renal replacement therapy databases can be divided into three categories according to the three modalities of RRT available under the NHSO’s disease management. These are 1) peritoneal dialysis, 2) hemodialysis, and 3) renal transplant. These three databases are separately kept and administered. All RRT units are obliged to use these databases in activities regarding RRT service provision, such as patient registration, records of given services, medications, and laboratory tests.
iv. Mortality database
The mortality database was obtained from the Ministry of Interior’s civil vital registry. Data from the civil vital registry have been computerised since 1980 and use ICD‐10 as diagnosis code to identify causes of death. One drawback of the mortality database is the reliability of the reported causes of death. The majority of deaths (about 65%) in Thailand occur outside hospitals and in the absence of medical examiners. In most cases, causes of these deaths are recorded by nonmedical civil registrars based on lay reports from relatives, occasionally informed by medical opinion obtained during the illness leading to death (Tangcharoensathien, Faramnuayphol et al. 2006). Even in the case of deaths which occurred in hospitals, a study in Thailand reviewed cause‐of‐
death certificates and compared to the patients’ medical record found that 51%
of deaths contained certification errors (Pattaraarchachai, Rao et al. 2010).
Additionally, ESRD patients usually suffer from multiple illnesses, therefore ESRD may not be reported as their cause of death. For these reasons, this current study avoided bias by using all‐cause of death instead of cause‐specific death.
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4.4.2.3 Cohort selection
Cohorts in this study were limited to adult UCS members aged 20 to 89 who were diagnosed with ESRD. ESRD patients were selected from patients who had ICD‐10 code N180 or N1858 in either primary diagnosis or secondary diagnosis in NHSO databases. The patient’s first hospitalisation or first visit with end‐
stage renal disease diagnosis (ICD‐10=N180 or N185) in the obtained databases were used to define the point at which a patient started having end‐stage renal disease.
There are few ESRD patients younger than 20 or older than 90 years, patients outside these age groups give unstable rates and may affect the reliability of analyses, therefore they are not included in the analyses of this study.
To assess registration rates of the RRT programme, the study period was 1 January 2008 to 31 December 2013. The follow up period for assessing death rates was from 1 January 2005 to 31 December 2013, except the death rate among RRT patients, data were collected from 1 January 2008 to 31 December 2013. Censoring for deaths was at the end of the data collection period (31 December 2013).
4.4.2.4 Variables
Three main variables in this study (age, period, and cohort) were modelled in terms of the year unit. Age of the entry into the RRT programme model was determined by the time between the year of birth and the year of registration in the programme, and age in the mortality model referred to the time between the year of birth and the year of death. The period in all models denoted
calendar years of studied period and cohort was the individuals’ years of birth.
In entry into the RRT programme models, the three modalities of RRT were separately used as covariates.
8 The ICD10 code for ESRD stage 5 was changed to N185 in April 2012.
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4.4.2.5 Data analysis
Data were analysed in two ways; descriptive analysis and age‐period‐cohort analysis. Each analysis is presented in two sections according to the two
models: entry into the RRT programme model and mortality model. Within the entry into the RRT programme model, the three modalities of renal replacement therapy are separately presented.
i. Descriptive analysis
According to Carstensen (2005), before conducting the age‐period‐cohort analysis, it is important to look at rates of observed events and explore whether rates are proportional between periods or cohorts.
In the descriptive analysis, age groups were first tabulated against the calendar year (or referred to as period) and the year of birth (or referred to as cohort) to display numbers of events (either numbers of registration of each modality or deaths). Next, numbers of events (registrations or deaths) were computed as rates and then graphically presented as rates for age at registration (or age at death), for calendar year, and for birth cohort.
Descriptive analyses of this study provide four classical plots:
a) Rate versus age and period: observations within each period are connected,
b) Rate versus age and period: observations within each birth cohort group are connected,
c) Rate versus period and age: observations within each age group are connected, and
d) Rate versus cohort and age: observations within each age group are connected.
The first (a) and the third (c) plots will exhibit fairly parallel lines if age‐specific rates are proportional between periods in the graphs. The second (b) and the fourth (d) plots will also show fairly parallel lines if rates are proportional to
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cohorts. These plots can be used as the first overview of the data but may not reflect the result of the entire analysis (Carstensen 2005).
In mortality models, age‐standardised mortality rates (ASMR) for UCS patients aged 20‐89 years who were on RRT and those of overall patients who had ESRD diagnosis are presented. All ASMRs were calculated using the World Health Organization’s world population in the year 2000 as the standard.
ii. Age-period-cohort analysis
The general form of the age‐period‐cohort model for rates (a, p) is:
ln [(a, p)] = f(a) + g(p) + h(c)
where f, g and h are functions, and a, p and c are age, period and cohort
respectively. This model can be used to predict the incidence or mortality rate for any combined effect of age, period, and cohort. However, due to the direct relationship between the terms where date of registration (or death) is the sum of the date of birth and the age at death (or death), p=c+a, there will be a
constraint in any model that includes these three variables on a linear scale.
Consequently, the components of this model cannot be directly determined by conventional linear regression (Clayton and Schifflers 1987; Carstensen 2007;
Yang and Land 2008).
Age, period and cohort effects need to be modeled in order to separate their effects. Various approaches have been introduced to cope with the so‐called identifiability problem (Clayton and Schifflers 1987; Carstensen 2007; Yang and Land 2008). Clayton and Schifflers (1987) described an approach to model mortality rates in terms of age, period, and cohort over time. Their proposed models were to overcome problems of the constraint in any model which included age, period, and cohort variables on a linear scale. This is in line with Heuer (1997), Holford (1983), and Carstensen (2007)’s parametisation technique.
This study used restricted cubic (natural) splines to model effects of age, period, and cohort within a Generalised Linear Model framework with a Poisson family
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error structure, a log link function and an offset of log (person risk‐time) which was suggested by Carstensen (2007). This was done by adding a drift term (a combined slope for period and cohort effects) with a selected number of
parameters (or knots) to either period or cohort effect. Placement of the drift on period or cohort depended on the subject of interest.
In this study, the main focus was on the effect of period, therefore the drift was allocated to the period variable. In the analysis, a point of period was fixed, and cohort fitted values were constrained to have zero slope. Age effect was then interpreted as age specific rate regarding the reference period.
As a result, the age‐period model was written as the first derivative functions of age, f(a) and period, g(p)as:
ln[(a,p)] = f(a) + g(p)
When a non‐linear regression model is estimated, the multiplicative age‐period model can be fitted by choosing a reference period p0 and a constraint g(p0)=0.
The model can be expressed as the function of rate as:
ln [(a,p)] = fp0 (a) + (p-p0) + g(p);
where fp0 (a) is the function for age, denoting age‐specific rates in the reference period, p0; is the slope of the log‐linear trend in period (the drift); and g( p) is the period function, which can be interpreted as a log relative risk of any period compared to the reference period, p0.
All tabulations of cohorts and population, descriptive analysis, and age‐period‐
cohort modelling were conducted using Stata version 12. Only goodness‐of‐fit in age‐period‐cohort analyses was assessed using R studio. All confidence
intervals are 95% confidence intervals.
4.4.3 Objective 3 Long term projections of RRT population and