Antibiotics-use in utero and early-life and risk of chronic childhood conditions in New Zealand: a methods protocol using linked data

doi:10.21203/rs.3.rs-4357022/v1

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Study protocol

Antibiotics-use in utero and early-life and risk of chronic childhood conditions in New Zealand: a methods protocol using linked data

https://doi.org/10.21203/rs.3.rs-4357022/v1

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Background

The incidence of many common chronic childhood conditions has increased globally in the past few decades. A potential role for antibiotic-(over)-use has been suggested with dysbiosis of the gut microbiome hypothesised to play a key role. This linkage study aims to assess the role of antibiotic-use in utero and in early-life in the development of Type 1 Diabetes (T1D), Attention Deficit Hyperactive Disorder (ADHD) and Inflammatory Bowel Disease (IBD).

Methods

The study design involves several retrospective cohort studies using linked administrative health and social data from Statistics New Zealand’s Integrated Data Infrastructure. It uses data for all children, and their mothers, born in New Zealand between October 2005 and December 2010 (n = 334,204). Children’s antibiotics-use are identified for four time periods (pregnancy, ≤ 1 year, ≤ 2 years, and ≤ 5 years) and the development of T1D, ADHD, and IBD is measured from the end of the antibiotics-use periods until death, emigration, or the end of the follow-up period (2021), whichever came first. Children who emigrated or died before the end of the antibiotics-use period are excluded. Cox proportional hazards regression models are used whilst adjusting for a range of potential confounders.

Discussion

These studies, using detailed, complete, and systematically collected antibiotic prescription data, will provide critical new knowledge regarding the role of antibiotics in the development of common chronic childhood conditions. Thus, it has the potential to contribute to the development of primary prevention strategies, through, for example, targeted changes in antibiotic-use.

Early-childhood

chronic childhood conditions

antibiotics

data-linkage

study protocol

routine data

The incidence of many chronic childhood conditions such as Type 1 Diabetes (T1D), Attention Deficit Hyperactive Disorder (ADHD), and Inflammatory Bowel Disease (IBD) has increased globally in the last two decades (1). The total global incidence of childhood and adolescent T1D is larger than previously estimated, with nearly one-in-two children currently undiagnosed, with most underdiagnosis occurring in low-income countries (2). Current global incidence estimates range from 128,900 to 149,500 per annum (3, 4). For ADHD, whilst recent geographical estimates are unavailable, the highest incidence rates are reported in countries with a higher socio-demographic index (5). The global incidence of IBD is increasing steadily and varies greatly by geographical areas, with the highest annual pediatric incidence of IBD reported to be 23/100,000 person-years in Europe, 15.2/100,000 in North America, and 11.4/100,000 in Asia/the Middle East and Oceania (6).

In New Zealand, the annual incidence of T1D is reported to be 23/100,000 and this is increasing by 4.1% annually (7). For ADHD, it is estimated that 2–5% of school-aged children in New Zealand are affected by ADHD (8) with a recent study showing that the total ADHD medication dispensing prevalence had almost doubled from 516/100,000 in 2007/08 to 996/100,000 in 2016/17, with the highest dispensing prevalence reported for those aged 7–17 years (9). In the case of IBD, recent New Zealand estimates have shown age-specific incidence rates of 39.5/100,000, which is 1.6-fold greater than what was measured 10 years earlier; this is among the highest in the world (10).

The aetiology of these diseases is not well understood but environmental factors, genetics, immune-regulatory pathways, and microbial exposures have all been suggested to be important (11, 12). Frequent use of antibiotics may also be a risk factor, with gut microbiome dysbiosis hypothesized to play a key role (13).

Several studies have assessed associations between antibiotic-use and the development of these chronic conditions during both the prenatal period and early-life years (14–16), but results have been inconsistent with some showing positive associations (16) and others showing no association (17–19). These inconsistencies may, at least in part, be explained by limitations in study design. For example, antibiotic-use is often assessed through recall, which is vulnerable to bias. Also, studies often rely on short-term prescription history prior to disease onset, which may result in issues of reverse causality (if antibiotics were prescribed to treat early symptoms of the disease itself (20, 21)). Further, studies have often focused on only one specific class of antibiotics without taking into account the full spectrum of antibiotics used (21). Importantly, most research has been conducted in Europe (particularly Scandinavia) and the US and it remains unclear whether results can be extrapolated to other parts of the world including New Zealand, which, compared to other OECD countries, including Scandinavia (22), is known to have a very high use of antibiotics among children (23).

The series of linked cohort studies for which the methods are described in this protocol paper will assess associations between prenatal and early-life antibiotics-use and the development of childhood T1D, ADHD and IBD. These studies, using detailed, complete, and systematically collected antibiotic prescription data, will provide critical new knowledge regarding the role of antibiotics in the development of these common chronic childhood conditions. Thus, it has the potential to contribute to the development of primary prevention strategies, through, for example, targeted changes in antibiotic-use.

The central hypothesis of this study is: early life antibiotic-use is associated with the development of childhood T1D, ADHD and IBD.

Study Design, setting and population

To date, data for all children born in New Zealand between October 2005 and December 2010 (n = 334,204) and their mothers have been extracted from Department of Internal Affairs (DIA) births data in the IDI. Children’s antibiotics-use has been defined for four time periods (pregnancy, ≤ 1 year, ≤ 2 years, and ≤ 5 years). The development of T1D, ADHD, and IBD (which consists of Crohn’s disease (CD) and ulcerative colitis (UC)) has been measured from the end of the antibiotics-use periods until death, emigration, or the end of the study in 2021, whichever came first, accumulating approximately 3,000,000 person-years. Children who emigrated overseas or died before the end of the antibiotics-use period have been excluded from the analysis, as they cannot be followed for the occurrence of these chronic childhood conditions. At the end of follow-up, children had reached an age of 11–16 years.

Data Sources

The IDI is a database of de-identified administrative and survey data about people and households in New Zealand (24). It includes data about health, education, income, social support payments, migration, and other life events, which can be linked at the individual level. The IDI provides a longitudinal record of events and is a growing resource. As of September 2018, the IDI holds over 166 billion pieces of information from more than 14 organisations (24, 25). Table 1 lists the datasets that are being used for this study with a brief description of the data and the variables extracted from these datasets.

Table 1

Data collections available within the Integrated data Infrastructure (IDI) for linking cohorts’ demographic data with antibiotics-use and selected health outcomes

(Source: Adapted from Statistics New Zealand).
Data collections	Descriptions	Characteristics/ variables extracted
Births (from 1840) This data collection was used to define the cohort and identify the mothers of the children.	This collection holds all births in New Zealand, including month and year of birth, sex, ethnicity, first and second parent as recorded on birth registration, and their sex, age, ethnicity, type of relationship, weight at birth, gestation, and their age.	Sex, date of birth, birth weight, ethnicity
Pharmaceutical data (from 2005) This data collection was used to analyse antibiotic prescription.	This collection holds claim and payment information from pharmacists for subsidised medicines including Pharmaceutical Management Agency (PHARMAC^#) identifier of primary active chemical ingredient, quantity, number of repeats, and date of dispensing.	Date of prescription dispensing, Anatomical Therapeutic Chemical (ATC) codes for medicines. Including antibiotics and treatments for: T1D, ADHD and IBD
Maternity (from 2003) This data collection was used to calculate gestational period, and maternal age at birth and identify the mode of delivery.	The National Maternity Collection provides statistical, demographic, and clinical information about selected publicly funded maternity services up to nine months before and three months after a birth.	Mothers date of birth, ethnicity, last date of menstruation, mode of delivery, maternal age at delivery
Mortality (from 1998) This data was used to identify the date of death.	This collection holds underlying cause of death for all deaths registered in New Zealand using the International Classification of Disease codes (ICD-10 AM), including all registered foetal deaths, and date of death.	Date of death
International travel and migration (from 1997) This data collection was used to identify children who emigrated from New Zealand.	This collection holds arrival and departure records and migration records.	Date of departure
Laboratory Claims (from 2003) This data was used to obtain laboratory testing information.	This collection holds primary-care test subsidies.	Laboratory test(s) conducted (results of tests are not available) Including testing for: T1D, ADHD and IBD
National Non-Admitted Patient Collection (NNPAC) (from 2007) This data was used to identify any diagnosis procedure for non-admitted patients	NNPAC provides national consistent data on non-admitted patient (outpatient and emergency department) activity.	Diagnosis for various health conditions including: T1D, ADHD and IBD
Publicly funded hospital discharges (from 1998) This data was used to identify the principal and additional reasons for hospitalisation and procedure performed during hospital stay.	This collection contains summarised information detailing publicly funded hospital discharges and procedures by New Zealand hospitals using the International Classification of Disease codes (ICD-10 CM).	Disease/procedure classification. Diagnosis for various health conditions including: T1D, ADHD and IBD

^#PHARMAC is a government agency in New Zealand responsible for managing the funding and procurement of pharmaceuticals and medical devices.

Definition of antibiotic exposure

Antibiotic exposures in utero and for the first five years of life are identified for all cohort members born between October 2005 to December 2010 from pharmaceutical data. Dispensing dates and dose and number of purchases are identified, and each antibiotic prescription is categorised by: (1) class, according to the Anatomical Therapeutic Chemical (ATC) classification J01 ‘Antibiotics for systemic use’ (e.g., penicillin’s, cephalosporins, sulphonamides); (2) spectrum i.e., broad, or narrow; and (3) whether antibiotics target gram positive or gram-negative bacteria, or both. These categorisations of individual antibiotics are provided in Table 2.

Table 2

**Antibiotics by class and spectrum of activity**
Class of antibiotics	Chemical name of antibiotic used among the cohort	Spectrum of activity (Narrow/ Broad)	Antibiotics targets Gram + ve / Gram -ve bacteria
Cephalosporins and Cephamycins	Cefaclor monohydrate	Moderate	Both
	Cefalexin	Moderate	Both
	Cefamandole nafate	Broad	Both
	Cefazolin	Moderate	Both
	Cefoxitin sodium	Moderate	Both
	Ceftazidime	Broad	Both
	Ceftriaxone	Broad	Both
	Cefuroxime axetil	Moderate	Both
	Cefuroxime sodium	Moderate	Both
	Cephalothin sodium	Broad	Both
	Cephradine	Broad	Both
Macrolides	Azithromycin	Broad	Positive
	Clarithromycin	Broad	Positive
	Erythromycin	Broad	Positive
	Erythromycin (as lactobionate)	Broad	Positive
	Erythromycin estolate	Broad	Positive
	Erythromycin ethyl succinate	Narrow	Positive
	Erythromycin stearate	Narrow	Positive
	Roxithromycin	Broad	Positive
Other Antibiotics	Aztreonam	Broad	Negative
	Chloramphenicol	Broad	Both
	Chloramphenicol sodium succinate	Broad	Both
	Ciprofloxacin	Broad	Both
	Clindamycin	Broad	Both
	Colistin sulphomethate	Broad	Positive
	Fleroxacin	Broad	Both
	Framycetin sulphate	Broad	Both
	Gentamicin sulphate	Broad	Both
	Imipenem	Broad	Both
	Levofloxacin	Broad	Both
	Lincomycin	Broad	Both
	Lincomycin hydrochloride	Narrow	Positive
	Moxifloxacin	Broad	Both
	Neomycin sulphate	Broad	Both
	Ofloxacin	Broad	Both
	Paromomycin	Broad	Positive
	Pyrimethamine	Broad	Both
	Sodium Fusidate [fusidic acid]	Narrow	Both
	Spectinomycin hydrochloride	Moderate	Both
	Spiramycin	Broad	Both
	Sulfadiazine sodium	wide	Both
	Sulphadiazine	Broad	Both
	Tobramycin	Broad	Both
	Triacetyloleandomycin	Broad	Positive
	Trimethoprim	Broad	Both
	Trimethoprim with sulphamethoxazole [Co-trimoxazole]	Broad	Both
	Vancomycin	Narrow	Positive
Penicillins	Amoxicillin	Broad	Both
	Amoxicillin with clavulanic acid	Broad	Both
	Amoxycillin Clavulanate	Broad	Both
	Benzathine benzylpenicillin	Narrow	Both
	Benzylpenicillin sodium [Penicillin G]	Narrow	Both
	Dicloxacillin	Narrow	Both
	Flucloxacillin	Narrow	Both
	Flucloxacillin magnesium	Narrow	Both
	Penicillin G benzathine [Benzathine benzylpenicillin]	Narrow	Both
	Phenoxymethylpenicillin (Penicillin V)	Narrow	Both
	Piperacillin	Broad	Both
	Pivampicillin	Broad	Both
	Pivmecillinam Hydrochloride	Narrow	Both
	Procaine penicillin	Narrow	Both
	Ticarcillin	Broad	Both
Tetracyclines	Demeclocycline hydrochloride	wide	Both
	Doxycycline	Broad	Both
	Lymecycline	Broad	Both
	Minocycline hydrochloride	Broad	Both
	Rolitetracycline	Broad	Both
	Tetracycline	Broad	Both
	Tetracycline hydrochloride	Broad	Both

(Source: Drugs & Medications A to Z – Drugs.com)

Definition of health outcomes

Selected health outcomes of the study population are determined through linkage with the following data collections: (1) hospital discharges; (2) pharmaceutical data (3) non-admitted patient collection and (4) laboratory claims, for the period starting from birth or end of antibiotics exposure period of each child to the end of 2021. The specific case definitions, including the ICD-10 codes corresponding to the health outcomes under consideration, are provided in Table 3. To ascertain the prevalence of T1D, three distinct algorithms were used to identify cases, facilitating a comprehensive comparison of results to ensure consistency.

For IBD, the identification of cases, as outlined in Table 3, will be subject to further validation against several cohorts of IBD patients obtained from collaborating gastroenterologists. This validation will involve exploring various combinations of medications prescribed for IBD, including those listed in Table 3. The analysis aims to provide insights into the diversity of medication regimens associated with IBD cases. Any refinements or enhancements to the algorithms, as well as insights gained from the medication combination analysis, will be documented, and incorporated into the final analysis.

Table 3

Health outcome (T1D, ADHD and IBD) classification, corresponding ICD-10 codes and case definitions
Health outcome of interest	ICD-10 Codes	ICD-10 Codes Description (Source: www.icd10data.com)	Case Definitions
Type 1 Diabetes (T1D)	E10	T1D without complications	Three different algorithms are used to identify cases of T1D: Algorithm 1: An individual is identified as a case if they meet one of the following criteria: 1) hospital discharge with one or more ICD-10 codes as outlined in this table; OR 2) prescription of an insulin medication during the study period. Algorithm 2: A base dataset for all possible indications of diabetes (i.e. Type 1 and Type 2 diabetes) is extracted first from the cohort dataset. Further processing is based on a previously defined algorithm (26), which identifies an individual as a case if they meet all of the following criteria during the study period: 1) prescription of an insulin medication; AND 2) not dispensed oral hypoglycaemics or alpha glucosidase inhibitors; AND 3) hospital discharges without a T2D diagnosis and at least one hospital discharge with a T1D diagnosis; AND 4) did not die during the study period with a T2D diagnosis in death records; AND 5) no hospital discharges with cystic fibrosis, pancreatectomy, or neonatal diabetes mellitus before insulin dispensing. Algorithm 3: A base dataset for all possible indications of diabetes (i.e. Type 1 and Type 2 diabetes) is extracted first from the cohort dataset. Further processing is based on a previously identified algorithm in “Health Tracker”, that was developed by the NZ Ministry of Health (27), which identifies an individual as a T1D case if they meet one of the following criteria during the study period: 1) one or more attendances for NNPAC services under T1D purchase unit codes; OR 2) discharge/diagnosis events/prescriptions related to T1D in the National Minimum Dataset (NMDS)/ Mental Health Information National Collection (MHINC)/ Programme for Integration of Mental Health Data (PRIMHD) /Pharmaceutical Collection (PHARMS). Individuals can also be identified as having T1D based on having discharge or other diagnosis events related to T1D and T2D, along with specific prescription patterns, such as no oral hypoglycemic/metformin prescription and one or more insulin prescriptions. Age-specific criteria further refine the classification for individuals aged 0–14 years or 15 years and older based on the distribution of T1D and T2D events. The year of diagnosis for T1D will be based on the date of the first insulin prescription.
	E10.1	T1D with ketoacidosis
	E10.2	T1D with kidney complications
	E10.3	T1D with ophthalmic complications
	E10.4	T1D with neurological complications
	E10.5	T1D with circulatory complications
	E10.6	T1D with other specified complications
	E10.8	T1D with unspecified complications
	E10.9	T1D without complications
	E10.10	T1D with ketoacidosis without coma
	E10.11	T1D with ketoacidosis with coma
	E10.13	Other specified diabetes mellitus
Attention Deficit Hyperactive Disorder (ADHD)	F90.0	ADHD, predominantly inattentive type	An individual is identified as a case if they meet one of the following criteria: 1) hospital discharge with one or more of the ICD-10 codes outlined in this table; OR 2) prescription of any one of the following medications: methylphenidate (Concerta, Ritalin, Rubifen), dexamphetamine, or atomoxetine (Strattera) during the study period. The ADHD prescriptions listed above are treatments in New Zealand specific to ADHD but maybe also prescribed for the rare childhood condition of narcolepsy. The year of diagnosis will be based on the date of the first ADHD medication prescription.
	F90.1	ADHD, predominantly hyperactive type
	F90.2	ADHD, combined type
	F90.8	ADHD, other type
	F90.9	ADHD, unspecified type
Inflammatory Bowel Disease (IBD)	K50	Crohn's disease [regional enteritis]	An individual is identified as a case if they meet one of the following criteria: 1) hospital discharge with one or more of the ICD-10 codes outlined in this table; OR 2) Prescription of medications including, but not limited to, combinations involving 5-aminosalicylic acid (5-ASA), corticosteroids, and immunomodulators such as infliximab and adalimumab depending on disease subtype and severity, reflecting the individualised nature of IBD treatment strategies (28). The year of diagnosis will be based on the date of the first IBD medication prescription.
	K51	Ulcerative colitis
	K52	Other and unspecified noninfective gastroenteritis and colitis

Other variables

Fixed covariates/confounders that will be considered in the analyses include sex, ethnicity, deprivation index (based on meshblock)(29) birth weight, gestation, mode of delivery, rurality, and maternal age. Time-dependent covariates include hospitalisation for infections and other chronic diseases, and selected prescription medications (e.g., paracetamol, antivirals, antifungals).

Follow-up of vital status and New Zealand residency

Linkage to border movements and mortality data is used to determine whether cohort members are still alive and are based in New Zealand. Those who have emigrated or died prior to their 5th birthday are excluded from the analyses; the follow-up time for those who died or emigrated after the 5th birthday is censored up to that point, which means that the event of interest or health outcome being investigated may not be observed for some individuals.

Statistical analysis

To date, the primary focus has been on data preparation that consisted of: (1) constructing the cohort through data linkage; (2) identifying the antibiotics exposure variables for all cohort members; (3) identifying other variables, including confounders for all cohort members; and (4) identifying T1D, ADHD and IBD cases within the cohort using the definitions described in Table 3. The next stage involves analyses that focus on assessing associations between antibiotics-use and the health outcomes described above. For this, we will use Cox proportional hazards regression, with attained age as the analysis time scale. As noted before, children have been followed-up until the estimated date of diagnosis, emigration from New Zealand, death, or the end of the study period (31 December 2021), whichever comes first.

For each health outcome, analyses will be conducted to measure associations with antibiotics-use during specific early life periods (pregnancy, ≤ 1year, ≤ 2years and ≤ 5 years, as well as combinations of these periods). Antibiotics-use will be based on the number of prescriptions, which will enable the assessment of dose-response associations. In addition to considering all antibiotic classes combined we will also conduct analyses where antibiotics will be grouped into different classes/categories (Table 2); this will provide insights into which specific groups of antibiotics may be most strongly associated with the three outcomes of interest. Analyses will be stratified by mode of delivery to assess whether associations may be different in different subgroups (effect modification) as has been shown for cesarean section births, with larger effect sizes shown for associations between antibiotics-use and T1D for caesarean section births (16, 30). In addition to stratified analyses, we will also assess the role of potential confounders such as sex, prioritised ethnicity, deprivation index and rurality using multivariable analyses.

Nested case-control analyses will be conducted as an additional way to address potential bias and confounding. Controls will be matched to cases on year and month of birth, sex, ethnicity, and other potential confounding factors such as residence and deprivation. In addition, to address potential medical surveillance bias, matched controls that occur in the same data collections as the cases will be selected. Nested case-control analyses will also enable the evaluation of possible reverse causation (i.e., the health outcome of interest resulting in the prescription of antibiotics rather than the other way around) by disregarding antibiotics-use in the 6 months before diagnosis of the cases and the equivalent time point of the matched controls. Further control for confounding by maternal factors will be achieved through within-mother analysis of disease-discordant pairs of siblings. Factors remaining constant between pregnancies could, for example, be the mother’s attitude towards antibiotic prescriptions as well as the GP’s antibiotic prescription practices, which will influence the child’s exposure to antibiotics; other types of analyses can typically not adjust for this.

STUDY POWER

Based on national and international data we have assumed that at least half of the children will have been prescribed antibiotics within the first year of life (52% in a Finnish study, 15% for specific antibiotic classes (31)). Based on age-specific statistics of the Virtual Diabetes Register (VDR), which is an annually updated national register of all patients with diabetes mellitus from 2010–2015, we estimate that 400 cases of T1D can be identified within the cohort (32). Furthermore, based on hospitalisation data, as per the age-specific data we estimate that at least 200 IBD cases (170 CD and 30 UC) can be included in the study based on hospitalisation data. Finally, for ADHD, and as noted earlier, medication dispensing has doubled between 2007/8 to 2016/17 from 516 per 100,000 to 996 per 100,000 in New Zealand (9). While a breakdown by age group is not available, we estimate that at least 1,000 cases can be identified in the cohort. This is a conservative estimate based on our experience of other IDI projects (33) and it is likely that this number is substantially higher (up to 3% of the study population). Thus, we assume that case-sets will have a minimum size of 1,000 for ADHD, 400 for T1D and 200 for IBD. Hazard ratio estimates (HR) that are detectable with 80% power (p < 0.05, 2-sided) under different population size and exposure prevalence scenarios are summarised in Table 4.

Assuming an exposure prevalence of 33%, the study has 80% power to detect a hazard ratio (HR) of 1.2 for ADHD, 1.4 for T1D and 1.5 for IBD. Analyses of specific strata of the study population (e.g., based on sex or ethnicity) will have sufficient study power to detect similar effect sizes (Table 4). Considering a lower exposure frequency of 10% (e.g., for specific antibiotic classes), the study has 80% power to detect a HR of 1.3 for ADHD, 1.4 for T1D and 1.7 for IBD. Assuming a further reduction in exposure frequency of 5% (e.g., for specific antibiotics), the study has 80% power to detect a HR of 1.5 for ADHD, 1.7 for T1D and 2.0 for IBD (Table 4).

Table 4

Study power: Hazard ratio (HR) detectable (power 80%, p < 0.05, 2-sided) under different population size and exposure prevalence scenarios.
		scenarios for ADHD					scenarios for T1D					scenarios for IBD
		cases	exposure prevalence				cases	exposure prevalence				cases	exposure prevalence
strata	study population		50 %	33 %	10 %	5 %		50 %	33 %	10 %	5 %		50 %	33 %	10 %	5 %
	n	n	HR	HR	HR	HR	n	HR	HR	HR	HR	n	HR	HR	HR	HR
all	300,000	1000	1.2	1.2	1.3	1.5	400	1.3	1.4	1.5	1.7	200	1.5	1.5	1.7	2.0
sex	150,000	500	1.3	1.3	1.5	1.6	200	1.5	1.5	1.7	2.0	100	1.7	1.7	2.0	2.4
ethnicity	60,000	200	1.5	1.5	1.7	2.0	80	1.8	1.8	2.2	2.5	40	2.2	2.2	2.7	3.2

ETHICS

The study was approved by Human Research Ethics Committee of the University of Otago (Reference number: HD21/053). Microdata access approval for the project was provided by Statistics New Zealand.

Although data within the IDI are fully deidentified there are several requirements that govern the use of IDI data that this study will adhere to. These are: 1) statistical outputs can only be disseminated after outputs have been checked and approved by Statistics New Zealand; 2) the IDI confidentiality rules require the random rounding of counts up or down to the next multiple of 3; and 3) the suppression of counts and associated results of analyses on samples smaller than six.

Antibiotics are a widely used in human populations, particularly children. It is therefore important to assess and quantify potential adverse effects of this exposure at a population level, so that they can be balanced against the many benefits of these therapies. The unique data infrastructure established through Statistics New Zealand’s IDI provides a robust way of assessing such relationships across a large cohort, consisting of the entire New Zealand population who can be followed for many years. Findings will be of relevance both locally and internationally.

The proposed series of studies, which are large by international standards and based on detailed, complete, and objectively collected antibiotic prescription data, will provide critical new knowledge regarding the role of antibiotics in the development of common chronic childhood conditions. As such, it will provide an important addition to the limited number of studies conducted in humans and has the potential to contribute to the development of feasible avenues for primary prevention, through e.g., targeted changes in antibiotic-use in both quantity and type and/or targeted use of e.g. pre- or probiotics.

The focus of this study is on the effects of antibiotics on the host, but the results are also relevant to the broader issue of antibiotic over-use and its link with increased microbial resistance. New Zealand has comparatively low rates of antimicrobial resistance (34, 35). However, resistance has steadily increased, and experience gained from other countries suggests that this will become more common in New Zealand, raising patient-risk and costs for the health system (36). Antimicrobial resistance is considered one of the biggest man-made public health threats of modern times (34). Clinicians and public health researchers have advocated that New Zealand needs to urgently institute a range of measures to significantly reduce antimicrobial consumption (37, 38). A systematic review of interventions aimed at reducing antibiotic prescribing for respiratory tract infections across several settings has led to substantial reductions (39), although efforts in New Zealand have not yet resulted in a decrease of antibiotic consumption (36). This study may provide additional evidence for the need to reduce (unnecessary) antibiotic consumption both in New Zealand and internationally.

ADHD: Attention Deficient Hyperactive Disorder

ATC: Anatomical Therapeutic Chemical

CD: Crohn’s Disease

DIA: Department of Internal Affairs

HR: Hazard ratio

IBD: Inflammatory Bowel Disease

ICD-10 CM: International Classification of Diseases, Tenth Revision, Clinical Modification

IDI: Integrated Data Infrastructure

MHINC : Mental Health Information National Collection

NMDS: National Minimum Dataset

NNPAC: National Non-Admitted Patient Collection

OECD: Organisation for Economic Co-operation and Development

OR: Odds ratio

PHARMAC: Pharmaceutical Management Agency in New Zealand

PHARMS: Pharmaceutical Collection

PRIMHD: Programme for Integration of Mental Health Data

RR: Relative Risk

T1D: Type 1 Diabetes

T2D: Type 2 diabetes

UC: Ulcerative Colitis

Author Contribution

JD, AM, and MB conceived the original idea for this data linkage study. All authors contributed to the development of the study methodology. SR wrote the first draft of the manuscript, with assistance from JD, AM, MC, AK, and MB. All authors contributed to the writing of the paper and read and approved the final manuscript.

FUNDING AND ACKNOWLEDGEMENT

Funding for this study is provided by the New Zealand Health Research Council (Reference ID: 18/509).

COMPETING INTERESTS

The authors declare that they have no competing interests.

AVAILABILITY OF DATA AND MATERIALS

The datasets generated and/or analysed for this study are not publicly available. Statistics New Zealand’s IDI microdata is available for research purposes and access is granted based on meeting the conditions set by Stats NZ through a secure Lab environment.

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No competing interests reported.

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Antibiotics-use in utero and early-life and risk of chronic childhood conditions in New Zealand: a methods protocol using linked data

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Abstract

Background

Methods

Discussion

BACKGROUND

METHODS AND ANALYSIS

Study Design, setting and population

Data Sources

Definition of antibiotic exposure

Definition of health outcomes

Other variables

Follow-up of vital status and New Zealand residency

Statistical analysis

STUDY POWER

ETHICS

DISCUSSION

Abbreviations

Declarations

Author Contribution

References

Additional Declarations

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