First Nations, Metis and Inuit health: published tables, distinctions and governance

Whose data these are

Two sets of principles govern Indigenous data in Canada.

• OCAP® comes from the First Nations Information Governance Centre. It stands for Ownership, Control, Access and Possession. It says First Nations own their information and decide how it is collected, used and shared.
• The CARE Principles come from Carroll et al. (2020). They stand for Collective benefit, Authority to control, Responsibility and Ethics, and apply to Indigenous data everywhere.

Walter and Andersen (2013) set out what a quantitative methodology looks like when it starts from Indigenous standpoints. Walter (2016) names the pattern it pushes against: statistics about Indigenous peoples clustered around five Ds, namely disparity, deprivation, disadvantage, dysfunction and difference.

The package follows these principles in six ways.

• Only open aggregate data. The data service mirrors only aggregate tables published under the Statistics Canada Open Licence. It never holds First Nations-held survey data, such as the First Nations Regional Health Survey. Those are reached through the FNIGC, on its terms.
• Distinctions-based. First Nations, Metis and Inuit are reported separately. A pooled “Indigenous” total is marked wherever it appears. dstntst() shows when pooling would hide a difference.
• Off-reserve coverage. The First Nations figures in these surveys are for people living off reserve. Reserves are outside the surveys’ coverage.
• Suppressed cells stay suppressed. A cell is suppressed (x) to protect the people in it, and small communities most of all. F cells are too unreliable to publish. Both become NA, and nothing in the package imputes them or works them back from totals.
• Strengths beside gaps. Wellness is reported beside distress, and frmaud() checks how deficit-framed a report is.
• A gap is not a deficit of the people. A difference between groups measures the effects of colonial policy, the residential schools and unequal services. Report it with that context, and with the communities’ own interpretation where they have given one.

Reading a table as published

A Statistics Canada table puts each estimate and its limits on separate rows. scests() puts them side by side. It reads every layout the Indigenous tables use:

• confidence limits named in any of their forms;
• coefficients of variation, or standard errors, in place of limits;
• several measures per cell;
• tables with no limits at all.

d <- data.frame(REF_DATE = "2022", GEO = "Canada",
                `Indigenous identity` = rep(c("First Nations", "Metis"), each = 4),
                Statistics = rep(c("Number of persons", "Percent", "Low 95% confidence interval",
                                   "High 95% confidence interval"), 2),
                UOM = rep(c("Persons", "Percent", "Percent", "Percent"), 2),
                VALUE = c(415000, 47.1, 45.2, 49.0, 320000, 50.3, 48.1, 52.5),
                STATUS = "", check.names = FALSE)
scests(d)
#>   REF_DATE    GEO Indigenous identity           measure estimate lower upper
#> 1     2022 Canada       First Nations Number of persons 415000.0    NA    NA
#> 2     2022 Canada       First Nations           Percent     47.1  45.2  49.0
#> 3     2022 Canada               Metis Number of persons 320000.0    NA    NA
#> 4     2022 Canada               Metis           Percent     50.3  48.1  52.5
#>       cisou quality
#> 1      <NA>      ok
#> 2 published      ok
#> 3      <NA>      ok
#> 4 published      ok

A mirrored table loads by its id. This needs the data service key, so it is not run here:

indtabs("mental health")
e <- scests("41100067")

Perceived mental health, against non-Indigenous adults

The share of adults who rate their mental health very good or excellent, 2019/2022, from table 13-10-0924-01:

h <- indiex("health")
mh <- h[h$period == "2019/2022" & h$age == "Total, 18 years and over" &
          h$indicator == "Perceived mental health, very good or excellent", ]
g <- indgap(mh, "group", "estimate", "lower", "upper", qual = "quality")
g
#>           group estimate refest     ratio   ratiolo   ratioup difference
#> 1 First Nations     45.8   61.5 0.7447154 0.7001926 0.7920694      -15.7
#> 2         Métis     52.8   61.5 0.8585366 0.8130217 0.9065995       -8.7
#> 3         Inuit     47.8   61.5 0.7772358 0.6785276 0.8903034      -13.7
#>       diflo      difup usewithcau poogrp
#> 1 -18.53593 -12.864070      FALSE  FALSE
#> 2 -11.58531  -5.814692      FALSE  FALSE
#> 3 -20.16568  -7.234321      FALSE  FALSE
plot(g)

Before pooling the three groups into one “Indigenous” figure, test whether they differ:

dt <- dstntst(mh[mh$group != "Non-Indigenous", ], "group", "estimate", "lower", "upper")
dt
#> Distinctions test across 3 groups: Q = 11.97 on 2 df, p = 0.00252, I^2 = 83%
#> Pooled (inverse-variance) 49.11; the groups differ: report each one, not the pooled figure
plot(dt)

The whole scale, not one cut of it

“Very good or excellent” cuts a five-point scale at one place (the table publishes it in four categories, with fair and poor together). ordcmp() compares the whole distribution. It gives the probability that a person in one group rates their mental health higher than a person in the reference group, with ties counted half (Agresti 1980). The data are from the 2022 Indigenous Peoples Survey, table 41-10-0067-01:

r <- indiex("mentalhealth")
r <- r[r$region == "Canada", ]
oc <- ordcmp(r, "group", "rating", "estimate", "lower", "upper",
                      lvls = c("Fair or poor", "Good", "Very good", "Excellent"),
                      ref = "M\u00e9tis", qual = "quality")
oc
#>           group reference   probbet     lower     upper      delta     neff
#> 1 First Nations     Métis 0.4653023 0.4531475 0.4774984 -0.0693954 4021.444
#> 2         Inuit     Métis 0.5062337 0.4928231 0.5196353  0.0124674 2791.908
#>    neffref usewithcau poogrp
#> 1 3884.405      FALSE  FALSE
#> 2 3884.405      FALSE  FALSE
plot(oc)

Age standardisation

First Nations, Metis and Inuit populations are younger than the non-Indigenous population, and many outcomes vary steeply with age. stdprev() standardises survey percentages to one age structure. Here it is applied to serious thoughts of suicide in the past year, from the 2017 Aboriginal Peoples Survey (table 41-10-0021-01):

si <- indiex("suicide")
si <- si[si$measure == "Seriously contemplated suicide in the past 12 months" &
           si$age != "Total, 15 years and over", ]
si <- si[order(si$group, si$age), ]
sp <- stdprev(si$estimate, si$lower, si$upper, std = c(20, 18, 17, 17, 28),
                             grp = si$group, qual = si$quality)
sp
#>           group estimate    lower    upper        se usewithcau
#> 1 First Nations    5.792 5.141142 6.442858 0.3320767       TRUE
#> 2         Inuit    5.940 5.036123 6.843877 0.4611700       TRUE
#> 3         Métis    4.742 4.125043 5.358957 0.3147799       TRUE
plot(sp)

For counts of events (deaths, hospitalisations), agestd() gives directly standardised rates with the Fay-Feuer gamma interval, and rategap() compares two of them.

Wellness beside distress

Indigenous scholars of mental health describe it as wellness, and as a process of healing, grounded in community, cultural identity, a holistic view and interdependence (Stewart 2008). They ask that strengths be reported together with disparities (Stewart 2020).

The estimators in this section and the next put that into how published tables are analysed. They are our translation, inspired by that work, not methods its authors state, and they say what they cannot show.

welprof() puts wellness indicators first and distress after them. This example uses the 2024 well-being table, 41-10-0082-01:

wb <- indiex("wellbeing")
w <- welprof(wb, "group", "indicator", "estimate", "lower", "upper",
                      well = c("Life satisfaction rating of 8, 9 or 10",
                                   "Sense of meaning and purpose rating of 8, 9 or 10",
                                   "Always or often has a hopeful outlook"),
                      distress = c("Life satisfaction rating between 0 and 5",
                                   "Rarely or never has a hopeful outlook"),
                      qual = "quality")
plot(w)

frmaud(w)
#> 40% of the indicators reported are deficit-framed.

dualcont() places each group on two axes, wellness and distress. They are related but distinct (Keyes 2002, 2005):

dc <- dualcont(w, "Always or often has a hopeful outlook", "Life satisfaction rating between 0 and 5")
dc
#>           group wellness wello welup distress distrlo distrup strratio  ratiolo
#> 1 First Nations     50.2  45.9  54.5     31.4    27.4    35.7 1.598726 1.365446
#> 2         Métis     52.3  47.6  57.0     28.4    24.0    33.1 1.841549 1.531631
#> 3         Inuit     54.4  45.7  62.8     25.2    19.4    31.8 2.158730 1.609182
#>    ratioup usewithcau
#> 1 1.871862      FALSE
#> 2 2.214178      FALSE
#> 3 2.895953      FALSE
plot(dc)

Holistic balance

Wellness is a balance among the domains of a person; illness is a loss of that balance (Stewart 2008; Gabriel, Mirza and Stewart 2022). An average of domains cannot show balance. holbal() measures it as the ratio of the geometric mean of the domain levels to their arithmetic mean. That ratio is 1 when every domain is level, and falls as one domain falls behind the others.

Which indicator stands for each domain is a choice for the people whose data these are to make. The choice below is one reading of it:

dom <- c("Perceived health, very good or excellent" = "physical",
         "Perceived mental health, very good or excellent" = "mental",
         "Life satisfaction, satisfied or very satisfied" = "emotional",
         "Sense of belonging to local community, somewhat strong or very strong" = "community")
d <- h[h$period == "2019/2022" & h$age == "Total, 18 years and over" & h$indicator %in% names(dom), ]
d$domain <- dom[d$indicator]
hb <- holbal(d, "group", "domain", "estimate", "lower", "upper", qual = "quality")
hb
#>            group  level    lvllo    lvlup   balance     ballo     balup
#> 1  First Nations 59.525 58.25190 60.79810 0.9641371 0.9582021 0.9696524
#> 2          Métis 63.725 62.43592 65.01408 0.9751839 0.9707913 0.9791662
#> 3          Inuit 63.800 60.74133 66.85867 0.9699605 0.9562503 0.9812743
#> 4 Non-Indigenous 69.800 69.60196 69.99804 0.9846768 0.9842679 0.9850811
#>     weadom usewithcau
#> 1 physical      FALSE
#> 2 physical      FALSE
#> 3   mental      FALSE
#> 4 physical      FALSE
plot(hb)

A community that weighs the domains differently sets weights. The result says which weights it used.

The four directions

The First Nations Mental Wellness Continuum Framework (Health Canada and Assembly of First Nations 2015) describes mental wellness in four directions: hope, belonging, meaning and purpose. mhcont() lays out the published proxies for each direction. It says which directions the tables do not measure, and does not fill them in:

mw <- mhcont(wb, "group", "indicator", "estimate", "lower", "upper",
        hope = "Always or often has a hopeful outlook",
        meaning = "Sense of meaning and purpose rating of 8, 9 or 10",
        purp = "Sense of meaning and purpose rating of 8, 9 or 10", qual = "quality")
mw[, c("group", "indicator", "domain", "estimate", "shared")]
#>            group indicator                                            domain
#> 1  First Nations      hope             Always or often has a hopeful outlook
#> 2  First Nations belonging                                no published proxy
#> 3  First Nations   meaning Sense of meaning and purpose rating of 8, 9 or 10
#> 4  First Nations   purpose Sense of meaning and purpose rating of 8, 9 or 10
#> 5          Métis      hope             Always or often has a hopeful outlook
#> 6          Métis belonging                                no published proxy
#> 7          Métis   meaning Sense of meaning and purpose rating of 8, 9 or 10
#> 8          Métis   purpose Sense of meaning and purpose rating of 8, 9 or 10
#> 9          Inuit      hope             Always or often has a hopeful outlook
#> 10         Inuit belonging                                no published proxy
#> 11         Inuit   meaning Sense of meaning and purpose rating of 8, 9 or 10
#> 12         Inuit   purpose Sense of meaning and purpose rating of 8, 9 or 10
#>    estimate shared
#> 1      50.2  FALSE
#> 2        NA  FALSE
#> 3      47.5   TRUE
#> 4      47.5   TRUE
#> 5      52.3  FALSE
#> 6        NA  FALSE
#> 7      51.6   TRUE
#> 8      51.6   TRUE
#> 9      54.4  FALSE
#> 10       NA  FALSE
#> 11     50.5   TRUE
#> 12     50.5   TRUE
plot(mw)

Healing as a process

A process shows in change. healchg() gives the change between survey periods, wellness first. It uses the two periods of table 13-10-0924-01:

hc <- healchg(h[h$age == "Total, 18 years and over", ], "group", "indicator", "period",
                     "estimate", "lower", "upper", from = "2015/2018", to = "2019/2022",
                     qual = "quality",
                     well = c("Perceived mental health, very good or excellent",
                                  "Sense of belonging to local community, somewhat strong or very strong"))
hc <- hc[hc$indicator %in% c("Perceived mental health, very good or excellent",
                             "Sense of belonging to local community, somewhat strong or very strong",
                             "Mood disorder", "Anxiety disorder"), ]
plot(hc)

Between the two periods, which span the COVID-19 pandemic, the share rating their mental health very good or excellent fell for First Nations, Metis and non-Indigenous adults. The Inuit interval includes no change. Because the reference group fell too, a group’s change is read against the reference group’s change, not on its own.

Connection across places

The land is “an instrumental part of all aspects of Indigenous life including health and well-being” (Mashford-Pringle and Stewart 2019). Health and education are interconnected (Mashford-Pringle 2016). Across places, connections of this kind leave a trace: do two indicators rise together where people live?

connassoc() measures this with Spearman’s rho. Its p-value is exact for up to eight places, and a Monte Carlo estimate beyond. Its band is a sensitivity band, not a confidence interval: it shows how far the published sampling error alone moves rho for these places. Here, by region, it asks whether First Nations and Metis people’s mental health rise together, using table 41-10-0067-01:

r <- indiex("mentalhealth")
r <- r[r$rating == "Excellent or very good" & r$region != "Canada", ]
pair <- merge(r[r$group == "First Nations", ], r[r$group == "M\u00e9tis", ], by = "region",
              suffixes = c("fn", "me"))
ca <- connassoc(pair, "region", "estimatefn", "estimateme", "lowerfn", "upperfn",
                             "lowerme", "upperme")
ca
#> Rank correlation across 11 places: rho = 0.52, permutation p = 0.106; with the published sampling error, 95% interval -0.19 to 0.77
#> An association across places: it does not show that one causes the other for any person.
plot(ca, xlab = "First Nations: excellent or very good (%)",
     ylab = "M\u00e9tis: excellent or very good (%)")

This is an association across places. It cannot say that one thing makes a person well.

Community-set weights, and the Community Well-Being index

commidx() combines protective and risk factors with weights the community sets. It follows the structure of Inuit Tapiriit Kanatami’s National Inuit Suicide Prevention Strategy (2016). The function never chooses the weights. A factor the tables do not publish leaves the index NA; it is not filled in.

h2 <- h[h$period == "2019/2022" & h$age == "Total, 18 years and over", ]
ci <- commidx(h2, "group", "indicator", "estimate", "lower", "upper", qual = "quality",
        w = c("Sense of belonging to local community, somewhat strong or very strong" = 2,
                    "Has a regular healthcare provider" = 1,
                    "Perceived life stress, most days quite a bit or extremely stressful" = -2,
                    "Heavy drinking" = -1))
plot(ci)

cwbscr() computes Indigenous Services Canada’s Community Well-Being index from census profile figures: income, education, housing and labour force activity. The index measures socio-economic conditions only. It is not a measure of wellness as First Nations and Inuit define it.

The 2021 Census

The Indigenous Population Profile of the 2021 Census gives counts for every geography, by gender, age group and about two thousand characteristics, with First Nations, Metis, Inuit and non-Indigenous people in their own columns. In the provincial table, residence on and off reserve is split out too. cnsprof() reads it with the hierarchy of the characteristics. profshr() takes a share with an interval for the 25% long-form sample and the random rounding of the counts.

Foster children among children under 15, Canada, 2021:

p <- cnsprof(indiex("censuspop"))
fc <- profshr(p, "Foster children", "Total - Children in census families")
fc <- fc[fc$geo == "Canada" & fc$age == "0 to 14 years" &
           fc$group %in% c("First Nations", "M\u00e9tis", "Inuit", "Non-Indigenous"), ]
fc[, c("residence", "group", "numerator", "denominator", "estimate", "lower", "upper")]
#>                               residence          group numerator denominator
#> 256 Total - Residence on or off reserve  First Nations     11735      265405
#> 257                          On reserve  First Nations      2350       90270
#> 258                         Off reserve  First Nations      9385      175135
#> 340 Total - Residence on or off reserve          Métis      1975      134670
#> 341                          On reserve          Métis        15         610
#> 342                         Off reserve          Métis      1960      134060
#> 424 Total - Residence on or off reserve          Inuit       770       21525
#> 425                          On reserve          Inuit        35         100
#> 426                         Off reserve          Inuit       735       21425
#> 676 Total - Residence on or off reserve Non-Indigenous     12705     5502310
#> 677                          On reserve Non-Indigenous        20        4070
#> 678                         Off reserve Non-Indigenous     12690     5498235
#>       estimate      lower      upper
#> 256  4.4215444  4.2880409  4.5590064
#> 257  2.6033012  2.5989608  2.6076487
#> 258  5.3587233  5.1789488  5.5443733
#> 340  1.4665479  1.3593905  1.5820167
#> 341  2.4590164  1.8933392  3.1882099
#> 342  1.4620319  1.3548094  1.5776046
#> 424  3.5772358  3.1713963  4.0328470
#> 425 35.0000000 30.9738945 39.2518422
#> 426  3.4305718  3.0324927  3.8788168
#> 676  0.2309030  0.2240599  0.2379546
#> 677  0.4914005  0.4040293  0.5975523
#> 678  0.2308013  0.2239573  0.2378541
plot(fc[fc$residence == "Total - Residence on or off reserve", ])

The overrepresentation of First Nations, Metis and Inuit children in care is the subject of the first five Calls to Action of the Truth and Reconciliation Commission.

Knowledge of an Indigenous language (all ages):

know <- "Total - Knowledge of Indigenous languages for the population in private households - 25% sample data"
la <- profshr(p, "Indigenous languages", know)
la[la$geo == "Canada" & la$age == "Total - Age groups" & la$residence == "Total - Residence on or off reserve" &
     la$group %in% c("First Nations", "M\u00e9tis", "Inuit"), c("group", "estimate", "lower", "upper")]
#>             group  estimate    lower     upper
#> 379 First Nations 17.530439 17.40473 17.656859
#> 505         Métis  1.332062  1.28369  1.382232
#> 631         Inuit 58.130139 57.49824 58.759377

The census age structure of the Indigenous population is the standard to use when age-standardising First Nations, Metis and Inuit rates (Kumar and Tjepkema 2019):

cnsstd(p)
#>                  age population     weight
#> 1       0 to 4 years     137510 0.07608819
#> 2       5 to 9 years     155565 0.08607853
#> 3     10 to 14 years     166135 0.09192722
#> 4     15 to 19 years     148820 0.08234633
#> 5     20 to 24 years     136070 0.07529140
#> 6     25 to 34 years     263655 0.14588780
#> 7     35 to 44 years     217990 0.12062006
#> 8     45 to 54 years     206360 0.11418485
#> 9     55 to 64 years     202785 0.11220670
#> 10    65 to 74 years     119410 0.06607295
#> 11 75 years and over      52945 0.02929597

A Community Well-Being score from the public profile (income, education, housing, labour force), by distinction. The household table splits Indigenous from non-Indigenous households only, so First Nations, Metis and Inuit people share the Indigenous households’ housing component. ?cnscwb explains where it departs from the Department’s own index:

h <- cnsprof(indiex("censushh"))
cw <- cnscwb(p, h)
cw[cw$geo %in% c("Canada", "Nunavut"), c("geo", "group", "income", "education", "housing", "labour", "cwb")]
#>        geo          group    income education  housing labour      cwb
#> 1   Canada  First Nations  71.03051  51.62081 91.87864  75.85 72.59499
#> 2   Canada          Métis  78.72661  59.55861 91.87864  83.15 78.32846
#> 3   Canada          Inuit  72.12712  38.50607 91.87864  74.75 69.31546
#> 4   Canada Non-Indigenous  84.37515  68.01121 95.98845  86.30 83.66870
#> 53 Nunavut  First Nations        NA        NA       NA     NA       NA
#> 54 Nunavut          Métis        NA        NA       NA     NA       NA
#> 55 Nunavut          Inuit  70.22000  28.49462 70.82270  69.85 59.84683
#> 56 Nunavut Non-Indigenous 100.00000  79.89287 93.30144  94.85 92.01108
plot(cw)

Intergenerational exposure

Studies of the residential schools ask whether harm passes down families. Among them are Bombay, Matheson and Anisman (2014), Kaspar (2014), Hackett, Feeny and Tompa (2016), and McQuaid et al. (2017).

• genexpo() codes how many of the two generations before a respondent attended. An unknown generation stays unknown.
• exptrnd() gives the odds ratio at each level, and the Cochran-Armitage test for a trend.

The counts below are made up, to show the computation:

genexpo(parent = c(TRUE, FALSE, TRUE, NA), gparent = c(TRUE, FALSE, FALSE, TRUE))
#> [1]  2  0  1 NA
et <- exptrnd(cases = c(60, 95, 70), n = c(500, 520, 300))
plot(et)

Individual-level data of this kind belong to the communities who collected them. These functions are for analyses done under their authority, or on tables they have published.

The Calls to Action

Call to Action 19 asks for “measurable goals to identify and close the gaps in health outcomes between Aboriginal and non-Aboriginal communities”, for indicators that include infant mortality, suicide, mental health, addictions, life expectancy and chronic diseases. trcind() lists which published table measures each of them. trchlthgap() measures every gap against non-Indigenous people on one scale, the disadvantage ratio: the ratio of the two estimates, turned so that above 1 means the group fares worse, with its interval. It is a ratio of published estimates, not a relative risk.

g <- trchlthgap()
g[g$measure %in% c("Life expectancy at birth", "Infant mortality rate"),
  c("measure", "period", "group", "estimate", "refest", "disratio", "lower", "upper")]
#>                     measure    period
#> 34 Life expectancy at birth 2016/2021
#> 35 Life expectancy at birth 2016/2021
#> 36 Life expectancy at birth 2016/2021
#> 37 Life expectancy at birth 2016/2021
#> 38 Life expectancy at birth 2016/2021
#> 39 Life expectancy at birth 2016/2021
#> 40    Infant mortality rate 2014/2016
#> 41    Infant mortality rate 2014/2016
#> 42    Infant mortality rate 2014/2016
#> 43    Infant mortality rate 2014/2016
#> 44    Infant mortality rate 2014/2016
#>                                                group estimate refest disratio
#> 34                                             Métis    80.47  84.99 1.056170
#> 35                                             Inuit    71.91  84.99 1.181894
#> 36              First Nations (Registered or Treaty)    73.98  84.99 1.148824
#> 37  First Nations (Registered or Treaty), on reserve    71.42  84.99 1.190003
#> 38 First Nations (Registered or Treaty), off reserve    76.04  84.99 1.117701
#> 39          First Nations, without registered status    79.23  84.99 1.072700
#> 40                                             Métis     5.20   4.20 1.238095
#> 41                                             Inuit    13.50   4.20 3.214286
#> 42              First Nations (Registered or Treaty)     9.60   4.20 2.285714
#> 43  First Nations (Registered or Treaty), on reserve    10.00   4.20 2.380952
#> 44 First Nations (Registered or Treaty), off reserve     8.10   4.20 1.928571
#>        lower    upper
#> 34 1.0497867 1.062592
#> 35 1.1636936 1.200379
#> 36 1.1433513 1.154323
#> 37 1.1833943 1.196648
#> 38 1.1078697 1.127620
#> 39 1.0617669 1.083745
#> 40 0.8366176 1.832235
#> 41 2.0315414 5.085613
#> 42 1.7776500 2.938987
#> 43 1.4796282 3.831323
#> 44 1.3632810 2.728262
plot(g)

Life expectancy at birth in 2016/2021 was 71.4 years for First Nations people living on reserve and 71.9 for Inuit, against 85.0 for non-Indigenous people. Infant mortality among Inuit was 13.5 per 1,000 live births in 2014/2016, against 4.2.

How fast the Calls themselves are being completed:

The Truth and Reconciliation Commission made 94 Calls to Action in 2015. trccmpl() holds the number completed each year, as the Yellowhead Institute counts them (Jewell and Mosby 2021, 2022). trcprog() takes that pace as a Poisson rate:

p <- trcprog()
p
#>   completed remaining years     rate    ratelo   rateup   cmplby cmplbyearly
#> 1        13        81  7.04 1.846591 0.9832319 3.157727 2066.865    2048.651
#>   cmplbylate
#> 1   2105.381
plot(p)

The projection only extends the pace so far. It is not a forecast.

Cultural continuity

Chandler and Lalonde (1998) found that British Columbia First Nations communities with more markers of cultural continuity had far fewer youth suicides. Hallett, Chandler and Lalonde (2007) found the same for knowledge of an Indigenous language. Their unit was the community. Across provinces and territories, the published tables allow a coarser question: where more First Nations people know an Indigenous language, do more rate their mental health highly?

prof <- cnsprof(indiex("censuspop"))
la <- profshr(prof, "Indigenous languages", know)
la <- la[la$age == "Total - Age groups" & la$group == "First Nations" & la$geo != "Canada" &
           la$residence == "Total - Residence on or off reserve", ]
mh2 <- indiex("mentalhealth")
mh2 <- mh2[mh2$rating == "Excellent or very good" & mh2$group == "First Nations", ]
both <- merge(la[c("geo", "estimate", "lower", "upper")], mh2[c("region", "estimate", "lower", "upper")],
              by.x = "geo", by.y = "region", suffixes = c("lang", "mh"))
cc <- connassoc(both, "geo", "estimatelang", "estimatemh", "lowerlang", "upperlang",
                             "lowermh", "uppermh")
cc
#> Rank correlation across 8 places: rho = 0.47, exact p = 0.247; with the published sampling error, 95% interval -0.31 to 0.79
#> An association across places: it does not show that one causes the other for any person.
plot(cc, xlab = "First Nations who know an Indigenous language (%)",
     ylab = "First Nations: mental health excellent or very good (%)")

Across these eight provinces and territories, the rank correlation is positive but far from settled: its exact p-value is large and its sensitivity band includes zero. That is not evidence for or against the communities’ findings. Eight places are far too few, a province mixes many communities, and the two measures come from different surveys and years. The community-level design needs the census profile of each First Nations community (98-510-X2021002 at the data service), and outcomes measured for the same communities.

Your own data

Nothing here is tied to Canada or to these tables. Each estimator takes a data frame with a group column, an estimate and its limits. A table from another agency needs two steps first: limits from whatever it publishes in their place (limfrom() takes a standard error, a coefficient of variation or a margin of error), and its quality flags mapped onto suppressed and use-with-caution (qltyrcd()). Here, made-up numbers in an Australian layout, with relative standard errors and that agency’s flags:

own <- data.frame(group = c("Aboriginal", "Torres Strait Islander", "Non-Indigenous Australians"),
                  pct = c(31.2, 27.5, 18.9), rse = c(4.1, 22.0, 1.2), flag = c("", "*", ""))
own <- cbind(own, limfrom(own$pct, cv = own$rse)[c("lower", "upper")])
own$quality <- qltyrcd(own$flag, suppr = "**", caution = "*")
indgap(own, "group", "pct", "lower", "upper", ref = "Non-Indigenous Australians",
               qual = "quality")
#>                    group estimate refest    ratio   ratiolo  ratioup difference
#> 1             Aboriginal     31.2   18.9 1.650794 1.5179467 1.795267       12.3
#> 2 Torres Strait Islander     27.5   18.9 1.455026 0.9167364 2.309390        8.6
#>       diflo    difup usewithcau poogrp
#> 1  9.753713 14.84629      FALSE  FALSE
#> 2 -3.266111 20.46611       TRUE  FALSE

Name the groups as the peoples concerned name themselves, and give pooled any row that pools several of them, so it is never set beside its own parts.

What the package does not compute

Some instruments are individual questionnaires, and aggregate tables cannot reproduce them:

• the Native Wellness Assessment (Fiedeldey-Van Dijk et al. 2017);
• the Cultural Connectedness Scale (Snowshoe et al. 2015);
• the Historical Loss Scale (Whitbeck et al. 2004).

Two-Eyed Seeing (Bartlett, Marshall and Marshall 2012) is a way of bringing knowledges together, not a formula. The package follows it by reporting results beside one another without merging them into one index (dualcont(), welprof()).

Acknowledgements

The estimators for holistic balance, wellness beside distress, the four directions, healing as a process and connection across places put to quantitative use the frameworks of Professor Suzanne Stewart (Yellowknives Dene First Nation), Director of the Waakebiness-Bryce Institute for Indigenous Health at the University of Toronto’s Dalla Lana School of Public Health, and Professor Angela Mashford-Pringle (Timiskaming First Nation), its Associate Director. This work stands on theirs. Any error in turning their qualitative work into estimators is the package’s, not theirs.

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