To illustrate, 2 outbreak investigations of CA-MRSA infec- tions in football teams revealed that the responsible CA-MRSA clone was not found in the nares of infected players, uninfected teammates/staff, or the environment [7, 33].

This suggests that the strain was acquired from a nonnasal endogenous source or an environmental source.

An investigation of community- dwelling rural Alaskans with CA-MRSA infection found that the majority of cases of S. aureus disease (185%) were caused by MRSA, but most nasal colonizations (67%) in infected per- sons, household members, and uninfected control subjects in- volved MSSA [34].

Taken together, these studies suggest that CA-MRSA infection was not "the tip of the iceberg" that rep- resented a clinical progression of massive background coloni- zation.

Instead, the findings suggested that CA-MRSA nasal colonization, while uncommon, could be associated with an uncommonly high risk of CA-MRSA infection.

Alternatively, CA-MRSA could be acquired from nonnasal body sites or con- taminated environments.


Data on CA-MRSA infection among MSM-especially HIV- infected MSM-have provided additional insight into the role of colonization in CA-MRSA infection.

CA-MRSA infection among MSM has been associated with high-risk behaviors, in- cluding use of methamphetamines and other illicit drugs, high- risk sexual behavior, use of the Internet to find sexual contacts, occurrence of skin-abrading sex, and history of sexually trans- mitted infections [10, 35-37].

CA-MRSA infection often occurs at sites in which skin-skin contact occurs during sexual activ- ities.

In one investigation, 32 (27%) of 118 MSM in San Fran- cisco, California, and 47 (39%) of 121 MSM from Boston, Massachusetts, had CA-MRSA infection involving the buttocks and/or genitoperineal sites [38].

Others have reported the oc- currence of CA-MRSA infection in the buttocks and genito- perineal sites associated with heterosexual sexual activity [39].

The high disease burden involving buttocks and/or genitoper- ineal sites caused by CA-MRSA suggests that CA-MRSA strains may be transmitted during sexual contact as a result of direct skin-skin contact and may even be considered to be an agent of sexually transmitted infection.


Another investigation has shed light on the pathogenesis of CA-MRSA in MSM.

In a point prevalence study of MRSA colonization among community-dwelling HIV-infected MSM (among whom CA-MRSA infections occurred frequently), na- sal MRSA colonization was uncommon (7 [4%] of 158 MSM); it was relatively uncommon when compared with MSSA col- onization (36 [23%] of 158 MSM) [40].

Among the 6 patients with recent, prior MRSA infection, only 1 was colonized with MRSA, suggesting that nasal colonization may have only a modest role in infection pathogenesis and that the CA-MRSA infection was acquired via other routes.


Other data support the role of nonnasal colonization in the pathogenesis of CA-MRSA.

One investigation of 65 patients with acute CA-MRSA infection found that 40% were colonized with MRSA and that the prevalences of MRSA colonization in the nares, axilla, inguinal area, and rectum were 26%, 8%, 20%, and 15%, respectively [41].

In contrast, in 35 subjects with acute CA-MSSA, HA-MRSA, or HA-MSSA infection, nonnasal S. aureus colonization was rare (3% of subjects).

This suggests that colonization patterns in CA-MRSA infection may be dis- tinct from the patterns of traditional S. aureus infection and thus play a role in, for example, the sexual transmission of CA- MRSA.


Outbreaks of CA-MRSA infection have repeatedly occurred among athletes.

In these outbreaks, players are believed to spread the infection through repeated skin-skin contact, es- pecially contact between broken skin, which occurs in games and practices [33].

In theses outbreaks, CA-MRSA infection was associated with exposures to various contaminated fomites, including whirlpools, shared razors, and shared towels [7, 33].

Other fomites implicated in outbreaks of sports team-associ- ated CA-MRSA infection include benches, body suits worn by fencers, and even a bar of soap [6, 33, 42].

In non-sports team outbreaks, sauna benches have been implicated [34].

In non- outbreak settings, close contact with a person who has a skin infection was also associated with CA-MRSA infection [43].

Because S. aureus can colonize commonly touched household objects, such as toilet handles, doorknobs, and kitchen sinks [44], it is feasible that households can serve as reservoirs for CA-MRSA strains and may play an important role in recurrent infections that can plague households [20, 21, 23].


Environmental sources of S. aureus as a cause for infection are not new or unique to CA-MRSA infection.

In hospitals, S. aureus and MRSA have been isolated from stethoscopes, pagers, bed spaces, workstations, and other places [45-47].

Aggressive control of MRSA strains in the environment has contributed to effective strategies to prevent MRSA infection.

For example, in The Netherlands, the so-called "search-and-destroy" policy has minimized HA-MRSA infections to such a degree that they have become uncommon, and the rate of HA-MRSA in The Netherlands is far below the rates in neighboring countries [48].

One key component of the search-and-destroy method is ag- gressive environmental (hospital ward) cleaning, in which wards where MRSA has been isolated are closed and extensively cleaned [49].

Although the reason behind the success of the search-and-destroy method is almost certainly multifactorial (e.g., it also involves aggressive screening and decolonization of patients and health care workers, among other measures) [49], its heavy emphasis on environmental decontamination may be a key component.

Its success compared with other MRSA control measures lends credence to the notion that en- vironmental sources are an important component of MRSA pathogenesis.


Clearly, the dynamics of community living differ markedly from the dynamics of health care settings.

On the basis of CA- MRSA outbreak data, it is possible that infection pathogenesis relies much more on acquisition from environmental sources and less on antecedent nasal colonization.

In fact, a conceptual model of CA-MRSA transmission highlights noncolonization routes of CA-MRSA acquisition.


This model, the "Five Cs of CA-MRSA Transmission" [50, 51], was developed by the Cen- ters for Disease Control and Prevention and suggests that MRSA infection results from the following constellation of risks: (1) contact (direct skin-skin contact); (2) cleanliness; (3) compromised skin integrity; (4) contaminated objects, surfaces, and items; and (5) crowded living conditions.

Some have added a 6th "C": exposure to antibiotic capsules (and tablets, liquids, etc.) on the basis of data that prior antibiotic use is a risk for CA-MRSA acquisition [33, 34, 52].


The 5 Cs model is impor- tant in that it helps explain CA-MRSA infections found in outbreak situations.

This model's role outside of outbreaks may not be as clear, because crowding living situations may not play a major role (e.g., New York City, to date, has experienced a relatively low rate of CA-MRSA infection) [53].

Taken in sum, data from outbreaks of CA-MRSA infection and from endemic infections suggest that antecedent nasal colonization may play a much less prominent role in the pathogenesis of CA-MRSA than was previously believed.


The prevalence of MRSA disease in the United States has in- creased dramatically as a result of the emergence of the CA- Downloaded from https://academic.oup.com/cid/article/46/5/752/287593 by Robert Crown Law Library user on 06 October 2025 CLINICAL PRACTICE • CID 2008:46 (1 March) • 755 MRSA clone USA300.

First isolated in 2000 [54], USA300 has been implicated in epidemiologically unassociated outbreaks in at least 38 US states (figure 1) [5-7, 10, 20, 33, 39, 42, 52, 54- 68].

Although many USA300 infections occur in the context of outbreaks, many are sporadic and do not involve any well- defined populations [69, 70].

USA300 is now also epidemic in many communities across the United States and accounts for 150% of all S. aureus infections [53, 71, 72].

Longitudinal anal- ysis of S. aureus disease at an urban medical center in the United States illustrates the epidemic character of this clone (figure 2) [73, 74].

The frequency of CA-MRSA disease increased 115- fold between 1996 and 2004, resulting in a concomitant dou- bling of overall S. aureus disease.

This epidemic could be at- tributed entirely to the rapid dissemination of a single clone of this species: from 1 case involving USA300 in 2000 to 1021 cases involving USA300 in 2004 (figure 2).


The international spread of USA300 has been recently re- ported, with outbreaks of infection in Canada and many Eu- ropean countries [75-81].

Exportation of USA300 may be fa- cilitated by a history of recent travel to the United States [75- 77].

It is of interest that USA300 first gained a foothold in Canada among persons with a history of incarceration, illicit drug use, and homelessness [82]; the same risk factors were noted among the early patients infected with the USA300 strain described in the United States [5, 54, 55, 73], suggesting that USA300 may emerge in the more marginalized populations with increased frequency of person-person contact.

However, this strain has clearly also established itself in nonmarginalized populations, such as healthy children [72].

The long history of effective MRSA control and prevention in some countries, such as The Netherlands, could be unraveled by community dissem- ination of USA300.


USA300 has become such a globally successful CA-MRSA clone in terms of causing human disease that it is reasonable to think of it as the prototypical CA-MRSA strain.

Thus, un- derstanding the pathogenesis of USA300 infection may be crit- ical to understanding the pathogenesis of CA-MRSA infection.

Its unique molecular characteristics may also be key compo- nents of the pathogenesis of CA-MRSA.


Presence of Panton-Valentine leukocidin (PVL) is epidemio- logically strongly associated with USA300 and other CA-MRSA strains.

The vast majority PVL-positive S. aureus isolates found worldwide are clustered in just 4 CA-MRSA lineages: ST1/ MW2, the US Midwest clone; ST8/USA300, the epidemic-prone American clone; ST30/USA1100, the Southwest Pacific Oceania clone; and ST80, the European clone.

The linkage of PVL to the 4 CA-MRSA genetic backgrounds may explain some of the unique epidemiological properties associated with CA-MRSA Downloaded from https://academic.oup.com/cid/article/46/5/752/287593 by Robert Crown Law Library user on 06 October 2025 756 • CID 2008:46 (1 March) • CLINICAL PRACTICE strains, although PVL's role in the spread of CA-MRSA is not clear [83].

The role of PVL in disease pathogenesis is also controversial.

Some animal studies have failed to find PVL as a major virulence factor [84], although others have found that it has a role in the pathogenesis of the pulmonary infection [85].


USA300 and other CA-MRSA strains also contain a different staphylococcal chromosomal cassette mec (SCCmec), confer- ring resistance to the entire class of b-lactam antibiotics.

In the absence of antibiotic treatment, SCCmec is thought to reduce the biological fitness of MRSA [86-88].

CA-MRSA, however, carries an allotype of SCCmec that is smaller in size than those typically found in nosocomial strains; this may impose only a slight cost to fitness, because it does not contain resistance genes other than mecA, which encodes an altered target penicillin- binding protein 2a.

CA-MRSA may also contain other genomic adaptations that allow it to overcome the presumed fitness cost of SCCmec.

Integrated next to the type IV SCCmec element is a putative pathogenicity island called type I arginine catabolic mobile element (ACME) [89].

Type I ACME is uniquely found in USA300 and not in other S. aureus strains.

The physical linkage between ACME and type IV SCCmec suggests that se- lection for enhanced fitness could coselect for increasing fre- quencies of not only b-lactam resistance but also other mul- tidrug resistant determinants, including multidrug resistance conjugative plasmids, which are carried by a subset of USA300 strains [89].


The ACME-SCCmec linkage is of great interest, because it was first discovered in the USA300 genome [89].

Horizontal acquisition of ACME from the ubiquitous skin commensal Staphylococcus epidermidis may enhance the growth and sur- vival of USA300 in the host-particularly on the human skin [89].

Deletion of ACME resulted in attenuated virulence of a clinical USA300 in a rabbit infection model [90].

Because the type I ACME found in USA300 has not been found in other CA-MRSA lineages, ACME could contribute to the epidemic character unique to this clone.

Indeed, only USA300 exhibits enhanced capacity to cause widespread epidemic disease and to displace other ACME-negative CA-MRSA clones, including ST1/MW2, ST30/USA1100, and ST80 [57, 73, 76, 91].

More- over, recent comprehensive proteomic analysis revealed that USA300 produced many virulence determinants in greater abundance than did ST1/MW2, including a-hemolysin, enter- otoxins Q and K, staphylokinase, and proteases [92].

In sum- mary, there are likely pathogen-level factors in CA-MRSA strains that enhance its pathogenicity.

Compared with other CA-MRSA strains, USA300 strains may have features that are particularly well suited to cause disease, although these features are, at this time, poorly understood.


The role of human host defenses against S. aureus is a final key factor in the pathogenesis of CA-MRSA and S. aureus in- fections.

Clearly, qualitative deficiencies in neutrophil function are associated with S. aureus infection [93].

Other host factors associated with infection risk include antimicrobial peptides [94], although our current understanding of host response to S. aureus is very limited.


Skin integrity is also key to prevention of S. aureus infection [33].

A distinct feature of CA-MRSA disease is the high disease burden among healthy community-dwelling individuals.

Many patients with CA-MRSA infection, however, have reported a breach in skin integrity that served as the portal of infection.

For example, skin infections often resulted from activities that resulted in skin trauma, such as those resulting from physical


Downloaded from https://academic.oup.com/cid/article/46/5/752/287593 by Robert Crown Law Library user on 06 October 2025 CLINICAL PRACTICE • CID 2008:46 (1 March) • 757 contact sports [7, 33, 42], injection drug use [95], and shaving of the genital area before sexual activity [39, 96].

Many children do not have recognized breeches in skin integrity before in- fection.

Whether this means there was none or that these breeches were not recognized or recalled is not clear.

The role of other host defenses in the pathogenesis of CA-MRSA, how- ever, remains poorly understood.


Data from outbreaks of infection and from endemic infections suggest that CA-MRSA behaves in a manner that is patho- genically distinct from HA-MRSA.

Although longitudinal nat- ural history studies have not been performed, epidemiologic and basic studies suggest that nasal colonization plays a less prominent role in the transmission and pathogenesis.

These studies also suggest that CA-MRSA colonization may not commonly precede infection.

Direct person-person and fo- mite-person transmission appear to be important routes of CA-MRSA infection.

It is possible that the genetically distinct CA-MRSA strains have important nonnasal niches (e.g., in- guinal regions) that act as reservoirs for transmission (e.g., via sexual behavior) or perhaps even reinfection.

Unique mo- lecular characteristics of CA-MRSA strains-especially the USA300 strain-may facilitate their pathogenicity and spread.

Host factors play an important but poorly understood role in CA-MRSA pathogenesis.


There are now compelling data suggesting that the patho- genesis of CA-MRSA is unique and distinct from that of HA- MRSA.

Rather than using the model of HA-MRSA that asserts that there is a stepwise progression from MRSA colonization to MRSA infection [26], we think that a model for CA-MRSA pathogenesis should consider the distinct epidemiological phe- nomena of CA-MRSA (figure 3).

Reliance on models of HA- MRSA transmission for prevention of CA-MRSA infection may result in the development of suboptimal prevention strategies.

For example, decolonization strategies that rely only on nasal decolonization may not be successful, because they do not ad- dress nonnasal colonization or decolonization of the patient's environs.

Prevention efforts may require total body decoloni- zation, decolonization of close or household contacts, and/or decolonization of the patient's household or workplace.

Suc- cessful efforts to prevent CA-MRSA reinfection or transmission will need to be based on a firm understanding of the unique CA-MRSA pathogenesis.

Longitudinal investigations will be key in helping us further understand CA-MRSA pathogenesis.


Background. Community-associated Staphylococcus aureus infections often affect multiple members of a household.

We compared 2 approaches to S. aureus eradication: decolonizing the entire household versus decolonizing the index case alone.


Methods.

An open-label, randomized trial enrolled 183 pediatric patients (cases) with community-onset S. aureus skin abscesses and colonization of anterior nares, axillae, or inguinal folds from 2008 to 2009 at primary and tertiary centers.

Participants were randomized to decolonization of the case alone (index group) or of all household members (household group).

The 5-day regimen included hygiene education, twice-daily intranasal mupirocin, and daily chlorhexidine body washes.

Colonization of cases and subsequent skin and soft tissue infection (SSTI) in cases and household contacts were ascertained at 1, 3, 6, and 12 months.


Results.

Among 147 cases with 1-month colonization data, modified intention-to-treat analysis revealed S. aureus eradication in 50% of cases in the index group and 51% in the household group (P = 1.00).

Among 126 cases completing 12-month follow-up, S. aureus was eradicated from 54% of the index group versus 66% of the household group (P = .28).

Over 12 months, recurrent SSTI was reported in 72% of cases in the index group and 52% in the household group (P = .02).

SSTI incidence in household contacts was significantly lower in the household versus index group during the first 6 months; this trend continued at 12 months.


Conclusions.

Household decolonization was not more effective than individual decolonization in eradicating community-associated S. aureus carriage from cases.

However, household decolonization reduced the incidence of subsequent SSTI in cases and their household contacts.

Clinical Trials Registration.


The incidence of staphylococcal skin and soft tissue infection (SSTI) has risen dramatically over the past decade [1, 2].

This epidemic has been driven largely by a community-associated Staphylococcus aureus clone designated USA300, which includes methicillin-resistant and methicillin-sensitive strains [3, 4].

Recurrent S. aureus SSTIs are especially problematic; recurrence rates >20% over 3 months have been reported [5–7].

Community-associated S. aureus infections cluster within households [8–10].

We previously demonstrated that children were at a 7-fold greater risk for developing SSTI if a household member had a recent SSTI [11].

Although community-associated methicillin-resistant Staphylococcus aureus (MRSA) transmission dynamics are not well established, it is believed that household contacts may serve as reservoirs for S. aureus transmission.

Thus, patients treated for S. aureus infection may reacquire the organism from colonized household contacts, potentially placing them at risk for recurrence.


Staphylococcus aureus nasal colonization is a risk factor for SSTI development [11–13].

Decolonization measures used to prevent healthcare-associated MRSA infections (eg, mupirocin ointment and chlorhexidine antiseptic) [14–18] are often prescribed for patients in community settings in an effort to prevent recurrent SSTI [19].

However, it is unclear whether decolonization should be performed by all household members or only by affected individuals.


The primary objective of this study was to compare S. aureus eradication from index cases when decolonization measures were performed by the index case alone compared with measures performed by all household members.

Secondary objectives included comparing incidence of SSTI in index cases and household contacts between treatment groups and evaluating adherence to decolonization measures.

We hypothesized that decolonization of all household members would be twice as effective in eradicating index case S. aureus carriage compared with decolonization of the index case alone.


METHODS Study Design The Staphylococcus aureus Decolonization Study (SuDS) was an open-label, randomized controlled trial.

The Washington University Institutional Review Board approved this study.


Definitions A case was defined as a pediatric patient with community-onset S. aureus SSTI and S. aureus colonization.

A household contact was defined as an individual spending more than half of each week in the case’s household.

Decolonization was defined as use of antimicrobial agents to eliminate S. aureus carriage [20].

Eradication was defined as absence of S. aureus carriage at 3 sampled body sites.


Participants Recruitment for this study was a 2-step process.

First, patients aged 6 months to 20 years with acute community-onset SSTI requiring drainage were screened at the St Louis Children’s Hospital (SLCH) ambulatory wound center and emergency department and from 9 community pediatric practices affiliated with a practice-based research network in metropolitan St Louis (Figure 1).

Patients were excluded from screening if they had an indwelling catheter, percutaneous medical device, or postoperative wound infection; were undergoing dialysis; or resided in a long-term care facility.

At screening, verbal informed consent and demographic information were obtained.

Culture swabs (BBL Liquid Stuart; Becton Dickinson [BD], Sparks, Maryland) were collected from the anterior nares, axillae, and inguinal folds.

Wound culture results were obtained from the SLCH microbiology laboratory or the primary provider to confirm S. aureus as the infecting organism.

Second, patients with both S. aureus (MRSA or methicillin-sensitive S. aureus [MSSA]) colonization and infection were subsequently invited to return to SLCH for trial enrollment.

To assess colonization status of the cases’ household contacts, swabs (BBL Liquid Amies; BD) from the anterior nares, axillae, and inguinal folds, accompanied by directions for culture collection, were mailed to the cases’ homes (a procedure validated previously by our group and others) [21, 22].


Study Intervention and Randomization Trial enrollment was conducted in the SLCH Clinical Research Center (CRC) from July 2008 to November 2009, after the case’s acute SSTI had healed (median time from screening, 21 days; interquartile range,15–31 days; no difference between treatment groups).

At trial enrollment, written informed consent was provided for the case and each household contact.

Participant assent was obtained for minors ≥7 years of age.

Randomization was conducted with an Internet-based, computer-generated randomization schedule (Randomization.com) using permutation blocks of 6 and 8.

The designated intervention for each participant was sealed inside a sequentially numbered security envelope and opened at enrollment by a research coordinator.

All participants received a standardized hygiene curriculum advising them to avoid sharing personal hygiene items (eg, razors, brushes, and towels), to use liquid pump or pour soaps and lotions (rather than bar soaps and jars of lotion), and to launder (in hot water) bed linens at least once weekly and towels and washcloths after each use.

The 5-day decolonization regimen included twice-daily application of 2% mupirocin ointment to the anterior nares with a sterile cotton applicator and daily use of Hibiclens (4% chlorhexidine gluconate, Mölnlycke Health Care, Norcross, Georgia) in the bath or shower.

Participants were instructed to apply the Hibiclens with the hands or with a clean washcloth and wash all body parts, excluding the face, followed by a thorough rinse with water.

Participants were randomized to 1 of 2 study groups: the index group in which only the case was instructed to perform decolonization, and the household group in which the case and all household contacts (>6 months of age and not pregnant) were instructed to perform decolonization.

Oral and printed instructions were provided to study participants.

Study staff demonstrated application of mupirocin ointment to the anterior nares.

Participants were provided with all study materials needed to complete decolonization measures at home.


Data Collection at Baseline and Follow-up A standardized questionnaire was administered to each case at enrollment to collect demographic information for each case and household contact, details of the household environment, and case characteristics.

One week after enrollment, a parent or guardian of each case was telephoned to assess adherence and adverse reactions to the decolonization protocol.

Index cases were followed longitudinally with visits 1, 3, 6, and 12 months after enrollment at the SLCH CRC or the primary provider’s office.

To assess S. aureus colonization status of the case, nasal, axillary, and inguinal swabs were collected at each follow-up visit.

A survey was administered to ascertain interval development of SSTI in the case or any household contact.

Descriptions of SSTI included skin abscess or boil, cellulitis, impetigo, or spider bite (because community-associated MRSA abscesses may be mistaken for spider bites) [4].

All follow-up was completed by November 2010.

Participants remained blinded to colonization culture results until completion of their 12-month follow-up.

Over the 1-year study, 22 cases completed ≥1 follow-up visit by mail due to the distance of their home from SLCH.

For these cases, the follow-up survey was conducted by telephone, and swabs were mailed for home collection as detailed above.

For cases unable to be contacted for follow-up, SLCH medical records were examined to identify recurrent SSTI.

In addition, medical record review was performed at the primary provider’s office for cases reporting recurrent SSTI for which they sought medical care.


Study Outcomes The primary outcome measure was S. aureus eradication of the case 1 month after randomization.

Secondary outcomes were S. aureus eradication from the case at 3, 6, and 12 months; incidence of SSTI in the case or household contacts over 12 months; and adherence to decolonization measures.


Laboratory Methods Colonization culture swabs were incubated in tryptic soy broth with 6.5% sodium chloride (BBL; BD) overnight at 35°C.

The broth was plated to trypticase soy agar with 5% sheep’s blood (BBL; BD) and incubated at 35°C overnight.

Staphylococcus aureus isolates were identified, and antibiotic susceptibility testing was performed according to Clinical and Laboratory Standards Institute procedures as previously described [23, 24].

Laboratory personnel were blinded to randomization assignments.

Swabs collected at home all yielded normal flora for the appropriate body site, suggesting that samples were indeed representative of these sites.


Sample-Size Calculation and Statistical Analysis We anticipated that 50% of cases would achieve S. aureus eradication when measures were performed by the case alone ([25] and Fritz, unpublished data).

On this basis, 65 participants were needed in each group to detect 50% relative reduction in S. aureus colonization in the case at 1 month (α = .05, study power 80% by Pearson’s χ2 test).

We anticipated 25% attrition and therefore enrolled 183 households.

Statistical analyses were performed with SPSS for Windows 17.0 (IBM SPSS, Chicago, Illinois).

Baseline characteristics between treatment groups were evaluated by the Student t test (continuous data) and Pearson’s χ2 test or Fisher’s exact test (categorical data).

Outcome measures were evaluated by modified intention-to-treat analysis at each time point by Fisher’s exact test.

Specifically, participants were analyzed in the arm to which they were assigned, and the analysis for each time point included participants with evaluable data at that time point.

All tests of significance were 2-tailed.

P values ≤.05 were considered significant.

Odds ratios were considered significant if the 95% confidence interval did not include 1.

Relationships between treatment group and outcomes (index case S. aureus eradication and recurrent SSTI) were evaluated using multivariable binary logistic regression, adjusting for potential confounding characteristics that differed significantly between treatment groups at baseline; adjusted odds ratios were reported.


RESULTS Baseline Patient Characteristics One hundred eighty-three cases and their households were enrolled in the study; 91 were randomized to the household group and 92 to the index group (Figure 1).

The mean age (SD) of the cases was 6.0 (5.8) years.

Females (58%) and African Americans (58%) were predominant.

Characteristics of cases in the treatment groups were similarly distributed at baseline, except that those randomized to the household group were less likely than those in the index group to have Medicaid or no health insurance (P = .02) and more likely to be colonized with MRSA (P = .04) (Table 1).


Primary and Secondary Outcomes S. aureus Eradication at 1 Month Among 147 cases with 1-month colonization data, S. aureus was eradicated from 51% in the household group and 50% in the index group (P = 1.00) (Figure 2).


S. aureus Eradication at 3, 6, and 12 Months At 3 months, cases in the household group had a higher rate of S. aureus eradication (72%) than those in the index group (54%; P = .05); this difference was not significant when adjusting for baseline differences between treatment groups (P = .07).

Eradication rates at 6 and 12 months did not differ between groups (Figure 2).

When stratified by MRSA and MSSA baseline colonization, eradication rates between treatment groups did not differ significantly (data not shown).


Recurrent SSTI in Cases At 1 month, recurrent SSTI was self-reported for 15% of case in the household group and 26% in the index group (P = .12).

Assessment of the cumulative recurrence of self-reported SSTI revealed that cases in the household group were less likely than those in the index group to report recurrent SSTI after 3 months (28% vs 47%; P = .02), 6 months (38% vs 61%; P = .008), and 12 months (52% vs 72%; P = .02) (Figure 3A).

Recurrent SSTI rates did not differ when stratified by baseline MRSA versus MSSA infection (data not shown).

Of 175 self-reported recurrent SSTIs in 90 cases, medical care was sought for 117 (67%) SSTIs.

Of these, 56 (48%) cases reported requiring a drainage procedure, and 93 (79%) reportedly were prescribed antibiotics.

Of the 117 SSTIs for which cases reported seeking medical care, 107 (91%) were confirmed upon review of medical records.

At 1 month, recurrent SSTI was documented by a physician in 13% of cases in the household group and 19% of cases in the index group (P = .38).

Cases in the household group were less likely than those in the index group to have a physician-documented recurrent SSTI after 3 months (15% vs 34%; P = .007), 6 months (22% vs 43%; P = .008), and 12 months (36% vs 55%; P = .03) (Figure 3B).

In multivariable analyses, adjusting for baseline differences between treatment groups (insurance and MRSA colonization status) did not change the effect size of the relationship between treatment group and recurrent SSTI in cases (Figure 3A and 3B).


SSTI in Household Contacts Household contacts of cases in the household group were less likely than those in the index group to report SSTI at 1 month (2% vs 7%; P = .005), 3 months (4% vs 10%; P = .01), and 6 months (9% vs 16%; P = .04).

At 12 months, a trend remained toward decreased SSTI among household contacts in the household group (16% vs 22%, P = .10) (Figure 4).


Protocol Adherence No serious adverse events were reported.

Of 162 (88%) cases providing information, 35 (22%) reported side effects, including dry skin (23 [14%]), rash (9 [6%]), and hives (3 [2%]).

Most parents of cases (85%) found the protocol easy to implement.

Parents perceived chlorhexidine body washes to be the easiest study component, whereas washing towels after each use was perceived to be the most difficult.

Cases were considered adherent to the decolonization protocol if they used mupirocin twice daily and chlorhexidine daily for ≥5 days and followed ≥3 hygiene recommendations.

Duration of use of study medications by cases ranged from 0 to 30 (median, 5) days.

The majority (131 of 162 [81%]) of cases were adherent with the protocol (index group, 69 of 84 [82%]; household group, 62 of 78 [80%]; P = .69).

In the household group, 74% (220 of 299) of the household contacts performed decolonization measures for ≥5 days as prescribed.

Parents and siblings (77%) were more likely to be adherent than other household members (63%; P = .03).

Twenty-nine (10%) of 295 household contacts in the index group (instructed not to perform decolonization) reported using the study materials.


DISCUSSION While data are emerging regarding the effectiveness of regimens to eradicate S. aureus from patients in community settings, the optimal approach to decolonization remains unclear [20, 25–27].

Community-associated S. aureus is a disease affecting households; in the present study, we evaluated an individual versus household approach to S. aureus eradication.

We found that S. aureus eradication from cases did not differ between treatment groups over 12 months.

Interestingly, we found that a household decolonization approach resulted in fewer subsequent SSTIs in both cases and their household contacts.

The finding of reduced SSTI despite no difference in eradication rates between treatment groups is surprising given the fact that endogenous S. aureus colonization is a predictor of nosocomial infection [28–30].

However, in the community setting, other factors may contribute to S. aureus pathogenesis and SSTI development [31].

Some authors propose that skin-to-skin and skin-to-fomite contact may play a greater role in S. aureus pathogenesis than does endogenous colonization [31].

This model of transmission is supported by the observation that not all patients with S. aureus SSTI are colonized with S. aureus [31, 32].

In the present study, 20% of screened patients with confirmed S. aureus SSTI were not colonized with S. aureus in the nares, axillae, or inguinal folds.

Furthermore, while many patients with SSTI are S. aureus carriers, an endogenous colonizing strain may not be the strain causing disease.

In a study of pediatric patients with S. aureus SSTI and nasal colonization, only 59% were colonized with a strain concordant with the strain recovered from the infection site [6].

This suggests that acquisition of a new strain (via person-to-person contact or from environmental surfaces) results in symptomatic infection.

A better understanding of the molecular epidemiology of community-associated S. aureus colonization and infection is essential to developing interventions that will improve patient outcomes.


The household decolonization data presented here can inform a common clinical practice that had been previously unstudied.

In a 2006 survey of infectious diseases consultants inquiring about strategies to prevent recurrent community-associated SSTIs, approximately half the respondents reported prescribing decolonization measures for all family members, while 4% treated only colonized family members [19].

The effectiveness of decolonization measures in reducing SSTI might be similar if measures were targeted only at S. aureus carriers; such an approach would decrease the burden and cost of performing these measures.

However, culturing all household members may not be practical in a community setting.

If decolonization measures are prescribed for patients with recurrent SSTI, the findings of this study support an approach directed at all household members.


This study has several limitations.

We did not directly observe the performance of decolonization measures by participants, although 81% of cases and 74% of household contacts asked to perform the protocol reported adherence with prescribed measures.

Additionally, 10% of household contacts not assigned to the protocol used the index case’s materials.

Although we recognize the limitations of self-reporting an SSTI, our conclusions are also borne out by the analysis limited to physician-documented SSTIs.

The decolonization regimen selected for this trial (intranasal mupirocin and chlorhexidine body washes) was selected based on data from healthcare-based trials [17, 18, 33] and the popularity of this regimen among infectious disease practitioners for patients in the community [19].

Bleach and chlorhexidine-impregnated cloths are alternatives for decolonization [34–36].

Finally, because we hypothesized that the effect size would be greater earlier in the follow-up period (within the first 3 months), we modeled the outcomes individually at each time point; alternative modeling strategies (eg, generalized hierarchical linear modeling) might also have been considered.


This study has unique strengths.

Though nasal screening is frequently the sole means used to detect S. aureus colonization [37–40], other sites [38, 41–43] may represent important niches for S. aureus colonization.

We sampled 3 body sites that yielded important data on the distinct colonization patterns of community-associated S. aureus [22, 31].

Indeed, nearly one-third of colonized cases and household contacts would not have been detected had only the nares been sampled.

Also, due to the breadth of age, race, household size, and geographic distribution of our participants, these results may be generalizable to other populations affected by community-associated S. aureus.

The ongoing community-associated S. aureus epidemic is a significant public health burden that affects millions of individuals.

This entity is particularly challenging within households, where the reservoir of S. aureus may perpetuate ongoing disease.

At present, we lack effective validated methods to prevent these infections [20].

This is the first randomized trial of S. aureus decolonization strategies in the community setting to demonstrate a reduction in the burden of SSTI [25–27].

However, this study is only a first step in demonstrating that a household approach can decrease the impact of S. aureus, as >50% of cases in the household group reported recurrent SSTI over a 1-year period.

Further studies elucidating the roles of household colonization pressure and household environmental contamination in community-associated S. aureus transmission and disease are needed to guide subsequent trials of optimized strategies for interrupting staphylococcal transmission.


Community-associated Staphylococcus aureus skin and soft tissue infections (SSTIs) are a common reason healthy and immunocompromised persons seek medical attention [1, 2].

In the United States, the emergence of community-associated methicillin-resistant S. aureus (CA-MRSA) infections has dramatically shifted SSTI epidemiology.

Visits to US physician offices, hospital outpatient departments, and emergency departments for SSTIs increased by 50% between 1997 and 2004 [3], and hospital admissions increased by 29% during 2000–2004 [4].

These increases appear to be driven by the dramatic increase in CA-MRSA infections [3, 4].

One notable aspect of CA-MRSA skin infections is their predilection to cause subsequent disease.

Recurrent SSTIs are frequently encountered in patients with community-associated S. aureus and CA-MRSA [1], and recurrence rates exceeding 50% have been described in selected populations [5].

Unfortunately, there are few data to guide clinicians and patients on optimal methods for prevention.


In this issue of Clinical Infectious Diseases, Fritz et al [6] present results of a randomized clinical trial on prevention of community-associated S. aureus SSTI infection.

The investigators randomized children with S. aureus infection plus S. aureus colonization in the nares, axilla, or inguinal folds into a 5-day decolonization regimen that targeted either the infected child alone or the entire household.

The investigators exploited the knowledge that many households’ members are also colonized with the infecting isolate [7] and thus may serve as reservoirs for pathogenic S. aureus strains.

Results of this trial are intriguing.

First, although there were no significant differences in colonization eradication of case (index) subjects between treatment allocation groups (54% vs 66%; P = .28), self-reported infection rates differed between groups (72% in the index group vs 52% in the household decolonization group; P = .02).

Second, the regimen under study was brief (5 days), and household implementation was described as easy and relatively well tolerated.


Like any important clinical trial, findings from Fritz et al [6] raise more questions than they answer.

To frame these issues, it is useful to look at the findings in the context of the clinician or patient faced with community-associated S. aureus infection, specifically: Is decolonization of S. aureus after S. aureus infection needed?

Interestingly, the study design of Fritz et al does not answer the key question: Does S. aureus decolonization prevent subsequent S. aureus infection?

At this time, there are no data from randomized clinical trials showing that decolonization reduces recurrent infections compared with no decolonization effort.

We still lack clear evidence to demonstrate the efficacy of S. aureus eradication of the index child compared with no decolonization.

Despite the lack of an untreated control group, the finding that decolonization of the household unit was more effective at preventing infections than was decolonization of just the index patient is important and supports the concept that decolonization may be an effective tool to prevent recurrent infection.


During the physician-patient dialogue on S. aureus decolonization, the prudent clinician should recommend the regimen described by Fritz et al as a safe and well-tolerated protocol.

Other decolonization regimens, for example, bleach baths [2], remain unproven.

We must remind ourselves that patients and parents in this investigation constituted a relatively motivated group that agreed to participate in a 12-month trial involving the entire family.

Not all households may be so motivated.

Offering this regimen after a single infection may be futile unless the family is highly motivated.

We must also remind ourselves and our patients that even among those randomized to household decolonization, 52% of index subjects reported recurrent disease.


Do I need to check for colonization prior to household decolonization?

The investigators found that only 80% of children with S. aureus SSTIs were colonized with S. aureus and therefore eligible for trial participation.

They also correctly note that even if colonization is found, more than one-third of subjects could be colonized with strains different from the infecting isolate.

Thus, this trial does not address whether index patients without detected S. aureus colonization would benefit from household decolonization.

Trial findings suggest that before considering decolonization, clinicians should screen the nares, axilla, and inguinal folds for S. aureus colonization.

( If the index patient had MRSA, then testing solely for MRSA would likely be sufficient.) If colonization is detected, then motivated, willing families should be offered decolonization.

If colonization is absent, clinicians must convey that decolonizing household members may result in no benefit.


What about adults with S. aureus infection?

Adults are not just big children.

Their relative independence leads them to behaviors and exposures that may result in acquisition of S. aureus infections in different manners than children.

The relatively low proportion of MRSA-colonized patients with MRSA infection in some investigations argues that adults may be acquiring pathogenic isolates from fomites or other persons [8].

Thus, it is unclear whether results of this trial would be applicable to adults.

If the regimen is offered to households of index adults, we must be honest with our patients and let them know that this regimen’s efficacy, while promising, is unproven.


Given the low risk of the decolonization regimen, is there any downside to household decolonization?

The intervention used by Fritz et al, although relatively easy to use, is far from perfect.

The investigators did not quantify whether infecting or colonizing isolates developed mupirocin or chlorhexidine resistance.

Although the likelihood of colonized patients’ S. aureus isolates developing mupirocin resistance during the therapeutic regimen described by Fritz et al is probably very low [9], increasing use of mupirocin could drive the prevalence of mupirocin-resistant S. aureus higher, which may in turn lead to treatment failures.

Therefore, we must remain judicious in our use of this agent in medical institutions and the outpatient setting.

Fortunately, chlorhexidine resistance appears to be rare [10]; however, our understanding of the mechanism and its prevalence is in its infancy.

In addition, although well tolerated, chlorhexidine—including the product used for the trial (Hibiclens)—is complicated to use optimally.

Patients need to avoid getting the product in open wounds and in mucous membranes.

The product works best when it is left on for many hours and is easily inactivated by soap, creams, and lotions [11].

Washing clothes and linens with residual chlorhexidine using bleach containing detergents will cause staining.

Thus, prescribing chlorhexidine requires considerable patient education.

Fortunately, materials on proper use are available on the Internet [12], but practitioners need to familiarize themselves with proper use of both mupirocin and chlorhexidine and provide detailed verbal and written materials on optimal use.


So is current evidence limiting us to just recommend decolonization to children with S. aureus infections known to be colonized with S. aureus and willing to have their household use this regimen? From an evidence-based medicine perspective, the answer is yes. That said, the burden of suffering from recurrent S. aureus infections is substantial. And we owe it to our patients to make the best recommendations on prevention on the basis of our contemporary understanding of the literature available to us.

If an adult suffering from recurrent S. aureus SSTI asks for help preventing subsequent infections, it would be a disservice not to counsel the patient about the pros and cons of decolonization therapy.

But we need to acknowledge that we do not know whether this regimen works and that there may be adverse effects.

Given the inordinate amount of physical and psychological suffering caused by S. aureus infections [13], the knowledge that something promising but unproven can be tried may provide some comfort.

Hopefully, in the future, more definitive trials on community-associated S. aureus infection prevention in adults and other populations will be able to provide additional key critical evidence about prevention of this very common infection.


The Global Navigation Satellite System (GNSS) receivers, which are widely distributed on the Earth's surface, are very useful for monitoring changes in the ionosphere.

The ionosphere is the largest source of error in signal propagation from GNSS satellites to single-frequency GNSS receivers.

Accurately estimating the total electron content (TEC) in the ionosphere can improve the position accuracy for the GNSS users.

GNSS TEC can be obtained easily from the geometry-free linear combination (P1-P2 or C1-P2) using data of different frequencies.

However, GNSS satellites and ground receivers have their own hardware biases, which act as large sources of error in calculating the ionospheric TEC.

These hardware-associated biases still remain in the ionospheric TEC after subtracting the measurements at two different frequencies.

These differences in hardware biases inherent in different GNSS code measurements are called differential code biases (DCBs).

Methods for estimating the DCB have been reported in many studies [1-9].


The GNSS satellite and receiver DCB (rDCB) values are different for each satellite and receiver.

In general, the DCBs of the GNSS receivers are larger than those of the GNSS satellites.

GNSS satellites have DCB values of several nanoseconds (ns), while those of GNSS receivers can reach up to a few tens of ns.

Wilson and Mannucci [10] demonstrated that global positioning system (GPS) satellite DCBs have long-term stability with a root mean square (RMS) error of approximately 0.2 ns.

Hernandez-Pajares et al. [ 11] showed that GPS satellite DCBs will remain in the range of several ns for about 10 years.

They also observed some periodic behaviors in them, and suggested that these could be related to the ionospheric TEC.


GNSS DCBs are affected by various factors, such as data quality, estimation method, receiver type, and antenna characteristics.

The diurnal variations of the rDCB can differ depending on the temperature of the hardware inside the receiver [12-15].

Mannucci et al. [ 16] suggested that the rDCB value can be more stable and more accurate when the ionospheric activity is in a somewhat quiet condition than when it is in a disturbed condition.

Furthermore, Zhang et al. [ 17] reported that geomagnetic conditions can affect the accuracy of rDCB values.

They noted that there was a difference in rDCB between disturbed and quiet geomagnetic conditions.

Recently, Choi and Lee [18] also reported that the rDCB can change significantly depending on the grounding status of the antennas at GNSS sites.


The International GNSS Service (IGS) has been providing reliable Global Ionosphere Map (GIM) products since 1998 [19,20].

There are several international associate analysis centers (IAACs) such as the Center for Orbit Determination in Europe, the Jet Propulsion Laboratory, the European Space Operations Center of the European Space Agency, the Technical University of Catalonia, and the GNSS Research Center of Wuhan University.

Typically GIM products are provided on a daily basis by each IAAC and IGS.

In addition, they contain DCBs for the GNSS satellites and the IGS stations.

DCBs and their associated RMS values are available in the daily GIM products.


The variability of DCBs related to ionospheric TEC changes has been reported by previous studies.

For example, Zhang et al. [ 21] reported that DCBs are related to ionospheric variability.

Considering the basic ionospheric approximation in the DCB estimation approach, they suggested that the day-to-day variation of GPS satellite DCBs is associated with the strength of the ionospheric day-to-day variation.

Themens et al. [ 22] investigated that the behavior of GPS rDCB in the high-latitude region shows seasonal and temperature variability.

In addition, ionospheric gradients at middle- and low-latitude regions can generate seasonal and solar-cycle variations of rDCB [23,24].

However, a few case studies of DCB variability related to ionospheric change have been conducted in low-latitude or high-latitude regions.


In this study, we focus on the relationship between the stability of rDCB and ionospheric TEC change with solar activity during a period of three years from 2014 to 2016.

This period corresponds to solar cycle 24, and has a gradual decrease in the number of sunspots.

The stability of the rDCB is analyzed in terms of rDCB RMS value, which can be obtained from the European Space Agency hourly rapid GIM (EHRG) products.

Using the EHRG GIMs, we investigate vertical total electron content (VTEC), rDCB, and rDCB RMS values for the GNSS stations in three different latitude regions.

In addition, correlation coefficients are considered to measure the linear relationships between pairs of variables (VTEC and rDCB, VTEC and rDCB RMS).


EHRG GIM products are generated on a daily basis using data from about 220 GNSS stations of the IGS.

They are modeled using the ionospheric model, based on spherical harmonic expansion up to degree and order 15, which are provided by the IGS.

GIM products can be downloaded via the Crustal Dynamic Data Information System server (ftp://cddis.gsfc.nasa.gov/).

As of 2014, EHRG GIM products provided in IONosphere EXchange (IONEX) format have the vertical TEC (VTEC) with a temporal resolution of 1 hour, and a spatial resolution with 2.5 • in latitude and 5 • in longitude, respectively. They also include the P1-P2 DCB for all GPS satellites, and rDCB for ground stations for each day.


Ionospheric TEC can be calculated from the geometry-free linear combination using dual-frequency GNSS measurements.

The electron density in the ionosphere along the line of sight from a satellite to receiver is referred to as TEC.

It can be directly calculated using Equation (1).


where STEC represents the slant TEC.

P 1 and P 2 are GNSS pseudoranges.

f 1 and f 2 are the frequencies of GNSS signals.

d s and d r are GNSS satellite DCB and receiver DCB, respectively.

To estimate the GNSS satellite and receiver DCBs, constraint condition is required to separate the satellite and receiver DCBs.

It is assumed that the sum of all available GNSS satellite DCBs is zero.


In general, GIMs use the spherical harmonic expansion method to derive the spatial distribution of ionospheric TEC.

The spherical harmonic expansion technique is shown in Equation (2) [25,26].


where, VTEC is the vertical TEC, β and s are the geocentric latitude and solar-fixed longitude of ionospheric pierce points, respectively.

N denotes the maximum degree of the spherical harmonic expansion.

P nm represents the normalized associated Legendre function with the degree n and the order m. C nm and S nm are the TEC unknown coefficients of spherical harmonics.

F is a slant factor.

STEC is converted to VTEC by a slant factor.


The sunspot number associated with solar activity was analyzed to investigate the relationship between the stability of rDCB and changes in the ionospheric TEC.

Figure 1 shows the daily sunspot numbers during the period from 2011 to 2016.

Data on sunspot numbers is available at http: //www.sidc.be/silso/ssngraphics.

In Figure 1, the yellow solid line indicates the daily sunspot number and the red solid line represents the smoothed daily sunspot number with sinusoidal harmonic curve fitting.

Within solar cycle 24, solar activity reached its maximum in April 2014 and has weakened in the number of sunspots since then.

In the present study, we consider ionospheric TEC changes and rDCB stability in a period of weakening solar activity from 2014 to 2016.


Figure 2 shows the locations of the six GNSS reference stations selected to investigate the relationship between ionospheric TEC and rDCB, and between ionospheric TEC and rDCB stability.

The locations of GNSS reference stations are considered in terms of three latitude zones: low latitude (20 • S-20 • N), mid-latitude (20 • S-60 • S, 20 • N-60 • N) and high latitude (60 • S-90 • S, 60 • N-90 • N), respectively.

There are two GNSS stations in each latitude zone.

Table 1 shows the receiver and antenna types at the six GNSS reference stations from 2014 to 2016.

The IGS network consists of more than 500 GNSS stations distributed around the world.

All selected stations in this study are included in the IGS network.

We used two major considerations when selecting these GNSS stations.

First, the GNSS receivers and antennas should not be replaced during the three years from 2014 to 2016.

Second, the DCBs should be reliably provided with daily values in GIM products.

In addition, different GNSS receiver models were considered to exclude receiver-type dependency.

Other GNSS sites, except for NKLG and WILL, are equipped with different receivers as listed in Table 1.


Figure 3 shows the time series of vertical TEC units (VTEC) (top panel), rDCB (middle panel), and rDCB RMS (bottom panel) for each GNSS station from 2014 to 2016.

The amplitude of the VTEC values at all stations (DGAR, NKLG, GRAS, WILL, MAW1, and THU2) decreased gradually with solar activity.

The rDCB RMS values are multiplied by the number 50 because their values are too small.

Therefore, we can see clear variations in the rDCB RMS*50 time series.


Figure 3a,b show the time-dependent changes of the above quantitative variables at the DGAR and NKLG stations, which are located at the Earth's equatorial region.

The data curves at mid-latitudes (GRAS and WILL) and higher latitudes (MAW1 and THU2) are shown in Figure 3c-f.


The most dominant feature in the VTEC, rDCB, and rDCB RMS*50 time series is the annual frequency component.

A strong semiannual component is also observed in low- and mid-latitude regions.

Zhao et al. [ 27] investigated TEC climatology from the global ionosphere maps; they reported that the semiannual behavior of the ionospheric TEC is more significant in low-latitude regions than in high-latitude regions, which is consistent with our results.


As a matter of fact, the amplitude of VTEC at MAW1 in the Southern Hemisphere is higher than that at THU2 in the Northern Hemisphere.

Therefore, at high-latitudes, the rDCB does not seem to be significantly correlated with TEC variations.


A polynomial fitting method with 10 degrees is considered to show clear periodic features in the data time series.

As shown in Figure 3, the magnitude of the VTEC is normally larger in low-latitude regions, while it is smaller in higher latitude regions.

The effect of solar activity on ionospheric TEC can be stronger at low latitudes than at mid- or high-latitudes.

At all stations, periodic variations in the rDCB and rDCB RMS*50 time series are similar to changes in the VTEC time series.


To investigate the relationship between the two different variables, a Pearson correlation was considered.

The Pearson correlation is the most widely used to find the linear relationship between normally distributed variables.

Therefore, Pearson's coefficient was used to calculate the linear relationship between two variables according to the following Equation (3).


where µA and σA are the mean and standard deviation of the variableA, respectively.

µB and σB are the mean and standard deviation of the variable B. The index, R, which indicates the correlation between the two variables, is calculated by the following Equation (4).


Since the variables A and B always have a direct correlation to themselves, the diagonal elements ρ(A, A) and ρ(B, B) of the matrix are necessarily equal to one.

R is simply defined as ρ(A, B).

Several approaches have been suggested to interpret the correlation coefficient with respect to different cutoff points.

In this study, we choose a classification proposed by Schober et al. [ 28].

They categorised the absolute magnitude of the correlation coefficient into five categories as follows: 0.00-0.10 (negligible correlation), 0.10-0.39 (weak correlation), 0.40-0.69 (moderate correlation), 0.70-0.89 (strong correlation), and 0.90-1.00 (very strong correlation).


Figure 4 shows different correlation coefficients between rDCB and VTEC, and between rDCB RMS and VTEC, respectively.

In Figure 4a-f, the upper panels show the correlation between rDCB and VTEC, while the lower panels show the correlation between rDCB RMS and VTEC.

The value of R between rDCB and VTEC ranges from 0.33 to 0.78, while R between rDCB RMS and VTEC varies from 0.50 to 0.94.


Two GNSS stations (DGAR and NKLG) in the equatorial regions showed a positive linear relationship between rDCB and VTEC.

Their R values were 0.33 and 0.40, respectively.

According to the classification defined by Schober et al. [ 28], the DGAR and NKLG stations can be interpreted as having a weak or moderate positive correlation between the two variables.


On the other hand, rDCB RMS*50 and VTEC are highly correlated.

As shown in Figure 4a,b, the R values for this correlation are 0.87 and 0.94, respectively.

In mid-latitude regions (GRAS and WILL), the R values between VTEC and rDCB are 0.78 and 0.73, respectively.

This indicates that there is a strong correlation between the two variables in mid-latitude regions.

In addition, the relationship between VTEC and rDCB RMS*50 at mid-latitudes was investigated.

The R values for this correlation were 0.60 and 0.68, respectively.

We can see that there is a clear difference between these variables in the low-latitude and mid-latitude regions.


The correlation between ionospheric TEC and rDCB stability in low-latitude regions is higher than that in mid-latitude regions.

Conversely, the correlation between ionospheric TEC and rDCB is relatively higher in mid-latitude regions.

To investigate the latitudinal dependency of the correlation in more detail, we considered two additional GNSS stations (MAW1 and THU2) in high-latitude regions.

As shown in Figure 4e,f, the two stations show different correlations.

At MAW1 station in the southern hemisphere, the correlation between VTEC and rDCB was slightly smaller than that between VTEC and rDCB RMS.

In contrast, the THU2 site in the highest latitude of the northern hemisphere, the correlation (R~0.64) between VTEC and rDCB was a little larger than that (R~0.50) between VTEC and rDCB RMS.


As a result, correlations between VTEC and rDCB RMS are closely associated with a latitudinal dependency.

R between VTEC and rDCB is the smallest at low-latitude regions.

The mid-latitude regions exhibit the maximum value of R. Ionospheric TEC variability in mid-latitude is generally less irregular compared to that in low and high latitudes.

The estimation of rDCB at mid-latitude is also expected to be stable.

It can mainly be associated with the effect of ionospheric irregularities like rapid plasma fluctuation, which is known as ionospheric scintillation.

Ionospheric irregularities occur more often in low- and high-latitudes than in mid-latitudes [29].

They can affect the amplitude and phase of GNSS signals rapidly and seem to degrade the accuracy of rDCB.

Therefore, the correlation between VTEC and rDCB itself at mid-latitude may be relatively higher than that at low and high latitudes.

On the other hand, R between VTEC and rDCB RMS*50 becomes smaller on average in high-latitude regions.

The peak value of R is found in low-latitude regions.

The latitude-dependent tendency for these correlations is clearly seen in the bottom panels of Figure 4a-f.


To further analyze the latitudinal dependency of the correlation between VTEC and rDCB RMS in more detail, data from the 7 additional GNSS stations were examined.

The receiver and antenna types at the 7 additional GNSS stations from 2014 to 2016 are listed in Table 2.

Figure 5 shows the locations of the 13 GNSS stations and the R values between VTEC and rDCB RMS for three years.

The numbers in parentheses in Figure 5 represent the R values.

We can also see the apparent latitudinal dependency from the R values of the 13 GNSS stations.


The three GNSS stations (NKLG, DGAR, and DARW) at low latitudes have large R values above 0.86.

In particular, as the latitude increases from the equator, the R value decreases slightly.

There are 6 GNSS stations (WILL, GRAS, JPLM, ULAB, SUWN, and CORD) in mid-latitude regions.

Their R values range from 0.6 to 0.8.

The averaged value of R is approximately 0.69, which is smaller than that on average in low-latitude regions.

The four GNSS stations (THU2, SCOR, MAW1, and CAS1) at high latitudes have weak correlations compared to those at low and mid-latitudes.

The R value ranges from 0.49 to 0.61.


Interestingly, the R values in high-latitudes of the Southern Hemisphere were greater than those in high-latitudes of the Northern Hemisphere.

R between VTEC and rDCB RMS can be associated with the magnitude of TEC following the latitudinal dependency.

As shown in Figure 3e-f, the magnitude of TEC is higher at the Southern Hemisphere compared to the Northern Hemisphere during three years from 2014 to 2016.

Therefore, the asymmetry of the ionosphere in different hemispheres can affect R between VTEC and rDCB RMS.


The ionosphere over equatorial and low-latitude regions can be strongly influenced by phenomena such as daytime spread-F, equatorial ionization anomalies, and intense irregularities in TEC distributions.

These effects in the equatorial and low-latitude regions can affect GPS signals passing through the ionosphere.

In particular, the dynamically changing ionosphere in the equatorial regions may have a large effect on the stability of the receiver DCB.

Therefore, a strong ionospheric activity might induce a high correlation between TEC and receiver DCB stability.

In high-latitude regions, however, the intense ionospheric TEC irregularities are presumed to be caused by auroral activities and high-speed plasma convection [30].

The magnitude of TEC at high latitudes is about three times smaller than that at low latitudes [31].

The correlation between TEC and rDCB in low-latitude regions is smaller than that in high-latitude regions.

On the other hand, the amplitude of TEC decreases with higher latitudes.

This may also be associated with rapid TEC changes, or to the magnitude of TEC.


There was a high correlation between TEC and rDCB stability at low latitudes.

For a better analysis of these characteristics, we considered two GNSS stations (BOGT and KOUR) located in South America, as shown in Figure 6.

The geographic latitudes of BOGT and KOUR stations at the equatorial region are about 4.64 and 5.25 degrees, respectively.

For three years from 2014 to 2016, the BOGT and KOUR sites had operated GNSS receivers (JAVAD TRE_3 DELTA and SEPT POLARX4) of different type models.

The rDCB may be different depending on the receiver type.

Even the same receiver models have different rDCB values [18,30].

Therefore, it may be important to investigate the correlation in the GNSS stations with different receiver models.

Figure 6b shows the time series of the VTEC and rDCB RMS*50 at BOGT (top panel) and KOUR (lower panel), respectively.

The blue solid line denotes the changes in VTEC for three years from 2014 to 2016.

Purple circles represent the changes in the rDCB RMS*50.

The linear correlation between those variables is displayed in the upper right corner of each panel.


The VTEC values have been decreasing gradually since 2014, as seen in the upper panel of Figure 6b.

The maximum value of the TEC peaked in 2014, and reached its minimum value at the end of 2016.

We can also observe a clear annual and semi-annual variation of the VTEC.

The VTEC was higher at the equinoxes than at the solstices during the period from 2014 to 2016.

The magnitude of TEC was highest at the March equinox during this period, and lowest at the June solstice.


This is fully consistent with the results reported by Akala et al. [ 32].

They attribute to increasing in ion production rate in the equinox and low ionization resulting from reduced production rate in the solstice during this period.


As shown in the upper panel of Figure 6b, the changes in rDCB RMS were very similar to the changes in VTEC.

We calculated that the value of R between VTEC and rDCB RMS was about 0.90.

From the results in the bottom panel of Figure 5b, the KOUR station with R of 0.91 shows very similar features to the BOGT station.

Changes in VTEC could be similar because the two stations have similar latitudes and are located close to each other.

It is noted that although the two stations use different GNSS receiver models, the changes in rDCB RMS are similar.

This suggests that the ionospheric TEC at low latitudes and equatorial regions is strongly related to the stability of the rDCB.


In addition, we have analyzed the changes in rDCB and rDCB RMS as the model of the GNSS receiver is changed.

Figure 7 shows the time series of rDCB and rDCB RMS at the ALIC GNSS station (GLAT: 23.7 • S, GLON: 133.8 • E) from 2014 to 2016.

The red solid lines represent the polynomial-fitted curve of the data.


As shown in the upper panel of Figure 7, the rDCB values have been stable since 2014 with only small changes.

However, they show a large discrepancy in early 2016.

This is due to the replacement of the GNSS receiver.

In Figure 7, the blue vertical dotted line indicates when the receiver was replaced.

The large change in the rDCB is exactly in line with receiver replacement.

The GNSS receiver in the ALIC station was changed from the 'LEICA GRX1200GGPRO' to the 'LEICA GR25 model in February 2016.

The rDCB value of the ALIC station changed from about 12 to 21 ns with the receiver replacement.


The lower panel of Figure 7 shows the rDCB RMS time series for three years.

The rDCB RMS reaches its maximum value in early 2014, and then tends to decrease gradually.

In addition, rDCB RMS has periodic and time-varying features.

It is noteworthy that the rDCB RMS of the ALGO station did not change significantly despite the replacement of the receiver in early 2016.

Changes in the rDCB RMS, even after receiver replacement, have continuity.

This indicates that the stability of the rDCB can also be independent of the GNSS receiver model.


To investigate the correlation between VTEC changes and rDCB stability before and after replacing the receiver, we plotted the time series of VTEC and rDCB RMS*50 for the same period of time.

In Figure 8, the blue solid line and the brown solid line represent the VTEC and rDCB RMS changes, respectively.

They represent a polynomial fitting of the data.

The time series of the two variables show similar features, such as periodicity and damping.

R between the two variables is about 0.92, which means that there is a very strong correlation.

Therefore, we found that the ionospheric TEC and rDCB stability have a very strong correlation even after the replacement of the receiver.


According to Kao et al. [ 33], there are various factors affecting GNSS rDCB estimation accuracy.

They demonstrated that the estimation accuracy of rDCB is lower in the low-latitude regions because TEC changes in low-latitude regions are more complicated than those in mid-latitude regions.

In this study, we showed that the values of R between TEC and rDCB RMS were also larger in low-latitude regions than those in mid-latitude or high-latitude regions.

Besides, this may be explained that rDCB stability is poor due to the complex structure of ionospheric TEC in low-latitude regions.

It can affect the accuracy of ionospheric TEC estimation.

Eventually, the estimation of real-rDCB can increase the accuracy of TEC estimation, which will contribute to improving the position accuracy of the GNSS users.


In this study, we have investigated the relationship between VTEC and rDCB, and VTEC and rDCB stability, during the three years from 2014 to 2016.

Pearson's linear correlation values were considered in order to quantify the relationships between the variables.

In addition, because the magnitude of the GNSS TEC has a latitudinal dependency, we used data from 13 GNSS stations located in low-, middle-, and high-latitude regions.


The time series of VTEC, rDCB, and rDCB RMS at each GNSS station from 2014 to 2016 were analyzed.

We could see clear periodic features in time series at different latitudes.

The most prominent features were the annual and semiannual variations.

The semiannual variations were more significant in low-latitude and mid-latitude regions than in high-latitude regions.


In addition, these characteristics of rDCB and rDCB RMS have a similarity with those of VTEC, at all stations.

By calculating R between the variables, it can be shown that the correlation between TEC and rDCB is the smallest in low-latitude regions.

The mid-latitude regions exhibit the maximum value of R. This can be related to ionospheric TEC irregularities with different latitude regions.


In contrast, R values between TEC and rDCB RMS were greater in low-latitude regions, which can be associated with the magnitude of TEC.

A strong positive correlation (≥ 0.90) between TEC and rDCB RMS was also observed at the two additional GNSS stations in low-latitude regions.

This suggests that the correlation between the two different variables has a clear latitudinal dependence.


The rDCB values showed large differences before and after replacing GNSS receivers, which is consistent with earlier studies.

The rDCB RMS has periodic and time-varying features, and is not changed by the replacement of the receiver.

Changes in the rDCB RMS have continuity even after receiver replacement.

R between TEC and rDCB RMS after replacing the receiver was 0.92, indicating a very strong correlation.

Therefore, it can be noted that rDCB RMS is not related to the GNSS receiver model.

As a result, we found that ionospheric TEC and rDCB stability still have a very strong correlation.

Estimation of stable rDCB can contribute to improving the accuracy of the GNSS ionospheric TEC.

Therefore, applying the correlation between ionospheric TEC changes and rDCB stability in different latitude regions to the ionospheric TEC will allow for more accurate monitoring of GNSS ionospheric TEC.


Choi et al. [ 1] analyzed the correlation between the ionospheric total electron content (TEC) and the Global Navigation Satellite System (GNSS) receiver differential code bias (DCB) and concluded that the long-term variations of the receiver DCB are caused by the corresponding variations in the ionosphere.

Unfortunately, their method is problematic, resulting in conclusions that are not useful.

The long-term variations of the Global Positioning System (GPS) DCBs are primarily attributed to the GPS satellite replacement with different satellite block series under the zero-mean constraint condition, rather than the ionospheric variability.


In the ionospheric total electron content (TEC) retrieval, the differential code bias (DCB) caused by the Global Navigation Satellite System (GNSS) satellite transmitter and receiver hardware is one of the main error sources, and it has been widely studied.

Choi et al. [ 1] have recently proposed a new aspect of the correlation between the ionospheric TEC and the GNSS receiver differential code bias (rDCB).

They stated that “the characteristics of rDCB and rDCB root mean square (RMS) have a similarity with those of vertical TEC (VTEC), at all stations”, and they calculated Pearson’s coefficient (R) between the TEC and rDCB and showed that “the correlation between TEC and rDCB is the smallest in low-latitude regions.

The mid-latitude regions exhibit the maximum value of R”.

Supposedly, the GNSS DCB should be related with the transmitter hardware and its environment, as expected.

Studies also showed that the variations of the Global Positioning System (GPS) satellite DCB and receiver DCB are not caused by the ionospheric variability [2,3].

However, Choi et al. [ 1] might have advertently or inadvertently overlooked the work of Zhong et al. [ 2,3].

As discussed subsequently, it does not make sense to utilize the comparison between the ionospheric TEC and the GNSS receiver DCB to demonstrate their cause–effect relationship.

Thus, the correlation analysis between TEC and rDCB as described by Choi et al. [ 1] is not meaningful.


Zhong et al. [ 2,3] have assessed the variations of the GPS satellite DCB and receiver DCB.

Note that in the DCB estimation, only the combined satellite–receiver DCB (the sum of the GPS DCB and receiver DCB) can actually be determined, so that an additional constraint condition is required to further separate the GPS DCB and receiver DCB.

Generally, the zero-mean condition imposed on all satellite DCBs (i.e., the daily mean of all GPS DCBs is set as zero) is introduced for the DCB separation [4].

Figure 1 shows the comparisons of the GPS DCB of Pseudo Random Number (PRN) 13 from the International GNSS Service (IGS) [5] and solar activity proxy F10.7 during 1998–2020.

It seems that the GPS DCB also showed a solar-cycle-like variation [6], if only the period of 2002–2014 is considered.

However, this is not the case for the whole period of 1998–2020.

Figure 2 presents the lifetime of each GPS satellite from 1998 to 2020.

It is clear that the new satellite block series are replacing the old ones gradually.

From the GPS DCB values on 1 January 2015 as displayed at the upper left corner of Figure 2, it is evident that the GPS DCBs from different satellite block series show a significant difference.

Under the zero-mean condition imposed on all GPS DCBs, when a new GPS satellite replaces a decommissioned one with a different block series, the GPS DCB values change relatively to satisfy the zero-mean condition.

Specifically, when a new GPS satellite with higher (lower) DCB values comes into service, all the GPS DCB values descend (increase).

Thus, the long-term variations of the GPS DCBs are not caused by the ionospheric variability, while they are attributed to the GPS satellite replacement with different satellite block series under the zero-mean condition [2].

Correspondingly, the estimated receiver DCB will also change, as the daily combined satellite–receiver DCB is constant.


Note that only after the effect of GPS satellite replacement is removed, the DCB could be used for comparison or periodic spectral analysis.

To remove the effect of GPS satellite replacement, we can reconstruct the DCB value under a new constraint condition imposed only on continuously operating GPS satellites during a specific period.

The new constraint condition for the GPS DCBs is expressed as follows: where DCBs,rec denotes the reconstructed GPS DCB, n is the number of the selected GPS satellites, and C is a constant value.

During the period of 2005–2019, nine continuously operating GPS satellites (PRN 11, 13, 14, 16, 19, 20, 22, 23, and 28) are selected.

For each day, the offset value is calculated based on the mean value of the GPS DCBs of the selected satellites: Then, the reconstructed GPS DCB (DCBs,rec) can be obtained from the original GPS DCB and the offset value as follows: The receiver DCB (DCBr,rec) can be also obtained in the similar way, but with an opposite sign on the offset value: The reconstructed GPS DCB of PRN28 is shown in Figure 3a as an example.

On the long-term scale, the reconstructed GPS DCB only showed a slight variation of less than 1 TECU.

In addition, the reconstructed GPS DCB presented short-term periodic variations, which is less than one year.


The Lomb–Scargle periodograms of the original and reconstructed GPS DCBs are shown in Figure 3b.

It is clear that the original GPS DCB presented multiple periodic components, while some periodic components in the reconstructed GPS DCB became much weaker.

This indicates that DCB reconstruction could filter out the artificial periodic variations.

In addition, it is interesting that there is a prominent periodic component of about 175 days in Figure 3b, which is a little shorter than half of a sidereal year (182.625 days).

Zhong et al. [ 3] demonstrated that the periodic variation of CHAMP satellite receiver DCB is mainly attributed to its hardware thermal status.

Here, we calculated the daily mean solar angle on the zenith side of the GPS satellite to represent its hardware thermal status, as shown in Figure 3c.

The periodogram of the daily mean solar angle in Figure 3d displayed a clear periodic component of about 175 days, which is generally in agreement with that of the GPS DCB.

This period is matched with the period of a GPS draconitic year, which is equal to about 351.5 days.

The GPS draconitic year is the time taken for the Sun to complete one revolution (return to the same point in space) with respect to the GPS orbital nodes, as seen from the Earth [7,8].

In one GPS draconitic year, there are exactly two eclipsing periods, which means that after 175.75 days, the elevation of the Sun above the orbital plane is the same, and the direction of the relative change in the height of the Sun is also the same.

As a result of the Sun illumination and attitude of GPS satellites [9], every 175.75 days the Sun illuminates the same parts (electron circuits) of the GPS satellite in the same manner, which may change the hardware thermal status with the same period and further affect the DCB values.

The periodic component of about 175 days can be seen in DCB around half of the total GPS satellites (not shown), but it is still not clear whether the effect of hardware thermal status on DCB is consistent for different GPS satellite block series.

Many other GNSS-derived parameters are characterized by the draconitic repeatabilities or their harmonics, such as GNSS-based Earth Rotation Parameters [10].

Overall, both the solar-cycle-like and semiannual-like variations of the GPS DCB are artificial and not caused by the ionospheric variability.


Figure 4 shows the original and reconstructed receiver DCBs for station GRAS as an example, as used in Choi et al. [ 1].

Again, the original receiver DCB presented a declined trend from 2014 to 2016, when F10.7 was mainly decreasing.

However, the reconstructed receiver DCB only changed slightly during 2012–2017.

The periodograms showed that the receiver DCB also presents a significant periodic component of about 176 days, which should be associated with the GPS draconitic year, as discussed above.

In addition, there is a significant annual periodic component (365 days).

The short-term variations of the receiver DCB are attributed to various factors, such as the hardware temperature [11].

From the comparison between surface temperature and the reconstructed receiver DCBs from many IGS stations (not shown), it illustrated that when the surface temperature reached its maximum (minimum), some of the reconstructed receiver DCBs also presented the maximum (minimum) values.

Thus, the annual periodic component could probably be associated with the variations of the surface temperature.

However, the manufacturer and model, antenna mounting position, cable construction, and thermal control could be complicated and different for each receiver, so it is difficult to draw a unified conclusion in respect of short-term variations of the receiver DCB.


As discussed above, both the long-term and short-term variations of the GPS DCBs and receiver DCBs are accessed.

Though during specific periods, they were similar to the ionospheric variations by coincidence, they are not caused by the ionospheric variability physically.

In the ionospheric TEC/DCB estimation, it is generally assumed that the ionosphere satisfies the spherically symmetric hypothesis or spherical harmonic expansion distribution.

When the actual ionospheric distributions deviate from the assumption, the TEC measurement used in the DCB determination is subject to a large number of small random errors, and the probability distribution for the total statistical error is driven toward the Gaussian distribution, indicated by higher RMS (lower stability) for DCB.

In TEC/DCB estimation, the larger the ionospheric TEC is, the greater the deviation from the spherically symmetric assumption would be, thus causing higher RMS (lower stability) for DCB, as presented by Choi et al. [ 1].

However, as mentioned above, in the physical sense the ionospheric TEC cannot alter the rDCB, which is essentially subject to the receiver hardware.


In summary, Choi et al. [ 1] concluded arbitrarily that the long-term variations of the receiver DCB are caused by the ionospheric variability on the basis of the problematic method.

They were not aware of the effect of the GPS satellite replacement on the DCB determination.

Overall, the study of Choi et al. [ 1] could introduce confusion and be misleading for the GNSS and the ionospheric communities.


Choi et al. ( 2019) suggested that ionospheric total electron content (TEC) and receiver differential code bias (rDCB) stability have a strong correlation during a period of two years from 2014 to 2016.

This article is a response to Zhong et al. ( 2020), who pointed out that the long-term variations of the GPS DCBs are mainly attributed to the satellite replacement rather than the ionospheric variability.

In this issue, we investigated the center for orbit determination in Europe (CODE) Global Ionosphere Maps (GIM) products from 2000 to 2020.

In this study, changes in TEC and receiver DCB (rDCB) root mean squares (RMS) at Bogota (BOGT) station still have a clear correlation.

In addition, there was a moderate correlation between satellite DCB RMS and rDCB RMS.

As a result, we suggest that rDCB can be affected simultaneously by GPS sDCB as well as ionospheric activity.

The Global Navigation Satellite System (GNSS) Differential Code Biases (DCB) is the largest error source in estimating the total electron content (TEC) of the ionosphere.

Many studies have reported that DCB is affected by a variety of factors [1-6].

There can be significant jumps [7] and other features in DCB related to the replacement of the receiver and antenna.


Choi et al. [ 8] showed that ionospheric TEC and receiver DCB (rDCB) root mean squares (RMS) have a strong correlation during a period of two years from 2014 to 2016.

In addition, they focused on the relation between rDCB stability and ionospheric activity rather than rDCB itself.

However, Zhong et al. [ 9] pointed out that the long-term variations of the GPS DCBs are mainly attributed to the satellite replacement rather than the ionospheric variability.

Interestingly, they also focused on the GPS DCB changes and did not mention the relationship between rDCB stability and ionospheric activity.

As suggested by Zhong et al. [ 9], rDCB and satellite DCB may have a significant correlation.

In this issue, Choi et al. [ 8] suggested that changes in the rDCB RMS still have continuity after the replacement of a receiver.

Replacing hardware such as GNSS receivers or satellites can seriously affect the DCB.

However, large changes in DCB with hardware replacement may be usually instantaneous.

For more reliable analysis of the results presented by Choi et al. [ 8], we investigated the center for orbit determination in Europe (CODE) Global Ionosphere Maps (GIM) data from 2000 to 2020.

Figure 1 shows the time series of vertical TEC units (VTECU), rDCB, and rDCB RMS (Stability) at BOGT station, respectively.

A band-pass filter was applied to isolate specific changes for GIM-TEC and rDCB RMS.


As shown in Figure 1a, the variations of VTEC are closely related to solar activities.

They also show an exact annual and semi-annual cycle.

To analyze the relationship between VTEC and rDCB RMS during this period, we calculated band-pass filtered (0~0.05 Hz) rDCB RMS plotted in Figure 1a as a red solid line.

In CODE’s GIM products, the rDCB RMS values of the BOGT station exist only from 2003 to 2020.

Filtered rDCB RMS showed annual and semi-annual patterns similar to GIM-TEC.

However, there were no regular changes in rDCB RMS from 2006 to 2011, and solar activity was relatively weak during this period.

Nevertheless, changes in VTEC and rDCB RMS appear to have a clear correlation.

Figure 1b shows the time series of GPS rDCB at BOGT station (4.64°N, 74.08°W) from about 2003 to 2020.

A significant jump in rDCB was detected due to receiver replacement.

Changes in rDCB do not show any regular patterns similar to VTEC.

Zhong et al. [ 9] suggested that the variations of rDCB are not caused by the ionospheric variability, which may be consistent with our results.

The blue dashed rectangles “A” and “B” in Figure 1b represent the changes of rDCB in low- and high-ionospheric activity, respectively.

The standard deviations (STD) of rDCB in the two sections “A” and “B” were about 0.48 and 0.80, respectively.

Variations in rDCB can see to be unstable during high ionospheric activity.

On the other hand, they are stable when ionospheric activity is relatively low.

Therefore, rDCB changes can be associated with ionospheric activity.

In addition, as shown in Figure 1c, rDCB RMS values were small and stable during low-ionospheric activity.


Figure 2 presents the time series of GPS satellite P1-P2 DCB (sDCB), band-pass filtered (0~1.5Hz) sDCB, and rDCB RMS from 2000 to 2020, respectively.

Changes in DCB for all GPS satellites are shown in Figure 2a.

In Figure 2a, it is not easy to distinguish specific correlations with ionospheric activity in GNSS sDCB changes.

To analyze the relationship between sDCB and rDCB, we considered the most stable satellites with no PRN changes [7,10].

As plotted in Figure 2a, GPS PRN 28 was selected.

Zhong et al. [ 9] also reported that the ionospheric activity does not cause the long-term variations of the GPS sDCB.

However, sDCBs have relatively large RMS values for solar maximum from about 2000 to 2003, as shown in Figure 2b.

In addition, the filtered sDCB RMS values for GPS PRN 28 show very similar periodic patterns.

These cycles can be coincident with a period of a draconitic year of GPS satellites suggested by Zhong et al. [ 9].

Furthermore, there was a significant jump in changes in GPS PRN 28 sDCB RMS in 2015.

In early 2015, CODE changed a solar radiation pressure (SRP) model from the old empirical CODE orbit model (ECOM) to the new ECOM2 for GNSS orbit determination [11].

A change in the CODE SRP model can cause DCB variations.

For the relationship between sDCB RMS and rDCB RMS, we plotted the band-filtered rDCB RMS as the blue dots in Figure 2b.

There is some correlation between the two variables.

The correlation coefficient (R) between the two variables was about 0.58.

It indicates that there is a moderate correlation between sDCB RMS and rDCB RMS.


Choi et al. [ 8] suggested that the correlation between TEC and rDCB RMS shows the apparent latitudinal dependency with ionosphere variability.

It can be related to TEC distributions.

Zhang et al. [ 12] reported that the accuracy of rDCB estimation is dependent on the receiver’s location.

The rDCB estimation accuracy can be lower in low-latitude regions because TEC distributions at low latitudes are much more complicated than those at mid and high latitudes [13,14].

Strugarek et al. [ 15] reported that the GNSS signal is more affected during the periods of the high ionospheric activity, which causes problems with precise orbit determination of low Earth orbit satellites.

What we emphasize is that it can be difficult to specifically explain the latitudinal dependency of rDCB with changes in GPS sDCB only.

In general, the global ionospheric TEC is modeled by the spherical harmonic function.

To separate the satellite DCBs and rDCBs in ionospheric GPS-TEC estimation, we consider a constraint that the sum of all GPS satellite DCBs becomes zero.

Under this condition, variables such as TEC, satellite DCBs, and rDCBs have to be calculated simultaneously.

Each of the estimated variables can be influenced by each other.

As suggested by Zhong et al. [ 9], there was a clear correlation between sDCB and rDCB.

However, we suggest that variables are not estimated as an independent variable but are correlated.

Therefore, rDCB can be affected simultaneously by GPS sDCB as well as ionospheric activity.


Iron deficiency is one of the most prevalent co-morbid conditions in chronic heart failure.

In the absence of any iron treatment, it is estimated that up to 50% of patients with heart failure have low levels of available iron.

1 The prevalence of iron deficiency is higher in the more advanced stages of heart failure (NYHA class III and IV), in females, and in patients with high levels of inflammatory markers (such as C-reactive protein) as well as increased levels of NT-proBNP, 2,3 but, even in patients at lower risk such as those with NYHA class I or II, the prevalence remains >30%.

1,2 These estimates include both absolute iron deficiency (in which iron stores are low) and functional iron deficiency (in which iron supply is inadequate to meet the demand for erythropoiesis and other cellular functions despite normal or abundant body iron stores).

The two states have different aetiologies.

Various pathological changes in chronic heart failure predispose patients to absolute iron deficiency, which is usually defined as ferritin <100 ng/mL. 4 -6 Poor nutrition is frequent, and patients with concomitant renal disease may be advised to follow a low-protein (and thus low-iron) diet.

7 Low intake of iron can be compounded by physiological and functional changes to the intestinal wall which may impair iron uptake from the gut, including the presence of mucosal oedema, and reduced gastrointestinal blood flow 8 (Table 1).

Patients with heart failure are also at risk of iron loss secondary to gastritis or ulceration caused by concomitant phar- macotherapy, and from proteinuria arising from chronic renal disease.

9,10 Lastly, administration of antiplatelet drugs or anticoag- ulation agents increases susceptibility to bleeding, with consequent blood loss.

11 Functional iron deficiency, in contrast, arises from the generalized inflammatory state which characterizes heart © 2015 The Authors.

European Journal of Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. 249 failure.

Increased levels of proinflammatory cytokines such as interleukin-6 (IL-6) enhance production and release of hepcidin, an acute phase protein synthesized in the liver.

Hepcidin regulates the release of stored iron from enterocytes and hepatocytes by causing degradation of the iron transporter protein, ferroportin.

As a result, high levels of hepcidin can 'trap' iron in storage cells.

6,12,13 In a recent study published by Jankowska et al., the early stages of systolic heart failure were characterized by high levels of circulating hepcidin.

14 However, hepcidin levels declined as the severity of heart failure progressed, and as iron deficiency developed.

Moreover, low hepcidin was independently associated with unfavourable outcome.

14 The authors hypothesized that the pathophysiological mechanisms underlying iron deficiency in heart failure are markedly different from those in chronic inflammatory diseases, a possibility that requires further evidence to be con- firmed.

Functional iron deficiency is usually defined in heart failure as ferritin <300 ng/mL with transferrin saturation (TSAT) <20%.


While iron deficiency is usually identified in the clinic based on levels of ferritin and TSAT, due to their ease of use and wide availability, it should be noted that either inflammation or oxidative stress may artificially increase ferritin concentrations, regardless of actual iron status.

In contrast to other clinical settings, the ferritin threshold defining deficient iron storage is increased to 100 ng/mL in heart failure patients to take account of the chronic inflammatory state.

The recent FAIR-HF 15 and CONFIRM-HF 16 trials achieved beneficial results when ferritin and TSAT were used to define inclusion criteria and thresholds for maintenance treatment, and the European Society of Cardiology (ESC) Guidelines for heart failure 2012 included cut-off values for iron deficiency based on the FAIR-HF study.

17 Nevertheless, diagnosis based on ferritin and TSAT may be inadequate in advanced disease 18 or acute heart failure, 4,19 when ferritin becomes an unreliable marker.

Iron deficiency can therefore be underdiagnosed in these types of patients.

Measurement of soluble transferrin receptor, hepcidin, or reticulocyte haemoglobin (Hb) content may offer a more sensitive assessment of iron status and response to therapy, 4 and these techniques may become more widely adopted in the future.


Iron deficiency is closely associated with low Hb levels in patients with heart failure.

3,20 A large population-based study in Canada has suggested that anaemia of chronic disease predominates, followed by iron deficiency anaemia, 21 but the two types both primarily arise from insufficient iron available for haematopoiesis in the erythrocytes.


Conventionally, attention has focused on the effect of inadequate iron supply on erythropoiesis, and the resulting risk of anaemia.

Awareness is growing, however, of the many haematopoietic func- tions of iron.

Iron is an essential cofactor for haem and non-haem proteins involved in cellular activities such as oxygen storage (as a component of myoglobin), generation of cellular energy (as part of oxidative enzymes and mitochondrial respiratory chain proteins) in skeletal muscle and myocardiocytes, and the synthesis and degra- dation of lipids, carbohydrates, and nucleic acids.

22 In patients with heart failure, it has been shown that the myocardium has a reduced iron content, 23 and it is possible that myocardial iron depletion plays a role in deteriorating systolic function.


Several observational studies have assessed the asso- ciation between iron deficiency and outcomes in patients with heart failure (Table 2).

1 -3,21,24-27 Regardless of whether or not patients had anaemia, or the severity of heart failure, the presence of iron deficiency showed a significant association with mortality after adjustment for confounding factors in most studies.

1,2,24,25 Only in one analysis, a community-based study in the USA, was no correlation found, but in this analysis disease severity (measured with NYHA functional class and NT-proBNP levels) was not assessed.

3 Overall, the increase in mortality risk appears to be in the order of 40-50%.